education and health research questions

• Open access
• Published: 06 April 2020

The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015

• Viju Raghupathi 1 &
• Wullianallur Raghupathi 2  

Archives of Public Health volume  78 , Article number:  20 ( 2020 ) Cite this article

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A clear understanding of the macro-level contexts in which education impacts health is integral to improving national health administration and policy. In this research, we use a visual analytic approach to explore the association between education and health over a 20-year period for countries around the world.

Using empirical data from the OECD and the World Bank for 26 OECD countries for the years 1995–2015, we identify patterns/associations between education and health indicators. By incorporating pre- and post-educational attainment indicators, we highlight the dual role of education as both a driver of opportunity as well as of inequality.

Adults with higher educational attainment have better health and lifespans compared to their less-educated peers. We highlight that tertiary education, particularly, is critical in influencing infant mortality, life expectancy, child vaccination, and enrollment rates. In addition, an economy needs to consider potential years of life lost (premature mortality) as a measure of health quality.

Conclusions

We bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health. Our country-level findings on NEET (Not in Employment, Education or Training) rates offer implications for economies to address a broad array of vulnerabilities ranging from unemployment, school life expectancy, and labor market discouragement. The health effects of education are at the grass roots-creating better overall self-awareness on personal health and making healthcare more accessible.

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Introduction

Is education generally associated with good health? There is a growing body of research that has been exploring the influence of education on health. Even in highly developed countries like the United States, it has been observed that adults with lower educational attainment suffer from poor health when compared to other populations [ 36 ]. This pattern is attributed to the large health inequalities brought about by education. A clear understanding of the health benefits of education can therefore serve as the key to reducing health disparities and improving the well-being of future populations. Despite the growing attention, research in the education–health area does not offer definitive answers to some critical questions. Part of the reason is the fact that the two phenomena are interlinked through life spans within and across generations of populations [ 36 ], thereby involving a larger social context within which the association is embedded. To some extent, research has also not considered the variances in the education–health relationship through the course of life or across birth cohorts [ 20 ], or if there is causality in the same. There is therefore a growing need for new directions in education–health research.

The avenues through which education affects health are complex and interwoven. For one, at the very outset, the distribution and content of education changes over time [ 20 ]. Second, the relationship between the mediators and health may change over time, as healthcare becomes more expensive and/or industries become either more, or less hazardous. Third, some research has documented that even relative changes in socioeconomic status (SES) can affect health, and thus changes in the distribution of education implies potential changes in the relationship between education and health. The relative index of inequality summarizes the magnitude of SES as a source of inequalities in health [ 11 , 21 , 27 , 29 ]. Fourth, changes in the distribution of health and mortality imply that the paths to poor health may have changed, thereby affecting the association with education.

Research has proposed that the relationship between education and health is attributable to three general classes of mediators: economic; social, psychological, and interpersonal; and behavioral health [ 31 ]. Economic variables such as income and occupation mediate the relationship between education and health by controlling and determining access to acute and preventive medical care [ 1 , 2 , 19 ]. Social, psychological, and interpersonal resources allow people with different levels of education to access coping resources and strategies [ 10 , 34 ], social support [ 5 , 22 ], and problem-solving and cognitive abilities to handle ill-health consequences such as stress [ 16 ]. Healthy behaviors enable educated individuals to recognize symptoms of ill health in a timely manner and seek appropriate medical help [ 14 , 35 ].

While the positive association between education and health has been established, the explanations for this association are not [ 31 ]. People who are well educated experience better health as reflected in the high levels of self-reported health and low levels of morbidity, mortality, and disability. By extension, low educational attainment is associated with self-reported poor health, shorter life expectancy, and shorter survival when sick. Prior research has suggested that the association between education and health is a complicated one, with a range of potential indicators that include (but are not limited to) interrelationships between demographic and family background indicators [ 8 ] - effects of poor health in childhood, greater resources associated with higher levels of education, appreciation of good health behaviors, and access to social networks. Some evidence suggests that education is strongly linked to health determinants such as preventative care [ 9 ]. Education helps promote and sustain healthy lifestyles and positive choices, nurture relationships, and enhance personal, family, and community well-being. However, there are some adverse effects of education too [ 9 ]. Education may result in increased attention to preventive care, which, though beneficial in the long term, raises healthcare costs in the short term. Some studies have found a positive association between education and some forms of illicit drug and alcohol use. Finally, although education is said to be effective for depression, it has been found to have much less substantial impact in general happiness or well-being [ 9 ].

On a universal scale, it has been accepted that several social factors outside the realm of healthcare influence the health outcomes [ 37 ]. The differences in morbidity, mortality and risk factors in research, conducted within and between countries, are impacted by the characteristics of the physical and social environment, and the structural policies that shape them [ 37 ]. Among the developed countries, the United States reflects huge disparities in educational status over the last few decades [ 15 , 24 ]. Life expectancy, while increasing for all others, has decreased among white Americans without a high school diploma - particularly women [ 25 , 26 , 32 ]. The sources of inequality in educational opportunities for American youth include the neighborhood they live in, the color of their skin, the schools they attend, and the financial resources of their families. In addition, the adverse trends in mortality and morbidity brought on by opioids resulting in suicides and overdoses (referred to as deaths of despair) exacerbated the disparities [ 21 ]. Collectively, these trends have brought about large economic and social inequalities in society such that the people with more education are likely to have more health literacy, live longer, experience better health outcomes, practice health promoting behaviors, and obtain timely health checkups [ 21 , 17 ].

Education enables people to develop a broad range of skills and traits (including cognitive and problem-solving abilities, learned effectiveness, and personal control) that predispose them towards improved health outcomes [ 23 ], ultimately contributing to human capital. Over the years, education has paved the way for a country’s financial security, stable employment, and social success [ 3 ]. Countries that adopt policies for the improvement of education also reap the benefits of healthy behavior such as reducing the population rates of smoking and obesity. Reducing health disparities and improving citizen health can be accomplished only through a thorough understanding of the health benefits conferred by education.

There is an iterative relationship between education and health. While poor education is associated with poor health due to income, resources, healthy behaviors, healthy neighborhood, and other socioeconomic factors, poor health, in turn, is associated with educational setbacks and interference with schooling through difficulties with learning disabilities, absenteeism, or cognitive disorders [ 30 ]. Education is therefore considered an important social determinant of health. The influence of national education on health works through a variety of mechanisms. Generally, education shows a relationship with self-rated health, and thus those with the highest education may have the best health [ 30 ]. Also, health-risk behaviors seem to be reduced by higher expenditure into the publicly funded education system [ 18 ], and those with good education are likely to have better knowledge of diseases [ 33 ]. In general, the education–health gradients for individuals have been growing over time [ 38 ].

To inform future education and health policies effectively, one needs to observe and analyze the opportunities that education generates during the early life span of individuals. This necessitates the adoption of some fundamental premises in research. Research must go beyond pure educational attainment and consider the associated effects preceding and succeeding such attainment. Research should consider the variations brought about by the education–health association across place and time, including the drivers that influence such variations [ 36 ].

In the current research, we analyze the association between education and health indicators for various countries using empirical data from reliable sources such as the Organization for Economic Cooperation and Development (OECD) and World Bank. While many studies explore the relationship between education and health at a conceptual level, we deploy an empirical approach in investigating the patterns and relationships between the two sets of indicators. In addition, for the educational indicators, we not only incorporate the level of educational attainment, but also look at the potential socioeconomic benefits, such as enrollment rates (in each sector of educational level) and school life expectancy (at each educational level). We investigate the influences of educational indicators on national health indicators of infant mortality, child vaccinations, life expectancy at birth, premature mortality arising from lack of educational attainment, employment and training, and the level of national health expenditure. Our research question is:

What are some key influencers/drivers in the education-health relationship at a country level?

The current study is important because policy makers have an increasing concern on national health issues and on policies that support it. The effect of education is at the root level—creating better overall self-awareness on personal health and making healthcare more accessible. The paper is organized as follows: Section 2 discusses the background for the research. Section 3 discusses the research method; Section 4 offers the analysis and results; Section 5 provides a synthesis of the results and offers an integrated discussion; Section 6 contains the scope and limitations of the research; Section 7 offers conclusions with implications and directions for future research.

Research has traditionally drawn from three broad theoretical perspectives in conceptualizing the relationship between education and health. The majority of research over the past two decades has been grounded in the Fundamental Cause Theory (FCT) [ 28 ], which posits that factors such as education are fundamental social causes of health inequalities because they determine access to resources (such as income, safe neighborhoods, or healthier lifestyles) that can assist in protecting or enhancing health [ 36 ]. Some of the key social resources that contribute to socioeconomic status include education (knowledge), money, power, prestige, and social connections. As some of these undergo change, they will be associated with differentials in the health status of the population [ 12 ].

Education has also been conceptualized using the Human Capital Theory (HCT) that views it as a return on investment in the form of increased productivity [ 4 ]. Education improves knowledge, skills, reasoning, effectiveness, and a broad range of other abilities that can be applied to improving health. The third approach - the signaling or credentialing perspective [ 6 ] - is adopted to address the large discontinuities in health at 12 and 16 years of schooling, which are typically associated with the receipt of a high school diploma and a college degree, respectively. This perspective considers the earned credentials of a person as a potential source that warrants social and economic returns. All these theoretical perspectives postulate a strong association between education and health and identify mechanisms through which education influences health. While the HCT proposes the mechanisms as embodied skills and abilities, FCT emphasizes the dynamism and flexibility of mechanisms, and the credentialing perspective proposes educational attainment through social responses. It needs to be stated, however, that all these approaches focus on education solely in terms of attainment, without emphasizing other institutional factors such as quality or type of education that may independently influence health. Additionally, while these approaches highlight the individual factors (individual attainment, attainment effects, and mechanisms), they do not give much emphasis to the social context in which education and health processes are embedded.

In the current research while we acknowledge the tenets of these theoretical perspectives, we incorporate the social mechanisms in education such as level of education, skills and abilities brought about by enrollment, school life expectancy, and the potential loss brought about by premature mortality. In this manner, we highlight the relevance of the social context in which the education and health domains are situated. We also study the dynamism of the mechanisms over countries and over time and incorporate the influences that precede and succeed educational attainment.

We analyze country level education and health data from the OECD and World Bank for a period of 21 years (1995–2015). Our variables include the education indicators of adult education level; enrollment rates at various educational levels; NEET (Not in Employment, Education or Training) rates; school life expectancy; and the health indicators of infant mortality, child vaccination rates, deaths from cancer, life expectancy at birth, potential years of life lost and smoking rates (Table 1 ). The data was processed using the tools of Tableau for visualization, and SAS for correlation and descriptive statistics. Approaches for analysis include ranking, association, and data visualization of the health and education data.

Analyses and results

In this section we identify and analyze patterns and associations between education and health indicators and discuss the results. Since countries vary in population sizes and other criteria, we use the estimated averages in all our analyses.

Comparison of health outcomes for countries by GDP per capita

We first analyzed to see if our data reflected the expectation that countries with higher GDP per capita have better health status (Fig. 1 ). We compared the average life expectancy at birth, average infant mortality, average deaths from cancer and average potential year of life lost, for different levels of GDP per capita (Fig. 1 ).

Associations between Average Life Expectancy (years) and Average Infant Mortality rate (per 1000), and between Deaths from Cancer (rates per 100,000) and Average Potential Years of Life Lost (years), by GDP per capita (for all countries for years 1995–2015)

Figure 1 depicts two charts with the estimated averages of variables for all countries in the sample. The X-axis of the first chart depicts average infant mortality rate (per 1000), while that of the second chart depicts average potential years of life lost (years). The Y-axis for both charts depicts the GDP per capita shown in intervals of 10 K ranging from 0 K–110 K (US Dollars). The analysis is shown as an average for all the countries in the sample and for all the years (1995–2015). As seen in Fig. 1 , countries with lower GDP per capita have higher infant mortality rate and increased potential year of life lost (which represents the average years a person would have lived if he or she had not died prematurely - a measure of premature mortality). Life expectancy and deaths from cancer are not affected by GDP level. When studying infant mortality and potential year lost, in order to avoid the influence of a control variable, it was necessary to group the samples by their GDP per capita level.

Association of Infant Mortality Rates with enrollment rates and education levels

We explored the association of infant mortality rates with the enrollment rates and adult educational levels for all countries (Fig. 2 ). The expectation is that with higher education and employment the infant mortality rate decreases.

Association of Adult Education Levels (ratio) and Enrollment Rates (ratio) with Infant Mortality Rate (per 1000)

Figure 2 depicts the analysis for all countries in the sample. The figure shows the years from 1995 to 2015 on the X axis. It shows two Y-axes with one axis denoting average infant mortality rate (per 1000 live births), and the other showing the rates from 0 to 120 to depict enrollment rates (primary/secondary/tertiary) and education levels (below secondary/upper secondary/tertiary). Regarding the Y axis showing rates over 100, it is worth noting that the enrollment rates denote a ratio of the total enrollment (regardless of age) at a level of education to the official population of the age group in that education level. Therefore, it is possible for the number of children enrolled at a level to exceed the official population of students in the age group for that level (due to repetition or late entry). This can lead to ratios over 100%. The figure shows that in general, all education indicators tend to rise over time, except for adult education level below secondary, which decreases over time. Infant mortality shows a steep decreasing trend over time, which is favorable. In general, countries have increasing health status and education over time, along with decreasing infant mortality rates. This suggests a negative association of education and enrollment rates with mortality rates.

Association of Education Outcomes with life expectancy at birth

We explored if the education outcomes of adult education level (tertiary), school life expectancy (tertiary), and NEET (not in employment, education, or training) rates, affected life expectancy at birth (Fig. 3 ). Our expectation is that adult education and school life expectancy, particularly tertiary, have a positive influence, while NEET has an adverse influence, on life expectancy at birth.

Association of Adult Education Level (Tertiary), NEET rate, School Life Expectancy (Tertiary), with Life Expectancy at Birth

Figure 3 show the relationships between various education indicators (adult education level-tertiary, NEET rate, school life expectancy-tertiary) and life expectancy at birth for all countries in the sample. The figure suggests that life expectancy at birth rises as adult education level (tertiary) and tertiary school life expectancy go up. Life expectancy at birth drops as the NEET rate goes up. In order to extend people’s life expectancy, governments should try to improve tertiary education, and control the number of youths dropping out of school and ending up unemployed (the NEET rate).

Association of Tertiary Enrollment and Education with potential years of life lost

We wanted to explore if the potential years of life lost rates are affected by tertiary enrollment rates and tertiary adult education levels (Fig. 4 ).

Association of Enrollment rate-tertiary (top) and Adult Education Level-Tertiary (bottom) with Potential Years of Life Lost (Y axis)

The two sets of box plots in Fig. 4 compare the enrollment rates with potential years of life lost (above set) and the education level with potential years of life lost (below set). The analysis is for all countries in the sample. As mentioned earlier, the enrollment rates are expressed as ratios and can exceed 100% if the number of children enrolled at a level (regardless of age) exceed the official population of students in the age group for that level. Potential years of life lost represents the average years a person would have lived, had he/she not died prematurely. The results show that with the rise of tertiary adult education level and tertiary enrollment rate, there is a decrease in both value and variation of the potential years of life lost. We can conclude that lower levels in tertiary education adversely affect a country’s health situation in terms of premature mortality.

Association of Tertiary Enrollment and Education with child vaccination rates

We compared the performance of tertiary education level and enrollment rates with the child vaccination rates (Fig. 5 ) to assess if there was a positive impact of education on preventive healthcare.

Association of Adult Education Level-Tertiary and Enrollment Rate-Tertiary with Child Vaccination Rates

In this analysis (Fig. 5 ), we looked for associations of child vaccination rates with tertiary enrollment and tertiary education. The analysis is for all countries in the sample. The color of the bubble represents the tertiary enrollment rate such that the darker the color, the higher the enrollment rate, and the size of the bubble represents the level of tertiary education. The labels inside the bubbles denote the child vaccination rates. The figure shows a general positive association of high child vaccination rate with tertiary enrollment and tertiary education levels. This indicates that countries that have high child vaccination rates tend to be better at tertiary enrollment and have more adults educated in tertiary institutions. Therefore, countries that focus more on tertiary education and enrollment may confer more health awareness in the population, which can be reflected in improved child vaccination rates.

Association of NEET rates (15–19; 20–24) with infant mortality rates and deaths from Cancer

In the realm of child health, we also looked at the infant mortality rates. We explored if infant mortality rates are associated with the NEET rates in different age groups (Fig. 6 ).

Association of Infant Mortality rates with NEET Rates (15–19) and NEET Rates (20–24)

Figure 6 is a scatterplot that explores the correlation between infant mortality and NEET rates in the age groups 15–19 and 20–24. The data is for all countries in the sample. Most data points are clustered in the lower infant mortality and lower NEET rate range. Infant mortality and NEET rates move in the same direction—as infant mortality increases/decrease, the NEET rate goes up/down. The NEET rate for the age group 20–24 has a slightly higher infant mortality rate than the NEET rate for the age group 15–19. This implies that when people in the age group 20–24 are uneducated or unemployed, the implications on infant mortality are higher than in other age groups. This is a reasonable association, since there is the potential to have more people with children in this age group than in the teenage group. To reduce the risk of infant mortality, governments should decrease NEET rates through promotional programs that disseminate the benefits of being educated, employed, and trained [ 7 ]. Additionally, they can offer financial aid to public schools and companies to offer more resources to raise general health awareness in people.

We looked to see if the distribution of population without employment, education, or training (NEET) in various categories of high, medium, and low impacted the rate of deaths from cancer (Fig. 7 ). Our expectation is that high rates of NEET will positively influence deaths from cancer.

Association of Deaths from Cancer and different NEET Rates

The three pie charts in Fig. 7 show the distribution of deaths from cancer in groups of countries with different NEET rates (high, medium, and low). The analysis includes all countries in the sample. The expectation was that high rates of NEET would be associated with high rates of cancer deaths. Our results, however, show that countries with medium NEET rates tend to have the highest deaths from cancer. Countries with high NEET rates have the lowest deaths from cancer among the three groups. Contrary to expectations, countries with low NEET rates do not show the lowest death rates from cancer. A possible explanation for this can be attributed to the fact that in this group, the people in the labor force may be suffering from work-related hazards including stress, that endanger their health.

Association between adult education levels and health expenditure

It is interesting to note the relationship between health expenditure and adult education levels (Fig. 8 ). We expect them to be positively associated.

Association of Health Expenditure and Adult Education Level-Tertiary & Upper Secondary

Figure 8 shows a heat map with the number of countries in different combinations of groups between tertiary and upper-secondary adult education level. We emphasize the higher levels of adult education. The color of the square shows the average of health expenditure. The plot shows that most of the countries are divided into two clusters. One cluster has a high tertiary education level as well as a high upper-secondary education level and it has high average health expenditure. The other cluster has relatively low tertiary and upper secondary education level with low average health expenditure. Overall, the figure shows a positive correlation between adult education level and compulsory health expenditure. Governments of countries with low levels of education should allocate more health expenditure, which will have an influence on the educational levels. Alternatively, to improve public health, governments can frame educational policies to improve the overall national education level, which then produces more health awareness, contributing to national healthcare.

Association of Compulsory Health Expenditure with NEET rates by country and region

Having explored the relationship between health expenditure and adult education, we then explored the relationship between health expenditure and NEET rates of different countries (Fig. 9 ). We expect compulsory health expenditure to be negatively associated with NEET rates.

Association between Compulsory Health Expenditure and NEET Rate by Country and Region

In Fig. 9 , each box represents a country or region; the size of the box indicates the extent of compulsory health expenditure such that a larger box implies that the country has greater compulsory health expenditure. The intensity of the color of the box represents the NEET rate such that the darker color implies a higher NEET rate. Turkey has the highest NEET rate with low health expenditure. Most European countries such as France, Belgium, Sweden, and Norway have low NEET rates and high health expenditure. The chart shows a general association between low compulsory health expenditure and high NEET rates. The relationship, however, is not consistent, as there are countries with high NEET and high health expenditures. Our suggestion is for most countries to improve the social education for the youth through free training programs and other means to effectively improve the public health while they attempt to raise the compulsory expenditure.

Distribution of life expectancy at birth and tertiary enrollment rate

The distribution of enrollment rate (tertiary) and life expectancy of all the countries in the sample can give an idea of the current status of both education and health (Fig. 10 ). We expect these to be positively associated.

Distribution of Life Expectancy at Birth (years) and Tertiary Enrollment Rate

Figure 10 shows two histograms with the lines representing the distribution of life expectancy at birth and the tertiary enrollment rate of all the countries. The distribution of life expectancy at birth is skewed right, which means most of the countries have quite a high life expectancy and there are few countries with a very low life expectancy. The tertiary enrollment rate has a good distribution, which is closer to a normal distribution. Governments of countries with an extremely low life expectancy should try to identify the cause of this problem and take actions in time to improve the overall national health.

Comparison of adult education levels and deaths from Cancer at various levels of GDP per capita

We wanted to see if various levels of GDP per capita influence the levels of adult education and deaths from cancer in countries (Fig. 11 ).

Comparison of Adult Education Levels and Deaths from Cancer at various levels of GDP per capita

Figure 11 shows the distribution of various adult education levels for countries by groups of GDP per capita. The plot shows that as GDP grows, the level of below-secondary adult education becomes lower, and the level of tertiary education gets higher. The upper-secondary education level is constant among all the groups. The implication is that tertiary education is the most important factor among all the education levels for a country to improve its economic power and health level. Countries should therefore focus on tertiary education as a driver of economic development. As for deaths from cancer, countries with lower GDP have higher death rates, indicating the negative association between economic development and deaths from cancer.

Distribution of infant mortality rates by continent

Infant mortality is an important indicator of a country’s health status. Figure 12 shows the distribution of infant mortality for the continents of Asia, Europe, Oceania, North and South America. We grouped the countries in each continent into high, medium, and low, based on infant mortality rates.

Distribution of Infant Mortality rates by Continent

In Fig. 12 , each bar represents a continent. All countries fall into three groups (high, medium, and low) based on infant mortality rates. South America has the highest infant mortality, followed by Asia, Europe, and Oceania. North America falls in the medium range of infant mortality. South American countries, in general, should strive to improve infant mortality. While Europe, in general, has the lowest infant mortality rates, there are some countries that have high rates as depicted.

Association between child vaccination rates and NEET rates

We looked at the association between child vaccination rates and NEET rates in various countries (Fig. 13 ). We expect countries that have high NEET rates to have low child vaccination rates.

Association between Child Vaccination Rates and NEET rates

Figure 13 displays the child vaccination rates in the first map and the NEET rates in the second map, for all countries. The darker green color shows countries with higher rates of vaccination and the darker red represents those with higher NEET rates. It can be seen that in general, the countries with lower NEET also have better vaccination rates. Examples are USA, UK, Iceland, France, and North European countries. Countries should therefore strive to reduce NEET rates by enrolling a good proportion of the youth into initiatives or programs that will help them be more productive in the future, and be able to afford preventive healthcare for the families, particularly, the children.

Average smoking rate in different continents over time

We compared the trend of average smoking rate for the years 1995–201 for the continents in the sample (Fig. 14 ).

Trend of average smoking rate in different continents from 1995 to 2015

Figure 14 depicts the line charts of average smoking rates for the continents of Asia, Europe, Oceania, North and South America. All the lines show an overall downward trend, which indicates that the average smoking rate decreases with time. The trend illustrates that people have become more health conscious and realize the harmful effects of smoking over time. However, the smoking rate in Europe (EU) is consistently higher than that in other continents, while the smoking rate in North America (NA) is consistently lower over the years. Governments in Europe should pay attention to the usage of tobacco and increase health consciousness among the public.

Association between adult education levels and deaths from Cancer

We explored if adult education levels (below-secondary, upper-secondary, and tertiary) are associated with deaths from cancer (Fig. 15 ) such that higher levels of education will mitigate the rates of deaths from cancer, due to increased awareness and proactive health behavior.

Association of deaths from cancer with adult education levels

Figure 15 shows the correlations of deaths from cancer among the three adult education levels, for all countries in the sample. It is obvious that below-secondary and tertiary adult education levels have a negative correlation with deaths from cancer, while the upper-secondary adult education level shows a positive correlation. Barring upper-secondary results, we can surmise that in general, as education level goes higher, the deaths from cancer will decrease. The rationale for this could be that education fosters more health awareness and encourages people to adopt healthy behavioral practices. Governments should therefore pay attention to frame policies that promote education. However, the counterintuitive result of the positive correlation between upper-secondary levels of adult education with the deaths from cancer warrants more investigation.

We drilled down further into the correlation between the upper-secondary education level and deaths from cancer. Figure 16 shows this correlation, along with a breakdown of the total number of records for each continent, to see if there is an explanation for the unique result.

Association between deaths from cancer and adult education level-upper secondary

Figure 16 shows a dashboard containing two graphs - a scatterplot of the correlation between deaths from cancer and education level, and a bar graph showing the breakdown of the total sample by continent. We included a breakdown by continent in order to explore variances that may clarify or explain the positive association for deaths from cancer with the upper-secondary education level. The scatterplot shows that for the European Union (EU) the points are much more scattered than for the other continents. Also, the correlation between deaths and education level for the EU is positive. The bottom bar graph depicts how the sample contains a disproportionately high number of records for the EU than for other continents. It is possible that this may have influenced the results of the correlation. The governments in the EU should investigate the reasons behind this phenomenon. Also, we defer to future research to explore this in greater detail by incorporating other socioeconomic parameters that may have to be factored into the relationship.

Association between average tertiary school life expectancy and health expenditure

We moved our focus to the trends of tertiary school life expectancy and health expenditure from 1995 to 2015 (Fig. 17 ) to check for positive associations.

Association between Average Tertiary School Life Expectancy and Health Expenditure

Figure 17 is a combination chart explaining the trends of tertiary school life expectancy and health expenditure, for all countries in the sample. The rationale is that if there is a positive association between the two, it would be worthwhile for the government to allocate more resources towards health expenditure. Both tertiary school life expectancy and health expenditure show an increase over the years from 1995 to 2015. Our additional analysis shows that they continue to increase even after 2015. Hence, governments are encouraged to increase the health expenditure in order to see gains in tertiary school life expectancy, which will have positive implications for national health. Given that the measured effects of education are large, investments in education might prove to be a cost-effective means of achieving better health.

Our results reveal how interlinked education and health can be. We show how a country can improve its health scenario by focusing on appropriate indicators of education. Countries with higher education levels are more likely to have better national health conditions. Among the adult education levels, tertiary education is the most critical indicator influencing healthcare in terms of infant mortality, life expectancy, child vaccination rates, and enrollment rates. Our results emphasize the role that education plays in the potential years of life lost, which is a measure that represents the average years a person would have lived had he/she not died prematurely. In addition to mortality rate, an economy needs to consider this indicator as a measure of health quality.

Other educational indicators that are major drivers of health include school life expectancy, particularly at the tertiary level. In order to improve the school life expectancy of the population, governments should control the number of youths ending up unemployed, dropping out of school, and without skills or training (the NEET rate). Education allows people to gain skills/abilities and knowledge on general health, enhancing their awareness of healthy behaviors and preventive care. By targeting promotions and campaigns that emphasize the importance of skills and employment, governments can reduce the NEET rates. And, by reducing the NEET rates, governments have the potential to address a broad array of vulnerabilities among youth, ranging from unemployment, early school dropouts, and labor market discouragement, which are all social issues that warrant attention in a growing economy.

We also bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health, at a macro level. The health effects of education are at the grass roots level - creating better overall self-awareness on personal health and making healthcare more accessible.

Scope and limitations

Our research suffers from a few limitations. For one, the number of countries is limited, and being that the data are primarily drawn from OECD, they pertain to the continent of Europe. We also considered a limited set of variables. A more extensive study can encompass a larger range of variables drawn from heterogeneous sources. With the objective of acquiring a macro perspective on the education–health association, we incorporated some dependent variables that may not traditionally be viewed as pure health parameters. For example, the variable potential years of life lost is affected by premature deaths that may be caused by non-health related factors too. Also there may be some intervening variables in the education–health relationship that need to be considered. Lastly, while our study explores associations and relationships between variables, it does not investigate causality.

Conclusions and future research

Both education and health are at the center of individual and population health and well-being. Conceptualizations of both phenomena should go beyond the individual focus to incorporate and consider the social context and structure within which the education–health relationship is embedded. Such an approach calls for a combination of interdisciplinary research, novel conceptual models, and rich data sources. As health differences are widening across the world, there is need for new directions in research and policy on health returns on education and vice versa. In developing interventions and policies, governments would do well to keep in mind the dual role played by education—as a driver of opportunity as well as a reproducer of inequality [ 36 ]. Reducing these macro-level inequalities requires interventions directed at a macro level. Researchers and policy makers have mutual responsibilities in this endeavor, with researchers investigating and communicating the insights and recommendations to policy makers, and policy makers conveying the challenges and needs of health and educational practices to researchers. Researchers can leverage national differences in the political system to study the impact of various welfare systems on the education–health association. In terms of investment in education, we make a call for governments to focus on education in the early stages of life course so as to prevent the reproduction of social inequalities and change upcoming educational trajectories; we also urge governments to make efforts to mitigate the rising dropout rate in postsecondary enrollment that often leads to detrimental health (e.g., due to stress or rising student debt). There is a need to look into the circumstances that can modify the postsecondary experience of youth so as to improve their health.

Our study offers several prospects for future research. Future research can incorporate geographic and environmental variables—such as the quality of air level or latitude—for additional analysis. Also, we can incorporate data from other sources to include more countries and more variables, especially non-European ones, so as to increase the breadth of analysis. In terms of methodology, future studies can deploy meta-regression analysis to compare the relationships between health and some macro-level socioeconomic indicators [ 13 ]. Future research should also expand beyond the individual to the social context in which education and health are situated. Such an approach will help generate findings that will inform effective educational and health policies and interventions to reduce disparities.

Availability of data and materials

The dataset analyzed during the current study is available from the corresponding author on reasonable request.

Abbreviations

Fundamental Cause Theory

Human Capital Theory

Not in Employment, Education, or Training

Organization for Economic Cooperation and Development

Socio-economic status

Andersen RM, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Mem Fund Q Health Soc. 1973;51(1):95–124.

Article   CAS   Google Scholar  

Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Social Behav. 1995;36(1):1–10.

Baker DP. The Schooled Society: The Educational Transformation of Global Culture. Stanford, CA: Stanford Univ. Press: 2014; 360.

Becker GS. Human capital: a theoretical and empirical analysis, with special reference to education. Chicago: Univ. Chicago Press; 1964.

Google Scholar  

Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. Oxford Acad J. 1979;109(2):186–204.

CAS   Google Scholar  

Crimmins EM, Kim JK, Vasunilashorn S. Biodemography: new approaches to understanding trends and differences in population health and mortality. Demography. 2010;47:S41–64.

Article   Google Scholar  

Spence M. Job market signalling. The Quarterly J Econ. 1973;87:355–79.

Cutler DM, Lleras-Muney A. Education and Health: Evaluating Theories and Evidence: NBER Working Papers; 2006. p. 12352.

Feinstein L. What are the effects of education on health? OECD Proceedings of the Copenhagen Symposium; 2006 . .

Folkman S, Lazarus RS. An analysis of coping in a middle-aged community sample. J Health Soc Behav. 1980;21(3):219–39.

Freedman VA, Martin LG. The role of education in explaining and forecasting trends in functional limitations among older Americans. Demography. 1999;36(4):461–73.

Freese J, Lutfey K. Fundamental causality: challenges of an animating concept for medical sociology. In: Pescosolido BA, Martin JK, McLeod JD, Rogers A, editors. Handbook of the Sociology of Health, Illness, and Healing: a blueprint for the 21st century. New York: Springer; 2011. p. 67–81.

Chapter   Google Scholar  

Fouweather T, Gillies C, Wohland P, Van Oyen H, Nusselde W, Robine J, Cambois E, Jagger C. Comparison of socio-economic indicators explaining inequalities in healthy life years at age 50 in Europe: 2005 and 2010. Eur J Pub Health. 2015;25:978–83.

George LK, Gwyther LP. Caregiver Weil-being: a multidimensional examination of family caregivers of demented adults. Gerontologist. 1986;26(3):253–9.

Goldman D, Smith JP. The increasing value of education to health. Soc Sc Med. 2011;72(10):1728–37.

Harper AC, Lambert LJ. The health of populations: an introduction. New York: Springer Publishing Company; 1994.

Health 2020: Education and health through the life-course. WHO Europe Sector Brief on Education Health; 2015. http://www.euro.who.int/__data/assets/pdf_file/0007/324619/Health-2020-Education-and-health-through-the-life-course-en.pdf?ua=1 .

Kino S, Bernabé E, Sabbah W. The role of healthcare and education systems in co-occurrence of health risk behaviours in 27 European countries. Eur J Public Health. 2018;28(1):186–92.

Landerman LR, Burns BJ, Swartz MS, Wagner HR, George LK. The relationship between insurance coverage and psychiatric disorder in predicting use of mental health services. Am J Psychol. 1994;151(12):1785.

Lynch SM. Cohort and life-course patterns in the relationship between education and health: a hierarchical approach. Demography. 2003;40(2):309–31.

Mackenbach J, Kunst A. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med. 1997;44(6):757–71. https://doi.org/10.1016/S0277-9536(96)00073-1 .

Article   CAS   PubMed   Google Scholar  

Manton KG, Corder L, Stallard E. Chronic disability trends in elderly United States populations: 1982-1994. Natl Acad Sci. 1997;94(6):2593–8.

Mirowski J, Ross CE. Education, learned effectiveness and health. London Rev Edu. 2005;3(3):205–20.

Montez JK, Berkman LF. Trends in the educational gradient of mortality among US adults aged 45 to 84 years: bringing regional context into the explanation. Am J Pub Health. 2014;104(1):e82–90.

Montez JK, Zajacova A. Trends in mortality risk by education level and cause of death among US white women from 1986 to 2006. Am J Pub Health. 2013;103:473–9.

Olshansky SJ, Antonucci T, Berkman L, Binstock RH, Boersch-Supan A, Cacioppo JT, Carnes BA, Carstensen LL, Fried LP, Goldman DP, Jackson J, Kohli M, Rother J, Zheng Y, Rowe J. Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Aff. 2012;31(8):1803–13.

Pamuk ER. Social-class inequality in infant mortality in England and Wales from 1921 to 1980. Eur J Popul 1988; 4 , 1–21, https://doi.org/10.1007/BF01797104.

Phelan JC , Link BG , Tehranifar P . Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications . J Health Soc Behav 2010 ; 51 : S28 S40 . doi : 10.1177/0022146510383498.

Renard F, Devleesschauwer B, Speybroeck N, Deboosere P. Monitoring health inequalities when the socio-economic composition changes: are the slope and relative indices of inequality appropriate? Results of a simulation study. BMC Public Health 2019; 19: 662. https://doi.org/10.1186/s12889-019-6980-1.

Ro A, Geronimus A, Bound J, Griffith D, Gee G. Educational gradients in five Asian immigrant populations: do country of origin, duration and generational status moderate the education-health relationship? Prev Med Rep. 2016;4:338–43.

Ross CE, Wu CL. The links between education and health. Am Soc Rev. 1995;60(5):719–45.

Shiels MS, Chernyavskiy P, Anderson WF, Best AF, Haozous EA. Diverging trends in premature mortality in the U.S. by sex, race, and ethnicity in the 21st century. Lancet. 2017;389:1043–54.

Tsou MT. Association of Education, health behaviors, concerns, and knowledge with metabolic syndrome among urban elderly in one medical Center in Taiwan. Int J Gerontology. 2017;11(3:138–43.

Wheaton B. Stress, personal coping resources, and psychiatric symptoms: an investigation of interactive models. J Health Soc Behav. 1983;24(3):208–29.

Williams DR, Collins C. US socioeconomic and racial differences in health: patterns and explanations. Ann Rev Soc. 1995;21:349–86.

Zajacova A, Lawrence EM. The relationship between education and health: reducing disparities through a contextual approach. Ann Rev Pub Health. 2018;39:273–89.

Zimmerman EB, Woolf SH. Understanding the relationship between education and health. Discussion Paper, Inst Med. Washington DC; 2014. https://nam.edu/wp-content/uploads/2015/06/BPH-UnderstandingTheRelationship1.pdf .

Zhong H. An over time analysis on the mechanisms behind the education–health gradients in China. China Econ Rev. 2015;34(C):135–49.

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Raghupathi, V., Raghupathi, W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Arch Public Health 78 , 20 (2020). https://doi.org/10.1186/s13690-020-00402-5

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School Success: An Opportunity for Population Health: Proceedings of a Workshop (2020)

Chapter: 2 the relationship between education and health, 2 the relationship between education and health 1.

To provide a foundation for the discussions, Steven Woolf of Virginia Commonwealth University’s Center on Society and Health gave a brief overview of why educational success matters for health. The discussion that followed was moderated by Joshua Sharfstein, Johns Hopkins Bloomberg School of Public Health. (Highlights of this session are presented in Box 2-1 .)

FACTORS THAT SHAPE HEALTH OUTCOMES

Woolf outlined five domains from the report U.S. Health in International Perspective: Shorter Lives, Poorer Health ( NRC and IOM, 2013 ) that are important to shaping health outcomes. These include health care and public health (which, he noted, account for only 10–20 percent of health outcomes); individual behaviors; the physical and social environment; social and economic factors (including education); and public policies and spending, which shape the other four domains (see Figure 2-1 ). Differences in health outcomes are related to differences in how people and communities experience each of these domains. Key factors impacting health outcomes include

___________________

1 Unless otherwise noted, as in the case of the question and answer/discussion period, this chapter represents the rapporteur’s synopsis of the presentation delivered by Steven Woolf of Virginia Commonwealth University and the statements have not been endorsed or verified by the National Academies of Sciences, Engineering, and Medicine.

• education and income (e.g., families with limited incomes cannot live in healthy neighborhoods);
• quality of housing (e.g., exposure to allergens that cause asthma, overcrowding);
• quality of food that is accessible to residents (convenient availability of fresh, nutritious foods versus unhealthy options);
• the built environment (e.g., opportunities for residents to safely exercise, walk, cycle, or play outside);
• proximity to highways, factories, and other sources of exposure to pollutants and toxic agents;
• access to primary care providers and quality hospitals;
• access to affordable and reliable public transit (for travel to jobs, health and child care, social services, etc.); and
• residential segregation or other features that isolate communities and stifle economic growth.

Education, income, and wealth are among the most powerful predictors of health outcomes, Woolf said.

EDUCATION AS A PREDICTOR OF HEALTH OUTCOMES

In the United States, the risk of dying from any cause (all-cause mortality) is directly related to educational attainment. Woolf described this relationship as a gradient: for both men and women, the more years of education an individual has, the lower the risk of death ( Ross et al., 2012 ). Similarly, people who have less educational attainment more frequently self-report fair or poor health ( Schiller et al., 2012 ). This association (between higher education and better health) is demonstrable across a range of different health outcomes, Woolf said, and he shared prevalence data by education for coronary heart disease, stroke, lung diseases, diabetes, kidney disease, and others ( Schiller et al., 2012 ).

There is a tendency in U.S. society to assume that health is primarily the result of health care, Woolf observed, but he said the assumption is incorrect. Analyzing data from Kaiser Permanente in Northern California, Woolf and colleagues found that the educational attainment–health outcome gradient persists even among patients in this integrated health system, whose members have equivalent access to health care. Factors outside of the health care system contribute to the differences in health outcomes by educational attainment.

Tremendous amounts of money are dedicated to health care in the United States, he said, but the importance of the social determinants of health, including education, is not always fully appreciated. He shared data that suggest that for every life saved by medical advances, seven

lives would be saved if all adults had the mortality rate of people with some college education ( Woolf et al., 2007 ).

Recognizing that patients who have less educational attainment are at greater risk for chronic diseases is important for clinicians, Woolf said, but there are also broader implications of this association for decision makers outside of the health sector, such as employers. A greater percentage of individuals with less educational attainment have difficulties with physical functioning—from walking, climbing steps, or handling small objects to lifting, carrying, or pushing large or heavy objects ( Schiller et al., 2012 ). An educated workforce is more capable of physically functioning in blue-collar jobs. In addition to higher productivity, a more educated employee population will experience lower health care costs, less absenteeism, and more presenteeism. 2

In a knowledge economy, it is difficult to separate the impact of education from that of income and wealth, Woolf said. People who have more education are more likely to obtain high-earning jobs and thus to have higher incomes and greater wealth. As with the education–health gradient, higher levels of income are associated with better health across a wide range of both physical and mental health outcomes ( Schiller et al., 2012 ). Woolf added that people with less educational attainment are more dramatically impacted by societal trends. For example, although life expectancy in industrialized countries has been increasing for the past century, U.S. life expectancy has decreased in recent years, and this trend has been more pronounced among adults who have not graduated from high school ( Olshansky et al., 2012 ). The factors behind this trend are complex. Woolf cited the work of Case and Deaton, who have drawn attention to the problem of “deaths of despair”: death rates from drug overdoses, alcoholism, and suicides have increased significantly since the 1990s. Case and Deaton showed that this increase was concentrated among middle-aged whites, especially among Americans with less educational attainment ( Case and Deaton, 2017 ).

UNDERSTANDING THE RELATIONSHIP BETWEEN EDUCATION AND HEALTH

Education can produce better health through multiple pathways (see Figure 2-2 ). For example, those who have more education have the ability to access more economic resources, such as better-paying jobs with health insurance benefits. Having those resources, in turn, allows them to live in healthier neighborhoods and avoid a range of health hazards, from

2 “Presenteeism” in this sense means the state of being present, as opposed to being present at work but not productive.

crime to air pollution. In what is called reverse causality, health can also influence educational outcomes. For example, proper management of conditions such as attention-deficit/hyperactivity disorder or asthma can improve a child’s academic success.

This education–health relationship is highly influenced by contextual factors, Woolf emphasized. Contextual factors are the conditions throughout a person’s life that can affect both education and health. These contextual factors, including both experiences and place, may often be the root cause of the correlation between education and health. For example, chronic stress and trauma are examples of contextual factors that can affect a child’s health trajectory and success in school. Research has shown that adverse childhood experiences can influence health throughout life, leading to higher risks of depression, substance abuse, and chronic diseases later in life ( Felitti et al., 1998 ). Place—the conditions in communities where people live—can also shape both health outcomes and educational outcomes. For example, life expectancy in Chicago varies as much as 20 years by census tract, with much lower life expectancies in Southside Chicago and similar areas. Maps reveal that the areas that tend to have lower life expectancy are also areas where educational attainment is the lowest.

In closing, Woolf noted that many efforts are under way to draw a connection between the community and a child’s experience in school.

As examples, he mentioned the Whole School, Whole Community, Whole Child model 3 that the Centers for Disease Control and Prevention developed and the Together for Healthy and Successful Schools Initiative being undertaken by Washington University in St. Louis. 4

Strength and Appreciation of the Data

Moderator Sharfstein asked about the extent to which the data on the importance of education for health are appreciated by health care leaders. Woolf responded that there has historically been a lack of awareness in the health care community about the importance of the social determinants of health. In recent years, however, health care systems have become more attentive to these issues, driven in part by health care reform. The Patient Protection and Affordable Care Act and other health reforms that

3 See https://www.cdc.gov/healthyschools/wscc/index.htm (accessed May 30, 2019).

4 See https://cphss.wustl.edu/items/healthy-schools (accessed May 30, 2019).

mandate efforts to improve population health outcomes and lower the use of health care services have emphasized the importance of addressing the social determinants of health. Although health care systems have been focusing on addressing factors such as unstable housing and food security, there is increasing interest in investing in education, including not only education for children but also skills training for adults to compete for better jobs.

The issue of correlation versus causation was raised by a participant relative to the data on the association between education and health. Woolf acknowledged the problem, noting that more prospective studies are needed to demonstrate that improvements in education will improve health outcomes. “Just giving out diplomas doesn’t save lives,” he said. That said, although arguments could be made about the magnitude of the impact, the concept that improving education will improve health outcomes has been well established by numerous examples worldwide. Woolf referred to a National Research Council (NRC) and Institute of Medicine (IOM) study comparing the health of Americans to that of people in other high-income countries ( NRC and IOM, 2013 ). The NRC and IOM committee found that life expectancy and other health outcomes in the United States were inferior to those in other high-income countries, across many different health metrics. A systematic examination of potential causes revealed differences compared to other countries across all five domains that shape health. Among these, education was a key factor: after World War II, Americans were the most educated people in the world, he said, but educational outcomes in the United States have not kept pace with progress in other high-income countries or even in some developing economies, such as South Korea. These countries have outperformed the United States in terms of their ability to educate young people and prepare them for successful careers.

Another key difference is that many other high-income countries invest more (per capita or as a proportion of their total spending) in social services, education, and other factors that improve health. From a policy perspective, Woolf said, the United States needs to shift its priorities as a way not only to improve health outcomes but also to strengthen its economic competitiveness with these other countries. If the U.S. workforce is less healthy than workers in other countries, the nation’s ability to challenge the economic performance of other countries is at risk if those kinds of investments are not made, he said.

Education and Health Inequities

Health inequities are a key health challenge in the United States, Sharfstein noted. He asked about the impact of educational challenges in

producing serious health inequities by race, location, or other key factors. The five domains that shape health outcomes also drive health inequities, Woolf responded. There are other factors that influence health inequities (e.g., the biological effects of experiencing racial discrimination and trauma), but racial and ethnic disparities in health are often mirrored by dramatic differences in educational outcomes, he said. For a variety of reasons (including racism), African Americans have, on average, a lower rate of graduation from high school and less success in obtaining 4-year degrees than white Americans. In marginalized communities, escaping the multigenerational cycle of poverty often depends on the ability of young people to get a good education. Woolf reiterated that education is important in shaping not only health outcomes but economic opportunity and social mobility. Investments are needed to address the gaps in education that often exist to a greater degree in marginalized populations, both to improve health outcomes and to end the negative economic cycle that has historically trapped these communities in a state of persistent disadvantage.

Higher Education

Sanne Magnan of the HealthPartners Research Institute asked whether young people are still being encouraged to pursue higher education the way they were after World War II and whether, given the expense of a college education, there should be more investment in craft, trade, and vocational education. Woolf agreed that a strong interest in higher education was evident after World War II, as exemplified by the GI Bill. He felt that although today’s world places a fair amount of pressure on high school students to perform well and try to get into the best schools, there are barriers to accessing a college education that prior generations did not face. He agreed that a 4-year degree was not the only way to break the cycle of poverty, adding that there is a great market demand for people who are trained in the trades and an underinvestment in vocational schools and community colleges.

Sally Kraft from Dartmouth-Hitchcock inquired about the existence of any research on whether innovative ways of delivering education at lower cost (e.g., open online courses) have the same impact on increasing educational attainment and the associated health and income outcomes. Woolf replied that although the question was a good one, he was not familiar with research on that topic.

Education and health care significantly influence well-being and health outcomes, especially throughout adolescence. In fact, doctors note that performance in school is highly reflective of a child's current and future health. Despite knowledge of this connection, pediatricians are rarely aware of their patients' school performance and have a limited understanding of the education system. Fostering collaboration and aligning efforts within the health and education sectors is a critical step towards building stronger and healthier communities.

On June 14, 2018, the National Academies convened a workshop to discuss how efforts within the health sector can support children's education from pre-kindergarten through 12th grade and to explore the barriers between these sectors. The committee also examined case examples of health-education collaboration and opportunities in policy. This publication summarizes the presentations and discussions from the workshop.

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Annual Review of Public Health

Volume 39, 2018, review article, open access, the relationship between education and health: reducing disparities through a contextual approach.

• Anna Zajacova 1 , and Elizabeth M. Lawrence 2
• View Affiliations Hide Affiliations Affiliations: 1 Department of Sociology, Western University, London, Ontario N6A 5C2, Canada; email: [email protected] 2 Department of Sociology, University of Nevada, Las Vegas, Nevada 89154, USA; email: [email protected]
• Vol. 39:273-289 (Volume publication date April 2018) https://doi.org/10.1146/annurev-publhealth-031816-044628
• First published as a Review in Advance on January 12, 2018
• Copyright © 2018 Anna Zajacova & Elizabeth M. Lawrence. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Adults with higher educational attainment live healthier and longer lives compared with their less educated peers. The disparities are large and widening. We posit that understanding the educational and macrolevel contexts in which this association occurs is key to reducing health disparities and improving population health. In this article, we briefly review and critically assess the current state of research on the relationship between education and health in the United States. We then outline three directions for further research: We extend the conceptualization of education beyond attainment and demonstrate the centrality of the schooling process to health; we highlight the dual role of education as a driver of opportunity but also as a reproducer of inequality; and we explain the central role of specific historical sociopolitical contexts in which the education–health association is embedded. Findings from this research agenda can inform policies and effective interventions to reduce health disparities and improve health for all Americans.

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Literature Cited

• Adler NE . , Cutler DM . , Fielding JE . , Galea S . , Glymour MM . 1.  , et al. 2016 . . Addressing social determinants of health and health disparities . Disc. Pap., Sept 19, Natl. Acad. Med. Vital Dir. Health Health Care , Washington, DC: . https://nam.edu/wp-content/uploads/2016/09/Addressing-Social-Determinants-of-Health-and-Health-Disparities.pdf [Google Scholar]
• Adler NE . , Ostrove JM . 2.  . 1999 . . Socioeconomic status and health: what we know and what we don't. . Ann. N. Y. Acad. Sci. 896 : : 3 – 15 [Google Scholar]
• Amin V . , Behrman JR . , Kohler H-P . 3.  . 2015 . . Schooling has smaller or insignificant effects on adult health in the US than suggested by cross-sectional associations: new estimates using relatively large samples of identical twins. . Soc. Sci. Med. 127 : : 181 – 89 [Google Scholar]
• Arendt JN . 4.  . 2005 . . Does education cause better health? A panel data analysis using school reforms for identification. . Econ. Educ. Rev. 24 : : 149 – 60 [Google Scholar]
• Armstrong EA . , Hamilton LT . 5.  . 2013 . . Paying for the Party . Cambridge, MA: : Harvard Univ. Press [Google Scholar]
• Augustine JM . , Cavanagh SE . , Crosnoe R . 6.  . 2009 . . Maternal education, early child care and the reproduction of advantage. . Soc. Forces 88 : : 1 – 29 [Google Scholar]
• Avendano M . , de Coulon A . , Nafilyan V . 7.  . 2017 . . Does more education always improve mental health? Evidence from a British compulsory schooling reform . Work. Pap., Health, Econom. Data Group, Dep. Econ., Univ. York. https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/1710.pdf [Google Scholar]
• Baker DP . 8.  . 2014 . . The Schooled Society: The Educational Transformation of Global Culture . Stanford, CA: : Stanford Univ. Press [Google Scholar]
• Baker DP . , Smith WC . , Muñoz IG . , Jeon H . , Fu T . 9.  , et al. 2017 . . The population education transition curve: education gradients across population exposure to new health risks. . Demography 54 : : 1873 – 95 [Google Scholar]
• Bauldry S . 10.  . 2014 . . Conditional health-related benefits of higher education: an assessment of compensatory versus accumulative mechanisms. . Soc. Sci. Med. 111 : : 94 – 100 [Google Scholar]
• Baum A . , Garofalo JP . , Yali AM . 11.  . 1999 . . Socioeconomic status and chronic stress: Does stress account for SES effects on health?. Ann. N. Y. Acad. Sci. 896 : : 131 – 44 [Google Scholar]
• Becker GS . 12.  . 1964 . . Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education . Chicago: : Univ. Chicago Press [Google Scholar]
• Behrman J . , Kohler H-P . , Jensen VM . , Pedersen D . , Petersen I . 13.  , et al. 2011 . . Does more schooling reduce hospitalization and delay mortality? New evidence based on Danish twins. . Demography 48 : : 1347 – 75 [Google Scholar]
• Behrman JR . , Xiong Y . , Zhang J . 14.  . 2015 . . Cross-sectional schooling-health associations misrepresented causal schooling effects on adult health and health-related behaviors: evidence from the Chinese Adults Twins Survey. . Soc. Sci. Med. 127 : : 190 – 97 [Google Scholar]
• Boardman JD . , Fletcher JM . 15.  . 2014 . . To cause or not to cause? That is the question, but identical twins might not have all of the answers. . Soc. Sci. Med. 127 : : 198 – 200 [Google Scholar]
• Böckerman P . , Maczulskij T . 16.  . 2016 . . The education-health nexus: fact and fiction. . Soc. Sci. Med. 150 : : 112 – 16 [Google Scholar]
• Bor J . , Cohen GH . , Galea S . 17.  . 2017 . . Population health in an era of rising income inequality: USA, 1980–2015. . Lancet 389 : : 1475 – 90 [Google Scholar]
• Bourdieu P . , Passeron J-C . 18.  . 1990 . . Reproduction in Education, Society and Culture , transl. R Nice . Newbury Park, CA: : Sage. (From French) [Google Scholar]
• Brand JE . , Xie Y . 19.  . 2010 . . Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education. . Am. Sociol. Rev. 75 : : 273 – 302 [Google Scholar]
• Breen R . , Jonsson JO . 20.  . 2005 . . Inequality of opportunity in comparative perspective: recent research on educational attainment and social mobility. . Annu. Rev. Sociol. 31 : : 223 – 43 [Google Scholar]
• Buchmann C . , DiPrete TA . , McDaniel A . 21.  . 2008 . . Gender inequalities in education. . Annu. Rev. Sociol. 34 : : 319 – 37 [Google Scholar]
• 22.  Bur. Labor Stat. 2017 . . College enrollment and work activity of 2016 high school graduates . News Release, April 27, Bur. Labor Stat ., Washington, DC: . https://www.bls.gov/news.release/pdf/hsgec.pdf [Google Scholar]
• Cahalan M . , Perna L . , Yamashita M . 23.  . 2016 . . Indicators of Higher Education Equity in the United States: 2016 Historical Trend Report . Washington, DC/Philadelphia, PA: : Pell Inst./Penn Alliance for High. Educ. Democr. (AHEAD) [Google Scholar]
• Calarco JM . 24.  . 2014 . . Coached for the classroom: parents’ cultural transmission and children's reproduction of educational inequalities. . Am. Sociol. Rev. 79 : : 1015 – 37 [Google Scholar]
• Campbell F . , Conti G . , Heckman JJ . , Moon SH . , Pinto R . 25.  , et al. 2014 . . Early childhood investments substantially boost adult health. . Science 343 : : 1478 – 85 [Google Scholar]
• Card D . , Lemieux T . 26.  . 1996 . . Wage dispersion, returns to skill, and black-white wage differentials. . J. Econ. 74 : : 319 – 61 [Google Scholar]
• Carnevale AP . 27.  . 2008 . . College for all?. Change 40 : : 22 – 31 [Google Scholar]
• Case A . , Deaton A . 28.  . 2015 . . Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. . PNAS 112 : : 15078 – 83 [Google Scholar]
• Case A . , Deaton A . 29.  . 2017 . . Mortality and morbidity in the 21st century. . Brookings Pap. Econ. Act. March 23. https://www.brookings.edu/bpea-articles/mortality-and-morbidity-in-the-21st-century/ [Google Scholar]
• Cataldi EF . , Green C . , Henke R . , Lew T . , Woo J . 30.  , et al. 2011 . . 2008–09 Baccalaureate and Beyond Longitudinal Study (B&B:08/09): First Look (NCES 2011–236) , ed. US Dep. Educ . Washington, DC: : Natl. Cent. Educ. Stat . https://nces.ed.gov/pubs2011/2011236.pdf [Google Scholar]
• Christenson BA . , Johnson NE . 31.  . 1995 . . Educational inequality in adult mortality: an assessment with death certificate data from Michigan. . Demography 32 : : 215 – 29 [Google Scholar]
• Clark D . , Royer H . 32.  . 2013 . . The effect of education on adult mortality and health: evidence from Britain. . Am. Econ. Rev. 103 : : 2087 – 120 [Google Scholar]
• Coburn D . 33.  . 2000 . . Income inequality, social cohesion and the health status of populations: the role of neo-liberalism. . Soc. Sci. Med. 51 : : 135 – 46 [Google Scholar]
• Collins R . 34.  . 1979 . . The Credential Society: An Historical Sociology of Education and Stratification . . New York: : Academic
• Crimmins E . , Kim J . , Vasunilashorn S . 35.  . 2010 . . Biodemography: new approaches to understanding trends and differences in population health and mortality. . Demography 47 : : S41 – 64 [Google Scholar]
• Cutler DM . , Lleras-Muney A . 36.  . 2008 . . Education and health: evaluating theories and evidence. . In Making Americans Healthier: Social and Economic Policy as Health Policy , ed. RF Schoeni, JS House, GA Kaplan, H Pollack , pp. 29 – 60 . New York: : Russell Sage Found. [Google Scholar]
• Cutler DM . , Lleras-Muney A . 37.  . 2010 . . Understanding differences in health behaviors by education. . J. Health Econ. 29 : : 1 – 28 [Google Scholar]
• Dowd JB . , Hamoudi A . 38.  . 2014 . . Is life expectancy really falling for groups of low socio-economic status? Lagged selection bias and artefactual trends in mortality. . Int. J. Epidemiol. 43 : : 983 – 88 [Google Scholar]
• Duke N . , Macmillan R . 39.  . 2016 . . Schooling, skills, and self-rated health: a test of conventional wisdom on the relationship between educational attainment and health. . Sociol. Educ. 89 : : 171 – 206 [Google Scholar]
• Eide ER . , Showalter MH . 40.  . 2011 . . Estimating the relation between health and education: What do we know and what do we need to know?. Econ. Educ. Rev. 30 : : 778 – 91 [Google Scholar]
• Eikemo TA . , Huisman M . , Bambra C . , Kunst AE . 41.  . 2008 . . Health inequalities according to educational level in different welfare regimes: a comparison of 23 European countries. . Sociol. Health Illn. 30 : : 565 – 82 [Google Scholar]
• Elo IT . , Preston SH . 42.  . 1996 . . Educational differentials in mortality: United States, 1979–85. . Soc. Sci. Med. 42 : : 47 – 57 [Google Scholar]
• Evans GW . , Kantrowitz E . 43.  . 2002 . . Socioeconomic status and health: the potential role of environmental risk exposure. . Annu. Rev. Public Health 23 : : 303 – 31 [Google Scholar]
• Ezzati M . , Friedman AB . , Kulkarni SC . , Murray CJ . 44.  . 2008 . . The reversal of fortunes: trends in county mortality and cross-county mortality disparities in the United States. . PLOS Med . 5 : : e66 [Google Scholar]
• Feinstein L . , Sabates R . , Anderson TM . , Sorhaindo A . , Hammond C . 45.  . 2006 . . What are the effects of education on health?. In Measuring the Effects of Education on Health and Civic Engagement: Proceedings of the Copenhagen Symposium , pp. 171 – 354 . Paris: : OECD [Google Scholar]
• Feldman JJ . , Makuc DM . , Kleinman JC . , Cornoni-Huntley J . 46.  . 1989 . . National trends in educational differentials in mortality. . Am. J. Epidemiol. 129 : : 919 – 33 [Google Scholar]
• Fletcher JM . 47.  . 2015 . . New evidence of the effects of education on health in the US: compulsory schooling laws revisited. . Soc. Sci. Med. 127 : : 101 – 7 [Google Scholar]
• Freese J . , Lutfey K . 48.  . 2011 . . Fundamental causality: challenges of an animating concept for medical sociology. . In Handbook of the Sociology of Health, Illness, and Healing: A Blueprint for the 21st Century , ed. BA Pescosolido, JK Martin, JD McLeod, A Rogers , pp. 67 – 81 . New York: : Springer [Google Scholar]
• Fujiwara T . , Kawachi I . 49.  . 2009 . . Is education causally related to better health? A twin fixed-effect study in the USA. . Int. J. Epidemiol. 38 : : 1310 – 22 [Google Scholar]
• George LK . 50.  . 2003 . . Life course research. . In Handbook of the Life Course , ed. JT Mortimer, JM Shanahan , pp. 671 – 80 . New York: : Springer Sci.+Bus. Media [Google Scholar]
• Gerdtham U-G . , Lundborg P . , Lyttkens CH . , Nystedt P . 51.  . 2016 . . Do education and income really explain inequalities in health? Applying a twin design. . Scand. J. Econ. 118 : : 25 – 48 [Google Scholar]
• Gerth H . , Mills CW . 52.  , eds. 1946 . . From Max Weber: Essays in Sociology . New York: : Oxford Univ. Press [Google Scholar]
• Goesling B . 53.  . 2007 . . The rising importance of education for health?. Soc. Forces 85 : : 1621 – 44 [Google Scholar]
• Grodsky E . , Jackson E . 54.  . 2009 . . Social stratification in higher education. . Teach. Coll. Rec. 111 : : 2347 – 84 [Google Scholar]
• Halpern-Manners A . , Schnabel L . , Hernandez EM . , Silberg JL . , Eaves LJ . 55.  . 2016 . . The relationship between education and mental health: new evidence from a discordant twin study. . Soc. Forces 95 : : 107 – 31 [Google Scholar]
• Hayward MD . , Hummer RA . , Sasson I . 56.  . 2015 . . Trends and group differences in the association between educational attainment and US adult mortality: implications for understanding education's causal influence. . Soc. Sci. Med. 127 : : 8 – 18 [Google Scholar]
• Heckman JJ . 57.  . 2006 . . Skill formation and the economics of investing in disadvantaged children. . Science 312 : : 1900 – 2 [Google Scholar]
• Hendi AS . 58.  . 2015 . . Trends in U.S. life expectancy gradients: the role of changing educational composition. . Int. J. Epidemiol. 44 : : 946 – 55 [Google Scholar]
• Hessel P . , Thomas JS . 59.  . 2016 . . Invited commentary to: Lynch and von Hippel “An education gradient in health, a health gradient in education, or a confounded gradient in both?. ” Soc. Sci. Med. 158 : : 168 – 70 [Google Scholar]
• Hill TD . , Needham BL . 60.  . 2006 . . Gender-specific trends in educational attainment and self-rated health, 1972–2002. . Am. J. Public Health 96 : : 1288 – 92 [Google Scholar]
• Hoffmann R . 61.  . 2008 . . Socioeconomic Differences in Old Age Mortality . Dordrecht, Neth: .: Springer Sci.+Bus. Media [Google Scholar]
• Holmes CJ . , Zajacova A . 62.  . 2014 . . Education as “the great equalizer”: health benefits for black and white adults. . Soc. Sci. Q. 95 : : 1064 – 85 [Google Scholar]
• Houle JN . 63.  . 2013 . . Disparities in debt: parents’ socioeconomic resources and young adult student loan debt. . Sociol. Educ. 87 : : 53 – 69 [Google Scholar]
• Houle JN . , Warner C . 64.  . 2017 . . Into the red and back to the nest? Student debt, college completion, and returning to the parental home among young adults. . Sociol. Educ. 90 : : 89 – 108 [Google Scholar]
• Hout M . 65.  . 2012 . . Social and economic returns to college education in the United States. . Annu. Rev. Sociol. 38 : : 379 – 400 [Google Scholar]
• Hummer RA . , Lariscy JT . 66.  . 2011 . . Educational attainment and adult mortality. . In International Handbook of Adult Mortality , ed. RG Rogers, EM Crimmins , pp. 241 – 61 . New York: : Springer [Google Scholar]
• Jemal A . , Ward E . , Hao Y . , Thun MJ . 67.  . 2005 . . Trends in the leading causes of death in the United States, 1970–2002. . JAMA 294 : : 1255 – 59 [Google Scholar]
• Johnson-Lawrence V . , Zajacova A . , Sneed R . 68.  . 2017 . . Education, race/ethnicity, and multimorbidity among adults aged 30–64 in the National Health Interview Survey. . SSM—Popul. Health 3 : : 366 – 72 [Google Scholar]
• Kao G . , Thompson JS . 69.  . 2003 . . Racial and ethnic stratification in educational achievement and attainment. . Annu. Rev. Sociol. 29 : : 417 – 42 [Google Scholar]
• Kirkpatrick Johnson M . , Staff J . , Schulenberg JE . , Patrick ME . 70.  . 2016 . . Living healthier and longer: a life course perspective on education and health. . In Handbook of the Life Course , ed. MJ Shanahan, JT Mortimer, M Kirkpatrick Johnson , pp. 369 – 88 . Cham, Switz: .: Springer Int. [Google Scholar]
• Kitagawa EM . , Hauser PM . 71.  . 1973 . . Differential Mortality in the United States: A Study in Socioeconomic Epidemiology . Cambridge, MA: : Harvard Univ. Press [Google Scholar]
• Krieger N . , Rehkopf DH . , Chen JT . , Waterman PD . , Marcelli E . , Kennedy M . 72.  . 2008 . . The fall and rise of US inequities in premature mortality: 1960–2002. . PLOS Med . 5 : : e46 [Google Scholar]
• Lawrence EM . 73.  . 2017 . . Why do college graduates behave more healthfully than those who are less educated?. J. Health Soc. Behav. 58 : : 291 – 306 [Google Scholar]
• Lawrence EM . , Rogers RG . , Zajacova A . 74.  . 2016 . . Educational attainment and mortality in the United States: effects of degrees, years of schooling, and certification. . Popul. Res. Policy Rev. 35 : : 501 – 25 [Google Scholar]
• Link BG . , Phelan J . 75.  . 1995 . . Social conditions as fundamental causes of disease. . J. Health Soc. Behav. 35 : : 80 – 94 [Google Scholar]
• Link BG . , Phelan JC . 76.  . 2006 . . Stigma and its public health implications. . Lancet 367 : : 528 – 29 [Google Scholar]
• Liu H . , Hummer RA . 77.  . 2008 . . Are educational differences in U.S. self-rated health increasing?: An examination by gender and race. . Soc. Sci. Med. 67 : : 1898 – 906 [Google Scholar]
• Lleras-Muney A . 78.  . 2005 . . The relationship between education and adult mortality in the United States. . Rev. Econ. Stud. 72 : : 189 – 221 [Google Scholar]
• Lundborg P . 79.  . 2013 . . The health returns to schooling—what can we learn from twins?. J. Popul. Econ. 26 : : 673 – 701 [Google Scholar]
• Lundborg P . , Lyttkens CH . , Nystedt P . 80.  . 2016 . . The effect of schooling on mortality: new evidence from 50,000 Swedish twins. . Demography 53 : : 1135 – 68 [Google Scholar]
• Lynch JL . , von Hippel PT . 81.  . 2016 . . An education gradient in health, a health gradient in education, or a confounded gradient in both?. Soc. Sci. Med. 154 : : 18 – 27 [Google Scholar]
• Mackenbach JP . , Stirbu I . , Roskam A-JR . , Schaap MM . , Menvielle G . 82.  , et al. 2008 . . Socioeconomic inequalities in health in 22 European countries. . N. Engl. J. Med. 358 : : 2468 – 81 [Google Scholar]
• Madsen M . , Andersen PK . , Gerster M . , Andersen A-MN . , Christensen K . , Osler M . 83.  . 2014 . . Are the educational differences in incidence of cardiovascular disease explained by underlying familial factors? A twin study. . Soc. Sci. Med. 118 : : 182 – 90 [Google Scholar]
• Marmot MG . , Bell R . 84.  . 2009 . . Action on health disparities in the United States: Commission on Social Determinants of Health. . JAMA 301 : : 1169 – 71 [Google Scholar]
• Martin LG . , Schoeni RF . , Andreski PM . 85.  . 2010 . . Trends in health of older adults in the United States: past, present, future. . Demography 47 : : S17 – 40 [Google Scholar]
• Masters RK . , Hummer RA . , Powers DA . 86.  . 2012 . . Educational differences in U.S. adult mortality: a cohort perspective. . Am. Sociol. Rev. 77 : : 548 – 72 [Google Scholar]
• Mayhew MJ . , Rockenbach AN . , Bowman NA . , Seifert TA . , Wolniak GC . 87.  , et al. 2016 . . How College Affects Students . Volume 3: 21st Century Evidence That Higher Education Works . San Francisco, CA: : Jossey-Bass [Google Scholar]
• Mazumder B . 88.  . 2008 . . Does education improve health? A reexamination of the evidence from compulsory schooling laws. . Econ. Perspect. 32 : : 2 – 16 [Google Scholar]
• Mazumder B . 89.  . 2012 . . The effects of education on health and mortality. . Nord. Econ. Policy Rev. 2012 : : 264 – 309 [Google Scholar]
• McDade TW . , Hawkley LC . , Cacioppo JT . 90.  . 2006 . . Psychosocial and behavioral predictors of inflammation in middle-aged and older adults: the Chicago health, aging, and social relations study. . Psychosom. Med. 68 : : 376 – 81 [Google Scholar]
• McDonough P . , Williams DR . , House JS . , Duncan GJ . 91.  . 1999 . . Gender and the socioeconomic gradient in mortality. . J. Health Soc. Behav. 40 : : 17 – 31 [Google Scholar]
• Meara ER . , Richards S . , Cutler DM . 92.  . 2008 . . The gap gets bigger: changes in mortality and life expectancy, by education, 1981–2000. . Health Aff . 27 : : 350 – 60 [Google Scholar]
• Mirowsky J . , Ross CE . 93.  . 2003 . . Education, Social Status, and Health . . New York: : Aldine de Gruyter
• Mirowsky J . , Ross CE . 94.  . 2008 . . Education and self-rated health: cumulative advantage and its rising importance. . Res. Aging 30 : : 93 – 122 [Google Scholar]
• Monnat S . 95.  . 2016 . . Deaths of despair and support for Trump in the 2016 presidential election . Res. Brief, Dec. 4, Pa. State Univ. Dep. Agric. Econ., Soc. Educ., State College, PA. http://aese.psu.edu/directory/smm67/Election16.pdf [Google Scholar]
• Montez JK . 96.  . 2017 . . Deregulation, devolution, and state preemption laws’ impact on US mortality trends. . Am. J. Public Health 107 : : 1749 – 50 [Google Scholar]
• Montez JK . 97.  . 2017 . . Do U.S. states’ socioeconomic and political contexts shape adult disability? Presented at NIA-Supported Network on Life Course Health Dynamics and Disparities in 21st Century America, Chicago [Google Scholar]
• Montez JK . , Hummer RA . , Hayward MD . 98.  . 2012 . . Educational attainment and adult mortality in the United States: a systematic analysis of functional form. . Demography 49 : : 315 – 36 [Google Scholar]
• Montez JK . , Zajacova A . 99.  . 2013 . . Trends in mortality risk by education level and cause of death among US white women from 1986 to 2006. . Am. J. Public Health 103 : : 473 – 79 [Google Scholar]
• Montez JK . , Zajacova A . , Hayward MD . 100.  . 2016 . . Explaining inequalities in women's mortality between US states. . SSM—Popul. Health 2 : : 561 – 71 [Google Scholar]
• Montez JK . , Zajacova A . , Hayward MD . 101.  . 2017 . . Disparities in disability by educational attainment across US states. . Am. J. Public Health 107 : : 1101 – 8 [Google Scholar]
• Muennig P . 102.  . 2015 . . Can universal pre-kindergarten programs improve population health and longevity? Mechanisms, evidence, and policy implications. . Soc. Sci. Med. 127 : : 116 – 23 [Google Scholar]
• Nash R . 103.  . 1990 . . Bourdieu on education and social and cultural reproduction. . Br. J. Sociol. Educ. 11 : : 431 – 47 [Google Scholar]
• Navarro V . , Shi L . 104.  . 2001 . . The political context of social inequalities and health. . Soc. Sci. Med. 52 : : 481 – 91 [Google Scholar]
• Pampel FC . , Krueger PM . , Denney JT . 105.  . 2010 . . Socioeconomic disparities in health behaviors. . Annu. Rev. Sociol. 36 : : 349 – 70 [Google Scholar]
• Piketty T . 106.  . 2014 . . Capital in the Twenty-First Century . Cambridge, MA: : Harvard Univ. Press [Google Scholar]
• Psacharopoulos G . , Patrinos HA . 107.  . 2004 . . Returns to investment in education: a further update. . Educ. Econ. 12 : : 111 – 34 [Google Scholar]
• Quiñones AR . , Markwardt S . , Botoseneanu A . 108.  . 2016 . . Multimorbidity combinations and disability in older adults. . J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 71 : : 823 – 30 [Google Scholar]
• Rogot E . , Sorlie PD . , Johnson NJ . 109.  . 1992 . . Life expectancy by employment status, income, and education in the National Longitudinal Mortality Study. . Public Health Rep . 107 : : 457 – 61 [Google Scholar]
• Rosenbaum J . 110.  . 2012 . . Degrees of health disparities: health status disparities between young adults with high school diplomas, sub-baccalaureate degrees, and baccalaureate degrees. . Health Serv. Outcomes Res. Methodol. 12 : : 156 – 68 [Google Scholar]
• Ross C . , Masters R . , Hummer RA . 111.  . 2012 . . Education and the gender gaps in health and mortality. . Demography 49 : : 1157 – 83 [Google Scholar]
• Ross CE . , Wu C-L . 112.  . 1995 . . The links between education and health. . Am. Sociol. Rev. 60 : : 719 – 45 [Google Scholar]
• Ryan CL . , Bauman K . 113.  . 2016 . . Educational attainment in the United States: 2015 . March, Rep. P20-578, US Census Bur ., Washington, DC: . https://www.census.gov/content/dam/Census/library/publications/2016/demo/p20-578.pdf [Google Scholar]
• Saha LJ . 114.  . 2008 . . Sociology of education. . In 21st Century Education: A Reference Handbook , ed. TL Good , pp. 299 – 307 . Thousand Oaks, CA: : Sage [Google Scholar]
• Sasson I . 115.  . 2016 . . Diverging trends in cause-specific mortality and life years lost by educational attainment: evidence from United States Vital Statistics Data, 1990–2010. . PLOS ONE 11 : : e0163412 [Google Scholar]
• Sasson I . 116.  . 2016 . . Trends in life expectancy and lifespan variation by educational attainment: United States, 1990–2010. . Demography 53 : : 269 – 93 [Google Scholar]
• Schoenborn CA . , Adams PF . , Peregoy JA . 117.  . 2013 . . Health Behaviors of Adults: United States, 2008–2010 . Vital Health Stat. Ser. 10 . Atlanta: : US Dep Health Hum. Serv., CDC, Natl. Cent. Health Stat . https://www.cdc.gov/nchs/data/series/sr_10/sr10_257.pdf [Google Scholar]
• Schoeni RF . , Freedman VA . , Wallace RB . 118.  . 2001 . . Persistent, consistent, widespread, and robust? Another look at recent trends in old-age disability. . J. Gerontol. Ser. B: Psychol. Sci. Soc. Sci. 56 : : S206 – 18 [Google Scholar]
• Schoeni RF . , Martin LG . , Andreski PM . , Freedman VA . 119.  . 2005 . . Persistent and growing socioeconomic disparities in disability among the elderly: 1982–2002. . Am. J. Public Health 95 : : 2065 – 70 [Google Scholar]
• Sewell WH . , Haller AO . , Portes A . 120.  . 1969 . . The educational and early occupational attainment process. . Am. Sociol. Rev. 34 : : 82 – 92 [Google Scholar]
• Shiels MS . , Chernyavskiy P . , Anderson WF . , Best AF . , Haozous EA . 121.  , et al. 2017 . . Diverging trends in premature mortality in the U.S. by sex, race, and ethnicity in the 21st century. . Lancet 389 : : 1043 – 54 [Google Scholar]
• Silles MA . 122.  . 2009 . . The causal effect of education on health: evidence from the United Kingdom. . Econ. Educ. Rev. 28 : : 122 – 28 [Google Scholar]
• Skalamera J . , Hummer RA . 123.  . 2016 . . Educational attainment and the clustering of health-related behavior among U.S. young adults. . Prev. Med. 84 : : 83 – 89 [Google Scholar]
• Sorlie PD . , Backlund E . , Keller JB . 124.  . 1995 . . US mortality by economic, demographic, and social characteristics: the National Longitudinal Mortality Study. . Am. J. Public Health 85 : : 949 – 56 [Google Scholar]
• Spence M . 125.  . 1973 . . Job market signalling. . Q. J. Econ. 87 : : 355 – 79 [Google Scholar]
• Stevens ML . , Armstrong EA . , Arum R . 126.  . 2008 . . Sieve, incubator, temple, hub: empirical and theoretical advances in the sociology of higher education. . Annu. Rev. Sociol. 34 : : 127 – 51 [Google Scholar]
• Stuber JM . 127.  . 2011 . . Inside the College Gates: How Class and Culture Matter in Higher Education . Lanham, MD: : Lexington Books [Google Scholar]
• Taylor SE . , Seeman TE . 128.  . 1999 . . Psychosocial resources and the SES‐health relationship. . Ann. N. Y. Acad. Sci. 896 : : 210 – 25 [Google Scholar]
• Temin P . 129.  . 2017 . . The Vanishing Middle Class: Prejudice and Power in a Dual Economy . Cambridge, MA: : MIT Press [Google Scholar]
• Tsai Y . 130.  . 2016 . . Education and disability trends of older Americans, 2000–2014. . J. Public Health 39 : : 447 – 54 [Google Scholar]
• Waite LJ . , Gallagher M . 131.  . 2002 . . The Case for Marriage: Why Married People Are Happier, Healthier and Better Off Financially . New York: : Random House [Google Scholar]
• Walsemann KM . , Gee GC . , Ro A . 132.  . 2013 . . Educational attainment in the context of social inequality: new directions for research on education and health. . Am. Behav. Sci. 57 : : 1082 – 104 [Google Scholar]
• Williams DR . 133.  . 1990 . . Socioeconomic differentials in health: a review and redirection. . Soc. Psychol. Q. 53 : : 81 – 99 [Google Scholar]
• Williams DR . , Collins C . 134.  . 1995 . . US socioeconomic and racial differences in health: patterns and explanations. . Annu. Rev. Sociol. 21 : : 349 – 86 [Google Scholar]
• Williams DR . , Jackson PB . 135.  . 2005 . . Social sources of racial disparities in health. . Health Aff . 24 : : 325 – 34 [Google Scholar]
• Willis PE . 136.  . 1977 . . Learning to Labor: How Working Class Kids Get Working Class Jobs . . New York: : Columbia Univ. Press
• Woolf SH . , Aron LY . 137.  . 2013 . . The US health disadvantage relative to other high-income countries: findings from a National Research Council/Institute of Medicine report. . JAMA 309 : : 771 – 72 [Google Scholar]
• Yoshikawa H . , Weiland C . , Brooks-Gunn J . 138.  . 2016 . . When does preschool matter?. Future Child . 26 : : 21 – 35 [Google Scholar]
• Zajacova A . 139.  . 2006 . . Education, gender, and mortality: Does schooling have the same effect on mortality for men and women in the US?. Soc. Sci. Med. 63 : : 2176 – 90 [Google Scholar]
• Zajacova A . , Dowd JB . , Aiello AE . 140.  . 2009 . . Socioeconomic and race/ethnic patterns in persistent infection burden among U.S. adults. . J. Gerontol. A: Biol. Sci. Med. Sci. 64 : : 272 – 79 [Google Scholar]
• Zajacova A . , Hummer RA . , Rogers RG . 141.  . 2012 . . Education and health among U.S. working-age adults: a detailed portrait across the full educational-attainment spectrum. . Biodemogr. Soc. Biol. 58 : : 40 – 61 [Google Scholar]
• Zajacova A . , Johnson-Lawrence V . 142.  . 2016 . . Anomaly in the education–health gradient: biomarker profiles among adults with subbaccalaureate attainment levels. . SSM — Popul. Health 2 : : 360 – 64 [Google Scholar]
• Zajacova A . , Montez JK . 143.  . 2017 . . Physical functioning trends among US women and men age 45–64 by education level. . Biodemogr. Soc. Biol. 63 : : 21 – 30 [Google Scholar]

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Health Education and Health Promotion: Key Concepts and Exemplary Evidence to Support Them

• First Online: 09 October 2018

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• Hein de Vries 8 ,
• Stef P. J. Kremers 8 &
• Sonia Lippke 9  

Health is regarded as the result of an interaction between individual and environmental factors. While health education is the process of educating people about health and how they can influence their health, health promotion targets not only people but also their environments. Promoting health behavior can take place at the micro level (the personal level), the meso level (the organizational level, including e.g. families, schools and worksites) and at the macro level (the (inter)national level, including e.g. governments). Health education is one of the methods used in health promotion, with health promotion extending beyond just health education.

Models and theories that focus on understanding health and health behavior are of key importance for health education and health promotion. Different classes of models and theories can be distinguished, such as planning models, behavioral change models, and diffusion models. Within these models different topics and factors are relevant, ranging from health literacy, attitudes, social influences, self-efficacy, planning, and stages of change to evaluation, implementation, stakeholder involvement, and policy changes. Exemplary health promotion settings are schools, worksites, and healthcare, but also the domains that are involved with policy development. Main health promotion methods can involve a variety of different methods and approaches, such as counseling, brochures, eHealth, stakeholder involvement, consensus meetings, community ownership, panel discussions, and policy development. Because health education and health promotion should be theory- and evidence-based, personalized interventions are recommended to take empirical findings and proven theoretical assumptions into account.

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Aarts, H., Paulussen, T., & Schaalma, H. (1997). Physical exercise habit: On the conceptualization and formation of habitual health behaviours. Health Education Research, 12 (3), 363.

Article   PubMed   Google Scholar  

Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology, 27 (3), 379–387.

Abraham, C., & Sheeran, P. (2003). Acting on intentions: The role of anticipated regret. The British Journal of Social Psychology, 42 (Pt 4), 495–511.

Abraham, C., & Sheeran, P. (2005). The Health Belief Model. In M. Conner & P. Norman (Eds.), Predicting health behaviour (pp. 28–80). Berkshire: Open University Press.

Google Scholar  

Abroms, L. C., & Maibach, E. W. (2008). The effectiveness of mass communication to change public behavior. Annual Review of Public Health, 29 , 219–234.

Adams, J., & White, M. (2003). Are activity promotion interventions based on the transtheoretical model effective? A critical review. British Journal of Sports Medicine, 37 (2), 106–114.

Article   PubMed   PubMed Central   Google Scholar  

Ajzen, I. (1988). Attitudes, personality, and behavior . Chicago: Dorsey Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 (2), 179–211.

Article   Google Scholar  

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior . Englewood Cliffs: Prentice-Hall.

Aldana, S. G., Merrill, R. M., Price, K., Hardy, A., & Hager, R. (2005). Financial impact of a comprehensive multisite workplace health promotion program. Preventive Medicine, 40 (2), 131–137.

Alderman, M. H., & Schoenbaum, E. E. (1975). Detection and treatment of hypertension at the work site. The New England Journal of Medicine, 293 (2), 65–68.

Armitage, C. J. (2009). Is there utility in the transtheoretical model? British Journal of Health Psychology, 14 (2), 195–210.

Armitage, C. J. (2004). Evidence that implementation intentions reduce dietary fat intake: A randomized trial. Health Psychology, 23 (3), 319.

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. The British Journal of Social Psychology, 40 (Pt 4), 471–499.

Armstrong, N., & Powell, J. (2008). Preliminary test of an Internet-based diabetes self-management tool. Journal of Telemedicine and Telecare, 14 (3), 114–116.

Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs, 70 (9), 1.

Ashford, S., Edmunds, J., & French, D. P. (2010). What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. British Journal of Health Psychology, 15 (Pt 2), 265–288.

Ausems, M., Mesters, I., van Breukelen, G., & De Vries, H. (2004). Effects of in-school and tailored out-of-school smoking prevention among Dutch vocational school students. Health Education Research, 19 (1), 51–63.

Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: Structure, process, and content. Psychological Bulletin, 120 (3), 338.

Aveyard, P., Massey, L., Parsons, A., Manaseki, S., & Griffin, C. (2009). The effect of Transtheoretical Model based interventions on smoking cessation. Social science and medicine, 68 (3), 397–403.

Babbin, S. F., Harrington, M., Burditt, C., Redding, C., Paiva, A., Meier, K., Oatley, K., McGee, H., & Velicer, W. F. (2011). Prevention of alcohol use in middle school students: Psychometric assessment of the decisional balance inventory. Addictive Behaviors, 36 (5), 543–546.

Bagozzi, R. P. (1992). The self-regulation of attitudes, intentions, and behavior. Social Psychology Quarterly, 55 (2), 178.

Bagozzi, R. P., & Dholakia, U. (1999). Goal setting and goal striving in consumer behavior. Journal of Marketing, 63 , 19–32.

Baker, D. W., Parker, R. M., Williams, M. V., & Clark, W. S. (1998). Health literacy and the risk of hospital admission. Journal of General Internal Medicine, 13 (12), 791–798.

Bandura, A. (1977). Social learning theory . New York: General Learning Press.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Englewood Cliffs: Prentice-Hall.

Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44 (9), 1175–1184.

Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28 (2), 117–148.

Bandura, A. (1997). Editorial. American Journal of Health Promotion, 12 (1), 8–10.

Bargh, J. A. (2005). Bypassing the will: Toward demystifying the nonconscious control of social behavior. In R. R. Hassin, J. S. Uleman, & J. A. Bargh (Eds.), The new unconscious (pp. 37–58). New York: Oxford University Press.

Bartholomew, L. K., Parcel, G. S., Kok, G., & Gottlieb, N. H. (2001). Intervention-mapping: Designing theory and evidence-based health promotion programs . Mountain View: Mayfield.

Bartholomew, L. K., Parcel, G. S., Kok, G., Gottlieb, N. H., & Fernández, M. E. (2011). Planning health promotion: An intervention mapping approach (3rd ed.). San Francisco: Jossey Bass.

Bentz, C. J., Bruce Bayley, K., Bonin, K. E., Fleming, L., Hollis, J. F., Hunt, J. S., LeBlanc, B., McAfee, T., Payne, N., & Siemienczuk, J. (2007). Provider feedback to improve 5A’s tobacco cessation in primary care: A cluster randomized clinical trial. Nicotine & Tobacco Research, 9 (3), 341–349.

Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda County residents. American Journal of Epidemiology, 109 (2), 186–204.

Beyer, J. M., & Trice, H. M. (1978). Implementing change: Alcoholism policies in work organization . New York: Free Press.

Blissmer, B., & McAuley, E. (2002). Testing the requirements of stages of physical activity among adults: The comparative effectiveness of stage-matched, mismatched, standard care, and control interventions. Annals of Behavioral Medicine, 24 (3), 181–189.

Bodenheimer, T., Lorig, K., Holman, H., & Grumbach, K. (2002). Patient self-management of chronic disease in primary care. JAMA, 288 (19), 2469–2475.

Boer, H., & Seydel, E. R. (1996). Protection motivation theory. In M. Conner & P. Norman (Eds.), Predicting health behavior (pp. 95–120). Buckingham: Open University Press.

Bolman, C., Arwert, T. G., & Vollink, T. (2011). Adherence to prophylactic asthma medication: Habit strength and cognitions. Heart & Lung, 40 (1), 63–75.

Bonfadelli, H. (2002). Medieninhaltsforschung: Grundlagen, Methoden, Anwendungen . Konstanz: UVK.

Borland, R., Balmford, J., Segan, C., Livingston, P., & Owen, N. (2003). The effectiveness of personalized smoking cessation strategies for callers to a Quitline service. Addiction, 98 (6), 837–846.

Botvin, G. J., Eng, A., & Williams, C. L. (1980). Preventing the onset of cigarette smoking through life skills training. Preventive Medicine, 9 (1), 135–143.

Botvin, G. J., & Griffin, K. W. (2004). Life skills training: Empirical findings and future directions. The Journal of Primary Prevention, 25 (2), 211.

Bracht, N. (1990). Community organization principles in health promotion: A five-stage model. In N. Bracht & L. Kingsbury (Eds.), Health promotion at the community level . Thousand Oaks: Sage.

Bracht, N. (1999). Health promotion at the community level: New advances . Thousand Oaks: Sage.

Brewer, N. T., Weinstein, N. D., Cuite, C. L., & Herrington, J. E. (2004). Risk perceptions and their relation to risk behavior. Annals of Behavioral Medicine, 27 (2), 125.

Brewer, N. T., Brewer, N. T., & Fazekas, K. I. (2007). Predictors of HPV vaccine acceptability: A theory-informed, systematic review. Preventive Medicine: An International Journal Devoted to Practice and Theory, 45 (2–3), 107.

Brinn, M. P., Carson, K. V., Esterman, A. J., Chang, A. B., & Smith, B. J. (2010). Mass media interventions for preventing smoking in young people. Cochrane Database of Systematic Reviews, 11 , CD001006.

Britt, E., Hudson, S. M., & Blampied, N. M. (2004). Motivational interviewing in health settings: A review. Patient Education and Counseling, 53 (2), 147–155.

Brodie, M., Flournoy, R. E., Altman, D. E., Blendon, R. J., Benson, J. M., & Rosenbaum, M. D. (2000). Health information, the Internet, and the digital divide. Health Affairs (Millwood), 19 (6), 255–265.

Brouwer, W., Oenema, A., Raat, H., Crutzen, R., de Nooijer, J., de Vries, N. K., & Brug, J. (2010). Characteristics of visitors and revisitors to an Internet-delivered computer-tailored lifestyle intervention implemented for use by the general public. Health Education Research, 25 (4), 585–595.

Brown, T., & Summerbell, C. (2009). Systematic review of school-based interventions that focus on changing dietary intake and physical activity levels to prevent childhood obesity: An update to the obesity guidance produced by the National Institute for Health and Clinical Excellence. Obesity Reviews, 10 (1), 110–141.

Brug, J., Lechner, L., & De Vries, H. (1995). Psychosocial determinants of fruit and vegetable consumption. Appetite, 25 (3), 285.

Brug, J., Steenhuis, I., van Assema, P., & de Vries, H. (1996). The impact of a computer-tailored nutrition intervention. Preventive Medicine, 25 (3), 236–242.

Brug, J., Steenhuis, I. H. M., Van Assema, P., Glanz, K., & De Vries, H. (1999). The impact of two computer tailored nutrition education interventions. Health Education Research, 14 , 249–256.

Brug, J., Conner, M., Harré, N., Kremers, S., McKellar, S., & Whitelaw, S. (2005). The transtheoretical model and stages of change: A critique. Observations by five commentators on the paper by Adams, J., & White, M. (2004). Why don’t stage-based activity promotion interventions work? Health Education Research, 20 (2), 244.

Bruvold, W. H. (1993). A meta-analysis of adolescent smoking prevention programs. American Journal of Public Health, 83 (6), 872.

Bryan, A., Fisher, J. D., & Fisher, W. A. (2002). Tests of the mediational role of preparatory safer sexual behavior in the context of the theory of planned behavior. Health Psychology, 21 (1), 71–80.

Campbell, M. K., Demark-Wahnefried, W., Symons, M., Kalsbeek, W. D., Dodds, J., Cowan, A., Jackson, B., Motsinger, B., Hoben, K., Lashley, J., Demissie, S., & McClelland, J. W. (1999). Fruit and vegetable consumption and prevention of cancer: The Black Churches United for Better Health project. American Journal of Public Health, 89 (9), 1390–1396.

Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought . New York: The Guilford Press.

Charles, C., Whelan, T., & Gafni, A. (1999). What do we mean by partnership in making decisions about treatment? BMJ, 319 (7212), 780–782.

Chinn, M. D., & Fairlie, R. W. (2007). The determinants of the global digital divide: A cross-country analysis of computer and internet penetration. Oxford Economic Papers, 16 , f-44.

Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98 (2), 310.

Collins, J. L. (1982, March). Self-efficacy and ability in achievement behavior. Paper presented at the annual meeting of the American Educational Research Association, New York.

Conner, M., & Norman, P. (2005). Predicting health behaviour: Research and practice with social cognition models . Maidenhead: Open University Press.

Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28 (15), 1429.

Conner, M., Godin, G., Sheeran, P., & Germain, M. (2013). Some feelings are more important: Cognitive attitudes, affective attitudes, anticipated affect, and blood donation. Health Psychology, 32 (3), 264.

Coster, S., & Norman, I. (2009). Cochrane reviews of educational and self-management interventions to guide nursing practice: A review. International Journal of Nursing Studies, 46 (4), 508–528.

Cramer, J. A. (2004). A systematic review of adherence with medications for diabetes. Diabetes Care, 27 (5), 1218–1224.

Cranney, M., Warren, E., Barton, S., Gardner, K., & Walley, T. (2001). Why do GPs not implement evidence-based guidelines? A descriptive study. Family Practice, 18 (4), 359–363.

Cutrona, S. L., Choudhry, N. K., Stedman, M., Servi, A., Liberman, J. N., Brennan, T., Fischer, M. A., Alan Brookhart, M., & Shrank, W. H. (2010). Physician effectiveness in interventions to improve cardiovascular medication adherence: A systematic review. Journal of General Internal Medicine, 25 (10), 1090–1096.

de Bruijn, G. J., Kremers, S. P., De Vries, H., Van Mechelen, W., & Brug, J. (2007). Associations of social-environmental and individual-level factors with adolescent soft drink consumption: Results from the SMILE study. Health Education Research, 22 (2), 227–237.

de Meij, J. S., Chinapaw, M. J., Kremers, S. P., Jurg, M. E., & Van Mechelen, W. (2010). Promoting physical activity in children: The stepwise development of the primary school-based JUMP-in intervention applying the RE-AIM evaluation framework. British Journal of Sports Medicine, 44 (12), 879–887.

de Nooijer, J., Lechner, L., & de Vries, H. (2002). Early detection of cancer: Knowledge and behavior among Dutch adults. Cancer Detection and Prevention, 26 (5), 362–369.

de Vet, E., de Nooijer, J., de Vries, N. K., & Brug, J. (2008a). Testing the transtheoretical model for fruit intake: Comparing web-based tailored stage-matched and stage-mismatched feedback. Health Education Research, 23 (2), 218–227.

de Vet, E., De Nooijer, J., De Vries, N. K., & Brug, J. (2008b). Do the transtheoretical processes of change predict transitions in stages of change for fruit intake? Health Education & Behavior, 35 (5), 603–618.

de Vet, E., Gebhardt, W. A., Sinnige, J., Van Puffelen, A., Van Lettow, B., & de Wit, J. B. (2011). Implementation intentions for buying, carrying, discussing and using condoms: The role of the quality of plans. Health Education Research, 26 (3), 443–455.

de Vries, H., & Backbier, E. (1994). Self-efficacy as an important determinant of quitting among pregnant women who smoke: The phi-pattern. Preventive Medicine, 23 (2), 167–174.

de Vries, H., & Brug, J. (1999). Computer-tailored interventions motivating people to adopt health promoting behaviours: Introduction to a new approach. Patient Education and Counseling, 36 (2), 99–105.

de Vries, H., & van Dillen, S. (2002). Prevention of Lyme disease in Dutch children: Analysis of determinants of tick inspection by parents. Preventive Medicine, 35 (2), 160–165.

de Vries, H., & van Dillen, S. (2007). I PLAN: Planning the prevention of Lyme disease, a health promotion approach. In P. van Nitch (Ed.), Research on Lyme disease . New York: Nova Science.

de Vries, H., Dijkstra, M., & Kuhlman, P. (1988). Self-efficacy: The third factor besides attitude and subjective norm as a predictor of behavioural intentions. Health Education Research, 3 (3), 273.

de Vries, H., Backbier, E., Kok, G., & Dijkstra, M. (1995). The impact of social influences in the context of attitude, self-efficacy, intention and previous behavior as predictors of smoking onset. Journal of Applied Social Psychology, 25 , 237–257.

de Vries, H., Mudde, A. N., Dijkstra, A., & Willemsen, M. C. (1998). Differential beliefs, perceived social influences, and self-efficacy expectations among smokers in various motivational phases. Preventive Medicine, 27 (5 Pt 1), 681–689.

de Vries, H., Mudde, A., Leijs, I., Charlton, A., Vartiainen, E., Buijs, G., Clemente, M. P., Storm, H., González Navarro, A., Nebot, M., Prins, T., & Kremers, S. (2003). The European Smoking Prevention Framework Approach (EFSA): An example of integral prevention. Health Education Research, 18 (5), 611–626.

de Vries, H., Lezwijn, J., Hol, M., & Honing, C. (2005). Skin cancer prevention: Behaviour and motives of Dutch adolescents. European Journal of Cancer Prevention, 14 (1), 39–50.

de Vries, H., Candel, M., Engels, R., & Mercken, L. (2006). Challenges to the peer influence paradigm: Results for 12–13 year olds from six European countries from the European Smoking Prevention Framework Approach study. Tobacco Control, 15 (2), 83–89.

de Vries, H., van’t Riet, J., Panday, S., & Reubsaet, A. (2007). Access point analysis in smoking and nonsmoking adolescents: Findings from the European Smoking Prevention Framework Approach study. European Journal of Cancer Prevention, 16 (3), 257–265.

de Vries, H., Kremers, S., Smeets, T., & Reubsaet, A. (2008a). Clustering of diet, physical activity and smoking and a general willingness to change. Psychology & Health, 23 (3), 265.

de Vries, H., van’t Riet, J., Spigt, M., Metsemakers, J., van den Akker, M., Vermunt, J. K., & Kremers, S. (2008b). Clusters of lifestyle behaviors: Results from the Dutch SMILE study. Preventive Medicine, 46 (3), 203–208.

de Vries, H., Kremers, S. P. J., Smeets, T., Brug, J., & Eijmael, K. (2008c). The effectiveness of tailored feedback and action plans in an intervention addressing multiple health behaviors. American Journal of Health Promotion, 22 (6), 417–425.

Dean, A. J., Walters, J., & Hall, A. (2010). A systematic review of interventions to enhance medication adherence in children and adolescents with chronic illness. Archives of Disease in Childhood, 95 (9), 717–723.

Della Mea, V. (2001). What is e-Health (2): The death of telemedicine? Journal of Medical Internet Research, 3 (2), e22.

Dempsey, A. R., Johnson, S. S., & Westhoff, C. L. (2011). Predicting oral contraceptive continuation using the transtheoretical model of health behavior change. Perspectives on Sexual and Reproductive Health, 43 (1), 23–29.

Dictionary, O. E. (2010). “Mass media, n.”, Oxford University Press.

Dijk, F., & de Vries, H. (2009). Smoke alert: A computer-tailored smoking-cessation intervention for Dutch adolescents. Documentation of a FCHE workshop held in Berlin, 18–19 September 2008. Research and Practice of Health Promotion, 14 , 34–46.

Dijkstra, A. (2005). Working mechanisms of computer-tailored health education: Evidence from smoking cessation. Health Education Research, 20 (5), 527–539.

Dijkstra, A., & De Vries, H. (1999). The development of computer-generated tailored interventions. Patient Education and Counseling, 36 (2), 193–203.

Dijkstra, A., & De Vries, H. (2000). Subtypes of precontemplating smokers defined by different long-term plans to change their smoking behavior. Health Education Research, 15 (4), 423–434.

Dijkstra, A., Bakker, M., & De Vries, H. (1997). Subtypes within a sample of precontemplating smokers: A preliminary extension of the stages of change. Addictive Behaviors, 22 (3), 327–337.

Dijkstra, A., De Vries, H., Roijackers, J., & van Breukelen, G. (1998). Tailored interventions to communicate stage-matched information to smokers in different motivational stages. Journal of Consulting and Clinical Psychology, 66 (3), 549–557.

Dijkstra, A., Conijn, B., & De Vries, H. (2006). A match–mismatch test of a stage model of behaviour change in tobacco smoking. Addiction, 101 (7), 1035–1043.

DiMatteo, M. R. (2004). Social support and patient adherence to medical treatment: A meta-analysis. Health Psychology, 23 (2), 207–218.

Dlamini, S., Taylor, M., Mkhize, N., Huver, R., Sathiparsad, R., de Vries, H., Naidoo, K., & Jinabhai, C. (2009). Gender factors associated with sexual abstinent behaviour of rural South African high school going youth in KwaZulu-Natal, South Africa. Health Education Research, 24 (3), 450–460.

Dunton, G. F., Lagloire, R., & Robertson, T. (2009). Using the RE-AIM framework to evaluate the statewide dissemination of a school-based physical activity and nutrition curriculum: “Exercise Your Options”. American Journal of Health Promotion, 23 (4), 229–232.

Dusenbury, L., Brannigan, R., Falco, M., & Hansen, W. B. (2003). A review of research on fidelity of implementation: Implications for drug abuse prevention in school settings. Health Education Research, 18 (2), 237–256.

Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. The American Psychologist, 49 (8), 709–724.

Etter, J. F. (2009). Comparing computer-tailored, internet-based smoking cessation counseling reports with generic, untailored reports: A randomized trial. Journal of Health Communication, 14 (7), 646–657.

Evans, R. I. (1976). Smoking in children: Developing a social psychological strategy of deterrence. Preventive Medicine, 5 (1), 122–127.

Evans-Whipp, T., Beyers, J. M., Lloyd, S., Lafazia, A. N., Toumbourou, J. W., Arthur, M. W., & Catalano, R. F. (2004). A review of school drug policies and their impact on youth substance use. Health Promotion International, 19 (2), 227.

Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7 (1), e11.

Faggiano, F., Vigna-Taglianti, F. D., Versino, E., Zambon, A., Borraccino, A., & Lemma, P. (2008). School-based prevention for illicit drugs use: A systematic review. Preventive Medicine, 46 (5), 385–396.

Farquhar, J. W., Fortmann, S. P., Flora, J. A., Taylor, C. B., Haskell, W. L., Williams, P. T., Maccoby, N., & Wood, P. D. (1990). Effects of communitywide education on cardiovascular disease risk factors. The Stanford Five-City Project. JAMA, 264 (3), 359–365.

Fazio, R. H. (1990). Multiple processes by which attitudes guide behaviour: The MODE model as an integrative framework. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 75–109). San Diego: Academic Press.

Feifer, C., Ornstein, S. M., Jenkins, R. G., Wessell, A., Corley, S. T., Nemeth, L. S., Roylance, L., Nietert, P. J., & Liszka, H. (2006). The logic behind a multimethod intervention to improve adherence to clinical practice guidelines in a nationwide network of primary care practices. Evaluation & the Health Professions, 29 (1), 65–88.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7 , 117.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research . Reading: Addison-Wesley.

Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach . New York: Taylor & Francis Group.

Fletcher, A., Bonell, C., & Hargreaves, J. (2008). School effects on young people’s drug use: A systematic review of intervention and observational studies. The Journal of Adolescent Health, 42 (3), 209–220.

Fleuren, M., Wiefferink, K., & Paulussen, T. (2004). Determinants of innovation within health care organizations: Literature review and Delphi study. International Journal for Quality in Health Care, 16 (2), 107–123.

Floyd, D. L., Prentice-Dunn, S., & Rogers, R. W. (2000). A meta-analysis of research on protection motivation theory. Journal of Applied Social Psychology, 30 (2), 407–429.

Fox, M. P. (2009). A systematic review of the literature reporting on studies that examined the impact of interactive, computer-based patient education programs. Patient Education and Counseling, 77 (1), 6–13.

Fransen, G. A. J., Mesters, I., Janssen, M. J. R., Knottnerus, J. A., & Muris, J. W. M. (2009). Which patient-related factors determine self-perceived patient adherence to prescribed dyspepsia medication? Health Education Research, 24 (5), 788–798.

Gaglio, B., Shoup, J. A., & Glasgow, R. E. (2013). The RE-AIM framework: A systematic review of use over time. American Journal of Public Health, 103 (6), e38–e46.

Gagne, C., & Godin, G. (2000). The theory of planned behavior: Some measurement issues concerning belief-based variables. Journal of Applied Social Psychology, 30 (10), 2173–2193.

Glanz, K., Rimer, B. K., & Viswanath, K. (2008). Health behavior and health education: Theory, research, and practice . San Francisco: Jossey-Bass.

Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89 (9), 1322–1327.

Glasgow, R. E., Lichtenstein, E., & Marcus, A. C. (2003). Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. American Journal of Public Health, 93 (8), 1261–1267.

Glynn, T. J. (1989). Essential elements of school-based smoking prevention programs. The Journal of School Health, 59 (5), 181–188.

Godin, G., & Conner, M. (2008). Intention-behavior relationship based on epidemiologic indices: An application to physical activity. American Journal of Health Promotion, 22 (3), 180–182.

Godin, G., & Kok, G. (1996). The theory of planned behavior: A review of its applications to health-related behaviors. American Journal of Health Promotion, 11 (2), 87–98.

Goebbels, A. F., Goebbels, A. F. G., Nicholson, J. M., Walsh, K., & De Vries, H. (2008). Teachers’ reporting of suspected child abuse and neglect: Behaviour and determinants. Health Education Research, 23 (6), 941–951.

Goetzel, R. Z., & Ozminkowski, R. J. (2008). The health and cost benefits of work site health-promotion programs. Annual Review of Public Health, 29 , 303–323.

Gollwitzer, P. M. (1990). Action phases and mind-sets. In E. T. Higgins & R. M. Sorrentino (Eds.), Handbook of motivation and cognition: Foundations of social behavior (Vol. 2, pp. 53–92). New York: Guilford Press.

Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54 (7), 493.

Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A Meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38 , 69–119.

Grant, R. W., Wald, J. S., Poon, E. G., Schnipper, J. L., Gandhi, T. K., Volk, L. A., & Middleton, B. (2006). Design and implementation of a web-based patient portal linked to an ambulatory care electronic health record: Patient gateway for diabetes collaborative care. Diabetes Technology & Therapeutics, 8 (5), 576–586.

Green, L. W. (1980). Health education planning: A diagnostic approach . Palo Alto: Mayfield.

Green, L. W., & Kreuter, M. W. (1991). Health promotion planning: An educational and environmental approach . Mountain View: Mayfield.

Green, L. W., & Kreuter, M. (1999). Health promotion planning: An educational and ecological approach . Mountain View: Mayfield.

Green, L. W., & Kreuter, M. W. (2005). Health program planning: An educational and ecological approach . New York: McGraw-Hill.

Grilli, R., Ramsay, C., & Minozzi, S. (2002). Mass media interventions: Effects on health services utilisation. Cochrane Database of Systematic Reviews, 1 , CD000389.

Groeneveld, I. F., Proper, K. I., Van der Beek, A. J., & Van Mechelen, W. (2010). Sustained body weight reduction by an individual-based lifestyle intervention for workers in the construction industry at risk for cardiovascular disease: Results of a randomized controlled trial. Preventive Medicine, 51 (3-4), 240–246.

Grol, R., & Jones, R. (2000). Twenty years of implementation research. Family Practice, 17 (Suppl 1), S32–S35.

Grube, J. W., Morgan, M., & McGree, S. T. (1986). Attitudes and normative beliefs as predictors of smoking intentions and behaviours: A test of three models. The British Journal of Social Psychology, 25 (Pt 2), 81–93.

Hagger, M. S., Lonsdale, A. J., Hein, V., Koka, A., Lintunen, T., Pasi, H., Lindwall, M., Rudolfsson, L., & Chatzisarantis, N. L. D. (2011). Predicting alcohol consumption and binge drinking in company employees: An application of planned behaviour and self-determination theories. British Journal of Health Psychology, 17 (2), 379–407.

Hall, K. L., & Rossi, J. S. (2008). Meta-analytic examination of the strong and weak principles across 48 health behaviors. Preventive Medicine, 46 (3), 266–274.

Hallfors, D., & Godette, D. (2002). Will the ‘principles of effectiveness’ improve prevention practice? Early findings from a diffusion study. Health Education Research, 17 (4), 461–470.

Handley, M., MacGregor, K., Schillinger, D., Sharifi, C., Wong, S., & Bodenheimer, T. (2006). Using action plans to help primary care patients adopt healthy behaviors: A descriptive study. Journal of American Board of Family Medicine, 19 (3), 224–231.

Hansen, W. B. (1992). School-based substance abuse prevention: A review of the state of the art in curriculum, 1980–1990. Health Education Research, 7 (3), 403–430.

Harakeh, Z., Scholte, R. H., Vermulst, A. A., de Vries, H., & Engels, R. C. (2004). Parental factors and adolescents’ smoking behavior: An extension of the theory of planned behavior. Preventive Medicine, 39 (5), 951–961.

Harden, A., Peersman, G., Oliver, S., Mauthner, M., & Oakley, A. (1999). A systematic review of the effectiveness of health promotion interventions in the workplace. Occupational Medicine (London), 49 (8), 540–548.

Harting, J., Rutten, G. M., Rutten, S. T., & Kremers, S. P. (2009). A qualitative application of the diffusion of innovations theory to examine determinants of guideline adherence among physical therapists. Physical Therapy, 89 (3), 221–232.

Hawkins, R. P., Kreuter, M., Resnicow, K., Fishbein, M., & Dijkstra, A. (2008). Understanding tailoring in communicating about health. Health Education Research, 23 (3), 454–466.

US Department of Health and Human Services. (1991). Healthy people 2000: National health promotion and disease prevention objectives and healthy schools. Journal of School Health, 61 (7), 298–328.

Heckhausen, H. (1991). Motivation and action . New York: Springer.

Book   Google Scholar  

Helmink, J. H., Meis, J. J., de Weerdt, I., Visser, F. N., de Vries, N. K., & Kremers, S. P. (2010). Development and implementation of a lifestyle intervention to promote physical activity and healthy diet in the Dutch general practice setting: The BeweegKuur programme. International Journal of Behavioral Nutrition and Physical Activity, 7 , 49.

Henderson, V. (1966). The nature of nursing . New York: Macmillan.

Hendriksen, E. S., Pettifor, A., Lee, S. J., Coates, T. J., & Rees, H. V. (2007). Predictors of condom use among young adults in South Africa: The Reproductive Health and HIV Research Unit National Youth Survey. American Journal of Public Health, 97 (7), 1241–1248.

Herzog, T. A., & Blagg, C. O. (2007). Are most precontemplators contemplating smoking cessation? Assessing the validity of the stages of change. Health Psychology, 26 (2), 222.

Hettema, J., Steele, J., & Miller, W. R. (2005). Motivational interviewing. Annual Review of Clinical Psychology, 1 , 91–111.

Hochbaum, G. M. (1971). Measurement of effectiveness of health education activites. International Journal of Health Education, 2 , 54–59.

Hoffman, D. L., & Novak, T. P. (1998). Bridging the racial divide on the Internet. Science, 280 , 390–391.

Hofmann, W., Friese, M., & Wiers, R. W. (2008). Impulsive versus reflective influences on health behavior: A theoretical framework and empirical review. Health Psychology Review, 2 , 111–137.

Holm, K., Kremers, S. P., & de Vries, H. (2003). Why do Danish adolescents take up smoking? European Journal of Public Health, 13 (1), 67–74.

Horowitz, S. M. (2003). Applying the transtheoretical model to pregnancy and STD prevention: A review of the literature. American Journal of Health Promotion, 17 (5), 304–328.

Hoving, E. F., Mudde, A. N., & de Vries, H. (2006). Smoking and the O pattern; predictors of transitions through the stages of change. Health Education Research, 21 (3), 305–314.

Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion: Psychological studies of opinion change . New Haven: Yale UP.

Huver, R. M. E., Engels, R. C. M. E., & de Vries, H. (2006). Are anti-smoking parenting practices related to adolescent smoking cognitions and behavior? Health Education Research, 21 (1), 66–77.

Huver, R. M., Engels, R. C. M. E., Vermulst, A. A., & de Vries, H. (2007). Is parenting style a context for smoking-specific parenting practices? Drug and Alcohol Dependence, 89 (2-3), 116–125.

Hyman, H. H., & Sheatsley, P. B. (1947). Some reasons why information campaigns fail. Public Opinion Quarterly, 11 , 412–423.

Ito, K. E., & Brown, J. D. (2010). To friend or not to friend: Using new media for adolescent health promotion. North Carolina Medical Journal, 71 (4), 367–372.

PubMed   Google Scholar  

Jackson, C., Lawton, R., Knapp, P., Raynor, D. K., Conner, M., Lowe, C., & Closs, S. J. (2005). Beyond intention: Do specific plans increase health behaviours in patients in primary care? A study of fruit and vegetable consumption. Social Science & Medicine, 60 (10), 2383.

James, S., Reddy, P., Ruiter, R. A., McCauley, A., & Borne, B. V. D. (2006). The impact of an HIV and AIDS life skills program on secondary school students in KwaZulu-Natal, South Africa. AIDS Educ Prev, 18 (4), 281–294.

Janis, I. L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment . New York: Free Press.

Janssen, E., van Osch, L., de Vries, H., & Lechner, L. (2011). Measuring risk perceptions of skin cancer: Reliability and validity of different operationalizations. British Journal of Health Psychology, 16 (Pt 1), 92–112.

Janssen, E., van Osch, L., Lechner, L., Candel, M., & de Vries, H. (2012). Thinking versus feeling: Differentiating between cognitive and affective components of perceived cancer risk. Psychology & Health, 27 (7), 767–783.

Janssen, E., van Osch, L., de Vries, H., & Lechner, L. (2013). Examining direct and indirect pathways to health behaviour: The influence of cognitive and affective probability beliefs. Psychology & Health, 28 (5), 546–560.

Janz, N. K., & Becker, M. H. (1984). The Health Belief Model: A decade later. Health Education Quarterly, 11 (1), 1–47.

Johnson, C. E., Mues, K. E., Mayne, S. L., & Kiblawi, A. N. (2008). Cervical cancer screening among immigrants and ethnic minorities: A systematic review using the Health Belief Model. Journal of Lower Genital Tract Disease, 12 (3), 232–241.

Joosten, E. A., DeFuentes-Merillas, L., De Weert, G. H., Sensky, T., Van Der Staak, C. P. F., & de Jong, C. A. (2008). Systematic review of the effects of shared decision-making on patient satisfaction, treatment adherence and health status. Psychotherapy and Psychosomatics, 77 (4), 219–226.

Jurg, M. E., Kremers, S. P., Candel, M. J., Van der Wal, M. F., & Meij, J. S. D. (2006). A controlled trial of a school-based environmental intervention to improve physical activity in Dutch children: JUMP-in, kids in motion. Health Promotion International, 21 (4), 320.

Kamarck, T. W., Manuck, S. B., & Jennings, J. R. (1990). Social support reduces cardiovascular reactivity to psychological challenge: A laboratory model. Psychosomatic Medicine, 52 (1), 42.

Kemp, R., Kirov, G., Everitt, B., Hayward, P., & David, A. (1998). Randomised controlled trial of compliance therapy. 18-month follow-up. The British Journal of Psychiatry, 172 , 413–419.

King, E. S., Rimer, B. K., Seay, J., Balshem, A., & Engstrom, P. F. (1994). Promoting mammography use through progressive interventions: Is it effective? American Journal of Public Health, 84 (1), 104–106.

Kirby, D. B., Laris, B. A., & Rolleri, L. A. (2007). Sex and HIV education programs: Their impact on sexual behaviors of young people throughout the world. The Journal of Adolescent Health, 40 (3), 206–217.

Knai, C., Pomerleau, J., Lock, K., & McKee, M. (2006). Getting children to eat more fruit and vegetables: A systematic review. Preventive Medicine: An International Journal Devoted to Practice and Theory, 42 (2), 85.

Kok, G., Lo, S. H., Peters, G. J. Y., & Ruiter, R. A. (2011). Changing energy-related behavior: An intervention mapping approach. Energy Policy, 39 , 5280–5286.

Kolbe, L. J. (1985). Why school health education? An empirical point of view. Health Education, 16 (2), 116–120.

Kremers, S. P. (2010). Theory and practice in the study of influences on energy balance-related behaviors. Patient Education and Counseling, 79 (3), 291–298.

Kremers, S. P., & Brug, J. (2008). Habit strength of physical activity and sedentary behavior among children and adolescents. Pediatric Exercise Science, 20 (1), 5–14. Discussion 14-7.

Kremers, S. P. J., Mudde, A. N., & de Vries, H. (2001). Subtypes within the precontemplation stage of adolescent smoking acquisition. Addictive Behaviors, 26 (2), 237.

Kremers, S. P., De Bruijn, G. J., Visscher, T. L., Van Mechelen, W., De Vries, N. K., & Brug, J. (2006). Environmental influences on energy balance-related behaviors: A dual-process view. International Journal of Behavioral Nutrition and Physical Activity, 3 , 9.

Kreuter, M. W., & Strecher, V. J. (1996). Do tailored behavior change messages enhance the effectiveness of health risk appraisal? Results from a randomized trial. Health Education Research, 11 (1), 97–105.

Kreuter, M. W., Farrell, D. W., Olevitch, L. R., & Brennan, L. K. (2000). Tailoring health messages: Customizing communication with computer technology . Mahwah: Lawrence Erlbaum Associates.

Kroeze, W., Werkman, A., & Brug, J. (2006). A systematic review of randomized trials on the effectiveness of computer-tailored education on physical activity and dietary behaviors. Annals of Behavioral Medicine, 31 (3), 205–223.

Kruger, S. (1991). The patient educator role in nursing. Applied Nursing Research, 4 (1), 19–24.

Kwak, L., Kremers, S. P., van Baak, M. A., & Brug, J. (2007). Formation of implementation intentions promotes stair use. American Journal of Preventive Medicine, 32 (3), 254.

Latham, G. P., & Locke, E. A. (2006). Enhancing the benefits and overcoming the pitfalls of goal setting. Organizational Dynamics, 35 (4), 332–340.

Lazarus, R. S. (1966). Psychological stress and the coping process . New York: McGraw-Hill.

Leung, L. (2008). Internet embeddedness: Links with online health information seeking, expectancy value/quality of health information websites, and Internet usage patterns. Cyberpsychology & Behavior, 11 (5), 565–569.

Leventhal, H. (1970). Findings and theory in the study of fear communications. In L. Berkowitz (Ed.), Advances in experimental social psychology . New York: Academic Press.

Leventhal, H., & Cameron, L. (1987). Behavioral theories and the problem of compliance. Patient Education and Counseling, 10 (2), 117.

Leventhal, H., Nerenz, D. R., & Steele, D. J. (1984). Illness representation and coping with health threats . In A. Baum, S. E. Taylor, & J. E. Singer (Eds.), Handbook of psychology and health (pp. 219–252). Hillsdale: Lawrence Erlbaum Associates.

Lewis, B. A., Williams, D. M., Neighbors, C. J., Jakicic, J. M., & Marcus, B. H. (2010). Cost analysis of Internet vs. print interventions for physical activity promotion. Psychology of Sport and Exercise, 11 (3), 246–249.

Lippke, S., & Plotnikoff, R. C. (2012). Testing two principles of the health action process approach in individuals with type 2 diabetes. Health Psychology .

Lippke, S., & Ziegelmann, J. P. (2008). Theory-based health behavior change: Developing, testing, and applying theories for evidence-based interventions. Applied Psychology, 57 (4), 698–716.

Lippke, S., Ziegelmann, J. P., & Schwarzer, R. (2004). Initiation and maintenance of physical exercise: Stage-specific effects of a planning intervention. Research in Sports Medicine, 12 (3), 221.

Lippke, S., Ziegelmann, J. P., & Schwarzer, R. (2005). Stage-specific adoption and maintenance of physical activity: Testing a three-stage model. Psychology of Sport and Exercise, 6 , 585–603.

Lippke, S., Schwarzer, R., Ziegelmann, J. P., Scholz, U., & Schüz, B. (2010). Testing stage-specific effects of a stage-matched intervention: A randomized controlled trial targeting physical exercise and its predictors. Health Education & Behavior, 37 (4), 533–546.

Liu, L. L., & Park, D. C. (2004). Aging and medical adherence: The use of automatic processes to achieve effortful things. Psychology and Aging, 19 (2), 318–325.

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. The American Psychologist, 57 (9), 705–717.

Lotrean, L. M., Dijk, F., Mesters, I., Ionut, C., & De Vries, H. (2010). Evaluation of a peer-led smoking prevention programme for Romanian adolescents. Health Education Research, 25 (5), 803.

Luszczynska, A., & Schwarzer, R. (2003). Planning and self-efficacy in the adoption and maintenance of breast self-examination: A longitudinal study on self-regulatory cognitions. Psychology & Health, 18 (1), 93–108.

MacGregor, K., Handley, M., Wong, S., Sharifi, C., Gjeltema, K., Schillinger, D., & Bodenheimer, T. (2006). Behavior-change action plans in primary care: A feasibility study of clinicians. The Journal of the American Board of Family Medicine, 19 , 215–223.

Marks, R., Allegrante, J. P., & Lorig, K. (2005). A review and synthesis of research evidence for self-efficacy-enhancing interventions for reducing chronic disability: Implications for health education practice (part II). Health Promotion Practice, 6 (2), 148–156.

Marlatt, G. A., & George, W. H. (1984). Relapse prevention: Introduction and overview of the model. British Journal of Addiction, 79 (3), 261–273.

Marshall, S. J., & Biddle, S. J. (2001). The transtheoretical model of behavior change: A meta-analysis of applications to physical activity and exercise. Annals of Behavioral Medicine, 23 (4), 229–246.

Martinez, M. (2008). High attrition rates in e-learning: Challenges, predictors, and solutions (p. 15). Santa Rosa: The E-Learning Developers’ Journal.

Mays, D., Streisand, R., Walker, L. R., Prokhorov, A. V., & Tercyak, K. P. (2012). Cigarette smoking among adolescents with type 1 diabetes: Strategies for behavioral prevention and intervention. Journal of Diabetes and its Complications, 26 (2), 148–153.

McGuire, W. J. (1985). Attitudes and attitude change. In G. Lindzey & E. Aronson (Eds.), Handbook of social psychology (Vol. II). New York: Lawrence Erlbaum Associates.

McKenzie, J. F., & Smeltzer, J. L. (2001). Planning, implementing and evaluating health promotion programs . Boston: Allyn and Bacon.

McLean, S. M., Burton, M., Bradley, L., & Littlewood, C. (2010). Interventions for enhancing adherence with physiotherapy: A systematic review. Manual Therapy, 15 (6), 514–521.

McLeroy, K. R., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Education Quarterly, 15 (4), 351–377.

Mercken, L., Snijders, T. A., Steglich, C., & de Vries, H. (2009). Dynamics of adolescent friendship networks and smoking behavior: Social network analyses in six European countries. Social Science & Medicine, 69 (10), 1506.

Mercken, L., Candel, M., Van Osch, L., & De Vries, H. (2010). No smoke without fire: The impact of future friends on adolescent smoking behaviour. British Journal of Health Psychology, 16 (Pt 1), 170–188.

Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal Psychology, 67 , 371–378.

Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people to change (2nd ed.). New York: Guilford Press.

Milne, S., Sheeran, P., & Orbell, S. (2000). Prediction and intervention in health-related behavior: A meta-analytic review of protection motivation theory. Journal of Applied Social Psychology, 30 (1), 106.

Milne, S., Orbell, S., & Sheeran, P. (2002). Combining motivational and volitional interventions to promote exercise participation: Protection motivation theory and implementation intentions. British Journal of Health Psychology, 7 (Pt 2), 163–184.

Montaño, D. E., & Kasprzyk, D. (2008). Theory of reasoned action, theory of planned behavior and the integrated behavioral model. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education (4th ed.). San Francisco: Jossey-Bass.

Moritz, S. E., Feltz, D. L., Fahrbach, K. R., & Mack, D. E. (2000). The relation of self-efficacy measures to sport performance: A meta-analytic review. Research Quarterly for Exercise and Sport, 71 (3), 280–294.

Morrison, V., & Bennett, P. (2008). An introduction to health psychology (2nd ed.). London: Pearson Publishing.

Mulvaney, S. A., Rothman, R. L., Wallston, K. A., Lybarger, C., & Dietrich, M. S. (2010). An internet-based program to improve self-management in adolescents with type 1 diabetes. Diabetes Care, 33 (3), 602–604.

Newman, I. M., Martin, G. L., & Ang, J. (1982). The role of attitudes and social norms in adolescent cigarette smoking. The New Zealand Medical Journal, 95 (715), 618–621.

Ni Mhurchu, C., Aston, L. M., & Jebb, S. A. (2010). Effects of worksite health promotion interventions on employee diets: A systematic review. BMC Public Health, 10 (62), 1.

Nichols, J., Schutte, N. S., Brown, R. F., Dennis, C. L., & Price, I. (2009). The impact of a self-efficacy intervention on short-term breast-feeding outcomes. Health Education & Behavior, 36 (2), 250–258.

Nigg, C. R., Albright, C., Williams, R., Nichols, C., Renda, G., Stevens, V. J., & Vogt, T. M. (2010). Are physical activity and nutrition indicators of the checklist of health promotion environments at worksites (CHEW) associated with employee obesity among hotel workers? Journal of Occupational and Environmental Medicine, 52 (Suppl. 1), S4–S7.

Noar, S. M., & Harrington, N. G. (2012). eHealth applications . New York: Routledge.

Noar, S. M., Benac, C. N., & Harris, M. S. (2007). Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychological Bulletin, 133 (4), 673–693.

Noar, S. M., Black, H. G., & Pierce, L. B. (2009). Efficacy of computer technology-based HIV prevention interventions: A meta-analysis. AIDS, 23 (1), 107.

Noar, S. M., Webb, E. M., Van Stee, S. K., Redding, C. A., Feist-Price, S., Crosby, R., & Troutman, A. (2011). Using computer technology for HIV prevention among African-Americans: Development of a tailored information program for safer sex (TIPSS). Health Education Research, 26 (3), 393–406.

Norman, G. J., Velicer, W. F., Fava, J. L., & Prochaska, J. O. (2000). Cluster subtypes within stage of change in a representative sample of smokers. Addictive Behaviors, 25 (2), 183.

Norman, P., Boer, H., & Seydel, E. R. (2005). Protection motivation theory. In M. Conner & P. Norman (Eds.), Predicting health behavior (pp. 81–126). London: Open University Press.

Norris, S. L., Engelgau, M. M., & Narayan, K. V. (2001). Effectiveness of self-management training in type 2 diabetes a systematic review of randomized controlled trials. Diabetes Care, 24 (3), 561–587.

Norris, S. L., Lau, J., Smith, S. J., Schmid, C. H., & Engelgau, M. M. (2002). Self-management education for adults with type 2 diabetes a meta-analysis of the effect on glycemic control. Diabetes Care, 25 (7), 1159–1171.

O’Leary, A. (1985). Self-efficacy and health. Behaviour Research and Therapy, 23 (4), 437.

Oei, T. P., & Burrow, T. (2000). Alcohol expectancy and drinking refusal self-efficacy: A test of specificity theory. Addictive Behaviors, 25 (4), 499–507.

Oenema, A., Brug, J., & Lechner, L. (2001). Web-based tailored nutrition education: Results of a randomized controlled trial. Health Education Research, 16 (6), 647–660.

Oostveen, T., Knibbe, R., & de Vries, H. (1996). Social influences on young adults’ alcohol consumption: Norms, modeling, pressure, socializing, and conformity. Addictive Behaviors, 21 (2), 187–197.

Orbell, S., & Sheeran, P. (1998). ‘Inclined abstainers’: A problem for predicting health-related behaviour. The British Journal of Social Psychology, 37 (Pt 2), 151–165.

Panday, S., Reddy, S. P., Ruiter, R. A., Bergström, E., & de Vries, H. (2005). Determinants of smoking cessation among adolescents in South Africa. Health Education Research, 20 (5), 586–599.

Parcel, G. S., Eriksen, M. P., Lovato, C. Y., Gottlieb, N. H., Brink, S. G., & Green, L. W. (1989). The diffusion of school-based tobacco-use prevention programs: Project description and baseline data. Health Education Research, 4 (1), 111.

Parker, R. M., & Jacobson, T. A. (2000). The role of health literacy in narrowing the treatment gap for hypercholesterolemia. The American Journal of Managed Care, 6 (12), 1340–1342.

Pelletier, K. R. (2009). A review and analysis of the clinical and cost-effectiveness studies of comprehensive health promotion and disease management programs at the worksite: Update VII 2004–2008. Journal of Occupational and Environmental Medicine, 51 (7), 822–837.

Perry, C. L., Kelder, S. H., & Klepp, K. I. (1994). Community-wide cardiovascular disease prevention in young people: Long-term outcomes of the class of 1989 study. European Journal of Public Health, 4 (3), 188–194.

Perry, C. L., Williams, C. L., Veblen-Mortenson, S., Toomey, T. L., Komro, K. A., Anstine, P. S., McGovern, P. G., Finnegan, J. R., Forster, J. L., Wagenaar, A. C., & Wolfson, M. (1996). Project Northland: Outcomes of a communitywide alcohol use prevention program during early adolescence. American Journal of Public Health, 86 (7), 956–965.

Peters, L. W., Wiefferink, C. H., Hoekstra, F., Buijs, G. J., ten Dam, G. T., & Paulussen, T. G. (2009). A review of similarities between domain-specific determinants of four health behaviors among adolescents. Health Education Research, 24 (2), 198–223.

Peterson, A. V., Jr., Kealey, K. A., Mann, S. L., Marek, P. M., & Sarason, I. G. (2000). Hutchinson Smoking Prevention Project: Long-term randomized trial in school-based tobacco use prevention – Results on smoking. Journal of the National Cancer Institute, 92 (24), 1979–1991.

Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion, central and peripheral routes to attitude change . New York: Springer.

Pinto, A. M., Heinberg, L. J., Coughlin, J. W., Fava, J. L., & Guarda, A. S. (2008). The Eating Disorder Recovery Self-Efficacy Questionnaire (EDRSQ): Change with treatment and prediction of outcome. Eating Behaviors, 9 (2), 143.

Plotnikoff, R. C., Lippke, S., Courneya, K. S., Birkett, N., & Sigal, R. J. (2008). Physical activity and social cognitive theory: A test in a population sample of adults with type 1 or type 2 diabetes. Applied Psychology, 57 (4), 628–643.

Plotnikoff, R. C., Lippke, S., Johnson, S. T., & Courneya, K. S. (2010a). Physical activity and stages of change: A longitudinal test in types 1 and 2 diabetes samples. Annals of Behavioral Medicine, 40 (2), 138–149.

Plotnikoff, R. C., Lippke, S., Trinh, L., Courneya, K. S., Birkett, N., & Sigal, R. J. (2010b). Protection motivation theory and the prediction of physical activity among adults with type 1 or type 2 diabetes in a large population sample. British Journal of Health Psychology, 15 (Pt 3), 643–661.

Pomp, S., Lippke, S., Fleig, L., & Schwarzer, R. (2010). Synergistic effects of intention and depression on action control: Longitudinal predictors of exercise after rehabilitation. Mental Health and Physical Activity, 2 , 78–84.

Presson, C. C., Chassin, L., Sherman, S. J., Olshavsky, R., Bensenberg, M., & Corty, E. (1984). Predictors of adolescents’ intentions to smoke: Age, sex, race, and regional differences. The International Journal of the Addictions, 19 (5), 503–519.

Elders, M. J. (1994). Preventing tobacco use among young people. A report of the Surgeon General. Executive summary. MMWR – Recommendations and Reports, 43 (RR-4), 1–10.

Price, V., & Zaller, J. (1993). Who gets the news? Alternative measures of news reception and their implications for research. Public Opinion Quarterly, 57 (2), 133.

Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51 (3), 390–395.

Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12 (1), 38–48.

Prochaska, J. O., Velicer, W. F., Rossi, J. S., Goldstein, M. G., Marcus, B. H., Rakowski, W., Fiore, C., Harlow, L. L., Redding, C. A., Rosenbloom, D., & Rossi, S. R. (1994). Stages of change and decisional balance for 12 problem behaviors. Health Psychology, 13 (1), 39–46.

Prochaska, J. O., Velicer, W. F., Redding, C., Rossi, J. S., Goldstein, M., DePue, J., Greene, G. W., Rossi, S. R., Sun, X., & Fava, J. L. (2005). Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms. Preventive Medicine, 41 (2), 406–416.

Prochaska, J. J., Spring, B., & Nigg, C. R. (2008). Multiple health behavior change research: An introduction and overview. Preventive Medicine, 46 (3), 181–188.

Puska, P. M., Puska, P. M. J., Barrueco, M., Roussos, C., Hider, A., & Hogue, S. (2005). The participation of health professionals in a smoking-cessation programme positively influences the smoking cessation advice given to patients. International Journal of Clinical Practice, 59 (4), 447–452.

Quinlan, K. B., & McCaul, K. D. (2000). Matched and mismatched interventions with young adult smokers: Testing a stage theory. Health Psychology, 19 (2), 165.

Rahimi, B., Timpka, T., Vimarlund, V., Uppugunduri, S., & Svensson, M. (2009). Organization-wide adoption of computerized provider order entry systems: A study based on diffusion of innovations theory. BMC Medical Informatics and Decision Making, 9 (1), 52.

Rakowski, W., Ehrich, B., Goldstein, M. G., Rimer, B. K., Pearlman, D. N., Clark, M. A., Velicer, W. F., & Woolverton III, H. (1998). Increasing mammography among women aged 40–74 by use of a stage-matched, tailored intervention. Preventive Medicine, 27 (5 Pt 1), 748–756.

Renner, B., Kwon, S., Yang, B.-H., Paik, K.-C., Kim, S. H., Roh, S., Song, J., & Schwarzer, R. (2008). Social-cognitive predictors of dietary behaviors in South Korean men and women. International Journal of Behavioral Medicine, 15 (1), 4–13.

Resnicow, K., & Vaughan, R. (2006). A chaotic view of behavior change: A quantum leap for health promotion. International Journal of Behavioral Nutrition and Physical Activity, 3 , 25.

Resnicow, K., DiIorio, C., Soet, J. E., Borrelli, B., Hecht, J., & Ernst, D. (2002). Motivational interviewing in health promotion: It sounds like something is changing. Health Psychology, 21 (5), 444.

Rigotti, N. A., Munafo, M. R., & Stead, L. F. (2008). Smoking cessation interventions for hospitalized smokers: A systematic review. Archives of Internal Medicine, 168 (18), 1950–1960.

Riper, H., Kramer, J., Smit, F., Conijn, B., Schippers, G., & Cuijpers, P. (2008). Web-based self-help for problem drinkers: A pragmatic randomized trial. Addiction, 103 (2), 218.

Rivis, A., & Sheeran, P. (2003). Descriptive norms as an additional predictor in the theory of planned behaviour: A meta-analysis. Current Psychology: Developmental, Learning, Personality, Social, 22 , 218–233.

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology: Interdisciplinary and Applied, 91 (1), 93.

Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Social psychophysiology . New York: Guilford Press.

Rogers, R. W. (2003). Diffusion of innovations (5th ed.). New York: Free Press.

Rohrbach, L. A., Grana, R., Sussman, S., & Valente, T. W. (2006). Type II translation: Transporting prevention interventions from research to real-world settings. Evaluation & the Health Professions, 29 (3), 302–333.

Rosenstock, I. (1974). Historical origins of the Health Belief Model. Health Education Monographs, 2 (4), 336.

Rosseel, J. P., Hilberink, S. R., Jacobs, J. E., Maassen, I. M., Plasschaert, A. J. M., & Grol, R. P. T. M. (2010). Are oral health complaints related to smoking cessation intentions? Community Dentistry and Oral Epidemiology, 38 (5), 470–478.

Ruiter, R. A., Kessels, L. T., Jansma, B. M., & Brug, J. (2006). Increased attention for computer-tailored health communications: An event-related potential study. Health Psychology, 25 (3), 300–306.

Rutter, D. R., Steadman, L., & Quine, L. (2006). An implementation intentions intervention to increase uptake of mammography. Annals of Behavioral Medicine, 32 (2), 127–134.

Salpeter, S. R., Buckley, N. S., Ormiston, T. M., & Salpeter, E. E. (2006). Meta-analysis: Effect of long-acting beta-agonists on severe asthma exacerbations and asthma-related deaths. Annals of Internal Medicine, 144 (12), 904–912.

Sarafino, E. P. (1994). Health psychology: Biopsychosocial interactions (2nd ed.). New York: Wiley.

Sarkar, U., Karter, A. J., Liu, J. Y., Adler, N. E., Nguyen, R., López, A., & Schillinger, D. (2011). Social disparities in internet patient portal use in diabetes: Evidence that the digital divide extends beyond access. Journal of the American Medical Informatics Association, 18 (3), 318–321.

Say, R. E., & Thomson, R. (2003). The importance of patient preferences in treatment decisions – Challenges for doctors. BMJ, 327 (7414), 542–545.

Schaalma, H., Kok, G., & Peters, L. (1993). Determinants of consistent condom use by adolescents: The impact of experience of sexual intercourse. Health Education Research, 8 (2), 255.

Schoenmakers, T., Wiers, R. W., & Field, M. (2008). Effects of a low dose of alcohol on cognitive biases and craving in heavy drinkers. Psychopharmacology, 197 (1), 169–178.

Schwarzer, R. (Ed.). (1992). Self-efficacy: Thought control of action . Washington, DC: Hemisphere.

Schwarzer, R. (2008a). Modeling health behavior change: The Health Action Process Approach (HAPA) . Available from: http://www.hapa-model.de/

Schwarzer, R. (2008b). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology. An International Review, 57 (1), 1.

Schwarzer, R., Schüz, B., Ziegelmann, J. P., Lippke, S., Luszczynska, A., & Scholz, U. (2007). Adoption and maintenance of four health behaviors: Theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Annals of Behavioral Medicine, 33 (2), 156.

Schwarzer, R., Luszczynska, A., Ziegelmann, J. P., Scholz, U., & Lippke, S. (2008). Social-cognitive predictors of physical exercise adherence: Three longitudinal studies in rehabilitation. Health Psychology, 27 (Suppl. 1), S54.

Schwarzer, R., Lippke, S., & Luszczynska, A. (2011). Mechanisms of health behavior change in persons with chronic illness or disability: The Health Action Process Approach (HAPA). Rehabilitation Psychology, 56 (3), 161.

Segaar, D., Willemsen, M. C., Bolman, C., & De Vries, H. (2007a). Nurse adherence to a minimal-contact smoking cessation intervention on cardiac wards. Research in Nursing & Health, 30 (4), 429–444.

Segaar, D., Bolman, C., Willemsen, M. C., & De Vries, H. (2007b). Identifying determinants of protocol adoption by midwives: A comprehensive approach. Health Education Research, 22 (1), 14–26.

Senge, P. (1990). The fifth discipline: The art and practice of the learning organization . New York: Currency Doubleday.

Sheeran, P., & Orbell, S. (2000). Using implementation intentions to increase attendance for cervical cancer screening. Health Psychology, 19 (3), 283–289.

Sheeran, P., Webb, T. L., & Gollwitzer, P. M. (2005). The interplay between goal intentions and implementation intentions. Personality and Social Psychology Bulletin, 31 (1), 87–98.

Sherif, M. (1935). A study of some social factors in perception. Archives of Psychology, 187 .

Simonds, S. K. (1978). Health education: Facing issues of policy, ethics, and social justice. Health Education Monographs, 6 (Suppl. 1), 18–27.

Skår, S., Sniehotta, F. F., Molloy, G. J., Prestwich, A., & Araujo-Soares, V. (2011). Do brief online planning interventions increase physical activity amongst university students? A randomised controlled trial. Psychology & Health, 26 (4), 399–417.

Skinner, C. S., Sykes, R. K., Monsees, B. S., Andriole, D. A., Arfken, C. L., & Fisher, E. B. (1998). Learn, share, and live: Breast cancer education for older, urban minority women. Health Education & Behavior, 25 (1), 60–78.

Slovic, P. (1987). Perception of risk. Science, 236 (4799), 280–285.

Slovic, P., & Peters, E. (2006). Risk perception and affect. Current Directions in Psychological Science, 15 (6), 322–325.

Smeets, T., Brug, J., & de Vries, H. (2008). Effects of tailoring health messages on physical activity. Health Education Research, 23 (3), 402–413.

Smerecnik, C., Quaak, M., van Schayck, C. P., van Schooten, F. J., & de Vries, H. (2011). Are smokers interested in genetic testing for smoking addiction? A socio-cognitive approach. Psychology & Health, 26 (8), 1099–1112.

Sniehotta, F. F. (2009). Towards a theory of intentional behaviour change: Plans, planning, and self-regulation. British Journal of Health Psychology, 14 (2), 261.

Sniehotta, F. F., Scholz, U., Schwarzer, R., Fuhrmann, B., Kiwus, U., & Völler, H. (2005). Long-term effects of two psychological interventions on physical exercise and self-regulation following coronary rehabilitation. International Journal of Behavioral Medicine, 12 (4), 244–255.

Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2006). Action plans and coping plans for physical exercise: A longitudinal intervention study in cardiac rehabilitation. British Journal of Health Psychology, 11 (Pt 1), 23–37.

Snyder, L. B., Hamilton, M. A., Mitchell, E. W., Kiwanuka-Tondo, J., Fleming-Milici, F., & Proctor, D. (2004). A meta-analysis of the effect of mediated health communication campaigns on behavior change in the United States. Journal of Health Communication, 9 (Suppl. 1), 71–96.

Soler, R. E., Leeks, K. D., Razi, S., Hopkins, D. P., Griffith, M., Aten, A., Chattopadhyay, S. K., Smith, S. C., Habarta, N., & Goetzel, R. Z. (2010). A systematic review of selected interventions for worksite health promotion. The assessment of health risks with feedback. American Journal of Preventive Medicine, 38 (2 Suppl), S237–S262.

Stock, J., & Cervone, D. (1990). Proximal goal-setting and self-regulatory processes. Cognitive Therapy and Research, 14 (5), 483–498.

Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8 (3), 220.

Strecher, V. J. (1999). Computer-tailored smoking cessation materials: A review and discussion. Patient Education and Counseling, 36 (2), 107–117.

Strecher, V. J., & Rosenstock, I. M. (1997). The Health Belief Model. In A. Baum et al. (Eds.), Cambridge handbook of psychology, health and medicine . Cambridge: Cambridge University Press.

Strecher, V. J., McEvoy DeVellis, B., Becker, M. H., & Rosenstock, I. M. (1986). The role of self-efficacy in achieving health behavior change. Health Education Quarterly, 13 (1), 73–92.

Strecher, V. J., Kreuter, M., Den Boer, D. J., Kobrin, S., Hospers, H. J., & Skinner, C. S. (1994). The effects of computer-tailored smoking cessation messages in family practice settings. The Journal of Family Practice, 39 (3), 262–270.

Strecher, V. J., Shiffman, S., & West, R. (2005). Randomized controlled trial of a web-based computer-tailored smoking cessation program as a supplement to nicotine patch therapy. Addiction, 100 (5), 682–688.

Strecher, V. J., McClure, J. B., Alexander, G. L., Chakraborty, B., Nair, V. N., Konkel, J. M., Greene, S. M., Collins, L. M., Carlier, C. C., & Wiese, C. J. (2008). Web-based smoking-cessation programs: Results of a randomized trial. American Journal of Preventive Medicine, 34 (5), 373–381.

Suissa, S., & Ernst, P. (2001). Inhaled corticosteroids: Impact on asthma morbidity and mortality. The Journal of Allergy and Clinical Immunology, 107 (6), 937–944.

Sutton, S. (2000). Interpreting cross-sectional data on stages of change. Psychology and Health, 15 (2), 163–171.

Swinburn, B., Egger, G., & Raza, F. (1999). Dissecting obesogenic environments: The development and application of a framework for identifying and prioritizing environmental interventions for obesity. Preventive Medicine, 29 (6 Pt 1), 563–570.

Taylor, S. E., Falke, R. L., Shoptaw, S. J., & Lichtman, R. R. (1986). Social support, support groups, and the cancer patient. Journal of Consulting and Clinical Psychology, 54 (5), 608–615.

Te Poel, F., Bolman, C., Reubsaet, A., & de Vries, H. (2009). Efficacy of a single computer-tailored e-mail for smoking cessation: Results after 6 months. Health Education Research, 24 (6), 930–940.

Thomas, R. (2002). School-based programmes for preventing smoking. Cochrane Database of Systematic Reviews, 4 , CD001293.

Thomas, R., & Perera, R. (2013). School-based programmes for preventing smoking. Cochrane Database of Systematic Reviews, 4 , CD001293.

Thurstone, L. L. (1931). Measurement of social attitudes. Journal of Abnormal and Social Psychology, 26 , 249–269.

Tichenor, P. J., Donohue, G. A., & Olien, C. N. (1970). Mass media flow and differential growth in knowledge. Public Opinion Quarterly, 34 .

Tu, H. T., & Cohen, G. R. (2008). Striking jump in consumers seeking health care information. Tracking Report, 20 , 1–8.

Turner, J. C. (1991). Social influence . Belmont: Wadsworth Publishing.

Tyler-Smith, K. (2006). Early attrition among first time eLearners: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking eLearning programmes. Journal of Online Learning and Teaching, 2 , 73–85.

Uchino, B. N. (2006). Social support and health: A review of physiological processes potentially underlying links to disease outcomes. Journal of Behavioral Medicine, 29 (4), 377.

USDHHS. (1980). Promoting health/preventing disease: Objectives for the nation . Washington, DC: US Government Printing Office.

USDHHS. (1990). The health benefits of smoking cessation. A report of the Surgeon General’s Office on Smoking and Health . Rockville: U.S. Govt. Printing Office.

Valente, T. W. (2002). Evaluating health promotion programs . New York: Oxford University Press.

van der Velde, F. W., Hooykaas, C., & van der Pligt, J. (1996). Conditional versus unconditional risk estimates in models of AIDS-related risk behaviour. Psychology & Health, 12 (1), 87.

van Keulen, H. M., Mesters, I., Ausems, M., Van Breukelen, G., Campbell, M., Resnicow, K., Brug, J., & De Vries, H. (2011). Tailored print communication and telephone motivational interviewing are equally successful in improving multiple lifestyle behaviors in a randomized controlled trial. Annals of Behavioral Medicine, 41 (1), 104–118.

van Osch, L., Reubsaet, A., Lechner, L., & de Vries, H. (2008a). The formation of specific action plans can enhance sun protection behavior in motivated parents. Preventive Medicine, 47 (1), 127–132.

van Osch, L., Reubsaet, A., Lechner, L., Candel, M., Mercken, L., & De Vries, H. (2008b). Predicting parental sunscreen use: Disentangling the role of action planning in the intention-behavior relationship. Psychology and Health, 23 (7), 829–846.

van Osch, L., Lechner, L., Reubsaet, A., & De Vries, H. (2010). From theory to practice: An explorative study into the instrumentality and specificity of implementation intentions. Psychology & Health, 25 (3), 351–364.

van Stralen, M. M., Kok, G., de Vries, H., Mudde, A. N., Bolman, C., & Lechner, L. (2008). The Active plus protocol: Systematic development of two theory-and evidence-based tailored physical activity interventions for the over-fifties. BMC Public Health, 8 (1), 399.

Vartiainen, E., Puska, P., Koskela, K., Nissinen, A., & Toumilehto, J. (1986). Ten-year results of a community-based anti-smoking program (as part of the North Karelia Project in Finland). Health Education Research, 1 (3), 175–184.

Vassallo, M., Saba, A., Arvola, A., Dean, M., Messina, F., Winkelmann, M., Claupein, E., Lähteenmäki, L., & Shepherd, R. (2009). Willingness to use functional breads. Applying the Health Belief Model across four European countries. Appetite, 52 (2), 452.

Velicer, W. F., Diclemente, C. C., Rossi, J. S., & Prochaska, J. O. (1990). Relapse situations and self-efficacy: An integrative model. Addictive Behaviors, 15 (3), 271–283.

Velicer, W. F., Prochaska, J. O., Bellis, J. M., DiClemente, C. C., Rossi, J. S., Fava, J. L., & Steiger, J. H. (1993). An expert system intervention for smoking cessation. Addictive Behaviors, 18 (3), 269–290.

Velicer, W. F., Prochaska, J. O., & Redding, C. A. (2006). Tailored communications for smoking cessation: Past successes and future directions. Drug and Alcohol Review, 25 (1), 49–57.

Vernon, S. W. (1999). Risk perception and risk communication for cancer screening behaviors: A review. Journal of the National Cancer Institute. Monographs, 25 , 101–119.

Verplanken, B., & Faes, S. (1999). Good intentions, bad habits, and effects of forming implementation intentions on healthy eating. European Journal of Social Psychology, 29 (5-6), 591.

Vries, H. D., & Mudde, A. N. (1998). Predicting stage transitions for smoking cessation applying the attitude-social influence-efficacy model. Psychology and Health, 13 (2), 369–385.

Wagner, T. H., Bundorf, M. K., Singer, S. J., & Baker, L. C. (2005). Free internet access, the digital divide, and health information. Medical Care, 43 (4), 415–420.

Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132 (2), 249–268.

Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12 (1), e4.

Weinstein, N. D. (1988). The precaution adoption process. Health Psychology, 7 (4), 355–386.

Weinstein, N. D., & Sandman, P. M. (1992). A model of the precaution adoption process: Evidence from home radon testing. Health Psychology, 11 (3), 170–180.

Weinstein, N. D., Lyon, J. E., Sandman, P. M., & Cuite, C. L. (1998). Experimental evidence for stages of health behavior change: The precaution adoption process model applied to home radon testing. Health Psychology, 17 (5), 445.

WHO. (1978). Declaration of Alma-Ata . International conference on Primary Health Care, Alma-Ata, USSR, 6–12 September. Geneva: WHO.

WHO. (1985). Prerequisites for health. In WHO Regional Office of Europe (Ed.), Targets for all: Targets in support of the European regional strategy for health for all . Copenhagen: WHO Regional Office of Europe.

WHO. (1986). Ottawa charter of health promotion . In Health promotion I , Ottawa.

U.S. Department of Health and Human Services. (2000). Healthy people 2010: Understanding and improving health (2nd ed.). Washington, DC: U.S. Government Printing Office.

WHO. (2013). In J. M. Pelikan, I. Kickbusch, F. Apfel, & A. D. Tsouros (Eds.), Health literacy; solid facts .

WHO, [WHO definition of Health]. (1946). Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference , New York, 19–22 June 1946; signed on 22 July 1946 by the representatives of 61 States (Official Records of the World Health Organization, no. 2, p. 100) and entered into force on 7 April 1948.

Wielm, A. G. (2004). Digital Nation: Towards an inclusive information society (pp. 133–134). Cambridge, MA: MIT Press.

Wiers, R. W., Bartholow, B. D., van den Wildenberg, E., Thush, C., Engels, R. C. M. E., Sher, K. J., Grenard, J., Ames, S. L., & Stacy, A. W. (2007). Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacology, Biochemistry, and Behavior, 86 (2), 263–283.

Wiers, R. W., Rinck, M., Kordts, R., Houben, K., & Strack, F. (2009). Retraining automatic action-tendencies to approach alcohol in hazardous drinkers. Addiction, 105 (2), 279–287.

Wilde, M. H., & Garvin, S. (2007). A concept analysis of self-monitoring. Journal of Advanced Nursing, 57 (3), 339–350.

Wilson, M. G., Holman, P. B., & Hammock, A. (1996). A comprehensive review of the effects of worksite health promotion on health-related outcomes. American Journal of Health Promotion, 10 (6), 429–435.

Winett, L. B., & Wallack, L. (1996). Advancing public health goals through the mass media. Journal of Health Communication, 1 (2), 173–196.

Ziegelmann, J. P., Lippke, S., & Schwarzer, R. (2006). Adoption and maintenance of physical activity: Planning interventions in young, middle-aged, and older adults. Psychology & Health, 21 (2), 145–163.

Ziegelmann, J. P., Luszczynska, A., Lippke, S., & Schwarzer, R. (2007). Are goal intentions or implementation intentions better predictors of health behavior? A longitudinal study in orthopedic rehabilitation. Rehabilitation Psychology, 52 (1), 97.

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de Vries, H., Kremers, S.P.J., Lippke, S. (2018). Health Education and Health Promotion: Key Concepts and Exemplary Evidence to Support Them. In: Fisher, E., et al. Principles and Concepts of Behavioral Medicine. Springer, New York, NY. https://doi.org/10.1007/978-0-387-93826-4_17

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Understanding the Relationship Between Education and Health

It is now widely recognized that health outcomes are deeply influenced by a variety of social factors outside of health care. The dramatic differences in morbidity, mortality, and risk factors that researchers have documented within and between countries are patterned after classic social determinants of health, such as education and income (Link and Phelan, 1995; CSDH, 2008), as well as placed-based characteristics of the physical and social environment in which people live—and the macrostructural policies that shape them.

A 2013 report from the National Research Council and the Institute of Medicine cited these socioecological factors, along with unhealthy behaviors and deficiencies in the health care system, as leading explanations for the “health disadvantage” of the United States. In a comparison of 17 high-income countries, age-adjusted all-cause mortality rates for 2008 ranged from 378.0 per 100,000 in Australia to 504.9 in the United States. The report found a pervasive pattern of health disadvantages across diverse categories of illness and injury that existed across age groups, sexes, racial and ethnic groups, and social classes (NRC and IOM, 2013).

Recent attention has focused on the substantial health disparities that exist within the United States, where life expectancy varies at the state level by 7.0 years for males and 6.7 years for females (NRC and IOM, 2013) but mortality and life expectancy vary even more substantially across smaller geographic areas such as counties (University of Wisconsin Population Health Institute, 2013; Kulkarni et al., 2011) and census tracts. In many U.S. cities, life expectancy can vary by as much as 25 years across neighborhoods (Evans et al., 2012). The same dramatic geographic disparities can be seen for other outcomes, such as infant mortality, obesity, and the prevalence of diabetes and other chronic diseases.

Of the various social determinants of health that explain health disparities by geography or demographic characteristics (e.g., age, gender, race-ethnicity), the literature has always pointed prominently to education. Research-based on decades of experience in the developing world has identified educational status (especially of the mother) as a major predictor of health outcomes, and economic trends in the industrialized world have intensified the relationship between education and health. In the United States, the gradient in health outcomes by educational attainment has steepened over the last four decades (Goldman and Smith, 2011; Olshansky et al., 2012) in all regions of the United States (Montez and Berkman, 2014), producing a larger gap in health status between Americans with high and low education. Among white Americans without a high school diploma, especially women, life expectancy has decreased since the 1990s, whereas it has increased for others (Olshansky et al., 2012). Death rates are declining among the most educated Americans, accompanied by steady or increasing death rates among the least educated (Jemal et al., 2008).

What accounts for the growing health advantages that exist among people with higher educational attainment? Is it what they learn in school, such as how to live a healthy lifestyle, or the socioeconomic advantages that come from an education? Or is the cross-sectional association between education and health more complex, involving numerous contextual covariables that provide a fuller back story? Despite decades of research documenting the connections between education and health, there is still much to learn about the mechanisms that enable this connection.

Unpacking the reasons for the connections between education and health is not just an exercise in scientific inquiry; it is also essential to setting policy priorities. As increasing attention is focused on the need to address social inequity in order to address health inequities, understanding the links between broad upstream factors such as education and health outcomes becomes a critical challenge. Awareness of the importance of education might help drive investment in education and improvements in educational policy.

Conceptual Framework

An overarching theoretical framework for the impact of social determinants on health is provided by an ecological model in which individuals and their behaviors are embedded, across the lifespan, within a framework of nested institutional contexts (IOM, 2000; see Figure 1). The individual and his or her characteristics are situated within and affected by the family and household, the community and its institutions (e.g., school, workplace, and civil institutions), and policies of the larger society. Each level brings access to opportunities, as well as constraints on actions and opportunities. Furthermore, these levels interact with one another, such that family resources, for example, may mediate or moderate the resources available within the community. Social scientists widely agree that unequal social status creates unequal access to resources and rewards. Social structure, as embodied in social position, structures individual behaviors and values and therefore affects many of the mediators in the relationship between education and health.

Education is one of the key filtering mechanisms that situate individuals within particular ecological contexts. Education is a driving force at each ecological level, from our choice of partner to our social position in the status hierarchy. The ecological model can therefore provide a context for the numerous ways in which education is linked to our life experiences, including health outcomes. It also provides a framework for understanding the ways in which educational outcomes themselves are conditioned on the many social and environmental contexts in which we live and how these, in turn, interact with our individual endowments and experiences.

Within this rich contextual framework, educational attainment (the number of years of schooling completed) is important but is far from the whole story. Educational attainment is often a key indicator in research studies, not least because it is often measured and recorded; life expectancy is compared by educational attainment because it is the only information about education recorded on death certificates. Besides such obvious measures of the quality of education as proficiency scores and understanding of mathematics, reading, science, and other core content, other dimensions of education are clearly important in the ecological context as well; cognitive development, character development, knowledge, critical thinking and problem solving are a few examples.

In addition, the relationship between years of education and health is not a purely linear function. As part of the literature attempting to clarify the functional form of the relationship between education and health, Montez et al. (2012) have documented a negative relationship between years of education and mortality risk for attainment less than high school graduation, a steep decline for high school graduates (with reduction of risk five times greater than attributable to other years of education), and a continued yet steeper negative relationship for additional years of schooling. The drop at high school graduation points to the importance of obtaining credentials in addition to the other benefits associated with educational attainment.

In this paper we review the health benefits associated with education, focusing on the primary mechanisms, both distal and proximate, by which education may be considered a driving force in health outcomes. We take a socioecological approach by presenting these concepts in a hierarchy, moving from the level of the person to the community/institution and then the larger social/policy context. Next, we turn to issues of causality that can make it difficult to draw conclusions about the relationship between education and health. These include reverse causality and selection , in which education may be impacted by ill health, and confounding , where both education and health are affected by some other causal factor(s) that may also provide important clues about the root causes of poor education and poor health.

The Health Benefits Associated with Education

Among the most obvious explanations for the association between education and health is that education itself produces benefits that later predispose the recipient to better health outcomes. We may think of these returns from education, such as higher earnings, as subsequent “downstream” benefits of education. Following the socioecological framework presented in the introduction, we describe a range of potential downstream impacts of education on health, starting with the ways individuals experience health benefits from education then going on to discuss the health-related community (or place-based) characteristics that often surround people with high or low education, and closing with the larger role of social context and social policy.

Impact at the Individual Level

Education can impart a variety of benefits that improve the health trajectory of the recipient. We discuss its role in enhancing noncognitive and cognitive skills and access to economic resources, and we highlight the impacts of these on health behaviors and health care usage. Although this section focuses specifically on the health benefits of education, we do so in full knowledge that education is also impacted by health, development, and a host of personal, community, and contextual factors.

Education Impacts a Range of Skills

Education contributes to human capital by developing a range of skills and traits, such as cognitive skills, problem-solving ability, learned effectiveness, and personal control (Mirowski and Ross, 2005). These various forms of human capital may all mediate the relationship between education and health.

Personality traits (also known as “soft” or noncognitive skills) are associated with success in later life, including employment and health. The “Big Five” personality factors include conscientiousness, openness to experience, extraversion, agreeableness, and neuroticism/emotional stability (Heckman and Kautz, 2012). Roberts et al. (2007) postulate three pathways whereby personality traits may impact mortality: through disease processes (e.g., response to stress), health-related behaviors, and reactions to illness. A review by Roberts et al. (2007) suggests that the strength of association between the “Big Five” personality traits and mortality is to that of IQ and stronger than socioeconomic status (SES). Although enduring, these skills are also mutable, and research indicates that educational interventions to strengthen these skills can be important, especially among children in disadvantaged areas, who may find it more difficult to refine these skills at home and in their social environments.

Personal control, also described in the literature in terms of locus of control, personal efficacy, personal autonomy, self-directedness, mastery, and instrumentalism (Ross and Wu, 1995), is another soft skill associated with educational attainment. According to the authors, “Because education develops one’s ability to gather and interpret information and to solve problems on many levels, it increases one’s potential to control events and outcomes in life. Moreover, through education one encounters and solves problems that are progressively more difficult, complex, and subtle, which builds problem-solving skills and confidence in the ability to solve problems” (Ross and Wu, 1995, p. 723).

Personal control can impact individuals’ attitudes and behaviors, potentially including health behaviors. Furthermore, an individual’s sense of mastery and control may mediate stress, possibly by facilitating better coping mechanisms. Lack of personal control, on the other hand, may provoke physiological responses, leading to suppression of the immune system (Ross and Wu, 1995, p. 723).

In addition to its impact on soft skills, education has the potential to impart skills in reading, mathematics, and science/health literacy that could contribute to an individual’s health. Learners of English as a second language are helped to overcome language barriers that can interfere with understanding of health needs. Education may also improve a range of other skills, such as cognitive ability, literacy, reaction time, and problem-solving. Pathways from these skills to health outcomes may be indirect, via attainment of better socioeconomic circumstances or behavior, but they may also apply directly in understanding the increasingly complex choices individuals face in understanding health priorities and medical care needs. Skills such as higher cognitive ability and health literacy may also lead directly to improved health outcomes because of an enhanced “ability to comprehend and execute complex treatment regimens” and better disease self-management (Maitra, 2010). A strong education may be important in both navigating health care and making choices about lifestyle and personal health behaviors.  Cutler and Lleras-Muney (2010) report that increased cognitive ability resulting from education contributes significantly to the education gradient in health behaviors.

Education Increases Economic and Social Resources

A large part of the impact of education on health flows through the attainment of economic resources, such as earnings and wealth, as well social resources such as access to social networks and support. Adults with more education are less likely to experience unemployment and economic hardship and will have greater access to a variety of important material, financial and social resources. Link and Phelan (1995) point out that the specific mechanisms linking SES to health have changed over time but that SES remains a fundamental social cause of disease because it involves “access to resources that can be used to avoid risks or to minimize the consequences of disease once it occurs” (p. 87).

Economic Resources

Adults with a higher education—especially in today’s knowledge economy—have conspicuous advantages in gaining employment and finding desirable jobs (see Figure 2). Advanced degrees give workers an advantage in obtaining rewarding jobs that offer not only higher salaries and job satisfaction but other health-related benefits, such as health insurance coverage, (Adults with health insurance in the United States use more physician services and have better health outcomes compared to uninsured or inconsistently insured adults [NRC, 2009; Freeman et al., 2008; Hadley, 2003]) worksite health promotion programs, and worksite policies that protect occupational safety. An inadequate education markedly increases the risk of unemployment. (In 2012, unemployment was 12.4 percent among adults who did not graduate high school, compared to 8.3 percent among adults with a high school diploma and 4.5 percent among college graduates [BLS, 2013]. 5 A body of evidence links unemployment to adverse health outcomes. For example, a higher percentage of employed persons reported in 2010 that they were in excellent or very good health [62.7 percent] than did persons who were unemployed for less than 1 year [49.2 percent] or unemployed for more than 1 year (39.7 percent). The unemployed also reported more physically and mentally unhealthy days in the past 30 days [Athar et al., 2013]).

Income and wealth are leading predictors of health status (CSDH, 2008; Braveman et al., 2010), and accumulated financial strain has been shown to impact health above and beyond the effects of income and wealth (Shippee et al., 2012). In today’s society, economic resources are inextricably linked to education. In 2012, the median wage for college graduates was more than twice that of high school dropouts and more than one and a half times higher than high school graduates (BLS, 2013). Weekly earnings rise dramatically for Americans with a college or advanced degree. A higher education has an even greater effect on lifetime earnings, a pattern that is true for men and women, for blacks and whites, and for Hispanics and non-Hispanics. (According to 2006–2008 data, the lifetime earnings of a Hispanic male are $870,275 for those with less than a ninth-grade education but $2,777,200 for those with a doctoral degree. The corresponding lifetime earnings for a non-Hispanic white male are $1,056,523 and $3,403,123 [Julian and Kominski, 2011]). Economic vulnerability can affect health through a cascade effect on the ability to acquire resources that are important to health: food, stable housing, transportation, insurance, and health care (Braveman, et al., 2011). People with low income are more likely to be uninsured and to be vulnerable to the rising costs of health care, which insurance carriers are increasingly shifting to patients through higher copayments, deductibles, and premiums (In 2012, one-fourth [24.9 percent] of people in households with annual income less than $25,000 had no health insurance coverage, compared to 21.4 percent of people in households with income ranging from $25,000 to $49,999; the figure was 15.0 percent in households with income ranging from $50,000 to $74,999 and 7.9 percent with income of $75,000 or more [DeNavas-Walt et al., 2013]). Individuals with higher incomes have more resources to purchase healthy foods, to afford the time and expenses associated with regular physical activity, to have easy transportation to health care facilities or work locations, and to afford health care expenses. (According to 2010 Behavioral Risk Factor Surveillance System [BRFSS] data, 27 percent of adults with less than a high school education reported not being able to see a physician because of cost, compared to 18 percent and 8 percent of high school and college graduates, respectively [CDC, 2014]). Accordingly, the costs of a healthy lifestyle pose more of a barrier for people with less education. The health implications of these financial barriers to health care are well documented: the uninsured are less likely to receive preventive care or help with disease management (HHS, 2013, p. 9-1), and they have a higher risk of death (IOM, 2003a).

Social Resources

Educational attainment is associated with greater social support, including social networks that provide financial, psychological, and emotional support. Social support includes networks of communication and reciprocity. Individuals in a social network can relay information, define norms for behavior, and act as modeling agents. Those individuals with higher levels of education may also have higher levels of involvement with civic groups and organizations. Conversely, low social support (i.e., not participating in organizations, having few friends, being unmarried, or having lower-quality relationships) is associated with higher mortality rates (Kaplan et al., 1994; Seeman, 1996) and poor mental health (Seeman, 1996). Berkman et al. (2000) linked social integration to health outcomes in a causal chain that begins with the macrosocial and ends with psychobiological processes. They propose several mechanisms through which social integration affects health: social support, social influence, social engagement/attachment, and access to goods and resources (Berkman et al., 2000, p. 846). Social connection can be an important buffer to the negative health consequences of health stressors. Marriage imparts benefits in longevity, but weaker network ties can also have important health effects, such as the effects of peers on behavior (Smith and Christakis, 2008). The effect of social networks on smoking cessation is a well-known example (Christakis and Fowler, 2008).

Impact at the Community Level

Individuals with more education benefit not only from the resources that schooling brings to them and their families but also from health-related characteristics of the environments in which they tend to live, work, and study. Although there are many methodological challenges in estimating community-level effects on individuals (Kawachi and Berkman, 2003; Kawachi and Subramanian, 2007), communities may confer a range of benefits or risks that can impact health. In the midst of growing recognition that “place matters” to health, many studies have tried to estimate neighborhood effects on outcomes such as child/youth educational attainment, behavioral/well-being outcomes, or health status and mortality. (For example, Ross and Mirowsky (2008), using multilevel analysis of survey data from Illinois, addressed the question of whether community SES impacts health above and beyond the contributions of individual SES measures. They found that individual-level indicators of SES explained most of the variation in physical functioning (about 60 percent) but that neighborhood-level measures had a significant influence as well.) Given the wide range of methodologies and data sources utilized, findings are not uniform among such studies, but there is general agreement that a relatively modest neighborhood effect exists independent of individual and family-level factors (Kawachi and Berkman, 2003; Leventhal and Brooks-Gunn, 2000; Steptoe and Feldman, 2001). (Kawachi and Berkman (2003) call attention to the methodological difficulties of estimating neighborhood effects while controlling for individual SES, when some neighborhood effects may operate via their impact on individual outcomes, thus “adjusting away the variation of interest” (p. 9). Winkleby et al. (2006) examined the interaction between neighborhood SES and individual SES and found that low SES individuals living in higher SES neighborhoods had higher mortality rates compared to low SES individuals living in low or moderate SES neighborhoods.)) Effects that appear to occur at the neighborhood level may represent aggregated individual characteristics (compositional effects), neighborhood variability (contextual effects), or local manifestations of larger-scale processes (e.g., higher-level planning or regulatory decisions) (Shankardass and Dunn, 2011). Furthermore, it is important to recognize the dynamic interaction that occurs between the individual and the environment (Rhodes et al., 2011) and conceptions of space as “relational geographies” (Conceptualization of space as a “relational geography” emphasizes aspects such as networks rather than boundaries, social rather than physical distance, mobility of populations, and dynamic characteristics of places (Cummins et al., 2007)) (Cummins et al., 2007).

At one level, community characteristics matter because access to resources that are important to health is contingent on community-level resources and institutions. Macintyre and Ellaway (2003) categorize these as physical features, services, sociocultural features, reputation, and availability of healthy environments at home, work, and play. Theories about the mechanisms by which social environments affect the health of individuals also focus on community characteristics such as social disorganization, social control, social capital, and collective efficacy (Sampson, 2003). Kawachi et al. (2013) note that communities with higher social capital tend to be more resilient in the face of disasters and are better able to employ informal control mechanisms to prevent crime.

Through a combination of resources and characteristics, communities expose individuals to varying levels of risk versus safety (e.g., crime, unemployment, poverty, and exposure to physical hazards) and provide different levels of resources (e.g., food supply, green space, economic resources, and health care). One notable resource that differs among communities is the quality of education available. Low-income neighborhoods often have fewer good schools, not least because public schools tend to be poorly resourced by low property taxes and cannot offer attractive teacher salaries or properly maintain buildings, supplies, and school safety. Adverse community factors can compound the difficulty that children face in obtaining a good education while also compromising their health trajectory.

Below we touch on several additional community characteristics that have been linked to health outcomes, including food access, spaces and facilities for physical activity, access to health care, community economic resources, crime and violence, and environmental exposure to toxins.

• Food access. Unhealthy eating habits are linked to numerous acute and chronic health problems, such as diabetes, hypertension, obesity, heart disease, and stroke as well as higher mortality rates, but access to healthier foods tends to be limited in neighborhoods with lower median incomes and lower levels of educational attainment. In one study, access (Defined as at least one healthier food retailer within the census tract or within one-half mile of tract boundaries) to healthier food outlets was 1.4 times less likely in census tracts with fewer college-educated adults (less than 27 percent of the population) than in tracts with a higher proportion of college-educated persons; these differences varied by region and were highest in the South and lowest in the West and Northeast (Grimmet al., 2013). Conversely, low-income neighborhoods often have an oversupply of fast-food restaurants, convenience stores, bodegas, liquor stores, and other outlets that sell little fresh produce but promote inexpensive calorie-dense foods and beverages.
• Spaces and facilities for physical activity. People with higher education and income are more likely to live in neighborhoods that provide green space (e.g., parks), sidewalks, and other places that enable residents to walk and cycle to work and shopping, exercise, and outside play. Lower-income neighborhoods and those with higher proportions of nonwhite residents are also less likely to have commercial exercise facilities (Powell et al., 2006). The health benefits of green space have been documented in urban environments, especially for lower-income, young, and elderly populations (Maas et al., 2006). A longitudinal study in Great Britain found immediate, positive mental health effects of moving to urban areas with more green space (Alcock et al., 2014).
• Access to health care. Because of the maldistribution of health care providers in the United States (HHS, 1998), access to clinicians and facilities tends to be in shortest supply in rural and low-income areas. Thus, apart from whether residents have the health insurance coverage and resources to afford health care, they may struggle to find local primary care providers, specialists, and hospitals that provide quality health care services.
• Community economic resources. The lack of jobs in low-income communities can exacerbate the economic hardship that is common for people with less education, who are more likely to live in communities with a weak economic base that is unattractive to businesses, employers, and investors and are thereby often caught in a self-perpetuating cycle of economic decline and marginalization.
• Crime and violence. Community crime rates can impact health through the direct effects of violent crimes on victims, such as trauma and high youth mortality rates. Crime can also affect health indirectly, such as through fear of crime (Stafford et al., 2007) or the cumulative stress of living in unsafe neighborhoods. The high incarceration rates of residents in some communities can have deleterious effects on social networks, social capital, and social control, further compromising public health and safety (Clear, 2007). The 2006 and 2007 rounds of the American Community Survey found that, among young male high school drop-outs, nearly 1 in 10 was institutionalized on a given day in 2006–2007 versus fewer than 1 of 33 high school graduates (Sum et al., 2009).
• Environmental exposure to toxins. People of color and those with less education are more likely to live in neighborhoods that are near highways, factories, bus depots, power plants, and other sources of air and water pollution. A large body of research on environmental justice has documented the disparate exposure of low-income and minority neighborhoods to hazardous waste, pesticides, and industrial chemicals (Bullard et al., 2011; Calnan and Johnson, 1985). This exposure to toxins is perhaps the most undiscriminating place-based characteristic because residents’ personal socioeconomic advantages (e.g., education, income) offer no protection against the adverse health consequences of inhalation or ingestion of such toxins.

The Larger Social Context and Social Policy

Health inequities are driven, in large part, by the social context in which people are born, live, and work, that is, the social policies that shape resources, institutions, and laws; the economic system through which material and financial resources are created and distributed; and the social norms that govern interactions. The conditions in which people live—for example, the built environment, public transportation, urban design, crime rates, food deserts, and the location of polluting factories—are determined by macrostructural policies and the cultural values that shape them. Formulation of effective analyses and solutions to problems affecting health must address factors that go beyond the level of the individual and proximal risk factors (O’Campo and Dunn, 2011). These influences have been recognized by organizations concerned with health outcomes locally, nationally, and internationally. The World Health Organization calls for improved living and working conditions, social protection policy supportive of all, reduced inequality, and strengthened governance and civil society (CSDH, 2008). Healthy People 2020 has many policy objectives for health, including improved environmental conditions (e.g., air/water quality and exposure to hazards), violence prevention, poverty reduction, and increased rates of postsecondary education. (See http://www.healthypeople.gov/2020/topicsobjectives2020/default.aspx.) The Place Matters team in Alameda County, California, has identified five policy areas to impact health outcomes locally: economics, education, criminal justice, housing, and land use and transportation (Alameda County Public Health Department, n.d.).

Decisions made by society, voters, and policy makers—both within and outside of government—exert deep influences on education itself, as well as on the institutions and resources that populate the socioecological framework linking education and health. For example, in other societies, the adverse health consequences of poverty are often buffered by social services that act to safeguard the health of children, young parents, and other vulnerable groups. Bradley et al. (2011) found that while most OECD (Organisation for Economic Co-operation and Development) countries spent more on social services than on health expenditures, the converse was true in the United States. (The average ratio of social to health expenditures in OECD countries from 1995 to 2005 was 2.0; the ratio in the U.S. was 0.91. [Bradley et al. 2011]). Likewise, economic policies have a large influence on the employment and wealth-building opportunities of workers. Major economic and technological shifts of the last few decades have favored “nontradable” service jobs in sectors such as government and health care while manufacturing jobs have moved to less developed countries in large numbers. Remaining jobs in the “tradable” sectors such as technology and finance increasingly require advanced skill sets (Spence and Hlatshwayo, 2011). These employment trends provide a critical context in the relationship between education and health—those unable to acquire the necessary education to be competitive in an increasingly restrictive job environment are vulnerable to long-term economic hardship. Educational opportunities, however, are not equally distributed in the United States. Public school funding, largely dependent on local property taxes, varies widely both within and between states. The best-funded school systems in the United States have per pupil expenditures almost four times the per pupil expenditures in the lowest spending schools. (In 2011, total state per pupil education expenditures ranged from $6,200 to $16,700; among the 100 largest school systems in the United States, per pupil funding ranges from $5,400 to almost $20,000 [Census Bureau, 2013]). Although early studies failed to find a strong relationship between school funding amounts and student achievement, more recent meta-analysis has confirmed the importance of school funding for individual achievement (Greenwald et al., 1996).

Inequality by gender, race, ethnicity, sexual orientation, and disability affect risks and opportunities for people throughout the world. Figure 3 (See Women’s earnings as percent of men’s in 2010. http://www.bls.gov/opub/ted/2012/ted_20120110.htm) shows persistent gender and race disparities in earnings. Ridgeway (2014) calls attention to the cultural as well as the material dimensions of inequality: “Cultural status beliefs work their effects on inequality primarily at the social-relational level by shaping people’s expectations for themselves and others and their consequent actions in social contexts” (p. 3). Social status hierarchies based on “categories” of difference solidify and perpetuate differentials in power and control of resources—thus leading to material inequalities. Indeed, income inequalities in the United States are significant and have become more pronounced, with wages at the lower or middle of the income distribution stagnating or falling while those at the top continue to rise. This division has continued during recovery from the Great Recession, during the first three years of which 95 percent of income gains accrued to the top 1 percent of earners (Saez, 2013). The Census Bureau reports that the Gini coefficient, which measures income inequality, has risen from 0.394 in 1970 to 0.469 in 2010; the share of household income earned by the bottom quintile was 3.3 percent in 2010, compared to 50.2 percent among the top quintile. (Table A-3: Selected Measures of Household Income Dispersion: 1967 to 2010. https://www.census.gov/hhes/www/income/data/historical/inequality/IE-1.pdf)

Historical, economic, and cultural factors play a central role in opportunities, values, and behaviors. The continuing racial residential segregation and increasing economic segregation of urban landscapes affect the life chances of those living in concentrated poverty “irrespective of personal traits, individual motivations, or private achievements” (Massey and Denton, 1993, p.3). Massey and Denton argue that residential segregation and “hypersegregation” expose residents to higher levels of social problems. Wilson (1987) links historical and economic factors in his description of the racial division of labor resulting from “decades, even centuries, of discrimination and prejudice” with the result that “because those in the low-wage sector of the economy are more adversely affected by impersonal economic shifts in advanced industrial society, the racial division of labor is reinforced” (p. 12).

Reverse Causality and Selection

Education’s association with health may reflect not only the health benefits of education but also a selection phenomenon caused by the detrimental effects of illness on educational success. Basch (2011) identifies five causal pathways by which health may impact motivation and ability to learn— sensory perceptions, cognition, school connectedness and engagement, absenteeism, and temporary or permanent dropping out (p. 596). For example, chronic health conditions can impact children’s development and educational performance (Taras and Potts-Datema, 2005). Such children are more likely to have absences for medical reasons and to be distracted by health concerns. Nonetheless, research evidence demonstrating that poor health has a causal relationship with educational outcomes is incomplete (Currie, 2009), and findings of the overall effects range from about 1.4 years reduced attainment (Estimated for 16-year-old white males from 1979 youth cohort of the National Longitudinal Surveys of Youth (NLSY) using a dynamic programming model of joint decisions of young men on schooling, work, health expenditure, and savings (Gan and Gong, 2007)) (Gan and Gong 2007) to about half a year (Goldman and Smith 2011), but there are notable exceptions. For example, evidence across countries and time periods demonstrate the harmful effect of low birth weight on education (Currie, 2009; Eide and Showalter, 2011). Disease, malnutrition, and prenatal and childhood exposures to toxins can also impact physical and cognitive development and educational achievement (Pridmore, 2007).

The extent to which reverse causality contributes to the association between education and health requires further study, but longitudinal data—the most compelling evidence to resolve the controversy—tend to suggest that most of the association is attributable to the downstream benefits of education. Eide and Showalter (2011) reviewed studies incorporating a range of methodologies that attempted to examine causal links between education and health outcomes. Studies of natural experiments in the United States (e.g., changes in compulsory school laws) generally found evidence for a causal link with mortality. Twin studies found evidence for causal links between years of schooling and self-reported health, the probability of being overweight (among men but not women), and the effects on college attendance on preventive health care later in life. Link and Phelan (1995) also discussed research attempting to show the direction of causality using quasi-experimental approaches, longitudinal designs, and analyses of risk factors that cannot be attributed to individual illness (e.g., plant closings). They concluded that these studies “demonstrated a substantial causal role for social conditions as causes of illness” (p. 83).

Conditions Throughout the Life Course That Affect Both Health and Education

A third way that education can be linked to health is when education acts as a proxy for factors throughout the life course—most notably in early childhood—that affect both education and health. For example, as noted earlier, the social and economic environment facing individuals and households and the stresses and allostatic load induced by material deprivation can affect success in school (and work) while also inducing biological changes and unhealthy behaviors that can increase the risk of disease. Although this can occur throughout the life course, increasing attention is being placed on the role of these factors on children before they reach school age.

Early Childhood Experiences

The education community has long understood the connections between early life experiences and educational success. It is well established that school readiness is enhanced by positive early childhood conditions—for example, fetal well-being and social-emotional development (Denhem, 2006), family socioeconomic status, (Children’s birth weight, developmental outcomes, health status (e.g., obesity and specific health conditions), disability, and success in school are strongly linked to parents’ education and family income and assets (Williams Shanks and Robinson, 2013; Chapman et al., 2008; Currie, 2009)) neighborhood socioeconomic status (Jencks and Mayer, 1990; Mayer and Jencks, 1989), and early childhood education (Barnett and Belfield, 2006) — but some of these same exposures also appear to be vital to the health and development of children and their future risk of adopting unhealthy behaviors and initiating adult disease processes.

Below are several examples from the literature of early childhood experiences that influence health.

• Low birth weight affects not only educational outcomes but also health and disability (Avchen et al., 2001).
• Nurturing relationships beginning at birth, the quality of the home environment, and access to stimulation provide a necessary foundation for children to grow and thrive (Heckman, 2006). One example of this is the importance of child-directed speech during infancy for language skills (Weisleder and Fernald, 2013). The effects of stress can be reduced when children have a responsive and supportive caregiver available to help cope with stress and provide a protective effect (Shonkoff and Garner, 2012).
• Unstable home and community life, such as economic factors, family transitions, housing instability, and school settings, can harm child development and later outcomes (Sandstromand Huerta, 2013). In one study, (Data were based on research by the National Poverty Center on the basis of the Michigan Recession and Recovery Study (MRRS) of adults ages 19–64 in southeastern Michigan. The researchers examined the relationship between various forms of housing instability and health, controlling for prior health problems and sociodemographic characteristics) homelessness and struggles with mortgage payments and foreclosure were predictive of self-rated health, and these combined with other categories (e. g., moved for cost in past 3 years, behind on rent) also predicted mental health problems (Burgard et al., 2012).
• Family and neighborhood socioeconomic status not only affect education but also predict developmental and health trajectories as children grow and develop (Case et al., 2002; Duncan et al.,1994). Duration and timing of childhood poverty are important. Longitudinal studies indicate that the largest effects of poverty on child outcomes are during early childhood development, when children experience poverty for multiple years, and when they live in extreme poverty (Brooks-Gunn and Duncan 1997). Guo (1998) also found that timing of poverty during early adolescence is important for adolescent achievement.

Biological Pathways

A growing body of research suggests that the similar root causes that lead children to poor educational outcomes and poor health outcomes may not operate via separate pathways but may relate to the biology of brain development and the pathological effects of early childhood exposure to stress and adverse childhood events (ACEs). Children in low SES households are more likely to experience multiple stressors that can harm health and development (Evans and Kim, 2010), mediated by chronic stress (Evans et al., 2011). These disruptions, along the pathways listed below, can thereby shape educational, economic, and health outcomes decades and generations later (NRC and IOM, 2000).

• Neuroanatomy and neuroplasticity: Infants and toddlers exposed to toxic stress, social exclusion and bias, persistent poverty, and trauma may experience changes in brain architecture and development that affect cognition, the ability to learn new skills, behavioral and stress regulation, executive function, and the capacity to adapt to future adversity (Hackman, 2010; Gottesman and Hanson, 2005).
• Endocrine disruption: Early life stressors also appear to cause physiological increases in allostatic load that promote stress-related diseases later in life (Shonkoff and Garner, 2012). Such stressors may, for example, disrupt the hypothalamic-pituitary-adrenal axis of the endocrine system and stimulating overproduction of stress-related hormones that are thought to adversely affect end organs and lead later in life to heart disease and other adult health problems (McEwen, 2012).
• Immune dysregulation: The release of interleukins and other immune reactant proteins is thought to create conditions of chronic inflammation that may increase the risk of heart disease and other chronic diseases later in life (McEwen, 2012).
• Epigenetic changes: Chronic stress is thought to affect methylation of DNA and cause epigenetic changes that “turn on” expression of genes that may cause cancer and other diseases (Zhang and Meaney, 2010).

Enhanced understanding of these biological pathways is shedding light on research, first reported in the 1990s, that called attention to the correlation between adult disease rates and a history of childhood exposure to ACEs. In a seminal study on the subject, the Adverse Childhood Experiences study, Felitti et al. (1998) surveyed more than 13,000 adult patients at Kaiser Permanente and asked whether they recalled exposure to seven categories of ACEs: psychological, physical, or sexual abuse; violence against the mother; living in a household with members who are substance abusers; mentally ill or suicidal; or imprisonment (Felitti et al., 1998). More than half the adults recalled ACEs as children, and those with greater trauma were more likely to report unhealthy behaviors as adults (e.g., smoking, physical inactivity, alcoholism, drug abuse, multiple sexual partners) and to have a history of depression or a suicide attempt. The researchers reported a dose-response relationship: those who recalled four categories of ACEs faced significant odds ratios for adult diseases, including ischemic heart disease (2.2), cancer (1.9), stroke (2.4), chronic lung disease (3.9), and diabetes (1.6).

The ACE study and subsequent studies with similar results relied on retrospective designs that faced the limitation of recall bias (relying on the memory of adults); recollections of ACEs were vulnerable to the criticism that sick adults might have skewed perceptions of their childhood experiences. Nevertheless, prospective studies that documented ACEs contemporaneously during childhood have also documented higher rates of disease when the children were followed into adulthood. The Centers for Disease Control and Prevention maintains a website that is cataloguing the burgeoning research on ACEs (CDC, 2014), and increasing attention is shifting toward strategies for policy and clinical practice to help ameliorate childhood exposure to ACEs and to buffer their adverse biological and psychosocial effects.

The building evidence that stress and other contextual factors can have effects on both education and health throughout the life course—as in the lasting effects on development, behavior, learning, and health of children—adds important insights for understanding the correlation between education and health. As discussed earlier in this paper, reverse causality plays some role in the association, and a much larger influence comes from the downstream benefits of education (e.g., greater socioeconomic resources and personal skills), but the upstream influence of adverse experiences on the young child also cannot be ignored. The effects of ACEs on the developing brain and on behavior can affect performance in school and explain setbacks in education—but it can also affect health outcomes. Thus, the correlation between reduced education and illness may have as much to do with the seeds of illness that are planted before children ever reach school age than with the consequences of education itself. They end up with fewer years of education and greater illness, but an important way to improve their health is to address the root causes that expose children to stress in the first place.

• Alameda County Public Health Department. n.d. Place matters. Available at: www.acphd.org/media/114473/placematters_overview.pdf (accessed April 10, 2014).
• Alcock, I., M. P. White, B. W. Wheeler, L. E. Fleming, and M. H. Depledge. 2014, Longitudinal effects on mental health of moving to greener and less green urban areas. Environmental Science and Technology 48(2):1247–1255. https://doi.org/10.1021/es403688w
• Athar, H. M., M. H. Chang, R. A. Hahn, E. Walker, and P. Yoon. 2013. Unemployment—United States, 2006 and 2010. CDC health disparities and inequalities report—United States, Centers for Disease Control. Morbidity and Mortality Weekly Report 62(3):27–32. Available at: http://www.cdc.gov/mmwr/pdf/other/su6203.pdf (accessed February 7, 2014).
• Avchen, R. N., G. Scott, and C. A. Mason. 2001. Birth weight and school-age disabilities: A population-based study. American Journal of Epidemiology 154(10):895–901. https://doi.org/10.1093/aje/154.10.895
• Barnett, W. S., and C. R. Belfield. Early childhood development and social mobility. 2006. The Future of Children 16(2):73–98. https://doi.org/10.1353/foc.2006.0011
• Basch, C. E. 2011. Healthier students are better learners: A missing link in school reforms to close the achievement gap. Journal of School Health 81(10):593–598. Available at: https://healthyschoolscampaign.org/wp-content/uploads/2017/03/A-Missing-Link-in-School-Reforms-to-Close-the-Achievement-Gap.pdf (accessed May 26, 2020).
• Berkman, L. F., T. Glass, I. Brissette, T. E. Seeman. 2000. From social integration to health: Durkheim in the new millennium. Social Science and Medicine 51:843–857. https://doi.org/10.1016/s0277-9536(00)00065-4
• Berkman, N. D., S. L. Sheridan, K. E. Donahue, D. J. Halpern, and K. Crotty. 2011. Low health literacy and health outcomes: An updated systematic review. Annals of Internal Medicine 155(2):97–107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005
• BLS (Bureau of Labor Statistics). 2013. Employment projections. Available at: http://www.bls.gov/emp/ep_chart_001.htm (accessed February 7, 2014).
• BLS. Women’s earnings as a percent of men’s in 2010. 2012. Available at: http://www.bls.gov/opub/ted/2012/ted_20120110.htm (accessed June 3, 2014).
• Boote, J., R. Telford, and C. Cooper. 2002. Consumer involvement in health research: A review and research agenda. Health Policy 61:213–236. https://doi.org/10.1016/s0168-8510(01)00214-7
• Bradley E. H., B. R. Elkins, J. Herrin, and B. Elbel. 2011. Health and social services expenditures: Associations with health outcomes. BMJ Quality and Safety 20:826–831. https://doi.org/ 10.1136/bmjqs.2010.048363.
• Braveman, P., S. Egerter, and C. Barclay. 2011. Issue Brief Series: Exploring the Social Determinants of Health: Income, Wealth, and Health. Princeton, NJ, Robert Wood Johnson Foundation. Available at: https://www.rwjf.org/en/library/research/2011/04/how-social-factors-shape-health1.html (accessed May 26, 2020).
• Braveman, P. A., C. Cubbin, S. Egerter, D. R. Williams, and E. Pamuk. 2010. Socioeconomic disparities in health in the United States: What the patterns tell us. American Journal of Public Health 100(Suppl. 1):S186–S196. https://doi.org/10.2105/AJPH.2009.166082
• Brooks-Gunn, J., and G. J. Duncan. 1997. The effects of poverty on children. The Future of Children: Children and Poverty 7:55–71. Available at: https://pubmed.ncbi.nlm.nih.gov/9299837/ (accessed May 26, 2020).
• Brotaneka, J. M., C. E. Seeley, and G. Flores. 2008. The importance of cultural competency in general pediatrics. Current Opinion in Pediatrics 20:711–718. https://doi.org/10.1097/MOP.0b013e328317efff
• Brownell, K. D., R. Kersh, D. S. Ludwig, R. C. Post, R. M. Puhl, M. B. Schwartz, and W. C. Willett. 2010. Personal responsibility and obesity: A constructive approach to a controversial issue. Health Affairs 29(3):379–387. https://doi.org/10.1377/hlthaff.2009.0739
• Bullard, R. D., G. S. Johnson, and A. O. Torres. 2011. Environmental health and racial equity in the United States: Building environmentally just, sustainable and livable communities. Washington, DC: American Public Health Association. Available at: https://hefn.org/learn/resource/environmental_health_and_racial_equity_in_the_united_states_building_environmentally (accessed May 26, 2020).
• Burgard, S., K. Seefeldt, and S. Zelner. 2012. Housing instability and health: Findings from the Michigan Recession and Recovery Study. Population Studies Center Research Report 12-749. Ann Arbor, MI: University of Michigan Institute for Social Research. Available at: http://www.psc.isr.umich.edu/pubs/pdf/rr12-749.pdf (accessed February 26, 2014).
• Calnan, M. and Johnson, B. 1985. Health, health risks, and inequalities: An exploratory study of women’s perceptions. Sociology of Health and Illness (7): 55-75. https://doi.org/10.1111/1467-9566.ep10831360
• Case, A., D. Lubotsky, and C. Paxson. 2002. Economic status and health in childhood: The origins of the gradient. American Economic Review 92:1308–1334. https://doi.org/10.1257/000282802762024520
• CDC (Centers for Disease Control). 2014. Behavioral Risk Factor Surveillance System, 2010 BRFSS data. Office of Surveillance, Epidemiology, and Laboratory Services. Available at: http://www.cdc.gov/brfss/data_tools.htm (accessed February 14, 2014).
• Chapman, D. A., K. G. Scott, and T. L. Stanton-Chapman. 2008. Public health approach to the study of mental retardation. American Journal on Mental Retardation 113(2):102–116. Available at: http://meridian.allenpress.com/ajidd/article-pdf/113/2/102/2027787/0895-8017(2008)113[102_phatts]2_0_co_2.pdf (accessed May 28, 2020).
• Christakis, N. A., and J. H. Fowler. 2008. The collective dynamics of smoking in a large social network. New England Journal of Medicine 358:2249–2258. https://doi.org/10.1056/NEJMsa0706154
• Clear, T. R. 2007. Impact of incarceration on community public safety and public health. In Public health behind bars: From prisons to communities, edited by R. B Greifinger, J. A. Bick, and J. Goldenson. New York: Springer. Pp. 13–24.
• CSDH (Commission on Social Determinants of Health). 2008. Closing the gap in a generation: Health equity through action on the social determinants of health. Geneva, Switzerland: World Health Organization, Commission on Social Determinants of Health. Available at: http://www.who.int/social_determinants/thecommission/finalreport/en/ (accessed April 11, 2014).
• Cummins, S., S. Curtis, A. V. Diez-Roux, and S. Macintyre. 2007. Understanding and representing “place” in health research: A relational approach. Social Science and Medicine 65:1825–1838. https://doi.org/ 10.1016/j.socscimed.2007.05.036
• Currie, J. 2009. Healthy, wealthy, and wise: Socioeconomic status, poor health in childhood, and human capital development. Journal of Economic Literature 47(1):87–122. https://doi.org/10.1257/jel.47.1.87
• Cutler, D. M., and A. Lleras-Muney. 2010. Understanding differences in health behaviors by education. Journal of Health Economics 29(1):1–28. https://doi.org/10.1016/j.jhealeco.2009.10.003
• DeNavas-Walt, C., B. D. Proctor, and J. C. Smith. 2013. Income, poverty, and health insurance coverage in the United States: Current population reports. U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau. September. Available at: http://www.census.gov/hhes/www/hlthins/ (accessed May 23, 2014).
• Denhem, S. A. 2006. Social-emotional competence as support for school readiness: What is it and how do we assess it? Early Education and Development 17(1):57–89. https://doi.org/10.1207/s15566935eed1701_4
• Duncan, G. J., J. Brooks-Gunn, and P. K. Klebanov. 1994. Economic deprivation and early childhood development. Child Development 65:296–318. https://doi.org/10.2307/1131385
• Eide, R. E., and M. H. Showalter. 2011. Estimating the relation between health and education: What do we know and what do we need to know? Economics of Education Review 30(5):778–791. Available at: http://www.sciencedirect.com/science/article/pii/S0272775711000525 (accessed May 28, 2020).
• Estabrooks, P. A., R. E. Lee, and N. C. Gyurcsik. 2003. Resources for physical activity participation: Does availability and accessibility differ by neighborhood socioeconomic status? Annals of Behavioral Medicine 25(2):100–104. https://doi.org/10.1207/S15324796ABM2502_05
• Evans, B. F., E. Zimmerman, S. H. Woolf, and A. D. Haley. 2012. Social determinants of health and crime in post-Katrina Orleans Parish. Richmond: Virginia Commonwealth University Center on Human Needs.
• Evans, G. W., J. Brooks-Gunn, and P. K. Klebanov. 2011. Stressing out the poor: Chronic physiological stress and the income-achievement gap. Community Investments 23(2):22–27. Available at: https://inequality.stanford.edu/sites/default/files/PathwaysWinter11_Evans.pdf (accessed May 28, 2020).
• Evans, G. W., and P. Kim. 2010. Multiple risk exposure as a potential explanatory mechanism for the socioeconomic status-health gradient. Annals of the New York Academy of Sciences 186(1):174–189. https://doi.org/10.1111/j.1749-6632.2009.05336.x
• Felitti, V. J., R. F. Anda, D. Nordenberg, D. F. Williamson, A. M.Spitz, V. Edwards, M. P. Koss, and J. S. Marks. 1998. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) study. American Journal of Preventive Medicine 14(4):245–258. https://doi.org/10.1016/s0749-3797(98)00017-8
• Freeman, J. D., S. Kadiyala, J. F. Bell, and D. P. Martin. 2008. The causal effect of health insurance on utilization and outcomes in adults: A systematic review of U.S. studies. Medical Care 46(10):1023–1032. https://doi.org/10.1097/MLR.0b013e318185c913
• Gan, L., and G. Gong. 2007. Estimating interdependence between health and education in a dynamic model. NBER Working Paper No. 12830, Cambridge, MA. Available at: http://www.nber.org/papers/w12830 (accessed April 2, 2014).
• Garrett, B. E., S. R. Dube, C. Winder, and R. S. Caraballo. 2013. Cigarette smoking—United States, 2006–2008 and 2009–2010. DC health disparities and inequalities report—United States, 2013. Centers for Disease Control. Morbidity and Mortality Weekly Report 62(3):81–84. Available at: http://www.cdc.gov/mmwr/pdf/other/su6203.pdf (accessed February 7, 2014).
• Gaston, G. B. 2013. African-Americans’ perceptions of health care provider cultural competence that promote HIV medical self-care and antiretroviral medication adherence. AIDS Care 25(9):1159–1165. Available at: http://www.tandfonline.com/doi/abs/10.1080/09540121.2012.752783#preview (accessed, May 23, 2014).
• Goldman, D., and J. P. Smith. 2011. The increasing value of education to health. Social Science and Medicine 72:1728–1737. https://doi.org/ 10.1016/j.socscimed.2011.02.047
• Gottesman, I. I., and D. R. Hanson. 2005. Human development: Biological and genetic processes. Annual Review of Psychology 56:263-286. https://doi.org/10.1146/annurev.psych.56.091103.070208
• Greenwald, R., L. V. Hedges, and R. D. Laine. 1996. The effect of school resources on student achievement. Review of Educational Research 66(3):361–396. https://doi.org/10.2307/1170528
• Grimm, K. A., L. V. Moore, and K. S. Scanlon. 2013. Access to health food retailers—Unites States, 2011. CDC health disparities and inequalities report—United States, 2013. Centers for Disease Control. Morbidity and Mortality Weekly Report 62(3):20–26. Available at: http://www.cdc.gov/mmwr/pdf/other/su6203.pdf (accessed February 7, 2014).
• Guo, G. 1998. The timing of influences of cumulative poverty on children’s cognitive ability and achievement. Social Forces 77(1):257–287. https://doi.org/10.2307/3006017
• Hackman, D. A., M. J. Farah, and M. J. Meaney. 2010. Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience 11: 651-659. https://doi.org/10.1038/nrn2897
• Hadley, J. 2003. Sicker and poorer—the consequences of being uninsured: A review of the research on the relationship between health insurance, medical care use, health, work, and income. Medical Care Research and Review 60(Suppl. 2):S3–S75. https://doi.org/10.1177/1077558703254101
• Heckman, J. J. 2006. Skill formation and the economics of investing in disadvantaged children. Science 312:1900–1902. Available at: http://jenni.uchicago.edu/papers/Heckman_Science_v312_2006.pdf (accessed May 28, 2020).
• Heckman, J. J., and T. Kautz. 2012. Hard evidence on soft skills. Labour Economics 19:451–464. https://doi.org/10.1016/j.labeco.2012.05.014
• HHS (Health and Human Services). 1998. Council on graduate medical education tenth report: Physician distribution and health care challenges in rural and inner-city areas. Public Health Service, Health Resources and Services Administration. Available at: http://www.hrsa.gov/advisorycommittees/bhpradvisory/cogme/Reports/tenthreport.pdf (accessed February 14, 2014).
• HHS. 2013. National healthcare quality report 2012. Agency for Healthcare Research and Quality, Rockville, MD. Available at: http://www.ahrq.gov/research/findings/nhqrdr/nhqr12/nhqr12_prov.pdf (accessed February 14, 2014).
• Institute of Medicine. 2000.  Promoting Health: Intervention Strategies from Social and Behavioral Research . Washington, DC: The National Academies Press. https://doi.org/10.17226/9939
• Institute of Medicine. 2003.  Hidden Costs, Value Lost: Uninsurance in America . Washington, DC: The National Academies Press. https://doi.org/10.17226/10719
• Institute of Medicine. 2003.  Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care . Washington, DC: The National Academies Press. https://doi.org/10.17226/12875
• Jemal, A., E. Ward, R. N. Anderson, T. Murray, and M. J. Thun. 2008. Widening of socioeconomic inequalities in U.S. death rates, 1993–2001. PLoS ONE 3(5):1–8. https://doi.org/10.1371/journal.pone.0002181
• National Research Council. 1990.  Inner-City Poverty in the United States . Washington, DC: The National Academies Press. https://doi.org/10.17226/1539
• Julian, T. A., and R. A. Kominski. 2011. Education and synthetic work-life earnings estimates. American Community Survey Reports, ACS-14. U.S. Census Bureau, Washington, DC. Available at: https://www.census.gov/library/publications/2011/acs/acs-14.html (accessed May 28, 2020).
• Kanny, D., Y. Liu, R. D. Brewer, and H. Lu. 2013. Binge drinking–United States, 2011. CDC health disparities and inequalities report—United States, 2013. Centers for Disease Control. Morbidity and Mortality Weekly Report 62(3):77–80. Available at: http://www.cdc.gov/mmwr/pdf/other/su6203.pdf (accessed February 7, 2014).
• Kant, A. K., and B. I. Graubard. 2007. Secular trends in the association of socio-economic position with self-reported dietary attributes and biomarkers in the U.S. population: National Health and Nutrition Examination Survey (NHANES) 1971–1975 to NHANES 1999–2002. Public Health Nutrition 10(2):158–167. https://doi.org/10.1093/ajcn/84.5.1215
• Kaplan, G. A., T. W. Wilson, R. D. Cohen, J. Kauhanen, M. Wu, and J. T. Salonen. 1994. Social functioning and overall mortality: Prospective evidence from the Kuopio Ischemic Heart Disease Risk Factor Study. Epidemiology 5(5):495–500. Available at: https://pubmed.ncbi.nlm.nih.gov/7986863/ (accessed May 28, 2020).
• Karlamangla, A. S., H. S. Burton, and T. E. Seeman. 2006. Reduction in allostatic load in older adults is associated with lower all-cause mortality risk: MacArthur studies of successful aging. Psychosomatic Medicine 68:500–507. https://doi.org/10.1097/01.psy.0000221270.93985.82
• Kawachi, I., and L. F. Berkman. 2003. Neighborhoods and health. New York: Oxford University Press.
• Kawachi, I., and S. V. Subramanian. 2007. Neighbourhood influences on health. Journal of Epidemiology and Community Health 61(1):3–4. https://doi.org/10.1136/jech.2005.045203
• Kawachi, I., S. Takao, and S. V. Subramanian, eds. 2013. Global perspectives on social capital and health. New York: Springer.
• Krueger, P. M., and V. Chang. 2008. Being poor and coping with stress: Health behaviors and the risk of death. American Journal of Public Health 98:889–896. https://doi.org/10.2105/AJPH.2007.114454
• Kulkarni, S. C., A. Levin-Rector, M. Ezzati, and C. J. Murray. 2011. Falling behind: life expectancy in U.S. counties from 2000 to 2007 in an international context. Population Health Metrics 9(1):16. https://doi.org/10.1186/1478-7954-9-16
• Leventhal, T., and J. Brooks-Gunn. 2000. The neighborhood they live in: The effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin 126(2):309–337. https://doi.org/10.1037/0033-2909.126.2.309
• Link, B. G., and J. Phelan. 1995. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior 36:80–94. https://doi.org/10.1177/0022146510383498
• Maas, J., R. A., Verheij, P. P. Groenewegan, S. de Vries, and P. Spreeuwenberg. 2006 Green space, urbanity, and health: How strong is the relation? Journal of Epidemiology and Community Health 60:587–592. https://doi.org/10.1136/jech.2005.043125
• Macintyre, S., and A. Ellaway A. 2003. Neighborhoods and health: An overview. In Neighborhoods and health, edited by I. Kawachi and L. F. Berkman. New York: Oxford University Press. Pp. 20–42.
• Maitra, S. 2010. Can patient self-management explain the health gradient? Goldman and Smith’s “Can patient self-management help explain the SES health gradient?” (2002) revisited. Social Science and Medicine 70:802–812. https://doi.org/10.1016/j.socscimed.2009.08.043
• Massey, D. S., and N. A. Denton. 1993. American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University Press.
• Mayer, S. E., and C. Jencks. 1989. Growing up in poor neighborhoods: How much does it matter? Science 243(4897):1441–1445. https://doi.org/10.1126/science.243.4897.1441
• McEwen, B.S. 2012. Brain on stress: How the social environment gets under the skin. Proceedings of the National Academy of Sciences 109(2):17180-17185. https://doi.org/ 10.1073/pnas.1121254109
• McEwen, B. S., and E. Stellar. 1993. Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine 153(18):2093–2101. Available at: https://pubmed.ncbi.nlm.nih.gov/8379800/ (accessed May 28, 2020).
• Mirowsky, J., and C. E. Ross. 2005. Education, learned effectiveness and health. London Review of Education 3(3):205–220. Available at: https://eric.ed.gov/?id=EJ821180 (accessed May 28, 2020).
• Mistry K. B., C. S. Minkovitz, A. W. Riley, S. B. Johnson, H. A. Grason, L. C. Dubay, and B. Guyer.. 2012. A new framework for childhood health promotion: the role of policies and programs in building capacity and foundations of early childhood health. American Journal of Public Health 102: 1688-1696. https://doi.org/10.2105/AJPH.2012.300687
• Mokdad, A. H., J. S. Marks, D. F. Stroup, and J. L. Gerberdin. 2004. Actual causes of death in the United States 2000. Journal of American Medical Association 291(10):1238–1245. https://doi.org/10.1001/jama.291.10.1238
• Montez, J. K., and L. F. Berkman. 2014. Trends in the educational gradient of mortality among U.S. adults aged 45 to 84 years: Bringing regional context into the explanation. American Journal of Public Health 104(1):e82–e90. https://doi.org/10.2105/AJPH.2013.301526
• Montez, J. K., R. A. Hummer, and M. D. Hayward. 2012. Educational attainment and adult mortality in the United States: A systematic assessment of functional form. Demography 45:315–336. https://doi.org/10.1007/s13524-011-0082-8
• Institute of Medicine. 2009.  America’s Uninsured Crisis: Consequences for Health and Health Care . Washington, DC: The National Academies Press. https://doi.org/10.17226/12511
• Institute of Medicine and National Research Council. 2000.  From Neurons to Neighborhoods: The Science of Early Childhood Development . Washington, DC: The National Academies Press. https://doi.org/10.17226/9824
• Institute of Medicine and National Research Council. 2013.  U.S. Health in International Perspective: Shorter Lives, Poorer Health . Washington, DC: The National Academies Press. https://doi.org/10.17226/13497
• O’Campo, P., and J. R. Dunn, eds. 2011. Rethinking social epidemiology. Dordrecht: Springer. Available at: http://www.cdc.gov/nchs/data/databriefs/db82.pdf (accessed May 23, 2014).
• Olshansky, S. J., T. Atonucci, L. Berkman, R. H. Binstock, A. Boersch-Supan, J. T. Cacioppo, B. A. Carnes, L. L. Carstensen, L. P. Fried, D. P. Goldman, J. Jackson, M. Kohli, J. Rother, Y. Zheng, and J. Rowe. 2012. Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Affairs 31:1803-1813. https://doi.org/ 10.1377/hlthaff.2011.0746
• Pampel, F. C., P. M. Krueger, and J. T. Denney. 2010. Socioeconomic disparities in health behaviors. Annual Review of Sociology 36:349–370. https://doi.org/10.1146/annurev.soc.012809.102529
• Pearlin, L. I. 1989. The sociological study of stress. Journal of Health and Social Behavior 30(3):241–256. https://doi.org/10.2307/2136956
• Powell, L. M., S. Slater, F. J. Chaloupka, and D. Harper. 2006. Availability of physical activity-related facilities and neighborhood demographic and socioeconomic characteristics: A national study. American Journal of Public Health 96:1676–1680. https://doi.org/10.2105/AJPH.2005.065573
• Pridmore, P. 2007. Impact of health on education access and achievement: A cross-national review of the research evidence. Create Pathways to Access Research Monograph No. 2. Consortium for Research on Educational Access, Transitions and Equity. Institute of Education, University of London. Available at: http://files.eric.ed.gov/fulltext/ED508614.pdf (accessed April 29, 2014).
• Rhodes, T., K Wagner, S. A. Strathdee, K. Shannon, P. Davidson, and P. Bourgois. 2011. Structural violence and structural vulnerability within the risk environment: Theoretical and methodological perspectives for a social epidemiology of HIV risk among injection drug users and sex workers. In Rethinking social epidemiology, edited by P. O’Campo and J. R. Dunn. Dordrecht: Springer. Pp 205–230.
• Ridgeway, C. L. 2014. Why status matters for inequality. American Sociological Review 79(1):1–16. Available at: https://www.asanet.org/sites/default/files/savvy/journals/ASR/Feb14ASRFeature.pdf (accessed May 28, 2020).
• Roberts, B. W., N. R. Kuncel, R. Shiner, A. Caspi, and L. R. Goldberg. 2007. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science 2(4):313–345. https://doi.org/10.1111/j.1745-6916.2007.00047.x
• Rosas-Salazar, C., A. J. Apter, G. Canino, and J. C. Celedon. 2008. Neighborhood socioeconomic status and health: Context or composition? City and Community 7(2):163–179. https://doi.org/10.1111/j.1540-6040.2008.00251.x
• Rosas-Salazar, C., A. J. Apter, G. Canino, and J. C. Celedon. 2012. Health literacy and asthma. Journal of Allergy and Clinical Immunology 129(4):935–942. https://doi.org/10.1016/j.jaci.2012.01.040
• Ross, C. E., and C. L. Wu. 1995. The links between education and health. American Sociological Review 60(5):719–745. https://doi.org/10.2307/2096319
• Saez, E. 2013. Striking it richer: The evolution of top incomes in the United States (updated with 2012 preliminary estimates). Available at: http://elsa.berkeley.edu/~saez/saez-UStopincomes-2012.pdf (accessed May 23, 2014).
• Sampson, R. J. 2003. Neighborhood-level context and health: Lessons from sociology. In Neighborhoods and health, edited by I. Kawachi and L. F. Berkman. New York: Oxford University Press. Pp. 132–146.
• Sandstrom, H., and S. Huerta. 2013. The negative effects of instability on child development: A research synthesis. Low-Income Working Families Discussion Paper 3. Washington, DC: Urban Institute. Available at: https://www.urban.org/sites/default/files/publication/32706/412899-The-Negative-Effects-of-Instability-on-Child-Development-A-Research-Synthesis.PDF (accessed May 28, 2020).
• Seeman, T. E. 1996. Social ties and health: The benefits of social integration. Annals of Epidemiology 6(5):442–451. https://doi.org/10.1016/s1047-2797(96)00095-6
• Seeman, T. E., B. H. Singer, J. W. Rowe, R. I. Horwitz, and B. S. McEwen. 1997. Price of adaptation: Allostatic load and its health consequences. Archives of Internal Medicine 157:2259–2268. Available at: https://pubmed.ncbi.nlm.nih.gov/9343003/ (accessed May 28, 2020).
• Shankardass, K., and J. R. Dunn. 2011. How goes the neighbourhood? Rethinking neighbourhoods and health research in social epidemiology. In Rethinking social epidemiology, edited by P. O’Campo and J. R. Dunn. Dordrecht: Springer. Pp 137–156.
• Shippee, T. P., L. R. Wilkinson, and K. F. Ferraro. 2012. Accumulated financial strain and women’s health over three decades. Journals of Gerontology Series B: Psychological Sciences and Social Sciences I 67(5):585–594, https://doi.org/10.1093/geronb/gbs056
• Shonkoff, J. P., and A. S. Garner. 2012. The lifelong effects of early childhood toxic stress. Pediatrics 129(1):e232–e246. https://doi.org/10.1542/peds.2011-2663
• Shonkoff J. P., W. T. Boyce, and B. S. McEwen. 2009. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301(21): 2252-2259. https://doi.org/10.1001/jama.2009.754
• Smith, K. P., and N. A. Christakis. 2008. Social networks and health. Annual Review of Sociology 34:405–429. Available at: http://www.pinniped.net/smith2008.pdf (accessed May 28, 2020).
• Spence, M., and S. Hlatshwayo. 2011. The evolving structure of the American economy and the employment challenge. New York: Council on Foreign Relations. Available at: http://www.cfr.org/industrialpolicy/evolving-structure-american-economy-employment-challenge/p24366 (accessed April 9, 2014).
• Stafford, M., T. Chandola, and M. Marmot. 2007. Association between fear of crime and mental health and physical functioning. American Journal of Public Health 97:2076–2081. https://doi.org/10.2105/AJPH.2006.097154
• Steptoe, A., and P. J. Feldman. 2001. Neighborhood problems as sources of chronic stress: Development of a measure of neighborhood problems and associations with socioeconomic status and health. Annals of Behavioral Medicine 23:177–185. https://doi.org/10.1207/S15324796ABM2303_5
• Sum, A., I. Khatiwada, J. McLaughlin, and S. Palma. 2009. The consequences of dropping out of high school: Joblessness and jailing for high school dropouts and the high cost for taxpayers. Boston: Center for Labor Market Studies, Northeastern University. Available at: https://www.issuelab.org/resource/the-consequences-of-dropping-out-of-high-school-joblessness-and-jailing-for-high-school-dropouts-and-the-high-cost-for-taxpayers.html (accessed May 28, 2020).
• Taras, H., and W. Potts-Datema. 2005. Chronic health conditions may also impact children’s development and educational performance. Journal of School Health 75(7):255–266. https://doi.org/10.1111/j.1746-1561.2005.00034.x
• Thoits, P. A. 2010. Stress and health: Major findings and policy implications. Journal of Health and Social Behavior 51(Suppl.):S41–S53. https://doi.org/10.1177/0022146510383499
• University of Wisconsin Population Health Institute. County Health Rankings 2013. Available at: www.countyhealthrankings.org (accessed June 3, 2014).
• U.S. Census Bureau. 2012. American Community Survey, 2009–2011. Detailed tables using American FactFinder. Available at: http://factfinder.census.gov/home (accessed December 12, 2012).
• U.S. Census Bureau. 2013. Public education finances: 2011. G11-ASPEF. Washington, DC: Government Printing Office. Available at: https://www2.census.gov/govs/school/11f33pub.pdf (accessed May 28, 2020).
• Weisleder, A., and A. Fernald. 2013. Talking to Children Matters: Early language experience strengthens processing and builds vocabulary. Psychological Science 24(11):2143–2152. https://doi.org/ 10.1177/0956797613488145
• Willems, S., S. De Maesschalck, M. Deveugele, A. Derese, and J. De Maeseneer. 2005. Socio-economic status of the patient and doctor-patient communication: Does it make a difference? Patient Education and Counseling 56:139–146. https://doi.org/10.1016/j.pec.2004.02.011
• Williams, D. R., and S. A. Mohammed. 2009. Discrimination and racial disparities in health: Evidence and needed research. Journal of Behavioral Medicine 32(1):20–47. https://doi.org/10.1007/s10865-008-9185-0
• Williams Shanks, T. R., and C. Robinson. 2013. Assets, economic opportunity and toxic stress: A framework for understanding child and educational outcomes. Economics of Education Review 33:154–170. Available at: http://www.sciencedirect.com/science/article/pii/S0272775712001458 (accessed June 3, 2014).
• Wilson, J. W. 1987. The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.
• Winkleby, M., C. Cubbin, and D. Ahn. 2006. Effect of cross-level interaction between individual and neighborhood socioeconomic status on adult mortality rates. American Journal of Public Health 96:2145–2153. https://doi.org/10.2105/AJPH.2004.060970
• Zhang, T. Y. and M. J. Meaney. 2010. Epigenetics and the environmental regulation of the genome and its function. Annual Review of Psychology 61:439-466. https://doi.org/10.1146/annurev.psych.60.110707.163625

https://doi.org/10.31478/201406a

Suggested Citation

Zimmerman, E. and S. H. Woolf. 2014. Understanding the Relationship Between Education and Health. NAM Perspectives. Discussion Paper, National Academy of Medicine, Washington, DC. https://doi.org/10.31478/201406a

The views expressed in this discussion paper are those of the authors and not necessarily those of the authors’ organization or of the Institute of Medicine. The paper is intended to help inform and stimulate discussion. It has not been subjected to the review procedures of the Institute of Medicine and is not a report of the Institute of Medicine or of the National Research Council.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

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 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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Research Question Examples 🧑🏻‍🏫

25+ Practical Examples & Ideas To Help You Get Started 

By: Derek Jansen (MBA) | October 2023

A well-crafted research question (or set of questions) sets the stage for a robust study and meaningful insights.  But, if you’re new to research, it’s not always clear what exactly constitutes a good research question. In this post, we’ll provide you with clear examples of quality research questions across various disciplines, so that you can approach your research project with confidence!

Research Question Examples

• Psychology research questions
• Business research questions
• Education research questions
• Healthcare research questions
• Computer science research questions

Examples: Psychology

Let’s start by looking at some examples of research questions that you might encounter within the discipline of psychology.

How does sleep quality affect academic performance in university students?

This question is specific to a population (university students) and looks at a direct relationship between sleep and academic performance, both of which are quantifiable and measurable variables.

What factors contribute to the onset of anxiety disorders in adolescents?

The question narrows down the age group and focuses on identifying multiple contributing factors. There are various ways in which it could be approached from a methodological standpoint, including both qualitatively and quantitatively.

Do mindfulness techniques improve emotional well-being?

This is a focused research question aiming to evaluate the effectiveness of a specific intervention.

How does early childhood trauma impact adult relationships?

This research question targets a clear cause-and-effect relationship over a long timescale, making it focused but comprehensive.

Is there a correlation between screen time and depression in teenagers?

This research question focuses on an in-demand current issue and a specific demographic, allowing for a focused investigation. The key variables are clearly stated within the question and can be measured and analysed (i.e., high feasibility).

Examples: Business/Management

Next, let’s look at some examples of well-articulated research questions within the business and management realm.

How do leadership styles impact employee retention?

This is an example of a strong research question because it directly looks at the effect of one variable (leadership styles) on another (employee retention), allowing from a strongly aligned methodological approach.

What role does corporate social responsibility play in consumer choice?

Current and precise, this research question can reveal how social concerns are influencing buying behaviour by way of a qualitative exploration.

Does remote work increase or decrease productivity in tech companies?

Focused on a particular industry and a hot topic, this research question could yield timely, actionable insights that would have high practical value in the real world.

How do economic downturns affect small businesses in the homebuilding industry?

Vital for policy-making, this highly specific research question aims to uncover the challenges faced by small businesses within a certain industry.

Which employee benefits have the greatest impact on job satisfaction?

By being straightforward and specific, answering this research question could provide tangible insights to employers.

Examples: Education

Next, let’s look at some potential research questions within the education, training and development domain.

How does class size affect students’ academic performance in primary schools?

This example research question targets two clearly defined variables, which can be measured and analysed relatively easily.

Do online courses result in better retention of material than traditional courses?

Timely, specific and focused, answering this research question can help inform educational policy and personal choices about learning formats.

What impact do US public school lunches have on student health?

Targeting a specific, well-defined context, the research could lead to direct changes in public health policies.

To what degree does parental involvement improve academic outcomes in secondary education in the Midwest?

This research question focuses on a specific context (secondary education in the Midwest) and has clearly defined constructs.

What are the negative effects of standardised tests on student learning within Oklahoma primary schools?

This research question has a clear focus (negative outcomes) and is narrowed into a very specific context.

Need a helping hand?

Examples: Healthcare

Shifting to a different field, let’s look at some examples of research questions within the healthcare space.

What are the most effective treatments for chronic back pain amongst UK senior males?

Specific and solution-oriented, this research question focuses on clear variables and a well-defined context (senior males within the UK).

How do different healthcare policies affect patient satisfaction in public hospitals in South Africa?

This question is has clearly defined variables and is narrowly focused in terms of context.

Which factors contribute to obesity rates in urban areas within California?

This question is focused yet broad, aiming to reveal several contributing factors for targeted interventions.

Does telemedicine provide the same perceived quality of care as in-person visits for diabetes patients?

Ideal for a qualitative study, this research question explores a single construct (perceived quality of care) within a well-defined sample (diabetes patients).

Which lifestyle factors have the greatest affect on the risk of heart disease?

This research question aims to uncover modifiable factors, offering preventive health recommendations.

Examples: Computer Science

Last but certainly not least, let’s look at a few examples of research questions within the computer science world.

What are the perceived risks of cloud-based storage systems?

Highly relevant in our digital age, this research question would align well with a qualitative interview approach to better understand what users feel the key risks of cloud storage are.

Which factors affect the energy efficiency of data centres in Ohio?

With a clear focus, this research question lays a firm foundation for a quantitative study.

How do TikTok algorithms impact user behaviour amongst new graduates?

While this research question is more open-ended, it could form the basis for a qualitative investigation.

What are the perceived risk and benefits of open-source software software within the web design industry?

Practical and straightforward, the results could guide both developers and end-users in their choices.

Remember, these are just examples…

In this post, we’ve tried to provide a wide range of research question examples to help you get a feel for what research questions look like in practice. That said, it’s important to remember that these are just examples and don’t necessarily equate to good research topics . If you’re still trying to find a topic, check out our topic megalist for inspiration.

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• Focus : This program educates patients and families about the genetic aspects of MODY and provides personalized diabetes management strategies.
• Topics Covered : Genetic testing, understanding MODY, personalized treatment plans, and lifestyle modifications.
• Format : Workshops, one-on-one sessions, and online resources.
• Focus : These support groups often include educational sessions tailored for families dealing with monogenic diabetes, including MODY.
• Topics Covered : Peer support, nutritional guidance, diabetes management, and psychological support.
• Format : In-person meetings, virtual meetings, and online forums.
• Focus : Aimed at young people with diabetes, this intervention includes education on diet, exercise, and diabetes management.
• Topics Covered : Nutritional education, exercise routines, and personalized diabetes care plans.
• Format : Workshops, group sessions, and individual counseling.
• Examples : Programs at institutions like the University of Exeter and the University of Chicago, which are known for their work in monogenic diabetes.
• Topics Covered : Genetic counseling, dietary advice, lifestyle management, and ongoing diabetes education.
• Format : Lectures, seminars, and interactive workshops.
• Focus : Many diabetes clinics offer specialized nutritional programs tailored to children and young people with MODY.
• Topics Covered : Personalized diet plans, understanding carbohydrate counting, managing blood glucose levels through diet, and healthy eating habits.
• Format : Nutritionist consultations, group classes, and online modules.
• Providers : Organizations like Diabetes UK and the American Diabetes Association (ADA) offer webinars and online courses focused on monogenic diabetes.
• Topics Covered : Genetic education, lifestyle management, and nutritional advice.
• Format : Webinars, online courses, and downloadable materials.
• Focus : Diabetes camps that include educational components specifically designed for children with MODY.
• Examples : Camps like Camp Sweeney and Camp Kudzu, which provide tailored education and support for managing diabetes.
• Topics Covered : Diabetes management, nutrition, physical activity, and peer support.
• Format : Camp sessions with interactive learning and activities.
• Focus : Many pediatric diabetes centers offer comprehensive education programs for children with MODY.
• Topics Covered : Diabetes management, understanding the genetic aspects of MODY, nutritional counseling, and psychosocial support.
• Format : Inpatient and outpatient educational sessions, workshops, and family counseling.
• Consult with your healthcare provider : They can often refer you to specialized programs and provide recommendations based on your location and needs.
• Reach out to diabetes organizations : Contact organizations like Diabetes UK, ADA, and other regional diabetes associations for information on available programs.
• Check with local hospitals and universities : Many have specialized diabetes education programs and may offer workshops and support groups.
• Explore online resources : Look for webinars, online courses, and support groups specifically tailored for monogenic diabetes.
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300+ Health Related Research Topics For Medical Students(2023)

In the world of academia and healthcare, finding the right health-related research topics is essential. Whether you are a medical student, a college student, or a seasoned researcher, the choice of your research topic greatly impacts the quality and relevance of your work. This blog, health related research topics, is your guide to selecting the perfect subject for your research.

In this post, we will share 5 invaluable tips to help you pick suitable health-related research topics. Additionally, we will outline the crucial elements that every health-related research paper should incorporate.

Furthermore, we’ve compiled a comprehensive list of 300+ health-related research topics for medical students in 2023. These include categories like mental health, public health, nutrition, chronic diseases, healthcare policy, and more. We also offer guidance on selecting the right topic to ensure your research is engaging and meaningful.

So, whether you are delving into mental health, investigating environmental factors, or exploring global health concerns, health-related research topics will assist you in making informed and impactful choices for your research journey, even within the hardest medical specialties .

What Is Health Research?

Table of Contents

Health research is like detective work to understand how our bodies work and how to keep them healthy. It’s like asking questions and finding answers about things like sickness, medicine, and how to live better. Scientists and doctors do health research to learn new ways to treat illnesses, like finding better medicines or discovering new ways to prevent diseases.

Health research is a puzzle, where scientists collect information, do experiments, and study many people to find out what makes us healthy or sick. They want to find clues and put them together to help us stay well and live longer. So, health research is like a quest to learn more about our bodies and find ways to make them work their best, keeping us happy and strong.

5 Useful Tips For Choosing Health Related Research Topics

Here are some useful tips for choosing health related research topics: 

Tip 1: Follow Your Interests

When picking a health research topic, it’s a good idea to choose something you’re curious and excited about. If you’re interested in a subject, you’ll enjoy learning more about it, and you’ll be motivated to do your best. So, think about what aspects of health catch your attention and explore those areas for your research.

Tip 2: Consider Relevance

Your research topic should be meaningful and have real-world importance. Think about how your research can contribute to solving health problems or improving people’s well-being. Topics that are relevant and can make a positive impact on health and healthcare are usually more valuable.

Tip 3: Check Available Resources

Before deciding on a research topic, make sure you have access to the necessary resources, like books, articles, or equipment. It’s important that you can find the information and tools you need to conduct your research effectively.

Tip 4: Keep It Manageable

Select a research topic that you can handle within the available time and resources. It’s better to choose a more focused and manageable topic rather than something too broad or complex. This way, you can delve deep into the subject and produce meaningful results.

Tip 5: Seek Guidance

Don’t hesitate to ask for guidance from teachers, professors, or experts in the field. They can help you refine your research topic, provide valuable insights, and suggest improvements. Seeking advice can make your research journey smoother and more successful.

Important Elements That Must Be Present In A Health Related Research Paper

Here are some important elements that must be present in a health related research paper: 

1. Clear Title and Introduction

A good health research paper needs a clear title that tells people what it’s about. The introduction should explain why the research is important and what the paper will discuss. It’s like the map that shows the way.

2. Methods and Data

You should describe how you did your research and the data you collected. This helps others understand how you found your information. It’s like showing your work in math so that others can check it.

3. Results and Conclusions

After doing your research, you need to show what you discovered. Share the results and what they mean. Conclusions tell people what you found out and why it’s important. It’s like the “So what?” part of your paper.

4. Citations and References

When you use other people’s ideas or words, you need to give them credit. Citations and references show where you got your information. It’s like saying, “I learned this from here.”

5. Clear Language and Organization

Make sure your paper is easy to read and well-organized. Use clear and simple language so that everyone can understand. Organize your paper logically, with a beginning, middle, and end, like a good story. This makes your research paper more effective and useful.

In this section, we will discuss 300+ health related research topics for medical students(2023): 

Health Related Research Topics

• How living choices affect health and how long people live.
• Ways to make it easier for people in underserved areas to get medical care.
• The role of DNA in determining susceptibility to different diseases.
• There are differences in health between race and ethnic groups and between socioeconomic groups.
• Checking how well health education programs encourage people to behave in a healthy way.
• The effects that stress has on the body and mind.
• Looking at the pros and cons of different vaccine plans.
• The link between how well you sleep and your general health.
• The use of technology to make health care better.
• How cultural beliefs and habits affect how people seek health care.

Mental Health Related Research Topics

• Identifying the factors contributing to the rise in mental health disorders among adolescents.
• Examining the effectiveness of different therapeutic approaches for treating depression and anxiety.
• How social media can hurt your mental health and self-esteem.
• We are looking into the link between traumatic events in youth and mental health problems later in life.
•  Stigma and racism in mental health care, and how they make people less healthy.
•  Ways to lower the suicide rate among people who are at high risk.
•  Exercise and other forms of physical action can help your mental health.
•  The link between using drugs and having mental health problems.
•  Mental health support for frontline healthcare workers during and after the COVID-19 pandemic.
•  Exploring the potential of digital mental health interventions and apps.

Health Related Research Topics For College Students

• The impact of college stress on physical and mental health.
•  Assessing the effectiveness of college mental health services.
•  The role of peer influence on college students’ health behaviors.
•  Nutrition and dietary habits among college students.
•  Substance use and abuse on college campuses.
•  Investigating the prevalence of sleep disorders among college students.
•  Exploring sexual health awareness and behaviors among college students.
•  Evaluating the relationship between academic performance and overall health.
•  The influence of social media on college students’ health perceptions and behaviors.
•  Ideas for getting people on college grounds to be more active and eat better.

Public Health Related Research Topics

• Evaluating the impact of public health campaigns on smoking cessation .
•  The effectiveness of vaccination mandates in preventing disease outbreaks.
•  Looking into the link between the health of the people in cities and the quality of the air.
•  Strategies for addressing the opioid epidemic through public health initiatives.
•  The role of public health surveillance in early disease detection and response.
•  Assessing the impact of food labeling on consumer choices and nutrition.
•  Looking at how well public health measures work to lower the number of overweight and obese kids.
•  The importance of water quality in maintaining public health.
•  This paper examines various strategies aimed at enhancing mother and child health outcomes in emerging nations.
•  Addressing the mental health crisis through public health interventions.

Mental Disorder Research Topics

• The mental health effects of social isolation, with a particular focus on the COVID-19 pandemic.
•  Exploring the relationship between mental health and creative expression.
•  Cultural differences influence the way in which mental health disorders are perceived and treated.
•  The use of mindfulness and meditation techniques in managing mental health.
•  Investigating the mental health challenges faced by LGBTQ+ individuals.
•  Examining the role of nutrition and dietary habits in mood disorders.
•  The influence of childhood experiences on adult mental health.
•  Innovative approaches to reducing the stigma surrounding mental health.
•  Mental health support for veterans and active-duty military personnel.
•  The relation between sleep disorders and mental health.

Nutrition and Diet-Related Research Topics

• The impact of dietary patterns (e.g., Mediterranean, ketogenic) on health outcomes.
•  Investigating the role of gut microbiota in digestion and overall health.
•  The effects of food labeling and nutritional education on dietary choices.
•  The correlation between chronic disease prevention and nutrition.
•  Assessing the nutritional needs of different age groups (children, adults, elderly).
•  Exploring the benefits and drawbacks of various diet fads (e.g., intermittent fasting, veganism).
•  The role of nutrition in managing obesity and weight-related health issues.
•  Studying nutrition and mental wellness.
•   Impact of food insecure areas on population health and diet.
•  Strategies for promoting healthy eating in schools and workplaces.

Chronic Disease Research Topics

• The contribution of inflammation to the progression and development of chronic diseases.
•  Evaluating the effectiveness of lifestyle modifications in managing chronic conditions.
•  The impact of chronic stress on various health conditions.
•  Investigating disparities in the management and treatment of chronic diseases among different populations.
•  Exploring the genetics of chronic diseases and potential gene therapies.
•  The impact that environmental factors, including pollution, have on the prevalence of chronic diseases.
•  Assessing the long-term health consequences of childhood obesity.
•  Strategies for improving the quality of life for individuals living with chronic diseases.
•  The importance of maintaining a healthy level of physical activity and exercise for both the prevention and treatment of chronic illnesses.
•  Investigating innovative treatments and therapies for chronic diseases, such as gene editing and personalized medicine.

Healthcare Policy and Access Research Topics

• Assessing how the Affordable Care Act affects healthcare access and outcomes.
•  Telehealth’s impact on rural healthcare access.
•  Investigating the cost-effectiveness of various healthcare payment models (e.g., single-payer, private insurance).
•  Assessing healthcare disparities among different racial and socioeconomic groups.
•  The influence of political ideologies on healthcare policy and access.
•  Healthcare professional shortage solutions, including nurses and doctors.
•  The impact of malpractice reform on healthcare quality and access.
•  Examining the role of pharmaceutical pricing and regulation in healthcare access.
•  The use of technology in streamlining healthcare administration and improving access.
•  Exploring the intersection of healthcare policy, ethics, and patient rights.

Environmental Health Research Topics

• The impact of climate change on public health, including increased heat-related illnesses and vector-borne diseases.
•  Studying air pollution’s effects on the cardiovascular and respiratory systems.
•  Assessing the health consequences of exposure to environmental toxins and pollutants.
•  Exploring the role of green spaces and urban planning in promoting public health.
•  The impact of water quality and sanitation on community health.
•  Strategies for minimizing the health risks linked with natural catastrophes and extreme weather events.
•  Investigating the health implications of food and water security in vulnerable populations.
•  The influence of environmental justice on health disparities.
•  Evaluating the benefits of renewable energy sources in reducing air pollution and promoting health.
•  The role of public policy in addressing environmental health concerns.

Infectious Disease Research Topics

• Tracking the evolution and spread of infectious diseases, including COVID-19.
•  Investigating the effectiveness of vaccination campaigns in preventing outbreaks.
•  Antimicrobial resistance and strategies to combat it.
•  Assessing the role of vector-borne diseases in global health, such as malaria and Zika virus.
•  The impact of travel and globalization on the spread of infectious diseases.
•  Strategies for early detection and containment of emerging infectious diseases.
•  The role of hygiene and sanitation in reducing infectious disease transmission.
•  Investigating the cultural factors that influence infectious disease prevention and treatment.
•  The use of technology in disease surveillance and response.
• Examining the ethical and legal considerations in managing infectious disease outbreaks.

Women’s Health Research Topics

• Exploring the gender-specific health issues faced by women, such as reproductive health and menopause.
• Investigating the impact of hormonal contraception on women’s health.
• Assessing the barriers to accessing quality maternal healthcare in low-income countries.
• The role of gender-based violence in women’s mental and physical health.
• Strategies for promoting women’s sexual health and reproductive rights.
• Exploring the relationship between breast cancer and genetics.
• The influence of body image and societal pressures on women’s mental health.
• Investigating healthcare disparities among different groups of women, including racial and ethnic disparities.
• Strategies for improving access to women’s healthcare services, including family planning and prenatal care.
• The use of telemedicine and technology to address women’s health needs, especially in remote areas.

Children’s Health Research Topics

• The impact of early childhood nutrition on long-term health and development.
• Environmental toxin exposure and child health.
• Assessing the role of parenting styles in children’s mental and emotional well-being.
• Strategies for preventing and managing childhood obesity.
• The influence of media and technology on children’s physical and mental health.
• Exploring the challenges faced by children with chronic illnesses and disabilities.
• The relevance of early child mental wellness and developmental condition intervention.
• Investigating the role of schools in promoting children’s health and well-being.
• Strategies for addressing child healthcare disparities, including access to vaccines and preventive care.
• Adverse childhood experiences and adult health.

Aging and Gerontology Research Topics

• Investigating the factors contributing to healthy aging and longevity.
• Assessing the impact of dementia and Alzheimer’s disease on elderly individuals and their families.
• Strategies for improving elder care services and addressing the aging population’s healthcare needs.
• Exploring the social isolation and mental health challenges faced by the elderly.
• The importance of nutrition and exercise in old age.
• Investigating the impact of age-related chronic diseases, such as arthritis and osteoporosis.
• Assessing the financial and ethical aspects of end-of-life care for the elderly.
• Strategies for promoting intergenerational relationships and support networks.
• The influence of cultural differences on aging and health outcomes.
• Exploring technology and innovation in elder care, including assistive devices and telemedicine.

Health Technology and Innovation Research Topics

• The impact of telemedicine and virtual health platforms on patient care and outcomes.
• Investigating the use of wearable health technology in monitoring and managing chronic conditions.
• Assessing the ethical and privacy considerations of health data collection through technology.
• Investigating medical diagnoses and treatment with AI and ML.
• The role of robotics in healthcare, including surgical procedures and elder care.
• Investigating the use of 3D printing in healthcare, such as prosthetics and medical devices.
• The influence of mobile health apps on patient engagement and self-care.
• Strategies for implementing electronic health records (EHRs) and interoperability.
• The impact of precision medicine and genomics on personalized healthcare.
• Exploring the future of healthcare delivery through telehealth, remote monitoring, and AI-driven diagnostics.

Global Health Research Topics

• Investigating the challenges of global health equity and healthcare access in low- and middle-income countries.
• Assessing the effectiveness of international health organizations in addressing global health crises.
• Resource-limited mother and child health strategies.
• Exploring the impact of infectious diseases in global health, including tuberculosis and HIV/AIDS.
• The role of clean water and sanitation in improving global health outcomes.
• Investigating the social determinants of health in different global regions.
• Assessing the impact of humanitarian aid and disaster relief efforts on public health.
• Strategies for combating malnutrition and food insecurity in developing countries.
• The influence of climate change on global health, including the spread of vector-borne diseases.
• Exploring innovative approaches to global health, such as community health workers and telemedicine initiatives.
• Exploring the artificial intelligence and machine learning in medical treatment.

Health Disparities and Equity Research Topics

• The impact of socioeconomic status on healthcare access and health outcomes.
• Strategies to decrease racial and ethnic disparities in maternal and child health.
• LGBTQ+ healthcare disparities and interventions for equitable care.
• Health disparities among rural and urban populations in developed and developing countries.
• Cultural competence in healthcare and its role in reducing disparities.
• The intersection of gender, race, and socioeconomic status in health disparities.
• Addressing health disparities in the elderly population.
• The role of discrimination in perpetuating health inequities.
• Strategies to improve healthcare access for individuals with disabilities.
• The impact of COVID-19 on health disparities and lessons learned for future pandemics.

Cancer Research Topics

• Advancements in precision medicine for personalized cancer treatment.
• Immunotherapy breakthroughs in cancer treatment.
• Environmental factors and cancer risk: A comprehensive review.
• The role of genomics in understanding cancer susceptibility.
• Cancer treatment and survivorship, as well as quality of life following cancer therapy.
• The economics of cancer treatment and its impact on patients.
• Cancer prevention and early detection strategies in underserved communities.
• Palliative care and end-of-life decisions in cancer patients.
• Emerging trends in cancer epidemiology and global burden.
• Ethical considerations in cancer clinical trials and research.

Pharmaceutical Research Topics

• Repurposing existing medications in order to address uncommon illnesses.
• The impact of nanotechnology in drug delivery and targeting.
• Pharmacogenomics and personalized medicine: Current status and future prospects.
• Challenges and opportunities in developing vaccines for emerging infectious diseases.
• Quality control and safety in the pharmaceutical manufacturing process.
• Drug pricing and access: A global perspective.
• Green chemistry approaches in sustainable pharmaceutical development.
• The part that artificial intelligence plays in the search for new drugs and their development.
• Biopharmaceuticals and the future of protein-based therapies.
• Regulatory challenges in ensuring drug safety and efficacy.

Epidemiology Research Topics

• Emerging infectious diseases and global preparedness.
• The COVID-19 pandemic will have long-term effect on the health of the general population.
• Social determinants of health and their impact on disease prevalence.
• Environmental epidemiology and the study of health effects of pollution.
• Big data and its role in modern epidemiological research.
• Spatial epidemiology and the study of disease clusters.
• Epidemiological aspects of non-communicable diseases (NCDs) like diabetes and obesity.
• Genetic epidemiology and the study of hereditary diseases.
• Epidemiological methods for studying mental health disorders.
• Epidemiology of zoonotic diseases and their prevention.

Alternative and Complementary Medicine Research Topics

• Efficacy and safety of herbal remedies in complementary medicine.
• Mind-body interventions and their role in managing chronic pain.
• Acupuncture and its potential in the treatment of various conditions.
• Integrating traditional and complementary medicine into mainstream healthcare.
• Yoga and meditation for stress reduction and mental health.
• Biofield therapies and their impact on well-being.
• Ayurvedic medicine and its modern applications in health and wellness.
• Chiropractic care and its use in musculoskeletal health.
• Ethical considerations in the practice and regulation of alternative medicine.
• Integrating traditional Chinese medicine into Western healthcare systems.

Occupational Health and Safety Research Topics

• Occupational hazards in healthcare settings and strategies for prevention.
• The impact of remote work on occupational health and well-being.
• Ergonomics and its role in preventing workplace injuries.
• Occupational exposure to hazardous chemicals and long-term health effects.
• Mental health in the office: Stress, burnout, and interventions.
• Occupational safety in the construction industry: Recent developments.
• Role of technology in enhancing workplace safety.
• Occupational health disparities among different industries and occupations.
• The economics of workplace safety and the cost-benefit analysis.
• Business impacts of OSHA regulations.

Addiction and Substance Abuse Research Topics

• The opioid epidemic: Current status and future strategies.
• Dual diagnosis: Co-occurring mental health disorders and substance abuse.
• Harm reduction approaches in addiction treatment.
• The role of family and social support in addiction recovery.
• Behavioral addictions: Understanding and treating non-substance-related addictions.
• Novel pharmacotherapies for addiction treatment.
• The impact of COVID-19 on substance abuse and addiction.
• Substance abuse prevention programs in schools and communities.
• Stigmatization of addiction and its impact on treatment-seeking behavior.
• Substance abuse in the elderly population: Unique challenges and solutions.

Biomedical Research Topics

• Recent advancements in gene editing technologies (e.g., CRISPR-Cas9).
• Regenerative medicine and tissue engineering for organ replacement.
• Bioinformatics and its role in analyzing large-scale biological data.
• Stem cell research and its important applications in regenerative medicine.
• Biomarker discovery for early disease detection and monitoring.
• Precision medicine and its potential to transform healthcare.
• The microbiome and its impacts on human health and disease.
• Aging-related research and interventions for healthy aging.
• Neurodegenerative diseases and potential therapeutic approaches.
• Biomedical ethics in the age of cutting-edge research.

Maternal and Child Health Research Topics

• The influence of the mother’s nutrition on the development and health of the fetus.
• Maternal mental health and its positive effects on child development.
• Preterm birth prevention and interventions for at-risk pregnancies.
• Neonatal screening and early diagnosis of congenital diseases.
• Breastfeeding promotion and support for new mothers.
• Pediatric immunization programs and vaccine hesitancy.
• Child obesity prevention and intervention strategies.
• Maternal and child health in low-resource and conflict-affected areas.
• Maternal mortality reduction and improving access to obstetric care.
• Adverse childhood experiences (ACEs) and their long-term health consequences.

Mental Health Stigma Research Topics

• Understanding the origins and perpetuation of mental health stigma.
• Media and pop culture’s impact on mental disease views.
• Reducing stigma in the workplace and promoting mental health support.
• Stigma associated with specific mental health conditions (e.g., schizophrenia, bipolar disorder).
• Intersectionality and how it influences mental health stigma.
• Anti-stigma campaigns and their effectiveness in changing public attitudes.
• Stigma in online communities and the role of social media in shaping opinions.
• Cultural and cross-cultural perspectives on mental health stigma.
• The impact of self-stigma on individuals seeking mental health treatment.
• Legislative and policy efforts to combat mental health stigma.

Health Education and Promotion Research Topics

• Health literacy and its impact on informed decision-making.
• Promoting healthy behaviors in schools and educational settings.
• Social marketing campaigns for health behavior change.
• Community-based health promotion programs in underserved areas.
• The role of technology and social media in health education.
• Tailoring health messages to diverse populations and cultural sensitivity.
• The use of behavioral economics in health promotion strategies.
• Investigating the effectiveness of school-based sex education programs.
• Health education for the elderly population: Challenges and solutions.
• Promoting mental health awareness and resilience through education.

Healthcare Quality and Patient Safety Research Topics

• Patient-centered care and its impact on healthcare quality.
• Reducing medical errors and negative events in healthcare settings.
• Continuous quality improvement in healthcare organizations.
• The role of healthcare accreditation in ensuring quality and safety.
• Patient engagement and shared decision-making in healthcare.
• Electronic health records and patient safety.
• The ethics of telling patients and families about medical blunders.
• Medication safety and preventing adverse drug events.
• Cultural competence in healthcare and its effect on patient safety.
• Disaster preparedness and response in healthcare settings.

Health Informatics and Data Analytics Research Topics

• Big data analytics in healthcare for predictive modeling.
• Artificial intelligence in medical image analysis and diagnostics.
• Health information exchange and interoperability challenges.
• Electronic health record (EHR) usability and user satisfaction.
• Patient data privacy and security in health informatics.
• Telemedicine and its impact on healthcare delivery and data management.
• Real-time monitoring and data analytics for disease outbreaks.
• Health informatics applications in personalized medicine.
• Natural language processing for clinical notes and text analysis.
• The role of data analyticsin enhancing healthcare quality and outcomes.

Neurological Disorders Research Topics

• Neuroinflammation in neurodegenerative diseases (e.g., Alzheimer’s and Parkinson’s).
• Stroke prevention and rehabilitation strategies.
• Advances in brain imaging techniques for diagnosing neurological disorders.
• Pediatric neurological disorders: Diagnosis and intervention.
• Neurogenetics and the role of genetics in neurological conditions.
• Traumatic brain injury: Long-term effects and rehabilitation.
• Neurorehabilitation and quality of life improvement in patients with neurological disorders.
• Neurological consequences of long COVID and post-viral syndromes.
• The gut-brain connection and its implications for neurological health.
• Ethical considerations in neurological research and treatment.

Bioethics in Health Research Topics

• Informed consent and its challenges in clinical trials and research.
• Ethical considerations in human genome editing and gene therapy.
• Allocation of healthcare resources and the principles of distributive justice.
• The ethics of organ transplantation and organ trafficking.
• End-of-life decision-making, including physician-assisted suicide.
• Ethical issues in the use of Artficial intelligence in healthcare decision-making.
• Research involving vulnerable populations: Balancing benefits and risks.
• Ethical considerations in global health research and disparities.
• Ethical implications of emerging biotechnologies, such as CRISPR-Cas9.
• Autonomy and decision-making capacity in healthcare ethics.

Read More 

• Biology Research Topics
• Neuroscience Research Topics

Points To Be Remembered While Selecting Health Related Research Topics

When selecting a health-related research topic, there are several important considerations to keep in mind to ensure your research is meaningful and effective. Here are 7 key points to remember:

• Interest and Passion: Choose a topic that is according to your interests you, as your enthusiasm will fuel your research.
• Relevance: Ensure your topic addresses a real health issue or concern that can make a positive impact.
• Resources Availability: Confirm that you have access to the necessary materials and information for your research.
• Manageability: Pick a topic that is not too broad, ensuring it’s something you can investigate thoroughly.
• Guidance: Seek advice from experts or mentors to refine your topic and receive valuable insights.
• Ethical Considerations : Always consider the ethical implications of your research and ensure it complies with ethical guidelines.
• Feasibility: Ensure that the research can be completed within the available time and resources.

In the ever-evolving landscape of health research, selecting the right topic is the foundation for meaningful contributions. This blog has provided a roadmap for choosing health-related research topics, emphasizing the importance of personal interest, relevance, available resources, manageability, and expert guidance. Additionally, it has offered 300+ research topics across various domains, including mental health, public health, nutrition, chronic diseases, healthcare policy, and more. 

In addition, with these insights, researchers, students, and healthcare professionals can embark on journeys that not only align with their passions but also address critical healthcare challenges. By making informed choices, we can collectively advance the frontiers of health and well-being.

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Phd in nursing science program, examples of broad clinical research questions include:.

• Does the administration of pain medication at time of surgical incision reduce the need for pain medication twenty-four hours after surgery?
• What maternal factors are associated with obesity in toddlers?
• What elements of a peer support intervention prevent suicide in high school females?
• What is the most accurate and comprehensive way to determine men’s experience of physical assault?
• Is yoga as effective as traditional physical therapy in reducing lymphedema in patients who have had head and neck cancer treatment?
• In the third stage of labor, what is the effect of cord cutting within the first three minutes on placenta separation?
• Do teenagers with Type 1 diabetes who receive phone tweet reminders maintain lower blood sugars than those who do not?
• Do the elderly diagnosed with dementia experience pain?
•  How can siblings’ risk of depression be predicted after the death of a child?
•  How can cachexia be prevented in cancer patients receiving aggressive protocols involving radiation and chemotherapy?

Examples of some general health services research questions are:

• Does the organization of renal transplant nurse coordinators’ responsibilities influence live donor rates?
• What activities of nurse managers are associated with nurse turnover?  30 day readmission rates?
• What effect does the Nurse Faculty Loan program have on the nurse researcher workforce?  What effect would a 20% decrease in funds have?
• How do psychiatric hospital unit designs influence the incidence of patients’ aggression?
• What are Native American patient preferences regarding the timing, location and costs for weight management counseling and how will meeting these preferences influence participation?
•  What predicts registered nurse retention in the US Army?
• How, if at all, are the timing and location of suicide prevention appointments linked to veterans‘ suicide rates?
• What predicts the sustainability of quality improvement programs in operating rooms?
• Do integrated computerized nursing records across points of care improve patient outcomes?
• How many nurse practitioners will the US need in 2020?

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• Study Protocol
• Open access
• Published: 26 August 2024

Learning effect of online versus onsite education in health and medical scholarship – protocol for a cluster randomized trial

• Rie Raffing 1 ,
• Lars Konge 2 &
• Hanne Tønnesen 1  

BMC Medical Education volume  24 , Article number:  927 ( 2024 ) Cite this article

123 Accesses

Metrics details

The disruption of health and medical education by the COVID-19 pandemic made educators question the effect of online setting on students’ learning, motivation, self-efficacy and preference. In light of the health care staff shortage online scalable education seemed relevant. Reviews on the effect of online medical education called for high quality RCTs, which are increasingly relevant with rapid technological development and widespread adaption of online learning in universities. The objective of this trial is to compare standardized and feasible outcomes of an online and an onsite setting of a research course regarding the efficacy for PhD students within health and medical sciences: Primarily on learning of research methodology and secondly on preference, motivation, self-efficacy on short term and academic achievements on long term. Based on the authors experience with conducting courses during the pandemic, the hypothesis is that student preferred onsite setting is different to online setting.

Cluster randomized trial with two parallel groups. Two PhD research training courses at the University of Copenhagen are randomized to online (Zoom) or onsite (The Parker Institute, Denmark) setting. Enrolled students are invited to participate in the study. Primary outcome is short term learning. Secondary outcomes are short term preference, motivation, self-efficacy, and long-term academic achievements. Standardized, reproducible and feasible outcomes will be measured by tailor made multiple choice questionnaires, evaluation survey, frequently used Intrinsic Motivation Inventory, Single Item Self-Efficacy Question, and Google Scholar publication data. Sample size is calculated to 20 clusters and courses are randomized by a computer random number generator. Statistical analyses will be performed blinded by an external statistical expert.

Primary outcome and secondary significant outcomes will be compared and contrasted with relevant literature. Limitations include geographical setting; bias include lack of blinding and strengths are robust assessment methods in a well-established conceptual framework. Generalizability to PhD education in other disciplines is high. Results of this study will both have implications for students and educators involved in research training courses in health and medical education and for the patients who ultimately benefits from this training.

Trial registration

Retrospectively registered at ClinicalTrials.gov: NCT05736627. SPIRIT guidelines are followed.

Peer Review reports

Medical education was utterly disrupted for two years by the COVID-19 pandemic. In the midst of rearranging courses and adapting to online platforms we, with lecturers and course managers around the globe, wondered what the conversion to online setting did to students’ learning, motivation and self-efficacy [ 1 , 2 , 3 ]. What the long-term consequences would be [ 4 ] and if scalable online medical education should play a greater role in the future [ 5 ] seemed relevant and appealing questions in a time when health care professionals are in demand. Our experience of performing research training during the pandemic was that although PhD students were grateful for courses being available, they found it difficult to concentrate related to the long screen hours. We sensed that most students preferred an onsite setting and perceived online courses a temporary and inferior necessity. The question is if this impacted their learning?

Since the common use of the internet in medical education, systematic reviews have sought to answer if there is a difference in learning effect when taught online compared to onsite. Although authors conclude that online learning may be equivalent to onsite in effect, they agree that studies are heterogeneous and small [ 6 , 7 ], with low quality of the evidence [ 8 , 9 ]. They therefore call for more robust and adequately powered high-quality RCTs to confirm their findings and suggest that students’ preferences in online learning should be investigated [ 7 , 8 , 9 ].

This uncovers two knowledge gaps: I) High-quality RCTs on online versus onsite learning in health and medical education and II) Studies on students’ preferences in online learning.

Recently solid RCTs have been performed on the topic of web-based theoretical learning of research methods among health professionals [ 10 , 11 ]. However, these studies are on asynchronous courses among medical or master students with short term outcomes.

This uncovers three additional knowledge gaps: III) Studies on synchronous online learning IV) among PhD students of health and medical education V) with long term measurement of outcomes.

The rapid technological development including artificial intelligence (AI) and widespread adaption as well as application of online learning forced by the pandemic, has made online learning well-established. It represents high resolution live synchronic settings which is available on a variety of platforms with integrated AI and options for interaction with and among students, chat and break out rooms, and exterior digital tools for teachers [ 12 , 13 , 14 ]. Thus, investigating online learning today may be quite different than before the pandemic. On one hand, it could seem plausible that this technological development would make a difference in favour of online learning which could not be found in previous reviews of the evidence. On the other hand, the personal face-to-face interaction during onsite learning may still be more beneficial for the learning process and combined with our experience of students finding it difficult to concentrate when online during the pandemic we hypothesize that outcomes of the onsite setting are different from the online setting.

To support a robust study, we design it as a cluster randomized trial. Moreover, we use the well-established and widely used Kirkpatrick’s conceptual framework for evaluating learning as a lens to assess our outcomes [ 15 ]. Thus, to fill the above-mentioned knowledge gaps, the objective of this trial is to compare a synchronous online and an in-person onsite setting of a research course regarding the efficacy for PhD students within the health and medical sciences:

Primarily on theoretical learning of research methodology and

Secondly on

◦ Preference, motivation, self-efficacy on short term

◦ Academic achievements on long term

Trial design

This study protocol covers synchronous online and in-person onsite setting of research courses testing the efficacy for PhD students. It is a two parallel arms cluster randomized trial (Fig.  1 ).

Consort flow diagram

The study measures baseline and post intervention. Baseline variables and knowledge scores are obtained at the first day of the course, post intervention measurement is obtained the last day of the course (short term) and monthly for 24 months (long term).

Randomization is stratified giving 1:1 allocation ratio of the courses. As the number of participants within each course might differ, the allocation ratio of participants in the study will not fully be equal and 1:1 balanced.

Study setting

The study site is The Parker Institute at Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Denmark. From here the courses are organized and run online and onsite. The course programs and time schedules, the learning objective, the course management, the lecturers, and the delivery are identical in the two settings. The teachers use the same introductory presentations followed by training in break out groups, feed-back and discussions. For the online group, the setting is organized as meetings in the online collaboration tool Zoom® [ 16 ] using the basic available technicalities such as screen sharing, chat function for comments, and breakout rooms and other basics digital tools if preferred. The online version of the course is synchronous with live education and interaction. For the onsite group, the setting is the physical classroom at the learning facilities at the Parker Institute. Coffee and tea as well as simple sandwiches and bottles of water, which facilitate sociality, are available at the onsite setting. The participants in the online setting must get their food and drink by themselves, but online sociality is made possible by not closing down the online room during the breaks. The research methodology courses included in the study are “Practical Course in Systematic Review Technique in Clinical Research”, (see course programme in appendix 1) and “Getting started: Writing your first manuscript for publication” [ 17 ] (see course programme in appendix 2). The two courses both have 12 seats and last either three or three and a half days resulting in 2.2 and 2.6 ECTS credits, respectively. They are offered by the PhD School of the Faculty of Health and Medical Sciences, University of Copenhagen. Both courses are available and covered by the annual tuition fee for all PhD students enrolled at a Danish university.

Eligibility criteria

Inclusion criteria for participants: All PhD students enrolled on the PhD courses participate after informed consent: “Practical Course in Systematic Review Technique in Clinical Research” and “Getting started: Writing your first manuscript for publication” at the PhD School of the Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.

Exclusion criteria for participants: Declining to participate and withdrawal of informed consent.

Informed consent

The PhD students at the PhD School at the Faculty of Health Sciences, University of Copenhagen participate after informed consent, taken by the daily project leader, allowing evaluation data from the course to be used after pseudo-anonymization in the project. They are informed in a welcome letter approximately three weeks prior to the course and again in the introduction the first course day. They register their consent on the first course day (Appendix 3). Declining to participate in the project does not influence their participation in the course.

Interventions

Online course settings will be compared to onsite course settings. We test if the onsite setting is different to online. Online learning is increasing but onsite learning is still the preferred educational setting in a medical context. In this case onsite learning represents “usual care”. The online course setting is meetings in Zoom using the technicalities available such as chat and breakout rooms. The onsite setting is the learning facilities, at the Parker Institute, Bispebjerg and Frederiksberg Hospital, The Capital Region, University of Copenhagen, Denmark.

The course settings are not expected to harm the participants, but should a request be made to discontinue the course or change setting this will be met, and the participant taken out of the study. Course participants are allowed to take part in relevant concomitant courses or other interventions during the trial.

Strategies to improve adherence to interventions

Course participants are motivated to complete the course irrespectively of the setting because it bears ECTS-points for their PhD education and adds to the mandatory number of ECTS-points. Thus, we expect adherence to be the same in both groups. However, we monitor their presence in the course and allocate time during class for testing the short-term outcomes ( motivation, self-efficacy, preference and learning). We encourage and, if necessary, repeatedly remind them to register with Google Scholar for our testing of the long-term outcome (academic achievement).

Outcomes are related to the Kirkpatrick model for evaluating learning (Fig.  2 ) which divides outcomes into four different levels; Reaction which includes for example motivation, self-efficacy and preferences, Learning which includes knowledge acquisition, Behaviour for practical application of skills when back at the job (not included in our outcomes), and Results for impact for end-users which includes for example academic achievements in the form of scientific articles [ 18 , 19 , 20 ].

The Kirkpatrick model

Primary outcome

The primary outcome is short term learning (Kirkpatrick level 2).

Learning is assessed by a Multiple-Choice Questionnaire (MCQ) developed prior to the RCT specifically for this setting (Appendix 4). First the lecturers of the two courses were contacted and asked to provide five multiple choice questions presented as a stem with three answer options; one correct answer and two distractors. The questions should be related to core elements of their teaching under the heading of research training. The questions were set up to test the cognition of the students at the levels of "Knows" or "Knows how" according to Miller's Pyramid of Competence and not their behaviour [ 21 ]. Six of the course lecturers responded and out of this material all the questions which covered curriculum of both courses were selected. It was tested on 10 PhD students and within the lecturer group, revised after an item analysis and English language revised. The MCQ ended up containing 25 questions. The MCQ is filled in at baseline and repeated at the end of the course. The primary outcomes based on the MCQ is estimated as the score of learning calculated as number of correct answers out of 25 after the course. A decrease of points of the MCQ in the intervention groups denotes a deterioration of learning. In the MCQ the minimum score is 0 and 25 is maximum, where 19 indicates passing the course.

Furthermore, as secondary outcome, this outcome measurement will be categorized as binary outcome to determine passed/failed of the course defined by 75% (19/25) correct answers.

The learning score will be computed on group and individual level and compared regarding continued outcomes by the Mann–Whitney test comparing the learning score of the online and onsite groups. Regarding the binomial outcome of learning (passed/failed) data will be analysed by the Fisher’s exact test on an intention-to-treat basis between the online and onsite. The results will be presented as median and range and as mean and standard deviations, for possible future use in meta-analyses.

Secondary outcomes

Motivation assessment post course: Motivation level is measured by the Intrinsic Motivation Inventory (IMI) Scale [ 22 ] (Appendix 5). The IMI items were randomized by random.org on the 4th of August 2022. It contains 12 items to be assessed by the students on a 7-point Likert scale where 1 is “Not at all true”, 4 is “Somewhat true” and 7 is “Very true”. The motivation score will be computed on group and individual level and will then be tested by the Mann–Whitney of the online and onsite group.

Self-efficacy assessment post course: Self-efficacy level is measured by a single-item measure developed and validated by Williams and Smith [ 23 ] (Appendix 6). It is assessed by the students on a scale from 1–10 where 1 is “Strongly disagree” and 10 is “Strongly agree”. The self-efficacy score will be computed on group and individual level and tested by a Mann–Whitney test to compare the self-efficacy score of the online and onsite group.

Preference assessment post course: Preference is measured as part of the general course satisfaction evaluation with the question “If you had the option to choose, which form would you prefer this course to have?” with the options “onsite form” and “online form”.

Academic achievement assessment is based on 24 monthly measurements post course of number of publications, number of citations, h-index, i10-index. This data is collected through the Google Scholar Profiles [ 24 ] of the students as this database covers most scientific journals. Associations between onsite/online and long-term academic will be examined with Kaplan Meyer and log rank test with a significance level of 0.05.

Participant timeline

Enrolment for the course at the Faculty of Health Sciences, University of Copenhagen, Denmark, becomes available when it is published in the course catalogue. In the course description the course location is “To be announced”. Approximately 3–4 weeks before the course begins, the participant list is finalized, and students receive a welcome letter containing course details, including their allocation to either the online or onsite setting. On the first day of the course, oral information is provided, and participants provide informed consent, baseline variables, and base line knowledge scores.

The last day of scheduled activities the following scores are collected, knowledge, motivation, self-efficacy, setting preference, and academic achievement. To track students' long term academic achievements, follow-ups are conducted monthly for a period of 24 months, with assessments occurring within one week of the last course day (Table  1 ).

Sample size

The power calculation is based on the main outcome, theoretical learning on short term. For the sample size determination, we considered 12 available seats for participants in each course. To achieve statistical power, we aimed for 8 clusters in both online and onsite arms (in total 16 clusters) to detect an increase in learning outcome of 20% (learning outcome increase of 5 points). We considered an intraclass correlation coefficient of 0.02, a standard deviation of 10, a power of 80%, and a two-sided alpha level of 5%. The Allocation Ratio was set at 1, implying an equal number of subjects in both online and onsite group.

Considering a dropout up to 2 students per course, equivalent to 17%, we determined that a total of 112 participants would be needed. This calculation factored in 10 clusters of 12 participants per study arm, which we deemed sufficient to assess any changes in learning outcome.

The sample size was estimated using the function n4means from the R package CRTSize [ 25 ].

Recruitment

Participants are PhD students enrolled in 10 courses of “Practical Course in Systematic Review Technique in Clinical Research” and 10 courses of “Getting started: Writing your first manuscript for publication” at the PhD School of the Faculty of Health Sciences, University of Copenhagen, Denmark.

Assignment of interventions: allocation

Randomization will be performed on course-level. The courses are randomized by a computer random number generator [ 26 ]. To get a balanced randomization per year, 2 sets with 2 unique random integers in each, taken from the 1–4 range is requested.

The setting is not included in the course catalogue of the PhD School and thus allocation to online or onsite is concealed until 3–4 weeks before course commencement when a welcome letter with course information including allocation to online or onsite setting is distributed to the students. The lecturers are also informed of the course setting at this time point. If students withdraw from the course after being informed of the setting, a letter is sent to them enquiring of the reason for withdrawal and reason is recorded (Appendix 7).

The allocation sequence is generated by a computer random number generator (random.org). The participants and the lecturers sign up for the course without knowing the course setting (online or onsite) until 3–4 weeks before the course.

Assignment of interventions: blinding

Due to the nature of the study, it is not possible to blind trial participants or lecturers. The outcomes are reported by the participants directly in an online form, thus being blinded for the outcome assessor, but not for the individual participant. The data collection for the long-term follow-up regarding academic achievements is conducted without blinding. However, the external researcher analysing the data will be blinded.

Data collection and management

Data will be collected by the project leader (Table  1 ). Baseline variables and post course knowledge, motivation, and self-efficacy are self-reported through questionnaires in SurveyXact® [ 27 ]. Academic achievements are collected through Google Scholar profiles of the participants.

Given that we are using participant assessments and evaluations for research purposes, all data collection – except for monthly follow-up of academic achievements after the course – takes place either in the immediate beginning or ending of the course and therefore we expect participant retention to be high.

Data will be downloaded from SurveyXact and stored in a locked and logged drive on a computer belonging to the Capital Region of Denmark. Only the project leader has access to the data.

This project conduct is following the Danish Data Protection Agency guidelines of the European GDPR throughout the trial. Following the end of the trial, data will be stored at the Danish National Data Archive which fulfil Danish and European guidelines for data protection and management.

Statistical methods

Data is anonymized and blinded before the analyses. Analyses are performed by a researcher not otherwise involved in the inclusion or randomization, data collection or handling. All statistical tests will be testing the null hypotheses assuming the two arms of the trial being equal based on corresponding estimates. Analysis of primary outcome on short-term learning will be started once all data has been collected for all individuals in the last included course. Analyses of long-term academic achievement will be started at end of follow-up.

Baseline characteristics including both course- and individual level information will be presented. Table 2 presents the available data on baseline.

We will use multivariate analysis for identification of the most important predictors (motivation, self-efficacy, sex, educational background, and knowledge) for best effect on short and long term. The results will be presented as risk ratio (RR) with 95% confidence interval (CI). The results will be considered significant if CI does not include the value one.

All data processing and analyses were conducted using R statistical software version 4.1.0, 2021–05-18 (R Foundation for Statistical Computing, Vienna, Austria).

If possible, all analysis will be performed for “Practical Course in Systematic Review Technique in Clinical Research” and for “Getting started: Writing your first manuscript for publication” separately.

Primary analyses will be handled with the intention-to-treat approach. The analyses will include all individuals with valid data regardless of they did attend the complete course. Missing data will be handled with multiple imputation [ 28 ] .

Upon reasonable request, public assess will be granted to protocol, datasets analysed during the current study, and statistical code Table 3 .

Oversight, monitoring, and adverse events

This project is coordinated in collaboration between the WHO CC (DEN-62) at the Parker Institute, CAMES, and the PhD School at the Faculty of Health and Medical Sciences, University of Copenhagen. The project leader runs the day-to-day support of the trial. The steering committee of the trial includes principal investigators from WHO CC (DEN-62) and CAMES and the project leader and meets approximately three times a year.

Data monitoring is done on a daily basis by the project leader and controlled by an external independent researcher.

An adverse event is “a harmful and negative outcome that happens when a patient has been provided with medical care” [ 29 ]. Since this trial does not involve patients in medical care, we do not expect adverse events. If participants decline taking part in the course after receiving the information of the course setting, information on reason for declining is sought obtained. If the reason is the setting this can be considered an unintended effect. Information of unintended effects of the online setting (the intervention) will be recorded. Participants are encouraged to contact the project leader with any response to the course in general both during and after the course.

The trial description has been sent to the Scientific Ethical Committee of the Capital Region of Denmark (VEK) (21041907), which assessed it as not necessary to notify and that it could proceed without permission from VEK according to the Danish law and regulation of scientific research. The trial is registered with the Danish Data Protection Agency (Privacy) (P-2022–158). Important protocol modification will be communicated to relevant parties as well as VEK, the Joint Regional Information Security and Clinicaltrials.gov within an as short timeframe as possible.

Dissemination plans

The results (positive, negative, or inconclusive) will be disseminated in educational, scientific, and clinical fora, in international scientific peer-reviewed journals, and clinicaltrials.gov will be updated upon completion of the trial. After scientific publication, the results will be disseminated to the public by the press, social media including the website of the hospital and other organizations – as well as internationally via WHO CC (DEN-62) at the Parker Institute and WHO Europe.

All authors will fulfil the ICMJE recommendations for authorship, and RR will be first author of the articles as a part of her PhD dissertation. Contributors who do not fulfil these recommendations will be offered acknowledgement in the article.

This cluster randomized trial investigates if an onsite setting of a research course for PhD students within the health and medical sciences is different from an online setting. The outcomes measured are learning of research methodology (primary), preference, motivation, and self-efficacy (secondary) on short term and academic achievements (secondary) on long term.

The results of this study will be discussed as follows:

Discussion of primary outcome

Primary outcome will be compared and contrasted with similar studies including recent RCTs and mixed-method studies on online and onsite research methodology courses within health and medical education [ 10 , 11 , 30 ] and for inspiration outside the field [ 31 , 32 ]: Tokalic finds similar outcomes for online and onsite, Martinic finds that the web-based educational intervention improves knowledge, Cheung concludes that the evidence is insufficient to say that the two modes have different learning outcomes, Kofoed finds online setting to have negative impact on learning and Rahimi-Ardabili presents positive self-reported student knowledge. These conflicting results will be discussed in the context of the result on the learning outcome of this study. The literature may change if more relevant studies are published.

Discussion of secondary outcomes

Secondary significant outcomes are compared and contrasted with similar studies.

Limitations, generalizability, bias and strengths

It is a limitation to this study, that an onsite curriculum for a full day is delivered identically online, as this may favour the onsite course due to screen fatigue [ 33 ]. At the same time, it is also a strength that the time schedules are similar in both settings. The offer of coffee, tea, water, and a plain sandwich in the onsite course may better facilitate the possibility for socializing. Another limitation is that the study is performed in Denmark within a specific educational culture, with institutional policies and resources which might affect the outcome and limit generalization to other geographical settings. However, international students are welcome in the class.

In educational interventions it is generally difficult to blind participants and this inherent limitation also applies to this trial [ 11 ]. Thus, the participants are not blinded to their assigned intervention, and neither are the lecturers in the courses. However, the external statistical expert will be blinded when doing the analyses.

We chose to compare in-person onsite setting with a synchronous online setting. Therefore, the online setting cannot be expected to generalize to asynchronous online setting. Asynchronous delivery has in some cases showed positive results and it might be because students could go back and forth through the modules in the interface without time limit [ 11 ].

We will report on all the outcomes defined prior to conducting the study to avoid selective reporting bias.

It is a strength of the study that it seeks to report outcomes within the 1, 2 and 4 levels of the Kirkpatrick conceptual framework, and not solely on level 1. It is also a strength that the study is cluster randomized which will reduce “infections” between the two settings and has an adequate power calculated sample size and looks for a relevant educational difference of 20% between the online and onsite setting.

Perspectives with implications for practice

The results of this study may have implications for the students for which educational setting they choose. Learning and preference results has implications for lecturers, course managers and curriculum developers which setting they should plan for the health and medical education. It may also be of inspiration for teaching and training in other disciplines. From a societal perspective it also has implications because we will know the effect and preferences of online learning in case of a future lock down.

Future research could investigate academic achievements in online and onsite research training on the long run (Kirkpatrick 4); the effect of blended learning versus online or onsite (Kirkpatrick 2); lecturers’ preferences for online and onsite setting within health and medical education (Kirkpatrick 1) and resource use in synchronous and asynchronous online learning (Kirkpatrick 5).

Trial status

This trial collected pilot data from August to September 2021 and opened for inclusion in January 2022. Completion of recruitment is expected in April 2024 and long-term follow-up in April 2026. Protocol version number 1 03.06.2022 with amendments 30.11.2023.

Availability of data and materials

The project leader will have access to the final trial dataset which will be available upon reasonable request. Exception to this is the qualitative raw data that might contain information leading to personal identification.

Abbreviations

Artificial Intelligence

Copenhagen academy for medical education and simulation

Confidence interval

Coronavirus disease

European credit transfer and accumulation system

International committee of medical journal editors

Intrinsic motivation inventory

Multiple choice questionnaire

Doctor of medicine

Masters of sciences

Randomized controlled trial

Scientific ethical committee of the Capital Region of Denmark

WHO Collaborating centre for evidence-based clinical health promotion

Samara M, Algdah A, Nassar Y, Zahra SA, Halim M, Barsom RMM. How did online learning impact the academic. J Technol Sci Educ. 2023;13(3):869–85.

Article   Google Scholar  

Nejadghaderi SA, Khoshgoftar Z, Fazlollahi A, Nasiri MJ. Medical education during the coronavirus disease 2019 pandemic: an umbrella review. Front Med (Lausanne). 2024;11:1358084. https://doi.org/10.3389/fmed.2024.1358084 .

Madi M, Hamzeh H, Abujaber S, Nawasreh ZH. Have we failed them? Online learning self-efficacy of physiotherapy students during COVID-19 pandemic. Physiother Res Int. 2023;5:e1992. https://doi.org/10.1002/pri.1992 .

Torda A. How COVID-19 has pushed us into a medical education revolution. Intern Med J. 2020;50(9):1150–3.

Alhat S. Virtual Classroom: A Future of Education Post-COVID-19. Shanlax Int J Educ. 2020;8(4):101–4.

Cook DA, Levinson AJ, Garside S, Dupras DM, Erwin PJ, Montori VM. Internet-based learning in the health professions: A meta-analysis. JAMA. 2008;300(10):1181–96. https://doi.org/10.1001/jama.300.10.1181 .

Pei L, Wu H. Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Med Educ Online. 2019;24(1):1666538. https://doi.org/10.1080/10872981.2019.1666538 .

Richmond H, Copsey B, Hall AM, Davies D, Lamb SE. A systematic review and meta-analysis of online versus alternative methods for training licensed health care professionals to deliver clinical interventions. BMC Med Educ. 2017;17(1):227. https://doi.org/10.1186/s12909-017-1047-4 .

George PP, Zhabenko O, Kyaw BM, Antoniou P, Posadzki P, Saxena N, Semwal M, Tudor Car L, Zary N, Lockwood C, Car J. Online Digital Education for Postregistration Training of Medical Doctors: Systematic Review by the Digital Health Education Collaboration. J Med Internet Res. 2019;21(2):e13269. https://doi.org/10.2196/13269 .

Tokalić R, Poklepović Peričić T, Marušić A. Similar Outcomes of Web-Based and Face-to-Face Training of the GRADE Approach for the Certainty of Evidence: Randomized Controlled Trial. J Med Internet Res. 2023;25:e43928. https://doi.org/10.2196/43928 .

Krnic Martinic M, Čivljak M, Marušić A, Sapunar D, Poklepović Peričić T, Buljan I, et al. Web-Based Educational Intervention to Improve Knowledge of Systematic Reviews Among Health Science Professionals: Randomized Controlled Trial. J Med Internet Res. 2022;24(8): e37000.

https://www.mentimeter.com/ . Accessed 4 Dec 2023.

https://www.sendsteps.com/en/ . Accessed 4 Dec 2023.

https://da.padlet.com/ . Accessed 4 Dec 2023.

Zackoff MW, Real FJ, Abramson EL, Li STT, Klein MD, Gusic ME. Enhancing Educational Scholarship Through Conceptual Frameworks: A Challenge and Roadmap for Medical Educators. Acad Pediatr. 2019;19(2):135–41. https://doi.org/10.1016/j.acap.2018.08.003 .

https://zoom.us/ . Accessed 20 Aug 2024.

Raffing R, Larsen S, Konge L, Tønnesen H. From Targeted Needs Assessment to Course Ready for Implementation-A Model for Curriculum Development and the Course Results. Int J Environ Res Public Health. 2023;20(3):2529. https://doi.org/10.3390/ijerph20032529 .

https://www.kirkpatrickpartners.com/the-kirkpatrick-model/ . Accessed 12 Dec 2023.

Smidt A, Balandin S, Sigafoos J, Reed VA. The Kirkpatrick model: A useful tool for evaluating training outcomes. J Intellect Dev Disabil. 2009;34(3):266–74.

Campbell K, Taylor V, Douglas S. Effectiveness of online cancer education for nurses and allied health professionals; a systematic review using kirkpatrick evaluation framework. J Cancer Educ. 2019;34(2):339–56.

Miller GE. The assessment of clinical skills/competence/performance. Acad Med. 1990;65(9 Suppl):S63–7.

Ryan RM, Deci EL. Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. Am Psychol. 2000;55(1):68–78. https://doi.org/10.1037//0003-066X.55.1.68 .

Williams GM, Smith AP. Using single-item measures to examine the relationships between work, personality, and well-being in the workplace. Psychology. 2016;07(06):753–67.

https://scholar.google.com/intl/en/scholar/citations.html . Accessed 4 Dec 2023.

Rotondi MA. CRTSize: sample size estimation functions for cluster randomized trials. R package version 1.0. 2015. Available from: https://cran.r-project.org/package=CRTSize .

Random.org. Available from: https://www.random.org/

https://rambollxact.dk/surveyxact . Accessed 4 Dec 2023.

Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ (Online). 2009;339:157–60.

Google Scholar  

Skelly C, Cassagnol M, Munakomi S. Adverse Events. StatPearls Treasure Island: StatPearls Publishing. 2023. Available from: https://www.ncbi.nlm.nih.gov/books/NBK558963/ .

Rahimi-Ardabili H, Spooner C, Harris MF, Magin P, Tam CWM, Liaw ST, et al. Online training in evidence-based medicine and research methods for GP registrars: a mixed-methods evaluation of engagement and impact. BMC Med Educ. 2021;21(1):1–14. Available from:  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439372/pdf/12909_2021_Article_2916.pdf .

Cheung YYH, Lam KF, Zhang H, Kwan CW, Wat KP, Zhang Z, et al. A randomized controlled experiment for comparing face-to-face and online teaching during COVID-19 pandemic. Front Educ. 2023;8. https://doi.org/10.3389/feduc.2023.1160430 .

Kofoed M, Gebhart L, Gilmore D, Moschitto R. Zooming to Class?: Experimental Evidence on College Students' Online Learning During Covid-19. SSRN Electron J. 2021;IZA Discussion Paper No. 14356.

Mutlu Aİ, Yüksel M. Listening effort, fatigue, and streamed voice quality during online university courses. Logop Phoniatr Vocol :1–8. Available from: https://doi.org/10.1080/14015439.2024.2317789

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Acknowledgements

We thank the students who make their evaluations available for this trial and MSc (Public Health) Mie Sylow Liljendahl for statistical support.

Open access funding provided by Copenhagen University The Parker Institute, which hosts the WHO CC (DEN-62), receives a core grant from the Oak Foundation (OCAY-18–774-OFIL). The Oak Foundation had no role in the design of the study or in the collection, analysis, and interpretation of the data or in writing the manuscript.

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RR, LK and HT have made substantial contributions to the conception and design of the work; RR to the acquisition of data, and RR, LK and HT to the interpretation of data; RR has drafted the work and RR, LK, and HT have substantively revised it AND approved the submitted version AND agreed to be personally accountable for their own contributions as well as ensuring that any questions which relates to the accuracy or integrity of the work are adequately investigated, resolved and documented.

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The Danish National Committee on Health Research Ethics has assessed the study Journal-nr.:21041907 (Date: 21–09-2021) without objections or comments. The study has been approved by The Danish Data Protection Agency Journal-nr.: P-2022–158 (Date: 04.05.2022).

All PhD students participate after informed consent. They can withdraw from the study at any time without explanations or consequences for their education. They will be offered information of the results at study completion. There are no risks for the course participants as the measurements in the course follow routine procedure and they are not affected by the follow up in Google Scholar. However, the 15 min of filling in the forms may be considered inconvenient.

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Raffing, R., Konge, L. & Tønnesen, H. Learning effect of online versus onsite education in health and medical scholarship – protocol for a cluster randomized trial. BMC Med Educ 24 , 927 (2024). https://doi.org/10.1186/s12909-024-05915-z

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DOI : https://doi.org/10.1186/s12909-024-05915-z

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eAppendix 1. CLS Background

eAppendix 2 . CPC Program Description

eAppendix 3. SDH Definitions and Justification

eTable 1. Index of Structural Equality and Support (I-SES) as Operationalized in Chicago Longitudinal Study

eAppendix 4. Covariates in Model Specification

eAppendix 5. Inverse Probability Weighting

eAppendix 6. Educational Attainment Mediator

eTable 2. Group Equivalence at Age 35 Follow Up and for Original Chicago Longitudinal Study Cohort (N=1,124)

eFigure. Standardized Mean Differences for 2 Child-Parent Center (CPC) Program Contrasts for Low (0-3), Middle (4-6), and Top (7-9) Scores on the I-SES [Index of Structural Equality and Support] for the Total Sample and by Neighborhood Poverty Status (40% or More vs. Less in Poverty by Child’s Age 3 years) as Assessed at Midlife

eAppendix 7. Alternative Model Estimates

eTable 3. Alternative Models for CPC Preschool Participation and Index of Structural Equality and Support (I-SES) at Midlife

eReferences.

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Morency MM , Reynolds AJ , Loveman-Brown M , Kritzik R , Ou S. Structural Equality and Support Index in Early Childhood Education. JAMA Netw Open. 2024;7(8):e2432050. doi:10.1001/jamanetworkopen.2024.32050

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Structural Equality and Support Index in Early Childhood Education

• 1 Institute of Child Development, University of Minnesota, Minneapolis
• 2 Human Capital Research Collaborative, University of Minnesota, Minneapolis

Question   Does a comprehensive early childhood education program promote engagement in more supportive and resource-rich communities in adulthood?

Findings   This cohort study that followed-up 1124 individuals from preschool age to adulthood found that participation in the Child-Parent Center early education program was associated with higher scores on the Index of Structural Equality and Support at midlife.

Meaning   These findings suggest that early childhood programs can strengthen sociostructural and community supports well into adulthood.

Importance   Whether early childhood education is associated with a wide range of adult outcomes above and beyond individual- and family-level outcomes is unknown. As a consequence of improving educational and career success, it is postulated that participation in high quality, comprehensive programs can promote residence in more supportive community contexts in adulthood.

Objective   To investigate whether participation in high-quality early childhood programs (ECP) in high-poverty neighborhoods is associated with neighborhood-level social determinants of health (SDH) at midlife.

Design, Setting, and Participants   This cohort study analyzed data from the Chicago Longitudinal Study, a prospective cohort investigation following-up 989 children aged 3 to 4 years attending the Child-Parent Center (CPC) preschool program between 1983 and 1985 and a comparison group of 550 children using a nonrandomized trial design. Participants from the original sample who completed a telephone interview on health and well-being between ages 32 and 37 years were included in this analysis. Data analysis was conducted from April to June 2024.

Exposure   Participation in a CPC program, which includes preschool (ages 3 to 4 years) and school-age (kindergarten through third grade), vs usual early education programs.

Main Outcomes and Measures   The study used a new SDH measure (Index of Structural Equality and Support [I-SES]) based on the Healthy People 2030 framework. This 9-item index score included neighborhood-level assessment, measurement of the quality of education and health services, and assessment of racial discrimination in social and community contexts. Years of education by age 34 years was assessed as the key mediator of influence.

Results   A total of 1124 individuals (mean [SD] age at survey completion, 34.9 [1.4] years; 614 women [54.6%]; 1054 non-Hispanic Black [93.8%]; 69 Hispanic [6.2%]; 1 non-Hispanic White [<0.1%]) were included in the study, of whom 740 were in the CPC cohort and 384 were in the comparison cohort. After adjustment for baseline attributes and attrition, compared with no CPC preschool, CPC preschool was associated with significantly higher mean (SD) I-SES scores (5.93 vs 5.53; mean difference, 0.40; 95% CI, 0.16-0.65; standardized mean difference = 0.22). Compared with CPC participation for 0 to 3 years, CPC participation for 4 to 6 years showed a similar pattern of positive associations (adjusted mean I-SES score, 5.97 vs 5.69; mean difference, 0.28; 95% CI, 0.06-0.50; P  = .01; SMD = 0.15). CPC participation had a larger-magnitude association with I-SES in married vs single-parent households. Years of education partially mediated the association of CPC with I-SES (up to 41%), especially among those growing up in the highest-poverty neighborhoods.

Conclusions and Relevance   This cohort study found that early childhood programming is associated with SDH in adulthood. These findings reinforce the importance of early childhood education in addressing health disparities and contributing to healthier, more equitable communities and suggest that educational attainment is a key mechanism for health promotion.

Among early life experiences, participation in high-quality early childhood programs (ECP) is associated with a wide range of adult outcomes that include greater economic well-being, better cardiovascular and mental health, and reduced involvement in the criminal justice system. 1 , 2 Due to the breadth of outcomes affected and the major role of educational success in creating cumulative advantages over time, 1 , 3 ECPs that engage families intensively at multiple ecological levels may have carryover benefits to community-level social determinants of health (SDH). 4 Whether it is neighborhood poverty or discrimination, these environmental stressors and other sociostructural factors have pervasive influences on health and well-being across the life course. 4 Living in economically disadvantaged areas can limit access to essential resources such as quality health care and safe housing. Finally, despite the detrimental role of systemic racism and discrimination in areas such as health care, housing, and employment, studies show that Black communities greatly value education and view it as an avenue to social mobility, reflecting the importance of drawing value and satisfaction from one’s education as an SDH. 5

One of the 5 overarching goals for Healthy People 2030 4 is to create social, physical, and economic environments that promote attaining the full potential for health and well-being for all. Emerging research continues to explore the fundamental contributors underlying SDH as well as the health-related sequelae of these conditions, typically delineating the underlying modifiable determinants of health and grouping them according to categories like health behaviors, economic stability, physical environment, community safety, and clinical care. 6 , 7 It is critical to adopt a holistic, upstream approach in SDH research to address risk and protective factors and behaviors, rather than disease outcomes, enabling the development of prevention and interventions to mitigate compounding health issues. Early childhood education is intertwined with SDH through its influence on educational attainment, nutrition, parental employment, and access to support services. 1 Investing in high-quality programs can have far-reaching outcomes for individuals’ health and well-being, playing a vital role in addressing health disparities and promoting overall population health, which indicates the importance of investigating the association of ECPs with composite measures of SDH.

In this study, we assess, to our knowledge, for the first time whether an evidence-based, comprehensive ECP in high-poverty neighborhoods is associated with a new SDH index at midlife based on the 5-component framework of Healthy People 2030. The SDH variables proposed for this index have strong empirical bases that associate educational attainment with social mobility, which motivated us to also assess whether educational attainment mediates this association. 2 - 5

This cohort study was approved by the University of Minnesota institutional review board. Participants provided written and oral informed consent upon survey initiation. The reporting of the study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. 8

Data were analyzed from the Chicago Longitudinal Study (CLS), a prospective cohort investigation following 989 children aged 3 to 4 years attending the Child-Parent Center (CPC) preschool program between 1983 and 1985 and a comparison group of 550 children using a nonrandomized trial design. 9 , 10 The comparison group participants attended the usual early education programs primarily in randomly selected schools matched to CPC locations based on poverty and neighborhood characteristics. A subset of participants from the original cohort completed a telephone interview on health and well-being between ages 32 and 37 years, which constituted our study sample. Some participants mailed in surveys. Questions concerned education, employment, health behavior, community resources, safety, and experiences of discrimination. Previous data from participants has been collected at ages 10 years, 15 to 18 years, 18 to 24 years, and 26 to 28 years. 10 , 11 Participant sociodemographic data was collected from various sources, such as children, parents, teachers, and school administrative records. Race and ethnicity of participants were ascertained through self-report. Race and ethnicity categories included non-Hispanic Black, Hispanic, and non-Hispanic White. Race and ethnicity were included because it is an important attribute of the study sample for description, life circumstances, and influences.

The CLS design (eAppendix 1 in Supplement 1 ) is methodologically strong in that the CPC group included all children enrolled in these centers (none were excluded) and that they resided in the highest poverty-level neighborhoods. 10 , 11 Any comparison group would by necessity be more advantaged in sociostructural attributes. These attributes minimize any potential selection bias. Moreover, all comparison group participants enrolled in alternative, usual treatment services (other preschool or kindergarten programs). Continuing school-age program enrollment was a combination of family and administrative selection and choice because children who moved from CPC schools left the program by definition, but schools also varied by number of years of school-age services offered (2 years vs 3 years). Assessment of key covariates and baseline attributes showed equivalence on nearly all factors, and this finding has been confirmed in many previous reports. 10 - 13 This included achievement growth prior to enrollment in school-age services for program and comparison groups, suggesting equal performance between groups and no positive selection that would confound estimates of outcomes for program duration. 13 , 14

CPC provides comprehensive education and family support services aimed at mitigating the impacts of poverty (eAppendix 2 in Supplement 1 ). 3 , 9 Enrichment experiences emphasize engagement at family, school, and community levels. After a half-day preschool program (3 hours, 5 days per week) at ages 3 and/or 4 years in small classes with child-teacher ratios of 17:2, CPC components in kindergarten through third grade include reduced class sizes (maximum of 25 students), teacher aides for each class, health services, continued parent involvement opportunities, and enriched classroom environments for enhancing holistic well-being, including physical health. Following 1 to 2 years of half-day preschool, services extend to third grade for a total of up to 6 years. The overarching goal is to promote school success, ultimately leading to better health and well-being over the life course. Many prior studies have documented program structures, impacts, and validity of program estimates. Findings have corroborated this goal with the hypothesis that benefits carryover to SDH. 3 , 5 , 11

The Healthy People 2030 SDH framework is comprised of the following components: economic stability, neighborhood and built environment, health care access and quality, education access and quality, and social and community context. 4 It is unique among sociostructural indexes in its focus on neighborhood-level assessment, measurement of the quality of services and experiences in community settings, and assessment of racial discrimination as part of social and community context. We created an overall measure using 9 dichotomous indicators for the 5 components called the Index of Structural Equality and Support (I-SES). The survey items make the index and were completed by both CPC and comparison participants. See eAppendix 3 and eTable 1 in Supplement 1 for detailed information on variable definitions. As a positive measure of supports at midlife, scores range from 0 to 9, with higher scores meaning greater endorsement of positive environment structures at midlife.

The family risk index comprised of 8 sociodemographic indicators measured by age 3 years (eg, high school dropout or income near the federal poverty level) and its squared term were also included to assess cumulative risk. Receipt of c hild welfare services and adverse child experiences from birth to age 5 years, whether the mother attended college, neighborhood poverty status by age 3 years, single-parent family status by age 3 years (from birth records), and self-reported chronic health conditions as assessed in the age 35-year survey were also included. Models with CPC preschool included school-age participation to adjust for the influence of later intervention.

Linear regressions were analyzed with inverse probability weighting (IPW) to adjust for attrition bias and 12 covariates, including baseline family socioeconomic status and neighborhood poverty (eAppendix 4 and eAppendix 5 in Supplement 1 ). SPSS software version 29 (IBM) was used to calculate 95% CIs, with a 2-tailed P  < .05 set as the level of significance. Standardized mean differences (SMDs) of 0.20 denote practical significance. They are equivalent to a 15% to 20% change near the midpoint of the outcome distribution. CPC preschool and CPC preschool plus school-age participation were analyzed separately along with 3 subgroups: household structure (married vs single-parent status), multiple family risk status, and neighborhood poverty at preschool entry. The mediator was years of education completed by age 34 years . It was taken from administrative records (eg, National Student Clearinghouse) and supplemented with survey reports over time (eAppendix 6 in Supplement 1 ).

We also examined the distribution of scores in three categories: low (0-3), middle (4-6), and top (7-9). This reveals if group differences were similar across the full range of structural supports. SMDs were calculated at each of these levels for the model adjusted for baseline covariates and attrition.

Mediation was assessed by the difference-in-difference method (or percentage reduction). This is the mean difference in program estimates between groups without the mediator and estimates between groups with the mediator included in the model, and then divided by the unmediated program coefficient. This proportion is multiplied by 100 to denote the percentage reduction in the program group difference associated with the mediator, which is years of education completed. This approach to mediation provides conservative estimates by definition because complex indirect effects through paths of intervening mediators are not considered. However, our estimates are readily interpretable as direct contributors to understanding long-term associations. Data analysis was completed from April to June 2024.

A total of 1124 individuals (mean [SD] age at survey completion, 34.9 [1.4] years; 614 women [54.6%]; 1054 non-Hispanic Black [93.8%]; 69 Hispanic [6.2%]; 1 White [<.01%]) were included in the study, of whom 740 were in the CPC cohort and 384 were in the comparison cohort ( Table 1 ). Of all participants, 560 (49.8%) resided in low-income neighborhoods. Participants had completed a mean (SD [range]) 12.90 (2.13 [7-22]) years of education by age 34 years, with 161 (14.3%) having received a bachelor’s degree or higher. The mean (SD) I-SES score for the entire cohort was 5.77 (1.84), with 281 cases (25.0%) with a score less than or equal to 4 and 414 cases (36.8%) with a score of 7 or greater. The unadjusted mean (SD) I-SES scores for the CPC preschool and comparison groups were 5.91 (1.84) and 5.53 (1.82), respectively, with values for continuing program group following a similar pattern. Study participants growing up in high poverty neighborhoods (>40% of residents below federal poverty level) had lower mean (SD) I-SES scores than the lower poverty group (5.73 [1.87] vs 5.82 [1.80]). At midlife, however, the differential was accentuated (mean [SD] score, 5.01 [1.87] vs 6.03 [1.76]). Table 2 shows that I-SES indicators were positively associated with educational attainment (years of education), the preeminent individual-level SDH in Healthy People 2030. The total index score had a correlation of with educational attainment ( r  = 0.21). Correlations with overall life satisfaction ( r  = 0.41) and self-rated health ( r  = 0.17) followed a similar pattern. See eTable 2 in Supplement 1 for group equivalence at age 35 years for the original CLC cohort.

Table 3 shows that after adjusting for baseline characteristics including early family and social environments, compared with no CPC, CPC preschool was associated with a significantly higher mean I-SES score (5.93 vs 5.53; mean difference, 0.40; 95% CI, 0.16-0.65; P  = .03; SMD = 0.22). A similar pattern of differences was found for adjusted mean I-SES scores for CPC preschool plus school-age participation (4 to 6 years) compared with 0 to 3 years of participation (5.97 vs 5.69; mean difference, 0.28; 95% CI, 0.06-0.50; P  = .01; SMD = 0.15).

The eFigure in Supplement 1 shows the pattern of adjusted program group differences (SMDs) at low, middle, and top categories of the I-SES distribution. For the total sample, X participants (12.4%) were in the low category, X (50.8%) in the middle category, and X (37.X%) in the top category. For the CPC preschool vs none contrast, program participants were more likely to be in the top group of I-SES scores of 7 to 9 of 9 points (SMD = 0.25). They were less likely to be in the lower 2 groups (CPC preschool, SMD  = −0.12; no CPC, SMD = −0.23). The pattern was similar for the dosage groups (4-6 years vs 0-3 years). When separated by neighborhood poverty status at the time of program participation, children in CPC growing up in relatively lower poverty settings (<40% of residents below poverty) experienced the largest benefits in I-SES. For the top score group, SMDs were 0.33 and 0.25, respectively, for CPC preschool vs none and higher vs lower dosage groups (eFigure in Supplement 1 ).

Subgroup findings overall showed similar associations across groups, but there were larger-magnitude associations among more advantaged groups. One significant subgroup interaction was identified. CPC preschool was had a larger-magnitude association with I-SES in married households (adjusted mean score, 6.36 vs 5.42; mean difference, 0.94; 95% CI, 0.46 to 1.44; P  < .001; SMD = 0.51) than in single-parent households (adjusted mean score, 5.65 vs 5.56; mean difference, 0.09; 95% CI, −0.06 to 0.62; P  = .67; SMD = 0.05). This pattern was also found for the dosage groups of 4 to 6 years vs fewer years ( Table 3 ). Similarly, the lower neighborhood poverty group had significantly higher adjusted mean I-SES scores, including both the preschool vs comparison contrast (6.07 vs 5.58; mean difference, 0.49; 95% CI, 0.14 to 0.84; P  = .02; SMD = 0.27) and preschool plus school age vs comparison contrast (6.07 vs 5.73; mean difference, 0.34; 95% CI, 0.03 to 0.65; P  = 0.1; SMD = 0.18). The lone comparison favoring higher risk groups was for family risk status, which was found in both the preschool vs comparison contrast (adjusted mean I-SES score, 5.91 vs 5.47; mean difference, 0.44; 95% CI, 0.13 to 0.71; SMD = 0.24) and preschool plus school age vs comparison contrast (adjusted mean I-SES score, 5.90 vs 5.65; mean difference, 0.25; 95% CI, −0.06 to 0.76; P  = X.X; SMD = 0.14). No differences for the full program groups were detected ( Table 3 ).

Alternative estimates support robustness (eAppendix 7 and eTable 3 in Supplement 1 ). Estimated program outcomes were similar between IPW and non-IPW models, suggesting that attrition occurred at random and was not associated with baseline characteristics (eAppendix 6 in Supplement 1 ).

For the mediation results for the total sample, years of education accounted for 16% to 18% of the association of CPC with I-SES. Mediation increased with economic disadvantage. Among those growing up in the highest poverty neighborhoods, 31% to 41% of the association was mediated by years of education ( Table 3 ). These values are above and beyond the influence of baseline characteristics and program participation. These results reflect only the direct association with educational attainment. More complex associations are possible. Robustness testing using different model specifications did not alter the pattern of findings and was consistent with results reported here (eTable 3 in Supplement 1 ).

The findings of this cohort study provide evidence suggesting that a multilevel, comprehensive-service ECP is associated with SDH in adulthood. To our knowledge, this is the first study to find such an association. Findings also document that this new index measure of SDH comprised of impactful neighborhood indicators can discriminate between the early life experiences of children who do or do not participate in intensive educational enrichment. The findings also establish that the benefits of ECPs extend beyond individual-level education and occupational success to the broader sociostructural environment. Although in general CPC participation had similar positive associations with I-SES across subgroups, the SMD for children growing up in married households exceeded those in single-parent households and in other subgroups by a factor of 2 or higher. This finding suggests that economic and family resources available in the early years of life create cumulative advantages that are unlikely to be overcome by social intervention alone, even comprehensive programs like CPC. Concerted efforts at multiple levels over extended periods of time, however, can improve well-being.

The finding that educational attainment, a leading individual-level SDH, accounted for a sizable share of observed differences for the most economically disadvantaged groups suggests that educational success is one mechanism for reducing disparities in supportive social environments. This finding is consistent with a large body of research demonstrating that ECPs have compensatory and protective effects for children and families growing up in the most economically disadvantaged communities. 1 , 2 , 9 However, only programs high in quality have these benefits, and the barriers to such quality have increased in recent years.

Nevertheless, the developmental origins of educational attainment are complex and involve socioeconomic position, home and school environments, motivational and socioemotional influences, and achievement behaviors. 3 , 11 , 15 Investigation of these and related influences were beyond the scope of the present study. Previous findings in the CLS and related studies show that the cumulative advantages initiated by ECPs are complex and circuitous, including individual and personal, family, school, and community processes. 3 , 11 The early cognitive and scholastic advantages of CPC, for example, carryover to strengthened parental involvement in school, enrollment in higher quality schools, avoidance of delinquent behaviors, and ultimately higher educational attainment. 3 , 11 This process and others warrant further investigation and confirmation, especially in comprehensive frameworks such as the 5-Hypothesis Model. 3

This study has limitations. The main limitation is that our SDH measure, although broad and based on a well-documented framework, may not fully represent community and structural influences. Moreover, results are correlational and warrant replication.

This cohort study found that ECP was associated with SDH in adulthood. These findings suggest that CPC and similar programs can contribute to broader efforts to mitigate health disparities and create healthier, more equitable communities. Educational attainment appears to be a key transmitter of observed benefits, which reinforces its importance as a major goal of ECPs.

Accepted for Publication: July 11, 2024.

Published: August 30, 2024. doi:10.1001/jamanetworkopen.2024.32050

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Morency MM et al. JAMA Network Open .

Corresponding Author: Arthur J. Reynolds, PhD, Human Capital Research Collaborative, University of Minnesota, 51 E River Rd, Minneapolis, MN 55455 ( [email protected] ).

Author Contributions: Ms Morency and Dr Reynolds had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Reynolds, Morency, Loveman-Brown, Kritzik.

Acquisition, analysis, or interpretation of data: Reynolds, Morency, Loveman-Brown, Ou.

Drafting of the manuscript: Reynolds, Morency, Loveman-Brown, Kritzik.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Reynolds, Morency, Ou.

Obtained funding: Reynolds.

Administrative, technical, or material support: Reynolds, Ou.

Supervision: Reynolds, Ou.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the National Institute of Child Health and Human Development (grant No. HD034294) and the Bill & Melinda Gates Foundation of Education (grant No OPP1173152).

Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We thank the Chicago Public School District and participating schools for cooperation in data collection and collaboration in this study. Finally, we are especially grateful to the children and families who have participated over many years and have been supremely generous with their time and input about their lives and for providing so many valuable insights.

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A Medical Education Research Library: key research topics and associated experts

Kaylee eady.

Faculty of Education, University of Ottawa, Ottawa, Ontario, Canada

Katherine A. Moreau

When clinician-educators and medical education researchers use and discuss medical education research, they can advance innovation in medical education as well as improve its quality. To facilitate the use and discussions of medical education research, we created a prefatory visual representation of key medical education research topics and associated experts. We conducted one-on-one virtual interviews with medical education journal editorial board members to identify what they perceived as key medical education research topics as well as who they associated, as experts, with each of the identified topics. We used content analysis to create categories representing key topics and noted occurrences of named experts. Twenty-one editorial board members, representing nine of the top medical education journals, participated. From the data we created a figure entitled, Medical Education Research Library. The library includes 13 research topics, with assessment as the most prevalent. It also notes recognized experts, including van der Vleuten, ten Cate, and Norman. The key medical education research topics identified and included in the library align with what others have identified as trends in the literature. Selected topics, including workplace-based learning, equity, diversity, and inclusion, physician wellbeing and burnout, and social accountability, are emerging. Once transformed into an open educational resource, clinician-educators and medical education researchers can use and contribute to the functional library. Such continuous expansion will generate better awareness and recognition of diverse perspectives. The functional library will help to innovate and improve the quality of medical education through evidence-informed practices and scholarship.

Introduction

Medical education research (MER) advances innovation in medical education and improves its quality. However, for novice clinician-educators, generating medical education scholarship can be daunting [ 1 ]. The ‘alien culture’ of MER, with its own concepts and processes [ 2 ], and time-constraints [ 3 ], may hinder clinician-educators from appropriately implementing evidence into their educational practices and scholarship [ 3 , 4 ].

To increase MER use, some have shed light on research areas of foci [ 5 , 6 ], demonstrated top citations [ 7 , 8 ], and spotlighted journal trends [ 9 , 10 ]. Such bibliometric studies provide glimmers into who is publishing MER and individual pieces of scholarship. However, they do not concisely present key MER topics and associated experts. They present findings in long word-based formats, which are unideal for clinician-educators’ needs [ 11 ]. Thus, we initiated the creation of a prefatory visual representation of current key MER topics and associated experts.

Medical education journal editors have unique insights into MER and are responsible for determining the value and significance of works submitted [ 12 ]. We contacted all editorial board members of the top 10 medical education journals, based on 2022 impact factors, via their public email addresses, provided an information letter, and invited them to participate in a one-on-one virtual interview. We asked them what they perceived as key MER topics and who they associated, as experts, with each. Interviewees provided prior informed verbal consent. Our University’s Office of Research Ethics and Integrity approved the study (#S-11-21-7569).

We conducted content analysis [ 13 ] to determine key MER topics and associated experts. We created initial categories based on the above-mentioned bibliometric studies. We individually read the transcripts and highlighted text representing topics, coded highlighted text using the predetermined categories, then created new categories for topics beyond the initial scheme. We resolved disagreements through discussion. We then reread the transcripts and searched for occurrences of named experts for each topic. We determined key topics to be those identified by at least two interviewees and included all experts identified.

Of the 81 members invited, 21 participated, representing nine of the top medical education journals: Academic Medicine, Advances in Health Sciences Education, BMC Medical Education, Canadian Medical Education Journal, Journal of Continuing Education in the Health Professions, Medical Teacher, Perspectives on Medical Education, Teaching and Learning in Medicine, and The Clinical Teacher. From the data we created Figure 1 , Medical Education Research Library .

Medical Education Research Library.

The library illustrates key MER topics and associated experts. The shelves represent the identified topics; the right bracketed number represents the number of interviewees that identified the topic. The topics are placed most to least identified, from top to bottom and left to right. The labeled paper organisers represent the identified experts within each topic; the number below represents the number of interviewees that identified that individual as an expert.

This Medical Education Research Library provides a prefatory visual representation of current key MER topics and associated experts. The topics align with trends identified in the literature, including topics of longstanding focus in MER. For example, previous research revealed the prevalence of topics related to learner characteristics, medical school selection, and assessment [ 6 , 7 , 14 ]. Authors identified topics related to physician competencies (skills, knowledge, attributes), curricula, and teaching as key [ 5–7 ]. Topics related to research methodology, program evaluation, and technology are gaining prominence [ 5 ]. This study highlights a growing interest in research related to workplace-based learning and an emerging interest in equity, diversity, and inclusion, physician wellbeing and burnout, and social accountability.

The library allows clinician-educators to see key MER topics and perceived experts when searching for evidence to support, improve, and innovate their practices and scholarship. Such knowledge and efficiency benefits manuscript publication and academic promotion [ 15 ]. The library encourages medical education researchers to reflect on where they might fit within the library and who they might engage as future collaborators, mentors, or reviewers. More so, it urges them to think about how they can grow the library and diversify the topics included [ 5 , 10 , 16–18 ]. It illustrates a limited number of experts, highlighting the need for collaborative efforts to bring recognition to others’ work. Concerningly, the identified experts are mostly males, from Western countries and high-ranking institutions. As such, the library presses that the community needs to address issues around who is considered legitimate in MER and better recognize and support diverse perspectives [ 10 ].

With this, we recognize that the library provides the perspective of one stakeholder group, that of journal editors; specifically, the perspectives of those who participated in this study. Our intention is to provide a prefatory visual representation for the medical education community to grow and diversify through research. We intend to spark conversations around what and who is perceived as key in MER as well as comments such as ‘Why isn’t topic X included?’ and ‘I am missing from topic X!’. With this, we will transform the Medical Education Research Library into a functional, open educational resource that serves to innovate and improve the quality of medical education through evidence-informed practices and scholarship.

Acknowledgments

We would like to thank Dennis Newhook and Mary-Ann Harrison of the Clinical Research Unit, Children’s Hospital of Eastern Ontario Research Institute, for their support and artistic creativity in helping us create the Medical Education Research Library .

Funding Statement

The author(s) reported there is no funding associated with the work featured in this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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Program Goal Program Details Scholar Eligibility Applicant Information Questions? Participating ICOs Contacts

Program Goal

The Environmental Justice (EJ) Scholars Program invites EJ expertise, knowledge, and skills from research scholars, academics, public health and health care professionals, and EJ leaders from community-based organizations to work with staff members at various NIH ICOs.

The EJ Scholars Program aims to:

• Build NIH capacity to advance EJ-related research, programs, and other NIH ICO projects.
• Increase staff and community awareness and skills to address EJ issues.
• Grow and strengthen the NIH network of EJ resource experts.
• Contribute to NIH goals to support underserved and under resourced communities.

The 2024-2025 application cycle for the first cohort is now open!  View the application instructions here: NIH EJ Scholars Application Instructions .

Program Details

Environmental Justice scholars will be hosted by one or more NIH Institute, Center, or Office (ICO) . During the program period, EJ scholars will collaborate with NIH staff on one or more research, education, or training relevant activities, contributing to the wider NIH community (see below). EJ scholars may partner with NIH intramural and/or extramural (grant funded) scientists on projects of shared interests.

Scholars are expected to dedicate up to 25% of their time for up to 10 months to support and collaborate with their host ICO(s). Scholars will work virtually, unless otherwise agreed upon with their host ICO(s). The scholar will work with the host ICO on the structure and terms of their work plan prior to position commencement.

Project Topics and Activities

Depending on ICO needs, scholars may support projects related to EJ topics such as:

• Climate change and climate justice.
• Diet, nutrition, and food justice.
• Mental health consequences of environmental exposures.
• Land use and transportation.
• Energy extraction and energy justice.
• Maternal and child health, pregnancy outcomes.
• Community engaged research approaches.
• Air quality, water pollution, and other environmental exposures.
• Environmental impacts across the life course.
• Interplay of environmental exposure, social determinants of health, and health disparities.
• Translating, communicating, and disseminating research findings to different audiences in culturally appropriate modalities.

Scholars will support their host efforts on a range of activities that may fall within the following areas:

Education and Training

• Host internal and external seminars
• Develop training workshops, courses, and modules
• Inform environmental health and EJ working groups

Public Engagement

• Develop infographics and other gray literature material
• Present at conferences
• Write journal articles
• Write opinion pieces, commentaries, and blog posts
• Speak at NIH-wide and IC-specific webinars

Data Collection, Analysis, and Utilization

• Establish or inform dataset workbooks
• Conduct portfolio analyses
• Write white papers and reports
• Write manuscripts
• Provide consultations

Scholar Eligibility

U.S.‐based or international mid‐career to senior scientist candidates and environmental justice community leaders from academic, non‐profit, or private sectors are eligible to apply. Candidates should have a strong publication record in environmental justice and related health sciences (may include gray literature, such as infographics, podcasts, curriculum, policy statements, or training materials).

• This program is not a postdoctoral training program.
• Candidates do not need to have an NIH funding record.
• U.S. citizenship is not required.
• Former EJ Scholars Program participants are ineligible for a second period of support.

The program is open to recognized leaders in environmental justice from the following groups:

• Academic and research institutions, including Historically Black Colleges and Universities, Tribal Colleges and Universities, and Minority Serving Institutions.
• Community, advocacy, charitable, and faith-based organizations.
• Health care and public health organizations.
• Tribal, state, and local government offices.

Scholars should have demonstrated expertise in EJ areas that may include, but are not limited to:

• Behavioral and social sciences research
• Community engagement and partnerships
• Community organizing
• Community-led and Tribal-led research/community science
• Data mapping/visualization
• Disaster response and research.
• Environmental exposure and risk assessment
• Intervention strategies
• Traditional ecological knowledge/Indigenous knowledge
• Community health work and training 
• Policy development and engagement
• Translational research
• Implementation Science
• Workforce training and development
• Women’s health research
• Inclusive health education and research
• Migrant/immigrant health and research
• Communication research
• Youth EJ Leadership training

Applicant information

The 2024-2025 EJ Scholars Program application is now open! Please submit your application package to [email protected] by Friday, October 11th, 2024, at 11:59 PM EDT.

• For more information about how to apply, view the application instructions .
• To learn more about ICO interests, see the ICO interest statements . Applicants are encouraged to review the statements before applying. Please note that the list of interested ICOs included in the document is not exhaustive

Please email: [email protected] .

Upcoming Events

• Informational Webinar - Friday, September 13, 2024 at 11:00 AM - 12:00 PM EDT (registration link forthcoming)” 

Participating NIH Institutes, Centers, and Offices

The following NIH Institutes, Centers, and Offices (ICO) have expressed interest in recruiting an EJ Scholar (see interest statements for more information about ICO interests). This list is not exclusive.

• All of Us Research Program (AoU)
• Fogarty International Center (FIC)
• National Institute on Aging (NIA)
• National Institute of Allergy and Infectious Diseases (NIAID)
• National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
• National Institute of Dental and Craniofacial Research (NIDCR)
• National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
• National Institute of Environmental Health Sciences (NIEHS)
• National Institute of Mental Health (NIMH)
• National Institute of Nursing Research (NINR)
• NIH Office of Behavioral and Social Sciences Research (OBSSR)
• NIH Office of Dietary Supplements (ODS)

EJ Scholars Program Points of Contact

Liam-ofallon.jpg.

Liam O’Fallon, M.A.

Health Specialist, NIEHS [email protected] 984-287-3298

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Jessica Au, M.P.P.

Program Specialist, NIEHS [email protected] 984-287-4672

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Juliette McClendon, Ph.D.

Program Director, NIMH [email protected] 301-379-0413

This page last reviewed on August 19, 2024

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