descriptive epidemiological case study

Outbreak Toolkit

Descriptive epidemiology, on this page, descriptive analyses.

Descriptive epidemiology describes the outbreak in terms of person, place and time. “Person” refers to socio-demographic characteristics of cases and includes variables such as age, ethnicity, sex/gender, occupation, and socioeconomic status. “Place” refers to spatial relationships that are important in describing the occurrence of illnesses and may include variables that describe clustering, rural-urban status, city, province/territory, or country. “Time” refers to the examination of when and over what time period the illnesses occur and may describe a point source epidemic, secular trends, or temporal clustering. Descriptive epidemiology forms one of the main parts of an epidemiological summary .

The goals of descriptive epidemiology in enteric outbreak investigations are:

• To assess trends in health and disease: illnesses are monitored in order to identify emerging problems (e.g., potential outbreaks). Comparisons can be made among population groups (e.g., different age groups, or sexes), geographic areas, and time periods.
• To identify problems and generate hypotheses (e.g., if illnesses are occurring in a specific demographic or geographic area, this could suggest initial hypotheses for the source of an outbreak). Hypotheses can then be tested using analytic methods , such as a case-control study.

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People’s socio-demographic characteristics and behaviors can increase or decrease their risk for developing an illness. For example, the elderly and very young are often at elevated risk for bacterial and viral infections. Average (median), minimum, and maximum ages of cases, as well as proportions of cases according to sex and other relevant variables, should be part of any descriptive analysis. Cross-tabulations among these variables may be calculated to identify inter-relationships. Analyses of case demographics may provide insight into the source of an outbreak, for example, if the majority of cases are female, young or old, or from a specific ethnic group or religious community.

Pathogens do not necessarily respect or follow political borders. An examination of the spatial associations of cases can play a key role in determining the source of the outbreak. For example, the distribution of cases amongst provinces could be a reflection of the availability of the contaminated food product (distributed in Provinces A and B, but not C). Maps may be a useful tool in describing these spatial associations.

Time is important in characterizing illness to assess if incidence rates or case numbers have increased or decreased over time and if there is seasonal variation. In outbreaks, the relationship between time and the number of illnesses is graphically displayed in an epidemic curve . The nature of the outbreak can often be deduced by the appearance of the epidemic curve and may reveal whether an exposure is attributable to a point-source, a continuing common source or is intermittent. The incubation period of a particular pathogen–the time between infection and symptom onset–in any given outbreak is another important aspect of time and also will affect the shape of an epidemic curve. 

• Case Study, Module 1 – Descriptive epidemiology 
• Case Study, Module 1 – Epi summaries
• Case Study, Module 2: Updated epi summary
• Descriptive epidemiology example: Milord, F., et al . 2012. Cyclospora cayetanensis: a description of clinical aspects of an outbreak in Quebec, Canada. Epidemiol Infect .140(4):626-32.

Toolkit line list and data dictionary

• This Microsoft Excel-based tool is designed to be used as a template for foodborne outbreak investigation line lists. Once data has been entered, common descriptive statistics are automatically calculated. A data dictionary describing each data field in the line list is available in the final tab.

Toolkit epi curve exercise

• This exercise shows how to make an epidemic curve in Microsoft Excel, where each case is represented by a single box using this data set .

Toolkit pivot tables exercise

• This exercise uses an outbreak line list to create PivotTables in Microsoft Excel and use them to extract information for descriptive epidemiological summaries and create epidemic curves.

Toolkit epidemiological summary template

• This Microsoft Word document provides a suggested template for the content and layout of an  epidemiological summary .

Toolkit outbreak investigation report template

• This Microsoft Word document provides a suggested template for the content and layout of an outbreak investigation report or final  epidemiological summary .

Descriptive and Analytical Epidemiology

• First Online: 13 December 2023

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• Kiran Sapkota 2  

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Both descriptive and analytical epidemiology are important for advancing clinical medicine and public health. Descriptive epidemiology assesses the burden and magnitude of health problems in a population, whereas analytical epidemiology identifies the causes and risk factors of health problems. This chapter provides the scopes, designs, data analytics approaches, ethical issues, and examples of various epidemiological studies. Descriptive epidemiological studies include: (1) case reports, (2) case series, (3) descriptive cross-sectional (prevalence) studies, and (4) descriptive cohort (incidence) studies. Analytical epidemiological studies include: (a) observational studies, such as (1) ecological studies (correlational studies), (2) analytical cross-sectional studies, (3) analytical cohort studies (prospective and retrospective), and (4) case–control studies, and (b) experimental studies, such as (1) community-based interventions and (2) clinical trials.

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Sapkota, K. (2024). Descriptive and Analytical Epidemiology. In: Mitra, A.K. (eds) Statistical Approaches for Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-031-41784-9_1

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What is Descriptive Epidemiology?

Public health experts play a crucial role in addressing global health challenges, from infectious diseases like Ebola and COVID-19 to the broader impact of climate disasters and social disruptions. In this ever-evolving landscape, the field of epidemiology stands at the forefront, conducting vital research and data analysis. As we delve into the topic of descriptive epidemiology, we aim to uncover the essential aspects of this field. What are the 4 types of descriptive epidemiological studies? How does epidemiology contribute to our understanding of health events? Join us in exploring these questions and more, gaining insights into the skills and qualifications required for a rewarding career in this impactful discipline.

Beyond the Headlines: The Crucial Impact of Public Health Experts on Global Well-Being

Public health experts have never been more important than they are in the world today. From cancer to Ebola to COVID-19, major health events affect us all. As globalization, climate disasters and economic and social disruptions expand, we need trained professionals to help mitigate those threats. To meet health needs, public health professionals continue to serve and protect through research, policymaking and administration in the field of infectious disease preparedness and prevention. Specialists in the field of epidemiology are responsible for some of today’s most important public health research and data analysis.

What is descriptive epidemiology? Keep reading to find out, and to explore the skills and qualifications necessary to pursue a career in this life-saving field.

Epidemiology is the science concerned with the factors that influence and determine the frequency and distribution of disease, injury, and other health-related events, and their causes in a defined human population. Its goal is to establish causal factors for health issues in order to improve the health and safety of entire populations, such as towns, countries, age groups or races. A health issue is anything that might affect health now or in the future: illness, accident, natural disaster, economic strife, and so on. For epidemiologists, “Who is most likely to be injured in an automobile accident?” can be just as valuable a line of inquiry as, “What part of the population is at highest risk for developing complications from the flu?”

Experts in two main branches of this science—analytical and descriptive epidemiology—work to decrease health events and diseases by understanding the risk factors for them. Both branches serve public health organizations by providing information that may reduce disease and other kinds of events affecting human health.

So what is descriptive epidemiology? It’s a specialty that evaluates and catalogs all the circumstances surrounding a person affected by a particular health event. The more fully a descriptive epidemiologist can describe people, places and times, and any correlations between the three, the more likely it is that patterns will emerge which can be considered risk factors for certain kinds of health issues.

Analytical epidemiologists use the data gathered by descriptive epidemiologists to look for patterns that suggest causes.

What Do Descriptive Epidemiologists Do?

In descriptive epidemiology, scientists examine and describe in detail the people, places and times related to public health events, in order to understand and reduce health risks. They consider the impact of demographic, geographic and socioeconomic factors. They also take into account behavioral influences such as diet, work schedule, exercise frequency, drug use and sexual habits, all of which may be involved in the epidemiologic triangle .

They ask questions known as the five Ws:

• What (is the health event or diagnosis)?
• When (did the health event occur)?
• Where (did it take place?
• Who (are the people involved and affected)?
• Why/how (did it happen)?

Of these five, they focus primarily on three:

People Who is affected? Descriptive epidemiology looks for the age, education, race, socioeconomic status, sex, gender, and access to health services of the people involved in health events. Specialists may look into religious, cultural and social influences, as well.

Time When and for how long do health events occur? Descriptive epidemiology tracks and records the dates and lengths of disease exposure and use of control measures. This can help determine whether a disease primarily occurs seasonally, such as influenza in winter, or at any time, such as hepatitis B.

Location This research details where health events take place. Descriptive epidemiologists detail the birthplace, place of residence, site of employment, treatment location and other relevant geographic locations of the people affected.

The information they gather helps descriptive epidemiologists formulate hypotheses about the sources of outbreaks and health events, which helps public health officials analyze data, identify risk factors and improve health outcomes.

What’s the Workplace in Descriptive Epidemiology?

The nature of the work at the heart of descriptive epidemiology can vary from that in other parts of the field. Descriptive epidemiologists travel to administer studies, interviews and surveys, which puts them on the ground level in communities with severe, acute public health crises—think global outbreaks and natural disasters—which are usually sudden, unexpected, and in need of time-sensitive response.

Descriptive epidemiologists may also attend and support educational events or aid local officials in implementing disease prevention strategies.

The majority of epidemiologists work in state government (35%) or local government (19%). 2 A significant number work in general hospitals (15%) and in research-teaching positions at universities (11%). 3 Descriptive epidemiologists often work for the Centers for Disease Control and Prevention, the World Health Organization, the National Institutes of Health or other government or global organizations whose goal is to help protect the public from health events.

The Growing Demand for Epidemiologists

As the COVID-19 pandemic has demonstrated, epidemiologists’ work is crucial in creating and ensuring healthy societies.

As many workers retire or make transitions to other jobs, we need new experts to fill the gaps. The U.S. Bureau of Labor Statistics projects that opportunities for epidemiologists will expand by 30%—much faster than the average growth rate—in the decade from 2020 to 2030. 2 The number of open positions will most likely hover around 900 each year.

Cutting-edge healthcare technology will continue to aid in the discovery of new diseases over the next decade. Additionally, according to the Centers for Disease Control and Prevention, an increasing number of hospitals are expected to join infection-tracking programs such as the National Healthcare Safety Network. 4 Both of these expansions will result in heightened demand for descriptive epidemiologists.

Becoming a Descriptive Epidemiologist

Becoming an epidemiologist requires education and training beyond a bachelor’s degree. There are no official national licensing or educational requirements, but a master’s-level degree—a Master of Science in Clinical Epidemiology , a Master of Public Health with a specialization in epidemiology , or another related degree—is the accepted standard. You may choose to complete doctoral studies in epidemiology or medicine, as well, particularly if your interests lie in clinical work.

To pursue a career in epidemiology , look into an accredited program with experienced, professional faculty . Coursework should focus on public health, biological and physical sciences, math and statistics. Specific specialty courses may cover chronic diseases, infectious diseases or research principles. Most reputable programs include a practicum as part of the required coursework.

In addition to the expertise you’ll gain through graduate work, success as an epidemiologist requires that you’re adept with:

Math and statistics. Your advanced statistical skills will help you design and administer studies and surveys.

Details. Precision and accuracy are essential as you move from observation and interview to conclusions.

Communication. Clear communication is key in effective work with other health professionals, and you’ll need to speak and write well to inform the public and community leaders about public health risks.

Critical thinking. You’ll be called upon to analyze data to determine the best responses to public health problems and health-related emergencies.

Teaching. Epidemiologists are often involved in educating the public about health risks and healthy living.

Your Expertise Can Save Lives for Generations

Expand your knowledge and advance your healthcare career with Kent State’s online Master of Science in Clinical Epidemiology . Study with our expert faculty and complete your degree entirely online and on your schedule. Explore the robust curriculum and bring your questions to one of our Admissions Advisors today.

• Retrieved on December 23, 2021, from cdc.gov/csels/dsepd/ss1978/lesson1/section6.html
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The design, applications, strengths and weaknesses of descriptive studies and ecological studies

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Descriptive studies are frequently the first step into a new line of enquiry, and as such have an important role in medical research, where their findings can prompt further study. Their function is to describe the “who, what, why, when, where” without regard to hypothesis, highlighting patterns of disease and associated factors.

Descriptive studies that examine individuals can take the form of case reports (a report of a single case of an unusual disease or association), case series (a description of several similar cases) and cross-sectional studies (see “ Cross-sectional, analytical and intervention studies ”).

Descriptive studies that examine populations, or groups, as the unit of observation are known as ecological studies. Ecological studies are particularly useful to conduct when individual-level data would either be difficult or impossible to collect, such as the effect of air pollution or of legislation. Examples of the use of ecological studies include:

• Correlating population disease rates with factors of interest, such as healthcare use
• Demonstrating changes in mortality over time (time series)
• Comparing the prevalence of a disease between different regions at a single point in time (geographical studies)

Ecological studies often make use of routinely collected health information, such as hospital episode statistics in the UK or infectious disease notifications, so their principal advantage is that they are cheap and quick to complete. However, where appropriate information is not readily available it is necessary to carry out special surveys to collect the raw data necessary for the study.

Application

All forms of descriptive study can be used to generate hypotheses of possible causes or determinants of disease. These hypotheses can then be tested using further observational or interventional studies. Case reports can identify novel associations, such as the development of a rare benign liver cancer in a woman taking oral contraceptives 1 . Case series are useful in identifying epidemics. For example, the presence of AIDS in North America was identified by the report of a cluster of homosexual men in Los Angeles with a similar clinical syndrome 2 .

Ecological studies are a useful means of performing international comparisons and studying group-level effects (for example, the correlation between deaths rates from cardiovascular disease and cigarette sales per capita ).

Strengths and Weaknesses

Descriptive (including ecological) studies are generally relatively quick, easy and cheap to conduct. Particular strengths of ecological studies include:

• Exposure data often only available at area level.
• Differences in exposure between areas may be bigger than at the individual level, and so are more easily examined.
• Utilisation of geographical information systems to examine spatial framework of disease and exposure.

Weaknesses of case reports and case series are that they have no comparison (control) group, they cannot be tested for statistical associations, and they are especially prone to publication bias (especially where case reports/series describe the effectiveness of an intervention).

Limitations of ecological studies include:

• Measures of exposure are only a proxy based on the average in the population. Caution is needed when applying grouped results to the individual level (ecological fallacy, below ).
• Potential for systematic differences between areas in recording disease frequency. For example there may be differences in disease coding and classification, diagnosis and completeness of reporting between different countries.
• Potential for systematic differences between areas in the measurement of exposures.
• Lack of available data on confounding factors.

Ecological fallacy

The ecological fallacy is an error in the interpretation of the results of an ecological study, where conclusions are inappropriately inferred about individuals from the results of aggregate data. The fallacy assumes that individual members of a group all have the average characteristics of the group as whole, when in fact any association observed between variables at the group level does not necessarily mean that the same association exists for any given individual selected from the group. For example, it has been observed that the number of televisions per capita is negatively associated with the rate of deaths from heart disease. However, it would be an ecological fallacy to infer that people who don’t own televisions die from heart disease 3 . Indeed, in this scenario there are other potentially causative factors that could be common to both, such as reduced physical activity or a poorer diet associated with less affluent societies.

Reasons for the ecological fallacy include the following:

• It is not possible to link exposure with disease in individuals - those with disease may not be the same people in the population who are exposed.
• The data used may have originally been collected for other purposes.
• Use of average exposure levels may mask more complicated relationships with the disease, such as the J-shaped relationship between alcohol consumption and heart disease.
• Inability to control for confounding.
• Schenken JR. Hepatocellular adenoma: relationship to oral contraceptives? JAMA 1976; 236: 559.
• Anon. Pneumocystis pneumonia: Los Angeles. MMWR Morb Mortal Wkly Rep 1981; 30: 250–52.
• Grimes DA, Schulz KF. Descriptive studies: what they can and cannot do. Lancet 2002;359:145-9.

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5 Study Designs Commonly used in Epidemiology

Study designs commonly used in epidemiology.

Learning Objectives

By the end of this chapter, the learner will be able to

• Describe the most common research study designs used in epidemiology
• Differentiate between Non-experimental Observational studies, and Experimental/Interventional epidemiological studies
• Differentiate among individual and population based studies, and also between observational, descriptive and analytic studies.
• Understand the use of randomization in experimental studies such as clinical trials, and other types of experimental field trials.

Introduction to the chapter

This chapter will present the most commonly used epidemiological study designs,listing main characteristics and then, focusing on their benefits, strengths, weaknesses, and uses in public health.

Most epidemiologists are trained to do their investigation based on a series of designs called, Study Designs. The study Designs commonly used in epidemiology are based on several premises, but a series of questions can help the investigator to decide what design best fits its needs, some of these questions are, what types of study designs are there? How and when we use specific type of study designs? Which study design is the most appropriate to use in certain investigations? The list of questions could continue but it is important to generate these questions in order to arrive to a decision of what fits better the investigators needs. Also, investigators need to be familiar with these study designs so they can use them when needed.

Main Question

What are study designs in epidemiology? Study designs refer to the different approaches mainly used to conduct research for investigative purposes. They are called, ‘designs’ because they represent a specific manner of conducting the research process, which is mainly based on the scientific method. Study designs are more of a framework to guide the researcher in the process. [1] And, although basically all research process starts with a research question, there is need to follow a process that will convert this research question into a hypothesis, and then, to a real life situation, or, scenario that need to be framed in order to arrive to valid conclusions. [2] In other words, study designs assists the researcher providing a type of road map that will help to not get lost. It is easy to get lost, especially when complex health phenomena is studied/researched, but the study design is expected to assist in providing direction. [3] In sum, study designs are road maps, or, frameworks that assist in the research/investigative process.

It is also probably useful to mention that some of the study designs mentioned here, are not only used in the sciences of epidemiology, they are used by other areas of study, especially those areas that belong to the social sciences, including public health, but also mathematics, statistics, and of course, medical sciences. So, this study designs are not unique to the field of epidemiology, but they are highly used in public health, and medical research.

Descriptive versus Analytic In a broadly manner, epidemiologic study designs can be divided into two broad categories: 1) Descriptive and 2) Analytic.

Descriptive studies as the word implies, ‘describe’ situations, problems, and other health phenomena (diseases, disorders, health behavior, healthy lifestyles, etc.). These type of study designs are mainly used to generate hypotheses, especially in those cases in which the health issue in study is unknown, or, there is not much information on it, or, simply, because the topic is a ‘new’ problem found in sciences, in this case, social sciences, including epidemiology. On the other hand, Analytic studies, ‘analyze’ the health phenomena, situation, or, problem.

More elements used to distinguish between descriptive and analytic research studies Since epidemiology is by nature quantitative, the division between descriptive and analytic studies can be also clearly recognized by the type of quantitative methods, in this case, the methodology, data collection, and statistical analyses that are used in the research process. For example, in descriptive study designs, the most common data analysis is the use of ‘descriptive statistics’ such as numbers, percentages, sums (total number of cases), mean, mode, standard deviation, etc. Since, the descriptive study is looking mainly for a ‘description’ of the problem, the use of descriptive analyses suffice for the type of research that mainly intents to generate hypotheses, or, to add more information for future studies.

Overview of Study Designs – the following is a list of study designs:

Descriptive Studies In this category, the following are the most commonly used/listed: a) Case Study or, Reports b) Case Series c) Cross-sectional studies d) Ecologic studies

Analytics Studies , since they are more complex, they are subdivided into two additional categories: Observational and Experimental.

Observational studies are mainly represented by the following: a) Cross-sectional studies – as you probably had noted, this category is shared also with the ‘descriptive’ category, which means that a cross-sectional study can be descriptive, observational, or, analytic. b) Case- control studies c) Cohort studies

Experimental studies In this category, it is customary to include the following: a) Clinical Trials b) Community Trials c) Other forms of experimental research.

Now, the question is, how do we know what study design to use? As it is seen (listed) above, there is a repertoire of study designs available to the investigator. The use of any of these designs depends on the mainly purpose/reason for the study, and also, the resources, which are mainly financial, and expertise of the researcher team.

How do we choose certain study design from the list of options? Many times, the answer to this question depends on the purposes/reasons for the research, or, investigation of a certain health phenomena. For example, if we want to conduct a study about a topic/area that is unknown, or, not well-known, and the research question is still a work in process, or, it is clear, but there is need to elaborate a hypothesis/or, hypotheses; then, the most appropriate study design in this case is the descriptive, why? It is because descriptive studies are commonly used for hypotheses generating. Does it mean that there is no other way to conduct the study? Not necessarily, because they may be another way to do the same, but over and over, especially in social sciences, public health, and specifically in epidemiology, descriptive study designs have been used for those purposes, in this case, hypotheses generating.

Another example that refers to the purposes/reasons for the research but that also refers to the financial aspects of medical, and public health research is the cohort study. In this case, if the main reason/purpose of the study is to find a causal relationship, the cohort study is the answer. This type of study design will help overtime to elucidate the associated risk factors, and social  health determinants that are related to the health problem that is researched. What is the only reason that stop the use of the cohort study? The main obstacle is financial, cohort studies can be highly costly, since, study subjects are basically follow over a long period of time until the health outcome is developed, as it is the case of for example the cardiovascular disease cohort studies conducted in the United States. More information about cohort studies will be provided/expanded later in the context of this chapter, and the overall book content itself. [5]

Descriptive Study Designs

The Case Studies/Case Series This study design is commonly used when there is no much information about the case, as it is the example of a recently reported disease, or, a disease that is very rare, so, the investigator wants to share the information with the scientific community, but since there is only one, or, three cases, it is much better to choose this study design, which sometimes become a case series, if a continuation of cases are reported in a limited fashion. The main limitation is that the cases are not necessarily representative of the general population, but the benefits are that the reported case brings an opportunity for future studies on the subject. [6] , [7]

In most epidemiological textbooks, the case studies are commonly used by the medical community to report a case, or, a series of cases that usually represent patients suffering from certain diseases. The medical model usually reports cases that are diseased. But the model hardly applied to other health phenomena, and since it is usually oriented to report cases in a limited fashion, there is small use of this design in epidemiology, which focuses on populations at large.

Ecologic Studies

For this study design, the unit of analysis is the group, not the individual. In this case, correlations are obtained between exposures rates and disease rates among different groups, or, populations. Because of the word, ‘ecologic’, ecological studies tend to be confused with ‘environmental’ studies, and this is possible especially if an environmental issue is studied using this design, but in general, ecologic studies refer to the group(s) investigated.

The ecological study provides a setting in which observations made at the group level may not represent the exposure-disease relationship at the individual level, this is called, the ecologic fallacy , which occurs when incorrect inferences about the individual are made from the group level data. [8] .

Essentially, this means that inferences from ‘the results of ecological studies can only be applied to the group but not to the individual’. You may said, why? And the answer is because the intention of the ecologic studies is to capture how the health events are affecting the group, the community and not the individual. An example of this, is this study done in alcohol consumption and coronary heart disease (CHD), see the image below that presents the respective information:

In the study of the image presented above, the data across countries (the ‘population’ or, ‘group’ mentioned in the definition of ecologic studies) showed that moderate use of alcohol was not beneficial to the heart, on the contrary, it increases the risk for CHD, why? It is the typical case in which what is true at the group or, population level, it is not true to the individual level. Studies done on individuals or, specific groups had shown that moderate alcohol consumption is beneficial to prevent CHD. [9]

As it has been presented in the example above, the ecologic fallacy which is part of the nature of an ecologic study, it is also considered a disadvantage of this type of study design. Another disadvantage is that ecologic studies could make imprecise measurement of exposure and disease. [10] .

On the other hand, the following are advantages of ecologic studies, they are quick, simple and less costly than other studies, and their completion is faster compared to other designs used in epidemiology and related sciences. One more advantage is that they can be used (and very useful in this sense) for generating hypotheses, especially when a disease is of unknown etiology.

Common uses of ecologic studies Specific applications of the ecologic study design has been classified by some authors as the following: geographical comparisons, time trends, migrants, occupation and social class. More details are included below:

Geographical comparisons, which for example can be used to find prevalence of risk factors by comparing incidence or, mortality in two, or, more geographic areas. Time trends, which essentially means to study the fluctuations on the incidence of chronic diseases which tend to change over time. Migrants , the study of migrant groups can be used to identify those factors that are predominantly genetic from those who are environmental, in this case, first or, second generation of an ethnic group maybe affected differently depending on their degree acculturation . Occupation and social class, in this case, it refers on how some morbidity and mortality area associated with certain occupations, and also with the socioeconomic status of the groups working on those type of jobs. [11]

Cross-Sectional Studies

This study is a commonly used design. As a way to understand the most basic principle of a cross sectional study, is to think on the total study population as a pie, in which each percentage represents a section of the pie, then, for study purposes only a piece (or, section) of the pie is investigated. Since in the majority of cases, the characteristics of a population are very similar, choosing to study one portion (section) of the pie (the population) will be representative of the total population. Assuming that we are talking about a study population, not the general population. This analogy takes us to the term, cross (cutting) section (a piece of the pie) study.

Since cross-sectional studies collect data at a point in time, they are commonly used to calculate prevalence for public health reports, and also for the designing and location of health services in a community. This study design is used frequently, especially when there is not much money to afford another type of study. So, cross sectional studies are very popular not only because they are less costly, but also they are fast to complete. Another great advantage of cross sectional studies is that they are used to generate hypotheses, or, specific research questions about exposure and disease, however, this type of study does not address the issue of temporality , due to being one shot only (one point in time), it does not provide information to know what was first between cause and effect. [12]

Example An example of this type of study design is, an Australian cross-sectional study on the effects of screen and non-screen sedentary time in adolescents, and how these types of behaviors affect their weight and overall well-being. The study found that although screen sedentary time (SST) is a contributing factor in the amount of fatness observed in school-age children and adolescents, there are two other factors that need to be studied if a significant change is expected to be observed, and these are active lesson breaks in the classroom, or active transport to school. These two last factors are part of what the study called, the NSST or, Non-screen sedentary time. [13] As part of the study design (cross-sectional), some of the study variables are presented in the figure below (taken directly from the published article), see below:

Finally, one of the major benefits of cross sectional studies is they are quick (compared to other type of designs such as the cohort study) to complete, making them very efficient in terms of time and cost.

An example of a case control study is the work on a group of investigators who compared the Impact of windows and daylight exposure on overall health and sleep quality of office workers. The results showed that workers in windowless environments tend to experience limitations in their role in terms of physical problems and vitality and some sleep disturbances. When the two groups were compared, workers with windows had more light exposure, more physical activity, and longer sleep duration. [15] A selected image included in the article shows graphically how two (more variables were studied) of the characteristics between both groups show clearly the difference that makes to have windows or, not in the workplace.

Common uses of the case-control study design Because of all of the mentioned characteristics, the case control-study design is used to find the prevalence in the community of certain diseases, and from their results, a subsequent study is designed. Also, the case-control study design has been used especially for the study of rare diseases.

The selection of the cases and the controls in the case-control study design This is an important step in the use of the case-control study design. The cases need to be selected based on a set of criteria, which defines the characteristics and manifestation of the disease, including laboratory and other medical tests such as imaging, including x-rays. Then, these criteria is used to identified the cases. And, what about the controls, how are those individuals found? The controls  can be for example patients from the same clinic/hospitals as the cases, or, the same population, for example, college campus, or, factory workers. The major characteristic of the controls is the similarity in terms of the selection criteria used for the cases, so, a comparison can be established. When there is time to assess the risk, both groups cases and controls are included in the statistical calculations based on the exposed, and not exposed criteria and the development of the disease under study. It is important to note that the word, ‘exposed’ here is a matter of semantics, because the exposed do not necessarily existed, they are cases (they have the disease already). [16]

Cohort Study This type of study design is considered the prototype (the model) of an almost ‘perfect’ design to investigate causality. In the cohort study, the main measure of disease frequency is, incidence. The following diagram presents how the study population is selected, and the major steps in the implementation of this type of study design:

In the field of epidemiology is also accepted that cohort study address the issues of temporality (most studies do not) making possible to avoid logical errors. Cohort studies are usually conducted for longer periods of time compared to other designs; and the reason for it is that cohorts start with individuals who are free of the disease under study, and are followed up over the years to observe the development of this disease (or, group of diseases such as cardiovascular diseases). Due to the fact that most of the time, the data is collected in the future (from one specific point in time – the now, and the upcoming time of observation), the word retrospective is commonly used to reflect this concept, which makes most cohorts, examples of perspective studies. [17]

In some cases, a cohort study can be designed by using data that has already been collected (which resembles the case-control design), and since this is data collected in the past, then, the cohort is called, a retrospective cohort. And, when, retrospective data, present time data, and prospective data collection (which is essentially the classic cohort study) are included, the name of the cohort is, ambispective . But in reality most people when they hear the word, cohort, they are referring to prospective data collection studies (or, prospective cohorts). [18]

Cohort studies can be used to study more than one disease, or, multiple exposures, so, investigators can take some data (already collected in the cohort), and design a case-control study known as the ‘nested case-control,’ it is nested because it comes from inside the cohort. [19] , [20]

Because of all of the mentioned benefits and advantages of cohorts, especially the calculation of incidence about a disease, makes cohorts an ideal study design, but at the same time it limitation is that cohorts are highly expensive, and that is mainly due to the fact that they last for a long time, especially for those diseases that take a long time to develop, so, the investigators have to wait for a while before they see the first generation of cases. [21]

Examples of Cohort Studies To provide a mental picture of the cohort study design, I am including here, what I called, ‘Famous Cohorts in the United States.’ Famous because they reflect the reality of the country in terms of race segregation, and other socio-demographic factors that have shaped the country. The Framingham Heart Study

The Framingham Heart Study takes its name from the town in which the study was conducted, Framingham. Framingham is located in Massachusetts, United States, and it within Middlesex County and the MetroWest subregion of the Greater Boston metropolitan area. [22] A picture of this city is shown below:

The Framingham Heart Study is a classic example of a cohort study that assessed multiple exposures and multiple outcomes. This study, a collaboration between the US National Heart, Lung, and Blood Institute (a division of the National Institutes of Health) and Boston University, began in 1948 by enrolling just over 5,000 adults living in Framingham, Massachusetts. Investigators measured numerous exposures and outcomes, then repeated the measurements every few years. As the cohort aged, their spouses, children, children’s spouses, and grandchildren have been enrolled. [23]

The Framingham study is responsible for much of our knowledge about heart disease, stroke, and related disorders, as well as of the intergenerational effects of some lifestyle habits. [24] More information and a list of additional publications (more than 3,500 studies have been published using Framingham data) can be found extensively and especially in the project’s website.  [25]

The Bogalusa Heart Study Although, the Framingham Heart Study is a model cohort that influenced the work in public health and medicine. There was one flaw with the study, the participants were all white or, Caucasian; which from the beginning introduced a confounding factor that is race, which is a health determinant that is also linked to income, socio-economic status, social class, and among others, access to health services. So, to study cardiovascular disease beyond the white population generated the need to conduct a study on another major ethnic population group in the U.S. population, which is the black or African American community. Although not many studies on black, the Bogalusa Heart Study in Louisiana was born in 1972. Bogalusa is a small town in Louisiana (almost in the limit with Mississippi), it is mainly a biracial (black/white) rural community in which entire families have lived there for generations. The population in Bogalusa is mostly constant with few, or, no migration is ideal study population brought the attention of a famous Tulane University School of Public Health in New Orleans, Dr. Berenson, who lived through the entire duration of the study. [26] , [27] . See a picture of  the town, and also of Dr. Berenson:

He survived the study which was taken down after hurricane Katrina due to major damage by the lack of electricity in New Orleans during Katrina in which many of the study samples that were stored were damaged, and also, the lack of funding after the impacts of Hurricane Katrina devastation in New Orleans in 2005.

The Bogalusa Heart Study started as an epidemiological study of cardiovascular risk factors in children and adolescents; it eventually evolved into observations of young adults. This study main milestones confirmed the findings of the Framingham Heart Study, but also superseed in terms of adding new variables to the study of cardiovascular disease, especially with the findings of the presence of cardiovascular disease in children, which had not been studied before. The study reported an African American child who died of cardiovascular disease and had atherosclerotic deposits in his arteries at the age of eight years old. This finding moved the American Heart Association to recommend that children stop been fed with whole cow’s milk after the child is 1; recommending 2% cow’s milk for children over 1 years old in the U.S. population. Another major milestone of the Bogalusa heart study is that identified several risk factors such as obesity, essential hypertension linked to kidney disease, and also how early onset of diabetes can also increase highly the development of cardiovascular disease at earlier ages, a finding that was also new to the medical and public health community. [28] [29]

The San Antonio Heart Study

The San Antonio Heart Study conducted in Texas takes its name from this city. San Antonio, Texas, is a city of the Southern United States, and it is considered one of the seven most populous city in the U.S. [30]

The San Antonio Heart Study (SAHS), is another study that is no commonly mentioned in most epidemiology textbooks, and that brings another important perspective in the study of cardiovascular disease in the U.S. is the San Antonio Heart Study, which focused its efforts in identifying cardiovascular risk factors in the Latino population in the U.S. Again, as in the case of the Bogalusa Heart Study; the need to study in detail what happened to another major ethnic group in the U.S. was critical, what was found among Caucasians or, Whites in the U.S. cannot necessarily be applied (extrapolated) to the African American community, nor, to the Latino community, so, the study was justified. And its findings among others discovered that the Latino heart is hard to die. Among all of the major ethnic groups in the U.S., Latinos have the lowest rates of heart attacks after accounting for several confounding factors. [31] , [32] , [33]

Note: the above content about cohorts had presented the problem of heart disease among the white, black and Latino population in the U.S. but there is literature available about other ethnic groups in the U.S. such as Native Americans, Asians, and Pacific Islanders, however, those studies are not cohort studies, only reports about the health status of these mentioned groups, for example, there is one report about Native Americans in the U.S. [34]

Cohort Studies major disadvantages and historical ‘mistakes’ Besides the disadvantage already mentioned before in the content, that cohorts are very expensive (they usually costs millions over the years of duration), it is not possible to have a cohort for every disease that exist, or, that is highly prevalence in the population. There is another limitation of cohorts, they cannot be used for the study of rare diseases, because one of the characteristics of a rare disease is that it is not suffered by a great number of people in the population, making the sample usually small, or, it may take long times to get enough data that can be used meaningfully in the practice.

Another major disadvantage of cohorts, which is the reason I included the examples of the ‘famous cohorts in the U.S.,’ is that in this country, cohorts were linked to the problem of racial preference, the first ethnic group represented in a popular cohort such as the Framingham heart study is the white population, which excluded the other ethnic groups in the U.S. who also are affected by heart disease. This fact is an example of the historical exclusion of people of color and indigenous populations in the U.S., so, the history of major cohorts in the U.S. is also reflecting the need to study those oppressed, and ignored throughout history. Another observation in this context is, that for example, the Bogalusa Heart Study, and the San Antonio Heart Study are not well known in the scientific community, teaching medical schools, and other similar educational institutions do not know – or, pretend to not knowing about the existing of these studies, which provides extremely important data for the prevention of coronary heart disease in the nation.

Clinical Trials

Clinical Trials They are considered the highest level of the research designs discussed above in this chapter, and they are very much the standard design used especially for pharmaceutical companies to assess the effectiveness and safety of drugs, certain medical procedures, sophisticated medical equipment, etc. There at least two types of clinical trials as it was mentioned in the introduction of this chapter in the summary of types of study design; 1) Preventive or, Community Trials, and 2) Therapeutic clinical trials . The discussion in this chapter will be mainly focused in the second group or, therapeutic clinical trials, also, just called, clinical trials.

Community (or, preventive) trials These type of studies are used to determine the potential benefit of new policies and programs. They are called, community because it refers to the population, or, specific groups in the population. In general, the community trials will evaluate the impact of specific interventions that intent to produce changes in a target population. For example, the knowledge, attitudes and practices related to the Medicare program; or, the use (by the target population) of health care services to prevent and treat heart disease, etc.

The first step in the process of a community trial is to determine eligible communities, or, groups, and their willingness to participate. Then, baseline data is collected, this type of information can be for example, target population demographics, cultural traits, data from the national census, disease rates, etc., of the problem to be addressed in the intervention, and the collected information is also used for the control communities. In addition, the trial participants are selected by randomization (which is described in more details later in this section) and the selected individuals (or, groups) are followed over time. Data is entered, analyzed, and reports generated. Finally, the outcomes of interest are measured and used to assess the effectiveness, or, to identified weak points in the program intervention, and how to improve the quality of the programs and services offered to the target population, or, group. An example of a community trial and its protocol is summarized in the image below:

Advantages and disadvantages of community trials The major advantages of community trials is that are unique in providing information that be can used to estimate the impact of change in the behavior or modifiable exposure of the incidence of disease in a community or, group; and also, the effectiveness of services and programs offered to the target group. As any other study design, the community trials have some also some disadvantages, for example, in general they are considered inferior to clinical (therapeutic) trials – discussed in detail in the rest of this chapter); and that is because selection of participants into the study, delivery of the intervention, and monitoring of the study outcomes are not as strict (or, rigorous) as it is in a clinical trial. Other disadvantages is that the study results are affected by some population dynamics, especially secular trends because of the mobility, or, changes in the target population. Also, it is hard to avoid the influence of non intervention forces surrounding the study population or, group.

Clinical trials Since the content above has been mostly about the clinical community (or, preventive) trials. The information that follows will focus mainly in the conduction of therapeutic clinical trials, commonly called just, ‘clinical trials.’ What are clinical trials ? A common definition is that, clinical trials are planned experiments that assesses the efficacy of a treatment (or, medical procedure) in people. It is medical research involving people. In a clinical trial, the study outcomes in a treated group are compared with outcomes in an equivalent control group. Participants in both groups are enrolled, treated, and followed over the same time period. [35]

Methods commonly used in clinical trials There is a series of methods or, strategies used for clinical trials, and these are at the same time considered the major strengths of clinical trials. The most important are discussed in the following paragraphs. Study Protocol The clinical trials protocol is usually an extensive and detailed manual that outline the major steps of the study, especially outlining what could happen in certain situations during the completion of the study. For example, what to do is the investigators deviate from the originally planned study assignments of the participants? How many deviations in the protocol would  be allowed? It is customary that for example, no  more than three deviations would be allowed during the duration of the trial. Also, the protocol include the data collection instruments, data input procedures, and analyses once the data is collected. The presence of a protocol is a valuable tool to assure that the clinical trial is conducted under the required academic and scientific rigor.

One of the major elements of the mentioned document is to plan ahead for any deviation of the study protocol. To prevent for this to happen, planned crossovers are part of the protocol. In this case, the study participant may server as his/her own control. And, when unplanned situations occurs for example a change of treatment is requested by a study participant; this change is called, an unplanned crossover , which could exist  in for example situations in which the study participant request a change of treatment. It is recommended that no many unplanned crossovers occur during the duration of the trial, because it can compromise the study results, and the quality of the study in general.

Selection of the study participants One of the methods (and strengths) of clinical trials is the careful selection of study participants. For this purpose, research in clinical trials used what is known as randomization , , a statistical method to sort the possible study participants before they can participate in the study. Essentially, the randomization procedure allows to use random selection twice, for example, a person is assigned a number that will be randomly picked for the study participation, and then, if selected, the person (now, a study participant) will be randomly assigned to the drug, procedure, placebo or, no treatment branch of the trial.

Randomization is the preferred method for assigning subjects to the treatment or control conditions of a clinical trial. If not random assignment is used then, mixing of effects of the intervention can occur, which at the same time, create differences among the study participants in the trial.

Blinding An additional method or strategy commonly used in clinical trials is also one  of the major strengths of clinical trials is that they control bias, especially selection bias. And, to control for this, the procedure known as blinding is used.  At least three types of blinding are known, 1) single blinding, it is when the study participants (clients, patients) don’t know about the type of drug, or, medical procedure they have been assigned, or, they don’t know if the treatment they are receiving is placebo. 2) double blinding, it is when the client/patient doesn’t know what type of treatment or, placebo they are receiving (the clients/patient), or, the health care providers (doctors, nurses, technicians, etc.) are administering. 3) triple blind, the client/patient, the health care providers, nor the data collection and analysis have knowledge of the treatment given to the study participants. Additional blinding strategies can be used, but the mentioned here are the most commonly used. Phases of clinical trials

Strengths and limitations of clinical trials The major strengths of clinical trials is for the investigator to have the greatest control over the amount of exposure, the timing and the frequency of that exposure, and the observation period. Also, the use of randomization greatly reduces the likelihood that groups will differ significantly, which is of enormous benefit to the enhancement of the results. The limitations of clinical trials include ethical dilemmas such as how the benefits would outweigh the risk, how to protect the interests of the study participants and not only the investigators, when to stop a trial if a major adverse health outcome occurs during the completion of the study, and overall, how much of the information in the trial is shared with the study participant in the informed consent form.

Summary This chapter has covered the most common epidemiology study designs and its uses. These designs include: the case study/case reports, ecologic study, cross-sectional, case controls, cohort studies, and clinical trials. When they are classified by type of study, they can be descriptive or, analytic studies. The most common descriptive studies are, the case report/case series, ecologic and cross sectional studies. Analytic studies, they can be classified into intervention and observational.  Examples of analytic ‘intervention’ studies are, community (preventive) trials and clinical (therapeutic) trials. And, observational studies, examples, the case control, and the cohort studies. The use of these mentioned study designs depending of the research questions, the purposes of the study, and the availability of resources to conduct them. Investigators always look for those study designs that are relatively quick, less expensive, and efficient.

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observations made at the group level may not represent the exposure-disease relationship at the individual level

in epidemiology this term is used to refer to 'time' for example, the changes in disease prevalence over time.

the term refers to a defined unit, for example, a county, state, or, school district, etc.

Any program or other planned effort designed to produce changes in a target population. For example, health care use, etc.

essentially randomly selecting a study participant twice.

clinical trials are planned experiments that assesses the efficacy of a treatment (or, medical procedure) in people. It is medical research involving people.

Any planned, or, unplanned deviation of the study protocol.

To 'blind' the study participants, or, the providers or, both by not knowing to what clinical intervention of drug they are assigned or, participating, and it is commonly used in clinical trials.

Principles of Epidemiology Copyright © by H. Giovanni Antunez is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• v.24(6); 2014

Utilization and Application of Public Health Data in Descriptive Epidemiology

Descriptive epidemiology is defined as epidemiological studies and activities with descriptive components that are much stronger than their analytic components or that fall within the descriptive area of the descriptive-analytic spectrum. 1 Descriptive epidemiology deals with the occurrence of disease, in terms of both geographical comparisons and descriptions of temporal trends. 2 The methods used to identify the causes of chronic disease have evolved markedly over the past 20 years, particularly in the areas of epidemiological concepts, quantitative and statistical methods, case-control studies, and clinical epidemiology. 3 Epidemiological methods and biostatistics have especially become increasingly sophisticated. Descriptive studies are positioned at the base of the hierarchy of scientific evidence; nevertheless, their importance as the basic roots of the epidemiologic approach has not changed. In particular, disease prevalence and incidence data perform an essential role in both research and clinical settings.

This issue of Journal of Epidemiology includes three descriptive epidemiology studies from Japan regarding prevalence and incidence of disease. Dr. Kabeya and his colleagues present data on the prevalence of diabetes and distribution of HbA1c in Japan. 4 They estimated the prevalence of diabetes using data from a large-scale cohort study. Registered inhabitants aged 46–75 years from 10 public health center (PHC) areas were included in the initial survey, and those who received annual health checkups in each PHC-administered area were recruited. The age-standardized prevalence of diabetes in 55- to 74-year-old adults was 8.2% in the initial survey in the late 1990s and 10.6% at the 5-year follow-up. These findings suggest that a concerted effort to reduce the number of individuals who progress to diabetes is required to stop the diabetes epidemic.

In another descriptive study, Dr. Doi et al reported an analysis of cross-sectional data on amyotrophic lateral sclerosis (ALS). 5 Information on ALS patients who received financial aid for treatment of designated intractable diseases was collected from all 47 prefectural offices in Japan. The authors report that the annual crude prevalence and incidence per 100 000 people were 9.9 (95% CI, 9.7–10.1) and 2.2 (95% CI, 2.1–2.3), respectively, in a representative sample of the Japanese population. This is much lower than in the Caucasian populations of Europe and North America.

Dr. Chihara and his colleagues presented data on the incidence of myelodysplastic syndromes (MDS) in Japan. 6 MDS is a diverse group of clonal hematopoietic stem cell malignancies in the elderly that present with persistent bone marrow failure and peripheral blood cytopenia. The authors analyzed cancer registry data from the Monitoring of Cancer Incidence in Japan project, which was started in 2007 as a national project to pool prefecture-wide cancer registry data throughout Japan using a standardized protocol. The study showed that the age-adjusted incidence of MDS in Japan, standardized to the world population, was 1.6 cases per 100 000 for males and 0.8 cases for females in 2008. These rates are less than half of those in the United States and similar to those in China, though the authors pointed out that the Japanese registry data might have underestimated the incidence. MDS is common in the elderly, who are seldom thoroughly evaluated during diagnosis, so a substantial number of cases may be missed. Accurate evaluation of the health impact of MDS in Japan requires evaluation of disease mortality, continued surveillance, and improvement in the quality of cancer registry data.

It is difficult for a single study to estimate prevalence and incidence in rare diseases. Even for diseases that have high frequency, geographical variation needs to be considered for accurate nation-wide estimates. Utilization and application of public health data for descriptive studies can be useful; however, the use of public health databases for estimating disease prevalence and incidence is a developing field in Japan.

As shown in the study performed by Dr. Chihara, 6 cancer incidence data in Japan are available from population-based cancer registries (PBCRs). “The Cancer Registry Promotion Act” was enacted on December 6, 2013. This act regulates the collection, processing, release, and use of cancer registry data and clearly addresses the following: (1) cancer reporting will become a legislative duty of hospitals (>20 beds); (2) information collected in each PBCR must be registered in the new database system in the National Cancer Center, Tokyo (NCC); (3) the NCC will follow subjects in the database by linking to national death certificate data files, in order to calculate accurate cancer survival; and (4) the national government will provide financial support to PBCRs to cover part of the cost of registration. 7 It is expected that this system will promote the conduct of nationwide cohort studies, with cancer incidence, mortality, and survival as the outcome measures. 7

Furthermore, several national databases collect and store the profiles of health care institutions or individuals who have used health care services, such as the National Database and the Diagnosis Procedure Combination database. Because no database is perfect, linkage across these resources is crucial. 8 Japan has no unique personal identifiers at present. All Japanese citizens will be issued a national identification number, the so-called My Number, starting in January 2016. While concerns about privacy and security issues may arise, this identification number should be beneficial for medical applications, including research.

High-quality descriptive studies can provide fruitful scientific evidence and have societal relevance. Basic descriptions of the relationships between disease occurrence and the characteristics of person, place, and time remain the fundamental building blocks of epidemiology. 9 Descriptive analysis is an essential tool in hypothesis development for analytical studies and in monitoring public health policies. However, the basic infrastructure of disease registries and databases is currently insufficient. The disease registry systems and databases for many intractable diseases, such as Behçet’s disease and IgA nephropathy, can be useful for analyzing prognostic factors of these diseases.

Establishment of qualified databases in medical welfare and their mutual linkage plays a considerable role in improving the quality and quantity of research in the big-data era. Skills in data management and analysis are also required. Big data have the potential to revolutionize research, 10 and this new era also sheds light on the value of descriptive data. The ultimate goal of primary prevention is dependent on the effective interface of descriptive and analytic epidemiology, 9 and the importance of descriptive epidemiology is growing in Japan.

ACKNOWLEDGMENTS

Conflicts of interest: None declared.

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• Published: 04 September 2024

Incidence and prevalence of idiopathic inflammatory myopathies in Thailand from the Ministry of Public Health data analysis

• Tippawan Onchan 1 ,
• Chingching Foocharoen 1 ,
• Patnarin Pongkulkiat 1 ,
• Siraphop Suwannaroj 1 &
• Ajanee Mahakkanukrauh 1  

Scientific Reports volume  14 , Article number:  20646 ( 2024 ) Cite this article

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• Health care
• Rheumatology

The epidemiology of idiopathic inflammatory myopathies (IIMs) varies by country. Investigating the epidemiological profile among Thai IIMs could help to inform public health policy, potentially leading to cost-reducing strategies. We aimed to assess the prevalence and incidence of IIM in the Thai population between 2017 and 2020. A descriptive epidemiological study was conducted on patients 18 or older, using data from the Information and Communication Technology Center, Ministry of Public Health, with a primary diagnosis of dermatopolymyositis, as indicated by the ICD-10 codes M33. The prevalence and incidence of IIMs were analyzed with their 95% confidence intervals (CIs) and then categorized by sex and region. In 2017, the IIM cases numbered 9,074 among 65,204,797 Thais, resulting in a prevalence of 13.9 per 100,000 population (95% CI 13.6–14.2). IIMs were slightly more prevalent among women than men (16.8 vs 10.9 per 100,000). Between 2018 and 2020, the incidence of IIMs slightly declined from 5.09 (95% CI 4.92–5.27) in 2017 and 4.92 (95% CI 4.76–5.10) in 2019 to 4.43 (95% CI 4.27–4.60) per 100,000 person-years in 2020. The peak age group was 50–69 years. Between 2018 and 2020, the majority of cases occurred in southern Thailand, with incidence rates of 7.60, 8.34, and 8.74 per 100,000 person-years. IIMs are uncommon among Thais, with a peak incidence in individuals between 60 and 69, especially in southern Thailand. The incidence of IIMs decreased between 2019 and 2020, most likely due to the COVID-19 pandemic, which reduced reports and investigations.

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Introduction.

Idiopathic inflammatory myopathies (IIMs) are a group of connective tissue diseases characterized by muscle inflammation, pain, or weakness, that include dermatomyositis (DM), polymyositis (PM), overlap myositis (OM), sporadic inclusion body myositis (IBM), and necrotizing autoimmune myopathy (NAM) 1 . IIM patients have lower survival rates than the general population 2 . The early mortality rate was found to be high, ranging between 7.8 and 45%. Infections and cancers were the primary causes of death 3 .

The prevalence, incidence, and characteristics of IIMs vary by country. Extensive epidemiological studies have been conducted in various countries to determine the incidence of IIMs 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 . The studies conducted in Asia, Singapore, and Israel reported an incidence of 7.7 and 2.18 cases per million per year, respectively 14 , 15 . A systematic review found that the incidence of inflammatory myopathies ranged from 1.16 to 19 cases per million people per year, with a prevalence of 2.4 to 33.8 cases per 100,000. Although there was a significant variation in the results, no obvious geographical disparities were identified 17 , 20 . However, no research has been done to determine the prevalence of IIMs in Asia.

To date, there have been no epidemiological studies on the IIMs conducted in Thailand. Additionally, managing refractory IIMs presents challenges due to the high cost of intravenous immunoglobulin (IVIG) and rituximab treatments 1 . Thus, the purpose of this study was to assess the prevalence and incidence of IIMs in the Thai population between 2017 and 2020 using the ICD 10 codes obtained from the Ministry of Public Health’s Information and Communication Technology Center. These findings could then be used to inform and improve public health policies governing the care of individuals with IIMs, as well as to help reduce costs by allocating appropriate budgets.

A descriptive epidemiological study was carried out on the entire population of patients 18 years and up whose data had been recorded in the Information and Communication Technology Center, a database managed by the Ministry of Public Health ( https://www.moph.go.th ). This study focused on individuals from 1st January 2017 to 31th December2020 who had a primary diagnosis of dermatopolymyositis, as indicated by ICD-10 codes M33, including juvenile dermatomyositis, other dermatomyositis, polymyositis, and unspecified dermatopolymyositis. The diagnosis of dermatopolymyositis was made by the physicians who follow up with the patients. The dataset included data from a variety of healthcare providers, including the National Health Security Office (NHSO), the Civil Servants Benefit System from the Comptroller General’s Department, the Social Security Office, and cases involving self-payment. These hospitals maintained databases and the data was submitted and analyzed. The primary diagnosis for each patient was coded according to the International Classification of Disease, Tenth Revision (ICD-10). Demographic variables including age, sex, year of visit, hospital name, Amphoe (district), and province were incorporated into the collected data to prevent data duplication.

Statistical analysis

The analyzed data were presented from both a regional (northern, central, northeastern, eastern, western, and southern) and national perspective. Categorical data were presented as numbers and percentages, and the continuous data were expressed as means and standard deviations (SDs). The prevalence and incidence of IIMs were analyzed, along with their corresponding 95% confidence intervals (CIs). All patients were included to enhance the statistical power of the test. All data analyses were performed using the statistical software STATA (version 16.0; StataCorp, College Station, TX, USA).

The Information and Communication Technology Center, Ministry of Public Health, reported 9074 cases of IIMs in 2017 among 65,204,797 Thais, with 3478 (38.3 percent) men and 5596 (61.7 percent) women. These cases were diagnosed with dermatopolymyositis (ICD-10 codes M33). The female-to-male ratio in 2017 was 1.6:1, which remained stable between 2018 and 2020. During this time, there were 3332, 3228 and 2900 cases of IIM among 65,406,320, 65,557,054, and 65,421,139 Thai citizens, respectively. The peak age range was 50 to 59 years, accounting for 22.9%, 23.6%, 24.7%, and 23.5% of cases between 2017 and 2020, respectively. In 2017, the majority of patients (49.5%) were from northeastern Thailand, with an average age of 48.60 (± 18.06) years (Table 1 ).

In 2017, the prevalence of IIMs was 13.93 per 100,000 (95% CI 13.63–14.21), with a higher rate among women compared to men (16.84 (95% CI 16.4–17.28) vs 10.88 (95% CI 10.52–11.25) per 100,000) (Table 2 ). In 2017, the northeastern region had the highest number of IIM cases per 100,000 Thai population (20.49) (95% CI 19.89–21.10). The peak prevalence occurred between 60 and 69 years, with a rate of 26.82 per 100,000 (95% CI 25.51–28.19).

Between 2018 and 2020, the incidence of IIMs per 100,000 person-years was 5.09 (95% CI 4.92–5.27), 4.92 (95% CI 4.76–5.10), and 4.43 (95% CI 4.27–4.60), respectively. The majority of IIM cases were reported in southern Thailand, with rates of 7.60 (95% CI 6.74–8.53), 8.34 (95% CI 7.44–9.31), and 8.74 (95% CI 7.83–9.73) per 100,000 person-years from 2018 to 2020, respectively. Peak prevalence was observed in the age groups of 60 and 69 years in 2018 and 2020, with rates of 9.52 (95% CI 8.78–10.33) and 8.19 (95% CI 7.51–8.91) per 100,000 person-years, respectively. In 2019, the peak was between 50 and 59 years, with a rate of 8.32 (95% CI 7.75–8.92) per 100,000 person-years (Table 2 ).

In 2017, the northeastern region had the highest rate of IIM visits per 100,000 Thai population, with 4,492 cases (49.5%), while the central region followed with 2,141 cases (23.6%) (Fig.  1 A). In subsequent years, the number of new IIM cases was 1,581 cases (47.5%) in 2018 (Fig.  1 B), 1,306 cases (40.5%) in 2019 (Fig.  1 C), and 1,243 cases (42.9%) in 2020 (Fig.  1 D). The majority of these cases were concentrated in the northeastern region, particularly in the provinces of Surin, Roi-et, Nong Bua Lamphu, and Chaiyaphum.

The number of IIMs per 100,000 Thais by hospital was calculated based on the visit (the maps were generated using QGIS version 3.8.2, an open source, free geographic data system software; https://www.qgis.org/en/site/ ). ( A ) Number of IIMs in 2017 (prevalence cases), ( B ) incidence cases in 2018, ( C ) incidence cases in 2019, ( D ) incidence cases in 2020.

A limited number of epidemiological studies have been published as a result of the rarity of IIMs. This is the first epidemiological investigation in Thailand to specifically examine IIMs. For this comprehensive population study, the National Information and Communication Technology Center, a database of the Ministry of Public Health that includes all healthcare providers, provided the data. The purpose of this study was to ascertain the incidence and prevalence of IIMs among Thais throughout the COVID-19 pandemic.

The current study determined that the prevalence of IIMs among Thais was similar to that of the majority of IIMs worldwide, with a rate of 13.93 per 100,000 (95 percent CI 13.63–14.21) in 2017 and a general rate of 14 per 100,000 (95 percent CI 12.84–15.46) 17 . Essouma et al. likewise reported a prevalence of 11.49 cases per 100,000 in Africa in their systematic review 3 . Conversely, the majority of IIMs in the United States ranged between 3.45 and 21.42 cases per 100,000 4 , 5 , 6 , 8 , 9 , suggesting that subtypes and reporting locations varied. Northern Spain had a prevalence of 3 per 100,000, whereas England and France had 29.97 cases per 100,000 people, more than twice as many as Thais. Greece had the highest documented prevalence of IIMs, with 58 cases per 100,000 (triple the prevalence in Thailand) 16 , 17 , 21 . Meanwhile, no research has been conducted to determine the prevalence of IIMs in Asia.

Our study reveals a decline in the incidence of IIMs among Thais from 2017 to 2020, with rates lower than those reported in the United States between 2003 and 2008 4 , 5 . We observed only a slightly decrease incidence that would not be significant between 2018 and 2019. The decrease in the number of new cases from 2019 to 2020 could be attributed to the COVID-19 pandemic, which resulted in fewer reported cases and investigations involving IIMs. In contrast, our study showed a higher incidence of IIMs than studies conducted in Israel 15 . Furthermore, studies focusing on IIMs indicated that the incidence in Africa (in 2020), Australia (1980–2009), and Singapore (1986–1991) were comparable to our findings 3 , 10 , 12 , 13 , 14 , while England and France had the highest incidence rate from 1966 to 2013 17 . These findings confirm the variability of the findings.

Female cases predominate among IIMs in Thailand, which is consistent with previous research (Table 3 ). Our study revealed a female-to-male ratio of 1.6:1. In contrast, Lynn et al. reported a higher female-to-male ratio of 4:1 10 . However, Benbassat et al. found a male predominance 15 . According to the reported prevalence, PM and DM are more common in women, whereas IBM is more prevalent in men 21 , 22 , 23 , 24 . Dermatomyositis is more common in women than in men, and they can exhibit differences in certain manifestations, such as alopecia. The exact reason for this gender disparity is not yet known, but it is hypothesized to result from a combination of genetic, hormonal, and immunological factors 28 , 29 , 30 .

IIMs are most common in the late-middle-aged group, which is consistent with our findings. Our research revealed that the peak prevalence of IIMs occurs between 60 and 69 years. Patrick et al. demonstrated that IBM has a higher prevalence in the elderly population than DM and PM 13 . IBM is the most common subtype in men over the age of 50, while DM is more common than PM in individuals under the age of 50. Furthermore, IBM is more difficult to treat than PM and DM 25 , which can result in higher long-term treatment costs. However, it is important to note that our study does not include data on the subtypes.

Geographic factors may explain the differences between our findings and those of other studies. Aguilar-Vazquez et al. investigated the reported prevalence of myositis-specific antibodies (MSA) or myositis-associated antibodies (MAA) based on geographical location and UV radiation. According to their systematic review, the prevalence of anti-PL7, anti-Ro52, anti-La, and anti-Ku UV radiation levels and other environmental factors in IIM research. These findings indicate that geographical latitude remains a significant factor in the prevalence of certain myositis autoantibodies 20 . Since the majority of cases were concentrated in northeastern Thailand, more research is needed to investigate the association between geographic factors and MSA or MAA, as well as the potential impact on the clinical manifestation of IIMs in Thais.

This study has a few limitations. First, there is no comprehensive demonstration of IIM clinical subtypes or serology. Second, the classification criteria for diagnosing IIMs vary, potentially leading to misdiagnosis and underestimation of IIM cases. Third, cases may be overestimated when ICD-10 codes are used to identify IIMs because not all patients may meet the classification criteria. Furthermore, the decrease in both the frequency and quantity of records, as well as the reduction in ICD-10 records, could potentially be ascribed to the COVID-19 pandemic.

Our study possesses a number of strengths. It is the first epidemiological study among Thais to examine IIMs. Second, the extensive database administered by the Ministry of Public Health is regarded as reliable. Furthermore, our findings can be applied to other countries with similar geographic characteristics. However, further investigation is needed to explore the specifics of clinical subtypes, serology, response to treatment, prognosis, and cost-effectiveness. This research will help to inform advanced public policy in Thailand and improve our understanding of the disease.

The presence of IIMs in Thailand is unusual. It peaks among people aged 60 to 69, especially in southern Thailand. Furthermore, the frequency of IIM cases decreased significantly between 2019 and 2020, which was most likely influenced by the COVID-19 pandemic. As a result of this decline, fewer IIM-related cases and investigations have been reported.

Data availability

Data and materials are available from the correspondence author upon reasonable request.

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Acknowledgements

The authors thank (a) the Scleroderma Research Group and Faculty of Medicine, Khon Kaen University, for its support, (b) the Information and Communication Technology Centre, Ministry of Public Health, for access to the database, (c) Mr. Bryan Roderick Hamman—under the aegis of the Publication Clinic at Khon Kaen University, Thailand—for assistance in editing with the English-language presentation.

The Thailand National Science, Research, and Innovation Fund funded this study.

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Tippawan Onchan, Chingching Foocharoen, Patnarin Pongkulkiat, Siraphop Suwannaroj & Ajanee Mahakkanukrauh

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Conceptualization: TO, CF, AM Data curation: CF, SS, AM Formal analysis: CF Funding acquisition: TO, AM Methodology: CF Writing-original draft: TO, CF Writing-review & editing: TO, CF, PP, SS, AM. The authors consent to publication and grant the Publisher exclusive full copyright license.

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Onchan, T., Foocharoen, C., Pongkulkiat, P. et al. Incidence and prevalence of idiopathic inflammatory myopathies in Thailand from the Ministry of Public Health data analysis. Sci Rep 14 , 20646 (2024). https://doi.org/10.1038/s41598-024-71633-7

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Epidemiology of measles outbreaks, incidence and associated risk factors in Ethiopia from 2000 to 2023: a systematic review and meta-analysis

• Daniel Eshetu 1 ,
• Wagi Tosisa 2 ,
• Belay Tafa Regassa 2 ,
• Gadissa Bedada Hundie 3 &
• Andargachew Mulu 4  

BMC Infectious Diseases volume  24 , Article number:  914 ( 2024 ) Cite this article

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Although a universal vaccine is available and Ethiopia is working outstandingly towards measles elimination, a recurrent measles outbreak has occurred each year in different parts of the country. Therefore, understanding the epidemiology of measles cases, the incidence of confirmed measles virus cases and related risk factors is crucial. Here, we conducted a systematic review and meta-analysis to summarize information regarding the epidemiology, measles incidence rate and risk factors for national measles infections occurring in the past two decades, from 2000 to 2023.

Data from electronic databases, including PubMed, African Journal Online, WHO databases and Google Scholars, were searched to identify studies describing measles outbreaks, incidence rates and associated factors in Ethiopia that occurred between 2000 and 2023. Important basic information was extracted in an Excel spreadsheet and imported into Comprehensive Meta-analysis Software version 3 to evaluate the associations between measles outbreaks and different risk factors. We pooled the odds ratios (ORs) and 95% confidence intervals (CIs) for every included risk factor to evaluate the associations with measles outbreaks.

We included 36 studies involving 132,502 patients with confirmed measles cases in Ethiopia. The results of this systematic review and meta-analysis revealed that measles outbreaks were more frequently reported in the Oromia region (73,310 (33.1%)), followed by the Southern Nation Nationalities of Ethiopia region (29,057 (13.4%)). The overall pooled analysis indicated that the prevalence of measles susceptibility was 67.5% (95% CI: 67.3–67.8%), with an I 2 of 99.86% and a p value for heterogeneity < 0.0001. The non-vaccinated status of the children, their contact history with measles cases, their travel history, the presence of cases in family or neighbors, and malnourished patients were identified as factors associated with the high prevalence and recurrent measles infections in Ethiopia.

The results of this systematic review and meta-analysis indicated that the pooled prevalence of measles infection was high, which is a public health concern in Ethiopia. Thus, strengthening healthcare services, regular vaccination campaigns, and the integration of health education activities with other services may decrease the incidence rate.

Peer Review reports

Introduction

Measles is a highly contagious disease caused by the measles virus. The measles virus is an enveloped, single-stranded, negative-sense RNA virus that belongs to a member of the genus Morbillivirus in the family Paramyxoviridae [ 1 ]. The virus genome consists of six genes that encode eight viral proteins (six structural and two nonstructural proteins). The six structural proteins are the hemagglutinin (H) protein, fusion protein (F), nucleocapsid protein, phosphoprotein, matrix protein, and large protein [ 2 ]. These structural proteins, particularly the H and F proteins, are responsible for the interaction of the virus with the host cell and the fusion of the viral envelope with the plasma membrane to initiate infection [ 3 ]. After human-to-human contact via respiratory droplets, more than 90% of susceptible individuals may develop systemic infections such as fever, malaise, cough, rhinitis, conjunctivitis, and Koplik’s spots, followed by maculopapular rash [ 4 , 5 ].

Prior to the introduction of the measles vaccine in 1963, this viral disease caused more than 2 million deaths and 15,000–60,000 cases of blindness worldwide [ 6 ]. Owing to its high mortality and morbidity rates, in 2001, the American Red Cross, CDC, UNICEF, and WHO launched the Measles and Rubella Initiative (MRI), with the goal of reducing measles mortality by 90% in 2010 compared with the 2000 baseline [ 7 ]. These vaccination efforts resulted in a 79% reduction in global measles deaths, from 535,000 in 2000 to 139,300 in 2010 [ 8 ]. In 2011, members of the WHO Regional Office for Africa adopted a decision to eliminate measles by 2020. Member countries developed a measles elimination strategic plan to achieve the following goals by 2020: achieve and maintain measles incidence below 1 case per million population; achieve and maintain > 95% MCV1 coverage at the national and regional levels in all regions; maintain at least ≥ 95% SIA coverage; and incorporate the second dose of measles into the routine vaccination schedule [ 9 ].

Ethiopia is a strong supporter of membership in the WHO African Regional Office and advocates for a national measles strategic plan to control and ultimately eliminate measles by 2020 [ 10 ]. The country also launched a nationwide measles catch-up campaign (SIA) in 2009 and initiated field epidemiology and laboratory training programs [ 11 , 12 ]. In February 2019, Ethiopia incorporated the second dose of the measles vaccine (MCV2) into its routine vaccination schedule. However, despite these efforts, national plans to accelerate measles control by 2012 (< 5 cases per 1 million people per year) and eliminate measles by 2020 (< 1 case per 100,000 people per year) have not yet been achieved [ 13 ]. Nevertheless, measles is endemic in Ethiopia, and the annual rate of measles incidence has increased significantly. Currently, there is a high rate of measles incidence, with more than 50 cases per 1,000,000 people reported annually [ 14 ]. Ethiopia remains the 4th-leading country in the world in terms of the burden of measles cases and is experiencing an ongoing measles outbreak, with more than 6933 measles confirmed cases in 2023 [ 15 , 16 ].

Although the characteristics of measles infection have been well described, previous studies have focused mainly on descriptions of single measles outbreaks, and few studies have summarized the measles immunization situation in Ethiopia. In those studies, attention has been given to summarizing measles outbreaks, incidence, and risk factors. Therefore, we conducted a systematic review and meta-analysis to summarize information regarding the outbreaks, epidemiology, incidence and risk factors for measles infection in Ethiopia from 2000 to 2023.

Methods and materials

Search strategies.

Both published articles and unpublished reports were searched for primary studies through electronic databases, including PubMed, Scopus, African Journal Online, WHO, and Google Scholar, to identify studies describing measles outbreaks, incidence rates and associated factors in Ethiopia that occurred between 2000 and 2023 (Fig.  1 ). The selection of search terms was conducted in population, intervention, comparison, and outcome (PICO) format [ 17 ]. For the population search term “Ethiopia”, for the intervention search term “Measles risk factor”, the search terms used for comparison and outcome were “no measles” and “measles” or “measles” and “no measles”. These terms were searched individually in each database and then combined using “OR” and “AND”. We registered our protocol with the Prospero International Register of Systematic Reviews ( http://www.crd.york.ac.uk/PROSPERO/ ) 23/11/2023 (CRD42023482250). The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist [ 18 ] was utilized to present the findings of the epidemiology of measles outbreaks, incidence and associated factors in Ethiopia.

Inclusion and exclusion criteria

Articles that reported measles epidemiology, incidence, and associated risk factors that occurred in Ethiopia were eligible for this systematic review and meta-analysis. The search was limited to articles published between January 1, 2000, and May 1, 2023. Quantitative studies with sufficient basic information (measles outbreak location and epidemiological investigation about the outbreak) and a clear diagnosis of measles were included, irrespective of whether the study was implemented in a health facility and/or in the community. The exclusion criteria were as follows: research articles describing measles outbreaks outside of Ethiopia; reports or studies that lacked key information or were not related to the aim of this study; and measles outbreaks that occurred before January 1, 2000.

Data extraction

The relevant studies were identified and merged after duplicate studies were removed via EndNote X7. The data extraction format was applied by considering all the inclusion criteria to check consistency and ensure that all the pertinent information was addressed. The format includes author name, year of publication, year of measles outbreak, country, region, study design, study period, study setting, sample size, and risk factors for measles outbreak, including the number of patients (measles cases) and controls (non-measles cases). The three reviewers (DE, WT & BT) independently assessed the articles and extracted the information from each included study according to the predefined set of inclusion criteria. Any data discrepancy among the data reviewers was resolved by referring to the original study through discussion with fourth and fifth reviewers.

Data quality assessment and risk of bias

The qualities of the data or the selected articles were assessed according to the Joanna Brigg’s Institute (JBI) critical appraisal checklist, which contains 9 checklist items [ 18 ]. On the basis of the 9 points of the JBI checklist, the three authors (DE, WT & BT) assessed the overall methodological quality and evaluated the quality of the articles as low quality (< 5 out of 9), moderate quality (5–7), or high quality (> 7) (additional file 1). The assessments revealed that more than 90% of the eligible articles included in this systematic review and meta-analysis were high-quality, and these data were compiled into a standard table (Table  1 ).

Data synthesis

Both descriptive and statistical data synthesis approaches were used to present the findings of this systematic review and meta-analysis. A summary table was prepared to explain the characteristics of the included articles. We described the epidemiology of measles, its incidence and associated risk factors. We performed a statistical meta-analysis for the thirty-six included articles after organizing the data on an Excel spreadsheet and imported it to Comprehensive Meta-analysis Software version 3 to evaluate the associations between measles outbreaks and different risk factors. We pooled the odds ratios (ORs) and 95% confidence intervals (CIs) for every included risk factor to evaluate the associations with measles outbreaks. We assessed the level of heterogeneity across studies via both random effects and fixed effects models, reporting heterogeneity and overall p values. An I 2 value greater than 50% indicated high heterogeneity between studies; therefore, a random effects model was implemented.

Operational definition

A measles outbreak.

is the occurrence of five or more reported suspected cases of measles in one month per 100 000 people living in a geographical area.

Measles-confirmed cases

suspected measles cases that were reported from the surveillance system and confirmed by laboratory serological tests.

An epidemiologically linked case

refers to a suspected case that has been linked (in person, place, and time) to a laboratory-confirmed case.

Non-measles infected case

was defined as any notified case or suspected case that was measles-specific IgM negative after testing as per established laboratory protocols.

Measle incidence

The measles incidence was calculated by dividing measles-confirmed cases by the population of the year and then multiplying by one million to compute the measles incidence per million people for a single-year period or multiple-year period.

Study selection

Among the 354 records identified, 211 were screened after the removal of records duplicated in more than one electronic database, and 79 records were excluded because of an unrelated title and country. Following this, 57 records were excluded because they were abstracts only, letters to editors, poster papers, or guidelines, and 39 full records or articles were excluded because of the study period and incomplete information. Finally, 36 records or articles that met the critical appraisal checklists were included in this systematic review and meta-analysis, regardless of their study design (Fig.  1 ).

PRISMA (flow chart of study selection for epidemiology of measles outbreaks, incidence and associated factors in Ethiopia from 2000 to 2023: a systematic review and meta-analysis)

Epidemiology of measles cases in Ethiopia

Among all the selected studies, 227,250 suspected and 149,415 (65.75%) confirmed measles cases were reported from 2000 to 2023. Among these measles-confirmed cases, 62,521 (27.51%) were laboratory-confirmed (IgM + ve), 48,887 (21.51%) were epidemiologically linked, and 38,007 (16.72%) were clinically compatible. The Oromia region is one of the most affected regions, contributing approximately 90,610 (73.42%), followed by the Southern Nation Nationalities of Ethiopia region, which accounts for more than 29,057 (70.06%) measles-confirmed cases of the overall specified regions (Table  2 ).

The overall pooled prevalence of measles-confirmed cases was 67.6% (95% CI: 67.3–67.8%), with an I 2 of 99.86% and a p value for heterogeneity < 0.0001 (Table  3 ). This meta-analysis also described a subgroup analysis of the pooled prevalence of the different administrative regions of Ethiopia. The largest pooled prevalence of measles confirmed cases occurred in the Oromia region, with 72.9% (95% CI: 72.7–73.2%), followed by the Southern Nation Nationalities of Ethiopia region, with 69.3% (95% CI: 68.8–69.7%), and the Afar region, with 68.9% (66.6–71.2%). In a comparison of measles susceptibility based on investigation modalities, the prevalence of measles investigated by survey and by health facilities was 69.4% (95% CI = 69.2–69.6%; I 2  = 99.78%; P  < 0.0001) and 5.9% (95% CI = 5.6–6.3%; I 2  = 99.82%; P  < 0.0001), respectively (Table  3 ).

Measles incidence rate

The incidence rate of measles in Ethiopia has significantly varied between 2005 and 2023. A noticeable increase in the incidence rate started in 2008, with 42.4 cases per million people, and reached its peak in 2015, with 173.2 cases per million people. Although the incidence rate declined between 2016 and 2020, there was a surge in 2022, indicating that the incidence rate is fluctuating and not consistently declining (Fig.  2 ). This suggests that measures to control the disease have not been consistently effective.

Incidence rate of measles in Ethiopia (Epidemiology of measles outbreaks, incidence and associated factors in Ethiopia from 2000–2023: a systematic review and meta-analysis)

Factors associated with measles outbreaks

In our systematic review and meta-analysis, none vaccinated patients, having a contact history with measles patients, a non-educated mother, travel history to the measles site, a distance greater than 2 km from health facilities, the presence of cases in a family or neighbor, a non-ventilated house, and malnourished patients were statistically associated with measles outbreaks. However, poor knowledge of measles transmission, living in a room by more than five people, and previous measles infections were not statistically associated with measles outbreaks (Fig.  3 ).

Factors associated with measles cases in Ethiopia (Epidemiology of measles outbreaks, incidence and associated factors in Ethiopia from 2000–2023: a systematic review and meta-analysis)

The significant risk factors from a number of studies revealed that none vaccinated patients were 2.6 times more likely to have measles infections than were the vaccinated patients (2.63; 1.992–3.477), and a history of contact with measles patients made them nearly 3 times more likely to have measles infections (2.94; 2.198–3.928). Patients from illiterate mothers were 1.4 times more likely to have measles infections (1.38; 1.004–1.907), whereas patients with a travel history to the measles area were 1.7 times more likely to have measles infections (1.7; 1.209–2.341). On the other hand, the presence of cases in a family or neighbor makes them 2.2 times more likely to have measles infections, and taking two or more than two vaccine doses decreases the odds of contracting measles by 61% compared with those who do not take two or more vaccines (Table  4 ).

This systematic review and meta-analysis focused on describing the magnitude of the measles outbreak and identifying the risk factors for measles infection in Ethiopia. This study presents the epidemiology, incidence rate, and risk factors for measles outbreaks that occurred in the past two decades between 2000 and 2023.

The overall pooled prevalence of measles-confirmed cases among all regions was 67.6% (95% CI: 67.3–67.8%); however, a difference in the pooled prevalence of measles-confirmed cases was observed among the regions in our study. The Oromia region was the most affected region, with 72.9% (95% CI: 72.7–73.2%), followed by the Southern Nation Nationalities of Ethiopia region, with 69.3% (95% CI: 68.8–69.7%), and the Afar region, with 68.9% (66.6–71.2%). The disparity may be due to differences in early detection and confirmation of measles epidemics among regions, and the highest pooled prevalence may be due to the large susceptible population, which may contribute to the spread of the virus.

The measles incidence is high and has remained above 15 cases per million populations for the past twenty years. This reveals that the country is not on the right track to attain the measles elimination goal of less than 1 case per million people by 2020 [ 50 ]. According to the present study, the highest measles incidence rate (173.2 per 1,000,000 people) occurred in 2015, and the lowest measles incidence rate (15.8 per 1,000,000 people) occurred in 2018. The variation in incidence rates from year to year could be due to differences in measles outbreak duration, low routine measles vaccine coverage, poor living and nutritional conditions, measles susceptibility accumulation, or differences in clusters of non-immune individuals [ 51 ].

Our meta-analysis revealed that the non-vaccinated status of children, contact history with measles patients, travel history to measles endemic site, the presence of measles cases in family/neighbors and malnourished patients were statistically significant risk factors for the continued high incidence rate of measles in Ethiopia. Among those risk factors, the overall pooled prevalence of non-vaccinated children in our study was 2.63 (95% CI: 1.992–3.477). Thus, the likelihood of acquiring measles infection was approximately 2.6 times greater in unvaccinated patients than in vaccinated patients. This result is in line with studies conducted in Zimbabwe (3 times) [ 52 ] and Ethiopia (5 times) [ 53 ]. This may be due to the perceptions of societies about the importance of vaccines. These factors result in low vaccination coverage and inadequate herd immunity in the community.

The pooled estimate of patients who had a history of contact with measles patients revealed a 2.9-fold greater risk of developing measles infection than did those who had no known contact history. This finding is consistent with studies performed in Japan, mainland China and Taiwan in China [ 54 ]. This is because the measles virus is highly contagious and is transmitted either by direct or indirect contact with infected patients’ respiratory droplets, which increases the spread of the virus from person to person.

In many low-income countries, including Ethiopia, malnutrition is a great challenge and leads to increased susceptibility to infectious diseases [ 55 ]. In this study, the odds of acquiring measles infection in malnourished children were 5.99 times greater among children who were not malnourished (5.99; 95% CI 3.112–11.53). The findings of this meta-analysis are in line with those of a study conducted in Yemen [ 56 ]. The association between measles and malnutrition has been described, in that malnourished children are more likely to contract the measles virus than are well-nourished children. In temperate regions, virus transmission peaks in late winter and early spring and follows the rainy season in tropical regions [ 57 ]. However, in this study, knowledge about measles transmission was not statistically associated with measles outbreaks.

Although; considerable improvement was practiced to eliminate Measles in Ethiopia, the outbreaks is continue to occur in many parts of the country and becomes a cause of significant number of death. In this study, the pooled prevalence of measles infection was high (67.6%), which is a public health concern in Ethiopia. The Oromia region was the most affected region, followed by the Southern Nation Nationalities of Ethiopia region and the Afar region. The result of our study also shows a significant association in terms non-vaccinated status of the children, having a history of contact with measles cases, travel history, the presence of cases in families/neighbors and malnourished patients. Those risk factors were identified as factors associated with the high prevalence and recurrent measles infections in the country.

Thus, major efforts will be needed to strengthening healthcare services, regular vaccination campaigns, and the integration of health education activities with other services to reduce measles incidence rate.

Limitations

Our study had several limitations. First, since our systematic review and meta-analysis were based on reported or published papers, some measles outbreaks may not have been recorded. Therefore, the actual number of measles cases associated with these outbreaks was possibly greater than recorded. Second, measles is self-limiting, and most infections are asymptomatic; thus, some measles patients may become unidentified during measles outbreaks. Despite these limitations, we believe that our systematic review and meta-analysis provide useful information regarding the national epidemiology of measles outbreaks, incidence and important risk factors.

Data availability

The authors confirm that all relevant data were included in the manuscript and that we do not have any research data outside the submitted manuscript file.

Abbreviations

Centers for Disease Control

Confidence interval

Fusion protein

Hemagglutinin protein

Measles and Rubella Initiative

Measles vaccine

• Measles virus

Ribonucleic acid

World health origination

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Acknowledgements

We would like to acknowledge the Authors of each article included in this manuscript. We would also like to thank JBI for using their systemic review and meta-analysis guidance.

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Department of Microbiology, Yirgalem Hospital Medical College, Yirgalem, Ethiopia

Daniel Eshetu

Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Ambo University, Ambo, Ethiopia

Wagi Tosisa & Belay Tafa Regassa

Department of Microbiology, Immunology & Parasitology, St. Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia

Gadissa Bedada Hundie

Armauer Hansen Research Institute (AHRI), Jimma Road, Addis Ababa, Ethiopia

Andargachew Mulu

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DE, WT and BT. conceived, designed the study and led the protocol design, study design, the data acquisition and data extraction. DE. conducted the statistical analysis and wrote the draft. GB. and AM. critically revised and modify the manuscript for important intellectual content. All the authors have read and approved the final manuscript.

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Eshetu, D., Tosisa, W., Regassa, B.T. et al. Epidemiology of measles outbreaks, incidence and associated risk factors in Ethiopia from 2000 to 2023: a systematic review and meta-analysis. BMC Infect Dis 24 , 914 (2024). https://doi.org/10.1186/s12879-024-09828-6

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Received : 06 March 2024

Accepted : 28 August 2024

Published : 03 September 2024

DOI : https://doi.org/10.1186/s12879-024-09828-6

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