thesis on breast cancer risk factors

• Open access
• Published: 26 August 2024

Global burden of breast cancer and attributable risk factors in 204 countries and territories, from 1990 to 2021: results from the Global Burden of Disease Study 2021

• Rui Sha   nAff1 ,
• Xiang-meng Kong 2 ,
• Xin-yu Li 3 &
• Ya-bing Wang 1  

Biomarker Research volume  12 , Article number:  87 ( 2024 ) Cite this article

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Background and objective

Breast cancer is a leading cause of morbidity and mortality among women worldwide. This study aimed to assess the global burden of breast cancer and identify attributable risk factors across 204 countries and territories from 1990 to 2021.

Using data from the Global Burden of Disease Study 2021, we analyzed the incidence, mortality, disability-adjusted life years (DALYs), and risk factors associated with breast cancer. We obtained and analyzed the age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALYs rate from 1990 to 2021. We assessed geographical variations and the impact of the Socio-demographic Index (SDI) using regression analysis and stratification by SDI quintiles. Additionally, we estimated the risk factors attributable to breast cancer deaths and DALYs using the comparative risk assessment framework of the GBD study.

Globally, breast cancer incident cases increased from 875,657 in 1990 to 2,121,564 in 2021. The ASIR rose from 16.42 to 26.88 per 100,000 (95% CI: 1.54–1.60). High SDI regions showed the highest ASIR (66.89 per 100,000 in 2021), while Low SDI regions had the lowest (6.99 per 100,000 in 2021). The global ASDR decreased from 10.42 to 8.54 per 100,000, and the age-standardized DALYs rate decreased from 313.36 to 261.5 per 100,000 between 1990 and 2021. However, these improvements were not uniform across SDI regions. Risk factors included high body-mass index, alcohol use, tobacco, and high fasting plasma glucose, with variations across SDI regions.

The global burden of breast cancer has increased significantly from 1990 to 2021, with disparities observed across SDI regions. While high SDI areas show improvements in mortality and DALYs, lower SDI regions face increasing burdens. Targeted interventions addressing modifiable risk factors and improving healthcare access in less developed regions are crucial for reducing the global impact of breast cancer.

Introduction

Breast cancer (BC) remains a significant global health concern, representing a substantial burden on healthcare systems worldwide. As the most commonly diagnosed cancer among women globally, it continues to be a leading cause of cancer-related deaths [ 1 ].

Over the past three decades, the landscape of BC has evolved considerably. Advancements in early detection methods, improved treatment modalities, and increased awareness have contributed to changes in incidence, mortality, and survival rates [ 2 ]. However, these improvements have not been uniform across all regions, with significant disparities persisting between high-income and low-to-middle-income countries [ 3 ]. The etiology of BC is multifactorial, involving a complex interplay of genetic, environmental, and lifestyle factors [ 4 ]. Identifying and quantifying the contribution of various risk factors is crucial for developing effective prevention strategies and allocating resources appropriately [ 5 ].

The Global Burden of Disease (GBD) study provides a comprehensive framework to assess the impact of breast cancer across different regions and time periods [ 6 ]. The aim of this study was to describe the influence of geographical location, social-development index (SDI), age, and gender on the global trends in the incident cases, deaths, and DALYs of BC based on data from the GBD from 1990 to 2021 in 204 countries and territories.

Data source and disease definition

The GBD study, accessible through the GHDx online platform ( https://vizhub.healthdata.org/gbd-results/ ), served as the primary data source for this research on BC burden. This comprehensive initiative synthesizes epidemiological information from 204 countries and territories, offering comparative analyses of health losses attributed to 369 medical conditions and 88 risk factors. The methodological framework for data acquisition, processing, and analysis in the GBD 2021 study has been extensively documented in previous publications [ 7 , 8 ]. Utilizing the GBD 2021 dataset, we extracted annual statistics on BC incidence, mortality, DALYs, and their corresponding age-standardized rates (ASRs) for the period 1990–2021. BC cases were identified using the International Classification of Diseases, 10th revision (ICD-10) codes, encompassing C50-C50.629, C50.8-C50.929, Z12.3-Z12.39, Z80.3, Z85.3, and Z86.000. For this study, estimates and their corresponding 95% uncertainty intervals (UI) for the prevalence, incidence, and DALYs associated with BC were extracted from the GBD 2021 data. The calculation of DALYs attributed to BC involved the summation of years lived with disability and years of life lost (Supplementary Methods).

For GBD studies, the Institutional Review Board of the University of Washington reviewed and approved a waiver of informed consent. This work has been reported in line with the STROCSS criteria [ 9 ].

Sociodemographic Index

The SDI quantifies a country’s or region’s development level using fertility rate, education level, and per capita income data. Ranging from 0 to 1, a higher SDI indicates greater socioeconomic development [ 1 , 2 ]. The SDI is known to correlate with disease incidence and mortality rates. In this study, we classified countries and regions into five SDI categories (low, low-medium, medium, medium–high, and high) to examine the relationship between BC burden and socioeconomic development (Supplementary Methods).

Risk factors

Our study extends beyond the primary metrics of incidence, mortality, and DALYs to examine the impact of specific risk factors on BC burden. We focused on key factors identified in the GBD 2021 study: high body mass index, alcohol consumption, elevated fasting plasma glucose, and tobacco use. Our analysis incorporated data on BC-related DALYs and deaths attributable to these factors, stratified by region to elucidate geographical variations. To quantify the influence of these risk factors, we employed advanced methodologies, including DisMod-MR 2.1 and spatiotemporal Gaussian process regression [ 1 ]. These approaches allowed us to model exposure distributions across various demographics and locations. We established the theoretical minimum risk exposure level (TMREL) for each factor based on epidemiological evidence, representing the optimal exposure level for minimizing BC risk. By integrating exposure data, relative risk estimates, and TMRELs, we calculated population attributable fractions (PAFs) for each risk factor. These PAFs, stratified by location, age, sex, and year, quantify the potential reduction in BC burden if exposure to a given risk factor were reduced to its TMREL. To translate these fractions into meaningful health outcomes, we multiplied the PAFs by DALYs, providing estimates of the risk-attributable burden. This comprehensive approach not only highlights the direct impact of BC but also illuminates the contribution of modifiable risk factors. It offers a more nuanced understanding of the disease's burden and potential avenues for intervention across different populations. By identifying the relative importance of various risk factors in different regions, our study provides valuable insights for tailoring prevention strategies and public health initiatives to specific contexts.

Statistical analysis

In 2021, an extensive analysis was performed to evaluate the national burden of BC, encompassing its incidence, mortality, and DALYs. Furthermore, the study explored the sociodemographic factors influencing BC's impact, examining the distribution of the disease's burden across various age cohorts and between sexes. The temporal patterns of BC incidence, DALYs, and mortality were quantified using ASRs, DALYs, and estimated annual percentage changes (EAPCs). The ASR was computed per 100,000 individuals utilizing the subsequent formula:

( \(\alpha_i\) : the age-specific rate in i th the age group; w: the number of people in the corresponding i th age group among the standard population; A : the number of age groups)

The EAPC serves as a prevalent metric in epidemiological studies to ascertain temporal evolutions in ASRs of diseases. The coefficient, denoted as \(\upbeta\) , is derived from the natural logarithm of the ASRs. Herein, y represents ln(ASR) while x corresponds to the calendar years. The EAPC, accompanied by its 95% confidence interval (CI), was determined utilizing the ensuing linear regression model:

An upward trend is indicated when the lower limit of the 95% CIs exceeds 0, while a downward trend is suggested when the upper limit falls below 0. If the 95% CIs encompass 0, it signifies no statistically significant variation in trend patterns.

In this study, we utilized a Bayesian age-period-cohort (BAPC) model incorporating integrated nested Laplace approximations to project future trends in BC burden. Previous research has demonstrated that BAPC offers superior coverage and precision compared to alternative prediction methods [ 10 , 11 , 12 , 13 ]. The computational process was implemented using the R-package BAPC, following established protocols from prior studies [ 10 ]. Additionally, risk factors for BC were assessed. All analytical procedures and graphical representations were executed using the World Health Organization's Health Equity Assessment Toolkit and the R statistical computing environment (version 4.2.1).

Breast cancer incidence burden

Globally, the number of BC incident cases increased substantially from 875,657.23 in 1990 to 2,121,564.32 in 2021. The age-standardized incidence rate (ASIR) rose from 16.42 per 100,000 in 1990 to 26.88 per 100,000 in 2021. The EAPC was 1.57 (95% CI: 1.54-1.60), indicating a significant upward trend (Table 1 , Fig. 1 ). In addition, we found that BC incidence increased in all five SDI regions, with the highest number of BC incident cases in the High SDI region at 731761.8 (95% UI: 668800.23-764115.63). In 2021, the ASIR was highest in the High SDI region (66.89 per 100,000; 95% UI, 61.13-69.84); it was lowest in the Low SDI region (6.99 per 100,000; 95% UI, 6.17-7.84) (Table 1 ). Low-middle SDI region has the largest increase in cases compared to 1990 (3.5-fold increase, EAPC:3.31; 95%CI: 3.23-3.39).

Trends in breast cancer incidence, deaths and disability-adjusted life-years from 1990 to 2021

Regionally, the incidences increased in all 21 GBD regions between 1990 and 2021 with the largest increases in North Africa and Middle East (EAPC: 5.2; 95%CI: 4.99-5.4) and the lowest increases in High-income North America (EAPC: -0.1; 95%CI: -0.17 to -0.02) (Table 1 ). The ASIR was highest in High-income North America, at 80.51 per 100,000 persons (95% UI: 74.15-84.27), followed by Western Europe, at 76.65 per 100,000 persons (95% UI: 69.15-81.3). By contrast, low-income regions demonstrated significantly lower ASIRs, including Western Sub-Saharan Africa at 8.88 per 100,000 persons (95% UI: 6.71-11.68), Eastern Sub-Saharan Africa at 8.1 per 100,000 persons (95% UI: 6.85-9.57), Central Sub-Saharan Africa at 7.37 per 100,000 persons (95% UI: 5.51-9.64) (Table 1 ). China, the USA, and India were the 3 countries with the highest reported new cases of BC in 2021 while Nauru, Niue and Tokelau were the 3 countries with the least. Monaco, Bermuda, and France showed the highest ASIR while Somalia,Chad and Niger showed the lowest ASIR in 2021 (Fig. 2 , TableS1).

The global disease burden of breast cancer incidence rate for both sexes in 204 countries and territories

A significant positive relationship was found between the SDI and the ASIR (R: 0.71, p  < 0.001), suggesting that BC incidence is higher in more economically developed countries (Fig. S1). The relationship between ASIR and SDI for each of the 21 Global GBD regions is shown in Fig. S2. The ASIR tends to be numerically higher in regions with a higher SDI compared to those with a lower SDI.

Breast cancer deaths and DALY burden

In 2021, the worldwide number of deaths cases was 674199.41 (95% UI: 623371.55-720822.55) (TableS2), with an age-standardized deaths rates (ASDR) of 8.54 per 100,000 persons (95% UI: 7.9-9.13), showing a decrease of 1.88 per 100,000 persons from 1990 to 2021. The global DALYs for BC in 2021 was 20635718.18 (95% UI: 19358110.66-21993502.55) (Fig. 1 ; Table S3), with an age-standardized DALYs rates of 261.5 per 100,000 persons (95% UI: 245.31-278.7), which decreased by 51.86 between 1990 and 2021 (Fig. 1 ; Table S3). Among the five SDI regions, the High SDI region had the highest ASDR at 15.84 (95% UI: 13.99-16.81) and the highest age-standardized DALY rate at 399.21 (95% UI: 367.83-425.21). Notably, the High SDI region was the only one among the five SDI regions where both the ASDR and age-standardized DALY rate decreased over time, with an EAPC of -0.07 (95% CI: -0.14 to -0.01) for ASDR and -0.56 (95% CI: -0.59 to -0.52) for the DALY rate.

Among the five SDI regions, the Low-middle SDI region experienced the greatest increases in both ASDR and age-standardized DALY rate, with an EAPC of 2.24 (95% CI: 2.16 to 2.31) for ASDR and 2.27 (95% CI: 2.19 to 2.34) for the DALY rate.

Regionally, South Asia had the highest number of BC deaths throughout the study period, reaching 108,084.94 (95% UI: 94,490.36-123,378.98) cases in 2021. Meanwhile, High-income Asia Pacific showed the largest increase in BC deaths between 1990 and 2021, with an EAPC of 2.98 (95% CI: 2.91-3.06). Geographically, the ASDR was highest in Central Europe at 21.88 per 100,000 persons (95% UI: 19.93-23.72), followed by Western Europe at 21.46 per 100,000 persons (95% UI: 18.45-23.13). Among the 21 GBD regions, Central Asia, Western Europe, Australasia, and High-income North America showed a declining trend in ASDR. In 2021,China had the highest number of BC-associated deaths (91483.8; 95% UI, 71738.6-113710.5) (Table S2, Fig. 3 ). Monaco (46.2; 95% UI, 35.2-60.7) had the highest ASDR; Oman (1; 95% CI, 0.8-1.3) had the lowest ASDR (Table S2, Fig. 3 ). Turkey (EAPC, 4.74; 95% CI, 4.18-5.29) had the greatest increases in the mortality rate; the Afghanistan (EAPC, -0.61; 95% CI, -0.92–0.29) had the greatest decreases.

The global disease burden of breast cancer death rate for both sexes in 204 countries and territories

The age-standardized DALY rate increased in 15 regions, while it decreased in 6 GBD regions. The highest age-standardized DALY rates were observed in Central Europe (529.03; 95% UI: 488.00-572.35), Western Europe (491.05; 95% UI: 443.23-528.69), and Eastern Europe (479.03; 95% UI: 427.68-543.23) (Fig. 4 ). South Asia had the highest number of DALY cases (3,781,141.03; 95% UI: 3,308,591.50-4,332,368.78). North Africa and the Middle East showed the largest increase in the age-standardized DALY rate for breast cancer between 1990 and 2021, with an EAPC of 3.08 (95% CI: 2.90-3.26). In 2021, China had the highest number of DALYs (3029404.7; 95% UI, 2360641.2-3844035.9). (TableS3). Nauru had the highest rate of DALYs (640.8; 95% UI, 381.9-994.5) (TableS3, Fig. 4 ). Turkey (EAPC, 3.81; 95% CI, 3.09-4.54) had the greatest increase in DALYs rate; Denmark (EAPC, -2.76; 95% CI,-2.88–2.65) had the greatest decreases (TableS3).

The global disease burden of breast cancer DALYs rate for both sexes in 204 countries and territories

Age and sex patterns

In 2021, the highest global incidence rates of BC were observed in individuals aged 95 and older, with incidence rates increasing with advancing age.Globally, we found that compared to 1990, BC incidence rate in 2021 increased in the 40-74 year age group, remained relatively unchanged in the 15-39 year age group, and decreased in the 80-89 year age group (Fig. 5 ). In most of the five SDI regions, BC incidence increased across all age groups compared to 1990. However, in the High SDI region, incidence rates were lower in 2021 than in 1990 for the 15-89 year age group. BC incidence rates in High SDI regions are generally higher than in other regions, particularly for age groups older than 40 years. Starting from the age of 40, the incidence in High SDI regions is significantly higher compared to other regions and increases rapidly with advancing age. Overall, BC incidence was significantly higher in women than in men. However, we also found a significant acceleration in BC incidence among men in regions with High SDI, High-middle SDI, and Middle SDI (Fig. 1 ). In terms of mortality, we found a decrease in BC mortality rates worldwide compared to 1990 (Fig. S3). The highest mortality rate was observed in the over-95 age group, with an overall increase in mortality rates with advancing age. Over the 32-year period, mortality rates declined the most in High SDI regions. In contrast, mortality rates in less developed regions increased compared to 1990 (Fig. S3). The same trend was observed in DALYs (Fig. S4). The highest DALY rates were found in the over-95 age group, with an overall increase in DALY rates as age advanced. High SDI regions experienced the greatest reduction in DALY rates over the 32-year period. However, less developed regions showed an increase in DALY rates compared to 1990. This suggests that while the overall global burden of breast cancer has decreased since 1990, disparities exist between regions with different levels of socioeconomic development.

Breast cancer incidence by age group, global and 5 SDI regions

Globally, high body-mass index was the greatest contributor to DALYs. Other significant contributors included alcohol use, tobacco, and high fasting plasma glucose (Fig. 6 ). However, in Australasia, Western Europe, and High-income Asia Pacific, alcohol use was the greatest contributor to DALYs. BC DALYs attributable to high body-mass index varied by SDI quintiles in 2021. Globally, 5% of BC DALYs were attributable to high body-mass index. In the High SDI quintile, 7.8% of BC DALYs were attributable to high body-mass index, while in the Low SDI quintile, only 1.2% of BC DALYs were attributable to high body-mass index. In 2021, 2.7% of BC DALYs were attributable to tobacco, showing a decrease from previous years. In the High SDI quintile, 3.7% of BC DALYs were attributable to tobacco. Across the other 21 GBD regions, the proportion of BC DALYs attributable to smoking also decreased compared to 1990 (Fig. 6 ). In 2021, 4% of BC DALYs were attributable to high fasting plasma glucose, showing a slight increase from previous years. In the High SDI quintile, 4.9% of BC DALYs were attributable to high fasting plasma glucose, also demonstrating a slight increase. Across the other 21 GBD regions, the proportion of BC DALYs attributable to high fasting plasma glucose also increased compared to 1990 (Fig. 6 ). Globally and in the High SDI, High-middle SDI, and Middle SDI quintiles, high body-mass index was the greatest contributor to BC deaths. However, in the Low SDI quintile, high fasting plasma glucose was the greatest contributor to BC deaths. Overall, BC deaths from smoking and alcohol use decreased compared to 1990, while High body-mass index and High fasting plasma glucose increased compared to 1990 (Fig. 6 ).

The breast cancer DALYs and deaths attributable to risk factors compared in 1990 and 2021, globally and by region

Future forecasts of global burden of breast cancer

Fig.S5 shows the future forecasts of the GBD study for BC incidence. As illustrated in Fig. S5, the ASIR of BC worldwide is expected to remain relatively stable, with a projected ASIR of 24.53 per 100,000 in 2030. The ASIR for females is projected to reach 46.30 per 100,000. However, on a global scale, the absolute number of new BC cases is expected to continue increasing. Over time, the ASDR of BC in females decreased slightly and is projected to reach 19.59 per 100,000 in 2030 (Fig. S6). It is estimated that by 2030, the ASR of female BC DALYs will be 450.20/100,000; the ASR of BC DALYs in male will be 8.97 cases per 100,000 (Fig. S7).

Our study provides a comprehensive analysis of the global burden of BC from 1990 to 2021, revealing significant disparities across regions with varying SDI. The findings demonstrate a substantial increase in BC incidence worldwide, with the number of cases more than doubling over the study period. This trend aligns with previous studies that have reported rising breast cancer incidence globally [ 14 , 15 ].

The global ASIR increased from 16.42 per 100,000 in 1990 to 26.88 per 100,000 in 2021, with an EAPC of 1.57. This significant upward trend was observed across all SDI regions, with the highest ASIR in 2021 found in the High SDI region (66.89 per 100,000) and the lowest in the Low SDI region (6.99 per 100,000). These findings underscore the substantial disparities in BC incidence between regions of different socioeconomic development. The observed increase in BC incidence can be attributed to several factors. Improved screening programs and diagnostic techniques in many countries have led to earlier and more frequent detection of BC [ 16 ]. For instance, the widespread adoption of mammography screening in high-income countries has contributed to increased detection rates [ 17 ]. Similarly, the gradual implementation of screening programs in middle-income countries has contributed to rising incidence rates in these regions. Shifts in reproductive behaviors, particularly in high SDI regions, have been associated with increased BC risk. These changes include delayed childbearing, with the trend towards having first children at later ages linked to increased breast cancer risk [ 18 ]. Reduced parity, with women having fewer children or no children at all, is associated with a higher risk of BC. Decreased duration of breastfeeding, especially in high-income countries, may also contribute to increased risk. The globalization of Western lifestyles, especially in developing countries, has been implicated in the rising incidence of BC. Key aspects of this lifestyle shift include dietary changes, with increased consumption of processed foods, animal products, and foods high in saturated fats associated with higher BC risk. Reduced physical activity due to urbanization and technological advancements has led to more sedentary lifestyles, which is a known risk factor for BC [ 19 , 20 ]. Rising alcohol intake, particularly in women, has been linked to increased BC risk. The global increase in obesity rates, especially in middle- and high-income countries, is a significant contributor to rising BC incidence [ 20 ]. Obesity, particularly post-menopausal obesity, is a well-established risk factor for BC due to its effects on estrogen levels and inflammation. Exposure to environmental pollutants and endocrine-disrupting chemicals may also play a role in the increasing incidence of BC, although more research is needed to fully understand these associations. As global life expectancy has increased, more women are living to ages where BC risk is highest, contributing to the overall increase in incidence rates. The stark disparity in ASIR between High and Low SDI regions (66.89 vs 6.99 per 100,000 in 2021) reflects not only differences in risk factor profiles but also inequalities in healthcare access and quality. In low SDI regions, lower incidence rates may partially reflect underdiagnosis due to limited screening programs, inadequate healthcare infrastructure, and lower awareness. As these regions develop economically and adopt more Westernized lifestyles, they may face a "cancer transition," with increasing BC incidence rates. Understanding these multifaceted contributors to the global increase in BC incidence is crucial for developing targeted prevention strategies and allocating resources effectively. It highlights the need for comprehensive approaches that address modifiable risk factors, improve screening and early detection programs, and ensure equitable access to quality healthcare across all SDI regions. Furthermore, it underscores the importance of tailoring interventions to the specific needs and circumstances of different populations and regions to effectively combat the rising global burden of BC.

Regionally, we observed significant variations in BC incidence. North Africa and the Middle East showed the largest increase (EAPC: 5.2), while High-income North America experienced a slight decrease (EAPC: -0.1). These regional differences highlight the complex interplay of factors influencing BC incidence, including genetics, environmental exposures, and healthcare access. Our analysis of age patterns revealed that the highest global incidence rates of BC were observed in individuals aged 95 and older, with incidence rates generally increasing with advancing age. Compared to 1990, we found increased incidence rates in the 40–74 year age group in 2021, while rates remained relatively unchanged in the 15–39 year age group and decreased in the 80–89 year age group. These age-specific trends provide valuable insights for targeted screening and prevention strategies.

The study also highlighted significant gender disparities, with BC incidence being significantly higher in women than in men. This finding aligns with the well-established understanding that BC predominantly affects women, primarily due to biological factors such as hormonal influences and breast tissue composition. The female breast is more susceptible to carcinogenic changes due to its complex structure and cyclical hormonal fluctuations throughout a woman's life. Estrogen and progesterone, hormones that play crucial roles in female reproductive physiology, are also known to influence the development and progression of BC. These hormonal factors, combined with genetic predispositions and environmental influences, contribute to the higher incidence of BC in women. However, we observed a notable acceleration in BC incidence among men in regions with High SDI, High-middle SDI, and Middle SDI. This trend is particularly intriguing and raises several important questions about the changing landscape of male BC. While male BC remains relatively rare, accounting for less than 1% of all BC cases globally, the observed acceleration in these specific SDI regions suggests potential shifts in risk factors or detection practices. Several factors could contribute to this trend. Improved awareness and screening practices in higher SDI regions may lead to increased detection of male BC cases that might have gone undiagnosed in the past. Additionally, changes in lifestyle factors such as increased obesity rates, which is a known risk factor for male BC, could play a role in the rising incidence. Environmental exposures to endocrine-disrupting chemicals, which are more prevalent in industrialized regions, might also contribute to this trend. Furthermore, the aging population in higher SDI regions could be a factor, as the risk of BC in men, like in women, increases with age. This trend warrants further investigation to elucidate the specific factors driving the acceleration of male BC in these regions. Such research could provide valuable insights into the etiology of male BC and potentially reveal new risk factors or mechanisms of disease development.

Our analysis of risk factors reveals that high body-mass index is the greatest contributor to BC DALYs globally, followed by alcohol use, tobacco, and high fasting plasma glucose. In 2021, 5% of breast cancer DALYs were attributable to high body-mass index globally, with this proportion rising to 7.8% in the High SDI quintile. These findings underscore the importance of addressing modifiable risk factors in BC prevention strategies. The variation in risk factor contributions across SDI regions highlights the need for tailored interventions that consider local contexts and resources. The observed decrease in BC mortality rates in high SDI regions is encouraging and likely reflects advancements in treatment and early detection. The global ASDR decreased from 10.42 per 100,000 in 1990 to 8.54 per 100,000 in 2021. However, the persistent and increasing mortality rates in lower SDI regions emphasize the urgent need for improved access to quality healthcare and cancer control programs in these areas. Similarly, the global age-standardized DALY rate decreased from 313.36 per 100,000 in 1990 to 261.5 per 100,000 in 2021. The High SDI region was the only one among the five SDI regions where both the ASDR and age-standardized DALY rate decreased over time. This trend highlights the impact of advanced healthcare systems and effective cancer control strategies in more developed regions.

Our future forecasts suggest that while the global ASIR of breast cancer is expected to remain relatively stable (projected ASIR of 24.53 per 100,000 in 2030), the absolute number of new cases is likely to continue increasing. This projection underscores the ongoing challenge of breast cancer and the need for sustained efforts in prevention, early detection, and treatment.

Strengths and limitations

To the best of our knowledge, this GBD-based study represents the most comprehensive effort to date to analyze the global burden of BC, including incidence, mortality, and DALYs, as well as to assess the contribution of various risk factors and project future trends. However, our study has several limitations that should be considered when interpreting the results. Firstly, the quality and availability of data vary significantly across different countries and regions. Many low- and middle-income countries lack robust population-based cancer registries, which may lead to an underestimation or inaccurate representation of the true BC burden in these areas. This data gap is particularly pronounced in rural regions and areas with limited healthcare infrastructure, potentially skewing our global estimates. Secondly, while our study includes a range of risk factors, it is not exhaustive. The GBD 2021 study provides data on several behavioral and metabolic risks, but there are other potentially important factors that we were unable to assess. For instance, genetic predisposition, environmental exposures, and certain reproductive factors are not fully captured in our analysis. This limitation may result in an incomplete understanding of the full spectrum of BC risk factors and their relative contributions to the disease burden. Thirdly, our projections of the future BC burden are based on current trends and patterns. These forecasts do not account for potential future changes in risk factors, advancements in screening and treatment technologies, or shifts in healthcare policies. As such, they should be interpreted with caution and regularly updated as new data become available. Fourthly, while DALYs provide a comprehensive measure of disease burden, incorporating both mortality and morbidity, they may not fully capture the psychosocial impact of BC on patients and their families. The long-term effects of BC survivorship, including quality of life issues and economic burden, are not completely reflected in our DALY calculations. Lastly, our study relies on the methodological framework of the GBD study, which, although robust, has its own limitations. These include potential biases in data collection and modeling approaches, as well as assumptions made in the estimation process. Furthermore, the use of disability weights in DALY calculations is based on survey data and may not perfectly reflect the lived experiences of BC patients across different cultural contexts.

Despite these limitations, our study provides valuable insights into the global landscape of BC burden and offers a foundation for future research and policy development in BC prevention and control. We acknowledge these constraints to encourage cautious interpretation of our findings and to highlight areas for improvement in future studies.

In conclusion, our findings highlight the growing global burden of BC and the significant disparities between regions of different socio-economic development. The increasing incidence in lower SDI regions, coupled with persistent high rates in high SDI areas, calls for a multi-faceted approach to breast cancer control. This approach should include improving access to screening and treatment in less developed regions, implementing targeted prevention strategies, and addressing modifiable risk factors. Future research should focus on developing cost-effective interventions tailored to local contexts and resources to reduce the global burden of BC. Moreover, efforts should be made to improve data collection and quality in low-resource settings to enable more accurate monitoring of BC trends and the effectiveness of interventions.

Availability of data and materials

GBD study 2021 data resources were available online from the Global Health Data Exchange (GHDx) query tool ( http://ghdx.healthdata.org/gbd-results-tool ).

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Acknowledgements

Not applicable.

The conduct of this study was sponsored and funded by the Research Foundation for Talents of Yijishan Hospital of Wannan Medical College (Grant Number YR20220201) and Natural Science Research Project of Anhui Educational Committee (Grant Number 2023AH051770) to Rui Sha, Teaching and Research Project of Anhui Provincial Department of Education (Grant Number 2023jyxm1220), Anhui Provincial Health Commission Hengrui Innovative Drug Research Special Fund (Grant Number AHWJ2023BAc10053) and Teaching Quality and Reform Project of Wannan Medical College (Grant Number 2022jyxm60) to Ya-bing Wang.

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Present address: Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Zheshan West Rd No. 2, Wuhu , Anhui Province, 241001, China

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Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Zheshan West Rd No. 2, Wuhu , Anhui Province, 241001, China

Ya-bing Wang

Department of Cardiology, Shanghai Ninth People,s Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, Huangpu District, 200011, China

Xiang-meng Kong

Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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Supplementary Information

40364_2024_631_moesm1_esm.pdf.

Supplementary Material 1: Fig. S1. The correlation between age-standardized incidence rate and SDI in 2021 across 204 countries.

40364_2024_631_MOESM2_ESM.pdf

Supplementary Material 2: Fig. S2. The correlation between age-standardized incidence rate and SDI in 2021 globally and by region.

Supplementary Material 3: Fig. S3. Breast cancer deaths by age group, global and 5 SDI regions.

Supplementary material 4: fig. s4. breast cancer dalys by age group, global and 5 sdi regions., supplementary material 5: fig. s5. future forecasts of gbd in breast cancer incidence., supplementary material 6: fig. s6. future forecasts of gbd in breast cancer deaths., supplementary material 7: fig. s7. future forecasts of gbd in breast cancer dalys., supplementary material 8: table s1. incidence of breast cancer between 1990 and 2021 at the 204 countries level., supplementary material 9: table s2. deaths from breast cancerin 204 countries, globally and regionally., 40364_2024_631_moesm10_esm.docx.

Supplementary Material 10: Table S3. Disabdeathsility-adjusted life years from Breast Cancerin 204 Countries, Globally and Regionally.

Supplementary Material 11.

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Sha, R., Kong, Xm., Li, Xy. et al. Global burden of breast cancer and attributable risk factors in 204 countries and territories, from 1990 to 2021: results from the Global Burden of Disease Study 2021. Biomark Res 12 , 87 (2024). https://doi.org/10.1186/s40364-024-00631-8

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Breast cancer: A review of risk factors and diagnosis

Obeagu, Emmanuel Ifeanyi PhD a,* ; Obeagu, Getrude Uzoma BN Sc b

a Department of Medical Laboratory Science, Kampala International University, Kampala, Uganda

b School of Nursing Science, Kampala International University, Kampala, Uganda.

Received: 29 July 2023 / Received in final form: 15 December 2023 / Accepted: 18 December 2023

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

The authors have no funding and conflicts of interest to disclose.

How to cite this article: Obeagu EI, Obeagu GU. Breast cancer: A review of risk factors and diagnosis. Medicine 2024;103:3(e36905).

* Correspondence: Emmanuel Ifeanyi Obeagu, Department of Medical Laboratory Science, Kampala International University, Kampala, Uganda (email: [email protected] ).

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Breast cancer remains a complex and prevalent health concern affecting millions of individuals worldwide. This review paper presents a comprehensive analysis of the multifaceted landscape of breast cancer, elucidating the diverse spectrum of risk factors contributing to its occurrence and exploring advancements in diagnostic methodologies. Through an extensive examination of current literature, various risk factors have been identified, encompassing genetic predispositions such as BRCA mutations, hormonal influences, lifestyle factors, and reproductive patterns. Age, family history, and environmental factors further contribute to the intricate tapestry of breast cancer etiology. Moreover, this review delineates the pivotal role of diagnostic tools in the early detection and management of breast cancer. Mammography, the cornerstone of breast cancer screening, is augmented by emerging technologies like magnetic resonance imaging and molecular testing, enabling improved sensitivity and specificity in diagnosing breast malignancies. Despite these advancements, challenges persist in ensuring widespread accessibility to screening programs, particularly in resource-limited settings. In conclusion, this review underscores the importance of understanding diverse risk factors in the development of breast cancer and emphasizes the critical role of evolving diagnostic modalities in enhancing early detection. The synthesis of current knowledge in this review aims to contribute to a deeper comprehension of breast cancer’s multifactorial nature and inform future directions in research, screening strategies, and preventive interventions.

1. Introduction

One of the frequent and numerous malignant tumors that affect women is breast cancer. Breast cancer develops and occurs as a result of several internal and external factors. [ 1–3 ] Poor lifestyle choices, environmental factors, and social-psychological factors are all linked to its occurrence. It has been demonstrated that 5% to 10% of breast cancers can be attributed to genetic mutations and family history, and 20% to 30% of breast cancers can be attributed to factors that may be modifiable. [ 4 ] Breast cells are where breast cancer first develops. A collection of cancer cells known as a cancerous tumor is capable of spreading into and destroying nearby tissue. As well as spreading throughout the body, it can. Breast cells occasionally undergo changes that prevent them from growing or behaving normally. Non-cancerous breast conditions atypical hyperplasia and cysts may result from these changes. Additionally, they may result in benign tumors like intraductal papillomas. [ 5 ]

However, breast cancer can occasionally result from changes to breast cells. Breast cancer typically begins in the cells that line the ducts, which are the tubes that carry milk from the glands to the nipple. Ductal carcinoma is the name given to this subtype of breast cancer. The cells of the lobules, which are the collections of milk-producing glands, can also give rise to cancer. [ 6 , 7 ] Lobular carcinoma is the name of this type of cancer. Both ductal and lobular carcinomas can be in situ, which means the cancer is still present in the area where it first appeared and has not spread to adjacent tissues. They may also be invasive, which indicates that they have spread into the tissues around them. [ 8 ] Breast cancer can also manifest itself in less common forms. These include triple-negative breast cancer, breast Paget disease, and inflammatory breast cancer. Non-Hodgkin lymphoma and soft tissue sarcoma are uncommon forms of breast cancer. [ 9 ] Despite having a low incidence of breast cancer, studies show that it has been steadily rising in China. By 2022, the number of Chinese women who will have the disease will surpass 100 per 100,000, and there will be 2.5 million women with the disease overall, aged 35 to 49. Thus, it is crucial to research breast cancer risk factors to lower the disease’s incidence. [ 10 ] Breast cancer is the most prevalent cancer in women worldwide and the main reason why women die from cancer. About 630,000 women lost their lives to breast cancer in 2018, and there were approximately 2.09 million newly diagnosed cases. While there are regional variations in breast cancer incidence, it is rising. Due to China’s large population and high incidence of breast cancer, which ranks first globally and has increased over the past few years (17.6% and 15.6%, respectively), even though the incidence of breast cancer (36.1/105) and mortality (8.8/105) are both relatively low worldwide. The burden of the disease is rising alongside the incidence of breast cancer globally, which has grown to be a significant issue for global public health. [ 11 ] Breast cancer is a multifactorial disease with major genetic, environmental, and behavioral/lifestyle components. The objective of the current review was to investigate the epidemiology and associated risk factors of breast cancer globally to comprehend its prevalence and aid in early detection. The main risk factors for breast cancer are genetic factors, specifically family history; diet, and obesity, as the quality of life in our country improves, women are getting more and more obese, and their diet tends to be more and more high-fat; smoking and drinking; the other is ionizing radiation; still have, specifically menstruation, bear, and whether lactation, these factors also can affect the occurrence of breast cancer. To lessen the impact exogenous hormones, have on the body, we should try to avoid using cosmetics that contain estrogen in our daily lives. Around these appeals, there has been a lot of debate. As a result, it is essential to thoroughly examine the risk factors for breast cancer using meta-methods to direct clinical prevention and treatment. [ 12 ] We conducted a meta-analysis of breast cancer risk factors in Chinese women in the current study by gathering pertinent literature from 2001 to 2021, even though Chinese scholars have already done so. [ 13 ] Our goal was to provide fundamental information for the prevention of breast cancer in Chinese women. Something that raises your chance of getting cancer is a risk factor. A habit, substance, or illness could be the culprit. Numerous risk factors contribute to the majority of cancers. However, breast cancer can occasionally develop in women who don’t have any of the risk factors listed below. Women are more likely than men to develop breast cancer. Women are more likely to develop breast cancer when estrogen and progesterone are exposed to their breast cells.

Some breast cancers are aided in their growth by these hormones, particularly estrogen, which has been linked to breast cancer. Canada, the United States, and a few European nations are examples of high-income, developed nations where breast cancer is more prevalent. Age raises the likelihood of getting breast cancer. Women between the ages of 50 and 69 are the most common demographic for breast cancer. [ 14 ] The most frequently diagnosed cancer in women and a major global health concern is breast cancer. Researchers have found several risk factors that can raise a woman’s likelihood of getting breast cancer, even though the disease’s precise cause is still unknown. For early detection, prevention, and efficient management of breast cancer, [ 14 ] it is essential to comprehend these risk factors. With more than 1 in 10 new cancer diagnoses each year, breast cancer is the most common cancer in women. In the entire world, it is the second most typical cause of cancer-related death in females. Milk-producing glands are located in front of the chest wall on the anatomy of the breast. They rest on the pectoralis major muscle, and the breast is supported by ligaments that join it to the chest wall. The breast is made up of 15 to 20 lobes that are arranged in a circle. The size and shape of the breasts are determined by the fat that covers the lobes. Each lobe is made up of lobules that contain the glands that produce milk when hormones are stimulated. Breast cancer always progresses subtly. The majority of patients learn they have their illness while getting their regular screenings. Others may exhibit nipple discharge, a breast shape or size change, or an unintentionally discovered breast lump. Mastalgia is not unusual, though. Breast cancer diagnosis requires a physical examination, imaging, particularly mammography, and tissue biopsy. [ 14 ]

Early diagnosis increases the likelihood of survival. Poor prognosis and distant metastasis are caused by the tumor’s propensity to spread lymphatically and hemologically. This clarifies and highlights the significance of programs for breast cancer screening. [ 15 ] Anything that raises the possibility of developing cancer is a risk factor. It might be a habit, substance, or illness. Many risk factors combine to cause the majority of cancers. Women are more likely than men to develop breast cancer. Women are more likely to develop breast cancer when estrogen and progesterone are exposed to their breast cells. Breast cancer is more prevalent in high-income, developed nations like Canada, the United States, and some European nations. These hormones, particularly estrogen, are linked to the disease and promote its growth. As you get older, your risk of developing breast cancer rises. Most breast cancer cases in women are diagnosed between the ages of 50 and 69 years. [ 14 ]

2. Risk factors for developing breast cancer among women

2.1. personal history of breast cancer.

An increased risk of breast cancer recurrence exists in women who have previously experienced it. The second breast cancer may appear in the same breast as the first one or in a different breast. Although the majority of women who have ductal carcinoma in situ or lobular carcinoma in situ breast cancers do not recur, these women are at an increased risk of doing so. [ 16 ]

2.2. Breast and other types of cancer in the family history

The presence of breast cancer in one or more close blood relatives indicates that the disease runs in the family. More breast cancer cases than one might anticipate randomly occur in some families. It can be difficult to determine whether a family’s history of cancer is the result of coincidence, a common lifestyle, genes passed down from parents to children, or a combination of these factors. [ 17 ]

2.3. Mutations in the BRCA gene

An altered gene is referred to as a genetic mutation. Certain types of cancer may be more likely to develop as a result of some gene changes. A parent can pass on inherited gene mutations to their offspring. Only a small percentage of breast cancers (roughly 5%–10%) are brought on by inherited gene mutations. Normal human physiology includes both BRCA1 and BRCA2, which are breast cancer genes. As a result of what seems to be their involvement in regulating the growth of cancer cells, these genes are known as tumor suppressors. BRCA1 or BRCA2 gene mutations may cause them to lose their ability to regulate the development of cancer. Rarely occur these mutations. Roughly 1 in 500 people experience them. A mutated BRCA gene can be inherited by both men and women from either their mother or father. Children of those who carry the gene mutation may also inherit it. A child has a 50% chance of inheriting the gene mutation if 1 of the 2 copies of the BRCA gene has the mutation in 1 or both parents. A child also has a 50% chance of not inheriting the gene mutation, according to this. [ 18 ] According to studies, women who inherit BRCA1 or BRCA2 gene mutations have an 85% lifetime risk of developing breast cancer. Additionally, compared to other women, those who carry these inherited mutations are at an increased risk of developing breast cancer earlier in life. Breast cancer in both breasts is more likely to strike women who have the BRCA gene mutation. They are more likely to get cancer in the other breast if they have cancer in 1 breast. Ovarian cancer can strike a woman at any age if she carries a BRCA gene mutation. [ 19 ]

2.4. Large breasts

Compared to fatty tissue, dense breasts have more milk ducts, glands, and connective tissue. Breast density is a genetic trait. Compared to women with little or no dense breast tissue, women with dense breast tissue have a higher risk of developing breast cancer. Breast density can only be detected by a mammogram, but dense breasts also make the image more difficult to interpret. On a mammogram, dense tissue appears white, like tumors, while fatty tissue appears dark, concealing a tumor. [ 20 ]

2.5. The late menopause

The body’s level of hormones, primarily estrogen and progesterone, begins to decline as the ovaries stop producing them, resulting in menopause. A woman’s menstrual cycle is stopped as a result of this. Your cells are exposed to estrogen and other hormones for a longer period if you enter menopause later in life (after age 55). This raises the possibility of breast cancer. Likewise, breast tissue is exposed to estrogen and other hormones for a shorter period when menopause occurs earlier in life. A lower risk of breast cancer is associated with early menopause. [ 21 ]

2.6. Whether there are late or no pregnancies

Breast cells’ exposure to circulating estrogen is halted during pregnancy. It also reduces the overall number of menstrual cycles a woman experiences throughout her lifetime. A woman’s risk of breast cancer is marginally higher than it is for a woman who has at least one full-term pregnancy before the age of 30. Reduced risk of breast cancer is associated with early pregnancy. A woman is more protected from breast cancer the more children she has. Breast cancer risk is increased if a woman never conceives. [ 22 ]

2.7. Hormonal replacement treatment

According to the Women’s Health Initiative (WHI) study, estrogen alone increased breast cancer risk by about 1% per year and combined hormone replacement therapy (HRT) increased risk by about 8% per year. The study also discovered that, in comparison to a placebo, the risk increased even with relatively brief use of combined HRT. After stopping HRT for a few years, the higher risk seems to be gone. The WHI study also revealed that, among Canadian women aged 50 to 69, there was a notable decline in the number of new cases of breast cancer between 2002 and 2004. The use of combined HRT decreased at the same time as this drop. Other nations around the world, such as the United States, Australia, Germany, the Netherlands, Switzerland, and Norway, have also noticed this trend. The risks associated with the long-term use of combined HRT are now thought to outweigh the advantages. [ 23 ]

2.8. Being overweight

In post-menopausal women, obesity increases the risk of developing breast cancer. According to studies, women with a body mass index of 31.1 or higher who have never used HRT are 2.5 times more likely to develop breast cancer than those with a body mass index of 22.6 or lower. In particular, estrogens from the ovaries play a significant role in breast cancer. Many breast cancer risk factors are thought to be caused by the cumulative estrogen dose that the breast tissue absorbs over time. The majority of the body’s estrogen is produced by the ovaries, but after menopause, fat tissue only produces a small amount of estrogen. A higher estrogen level can result from having more fat tissue, which raises the risk of breast cancer. [ 24 ]

2.9. Estrogen

Breast cancer risk is linked to estrogens, both endogenous and exogenous. In premenopausal women, the ovary typically produces endogenous estrogen, and ovarian removal can lower the risk of breast cancer. HRT and oral contraceptives are the main exogenous estrogen sources. Since the 1960s, oral contraceptives have been extensively used, and their formulations have been improved to minimize side effects. The odd ratio is still higher than 1.5 for Iranian and African American female populations, though. Oral contraceptives do not, however, raise the risk of breast cancer in women who stop using them for more than 10 years. For menopausal or postmenopausal women, HRT entails the administration of exogenous estrogen or other hormones. The use of HRT can raise the risk of breast cancer, according to several studies. According to the Million Women Study in the UK, there is a 1.66 relative risk between those who currently use HRT and those who have never used it. A cohort study of 22,929 Asian women found that after using HRT for 4 and 8 years, respectively, hazard ratios (HRs) of 1.48 and 1.95 were found. After 2 years of stopping HRT, it has been demonstrated that the risk of breast cancer significantly declines. With a 3.6 HR for a new breast tumor, the recurrence rate is also high among breast cancer survivors who take HRT. Since the negative effects of HRT were revealed in 2003 based on the WHI randomized controlled trial, there has been a 7% decrease in the incidence rate of breast cancer in America. [ 25 ]

3. Breast cancer in women: Diagnosed through appended technologies

Breast tumors typically start as benign tumors or even metastatic carcinomas due to ductal hyperproliferation, which is then constantly stimulated by various carcinogenic factors. Breast cancer is initiated and progresses differently depending on the microenvironment of the tumor, such as stromal influences or macrophages. When only the stroma of the rat mammary gland was exposed to carcinogens—not the extracellular matrix or the epithelium—neoplasms could be induced. A mutagenic inflammatory microenvironment that macrophages can create can encourage angiogenesis and help cancer cells avoid immune rejection. Different DNA methylation patterns between the typical and tumor-associated microenvironments have been observed, suggesting that epigenetic changes in the tumor microenvironment can encourage carcinogenesis. Cancer stem cells (CSCs), a new subclass of malignant cells within tumors, have recently been identified and linked to tumor initiation, escape, and recurrence. This small population of cells, which may originate from stem cells or progenitor cells in healthy tissues, can regenerate itself and is resistant to traditional treatments like chemotherapy and radiotherapy. Ai Hajj was the first to identify breast cancer stem cells (bCSCs), and immunocompromised mice could develop new tumors from as few as 100 bCSCs. As opposed to basal stem cells, luminal epithelial progenitors are more likely to be the source of bCSCs. The self-renewal, proliferation, and invasion of bCSCs are mediated by signaling pathways that include Wnt, Notch, Hedgehog, p53, PI3K, and HIF. More research is nevertheless required to comprehend bCSCs and create fresh methods for their complete eradication. [ 19 ]

The CSC theory and the stochastic theory are 2 speculative theories for how breast cancer starts and spreads. According to the theory about CSCs, all subtypes of tumors are descended from the same stem cells or transit-amplifying cells. A variety of tumor phenotypes are caused by acquired genetic and epigenetic mutations in stem cells or progenitor cells. According to the stochastic theory, a single type of cell is the source of all tumor subtypes. Any breast cell can gradually develop random mutations, and when enough mutations have accumulated, the breast cell can transform into a tumor cell. Even though both theories have a lot of data to back them up, neither can fully explain how human breast cancer first developed. [ 26 ]

4. Biology-based breast cancer prevention

To enhance the quality of life for breast cancer patients, biological prevention, primarily known as monoclonal antibodies for the disease, has recently been developed. These monoclonal antibodies have human epidermal growth factor receptor 2 (HER2) as one of their primary targets. The HER2 protein is overexpressed or the HER2 gene is amplified in about 20% to 30% of all breast cancer cases. The first HER2-targeted medication to receive FDA approval is trastuzumab (Herceptin), a recombinant humanized monoclonal antibody. It can directly interact with the C-terminal region of domain IV in the extracellular region of HER2. Trastuzumab’s anti-tumor mechanism has not yet been fully understood. Trastuzumab may inhibit the growth and proliferation of cancer cells through several possible mechanisms, including activating the immune system against cancer cells through an effect known as antibody-dependent cell-mediated cytotoxicity, inhibiting the MAPK and PI3K/Akt pathways, and enlisting ubiquitin to internalize and degrade HER2. With an objective response rate of 26%, trastuzumab was initially used to treat metastatic breast cancer. Trastuzumab interacts favorably with other anti-tumor medications, including nimotuzumab, carboplatin, 4-hydroxycyclophosphamide, docetaxel, and vinorelbine, according to in vitro studies. According to the HERA and TRAIN trials, chemotherapy given in combination with adjuvant trastuzumab for a year can prolong disease-free survival in HER2+ breast cancer patients (HR = 0.76). Trastuzumab plus docetaxel was shown to be more effective than docetaxel alone in treating HER2-positive metastatic breast cancer, with an objective response rate of 50% versus 32%, in a randomized phase II trial carried out by Marty. Patients receiving trastuzumab, however, also experienced adverse effects like congestive heart failure and a decline in their left ventricular ejection fraction. [ 4 ]

5. Breast cancer in women is diagnosed

5.1. mammography.

Diagnostic mammography is an x-ray that creates an image of the breast using low radiation doses. It is used to follow up on unexpected findings from a clinical breast exam or a screening mammogram. It is also possible to use mammography during a biopsy to identify an abnormal area. [ 27 ]

5.2. Ultrasound

An ultrasound creates images of various body parts using high-frequency sound waves. It is used to determine whether a lump in the breast is a solid tumor or a cyst. Additionally, ultrasound can be used by medical professionals to direct them to the biopsy site. An ultrasound may be performed on women with advanced breast cancer to determine whether liver metastasis has occurred. [ 28 ]

5.3. Biopsy

Breast cancer can only be accurately identified through a biopsy. The purpose of a biopsy is to remove tissues or cells from the patient’s body for laboratory testing. The pathologist’s report will determine whether or not cancer cells were discovered in the sample. The type of biopsy performed will depend on whether the lump is palpable, meaning you can feel it, or non-palpable, meaning you can’t. To locate the area to be tested, the doctor may use ultrasound or mammography. The majority of biopsies are performed in a hospital, and once they are complete, you can leave for home. [ 29 ]

5.4. The core biopsy

Removes tissue from the body using a unique hollow needle. It is employed by doctors to obtain a sample from a breast region that is thought to be suspicious. During the procedure, they might take several samples from the area. To remove more tissue through the hollow needle, doctors occasionally use a special vacuum. Vacuum-assisted core biopsy is the name of this method. [ 17 ]

5.5. A lymph node biopsy

A lymph node biopsy is a surgical procedure that involves the removal of lymph nodes so they can be examined under a microscope to determine if they contain cancer. Breast cancer cells can separate from the tumor and move through the lymphatic system. Lymph nodes beneath the arm are where they might spread first. To help determine the stage of breast cancer, doctors count the number of lymph nodes that contain the disease. [ 30 ]

5.6. Fine needle aspiration

Removes a small amount of tissue from a lump using a syringe and a very thin needle. It helps doctors determine whether a lump is a cyst or a solid tumor. Whether a cancer is non-invasive or invasive cannot be determined by fine needle aspiration (FNA). [ 31 ] During the procedure, a healthcare professional inserts a thin needle into the breast lump, guided by palpation or imaging techniques such as ultrasound. A syringe attached to the needle is used to suction out cells or fluid from the lump. These cells or fluid samples are then examined under a microscope by a pathologist to determine if they are cancerous (malignant) or noncancerous (benign). FNA is a minimally invasive procedure that can provide valuable information about the nature of the breast lump. It helps in the diagnosis of breast cancer by analyzing the characteristics of the cells, aiding in determining the presence of cancerous cells, and guiding further diagnostic or treatment procedures. However, depending on the situation, additional tests like a core needle biopsy or surgical biopsy may be recommended for a more comprehensive evaluation.

6. Preventative measures and ongoing research efforts

Certainly, preventive measures and ongoing research efforts are crucial components in the fight against breast cancer.

7. Preventative measures

Encouraging women to undergo regular mammograms and screenings based on age and risk factors can aid in early detection, leading to better treatment outcomes. [ 32 ] Promoting a healthy lifestyle that includes maintaining a balanced diet, regular exercise, limiting alcohol consumption, avoiding smoking, and maintaining a healthy weight can reduce the risk of developing breast cancer. Encouraging breastfeeding, which has been shown to have protective effects against breast cancer, can be promoted as a preventive measure. [ 33 ] Providing comprehensive and accessible education on breast cancer risks, symptoms, and the importance of early detection can empower individuals to take proactive measures and seek timely medical attention. For individuals with a family history or known genetic mutations (like BRCA1 or BRCA2), genetic counseling and testing can help in assessing risks and making informed decisions about preventive measures. [ 34 ] Understanding the risks associated with certain hormone therapies and discussing alternatives with healthcare providers, particularly for menopausal symptoms, is important.

8. Ongoing research efforts

Research continues to develop targeted therapies that focus on specific genetic mutations or molecular markers associated with breast cancer, improving treatment efficacy while reducing side effects. [ 35 ] Investigating the role of immunotherapy in breast cancer treatment, harnessing the body’s immune system to target cancer cells, is an area of active research. Ongoing research aims to develop more sensitive and specific screening methods beyond mammography, including molecular imaging and blood-based biomarkers, for earlier and more accurate diagnosis. Studying genetic and epigenetic factors influencing breast cancer development helps in identifying new targets for therapy and understanding individual susceptibility. [ 36 ] Research focuses on identifying additional lifestyle modifications, medications, or interventions that can further reduce the risk of developing breast cancer. Collaborative research initiatives between countries and institutions aim to share data, resources, and expertise, advancing our understanding of breast cancer and improving treatment outcomes globally. By continuing to prioritize prevention through lifestyle modifications, early detection through effective screening, and investing in cutting-edge research, the hope is to reduce the incidence, morbidity, and mortality associated with breast cancer worldwide.

9. Conclusion

Breast cancer remains a significant global health concern, impacting millions of individuals each year. This review has underscored the multifaceted nature of breast cancer, highlighting various risk factors and diagnostic approaches crucial in understanding and managing this disease. Moreover, advancements in diagnostic techniques have significantly improved early detection and treatment outcomes. Mammography, alongside emerging technologies like magnetic resonance imaging and molecular testing, plays a pivotal role in identifying breast cancer at its early stages, enabling prompt intervention and potentially improving patient prognoses. Moving forward, continued research into identifying additional risk factors, enhancing screening methods, and developing targeted therapies remains imperative. Furthermore, promoting awareness, advocating for increased screening accessibility, and fostering global collaboration among medical professionals and researchers is crucial in the ongoing fight against breast cancer. A comprehensive approach that integrates research, education, early detection, and accessible healthcare services is essential in combating breast cancer and reducing its impact on individuals and societies worldwide.

Author contributions

Conceptualization: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Methodology: Emmanuel Ifeanyi Obeagu.

Resources: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Supervision: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Visualization: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Writing – original draft: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Writing – review & editing: Emmanuel Ifeanyi Obeagu, Getrude Uzoma Obeagu.

Validation: Getrude Uzoma Obeagu.

Abbreviations:

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breast cancer; cancers; diagnosis; mortality; prevention; risk factors

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Breast cancer—epidemiology, risk factors, classification, prognostic markers, and current treatment strategies—an updated review.

Simple Summary

1. introduction, 2. breast cancer epidemiology, 3. risk factors of breast cancer, 3.1. non-modifiable factors, 3.1.1. female sex, 3.1.2. older age, 3.1.3. family history, 3.1.4. genetic mutations, 3.1.5. race/ethnicity, 3.1.6. reproductive history, 3.1.7. density of breast tissue, 3.1.8. history of breast cancer and benign breast diseases, 3.1.9. previous radiation therapy, 3.2. modifiable factors, 3.2.1. chosen drugs, 3.2.2. physical activity, 3.2.3. body mass index, 3.2.4. alcohol intake, 3.2.5. smoking, 3.2.6. insufficient vitamin supplementation, 3.2.7. exposure to artificial light, 3.2.8. intake of processed food/diet, 3.2.9. exposure to chemical, 3.2.10. other drugs, 4. breast cancer classification, 4.1. histological classification, 4.2. luminal breast cancer, 4.3. her2-enriched breast cancer, 4.4. basal-like/triple-negative breast cancer, 4.5. claudin-low breast cancer, 4.6. surrogate markers classification, 4.7. american joint committee on cancer classification, 5. prognostic biomarkers, 5.1. estrogen receptor, 5.2. progesterone receptor, 5.3. human epidermal growth factor receptor 2, 5.4. antigen ki-67, 5.6. e-cadherin, 5.7. circulating circular rna, 5.9. microrna, 5.10. tumor-associated macrophages, 5.11. inflammation-based models, 5.11.1. the neutrophil-to-lymphocyte ratio (nlr), 5.11.2. lymphocyte-to-monocyte ratio, 5.11.3. platelet-to-lymphocyte ratio (plr), 6. treatment strategies, 6.1. surgery, 6.2. chemotherapy, 6.3. radiation therapy, 6.4. endocrinal (hormonal) therapy, 6.5. biological therapy, 7. conclusions, author contributions, conflicts of interest.

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Share and Cite

Łukasiewicz, S.; Czeczelewski, M.; Forma, A.; Baj, J.; Sitarz, R.; Stanisławek, A. Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review. Cancers 2021 , 13 , 4287. https://doi.org/10.3390/cancers13174287

Łukasiewicz S, Czeczelewski M, Forma A, Baj J, Sitarz R, Stanisławek A. Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review. Cancers . 2021; 13(17):4287. https://doi.org/10.3390/cancers13174287

Łukasiewicz, Sergiusz, Marcin Czeczelewski, Alicja Forma, Jacek Baj, Robert Sitarz, and Andrzej Stanisławek. 2021. "Breast Cancer—Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies—An Updated Review" Cancers 13, no. 17: 4287. https://doi.org/10.3390/cancers13174287

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• Research article
• Open access
• Published: 17 November 2020

Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

• Maria Escala-Garcia 1 ,
• Anna Morra 1 ,
• Sander Canisius 1 , 2 ,
• Jenny Chang-Claude 3 , 4 ,
• Siddhartha Kar 5 , 6 ,
• Wei Zheng 7 ,
• Stig E. Bojesen 8 , 9 , 10 ,
• Doug Easton 11 , 12 ,
• Paul D. P. Pharoah 11 , 12 &
• Marjanka K. Schmidt   ORCID: orcid.org/0000-0002-2228-429X 1 , 13  

BMC Medicine volume  18 , Article number:  327 ( 2020 ) Cite this article

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Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM).

We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors.

Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, P value [ P ] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors.

Conclusions

This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.

Peer Review reports

Breast cancer is a heterogeneous disease with a broad variation in prognosis [ 1 ]. Providing a precise prognostication for breast cancer patients is important in order to inform them accurately about the course of the disease and to allocate them to the right treatment [ 2 ]. To date, most commonly used prognostic factors relate to tumour characteristics and the extent of the disease at the time of diagnosis [ 2 ]. Many observational studies have evaluated the association of breast cancer risk and survival with other patient characteristics and lifestyle-related risk factors [ 3 , 4 , 5 ]. However, due to their observational nature, it is difficult for these studies to establish causation. Understanding whether or not the association between breast cancer survival and risk factors is causal might influence strategies to improve survival in breast cancer patients. In theory, randomised control trials (RCTs) provide a reliable method to evaluate the causal relationship between risk factors and survival [ 6 , 7 ], but they are often not feasible as they can be prohibitively expensive, time-consuming, and even unethical. If an RCT cannot be performed to assess the causal effect between a risk factor and the outcome of interest, methods using instrumental variables may be an alternative.

Mendelian randomisation (MR) is a popular analytical method that uses genetic variants as instrumental variables (i.e. genetic instruments). This methodology uses a genetic predictor for the risk factor. Because of the natural randomisation of alleles during meiosis, this genetic predictor will be independently distributed across a population. Theoretically, therefore, this genetic instrument is not affected by potential environmental confounding factors or by disease status. MR rests on three basic assumptions: (1) genetic variants are associated with the risk factor (relevance assumption), (2) those genetic variants are not associated with any known or unknown confounders (independence assumption), and (3) the genetic variants affect the outcome only through the risk factor (exclusion restriction assumption) [ 8 ]. Using a genetic score that combines multiple variants explaining a large R -squared of the risk factor can help reducing the probability of violating the first MR assumption and providing more powerful MR analyses. The third assumption is also known as independence from horizontal pleiotropy, which occurs when the genetic variants influence the outcome by means of other pathways independently of the risk factor [ 8 ]. Several methods and sensitivity tests exist to assess these assumptions [ 9 ].

In this study, we used MR analysis to evaluate the causal relationships between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index (BMI), height, mammographic density, menarche (age at onset), menopause (age at onset), physical activity, smoking, and type 2 diabetes mellitus (T2DM). Observational studies have provided evidence for the potential association of these risk factors and breast cancer survival, sometimes with conflicting results.

A population-based prospective study found that smoking before or after breast cancer diagnosis is associated with worse breast cancer survival [ 10 ]. Another meta-analysis of cohort studies concluded that current smoking is associated with worse breast cancer-specific survival compared to never smoking in breast cancer patients [ 11 ]. Obesity (BMI of ≥ 30.0) has been associated with worse breast cancer survival in a meta-analysis and systematic review [ 12 ]. In another review, obesity was associated with worse breast cancer prognosis for women of all ages [ 13 ]. For T2DM, a retrospective study of breast cancer patients found that diabetes was independently associated with poorer breast cancer prognosis [ 14 ]. In a population-based study, breast cancer-specific mortality was higher among women with diabetes compared to non-diabetic patients [ 15 ]. In relation to menstrual risk factors, a population-based study showed that early age at menarche was significantly associated with poorer survival but age at menopause did not have a significant impact [ 16 ]. The relationship between mammographic density and breast cancer survival has been studied in several cohort studies, but results have been inconclusive [ 17 , 18 , 19 ]. For other factors such as physical activity, the evidence is also not clear: in an RCT with an 8-year follow-up, no significant difference in disease-free survival was found between an exercise group and a usual care group [ 20 ]. To date, there is no evidence for an association between height or post-diagnosis alcohol consumption and breast cancer survival [ 21 ].

Our hypothesis was that some of these risk factors, for which there is evidence of an association with breast cancer survival based on observational data, might have a causal association with breast cancer-specific survival. We also aimed to investigate whether we could observe—or refute—an effect for the risk factors for which the association is not clear. We therefore performed a two-sample MR analysis using genetic variants and risk factor association summary estimates from the GWAS Catalog [ 22 ] and breast cancer survival summary estimates from the Breast Cancer Association Consortium (BCAC) cohort [ 23 ].

Selection of risk factors

We first considered the full list of breast cancer risk factors provided on the Cancer Research UK site [ 33 ] as of January 2020 (Additional file  1 : Table S1). From this list of 25 factors, we identified nine factors for which genome-wide association study (GWAS) data were available. Only GWASs that could be directly downloaded from GWAS Catalog [ 22 ] into TwoSampleMR [ 34 ] R package were considered. If there were multiple GWAS for one risk factor, we selected the study with the largest sample size from those that were predominantly of European ancestry (Table  1 ). We considered only genome-wide significant variants ( P  < 5 × 10 −8 ) to ensure that the association with the risk factor was robust (first MR assumption). Only single-nucleotide polymorphisms (SNPs) were considered as the reference panel did not include other types of variants. Variants correlated with the most significant SNPs were removed so that only uncorrelated variants remained in the analysis ( r 2  < 0.001). We calculated a priori power to detect an association at a significant level of 0.05 for each risk factor using the tool ( https://sb452.shinyapps.io/power ) [ 35 ]. We used the number of events ( n  = 7054) as sample size.

Breast cancer survival and genetic data

The breast cancer survival data was obtained from the Breast Cancer Association Consortium (BCAC). We analysed clinic-pathological data (database version 12) and genotype data from the OncoArray [ 36 ] and iCOGS arrays [ 37 ]. The analysis included 86,627 female patients of European ancestry diagnosed at age > 18 years with invasive breast cancer of any stage. The dataset included 7054 breast cancer-specific deaths. A total of 59,378 patients (4246 deaths) had ER-positive disease, and 13,692 (1733 deaths) had ER-negative disease. Genotypes for variants not present on the arrays were imputed using the Haplotype Reference Consortium [ 38 ] as reference panel. Details about the genotyping, sample quality control, and imputation procedure have been described previously [ 36 , 39 ]. Our analyses were based on SNPs that were imputed with imputation r 2  > 0.7 and had minor allele frequency > 0.01 in at least one of the two datasets (iCOGS or OncoArray).

Breast cancer survival estimates

We took the SNPs referred to in Table  1 as genetic instruments for each of the nine risk factors. For every SNP, we performed survival analyses to obtain survival estimates as described previously [ 23 ]. The analyses included the full OncoArray and iCOGS datasets. Time at risk was calculated from the date of diagnosis with left truncation for prevalent cases. Follow-up was right censored on the date of death, last date known alive if death did not occur, or at 15 years after diagnosis, whichever came first [ 39 ]. We estimated the association between the genetic instruments and breast cancer-specific survival using Cox proportional hazards regression [ 40 ]. The models were stratified by study and included the first two ancestry informative principal components, based on the genotyping array data as previously described, to adjust for population structure [ 36 , 37 ]. We analysed the OncoArray and iCOGS datasets separately and then combined the estimates using fixed-effect meta-analyses [ 39 ]. Analyses were carried out for all invasive breast cancer and for estrogen receptor (ER)-positive and ER-negative disease separately. Additional file  2 : Tables S1-S9 provides the full list of SNPs used and the corresponding estimates for the per-allele risk factor effect sizes and the per-allele survival log (hazard ratios).

MR statistical analyses and sensitivity diagnostics

We used the TwoSampleMR [ 34 ] R package to perform the two-sample MR analyses. We obtained the genetic instruments for the risk factors (MR-Base NHGRI-EBI GWAS Catalog [ 22 ], 29 August 2019 update), harmonised the SNP effects so they corresponded to the same allele for the risk factor and survival associations, and performed the sensitivity tests. We estimated the causal relationships between each of the sets of SNPs for the nine risk factors and breast cancer-specific survival using the inverse-variance weighted (IVW) method. We performed the analyses for all invasive breast cancer, ER-positive, and ER-negative separately. The association of BMI with breast cancer-specific survival was previously evaluated in an earlier, smaller version of the BCAC dataset ( n  = 36,210) [ 41 ]. In this analysis, we included more patients, updated follow-up, and a larger BMI GWAS genetic instrument. It has been suggested that the potential negative effect of BMI on survival is especially relevant in postmenopausal women [ 12 ]. Therefore, we also tested whether the BMI associations differed between pre- (age at diagnosis under 50 years, n  = 27,009 with 2680 breast cancer-specific deaths) and postmenopausal women (age at diagnosis 50 years or older, n  = 59,617 with 4374 breast cancer-specific deaths). Inclusion of even a small percentage of a different ethnic group can affect the interpretation and validity of the causal estimates [ 42 ]. Because the genetic instrument that we used for BMI had 19% of non-European participants, we performed an additional analysis using the BMI European-specific summary estimates from the same GWAS available at the author’s supplementary material [ 25 ] (61 SNPs after filtering, Additional file 2 : Table S10).

IVW assumes that none of the variants exhibit horizontal pleiotropy, which may not be true in practice. Therefore, we also used the MR-Egger regression method that allows variants to demonstrate unbalanced pleiotropic associations. That is, MR-Egger regression relaxes the requirement of no horizontal pleiotropy provided that the pleiotropic effects are not proportional to the effects of the variants on the risk factors of interest [ 8 , 9 ]. In comparison to the IWM, the MR-Egger method’s intercept is not constrained to zero and provides a statistical test of the extent to which this intercept differs from zero as a measure of unbalanced pleiotropic effects.

For the risk factors with a significant association based on the IVW method (false discovery rate [FDR] < 0.05), we ran the following sensitivity analyses: heterogeneity tests, funnel plots, and leave-one-out tests. To assess the robustness of the results of the IVW method, we applied other MR methods (simple mode, weighted median, and weighted mode). We also tested all associations by performing the analysis using a multivariable model. In the multivariable model, we used imputed phenotypes [ 43 ] and adjusted for the following known prognostic factors: age of the patients at diagnosis; tumour size; node status; distant metastasis status; grade; ER-, progesterone receptor, and HER2-status; and (neo) adjuvant chemotherapy, adjuvant anti-hormone therapy, and adjuvant trastuzumab. Because breast cancer survival can differ on the short or longer term, we also assessed whether or not the associations would hold for the 5-year horizon, which is typically used in breast cancer prognostication [ 44 ]. For this analysis, we reduced the follow-up time from 15 to 10 years ( n  = 85,470 with 6147 breast cancer-specific deaths) and 5 years ( n  = 79,183 with 3573 breast cancer-specific deaths). Both in the multivariable model and the shorter follow-up analyses, we performed the MR analyses separately for OncoArray and iCOGS datasets and meta-analysed the results.

Relationships between BMI, T2DM, and breast cancer survival

To ensure that the effects of BMI and T2DM were independent, we identified SNPs that overlapped between the genetic instruments for these risk factors. Two SNPs, rs7144011 and rs7903146, were present in both the BMI and T2DM instrumental variables, and 12 (six pairs) SNPs were in linkage disequilibrium (LD): rs2972144, rs4072096, rs1801282, rs1899951, rs2112347, rs2307111, rs4715210, rs72892910, rs244415, rs889398, rs6059662, and rs6142096. We removed those 14 SNPs from the analyses to reduce the likelihood of horizontal pleiotropy. To further isolate the association of T2DM alone, we performed a multivariable MR model [ 45 ] by additionally including the genetically predicted BMI score as a covariate in the analyses of T2DM.

We found a significant association between genetic liability to T2DM and breast cancer-specific survival ( P  < 0.05, Table  2 ). For all breast cancers, T2DM was associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.04–1.18, P value [ P ] = 0.003, FDR = 0.023) (Fig.  1 and Table  2 ). T2DM was also associated with worse breast cancer-specific survival when restricting to ER-positive cases. The effect in the ER-positive subtype was consistent (HR = 1.09, CI = 1.01–1.18, P  = 0.036, FDR = 0.324) with the effect in all breast cancers. We did not observe associations at FDR < 0.05 (Table  2 ) between survival, for all breast cancer or by ER-subtype, and any of the other risk factors: alcohol consumption, BMI, height, mammographic density, menarche, menopause, physical activity, and smoking. The estimates we obtained from the models adjusted by other known prognostic factors (Additional file 1 : Table S2) were comparable to the initial unadjusted analyses for all risk factors. Under the current sample size of our study ( n  = 86,627 and 7054 events), the power to detect a causal association varied considerably between risk factors (Additional file 1 : Table S3). The estimated power was the largest for age at menopause and lowest for physical activity.

Effect of the nine breast cancer risk factors on breast cancer-specific survival in all breast cancers. The y -axis shows the −log 10 ( P value) effect for the association. The x -axis corresponds to log (hazard ratio) effect for each of the traits on breast cancer survival. The risk factors with false discovery rate (FDR) < 0.05 are coloured in red; the size of the circle is proportional to the −log 10 (FDR)

Genetic association between BMI by menopausal status and breast cancer-specific survival

We found no association between BMI and breast cancer-specific survival in any of the analysed subtypes, nor by menopausal status ( P  > 0.05): premenopausal (HR = 1.06, CI = 0.78–1.44, P  = 0.710) or postmenopausal women (HR = 1.02, CI = 0.80–1.30, P  = 0.899). The estimate using the European-specific BMI genetic instrument (HR = 1.14, CI = 0.94–1.38, P  = 0.174) was also not significant.

Genetic association between T2DM and breast cancer-specific survival

The HR estimate for T2DM and survival among all invasive breast cancers (HR = 1.10) was higher than that for either ER-subtype individually (ER-positive: HR = 1.09; ER-negative: HR = 1.09). This reflected the fact that the patients without ER-status information ( n  = 13,557) had a larger risk estimate (HR = 1.19, CI = 1.02–1.39, P  = 0.023).

To further validate the association between T2DM and breast cancer-specific survival, we performed the analysis using a shorter follow-up. The results were significant and similar to the main analysis both for 10-year (HR = 1.12, CI = 1.05–1.19, P  = 0.0006) and for 5-year follow-up (HR = 1.13, CI = 1.04–1.23, P  = 0.005). We also tested the association in a model adjusted by other known prognostic factors. The association of T2DM with breast cancer-specific survival in the adjusted model was still significant (HR = 1.10, CI = 1.02–1.18, P  = 0.013), and the effect size remained similar to the main T2DM analysis (HR = 1.10, CI = 1.04–1.18, P  = 0.003). Finally, we tried to replicate the result using another large and well-powered GWAS, i.e. the T2DM summary estimates from the DIAGRAM GWAS which is a large meta-analysis of 32 studies comprising data for 898,130 individuals (74,124 T2DM cases and 824,006 controls) of European ancestry [ 46 ]. The genetic instrument for this dataset included 152 SNPs (12 SNPs overlapping with the T2DM genetic instrument we initially used, Additional file 2 : Table S11). The association of T2DM with breast cancer-specific survival using the replication dataset was significant (HR = 1.18, CI = 1.04–1.33, P  = 0.009) and similar to the initial result (HR = 1.10).

Association between T2DM and breast cancer-specific survival with BMI adjustment

To explore the potential confounding effect of BMI with T2DM, we performed an analysis adjusting for genetically predicted BMI. The effect of BMI in this analysis was not significant (HR = 1.02, CI = 0.85–1.24, P  = 0.809), and the effect of T2DM on survival was similar (HR = 1.10, CI = 1.04–1.17, P  = 0.002) to the main T2DM analysis (HR = 1.10, CI = 1.04–1.18, P  = 0.003).

Causal association between T2DM and breast cancer-specific survival

We used different variations of the MR method to assess possible violations of the MR assumptions. Figure  2 shows that the range of MR methods used (simple mode, weighted median, and weighted mode) to assess the sensitivity of the findings all gave similar effect size estimates. Additionally, there was no evidence of pleiotropy based on the MR-Egger intercept test (MR-Egger intercept = 0.003, P  = 0.68, Fig.  2 ). In analyses using funnel plot (Additional file 1 : Figure S1) and a leave-one-out test (Additional file 1 : Figure S2), there was no indication for violation of the assumptions, nor that the association was driven by any particular SNP.

Plot showing the effect sizes of the SNP effects on breast cancer-specific survival for all breast cancers ( y -axes) and the SNP effects on T2DM ( x -axes) with 95% confidence intervals. Each dot represents one of the 95 SNPs used in the T2DM genetic instrument. The slopes indicate the estimate for each of the five different MR tests

We performed a Mendelian randomisation analysis to explore the potential causal effects on breast cancer-specific survival of nine established risk factors for breast cancer: alcohol consumption, BMI, height, mammographic density, menarche, menopause, physical activity, smoking, and T2DM. We used survival estimates from 86,627 European breast cancer patients with invasive breast cancer (by far the largest such dataset) and summary data from the GWAS Catalog for the nine risk factors. We used the IVW method to estimate causal effects and performed a wide range of sensitivity analyses to test the robustness of our findings.

Our analysis showed an association between genetic liability to T2DM and worse breast cancer-specific survival. The IVW method result was consistent with the results of other complementary MR-methods, and the performed sensitivity analyses did not give any statistical indication for violations of the MR assumptions. Additionally, the T2DM GWAS used was reasonably powered, with an estimated heritability of ~ 20% [ 32 ], supporting the relevance assumption. There was no evidence that the SNPs were associated with breast cancer survival (exclusion restriction). Finally, the association remained significant when adjusting for other known prognostic factors and when shortening the follow-up time to 10 and 5 years.

Because obesity and T2DM share some biological features such as elevated insulin levels, hypertension, and chronic inflammation [ 47 ] and since higher BMI has been associated with increased incidence of T2DM [ 48 ], we explored a possible interaction between the two risk factors. First, we ensured that there were no common SNPs between the T2DM and BMI genetic instruments or SNPs in LD that could be driving the association. Second, we performed BMI-adjusted analyses which also showed that the association was being driven by T2DM and not by BMI.

Earlier literature suggests an association between diabetes and worse breast cancer-specific survival [ 49 , 50 , 51 ]. There is no clear evidence linking diabetes to any particular ER-status specific breast cancer subtype [ 52 ] that could explain the poorer survival in women with T2DM. The increased mortality in patients with T2DM might be explained by the effect of insulin resistance or hyperinsulinemia, since breast cancer cells might have a selective growth advantage because of insulin receptor overexpression [ 53 , 54 ]. However, to our knowledge, no functional studies to evaluate this have yet been carried out. An important point to consider when interpreting the results is that, when using a binary risk factors such as T2DM, the genetic instrument estimate will only represent the average causal effect of the exposure in a fraction of the studied population (named “genetic compliers”). Additionally, the latter would only be true assuming that the monocity assumption is plausible, which means that increasing number of alleles for an individual would increase (or maintain constant) the risk of having T2DM [ 55 ].

All the other risk factors gave null results. Some of these may reflect the fact that there is no true association, but others may be underpowered since the fraction of variation of the risk factor explained by the genetic instrument was too small. The heritability explained by identified SNPs, and hence the power of the genetic instruments, varies substantially between risk factors, e.g. ~ 20% for T2DM [ 32 ] versus only 1% for the mammographic density GWAS [ 27 ]. In addition, we only kept genome-wide significant SNPs and dropped all SNPs in LD or with low imputation quality in the BCAC dataset, so the explained variation that we could utilise was smaller. As GWAS become larger and more powerful genetic instruments are available, it may be possible to find associations that could not be identified here. However, for those risk factors with a predicted small genetic component (e.g. physical activity), their association with breast cancer survival might not be assessable using an MR framework [ 8 ]. A potential limitation of our study is that some patients in the breast cancer survival dataset were also included in the GWASs for the risk factors, mammographic density (~ 2.5% overlap) and age at menarche (~ 27%) and menopause (~ 21%). However, because the genetic instruments of age at menarche and menopause were relatively strong and there was little overlap for mammographic density, we may expect the bias caused by patient overlap to be small [ 56 ]. Finally, another potential reason for which we did not observe association for some risk factors might be due to selection bias. This type of collider bias can lead to an under- or overidentification of genetic risk factors for breast cancer survival due to a relationship between the genetic risk factor concerned and breast cancer incidence [ 57 ]. This could be the case for BMI, age at menopause and menarche, or height, which have been causally associated with breast cancer risk [ 58 ]. For other risk factors such as T2DM or smoking, MR studies of incidence could not provide evidence for a causal association [ 59 , 60 ], which makes these genetic instruments less likely to be affected by selection bias.

To further explore the link between BMI and breast cancer survival, we also tested separately for pre- and postmenopausal status, but there was no indication for an association in any of the menopausal groups. Despite the evidence for an association between BMI and breast cancer survival from observational studies [ 12 , 13 ], our analysis on BMI and breast cancer-specific survival did not confirm this. A possible explanation is that obesity is associated with other comorbid conditions [ 48 ] that lead to poorer overall, but no breast cancer-specific survival. Additionally, it has been suggested that obese patients might receive suboptimal chemotherapy treatment compared to regular weight women [ 61 ] and tumours are usually detected at a later stage in obese patients [ 62 ]. This would, if insufficiently corrected for, lead to an association between high BMI and worse breast cancer-specific survival in observational, but not in MR, studies. The different observations of the relationship between BMI and survival from MR versus observational studies resemble those of genetic BMI and breast cancer risk [ 63 ], which were also deviant from epidemiological studies. To date, there is not a clear answer as to whether and how high BMI directly influences the biology of cancer [ 64 ].

From a clinical point of view, our analysis suggests that genetic liability to T2DM may contribute to variation in breast cancer outcomes in women of European ancestry. Such a genetic predictor might be included in prognostication models aimed at identifying women most likely to benefit from specific interventions. Furthermore, even though T2DM has a genetic component, it is also influenced by environmental and lifestyle factors and is potentially preventable [ 65 ]. Although our study does not address this directly, it seems sensible to recommend intensified management of T2DM, including lifestyle changes, in breast cancer patients.

The main strength of our study is the use of the biggest breast cancer dataset available so far and the use of SNPs as genetic instruments to reduce potential confounding. Despite including more than 7000 breast cancer-specific deaths in the analyses, our study was not well powered especially for the analysis within the subset of ER-negative tumours (as indicated by the broad confidence intervals). Additional findings might be possible when there are larger sample sizes available and a more complete follow-up. We also lacked power to detect associations for certain risk factors that had only a handful of SNPs in their genetic instruments such as mammographic density and physical activity. Finally, our results are applicable to women of European ancestry only. In order to be able to generalise these findings to other ancestry groups, larger breast cancer datasets are needed for the other ethnicities.

This two-sample MR analysis suggests that genetic liability to T2DM might be a cause of reduced breast cancer-specific survival. Our study provides further evidence for the importance of promoting a healthier lifestyle to improve survival in breast cancer patients.

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Abbreviations

Body mass index

Breast Cancer Association Consortium

Confidence interval

Estrogen receptor

False discovery rate

Genome-wide association study

Hazard ratio

Inverse-variance weighted

Linkage disequilibrium

• Mendelian randomisation

Randomised control trial

Single-nucleotide polymorphism

Type 2 diabetes mellitus

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Acknowledgements

BCAC: We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians, and administrative staff who have enabled this work to be carried out. We acknowledge all contributors to the COGS and OncoArray study design, chip design, genotyping, and genotype analyses.

BCAC is funded by Cancer Research UK (C1287/A16563, C1287/A10118), by the European Union’s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST, respectively), and by the European Community’s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344), and the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from the European Community’s Seventh Framework Programme under grant agreement no. 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. M.E.G was funded by the Dutch Cancer Society (grant 2015-7632).

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Maria Escala-Garcia, Anna Morra, Sander Canisius & Marjanka K. Schmidt

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Sander Canisius

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

Jenny Chang-Claude

University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg (UCCH), Cancer Epidemiology Group, Hamburg, Germany

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

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Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK

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M.K.S., S.C., and M.E.G. designed the study. M.E.G. performed the main data analyses and drafted the initial manuscript. A.M. performed the adjusted analysis. M.K.S., S.C., and M.E.G. interpreted the data. All authors were involved in the data collection, commented on the drafts, and approved the final version of the manuscript.

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The study was performed in accordance with the Declaration of Helsinki. Summary estimates were used from previously reported studies that followed the appropriate institutional review and patient consent procedures and followed the procedure the Data Access Coordination Committee (DACC) of BCAC ( http://bcac.ccge.medschl.cam.ac.uk/ ).

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Additional file 1: table s1..

List of breast cancer risk factors as indicated by Cancer Research UK. Information in this table was taken directly from: https://www.cancerresearchuk.org/about-cancer/breast-cancer/risks-causes/risk-factors (January 2020). Table S2. Comparison of the effect of nine breast cancer risk factors on breast cancer-specific survival for all breast cancers in the unadjusted model (left) and in the adjusted model (right). The model was adjusted for the known prognostic factors: age of the patients at diagnosis, tumour size, node status, distant metastasis status, grade, ER-, progesterone receptor and HER2-status and (neo) adjuvant chemotherapy, adjuvant anti-hormone therapy and adjuvant trastuzumab. HR = Hazard Ratio. CI = 95% Confidence Interval. Table S3. Power (%) estimation by a range of Hazard Ratios (HR) for the analysis of MR associations between nine breast cancer risk factors and breast cancer-specific survival in all breast cancers. Figure S1. Funnel plot for T2DM and breast cancer-specific survival. The plot shows the effect estimate (b) of a particular SNP against the SNP expected precision (1/Standard Error (SE)). Asymmetry in the funnel plot is an indication of horizontal pleiotropy. The dark and light blue lines represent the MR-Egger and Inverse variance weighted slopes respectively. Figure S2. Leave-one-out plot for T2DM and breast cancer specific-survival showing the estimate effect by sequentially dropping one SNP at a time. Each black dot in the forest plot represents the MR results (IVW method) excluding that particular SNP. The result including all SNPs is shown in red at the bottom of the plot.

Additional file 2:

SNPs used in the analyses for the nine risk factors. The risk factor estimates (beta and standard error (SE)) and breast cancer-specific survival estimates for each SNP are included. Table S1. Alcohol consumption. Table S2. Body mass index. Table S3. Height. Table S4. Mammographic density. Table S5. Menarche. Table S6. Menopause. Table S7. Physical activity. Table S8. Smoking behaviour. Table S9. Type 2 diabetes mellitus. Table S10. Body mass index European-specific. Table S11. Type 2 diabetes mellitus replicate.

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Escala-Garcia, M., Morra, A., Canisius, S. et al. Breast cancer risk factors and their effects on survival: a Mendelian randomisation study. BMC Med 18 , 327 (2020). https://doi.org/10.1186/s12916-020-01797-2

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The figure shows the percentage of women who answered that having the other risk factor put a woman at greater risk compared with dense breasts. Family history is defined as having a mother or sister who has or had breast cancer. Other race included women identifying as mixed race or another race or ethnicity. Data were missing for the following categories: being overweight or obese, 23; having 1 or more drinks of alcohol per day, 23; first-degree family history of breast cancer, 15; never having children, 27; having a breast biopsy, 32; and race and ethnicity, 1.

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Beidler LB , Kressin NR , Wormwood JB , Battaglia TA , Slanetz PJ , Gunn CM. Perceptions of Breast Cancer Risks Among Women Receiving Mammograph Screening. JAMA Netw Open. 2023;6(1):e2252209. doi:10.1001/jamanetworkopen.2022.52209

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Perceptions of Breast Cancer Risks Among Women Receiving Mammograph Screening

• 1 The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire
• 2 Section of General Internal Medicine, Boston University Chobanian and Avedesian School of Medicine, Boston, Massachusetts
• 3 Department of Psychology, University of New Hampshire, Durham
• 4 Department of Radiology, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts
• 5 Dartmouth Cancer Center, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire

Question   How do women perceive the breast cancer risk associated with breast density, and how do they plan to mitigate their risk?

Findings   In this qualitative study of women aged 40 to 76 years, family history was perceived as the greatest risk factor for breast cancer. In interviews, few women perceived breast density as a risk factor, and one-third thought that they could not take any actions to reduce their breast cancer risk.

Meaning   Despite laws that require women to be notified about breast density, women did not describe a strong understanding of the risk associated with breast density relative to other breast cancer risk factors.

Importance   Breast density is an independent risk factor for breast cancer. Despite the proliferation of mandated written notifications about breast density following mammography, there is little understanding of how women perceive the relative breast cancer risk associated with breast density.

Objective   To assess women’s perception of breast density compared with other breast cancer risks and explore their understanding of risk reduction.

Design, Setting, and Participants   This mixed-methods qualitative study used telephone surveys and semistructured interviews to investigate perceptions about breast cancer risk among a nationally representative, population-based sample of women. Eligible study participants were aged 40 to 76 years, reported having recently undergone mammography, had no history of prior breast cancer, and had heard of breast density. Survey participants who had been informed of their personal breast density were invited for a qualitative interview. Survey administration spanned July 1, 2019, to April 30, 2020, with 2306 women completing the survey. Qualitative interviews were conducted from February 1 to May 30, 2020.

Main Outcomes and Measures   Respondents compared the breast cancer risk associated with breast density with 5 other risk factors. Participants qualitatively described what they thought contributed to breast cancer risk and ways to reduce risk.

Results   Of the 2306 women who completed the survey, 1858 (166 [9%] Asian, 503 [27%] Black, 268 [14%] Hispanic, 792 [43%] White, and 128 [7%] other race or ethnicity; 358 [19%] aged 40-49 years, 906 [49%] aged 50-64 years, and 594 [32%] aged ≥65 years) completed the revised risk perception questions and were included in the analysis. Half of respondents thought breast density to be a greater risk than not having children (957 [52%]), having more than 1 alcoholic drink per day (975 [53%]), or having a prior breast biopsy (867 [48%]). Most respondents felt breast density was a lesser risk than having a first-degree relative with breast cancer (1706 [93%]) or being overweight or obese (1188 [65%]). Of the 61 women who were interviewed, 6 (10%) described breast density as contributing to breast cancer risk, and 43 (70%) emphasized family history as a breast cancer risk factor. Of the interviewed women, 17 (28%) stated they did not know whether it was possible to reduce their breast cancer risk.

Conclusions and Relevance   In this qualitative study of women of breast cancer screening age, family history was perceived as the primary breast cancer risk factor. Most interviewees did not identify breast density as a risk factor and did not feel confident about actions to mitigate breast cancer risk. Comprehensive education about breast cancer risks and prevention strategies is needed.

Dense breasts, in which breasts are composed of more glandular tissue relative to fatty tissue, is an independent, nonmodifiable risk factor for breast cancer and can mask cancer on mammograms. 1 Dense breast tissue is present in 40% to 50% of women undergoing screening mammography 2 and is associated with a 1.2 to 4.0 times higher risk of breast cancer (depending on degree of density) compared with a 2.0 times higher risk associated with a first-degree family history of breast cancer. 3 - 6 Other known risk factors include obesity, alcohol consumption, parity, and having a prior breast biopsy (eAppendix 1 in Supplement 1 ). 3 , 7 , 8 Although how much each risk factor or combination of factors affects overall breast cancer risk has not been completely characterized, 7 knowledge about personal risk is necessary to promote engagement in prevention, particularly for modifiable contributors, such as alcohol consumption and obesity.

Aiming to increase awareness and empower women to make informed choices about supplemental screening, laws enacted across 38 states mandate that women receive written notification about their personal breast density and its potential health implications. 9 Although laws vary among states, 9 they share an underlying goal of informing women about their personal breast cancer risk to promote informed decision-making about breast cancer screening and early detection.

Prior studies 10 - 17 have evaluated the association of breast density notification laws with women’s awareness of their individual breast density, masking bias, and the risks associated with breast density. Qualitative studies have found that few women are aware of the legislation around breast density notification, 15 that some women find breast density notifications to be confusing, 17 and that, although most women understand that breast density could mask cancer on a mammogram, few know that breast density is an independent breast cancer risk. 13 Cross-sectional surveys have found variation in women’s knowledge about breast density as a risk factor 10 - 12 , 14 , 16 ; variation in knowledge across racial and ethnic groups, income, and educational levels 11 , 14 ; that most women were aware of masking bias 11 , 14 , 16 ; and that women in states that mandated breast density notification were more likely to report having dense breasts. 14

Although the current literature explores women’s knowledge about breast density, a systematic review 18 noted that little is known about whether women understand the risk associated with breast density compared with other risk factors or their approaches to mitigating risk. We used a national survey and qualitative interviews to examine how women perceive breast density’s cancer risk relative to other breast cancer risk factors and their understanding of actions they could take to reduce breast cancer risk.

This mixed-methods qualitative study included survey data from a national, random-digit-dialing telephone survey coupled with semistructured interviews with a subset of survey respondents. Survey questions examined women’s perception of breast density in relation to other known breast cancer risks; interviews explored women’s understanding of breast cancer risk factors and actions to mitigate risk. This mixed-methods approach allowed us to examine the scope of awareness and understanding. On the basis of prior literature demonstrating differences in perceptions by sociodemographic characteristics, 11 , 14 we examined whether risk perceptions varied by self-reported race and ethnicity and by literacy level (high literacy [HL] vs low literacy [LL]). This study was reviewed by the Boston University Medical Campus Institutional Review Board, which determined that the study met federal exemption criteria and provided a waiver of documentation of informed consent. Approval was for the qualitative interviews (survey work was conducted by an external survey firm) and at the time of transcription. All interview data was deidentified. The study followed the Standards for Reporting Qualitative Research ( SRQR ) for reporting qualitative data. 19

The sampling frame consisted of 2306 participants who completed a national, random-digit-dialing survey of the effect of states’ breast density notification laws on knowledge about breast cancer risks associated with breast density. Eligible participants were aged 40 to 76 years, reported having undergone mammography in the prior 2 years, had no history of breast cancer, and had heard of breast density. Within the population-based sampling, efforts were made to ensure a sufficient sample of women from diverse racial and ethnic backgrounds, from states with and without breast density notification laws, and with lower literacy levels, as detailed in prior publications. 20 , 21 Participants were asked in the survey to self-identify their race or ethnicity. We collected race and ethnicity data to allow for oversampling across some groups to ensure that we could conduct analyses that compared findings across groups.

After completing the survey, women who reported knowing their breast density were invited to participate in a qualitative interview. Those who responded affirmatively were called to schedule an interview. We purposively sampled equal numbers of women who identified as Black, Hispanic, White, or other race or ethnicity as well as those with HL vs LL. In the survey, participants were asked to self-identify their race from a list that included Asian, Black or African American, Native American, Pacific Islander, White, mixed race, or some other race. For these analyses, anyone who responded that they were Native American, Pacific Islander, mixed race, or some other race were classified as other race. For the qualitative interviews, we included respondents who were Asian in the other race category.

Breast density awareness and breast cancer risk questions were adapted from measures used in prior surveys, 10 , 11 , 22 with modified measures tested by patient advisory group members. Advisory group members also reviewed the interview guide. The survey firm, SSRS, conducted all surveys using a standardized interview approach (eAppendix 2 in Supplement 1 ). The cooperation rate for the overall survey was 85%. 20 Survey administration spanned July 1, 2019, to April 30, 2020, and took approximately 10 minutes. Qualitative interviews were conducted from February 1 to May 30, 2020, and lasted 30 to 45 minutes. Qualitative interviews followed a flexible, semistructured interview guide (eAppendix 3 in Supplement 1 ) and were audiorecorded and transcribed. All data were collected via telephone by trained interviewers.

This mixed-methods qualitative study focused on women’s perceptions of breast cancer risks, examining how women rate certain risks relative to the risk of breast density. Women were asked to compare the risk of breast density with 5 other breast cancer risk factors (having a first-degree relative with breast cancer, being overweight or obese, having more than 1 alcoholic drink per day, never having children, or having a prior breast biopsy). A review of data from the first 448 survey participants revealed that wording of the risk perceptions questions was confusing. We revised the questions and excluded those participants from analyses due to identified measurement error and incompatibility of responses with subsequent risk questions. For each risk factor, participants were asked the question, “Which do you think puts someone at greater risk for developing breast cancer? Having dense breasts or…” Risk factors were elicited in a random order to minimize ordering bias.

We characterized the proportion of women who said having dense breasts puts someone at a greater risk for developing breast cancer vs the alternative risk factor or “don’t know”; participants with missing responses were excluded from analyses (<1%). Bivariate χ 2 analyses assessed whether the proportion of women who said having dense breasts puts someone at greater risk for developing breast cancer was associated with participants’ race and ethnicity (coded as Asian, Black, Hispanic, White, and other category not listed) or literacy level (HL or LL). Low literacy was defined as either having less than a high school education or reporting sometimes, often, or always needing assistance to complete medical forms using the validated Single Item Literacy Screener. 23 We used SPSS statistics software, version 26 (IBM Inc). 24 Statistical significance was defined at α = .05. We followed the American Association for Public Opinion Research ( AAPOR ) reporting guidelines for survey data. 25

Women were asked in an open-ended fashion what they thought contributed to breast cancer risk and how they could reduce their breast cancer risk. To organize and support analyses, we developed an analytic memo that described all observed themes. 26 We used a matrix coding approach to guide development of themes and justify inclusion or exclusion of interviewees within themes. 27 This approach includes arranging data within a table where individual participants represent rows and themes represent columns. We analyzed whether themes varied across literacy levels or across racial and ethnic groups. Qualitative analyses were overseen by a doctoral-level health services researcher (C.M.G.) with expertise in qualitative methods. Two masters-level trained research coordinators and 1 doctoral student participated in data collection and analysis, including co-coding and consensus determination meetings.

Of the 2306 women who responded to the survey, 1858 (166 [9%] Asian, 503 [27%] Black, 268 [14%] Hispanic, 792 [43%] White, and 128 [7%] other race; 358 [19%] aged 40-49 years, 906 [49%] aged 50-64 years, and 594 [32%] aged ≥65 years) completed the revised risk perception questions and were included in the analysis ( Table 1 ). In comparing risk factors with the risk associated with breast density, 1706 women (93%) viewed family history of breast cancer as the greater risk, and 1188 (65%) felt that being overweight or obese was a greater risk than breast density. Half of respondents thought that breast density was a greater risk than not having children (957 [52%]), having more than 1 alcoholic drink per day (975 [53%]), or having a prior breast biopsy (867 [48%]) ( Figure ). A higher proportion of women with LL compared with women with HL rated breast density as a higher risk than family history (13% vs 7%; χ 2 1  = 12.99, P  < .001), alcohol consumption (60% vs 53%; χ 2 1  =  5.41, P  = .02), and never having children (60% vs 51%; χ 2 1  = 7.39, P  = .007). A higher proportion of Black women (290 [58%]) and Hispanic women (153 [58%]) rated dense breast as a higher risk than alcohol consumption compared with women of other races (χ 2 4 13.63, P  = .009). A total of 289 Black (58%) and 153 Hispanic (58%) women also rated dense breasts as a higher risk than nulliparity than women who identified as Asian (74 [45%]), White (377 [48%]), and other race (64 [52%]) (χ 2 4  = 17.48, P  = .002).

Among 61 women interviewed, few women perceived breast density as contributing to their risk of developing breast cancer. Most women correctly noted that breast density could make mammograms harder to read: “It’s difficult to detect subsequent lumps or potential problem areas because of the dense breast tissue.” (Black woman, HL, respondent 7). When asked about their personal risk factors for breast cancer, few women noted that breast density could be a risk factor. One woman described her concern by saying, “Maybe 10% more worried than I was before because of the dense tissue issues. Just a slight uptick, but it’s not overwhelming” (Hispanic woman, HL, respondent 17).

Women most frequently and confidently emphasized family history of cancer or genetic factors as contributing to their own breast cancer risk ( Table 2 ), and many viewed this as conferring very high levels of risk. One woman estimated her own risk as “probably 50/50 at this point since my mother had breast cancer” (Black woman, HL, respondent 5). Concurrently, women who had no known family history seemed to minimize the possibility of developing cancer: “I’m not worried about it because it does not run in my family. So I don’t have to worry about dodging that bullet” (Hispanic woman, LL, respondent 23).

Table 2 displays risk factors cited by women, ordered by the prevalence of the theme across participants. Reported risk factors included diet, lifestyle, smoking and environmental exposures, breast density, obesity, alcohol consumption, and reproductive history. Unlike family history, most women did not voice confidence in their understanding of other risk factors. Instead, they spoke about a series of behaviors and exposures that they perceived as related to their health overall: “We blame smoking for everything. So I’m sure smoking’s on there” (Black woman, HL, respondent 5). Few women stated that they had no knowledge of what breast cancer risk factors were: “I have no idea. All the stuff that’s been here on the news. This chemical, that chemical...” (Black woman, HL, respondent 8). We did not observe differences in understanding or perception of personal breast cancer risk by health literacy level or by racial or ethnic group.

When asked about actions that could reduce their breast cancer risk, many women described detection methods, such as breast self-examinations and mammograms, as prevention strategies. Among women who discussed mammograms or breast self-examinations, a small subset noted that screening methods would not prevent cancer but were useful for potentially detecting breast cancer earlier: “Well, if I go for my annual mammogram and do self-breast examination, I will catch whatever’s growing in my breast will be nipped in the bud...It will be taken care of before it gets out of control” (White woman, LL, respondent 54).

Women’s descriptions of risk mitigation focused on mammography, with descriptions conflating early detection and prevention. Other ideas for reducing personal breast cancer risk included improving diet, maintaining a healthy weight, quitting smoking, avoiding secondhand smoke, limiting alcohol, and exercising ( Table 3 ). Many women suggested behaviors that they thought could improve their overall health but expressed less certainty about the direct effect on their breast cancer risk: “I try to eat a healthier lifestyle, more in the vegetable fields, less in any kind of…dairy or red meat portions. I do exercise more, but I did that for my general health, not for breast cancer” (Hispanic woman, HL, respondent 17).

Many women (17 [28%]) stated that they were not sure if it was possible to reduce their breast cancer risk or that they did not know what actions they could take to reduce their risk: “Do people even know how to prevent breast cancer? I couldn’t even say” (woman of other race, HL, respondent 30). Neither health literacy level nor race or ethnicity appeared to differentiate how women perceived actions that they could take to reduce their breast cancer risk.

This mixed-methods qualitative study demonstrated that women perceived family history as the strongest risk factor for breast cancer, with mixed perceptions about other lifestyle or clinical risk factors in relation to breast cancer risk. Among interview respondents who knew their breast density, few women noted breast density as a breast cancer risk factor. Few women understood options for mitigating their personal breast cancer risk.

Despite breast density being associated with a 1.2 to 4 times higher risk of breast cancer, 1 , 5 , 6 few women perceived breast density to be a strong personal risk factor. This finding is not surprising because prior studies 11 , 14 have shown variable rates of women indicating that breast density contributed to breast cancer risk (23%-66%). Qualitative studies 13 , 17 , 28 , 29 of women receiving breast density notifications found that women did not fully understand the clinical term breast density . It is possible that notification language stressing the normality of dense breast tissue in the population confers a sense of reassurance that may contribute to the downplaying of breast density as a risk factor. 13 , 29

In both interviews and surveys, women perceived family history as highly deterministic of future breast cancer. Women without a family history believed they were safe or had limited risk based on this factor alone. Other studies 30 , 31 have similarly found that women with family histories of breast cancer perceived their personal risk of cancer to be higher than the estimated risk associated with their family history. The emphasis on family history may be in part a result of clinical elicitation of family and genetic risk factors, including the increased emphasis on genetic testing for BRCA1/2 genes, both clinically and in popular media. 32 , 33 A 2021 systematic review 34 found that in primary care, family history is often the only risk factor elicited to counsel patients on breast cancer risk. Thus, frequent health messaging around family history and breast cancer risk may play a role in how this sample of women perceived their own breast cancer risk. Interviewed women displayed little confidence in their ability to modify their cancer risk, suggesting a need for more comprehensive education about which risk factors are amenable to intervention.

Few women identified ways in which they could reduce their breast cancer risk. When mentioned, these actions included participating in regular screening, diet and exercise, and avoiding tobacco ( Table 3 ). Many women suggested that breast self-examinations were important to maintaining their breast health, but these examinations are no longer recommended because of a lack of evidence of benefit. 35 (p179) 36 Women also suggested actions that they thought were generally healthy lifestyle changes, but they were not confident these actions would alter their breast cancer risk. Women may benefit from general guidance and information about cancer prevention strategies, such as tools that can help patients understand overall cancer risk and prevention options. 37 Clinical treatments, such as chemoprevention agents, are available to reduce breast cancer risk for women at elevated risk (>1.7% 5-year risk as determined by a validated risk model) 38 , 39 but were not mentioned by any interviewees. This finding is not unexpected because chemoprevention is significantly underused by the eligible population, 40 - 42 despite being recommended for women at elevated risk. 43

This study has some limitations. Despite efforts to include a racially and ethnically diverse sample on the telephone survey panel, nonresponse bias could have influenced findings. The survey did not ask about women’s perception of the absolute risk associated with each risk factor, limiting our ability to draw conclusions about the accuracy of women’s risk perceptions. Interviewees reported being informed of their personal breast density, but we were unable to verify the nature or timing of this notification. We defined low literacy using a single-item literacy scale combined with educational level, which is an imprecise way to measure literacy, limiting our ability to draw conclusions about the direct effect of literacy on risk perception.

Our study, coupled with prior research, 12 , 14 , 18 , 20 suggests that understanding of breast density’s contribution to breast cancer risk remains underappreciated by many women. Most notifications encourage women to speak with their physicians, yet prior studies 15 , 44 - 47 found that many clinicians do not feel comfortable counseling on the implications of breast density and cancer risk. Efforts to communicate breast density in part are intended to align with evidence suggesting that breast cancer screening services should be tailored to personal risk to maximize the benefits and avoid undue harms, 48 - 50 rendering conversations about risk imperative. Women with dense breasts, and thus some elevated risk, are ideal candidates for risk assessment. Tools that incorporate breast density in risk measures, such as one from the Breast Cancer Surveillance Consortium, 51 , 52 can inform future screening behaviors, including the opportunity for supplemental screening. Supplemental screening not only can lead to increased rates of cancer detection but also may result in more false-positive results and recall appointments. 53 - 55 Because supplemental screening is not recommended for women at average risk, 55 clinicians should use risk assessment to guide discussions with patients about tradeoffs associated with supplemental screening.

Despite available guidance on breast cancer risk assessment to inform screening decisions, 56 , 57 such assessments are underused in primary care. 58 - 60 Reported barriers include inadequate time, lack of integrated tools, and uncertainty in interpreting results for decision-making. 58 A review 61 of interventions involving the use of risk assessment tools in primary care concluded that more comprehensive interventions that combined risk assessment with decision support were more likely to have an effect on behavior. In some cases, it may be beneficial to develop partnerships between primary care and radiology to help counsel women on appropriate supplemental screening and/or preventive measures. 62 In summary, future laws or regulations involving breast density notifications should ensure that communications promote a more comprehensive understanding of breast cancer risk to inform choices about screening and prevention.

Accepted for Publication: December 2, 2022.

Published: January 23, 2023. doi:10.1001/jamanetworkopen.2022.52209

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Beidler LB et al. JAMA Network Open .

Corresponding Author: Christine M. Gunn, PhD, Dartmouth Cancer Center, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Dartmouth College, 1 Medical Center Dr, Williamson Translational Research Bldg, Level 5, Lebanon, NH 03765 ( [email protected] ).

Author Contributions: Dr Wormwood had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Kressin, Battaglia, Gunn.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Beidler, Gunn.

Critical revision of the manuscript for important intellectual content: Kressin, Wormwood, Battaglia, Slanetz, Gunn.

Statistical analysis: Wormwood.

Obtained funding: Kressin.

Supervision: Slanetz, Gunn.

Conflict of Interest Disclosures: Dr Battaglia reported receiving grants from Boston Medical Center during the conduct of the study. Dr Slanetz reported receiving royalties from Wolters-Kluwer outside the submitted work and serving as subspecialty chair of the American College of Radiology Appropriateness Criteria Breast Imaging Panels. Dr Gunn reported receiving grants from the American Cancer Society during the conduct of the study and receiving grants from the National Cancer Institute and consultation fees from Gilead Sciences outside the submitted work.

Funding/Support: This study was supported by grant RSG-133017-CPHPS from the American Cancer Society (principal investigator, Dr Kressin). Dr Gunn was funded in part by the National Cancer Institute (K07CA221899; principal investigator, Dr Gunn).

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Disclaimer: The views expressed here do not necessarily reflect the views of the American Cancer Society.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: Ariel Maschke, MA, Magdalena Pankowska, MPH, and Cristina Araujo Brinkerhoff, MA (Section of General Internal Medicine, Boston University Chobanian and Avedisian School of Medicine) contributed to qualitative data collection activities. They were all employed by Boston Medical Center at the time of their involvement with the project, and their role on the project was associated with their positions; they were not compensated for their work.

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• Published: 18 May 2020

Knowledge of symptoms and risk factors of breast cancer among women: a community based study in a low socio-economic area of Mumbai, India

• Ranjan Kumar Prusty 1 ,
• Shahina Begum 1 ,
• Anushree Patil 2 ,
• D. D. Naik 1 ,
• Sharmila Pimple 3 &
• Gauravi Mishra 3  

BMC Women's Health volume  20 , Article number:  106 ( 2020 ) Cite this article

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Breast cancer (BC) is leading cancer among women in India accounting for 27% of all cancers among women. Factors that make the policymakers and public health system worried are rising incidence of breast cancer in India and more importantly high death rates among breast cancer patients. One of the leading causes of high breast cancer deaths is lack of awareness and screening leading to the late presentation at an advanced stage. Therefore, the current research aimed to understand the knowledge of breast cancer symptoms and risk factors among women in a low socio-economic area of Mumbai.

A cross-sectional study was conducted at Prabhadevi, Mumbai and primary data was collected from 480 women aged 18–55 years. Structured questionnaire was used to collect quantitative data pertaining to awareness, signs and symptoms of breast cancer. Bivariate and multivariate regression techniques were used for understanding of the socio-demographic differentials in breast cancer awareness among women.

The study found that around half (49%) of the women were aware of breast cancer. The women who were aware of breast cancer considered lump in breast (75%), change in shape and size of breast (57%), lump under armpit (56%), pain in one breast (56%) as the important and common symptoms. Less than one-fifth of the women who were aware of breast cancer reported early menstruation (5.6%), late menopause (10%), hormone therapy (13%), late pregnancy (15%) and obesity (19%) as the risk factors for breast cancer. The multivariate regression analysis showed women who had more than 10 years of schooling (Adjusted Odds Ratio: 3.93, CI: 2.57–6.02, P  < 0.01) were about 4 times more likely to be aware of breast cancer than women who had less than 10 years of schooling.

In conclusion, knowledge of danger signs and risk factors of breast cancer were low among women in the community. This may lead to late detection of breast cancer among women in the community. Therefore, the study calls for advocacy and larger intervention to enhance knowledge of breast cancer among women in the particular region with a special reference to women with low education.

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Cancer incidence and mortality are growing at a vigorous pace across the globe and this transition is most striking among emerging economies. Globally, one-fourth (25%) i.e. 2.1 million cases of all female cancer diagnosed in 2018 were of breast cancer [ 1 ]. It is most commonly diagnosed cancer among females in more than 150 countries. Out of these 150 countries, breast cancer is the leading cause of mortality among all female cancers in 100 countries. The recent GLOBOCAN 2018 report shows age-standardised breast cancer incidence rate per 100 thousand females was very high in Australia (94.2), Western Europe (92.6) and Northern Europe (90.1) whereas it was lowest in South–Central Asia (25.9) region. However, the mortality rate in South Asian countries is more or less similar with greater mortality rate among most developing countries [ 1 ].

In India, the age-adjusted incidence rate of breast cancer was 25.8 per 100,000 women making it leading cancer among Indian females in 2012 [ 2 ]. Although the incidence rate was lower than many developed countries, it’s rapidly rising in Indian cities and the mortality rates were more than the United Kingdom (UK) (12.7 in UK vs 17.1 in India per 100 thousand women) which had a high incidence rate of 95 per 100 thousand females. According to National Cancer Registry Programme and GLOBOCAN 2018, there were 1,62,468 new cases of breast cancer and 87,090 deaths were reported for breast cancer in India [ 3 ]. In addition, there is a huge spatial variation across the nation with highest rates found in North-Eastern Indian states and major metropolitan cities like Mumbai, New Delhi, Kolkata and Chennai [ 4 ]. Detection of malignancy at advanced stages mainly leads to high death rates in India [ 5 , 6 , 7 ]. Lack of knowledge of signs and symptoms is considered as one of the major reasons contributing to the late detection backed by cumbersome referral pathways for diagnosis, lack of proper regional centres for treatment, incomplete treatment due to high out of pocket expenditures and several socio-economic, geographical, and cultural barriers associated with women’s health [ 5 , 6 , 8 , 9 ]. The high death among women suffering from breast cancer is a concern for the national policymakers in addition to the increasing incidence rate.

There are multiple demographic, social and biomedical risk factors of breast cancer. Age of the women, early age at menarche, delayed first birth and menopause, nulliparity, short duration lactation, use of birth control pills, obesity, excess consumption of fats, hormone replacements and more importantly women having family history are considered as significant risk factors of breast cancer by various epidemiological and clinical studies [ 10 , 11 , 12 ]. One of the meta-analysis by Vishwakarma et al. [ 10 ] carried on 24 observational studies stated that highest odds ratio (OR) obtained for risk of breast cancer was among those who never had breastfeeding (pooled OR 3.69, 95% Confidence Interval = 1.70–8.01), never married women (pooled OR = 2.29, 95% CI = 1.65–3.17), and nulliparous women (pooled OR = 1.58, 95% CI = 1.21–2.06) [ 10 ]. One of the studies in South India found higher risk of breast cancer in urban area than rural areas [ 11 ]. This study also reported that the odds of breast cancer among urban women which increased with increase in proportion of overweight or obese (BMI-body mass Index > 25), size of the waist (> 85 cm) and size of hip (> 100 cm) among both pre-menopausal and post-menopausal women. Another study in rural Maharashtra found that most of the breast cancer cases were confined to women aged 40–49 years, home makers and upper economic strata group. Further, this study found breast cancer risk was 8 times higher among unmarried women, 3 times more among nulliparous women, 2 times more likely among post-menopausal women, 10 times more among those who had never breastfed, 1.5 times higher among women who were exposed to hormonal contraceptives and 4.5 time more likely among women with history of ovarian diseases than in comparison to married, non-nulliparous, premenopausal, women who ever breastfed, who have not been exposed to hormonal contraceptives, and women without any ovarian diseases respectively [ 12 ]. There are also studies which found difference in exposure to different type of environmental pollutants as a risk factor to breast cancer [ 13 ].

Several studies focused on different preventive and curative interventions which were carried both internationally and in India [ 14 , 15 , 16 , 17 , 18 , 19 ]. Although breast cancer prevention remains a baffling task due to involvement of multiple cell types at multiple stages, most intervention literature on breast cancer suggested that modifiable risk factors may be prevented through promotion of healthy diet, regular physical activities, regulating alcohol consumption and controlling weight which is likely to reduce the incidence of breast cancer in longer time period [ 20 ]. Further, literature also suggest that delay in detection leads to poor survival and early detection leads to better and economic treatment [ 21 , 22 , 23 ]. The delays were most among the older women and were mainly due to poor knowledge of symptoms and erroneous belief related to breast cancer and it’s treatment [ 22 ]. Therefore, the present paper tries to understand the knowledge of signs, symptoms and risk factors of breast cancer among women in the study area of Mumbai.

The study was concentrated to lower socio-economic area catered by Prabhadevi maternity home and health post which comes under Municipal Corporation of Greater Mumbai (MCGM). Mumbai has a mixed health care system, inclusive of services provided by local bodies, the government of Maharashtra and public trusts and private service providers. The MCGM runs a network of primary, secondary and tertiary level facilities through medical college and hospitals, municipal general hospitals and speciality hospitals, maternity homes, dispensaries and health posts. The primary healthcare services are provided by health posts and dispensaries whereas maternity home provides specialized delivery care. The health posts were established to provide primary health services mainly in slum areas. The Prabhadevi maternity home and health post provides both primary healthcare services and maternal health care to lower socio-economic population in the Prabhadevi area of Mumbai.

The data used for the current study came from primary data collected for baseline survey of a breast cancer intervention study. The tertiary cancer specialized hospitals bear most of the burden of screening and treatment of breast cancer in India. The primary healthcare facilities in India is not well equipped with required human resources and training for cancer screening leading to late detection of cancer. So, this intervention was to test screening of breast cancer at primary care level for early detection of breast cancer cases with the available resources at present. The Prabhadevi facility was chosen for this study because it is both women centric and provides primary health care services. The cross-sectional baseline survey was conducted during November 2018 to March 2019.

The details of inclusion and exclusion criteria, sample size, sampling procedure, data collection and analysis are given below:

Inclusion criteria

Women between 18 and 55 years of age were included in the study.

Exclusion criteria

Women who were already diagnosed with breast cancer and under treatment, pregnant women and lactating women were excluded from the study.

Sample size

About 80% of women aged 30–50 years were aware of breast cancer in Vikhroli, Mumbai [ 17 ]. However, our study focused on women 18–55 years of women. One of the study in similar settings at Delhi found around half (53%) of the women (aged 14–75 years) were aware of breast cancer [ 15 ]. Thereby considering 53% prevalence, 5% level of significance and 20% non-response rate, the required sample size was calculated as 478. Information was collected from 480 women participants.

Sampling procedure

The complete area under Prabhadevi maternity home and health post was identified through the map available with Municipal Corporation of Greater Mumbai (MCGM). This health post is located at G-South ward of Mumbai. With the help of MCGM record, the low-income group housings based on criteria set by Maharashtra Housing and Area Development Authority (MHADA) were identified. Around 76 thousand low income group community population (according to MHADA, Government of Maharashtra) is catered by Prabhadevi Maternity Home under Municipal Corporation of Greater Mumbai. The whole area with around 19 thousand households was divided into 16 sections of around 1000–1400 households based on areas covered by 16 Community Health Volunteers at the health post. Mapping and house listing of the selected area/community was done to prepare a list of households having eligible women. Systematic random sampling was used to select the 480 eligible women from the list . Kish grid method was used to select women in case more than one woman was found eligible in the selected household [ 24 ].

Data collection tools (baseline)

The tools were divided into two sections a) socio-economic background of the participants b) knowledge about breast cancer with questions related to awareness and practices (See supplementary file ). The socio-economic background section focused on collecting individual level information like age, education, religion, caste, marital status of the participants. The second section was used to assess the women’s knowledge regarding breast cancer, sign and symptoms, risk factors, Breast Self-Examination (BSE), and Clinical Breast Examination (CBE) using a structured questionnaire. Women participants were asked whether they had ever heard of breast cancer. Those who have heard of breast cancers were further asked about knowledge of breast cancer signs and symptoms, risk factors and current practices using closed response questions. The questionnaire was prepared using existing literature and in consultation with the study team as well as experts constituting of oncologists, gynaecologist, public health, and social scientist. The tools were translated to both Hindi and Marathi languages for the convenience of participants. These questions were pilot tested with 20 participants (10 Hindi and 10 Marathi questionnaires each) at a similar socio-economic setting of Mumbai. The results from this pilot testing were used for modification of the words for easy comprehension of the participants. The content validity was ensured through expert consultation and pilot testing of the questionnaire. The field investigators were trained for 1 day and made familiar with the questions and ways of asking the questions. The data was collected through face to face interview with participants. Regular back-checks were conducted at the office to ensure data quality. The response rate was 96% for this baseline study.

Statistical analysis

Univariate and bivariate analysis were performed using percentage and median to know the profile of study participants, proportion of women who were aware of symptoms, risk factors and screening methods and socio-economic differential in those symptoms and risk factors. Multivariate logistic regression was used to know the socio-demographic predictors of breast cancer awareness among women in the study area. The data were analysed using IBM SPSS 26.0 packages.

Dependent variables

Women were asked ‘Have you ever heard of breast cancer?’. The response ‘Yes’ is coded as 1 and response “No” was coded as 0. This is used as a proxy variable for breast cancer awareness. Bivariate and multivariate binary logistic regression analysis was performed to see the differential and predictors of awareness of breast cancer. The other dependent variables used were specific symptoms, signs and risk factors of breast cancer to see differential socio-economic characteristics.

Independent variables

Different socio-economic variables like age, religion, caste, working status, marital status, and years of schooling of women were used as independent variables in this study.

Ethical permission

The Indian Council of Medical Research-National Institute for Research in Reproductive Health (ICMR-NIRRH) Ethics Committee for clinical studies, Mumbai has approved this study in compliance with the Helsinki declaration. Written consent from the participants was obtained during data collection. The confidentiality of the data was maintained during all the stages of research- data collection, data cleaning, and dissemination of research results.

Profile of the study participants

The median age of the participants was 39 years and 98% of the women ever attended school. The median year of schooling was 12 years. The religious composition showed 93% of women were Hindu, 3 % of women were Buddhist/Neo-Buddhist and the remaining 4 % were from Christian, Jain, Muslim religions. More than two-thirds of the women (69%) were from upper caste or no caste groups whereas one-fourth of them were Other Backward Classes (OBC) and around 6% of the women were Scheduled Caste or Scheduled Tribe (SCs/STs). Only 16% of the women were employed. Majority of women (84%) were married and 77% of them had at least one child.

Breast cancer awareness

About half (49%) of these women were ever heard of breast cancer. Breast cancer awareness was poor among women educated upto high school (10th) or not educated with only one-third of (34%) them ever heard of it. Nearly two-thirds of the women (61%) educated above 10th standard (higher education) were aware of breast cancer. Breast cancer awareness was better among middle aged women (25–34 years) than in comparison to younger (18–24 years) and older women (Table  1 ). Majority of these women had heard about breast cancer through television (53%) or from a doctor (25%) (Fig.  1 ).

Different sources of knowledge of breast cancer among women (%) who were aware of it (N = 234)

Multivariate analysis

The binary logistic regression analysis showed that education was the only significant predictor of breast cancer awareness (Table 1 ). The education of women was significantly and positively associated with awareness of breast cancer. The women who had more than 10 years of schooling (AOR: 3.93, CI: 2.57–6.02, P  < 0.01) were about 4 times more likely aware of breast cancer than in comparison to women who had less than 10 years of schooling or no education.

Knowledge of different signs and symptoms

The knowledge of different symptoms among women ever heard of breast cancer ( N  = 234) is depicted in Fig.  2 . Lump in breast was considered as a symptom of breast cancer by three-fourths of women. Interestingly, less than half of the women said abnormal discharge or blood from nipple (48%), change in shape or size of nipple (48%) and change in skin colour (47%) as symptoms of breast cancer. Only two out of five women (40%) thought breast cancer can be hereditary (not shown in figure).

Percentage of women who had knowledge of different signs or symptoms of breast cancer

The Table  2 shows the socio-economic differential in knowledge of danger signs of breast cancer among the women who were aware of breast cancer. The knowledge of different symptoms was less among marginalized classes like Scheduled Caste, Tribe and Other Backward Classes (SC or ST or OBC) group than in comparison to the other higher caste groups. A greater proportion of women, who were working had knowledge of different signs and symptoms of breast cancer than in comparison to women who were not working. It was also observed from the study that unmarried women had greater knowledge of all symptoms than in comparison to married women. No clear differential was found among age groups of women. Around half of the women believed ‘breast cancer means losing one’s breast’. Most women knew that breast cancer is not communicable (Table  3 ).

Knowledge of risk factors

Understanding the risk factors of BC may help women in taking preventive measures. In this study, women who were aware of breast cancer ( N  = 234) were asked about the risk factors of breast cancer. The percentage of women who identified breast cancer risk factors are shown in Fig.  3 . Most women believed consumption of excess tobacco (45%) and alcohol (44%) leads to breast cancer followed by risk factors like past history of BC (39%), no breastfeeding (39%), consumption of high fat foods (34%) and family history (31%). The knowledge of important biological risk factors like early age of menstruation (6%) and late menopause (10%) were very low among the women, although they had heard of breast cancer.

Percentage women who identified the risk factors of breast cancer

The socio-economic differentials showed that with an increase in age of women, the knowledge of different risk factors goes down (Table  4 ). Further, the risk factors knowledge was slightly higher among higher educated women compared to the women who had education till secondary school (10th standard). Women from nuclear family, not working and married woman had lower knowledge of most of the risk factors than in comparison to women from joint family, working and unmarried women respectively. However, the overall knowledge of risk factors was low among all women even though they are aware of breast cancer.

Knowledge and practice of breast examination

Of all 480 women, only 6.5% of women knew that breast cancer can be detected through Breast Self-Examination (BSE). Around two out of five (42%) women said cancer in breast can be detected through clinical examination (Fig.  4 ). Our results showed that around 10% of the women had undergone breast cancer screening. However, only 3.1% were trained in BSE and 2.5% of them were performing BSE. Around 2% of the women were performing BSE monthly (Fig.  5 ). Almost all women (99.4%) were interested to learn BSE procedure besides three women who were shy of it (not shown in figure).

Percentage of women who are aware of breast cancer screening

Percentage of women who have undergone screening of breast cancer and performing self-examination of breasts

This study found that breast cancer knowledge among the women in the study area was poor. Only less than half of the women were aware of breast cancer. This proportion was found to be consistent with two of the studies in India conducted in Mumbai (2009) and Delhi (2015) and one studies conducted in Addis Ababa, Ethiopia [ 15 , 19 , 25 ]. On the contrary, a recent study in Mumbai among 18–70 years of women found higher (71%) proportion of knowledge about breast cancer symptoms [ 26 ]. Television was found to be the most important source of breast cancer awareness. Our analysis of these 480 women found education as one of the crucial socio-economic factors that influences breast cancer awareness in Mumbai. Our bivariate and multivariate results have also shown consistent results on educational level and breast cancer awareness. A study by Dey et al. (2015) in Delhi also found an association between education and breast cancer awareness [ 15 ].

It is important to note that though half of the women were aware of breast cancer, the knowledge of different symptoms was low among these women. Lump in breast is considered as danger sign by most of the women whereas more than half don’t think abnormal discharge/blood, change in shape or size, and change in colour of nipple as danger signs of breast cancer. Another study in Vikroli, Mumbai also found similar results with a very low percentage of women saying the change in shape/ size of breast, discharge from nipple and inverted nipple as danger signs of breast cancer [ 17 ]. The study by Somdatta and Baridalyne [ 16 ] also found similar outcomes in a resettlement colony of Delhi. In this study, better knowledge of danger signs or symptoms of breast cancer is observed among higher educated and working women than lower educated and not working women respectively. Breast cancer means losing one’s breast(s) was the most common misconception among women.

Like many other Indian studies, this study found the knowledge of risk factors was very low [ 5 , 15 , 16 , 17 , 25 ]. The women in the study identified excessive consumption of tobacco, alcohol consumption and past history as most important risk factors of breast cancer. However, very few women in the community were aware of the risk of breast cancer due to disruption in biological clock like early menarche, late menopause, and hormonal therapy. Further, it is found knowledge of preventable risk factors like hormone replacement therapy, first baby after the age of 30 years, obesity, and use of oral contraceptive pills were low among participants. In this study, we also observed low knowledge of breast screening procedure among women like self-breast examination and mammography. The practice of BSE was very low because they were not trained to about the procedures.

This study is limited to one low socio-economic area of Mumbai, therefore, cannot be generalized to other community. The knowledge of signs, symptoms and risk factors depend on the comprehension capability of the participants during the data collection. Further, the study is cross-sectional in nature and therefore, it is not possible to get any causal relationship between dependent and independent variables.

This study aimed to assess breast cancer awareness and knowledge of danger signs, symptoms, risk factors and concluded that knowledge of danger signs and risk factors of breast cancer among women in the community was low. Considering the fact that breast cancer has grown as an epidemic in the country, lower knowledge of symptoms and signs may lead to delay in treatment seeking among the women. Although further studies are required at the national level, the lower knowledge of breast cancer among women in one of the advanced metropolises in India calls for greater effort to enhance knowledge of women at the regional and national level. This study calls for intervention to enhance and improve knowledge of breast cancer among women in the particular region with a special reference to women with low educational level and marginalised community. Effective media platform like television can be used to promote breast cancer awareness and breast self-examination practices. Advocacy and health education related to breast cancer awareness and screening methods and their accessibility needs to be strengthened in government programme with focus in lower socio-economic areas. Further, preparing appropriate and specific content for health education with an emphasis on preventable risk factors and lifestyle modification will enhance the awareness level and strengthen practices for prevention and early detection breast cancer.

Availability of data and materials

The raw data used in this research is available with the researchers. Please send your inquiries to the corresponding author.

Abbreviations

Breast Cancer

Global Cancer Incidence, Mortality and Prevalence

Breast Self-Examination

Clinical Breast Examination

Scheduled Caste

Scheduled Tribe

Other Backward Classes

Adjusted Odds Ratio

Confidence Interval

United Kingdom

Body Mass Index

Municipal Corporation of Greater Mumbai

Maharashtra Housing and Area Development Authority

Indian Council of Medical Research

National Institute for Research in Reproductive Health

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Acknowledgements

The authors are also thankful to the Director, ICMR-NIRRH and collaborative partners-Tata Memorial Hospital and Municipal Corporation of Greater Mumbai (MCGM) for all support to conduct the study. We acknowledge the contribution of the data collectors and thank the participants of the study for their time and co-operation.

Authors received no specific funding for this paper. However, the main study received financial support from the Department of Health Research, Government of India (R.11012/06/2018-HR). The funding agency had no role in the design the study, collection, analysis, and interpretation of data and in writing the manuscript.

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Anushree Patil

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SB & RKP conceived, designed, and performed the experiments and analyses. RKP wrote the first draft of the manuscript and contributed reagents/materials/analysis tools: SB, AP, DDN, SP and GM have read and revised the manuscript. All authors read and approved the final manuscript.

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Shahina Begum (Corresponding Author) is Scientist ‘E’ and Head at Department of Biostatistics, ICMR-NIRRH, Mumbai, India. [email protected]

Ranjan Kumar Prusty is Scientist ‘B’ at ICMR-NIRRH, Mumbai, India

Anushree Patil is Scientist ‘E’ at ICMR-NIRRH, Mumbai, India

DD Naik is Senior Technical Officer-3 at ICMR-NIRRH, Mumbai, India

Sharmila Pimple and Gauravi Mishra are Professors at Department of Preventive Oncology, Centre for Cancer Epidemiology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India.

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Prusty, R.K., Begum, S., Patil, A. et al. Knowledge of symptoms and risk factors of breast cancer among women: a community based study in a low socio-economic area of Mumbai, India. BMC Women's Health 20 , 106 (2020). https://doi.org/10.1186/s12905-020-00967-x

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Understanding breast cancer risk factors: is there any mismatch between laywomen perceptions and expert opinions

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Women’s perception and knowledge of breast cancer signs, symptoms, and risk factors could be conducive to breast cancer risk management and interventions. The present study aimed to explore Iranian laywomen perceptions and expert opinions regarding breast cancer risk factors.

This qualitative study was conducted from March to November 2019 in Mashhad, northeast of Iran. Through purposive sampling, 24 laywomen (women with and without BC) and 10 experts of different fields including oncology, surgery, gynecology and reproductive health were selected. Data collection was carried out using semi-structured interviews, which was mainly focused on the participants’ understanding and perception of BC risk factors. The data was analyzed utilizing conventional content analysis developed by Graneheim & Lundman. Components of trustworthiness, including credibility, dependability, confirmability, and transferability were considered.

The main category of risk factors, which emerged from the lay participants’ data analysis, were “unhealthy lifestyle and habits” , “hormonal influences”, “environmental exposures”, “Individual susceptibility “and “belief in supernatural powers”. The experts had similar perspectives for certain risk factors, yet not for all. The category of “Individual history of disease” was emerged only from experts’ interviews.

In the present study, the lay participants’ perception concerning BC risk factors was found to be a mixture of cultural beliefs and the scientific knowledge dispersed by the media, internet, and health services. Primary prevention approaches, including awareness of breast cancer risk factors, are required for women to make improved health-related choices.

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Introduction

Breast cancer (BC) is a multifactorial disease whose development involves various factors [ 1 ]. Some of the factors that can affect its risk include heredity and genetics and the factors affecting the amount of endogenous hormones, such as reproductive factors, exogenous hormone intake, lifestyle, anthropometric characteristics, increased breast density in mammography, and benign breast diseases [ 2 ].

Risk perceptions are contextual evaluations of knowledge, which help people to understand their vulnerability and make health-related decisions [ 3 ]. Since realistic and accurate risk perception can contribute to appropriate health behaviors, enhancing public awareness on risk factors of diseases is believed to be one of the most important objectives of risk communication [ 4 ].

According to a study on Iranian women’s perception regarding control and prevention of BC, lack of awareness of BC risk factors is a major issue that affecting women’s BC screening behaviors [ 5 ]. Certain studies have reported misconceptions about the risk factors of BC, such as the use of antiperspirants or breast size as the cause of BC [ 6 ].

A qualitative study to explore BC risk perception in women with increased risk showed that the interpretation of BC risk factors by the participants was superimposed with their sense of social norms about how they could understand and control their BC risk; they felt as though they had to rationalize thoughts and concepts that were inconsistent with their behavior [ 7 ]. Another paper was conducted on Iranian immigrants to explore their beliefs and explanations about BC and its causes. It revealed a hybrid of both traditional and scientific beliefs about BC and its risk factors [ 8 ]. Additionally, a study on women with a family history of BC demonstrated a lack of information in this group concerning advancing age, early age at menarche, and late age at menopause as risk factors for BC [ 9 ].

Several quantitative articles have also examined the extent to which public is aware of the risk factors of various types of cancer. The results of these studies have indicated that participants had limited knowledge about BC risk factors, such as obesity, oral contraceptives, late menopause, and early onset of menses [ 10 , 11 , 12 , 13 ].

Patients perceive the risk of illness based on previous personal or family experiences with their disease or information they have received from health care providers or the mass media. Risk perception may be specific to individuals and the risk perceived by lay people may differ significantly from those of the experts [ 14 ]. Therefore people’s perception and how they perceive the risk of illnesses in different communities require further studies [ 14 ]. The results of a systematic review of 22 studies indicated that laywomen beliefs about the causes of cancer may not always be consistent with the judgment of experts. Specifying the range of mismatch is of great importance in order to provide prevention programs and help the promotion in understanding laywomen support necessities [ 15 ]. The experts’ sources of knowledge and information about health risks are acquired through university textbooks, scientific journals, symposiums, meetings, and conferences, as well as experiences of medical doctors through their clinical practices [ 16 ]. Meanwhile, laywomen risk perception is intuitive and is less scientific than experts [ 17 ].

Under any circumstances, providing information about a disease and its management to a target group is impossible without taking their beliefs and values about disease risk factors into consideration [ 8 ]. Qualitative studies highlighted that culture, experiences, knowledge, and beliefs can affect individuals’ perceptions and attitudes toward diseases [ 18 , 19 ]. Thus, people’s perception and how they perceive the risk of an illnesses in different populations require further investigation via qualitative mode of inquiries. Accordingly, in the present study, the qualitative approach was adopted as risk perception is a complicated phenomenon and is affected by multiple factors. In order to investigate the perceptions of people and to provide an in-depth understanding of human behavior and demonstrate the variability within a given population, qualitative research was employed; it is known to be an appropriate method for providing a deeper understanding of individuals’ perceptions regarding BC risk factors [ 20 ].

Due to the insufficient studies on this subject using the qualitative approach in Iran, the current study was conducted to identify and compare perceived risk factors of BC among experts and laywomen in Iran.

Materials and methods

A qualitative descriptive study was employed to explore Iranian women’s perceptions and experts’ opinions about the risk factors of BC. This method is suitable for research that questions human views. Based on this method, reality varies from person to person and qualitative description seeks to accurately describe a phenomenon like risk perception while not getting too far from its literal description interpreting the results [ 21 ].

The laywomen in our study were recruited through purposive sampling method in Mashhad, a metropolis in northeastern Iran. The lay participants were divided into two groups; the first group comprised women with BC referred to hospitals of Mashhad University of Medical Sciences or private clinics in Mashhad for chemotherapy. The second group consisted of women without BC or any signs or symptoms of BC, who were apparently healthy; they were patients’ relatives, who were recruited from hospitals or clinics or were healthy people recruited from other settings, like universities or workplaces.

To identify and recruit eligible experts, snowball sampling technique was applied, which is a convenient sampling method. In this method, the subjects of a study introduce future subjects among their peers [ 22 ]. For this purpose, specialists of different fields, such as oncology, surgery and reproductive health, were asked to participate in this study. The experts who met the following criteria were eligible: 1) currently participating in BC management; and 2) having at least 5 years of BC management experience.

For data collection, an open-ended semi-structured interview guide was developed based on a review of the literature and discussion with the research team. The interview schedule consisted of two groups of questions: the main interview questions and probing questions. The interviews were conducted by the first author (EM) with a background in health research and clinical gynecology. Questions were about participants’ understanding and perception of BC risk factors. The questions for women without BC were: “How much do you think you are at risk for BC?, Is there anything you think can increase or decrease your risk of BC?” . The questions for women with BC were: “What do you think are the conditions in your life that have caused the disease? Is there anything you think can increase or decrease the risk of BC?”. The questions for experts were: “Based on your experience, what are the risk factors for BC? What are the factors that increase the prevalence of BC in Iranian women?”. Probing questions such as “Can you explain more?” Or “What do you mean by this sentence?” were also used to provide further explanations.

The interviews were conducted face to face and continued until achievement of data saturation when no new information emerged from the interviews [ 22 ]. They were conducted and analyzed in Persian language. Subsequently, only the passages used as quotes were translated in English. It is noteworthy that the quotations from non-English interviews could be utilized if the researchers have further awareness of sensibility toward dealing with language and translation issues [ 23 ]. For this reason, quotations were translated by a bilingual expert who was fluent in both Persian and English languages. The interviews lasted for approximately 70 to 90 min for the laywomen and 30 to 45 min for the experts. Interviews were audio-recorded and then transcribed verbatim by a member of the research team (EM). The data were collected from March to November 2019.

The analysis was carried out using conventional qualitative content analysis developed by Graneheim and Lundman [ 24 ]. To this end, de-contextualization and re-contextualization steps were performed; the text was primarily read several times to get a general sense and insight. Afterwards, the text was broken into pieces and the meaning units were identified. The meaning units were then condensed and coded. In the re-contextualization step, the codes were sorted based on their similarities and differences and the subcategories and categories were generated. Transcripts of the initial three interviews were reviewed by the research team members (AT, AE, and RLR), with the objective of establishing a preliminary coding pattern that was used for subsequent analyses. All the transcripts were then analyzed by two researchers (EM and AT) and the interpretations was discussed with the third researcher (RLR) who was an experienced scholar in qualitative methodology and the supervisor of the research project.

Since data collection and analysis were complementary, the process was iterative, reflexive, and interactive; for example, the data collected during subsequent interviews were used to modify code labels or meanings found during earlier interviews. After the initial coding of the all transcripts, the team members discussed the coding strategy and the initial codes to reach a consensus and merge individual codes into subcategories and then the main categories [ 25 ]. Data analysis was supported by MAXQDA 12 software.

The four criteria presented by Lincoln and Guba, namely credibility, reliability, confirmability, and transferability, were examined in order to confirm the validity and reliability of this qualitative study [ 26 ]. In order to provide credibility, the researcher continued the interviews until the achievement of data saturation and selecting the informant participants. The researcher’s interest in the field of study, prolonged engagement and immersion in the data, and interviewing various subjects were among the factors that guaranteed credibility. To meet dependability, the possibility of doing an audit trial was provided through keeping the records of the raw data, transcripts, and a reflexive journal, which could help other researchers to systemize, share, and cross-reference data to make the study and its findings auditable. In addition, the present study was conducted as a team-work with the supervision of experts, which made both data credibility and dependability possible.

To ensure confirmability, a number of qualitative researchers were consulted and the researcher tried to describe the method of the current study with details. To increase the transferability, purposive sampling was used and interviews were conducted with different participants with the maximum diversity and direct quotations and examples were provided.

Prior to each interview, the participants were explained the objectives of the research, the reason of recording the interview, voluntary participation, data confidentiality, and the right to withdraw from the study at any time without any prejudice; informed consent was also obtained. All the information obtained from the participants was kept confidential and anonymous. That said, the subjects were given codes used in the study instead of their names. Gift cards (500,000 Rials, equivalent to $12) were provided to all of them as an appreciation for their participation. Ethics approval was granted by the Mashhad University of Medical Sciences, Mashhad, Iran under the code of IR.MUMS.NURSEREC.1397.034.

Tables  1 and 2 display the characteristics of the participants. 24 laywomen and 10 experts were interviewed. Following coding the interviews, 492 initial codes were generated and the codes related to the research objectives were finally divided into eight subcategories and five categories for the lay participants and 12 subcategories and five categories for the experts (Tables  3 ).

Laywomen and experts had similar perspectives for some risk factors, but not for all of them.

Shared categories between laywomen and experts

There were several shared categories regarding risk factors of BC, including “unhealthy lifestyle and habits”, “hormonal influences”, “environmental exposures” and “Individual susceptibility “.

Unhealthy lifestyle and habits

There was consensus amongst participants (Laywomen and experts) that the individual lifestyle is likely to lead to the development of BC.

Unhealthy lifestyle behaviors

Almost all the participants were aware of a number of lifestyle-related risk factors whereas there was a lack of information about some other factors.

Shared perceived risks: Many lifestyle factors are likely to enhance the risk of developing BC. Some of the laywomen perceived the risk related to unhealthy lifestyle in agreement with the experts’ opinions. One of the lay participants said:

"I did not always like the habit of smoking of my husband. I used to say that a problem would finally happen for me or for our children, which did ... I knew that poor nutrition could lead to a variety of cancers but I did not know that being single and late marriage could also cause it (BC). I heard these things here … ". (P.5)

Similarly, one of the experts said:

"A factor that affects BC is inactivity. A sedentary lifestyle could be an effective factor even without obesity … ..Undoubtedly, diet, smoking, hookah, and passive smoking are among the risk factors, the person may not be a smoker or use hookah, but might be exposed to smoke in the family or workplace. Moreover, air pollution, unhealthy foods, full-fat diet, obesity, being overweight are the factors leading to BC. "(P.31)

Excessive and constant use of cosmetics was seen in both lay participants’ and experts’ opinions. One of the BC participants said:

"I am a person who always makes up. I believe there might be a relationship. Hair color and makeup … ."(P.12)

The risks perceived only by the laywomen: Some of the risk factors associated with an unhealthy lifestyle, which were perceived by the lay participants, were not mentioned by the experts like waxing. Some of the participants with BC, believed that BC could be contagious.

The risks mentioned only by the experts: Certain risk factors were only stated by the experts and none of the lay participants mentioned them, for instance, night-shift work and alcohol consumption.

Nutritional behaviors and habits

Regarding nutritional behaviors and habits, there were also similarities and differences concerning the perceived risks of both groups of the participants.

Shared perceived risks: The risk factors perceived and cited by both laywomen and experts included no use of fresh fruits and vegetables, use of processed, packed, and canned foods, and consumption of fast foods and food additives. One of the lay participants with a positive history of BC said:

"I have heard that chemical fertilizers are used indiscriminately in agriculture. Well, contaminated Fruits and meat and consumption of preservatives and fast foods are nowadays prevalent among people. These effects are not still clear; for example, ten years later, people might get BC while not knowing the reason . … ." (P.5)

An oncologist stated that:

“It is true that higher fat in the diet, vitamin D deficiency, omega 3 deficiency, and diets containing low fish and aquatic food could be risk factors for BC”. (P.25).

The risks perceived only by the laywomen: One of the participants diagnosed with BC said:

"I have seen that one of our relatives empties the water of kettle. She says boiling kettle water for several times is carcinogenic. She said it affects nerves and is carcinogen … .." (P.2)

A participant whose daughter was receiving chemotherapy stated:

"Palm oil is dangerous. I do not eat much meat at all. The doctor also told my daughter not to eat red meat now. Vegetables are better." (P.14)

The risks mentioned only by the experts : None of the participants were aware of high-calorie intake, Vitamin D deficiency, consumption of fatty foods and sauces, omega-3 deficiency, and changes in the diet of the community in general, as risk factors.

Hormonal influences

The perceived risk in this category was “Intake of exogenous hormones” and “individual’s reproductive history”.

Intake of exogenous hormones

All participants agreed on the use of exogenous hormones as a BC-related risk factor.

Shared perceived risks: The perceived risk of laywomen in this category was only the use of exogenous hormones, such as oral contraceptive pills and hormonal medications, which was in accordance with the experts’ opinions.

"One of the things I have heard is that I should not take hormonal pills...Even the contraceptive pills, as I heard, are not good at all." (P.16)

"Any use of exogenous hormones is important. Hormone replacement therapy is associated with a greater risk of BC. Once estrogen rises either endogenously or exogenously, a woman is more likely to develop BC..." (P.26)

Individual’s reproductive history

None of the laywomen were aware of reproductive history as a risk factor for BC.

The risks mentioned only by the experts : The factors related to the reproductive history were addressed only by the experts and all of them had a consensus in this regard. One surgeon said:

"Being nulligravid, having pregnancy after the age of 30 and limited parity, and pregnancy without breastfeeding could also be risk factors … .." (P.31)

Environmental exposure

Regarding environmental exposure, certain risk factors perceived by the laywomen were in line with experts’ opinion and some were different:

Exposure to magnetic fields

All participants took a variety of magnetic fields and environmental pollutants into account as BC risk factors.

Shared perceived risks : In this category, the laywomen perceived risks were related to exposure to electro-magnetic fields, such as telecommunication towers, modem, mobile phones, and Wi-Fi waves. A 30- years-old participant without BC stated:

"Large telecommunication towers noise. … . they have a terrible effect. I am sure it significantly affects our health … … At night, when I want to sleep, I charge my phone in another room." (P.16)

A housewife participant with BC stated: "I think these waves are very important. In some areas of Mashhad, there are much more waves. The telecommunication noise causes a lot of diseases, which (BC) has unfortunately increased so much in Iran … ." (P.11)

Nonetheless, the experts had different opinions in this regard although some experts had similar views with the laywomen. One of the oncologists with 28 years of experience said:

"It is unlikely that these waves could change DNA due to their long wavelength and low frequency. They cannot penetrate DNA. Theoretically, the waves from the electromagnetic spectrum can be carcinogenic if they are ionized. That means short wavelengths, high frequencies, and high-energies waves can damage DNA. "(P.26)

The most frequently mentioned risks about environmental pollution concerned water and air pollution.

Exposure to ionizing waves

The risks mentioned only by the experts : No perceived risks of exposure to ionizing waves was expressed by the laywomen. Meanwhile, the experts mentioned mammography or other diagnostic radiographies, chest radiography during puberty, chest radiotherapy for various causes, such as BC, and exposure to environmental ionizing radiation as BC risk factors:

"Ionizing waves, such as X-rays, and diagnostic imaging with X-ray, like CT and conventional radiographs, can all be carcinogenic." (P.26)

Individual susceptibility

Concerning individual susceptibility, there were similarities and differences between the laywomen and experts. The family and genetic background were mentioned in both groups. However, in the laywomen, “psychological factors” and in the group of experts, “anthropometric characteristics” and “demographic characteristics” were perceived.

Family and genetic background

There were differences and similarities between the perception of the participants concerning family and genetic background.

Shared perceived risks: The perceived risk factors by the laywomen were limited to family history of BC, ovarian cancer, hereditary background, and genetics. In this regard, one of the participants stated:

"We are 100% at risk. Because it is said that genetics plays a key role in the occurrence of this cancer and since my mother's BC was aggressive, we consulted several specialists; the first thing they said was that her daughters and sisters follow up the disease seriously...." (P.20)

The risks mentioned only by the experts : The experts pointed broader risk factors in this field, such as family history of colon and gastric cancer, paternal family history of BC, family history of glioma or multiple cancers in young relatives, the number of affected relatives, and history of BC in a male family member. One of the experts mentioned:

"The next issue is whether there was anyone in the family with cancer under the age of 40. Has anyone had a history of bilateral cancers? Did a man in their family have cancer? The presence of a father, brother, or close male relative who has had cancer is closely linked to the brca2 gene. After that, sarcoma, glioma, in other words, some brain tumors, in relatives under the age of 45 can increase the risk. One thing that is of particular importance is the high risk of BC if two or more people from the father's family of a woman have had cancers … " (P.29)

Psychological factors

Psychological-related factors were mainly perceived by the lay participants as BC risk factors.

The risks perceived only by the laywomen: One of the participants with a history of BC said:

"I think it could be more attributed to a nervous breakdown and not expressing problems. Those who are a bit introverted are more likely to get it (BC). I saw this in our family and my friends. BC is more prevalent in those who keep the problems to themselves compared with that in those who open up; for example, once they get upset and express their problem, they will get calm and everything will end. However, sensitive and emotional people are more at risk." (P.11)

Another participant in this regard said:

"I think it is just grief and a nervous shock at once. My father had a cardiac arrest four years ago. I was very dependent on him. He was fine and healthy. He came home from work and died suddenly. It was a huge shock to me ..." (P.10)

Anthropometric characteristics

The risks mentioned only by the experts : Anthropometric factors were only mentioned by the experts.

"An important factor in Asian and Iranian countries, and not in European and American populations, is that they have visceral or abdominal obesity. People with BC often have a specific phenotype that looks like a samovar. This means that their necks are shorter, their waist is bigger, and their hips are smaller … .." (P.28)

Demographic characteristics

The risks mentioned only by the experts : Some factors, like age and sex, were found in the experts’ interviews:

"Gender and age are also the most important risk factors (for BC). Nowadays, the observed and experienced risk factors are different from what scientific resources have suggested. In my opinion, the disease at the age of below 40 is more prevalent, which is on the contrary to what specialists previously believed. The number of these patients is relatively high. Still, the fact that the risk increases with age is undeniable". (P.25)

Different perceptions and opinions

On the other hand, there were certain categories mentioned in each group that were not expressed by the other group.

Belief in supernatural powers

The majority of the lay participants believed that God’s will and destiny were involved in BC occurrence.

One of the participants whose brother and child had serious renal and mental problems stated:

"What God wants will come forward no matter what happens. We have three patients in our family. What about the end? He puts this disease in me. We have no choice, but to cope." (P.4)

Moreover, one of the participants with BC said:

"It was always in my mind that I would probably take my mother's cancer gene and I finally got it. They say whatever you think will happen. I always told myself that my mother was stressful and therefore, got cancer. . It was always in my mind that I will definitely get cancer. … .. we might have made some mistakes. God wants to test us this way. "(P.9)

Individual history of diseases

A history of disease was mentioned by the experts, which the lay participants were not aware of.

Previous breast problems

According to the experts’ interviews, factors like atypical proliferative hyperplasia, personal history of BC, and most importantly bilateral BC and history of breast biopsy are also among the risk factors of this disease. An expert mentioned:

"Other breast disorders are also known as risk factors. Breast disorders which are more proliferative than non-proliferative diseases, particularly atypical types of proliferative diseases. All these may be highly important risk factors, such as hyperplasia or atypical dysplasia..." (P.26)

Health problems

The experts explained that certain diseases may increase the risk of BC such as uterine fibroids, immunodeficiency status, cholecystitis, and infertility. They were mentioned as BC risk factors:

"We know that women with high estrogen are more likely to get uterine fibroids and cholecystitis. If patients do not have cholecystitis by the time they are diagnosed with BC, we expect them to develop cholecystitis in the future, which is highly prevalent in BC patients. However, it must be further investigated in the general population; otherwise, we cannot rely on it." (P.29)

Some lay participants pointed out that the risk factors they found in scientific resources and health centers were not in accordance with the current scientific findings. One of the participants said:

"My mother did all the screenings. She has six children and all of them were breastfed completely. Our mother has used very few contraceptive pills during her life ... About my mother's lifestyle, she uses olive oil for cooking. Her nutrition is completely healthy. She always eats wholegrain bread. My mother is an active person and goes walking regularly. Thus, we were surprised when she was diagnosed with BC. My aunt is a sedentary person who does not follow a healthy lifestyle. She uses unhealthy oils and fried foods, but it (BC) did not happen to her. My mother has lived in a small and low-populated city without air pollution. I do not know what could be the reason behind the disease in my mother. As far as we know, nobody in our family has had breast cancer."(P.20)

Most experts stated that the BC risk factors in Iran are different from those in Western countries. According to them:

"Factors such as alcohol and smoking are not very common in our patients. A large percentage of our patients do not have a specific and bold risk factor. Another issue is ageing, which is being discussed all over the world; with the increase in age, the risk of BC increases. We also generally accept age as a risk factor. However, because our overall population structure is relatively younger, we generally see patients at a younger age" (P.22)

Another point in the laywomen’s interviews was the source of information about the risk factors and symptoms of BC. The participants obtained their information from sources such as physicians or counselors, Internet, social networks like Instagram, peer groups, television programs, and, to a very small number, through books. At the same time, they stated that information about BC is less accessible, simple, and understandable for Iranian laywomen.

One of them stated:

"I think we all go to a gynecologist at least once every two or three years. I think it is highly effective if the doctor gives a brief explanation (about BC risk factors, signs and symptoms). I see a lot of women whose cancer progressed because they did not have proper understanding of the disease … There are very few television programs about BC. If sometimes there is a TV program, it is either too long or specialists use medical terms that we do not understand. I think it is much better to speak briefly and understandably … " (P.25)

This qualitative study aimed to explore the Iranian women’s perception of the risk factors of BC and compare them to the experts’ opinions. The key findings obtained from the data analysis indicated that the lay participants’ perception of risk factors for BC was incomplete and there was an obvious gap between the public versus experts’ perceptions. This is indicative of the need to improve their perception of BC risk factors. According to our results, the lay participants had lack of risk perception and awareness of some of the risk factors, such as reproductive history, exposure to ionizing waves, certain diseases, such as breast benign disease and some types of cancers, alcohol consumption, radiation exposure, in addition to anthropometric, and demographic characteristics. According to the results of the present study, both groups of the participants (laywomen and experts) believed that in numerous cases, the risk factors of BC are different from the scientific literature because based on their experiences, most women with BC have none of these risk factors. The experts stated that many Iranian women with BC do not have any of the hormonal, nutritional, or lifestyle-related risk factors. The laywomen perceived this issue as a contradiction between their experiences with the experts’ opinions. As another finding, some of the factors that the laywomen mentioned as a risk factor of BC were on the contrary to the opinions of the experts and scientific texts, such as supernatural powers as a risk factor for BC. Also, the views of experts in various fields on the risk factors for BC were often similar because their views are based on scientific evidence. The only difference was the effect of exposure to electromagnetic fields, and the experts who disagreed on this were both experienced oncologists.

Published scientific literature on the risk factors of this disease has highlighted the importance of modifiable lifestyle-associated behaviors in controlling and modifying cancer risk [ 27 ]. The World Health Organization has indicated that more than 30.0% of cancer mortalities could be prevented by modifying or avoiding behavioral or lifestyle-related risk factors [ 28 ].

Some of the risk factors which were not properly perceived are among the modifiable risk factors. Awareness of such risk factors could be conductive to the improvement in health promoting behaviors, increase in health literacy, and thus, perceived control over the BC. One’s perceived ability to control his or her health contributes to adopting healthy behaviors, such as lifestyle improvements and participation in screening programs [ 29 ].

Based on the results obtained herein, all the participants believed that lifestyle-related factors, such as sedentary life, smoking, lack of regular checkups, and unhealthy diet could increase the risk of BC. Meanwhile, there was lack of perception and awareness among the laywomen about the relationship between alcohol consumption and overweightness or obesity with BC whereas they are of the most consistently reported risk factors in the literature and experts’ opinions. Other qualitative studies on BC risk perception, in line with our results, have revealed that many of the participants considered alcohol irrelevant to BC and this relationship was largely unknown [ 9 , 30 ]. Furthermore, in Iranian studies, the impact of increased body mass index on increasing the risk of BC has been confirmed [ 31 ].

The hormonal contraception methods, specifically OCPs, was identified as a risk factor of BC by all the participants. There was nevertheless a lack of perception and awareness about reproductive factors, including early age at menarche, late age at menopause, low parity or nulliparity, and no/low breastfeeding, as BC risk factors. Meanwhile, according to the experts’ opinions, endogenous reproductive risk factors can play a pivotal role in BC. In line with this result, in other studies in different countries, the factors linked to reproductive history, other than the use of contraception and hormones, were less frequently stated by the participants [ 9 , 32 ].

According to the experts’ opinions, one of the risk factors of BC was exposure to radiation for medical purposes (diagnostic or therapeutic). None of the laywomen identified medical diagnostic role as a risk factor for BC. In this study, the laywomen were more likely to attribute BC to non-ionizing than to ionizing radiation; this may reveal a poor understanding of the difference between the types of radiation. Similar results have been obtained in other papers [ 8 , 32 , 33 ].

According to the experts’ opinions, positive history of breast benign disease, breast atypical hyperplasia, or some problems of other organs, such as uterine fibroids, infertility, cancers in other parts of the body, and immunodeficiency, could be the risk factors of BC. However, none of the participants considered the role of the aforementioned diseases as BC risk factors. This has been also reported in other studies [ 5 , 34 ]. On the other hand, some expert opinions about the potential BC risk factors were not necessarily consistent with scientific literature, such as uterine fibroids as a risk factor. Herein, the experts had diverse opinions about some risk factors, such as stress, exposure to telecommunication noise, and excessive use of cosmetics. Since these factors remain controversial in the scientific evidence, the experts commented based on their personal experiences.

Superstitious beliefs exists throughout the world, which have been identified as a condition in which a person believes that certain actions can lead to certain consequences; this obviously has no scientific basis [ 35 ]. As a result, superstitious individuals may believe that diseases are controlled by unknown events and supernatural powers and therefore, find disease prevention strategies useless [ 36 ]; for instance, they assume that what you think about will happen or that showing happiness and joy in front of others will cause misery [ 8 ]. This kind of belief becomes destructive when it comes to health and affects people’s well-being since it becomes a part of the individual’s health beliefs [ 8 , 35 ].

In the present study, the laywomen’s perception about BC risk factors comprised a mixture of cultural beliefs and the scientific knowledge disseminated by the media, internet, and health services. Previous research has reported similar findings that TV and radio programs and internet were identified as the main sources of information about BC among women and men [ 34 , 37 ].

Generally, modifiable risk factors of BC, as reproductive factors, radiation exposure, intake of hormonal drugs, and lifestyle-related risk factors, were reported by experts, yet may be less well understood in the general population [ 38 ]. Increased laywomen’s perception and awareness of the modifiable risk factors of BC could increase the adherence to BC prevention strategies. Accordingly, improving public awareness and correcting women’s perception about BC risk factors and symptoms are believed to be of great necessity [ 39 ].

Strengths and limitations

The current study has certain strengths; primarily it is the first study in Iran exploring Iranian women’s perception of BC risk factors through a qualitative method. Secondly, the comparison between the understandings of laywomen with the opinions of experts could to some extent determine the gap between scientific and lay perception of risk factors. On top of that, the use of participants with and without BC could have contributed to more accurate understanding of the public attitude towards this disease. This would also help specialists to improve public awareness in this regard.

As a limitation of this study, these results are valid within the Iranian or similar cultural contexts due to the limited generalizability of qualitative studies. Therefore, further qualitative investigation in this field could be recommended in communities with different cultures.

In the present study, the lay participants’ perception about BC risk factors was found to comprise a mixture of cultural beliefs and the scientific knowledge dispersed by the media, internet, and health services. This study highlighted the need for health promotion and communication efforts in order to decrease the gap between lay and expert opinion on beliefs about the risk factors of BC. The highest proportion of the participants obtained their information from the television/radio for the first time. It seems necessary that mass media focus on refining general perception and increasing knowledge about BC in a transparent manner in their educational programs. In order to elevate awareness regarding BC, with focus on the role of screening and prevention, it is strongly suggested that a well-designed health education program be conducted to resolve the observed knowledge gaps. Programs that are accessible for women and consider their role in BC prevention should be included in the national strategies. For women to make risk-reducing changes in their lifestyle, they need to be educated about common BC risk factors.

The rich evidence generated by this qualitative research would assist clinicians regarding the range of variables that may affect the perception of risk factors by their clients. Moreover, it could be proposed that proper training and informative programs be considered when warning women about the risk of BC.

Availability of data and materials

All data generated or analyzed during this study are presented in this manuscript.

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Acknowledgements

The authors are grateful to Vice Chancellor for Research, Mashhad University of Medical Sciences, Mashhad, Iran for financial support of this project. Also, all participants who took part in the study including laywomen and experts are really appreciated for sharing their experiences.

The current qualitative study is part of the PhD thesis of the first author approved by Mashhad University of Medical Sciences, Mashhad, Iran, under code of IR.MUMS.NURSEREC.1397.034 (Grant ID: 970008).

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A. Taghipour

Present address: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

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Department of Midwifery, Faculty of Nursing and Midwifery, Mashhad Medical Sciences, Islamic Azad University, Mashhad, Iran

E. Manouchehri

Department of Midwifery, School of Nursing & Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran

E. Manouchehri & R. Latifnejad Roudsari

Department of Epidemiology, School of Public Health, Mashhad University of Medical Sciences, Mashhad, Iran

Behavioral Sciences Research Center, Life style Institute, Baqiyatallah University of Medical Sciences, Tehran, IR, Iran

Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, IR, Iran

Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

F. Homaei Shandiz

Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

R. Latifnejad Roudsari

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EM, AT, FH and RLR were the major contributors to the overall study conception and design. The interviews were performed by EM. Data analysis was performed by EM and supervised by RLR, AT and, AE. The manuscript was drafted by EM and revised critically in consultation with all authors. Also, all authors read and approved the final manuscript.

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Correspondence to R. Latifnejad Roudsari .

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Manouchehri, E., Taghipour, A., Ebadi, A. et al. Understanding breast cancer risk factors: is there any mismatch between laywomen perceptions and expert opinions. BMC Cancer 22 , 309 (2022). https://doi.org/10.1186/s12885-022-09372-z

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Research Article

A case-control study of breast cancer risk factors in 7,663 women in Malaysia

Roles Formal analysis, Visualization, Writing – original draft

Affiliations Department of Applied Mathematics, Faculty of Engineering, University of Nottingham Malaysia Campus, Semenyih, Selangor, Malaysia, Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia

Roles Writing – review & editing

Roles Methodology, Project administration, Supervision

Affiliation Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia

Roles Project administration

Roles Data curation

Roles Investigation

Roles Resources

Affiliation Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Affiliations Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia, Biomedical Imaging Department, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Affiliation Biomedical Imaging Department, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Roles Methodology

Affiliations Sime Darby Medical Centre, Subang Jaya, Selangor, Malaysia, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia Campus, Subang Jaya, Selangor, Malaysia

Roles Investigation, Methodology, Resources

Affiliation Department of Paediatrics, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Supervision

Affiliation Sime Darby Medical Centre, Subang Jaya, Selangor, Malaysia

•  [ ... ],

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing

* E-mail: [email protected]

• [ view all ]
• [ view less ]
• Min-Min Tan, 
• Weang-Kee Ho, 
• Sook-Yee Yoon, 
• Shivaani Mariapun, 
• Siti Norhidayu Hasan, 
• Daphne Shin-Chi Lee, 
• Tiara Hassan, 
• Sheau-Yee Lee, 
• Sze-Yee Phuah, 

• Published: September 14, 2018
• https://doi.org/10.1371/journal.pone.0203469
• Reader Comments

Breast cancer risk factors have been examined extensively in Western setting and more developed Asian cities/countries. However, there are limited data on developing Asian countries. The purpose of this study was to examine breast cancer risk factors and the change of selected risk factors across birth cohorts in Malaysian women.

An unmatched hospital based case-control study was conducted from October 2002 to December 2016 in Selangor, Malaysia. A total of 3,683 cases and 3,980 controls were included in this study. Unconditional logistic regressions, adjusted for potential confounding factors, were conducted. The breast cancer risk factors were compared across four birth cohorts by ethnicity.

Ever breastfed, longer breastfeeding duration, a higher soymilk and soy product intake, and a higher level of physical activity were associated with lower risk of breast cancer. Chinese had the lowest breastfeeding rate, shortest breastfeeding duration, lowest parity and highest age of first full term pregnancy.

Conclusions

Our study shows that breastfeeding, soy intake and physical activity are modifiable risk factors for breast cancer. With the increasing incidence of breast cancer there is an urgent need to educate the women about lifestyle intervention they can take to reduce their breast cancer risk.

Citation: Tan M-M, Ho W-K, Yoon S-Y, Mariapun S, Hasan SN, Lee DS-C, et al. (2018) A case-control study of breast cancer risk factors in 7,663 women in Malaysia. PLoS ONE 13(9): e0203469. https://doi.org/10.1371/journal.pone.0203469

Editor: Natarajan Aravindan, University of Oklahoma Health Sciences Center, UNITED STATES

Received: March 6, 2018; Accepted: August 21, 2018; Published: September 14, 2018

Copyright: © 2018 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data collected in this study are compliant with the Data Protection Act in Malaysia and can only be shared with research groups that contact Cancer Research Malaysia directly. All requests for data should be sent to Joanna Lim at the Data Access Committee of Cancer Research Malaysia using the following email address: [email protected] .

Funding: This study was supported by grants from Newton-Ungku Omar Fund [grant no: MR/P012930/1] and Wellcome Trust [grant no: v203477/Z/16/Z]. The Malaysian Breast Cancer Genetic Study was established using funds from the Malaysian Ministry of Science, and the Malaysian Ministry of Higher Education High Impact Research Grant [grant no: UM.C/HIR/MOHE/06]. The Malaysian Mammographic Density Study was established using funds raised through the Sime Darby LPGA tournament and the High Impact Research Grant. Additional funding was received from Yayasan Sime Darby, PETRONAS and other donors of Cancer Research Malaysia. The Newton-Ungku Omar Fund (grant no: MR/P012930/a), https://www.britishcouncil.my/programmes/newton-ungku-omar-fund was used to establish the cohort; Wellcome Trust (grant no: v203477/Z/16/Z), https://wellcome.ac.uk/funding , was used to establish the cohort; and Malaysian Ministry of Higher Education High Impact Research Grant (grant no: UM.C/HIR/MOHE/06, https://www.mohe.gov.my/en/initiatives-2/187-program-utama/penyelidikan/548-research-grants-information , was used to establish the cohort.

Competing interests: The authors have declared that no competing interests exist.

Breast cancer risk factors have been examined extensively and the common ones include early age of menarche, late age of menopause, short breastfeeding duration, late age of first full term pregnancy, nulliparity and low parity [ 1 – 5 ]. However, most of these studies were conducted predominantly in developed countries in a Western setting. Although a limited number of studies examining women living in Asian countries also supported the association of these common risk factors with breast cancer [ 6 – 10 ], they were conducted in the more developed Asian cities/countries, or have been limited to sample sizes of several hundred women and mostly limited to one ethnicity. Therefore, there is a need to conduct a more extensive study with a larger sample size to determine whether these risk factors also play a similar role among Asian populations in developing countries, as this evidence should contribute importantly to the development of appropriate strategies for breast cancer prevention and control in Asia.

Malaysia offers a unique opportunity to examine breast cancer risk factors in Asian populations because of its multi-cultural and multi-religious setting, both of which might influence lifestyle and reproductive characteristics, and hence, breast cancer risk. Notably, the three main ethnicities in Malaysia, namely, Malay, Chinese and Indian, represent the three largest ethnic groups in Asia. Breast cancer is the most common cancer among Malaysian women and accounted for 31% of total female cancers [ 11 ]. The age-adjusted breast cancer incidence in Malaysia is 47.4/100,000, about half of that in North America [ 12 ]. Chinese have the highest incidence (59.9/100,000) followed by Indians (54.2/100,000) and Malays (34.9/100,000) [ 11 ]. Like many developing Asian countries, Malaysia is undergoing a transition toward a Westernized diet that is high in fat and sugar, an increasingly sedentary lifestyle [ 13 ] and also experiencing changes in reproductive characteristics [ 14 ]. Thus, there is an urgent need to examine the impact of these changes on breast cancer risk.

In this paper, we report the association between clinical, exogenous hormonal, menstrual, reproductive, anthropometric and lifestyle factors with breast cancer from a hospital-based case-control study of 7,663 women in Malaysia. We also present the change of selected breast cancer-related factors across birth cohorts and their implication for breast cancer in Malaysia and potentially other developing Southeast Asian countries.

Materials and methods

The study was approved by the Independent Ethics Committee, Ramsay Sime Darby Health Care (reference nos: 201109.4 and 201208.1), and the Medical Ethics Committee, University Malaya Medical Centre (reference no: 842.9). All participants provided written informed consent. The study was performed in accordance with the Declaration of Helsinki.

The Malaysian Breast Cancer Genetic Study (MyBrCa), initiated in 2002, is a hospital-based case-control study of breast cancer risk factors. The study participants are recruited from two participating hospitals in Selangor, Malaysia: University Malaya Medical Centre (UMMC), a public hospital, and Subang Jaya Medical Centre (SJMC), a private hospital. All patients diagnosed clinically with breast carcinoma are eligible for inclusion as cases. Cases from UMMC were recruited since October 2002, and from SJMC, since September 2012. Controls are healthy women between ages 40 and 74 with no personal history of breast cancer and recruited in the Malaysian Mammography Study (MyMammo) at UMMC and SJMC. At SJMC, MyMammo is a subsidized opportunistic mammogram screening programme that was initiated in 2011; while at UMMC, MyMammo started recruitment in 2014 from patients attending routine opportunistic screening in UMMC.

All participants were interviewed by trained interviewers at the hospitals. The participants completed questionnaire that included items related to demographics, personal and family history of cancers, history of breast surgery, menstrual and reproductive history, use of oral contraceptive and hormone replacement therapy (HRT), breast cancer diagnosis (cases only) and history of and motivation of attending mammography screening (controls) only. The participants provided a blood sample that was processed and stored.

Statistical analysis

To date, a total of 4,056 cases and 4,145 controls were recruited and interviewed. Only participants recruited before 1 January 2017 were included in this study. After removing duplicates, males and non-breast cancer cases, the remaining cohort consists of 3,683 cases and 3,980 controls.

Ever had breast surgery was defined as whether the participant had surgery for a benign lump or cyst in the breast. Women who had sisters/mothers/daughters with breast cancer were categorized as having a first-degree family history of breast cancer. Ever used oral contraceptives and HRT was defined as at least one month of usage. Post-menopausal status was defined as no menses for the past one year. The participants were categorized as parous if they had at least one full term pregnancy (live or still birth). BMI was calculated as dividing weight (kg) by the square of height (m). Soy products intake included the consumption of tofu, fermented soybeans, tofu pudding, and soy products other than soymilk. The participants reported their average duration of strenuous, moderate and gentle physical activity of three periods: childhood (before 18 years old), young adulthood (18–30 years), and the recent years. Weekly metabolic equivalent (MET)-hours were obtained by multiplying the corresponding MET value of each intensity of physical activity (7, 4, 3 for strenuous, moderate and gentle activities, respectively) with the average time spent on physical activity [ 15 ].

Cases and controls were compared using chi-square tests for categorical variables and t-tests for continuous variables. Unconditional logistic regressions were conducted to assess the association between risk factors and breast cancer, adjusting for potential confounders and other risk factors. The first models were adjusted for age, ethnicity, and hospital; for history of breast surgery, and anthropometric and lifestyle variables, the models were adjusted for age, ethnicity and education, and only participants from private hospital were included. In the second models, other breast cancer risk factors such as age of menarche, age of menopause, ever had full term pregnancy, first degree family history of breast cancer, and age of first full term pregnancy were added when appropriate. Conditional logistic regression using hospital-, ethnicity- and age- (±5 years) matched samples and unconditional logistic regressions stratified by pre- and post-menopausal status were also conducted. However, the results were similar to the unconditional and unstratified analysis thus they are not reported here.

The participants were categorized based on their year of birth into four birth cohorts: those born before 1949, between 1950–59, between 1960–69, and after 1969, and their breast cancer risk factors were compared across the birth cohorts. To compare across ethnicity, analysis of variances (ANOVAs) were conducted for continuous variables while chi-square tests were conducted for categorical variables. To determine whether there was a change of trend in the selected variables across birth cohorts, trend analyses were conducted by entering the birth cohort variable as continuous parameter in the regression models.

All analyses were conducted using R [ 16 ].

Table 1 is the demographic comparisons of cases and controls. Controls were significantly older than cases, with mean ages of 54.0 years and 50.8 years, respectively (p<0.001) and significantly more controls had received secondary education. There were significantly more Chinese among the cases.

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https://doi.org/10.1371/journal.pone.0203469.t001

We conducted unconditional logistic regression to examine the association of clinical, exogenous hormonal, menstrual and reproductive factors with breast cancer ( Table 2 ). Compared with those who had never had breast surgery, participants who had breast surgery to remove cysts and lumps were 2.3 times (95% CI = 1.82–2.83) more likely to develop breast cancer after adjusting for demographics and other risk factors. First-degree family history of breast cancer was associated with 19% increased risk of breast cancer after adjusting for demographics and other risk factors. Post-menopausal women had a 52% increased risk of breast cancer after adjusting for demographics and other risk factors. The use of oral contraceptives and HRT were not significantly associated with breast cancer risk after adjustment of other breast cancer risk factors.

https://doi.org/10.1371/journal.pone.0203469.t002

Of the menstrual and reproductive factors examined, breastfeeding had the strongest protective effect against breast cancer ( Table 2 ). Among parous women, those who ever breastfed had 35% lower risk in the fully adjusted models; compared with those who did not breastfeed, the reduction of risk for those who breastfed between 1–12 months and those who breastfed more than 12 months was 30% and 70% respectively.

We also examined the association between anthropometric and lifestyle factors and breast cancer ( Table 3 ). A higher BMI was associated with a lower risk of breast cancer; those who are overweight (BMI = 23.0–27.4kg/m 2 ) had 33% reduced risk and those who are obese (BMI ≥ 27.5kg/m 2 ) had 53% reduced risk, after controlling for other risk factors ( Table 3 ). Those who consumed one cup or more soymilk per week and soy products once or more per week had 75% and 60% reduction in breast cancer risk, respectively. We did not find any significant association between smoking status and breast cancer. Women who drink less than 1 glass of alcohol per week and 1 glass per week or more had 55% and 48% reduced risk of breast cancer. It is noteworthy that the prevalence of those who reported alcohol intake in our cohort is low at 14%. A higher level of physical activity during childhood, young adulthood and recent period were also significantly associated with reduced risk of breast cancer before and after adjusting for other risk factors.

https://doi.org/10.1371/journal.pone.0203469.t003

Change of risk factors by birth cohorts

We examined the change of all risk factors across birth cohorts of both controls and cases in the three major ethnic groups in Malaysia and here we report the variables that had significantly changed across birth cohorts. Fig 1 showed the change of parity, age of first full term pregnancy, breastfeeding rate, breastfeeding duration and total soy intake. Compared with Indians and Malays, Chinese have the lowest parity, oldest age of first full term pregnancy, lowest breast feeding rate and shortest breastfeeding duration (p<0.001). All ethnic groups were experiencing significant reduction in parity (p<0.001 for all races) and significant increase of age of first full term pregnancy (p<0.001 for Chinese and p<0.001 for Malays and Indians) across birth cohorts.

https://doi.org/10.1371/journal.pone.0203469.g001

All ethnic groups had significant increase of breastfeeding rate across birth cohorts (p<0.001). The increase was more noticeable among Chinese; there was an increase from 50% among the oldest cohort to 79% among the youngest cohort. Only Chinese had a significant increase of breastfeeding duration across birth cohorts (p<0.001); however, breastfeeding duration among Chinese remained low compared with Malays and Indians.

There was a significant decrease of total soy intake among Chinese (p<0.001) and Malay (p<0.05) across birth cohorts. Compared with Malays and Indians, Chinese consumed significantly less soy products (p<0.001). However, the number of Malays and Indians who reported their intake of soy products were small compared with that of Chinese.

In this hospital-based case-control study of 7,663 Malaysian women, we showed that a higher breastfeeding rate and duration, soy intake and level of physical activity were associated with a reduced risk of breast cancer among Southeast Asian women. Although Southeast Asian countries are experiencing a substantial increase in the burden of breast cancer, there have been limited studies in risk factors for breast cancer in these populations. Before the Malaysian Breast Cancer Genetic study, the previous largest study on breast cancer risk factors in Southeast Asia was from Indonesia and included 526 cases and 1,052 controls [ 17 , 18 ]. A large scale prospective cohort study that followed 35,303 women in which 629 developed breast cancer has been conducted in Singapore, however, it focused mainly on soy intake and breast cancer risk and was limited to Chinese only [ 19 ]. Our current study included a large sample size and examined a wide range of breast cancer risk factors.

The strongest predictor of breast cancer in our study was breastfeeding, and the inverse association between breastfeeding and breast cancer risk is well documented [ 6 , 7 , 20 – 22 ]. Our study also showed an increasing trend of breastfeeding across birth cohorts in all ethnicity; however, among Chinese the breastfeeding rate and duration were still relatively low. The low breastfeeding rate and short breastfeeding duration may contribute to the highest breast cancer incidence (59.9/100,000) among Chinese in Malaysia compared with Indians (54.2/100,000) and Malays (34.9/100,000) [ 11 ]. Thus, the results of our study could be helpful in public health strategies to reduce risk of breast cancer through modifiable lifestyle choices including breastfeeding.

Our study also found that higher intake of soymilk and soy products is associated with lower risk of breast cancer. Soy is a major food in many parts of Asia and retrospective cross-sectional cohort studies in China and Japan show that increased soy protein intake is associated with reduced breast cancer risk in pre- and post-menopausal women [ 23 , 24 ]. A study in Singapore showed that increased soy intake was significantly associated with reduced breast cancer risk among pre-menopausal women but not post-menopausal women [ 9 ] while another study in China found no significant association between soy protein intake and breast cancer risk. While there is some heterogeneity across these Asian studies, meta-analyses of observational studies in both Caucasian and Asian countries have consistently shown that high soy intake is associated to a lower risk of breast cancer, particularly among Asian women [ 25 – 30 ]. Given that our results shows declining soy intake across birth cohorts, future studies are required to confirm the benefit of soy in reducing population risk of breast cancer, as well as to also identify effective strategies to increase soy intake among Asian women, for whom a soy intervention may be an affordable and acceptable strategy for breast cancer prevention.

Another lifestyle factor that is shown to be associated with decreased breast cancer risk in our study is physical activity. This is consistent with the latest World Cancer Research Fund report which showed strong probable evidence that regular physical activity of various intensity decreases the risk of breast cancer among post-menopausal women while among pre-menopausal women, regular vigorous physical activity is associated with reduced risk [ 31 ]. A recent systematic review evaluated 80 studies and found that moderate-vigorous physical activity is associated with lower breast cancer risk among pre-menopausal (RR = 0.80, 95% CI = 0.74–0.87) and post-menopausal cohort studies (RR = 0.79, 95% CI = 0.76–0.84) [ 32 ]. Another systematic review that examined the dose-response between physical activity and major non-communicable diseases, which included breast cancer, found that compared with insufficiently active women, the reduction of risk of breast cancer among the low active, moderately active and highly active was 3%, 6% and 14% respectively [ 33 ]. Compared with other populations, Malaysian women have a higher prevalence of physical inactivity [ 34 ] and in our study there was no significant change of physical activity across birth cohorts. Thus, there is a need to construct innovative strategy to increase the level of physical activity in order to reduce future breast cancer risk.

In our study, two risk factors were associated with breast cancer risk in the contradictory direction. First, the consumption of alcohol was associated with a decreased risk of breast cancer in our study. The association of alcohol consumption with increased breast cancer risk has long been established [ 35 ]. However, in our study, only 6% reported an intake of more than 1 glass of alcohol per week, which is low compared with other populations. The second risk factor that had a contradictory association with breast cancer in our study was a higher BMI, which was associated with a lower risk of breast cancer after adjustment for major breast cancer risk factors. Past studies have shown that a higher BMI is associated with increased risk among post-menopausal women and reduced risk among pre-menopausal women [ 36 ]. However, when stratified by menopausal status, our analysis showed that a higher BMI was still significantly associated with lower breast cancer risk in both pre- and post-menopausal women. More studies need to be conducted among the Malaysian women to further explore the link between BMI and breast cancer risk.

In addition, our study did not find a significant association between parity, age of first full term pregnancy, age of menarche and menopause and breast cancer, which is inconsistent with other studies [ 2 – 5 , 8 , 10 , 37 – 41 ]. Our study also found only a slight association between first-degree family history of breast cancer and increased risk of breast cancer risk, while other studies show that family history is strongly associated with increased breast cancer risk [ 20 , 21 , 39 , 41 – 44 ].

Since this is a hospital-based case-control study rather than population-based, it might be subject to selection bias. The two hospitals where our participants were recruited were located in urban areas and rural Malaysian women were not included. However, it is noteworthy that these hospitals treat more than 10% of the breast cancer cases in Malaysia. The controls of our study were enriched for women who had a family history of breast cancer because they were participants of an opportunistic mammography screening programme.

In conclusion, our study shows that breastfeeding, soy intake and physical activity are modifiable risk factors for breast cancer; and with the increasing incidence of breast cancer there is an urgent need to educate the women about lifestyle intervention they can take to reduce their risk of breast cancer.

Supporting information

S1 file. survey questions..

This file contains the questionnaire items used in this study.

https://doi.org/10.1371/journal.pone.0203469.s001

Acknowledgments

We want to thank Pui-Yoke Kwan, Norhashimah Hassan, Peter Choon-Eng Kang, In-Nee Kang, Kah-Nyin Lai, Hanis Hasmad, Jin-Tong Ng, Dr. Gaik-Theng Toh, Nancy Geen-See Tan, Dr. Suhaida Selamat, Dr. Rohaya Mohd Kasim, Dr. Malkit Kaur Dhillon, Dr. Thin-Chai Liu, Ernie Azwa, Hanani Che Halim, Leelavathy Krishnan, Don-Na Tan, Sweet-Lin Goh, Nur Naquiah Kamaruddin, Faridah Bakri, the participants of this study, and all staff at Cancer Research Malaysia, University Malaya, and Sime Darby Medical Centre who assisted in recruitment and interviews.

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Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative

Affiliations.

• 1 Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
• 2 Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA.
• 3 Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
• 4 Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
• 5 Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
• 6 Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98195, USA.
• 7 Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA.
• 8 Department of Epidemiology, University of Washington, Seattle, WA 98195, USA.
• 9 Center for Metabolic and Vascular Biology, College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA.
• 10 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
• 11 Department of Internal Medicine, Division of Medical Oncology, College of Medicine and Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA.
• PMID: 39195559
• PMCID: PMC11356420
• DOI: 10.3390/metabo14080463

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [ n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N -acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N -acetyl-glutamate, allantoin, N -acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine- N -oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Keywords: breast cancer; colorectal cancer; dietary biomarkers; metabolite predictors; metabolomics.

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Risk Factors Associated with Breast Cancer among Women in Addis Ababa, Ethiopia: Unmatched Case–Control Study

Lidia tolessa.

1 School of Nursing and Midwifery, College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia

Endalew Gemechu Sendo

2 Department of Nursing and Midwifery, College of Health Science, Addis Ababa University, Addis Ababa, Ethiopia

Negalign Getahun Dinegde

Assefa desalew.

Breast cancer is a common public health problem and the main cause of cancer-related death worldwide. There is a paucity of evidence on the risk factors of breast cancer in Ethiopia. Therefore, we aimed to identify the risk factors of breast cancer among women in Addis Ababa, Ethiopia.

We conducted an institutional-based unmatched case–control study with a sample of 348 women (116 cases and 232 controls). Participants were selected by a systematic random sampling technique. Data were collected using an interviewer-administered questionnaire. Data were entered using EpiData version 4.6 and analyzed using SPSS version 25. Multivariable analysis was carried out using the adjusted odds ratio (AOR) with a 95% confidence interval (CI). P-value of less than 0.05 was considered statistically significant.

The mean age (+SD) of the participants was 42.7 (±11.3) and 40.7 (±14.6) for the cases and controls, respectively. Early onset of menarche (AOR= 4.10; 95% CI: 1.84, 9.15), rural women (AOR= 3.64; 95% CI:1.38, 9.57), utilization of packed foods or drinks (AOR= 2.80; 95% CI:1.52, 5.15), and smoke-dried meat (AOR= 2.41; 95% CI:1.36, 4.27), family history of cancer (AOR= 2.11; 95% CI:1.04, 4.26), overweight and/or obesity (AOR= 2.38; 95% CI:1.31, 4.31), and women with one or less children (AOR= 1.86; 95% CI:1.01, 3.41) were associated factors with breast cancer risk.

In this study, early onset of menarche, rural women, utilization of packed foods or drinks and smoke-dried meat, family history of cancer, overweight and/or obesity, and women with one or fewer children were factors that increased breast cancer risk. Therefore, focusing on modifiable risk factors and increasing awareness of the community such as a healthy diet, promotion of breast self-examination, and creation of programs to increase women’s knowledge is important to reduce the increasing burden of breast cancer.

Breast cancer is the most common public health problem and the main cause of cancer-related death worldwide. 1–4 Breast cancer is a type of cancer characterized by uncontrolled development and the spread of abnormal breast cells. 5 , 6 Globally, approximately 2.1 million women, which account for 24.2% of new cancer cases and 627,000 (15%) deaths in 2018. 1 , 7 More than half of the incidence of breast cancer and 60% of deaths occur in low- and middle-income countries (LMICs). 1 , 8 For example, African countries had the highest age-standardized mortality rate (17.3 deaths per 100,000 annually) associated with breast cancer. 9 , 10

In Ethiopia, breast cancer incidence is rising and become the foremost common cancer, causing high rates of morbidity and mortality. 11 The incidence of breast cancer accounts for 15,244 (22.6%) all cases of cancer and 8,159 (17%) cancer mortality annually. 7 , 12 The age-standardized incidence rate of breast cancer was 40.6 per 100,000 females. 11 According to the Addis Ababa cancer registry, breast cancer incidence increased from 18 to 160 new cases between 2012 and 2018 in Addis Ababa. 13

Although breast cancer overwhelmingly occurs in high-income countries, recently, the burden has extended in LMICs due to factors such as a westernized lifestyle and urbanization. 1–4 Evidence indicates that breast cancer has several influences, including psychological distress to the patients, family members, reduced productivity, and increased cost of the health care system. 14–17 In African countries, including Ethiopia, the health care system cannot handle care and treatment for the expanding incidence rates of breast cancer due to inappropriate treatment strategies in place and economic barriers. Therefore, focusing on preventive strategies through appropriate identification and exposure reduction to established risk factors is important. 10 , 18

According to the World Health Organization (WHO) and American Cancer society, they recognized several risk factors for breast cancer, including early menarche, late menopause, null parity, late age at first pregnancy (>30 years), non-breastfeeding mothers, hormonal contraceptives, hormone therapy after menopause, alcohol, cigarette smoking, obesity, physical inactivity, diet, and family history of breast cancer. 6 , 19–21 Several risk factors are assumed to increase disease occurrence, which incorporates modifiable and non-modifiable risk factors. 6 , 22 However, several epidemiological studies conducted in different populations indicate that the incidence and mortality of breast cancer greatly vary between countries. This indicates that environmental and lifestyle factors may contribute to the development of breast cancer. 7 , 22–25

Primary prevention offers the greatest public health potential and most cost-effective long-term cancer control program. Approximately 50% of breast cancers could be prevented when a maximal benefit is achieved through prevention programs and lifestyle modification. 2 , 20 , 26 However, adequate information is not available to validate specific lifestyle modification programs, especially in LMICs. 2 , 20 , 26 Additionally, effective programs to prevent the onset of breast cancer have not been a priority for the health systems in LMICs, including Ethiopia, and there is a lack of awareness of the risk factors of cancer. 16

A study conducted in Africa on the management of breast cancer suggests that there is a research gap in LMICs. 27 The population of women in Ethiopia is diverse, with different lifestyle factors, cultures, economic statuses, and reproductive and breastfeeding practices. These factors affect the overall health conditions and make it an area of interest for investigating risk factors of breast cancer. Moreover, studies focused on identifying the risk factors in a local context play an important role in the prevention of breast cancer and would contribute to the prioritization of interventions. 10 , 18 Despite the abovementioned geographic and behavioral variation in breast cancer risk and increased incidence rate in low-income countries, including Ethiopia. There is a paucity of studies on risk factors of breast cancer in Ethiopia. Therefore, this study aimed to identify the risk factors associated with breast cancer among women attending Tikur Anbessa Specialized Hospital (TASH) and St. Paul’s Hospital Millennium Medical College (SPHMMC), Addis Ababa, Ethiopia, 2020.

Materials and Methods

Study setting and population.

The study participants were recruited from the two most common referral public hospitals (TASH and SPHMMC) in Addis Ababa, Ethiopia. We conducted an institutional-based unmatched case–control study from February 1 st to March 30, 2020. Women who have histologically confirmed breast cancer cases and unmatched noncancerous clients visiting the same hospital in the study period were included. However, women who had cancer from another site and disseminated to the breast, critically ill, and mentally handicapped patients were excluded. All women diagnosed clinically and histologically confirmed breast cancer were eligible for inclusion as cases. A potential control was a woman aged 20 years or older who had no prior history of breast cancer and who was unrelated to cases. Controls were relatively healthy women recruited from maternal and child health unit, family planning, and outpatient units for any illness other than breast cancer in two hospitals.

Sample Size and Procedure

The sample size was calculated using Epi-Info version 7.2 based on the following assumptions: 95% confidence interval, 80% power, and 1:2 ratio of cases to controls, 24.3% proportion of family history of breast cancer in Senegal Dakar with an odds ratio of 2.12 28 and a 10% non-response rate. This yields a maximum sample size of 116 cases and 232 controls. The hospitals were selected purposively because more than 90% of breast cancer patients were attending the oncology unit in these Hospitals in Addis Ababa, Ethiopia. The sample size was allocated using probability proportional to size based on the proportion of average monthly client flow reviewed from the registration book. Cases were selected for all women with confirmed breast cancer attending two hospitals during the two-month data collection period until the final sample size was reached. For every case, two controls were selected among healthy women between ages 20 to 74 years using systematic random sampling methods.

Data Collection Procedure

All participants were interviewed by trained interviewers at the hospitals. Data were collected by 6 nurses using a pretested, interviewer-administered questionnaire that was adapted and modified from published literature. 29 , 30 The questionnaires were translated into Amharic version, and then translated back to English to ensure its consistency. The questionnaire includes sociodemographic characteristics and behavioral, reproductive, and biological related factors. We defined women who had sisters or mothers or daughters with breast cancer were categorized as having a first-degree family history of breast cancer. Post-menopausal status was defined as no menses for the past one year. The participants were categorized as parous if they had at least one full-term pregnancy (live or stillbirth). The weight scale was calibrated at 0 with no object on it and placed on the level surface before the measurement was carried out. Height was measured with a stadiometer attached to the wall. A continuous checkup of the scale was performed for reliability. Body mass index (BMI) was calculated by BMI = weight (kg)/height square (M2). A BMI of 25.0 or more is overweight, more than 30 is obese, while the healthy range is 18.5 to 24.9.

Data Quality Control

Two days training was given to all the data collectors and supervisors. A pretest was carried out on 5% (in Zewuditu Hospital) of the total sample size before the actual data collection. Based on the findings of the pretest, some modifications were undertaken. The data collection process was closely supervised, and the completeness of each questionnaire was checked daily. During data cleaning, a logical checking technique was employed to identify errors. Finally, double data entry was performed to check the consistency of the data.

Data Processing Analysis

The data were entered into Epi Data version 4.6 and analyzed using Statistical Package for the Social Sciences (SPSS) Version 25. Descriptive statistics including frequencies and proportions were employed. The findings were presented using tables, figures, and graphs. Multivariable logistic regression was used to measure the association between covariates and outcome variable. Adjusting for potential confounders factors, the models were adjusted for age, anthropometric, lifestyle variables, age of menarche, age of menopause, and family history of breast cancer, were added when appropriate. Multi-collinearity was checked to see the linear correlation among the independent variables by using variance inflation factor (>10) and standard error (>2) and goodness-of-fit was checked by Hosmer-Lemeshow test (>0.05). Adjusted odds ratio (AOR) with 95% confidence intervals (CI) using a p-value<0.05 was considered a statistically significant association with the outcome variable.

Sociodemographic Characteristics

A total of 116 cases and 232 controls participated in the study, with a response rate of 100.0%. The mean (±SD) age of the participants was 42.7 (±11.3) and 40.7 (±14.6) for cases and controls, respectively. Of the participants, 67 (57.4%) cases and 96 (41.2%) controls were recruited from TASH. Ninety-eight (84.5%) cases and 223 (96.1%) controls were urban dwellers. Of these, more than half 98 (84.5%) of the cases and 162 (69.8%) controls were from Addis Ababa. Thirty-seven (31.9%) cases and 84 (36.2%) controls had college and above educational status. Regarding occupational status, 50 (43.1%) cases, and 91 (39.2%) controls were employed. More than half (55.5%) of the participants earn greater than 2000 Ethiopian birr (ETB) per month ( Table 1 ).

Sociodemographic Characteristics of Women among Cases and Controls at TASH and SPHMMC Addis Ababa, Ethiopia, 2020

Behavioral and Biological-Related Characteristics

Among the total participants, 37 (31.9%) cases and 58 (25.0%) controls had overweight and/or obesity. Among the study participants, 23 (19.8%) cases and 29 (12.5%) controls had a family history of cancers. Regarding physical activities, in most cases, 98 (84.5%) and 198 (85.3%) controls did not participate in regular physical exercise. More than half of the cases 79 (68.1%) and 132 (56.9%) controls eat less than seven servings of fruits and vegetables per week. Regarding exposure to smoked meat, 58 (50.0%) cases, and 70 (30.2%) controls were exposed. Thirty-nine (33.6%) cases and 46 (19.8%) controls used packed food or drinks. The majority of the participants, 84 (72.4%) cases and 181 (78.0%) control had no history of breast trauma ( Table 2 ).

Behavioral and Biological Related Characteristics of Women among Cases and Controls at TASH and SPHMMC Addis Ababa, Ethiopia, 2020

Reproductive Health-Related Characteristics

Among participants, 40 (34.5%) cases and 27 (11.6%) controls had seen their first menstrual period before the age of 12 years ( Figure 1 ). Sixty (44.1%) cases and 53 (39.0%) controls had a history of using contraceptives, nearly half (47.5%) of the cases and 21 (39.6%) controls used Oral contraceptives. Seventy-eight (67.2%) cases 181 (78.0%) controls had no history of abortion. From a total of women, the majority of cases 94 (81.0%) and 180 (77.6%) controls had ever breastfed their infants ( Table 3 ).

Reproductive Health-Related Characteristics of Women among Cases and Controls at TASH and SPHMMC Addis Ababa, Ethiopia, 2020

Distribution of women with age at menarche among cases and controls at TASH and SPHMMC Addis Ababa, Ethiopia, 2020.

Factors Associated with Breast Cancer Risk

In the multivariable logistic regression analysis, place of residence, use of packed foods or drinks, exposure to smoked-dried meat, age at menarche, BMI, family history of cancer, and having less than one child were independently associated with breast cancer risk. The risk of breast cancer was higher among rural women (AOR=3.64; 95% CI: 1.38, 9.57) than women who lived in urban areas. Women who used packed foods or drinks were nearly 3 times (AOR= 2.80; 95% CI: 1.52, 5.15) more likely to have breast cancer risk compared with their counterparts. Similarly, women who utilized smoke-dried meat were 2.41 times (AOR= 2.41; 95% CI: 1.36, 4.27) more likely to have breast cancer risk compared with their counterparts. Women with a family history of cancer were 2 times AOR= 2.11; 95% CI: 1.04, 4.26) more likely to be affected with breast cancer than their counterparts. Furthermore, women who had seen their first menstrual period before the age of 12 were almost 4 times (AOR= 4.10; 95% CI: 1.84, 9.15) more likely to have breast cancer risk compared with those women who had seen after the age of 12 years. Moreover, women who were overweight or obese were 2.38 times (AOR= 2.38; 95% CI: 1.31, 4.31) higher chance of getting the risk of breast cancer compared with the normal-weight women. Also, women with one or less child were 1.86 times (AOR=1.86; 95% CI: 1.01, 3.41) more likely to get the risk of breast cancer ( Table 4 ).

Bivariable and Multivariable Logistic Regression Analysis of Factors Associated with Breast Cancer Risk at TASH and SPHMMC Addis Ababa, Ethiopia, 2020

Note: * Statistically significant at P ≤ 0.05.

Abbreviations: COR, crude odds ratio; AOR, adjusted odds ratio.

The identification of modifiable factors in the local context may contribute to the development of prevention strategies that decrease breast cancer incidence and mortality. 31 , 32 To our knowledge, this study was the first to investigate breast cancer risk in Ethiopian women. The present study identified that rural residence, early onset of menarche, use of packed foods or drinks, smoked dried meat, BMI, family history of cancer, and less than one child were independently associated with the breast cancer risk.

In this finding, the place of residence had a significant association with breast cancer risk. The odds of breast cancer were approximately 4 times higher among women who were living in rural areas. This is consistent with a study conducted in the Central African Republic that showed that decreased odds of breast cancer were associated with living in urban areas. 31 However, it is inconsistent with the systematic review and meta-analysis indicating that residing in urban areas is associated with higher breast cancer incidence. 33 This might be due to differences in the economic condition of the population, educational status, and better awareness of risk factors in urban areas.

The risk of breast cancer may be reduced to the extent that one can make lifestyle changes consistent with modifiable risk factors. 34 According to this study, women who have used packed foods or drinks were nearly 3 times more likely to have breast cancer risk. This finding is supported by studies conducted in Iran that identified that the consumption of soft drinks and industrially produced juices was associated with a significantly increased risk of breast cancer. 35 , 36 Highly processed foods such as packaged foods, instant soups, reconstituted meats, frozen meals, and shelf-stable snacks also contain substances that may significantly increase the overall risk for cancer and breast cancer. 37 Moreover, exposure to smoke-dried meat increases breast cancer risk by 2 times. This finding is supported by studies that indicated processed meat intake was associated with increased breast cancer risk and statistically associated with an elevated risk of all-cause mortality. 38–40

Furthermore, the risk of breast cancer was positively associated with overweight and/or obesity. This finding is in agreement with many findings. 41–44 Even though, the exact mechanism behind the association between BMI and breast cancer risk is uncertain, there are some potential hypotheses. The positive association between BMI and breast cancer risk in women was speculated to result from the increased estrogens and inflammatory mediators within the larger fat reserves of women of higher BMI and contribute to the aggressive breast cancer phenotype in overweight and obesity. 42 , 45 , 46

Most of the well-known risk factors for breast cancer are related to the reproductive life of women: Women with early menarche (before age 12 years) have a higher breast cancer risk. 47 The present study found that the odds of breast cancer increase by 4 times among women with early menarche, which is consistent with studies conducted in India and Morocco. 48 , 49 This might be due to a woman’s risk of breast cancer being related to the estrogen and progesterone made by her ovaries (known as endogenous estrogen and progesterone). Being exposed for a long time and/or to high levels of these hormones has been linked to an increased risk of breast cancer. In the present findings, contraceptive use did not show a significant association with breast cancer. However, a study in Saudi Arabia 50 and Kazakhstan 51 showed that the odds of breast cancer were higher among women with long-term use of hormonal contraception. Furthermore, this study revealed that women with fewer children (none or one) increases breast cancer risk. This finding is supported by a similar study conducted in Vietnam. 52 We found a history of abortion as a risk factor for breast cancer in crude analysis. This is in line with a study that justified abortion disrupts the maturation process of the breast, it has increased breast cancer risk. 34 Besides, null or low parity was related to a higher risk of breast cancer in young women in southern Iran 53 and Morocco. 49 However, in the current study, parity, age at first live birth, and history of breastfeeding did not show a significant association with breast cancer. This difference might be due to the smaller sample size and poor age recording keeping system in the country.

Family history is an important risk factor for breast cancer and is significantly greater in women with a family history of the disease. 24 , 28 , 54 Similarly, in this study, family history has significantly associated with the risk of breast cancer. However, this finding is in contrast with the results of a previous similar study conducted in Vietnam, which indicated that there was no significant association between family history and breast cancer. 55 Further studies with high-level methodology may be needed to solicit the association between family history and risk of breast cancer.

Limitation of the study: This case–control study cannot determine cause and effect relationships. The findings of this study were based on self-report, as it was not possible to validate claims obtained from study subjects that could be subject to recall bias. A wide confidence interval is noted in some of the variables as a result of a small sample size.

In conclusion, factors such as the early onset of menarche, rural women, utilization of packed foods or drinks and smoke-dried meat, family history of cancer, overweight and/or obesity, and women with one or less child were factors which increased the risk of breast cancer. Therefore, focusing on modifiable risk factors and increasing awareness of the community such as a healthy diet and promotion of breast self-examination, and the creation of programs to increase women’s knowledge is important to reduce the increasing burden of breast cancer in the study setting. Further studies with larger sample sizes and high-level study designs are recommended to better understand the local risk factors.

Acknowledgments

We would like to thank Addis Ababa University for its financial and unreserved technical support. We would also like to extend our gratitude to the hospital heads, the study participants, the data collectors, and the supervisor for collaborations.

Funding Statement

This work was financially supported by Addis Ababa University, Ethiopia. The funder has no role in the study selection, data collection, analysis, conclusion, and interpretation.

Data Sharing Statement

All the data of this study are available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was conducted in accordance with the declaration of Helsinki. Ethical clearance was obtained from the Institutional Health Research Ethics Review Committee of the College of Health Sciences, Addis Ababa University (Ref. No. 047/20/SNM). Following approval, a written official letter of cooperation was given to the administrative health bureau and facilities. Informed written consent was obtained from all participants. Furthermore, confidentiality was assured throughout the process.

Author Contributions

All authors made a significant contribution in the conception, study design, execution, acquisition of data, analysis, and interpretation; took part in drafting, revising, or critically for important intellectual content; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

The authors declare that they have no competing interests.

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Breast Cancer Risk Factors

• Your risk for breast cancer is due to a combination of factors.
• The main factors that influence your risk include being a woman and getting older.

Studies have shown that your risk for breast cancer is due to a combination of factors. The main factors that influence your risk include being a woman and getting older. Most breast cancers are found in women who are 50 years old or older.

Some women will get breast cancer even without any other risk factors that they know of. Having a risk factor does not mean you will get the disease, and not all risk factors have the same effect. Most women have some risk factors, but most women do not get breast cancer. Talk with your doctor about ways you can lower your risk and about screening for breast cancer.

Risk factors you cannot change

• Getting older. The risk for breast cancer increases with age. Most breast cancers are diagnosed after age 50.
• Genetic mutations. Women who have inherited changes (mutations) to certain genes, such as BRCA1 and BRCA2, are at higher risk of breast and ovarian cancer.
• Reproductive history. Starting menstrual periods before age 12 and starting menopause after age 55 expose women to hormones longer, raising their risk of getting breast cancer.
• Having dense breasts. Dense breasts have more connective tissue than fatty tissue, which can sometimes make it hard to see tumors on a mammogram. Women with dense breasts are more likely to get breast cancer.
• Personal history of breast cancer or certain noncancerous breast diseases. Women who have had breast cancer are more likely to get breast cancer a second time. Some noncancerous breast diseases such as atypical ductal hyperplasia or lobular carcinoma in situ are associated with a higher risk of getting breast cancer.
• Family history of breast or ovarian cancer. A woman's risk for breast cancer is higher if she has a mother, sister, or daughter (first-degree relative) or multiple family members on either her mother's or father's side of the family who have had breast or ovarian cancer. Having a first-degree male relative with breast cancer also raises a woman's risk.
• Previous treatment using radiation therapy. Women who had radiation therapy to the chest or breasts (for instance, treatment of Hodgkin's lymphoma) before age 30 have a higher risk of getting breast cancer later in life.
• Exposure to the drug diethylstilbestrol (DES). DES was given to some pregnant women in the United States between 1940 and 1971 to prevent miscarriage. Women who took DES have a higher risk of getting breast cancer. Women whose mothers took DES while pregnant with them also may have a higher risk of getting breast cancer.

Risk factors you can change

• Not being physically active. Women who are not physically active have a higher risk of getting breast cancer.
• Being overweight or having obesity after menopause. Older women who are overweight or have obesity have a higher risk of getting breast cancer than those at a healthy weight.
• Taking hormones. Some forms of hormone replacement therapy (those that include both estrogen and progesterone) taken during menopause can raise risk for breast cancer when taken for more than 5 years. Certain oral contraceptives (birth control pills) also have been found to raise breast cancer risk.
• Reproductive history. Having the first pregnancy after age 30, not breastfeeding, and never having a full-term pregnancy can raise breast cancer risk.
• Drinking alcohol. Studies show that a woman's risk for breast cancer increases with the more alcohol she drinks.

Research suggests that other factors such as smoking, being exposed to chemicals that can cause cancer, and changes in other hormones due to night shift working also may increase breast cancer risk.

What Would You Tell Your Patients About Drinking Alcohol and Breast Cancer Risk?

CDC's Dr. Lisa Richardson explains the link between drinking alcoholic beverages and breast cancer risk in this video.

Who is at high risk for breast cancer?

If you have a strong family history of breast cancer or inherited changes in your BRCA1 and BRCA2 genes, you may have a high risk of getting breast cancer. You may also have a high risk for ovarian cancer.

Talk to your doctor about ways to reduce your risk, such as medicines that block or decrease estrogen in your body, or surgery.

"Having a family history increases cancer risk in both genders."‎

Breast cancer.

Talk to your doctor about when to start and how often to get a mammogram.

For Everyone

Public health.