Global Burden of Female Cancers Attributable to Lifestyle Risk Factors: A 1990-2021 Analysis from the Global Burden of Disease Study

Global Burden of Female Cancers Attributable to Lifestyle Risk Factors: A 1990-2021 Analysis from the Global Burden of Disease Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Burden of Female Cancers Attributable to Lifestyle Risk Factors: A 1990-2021 Analysis from the Global Burden of Disease Study Yuxuan Zhu, Qin DU, Yiyang Ma, Nanxin Xu, Yuhang Luo, Di Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6238383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cancer remains a leading cause of female mortality worldwide, especially in low- and middle-income countries. While lifestyle contributes to cancer causation, the extent of its burden on women across different populations remains unclear. Method: We conducted a study to investigate the role of six lifestyle factors—low physical activity, alcohol consumption, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI)—in the cancer burden among women globally, regionally, and nationally from 1990 to 2021. Result: Research shows that disability-adjusted life years (DALYs) and age-standardized mortality rates (ASRs) linked to health risks like high BMI, alcohol, tobacco, and poor diet are higher in high-SDI regions. Eastern Europe has the highest DALYs from high BMI (9.9%) and unhealthy diets (10.4%). Australia leads in alcohol-related DALYs (3.7%) and mortality (3.1%), while high-income North America has the highest tobacco-related DALYs (18%) and mortality (18.2%). Female cancer mortality rises with age, especially among women aged 40-60, with breast cancer DALYs increasing significantly. These insights aid in targeted cancer prevention strategies. Conclusion: These findings highlight the critical role of lifestyle factors in shaping global cancer epidemiology among women, providing a basis for developing targeted preventive measures and public health policies to reduce the disease burden on women. Women Cancer Lifestyle Risk Factors Global Burden of Disease Public Health Interventions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Cancer has become a leading global health threat, contributing significantly to female mortality and disability worldwide. According to data from the American Cancer Society(ACS), between 2015 and 2019, the annual incidence rates of breast, pancreatic, and corpus uteri cancers increased by 0.6–1%, while liver cancer rates in women surged by 2–3% annually. The GLOBOCAN project by the International Agency for Research on Cancer (IARC) predicts there will be nearly 20 million new cancer cases and 9.7 million cancer-related deaths globally in 2022, with women facing a 20% lifetime cancer risk and 12% cancer mortality probability. Breast cancer stands not only as the most prevalent cancer type among women but also as the foremost contributor to cancer-related deaths, surpassed only by lung and colorectal cancers in overall mortality [ 1 ] . According to data from The Lancet in 2023, breast, colorectal, lung, cervical, and thyroid cancers are the five most common types of cancer among women, accounting for 53.7% of all female cancer cases [ 2 ] . Despite medical advancements in early detection and targeted therapies, women worldwide face persistent barriers to timely cancer diagnosis and quality care. Social determinants including caregiving responsibilities, geographic constraints, and ethnic disparities frequently delay medical intervention. This reality underscores the urgency for better understanding the risk factors for cancer in female populations. With the continuous rise in cancer incidence, medical technology dedicated to conquering cancer is also advancing, especially in the improvement of early prevention and treatment methods, due to the innovation of early diagnosis and treatment technologies, including targeted therapies and immunotherapies. As cancer research deepens, researchers have gradually come to recognize that lifestyle factors are closely related to the risk of women developing tumors. Globally, the incidence and mortality rates of lung cancer among women remain alarmingly high. According to 2022 data, there were approximately 2.5 million new cases and more than 1.8 million deaths worldwide, representing 12.4% of all cancer diagnoses and 18.7% of cancer-related deaths. The accelerated decline in lung cancer mortality rates observed between 2014 and 2018 is strongly associated with the reduction in smoking prevalence [ 3 ] . While tobacco use remains the primary risk factor for lung cancer, a substantial proportion of cases occur among non-smokers, particularly among East Asian women. This phenomenon may be attributed to environmental factors such as exposure to air pollution and the use of solid fuels for household energy. Similarly, colorectal cancer, the third most common cancer globally [ 4 ] , shows strong association with unhealthy lifestyle factors, such as unhealthy diet, smoking, alcohol consumption, obesity, and lack of exercise. Moreover, geographical factors also affect the occurrence of cancer in women, including the implementation of organized and opportunistic screening programs, as well as variations in the prevalence and distribution of major risk factors, such as parity and age at first birth [ 5 ] . These findings further emphasize the close link between lifestyle factors and the risk of women developing cancers, highlighting the importance of adopting preventive measures and a healthy lifestyle [ 6 ] . Globally, the incidence and mortality rates of cancer among women are on the rise, particularly in low-income and middle-income countries. Changes in lifestyle, including unhealthy diets, lack of exercise, smoking, and alcohol consumption, are regarded as the primary factors driving the increase in the cancer burden. Given the aging global population and the westernization of lifestyles, it is anticipated that the burden of cancer among women will continue to grow over the coming decades. This study conducts a systematic analysis of the Global Burden of Disease (GBD) 2021 data, aiming to quantify the contribution of comprehensive lifestyle factors to the burden of female cancer at global, regional, and national levels from 1990 to 2021. The research analyze six lifestyle factors: low physical activity, alcohol, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI). By analyzing the data from 21 GBD regions and 5 Socio-demographic Index (SDI) regions, the study reveals the trends in the burden of cancer among women influenced by various risk factors, as well as the differences across different regions and at different levels of the Socio-demographic Index (SDI). We hope to provide a comprehensive perspective on how lifestyle factors impact the global burden of cancer among women and to offer scientific evidence for the development of effective public health interventions. Methods In this study, we analyzed data from the Global Burden of Disease Study 2021 (GBD 2021), with a particular focus on the impact of lifestyle-related risk factors on the incidence of cancer among women. GBD 2021 covers six lifestyle factors, including low physical activity, alcohol, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI), which are further used to estimate the burden of cancer in women. These data can be accessed through the GBD 2021 Data Sources tool, which provides detailed information on data sources on the website of the Institute for Health Metrics and Evaluation ([GBD 2021 Data Sources]( https://ghdx.healthdata.org/gbd-2021/sources )). GBD 2021 is a global collaborative project supported by more than 11,500 contributors from 164 countries. Through extensive data collection, review, and analysis, GBD 2021 systematically assesses global health status and disease burden. This information can be found on the website of the Institute for Health Metrics and Evaluation (IHME). The disease models for cancers in women are detailed in the GBD 2021 Methods Appendices, which can be accessed via the following link: [GBD Methods Appendices 2021 Cancers]( https://www.healthdata.org/gbd/methods-appendices-2021/cancers ). Using the GBD Results Tool, we extracted data on the incidence, mortality, and disability-adjusted life years (DALYs) for cancers in women from GBD 2021 ([GBD Results Tool]( https://vizhub.healthdata.org/gbd-results )). The world is divided into 21 GBD regions and 5 Socio-Demographic Index (SDI) regions. Age-Standardized Rates (ASR) and Estimated Annual Percentage Change (EAPC) are used to measure trends in the incidence and mortality of tumors caused by different risk factors in women. ASR trends are used to accurately depict changes in disease patterns in populations, while EAPC is used to quantify trends in ASR over time across different populations. Socio-Demographic Index: The Socio-Demographic Index (SDI) is a composite development status indicator closely related to health outcomes, calculated based on the geometric mean of three dimensions: the fertility rate of women under the age of 25, the average years of education for individuals aged 15, and above, and lagged distribution of per capita income. For GBD 2021, the final SDI values are multiplied by 100 for reporting, facilitating comparison and analysis. SDI values range from 0 (the theoretical minimum level of development related to health) to 100 (the theoretical maximum level). A recent GBD 2021 vertex paper describes the assembly method of SDI and divides 204 countries into five quintiles (low, low-middle, middle, high-middle, and high) based on the 2021 national-level SDI estimates [ 7 ] . Statistical Analysis: Using data from the United Nations' standard projection dataset for the year 2019, age-standardized incidence rates (ASR), such as age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized disability-adjusted life year rates (ASDR), were calculated. This dataset provides population data in five-year age groups from 1990 to 2030 ( https://population.un.org/wpp/Download/Standard/Population)(https://population.un.org/wpp/Download/Standard/Population/ ). The rates are derived based on the following formula: $$\:ASR=\frac{\sum\:_{i=1}^{A}{a}_{i}{w}_{i}}{\sum\:_{i=1}^{A}{w}_{i}}\times\:\text{100,000}$$ Where A represents the number of age groups, i denotes the i th age group, a i is the rate to be standardized, and w i is the population size of the standard population for the same age group. To assess changes in the disease burden of cancer among women caused by different risk factors, we introduced the Estimated Annual Percentage Change (EAPC) indicator. EAPC is calculated based on a regression model by fitting the natural logarithm of ASR to the calendar year, which is a widely adopted summary measure used to assess trends in ASR over a specific time interval. It is assumed that there is a linear relationship between the natural logarithm of ASR and time. The EAPC of ASR and its corresponding confidence interval (CI) can be calculated to illustrate the temporal pattern of ASR changes from 1990 to 2019: EAPC = 100 × (exp(β) − 1). EAPC is expressed on a scale of -1 to 1. An EAPC > 0 indicates an increase in ASR, while an EAPC < 0 indicates a decrease in ASR. All statistical analyses were performed in open-source software R (version 4.4.1). Result 1.Global, Regional, and National Burden of Cancer in Women In 2021, the incidence of cancer among women worldwide demonstrated significant geographical disparities[Figure1、Figure2]. It is estimated that there were 39,416,539 new cases of cancer among women globally, with an age-standardized incidence rate of 923.4 per 100,000 population[Table 1]. Among these cases, the number of deaths was approximately 4,286,993, with an age-standardized mortality rate of 93.6 per 100,000 population[Table 2]. Furthermore, the global burden of disability-adjusted life years (DALYs) attributed to cancer in women was 111,646,997, with an age-standardized DALY rate of 2,507.59 per 100,000 population[Table 3]. Among the 21 Global Burden of Disease (GBD) regions, High-income North America had the highest age-standardized incidence rate, reaching 3,484.32 per 100,000 population, while the southern sub-Saharan Africa had the highest age-standardized DALY rate, at 3,552.51 per 100,000 population. From 1990 to 2021, the age-standardized incidence rate of cancer among women worldwide showed a slight increasing trend, with an annual percentage change (EAPC) of 0.01. Meanwhile, the global age-standardized mortality rate and DALY rate showed a decreasing trend, with EAPC of -0.75 and − 0.98, respectively. Among the 5 Sociodemographic Index (SDI) regions and 21 GBD regions, Southern sub-Saharan Africa had the most significant increase in age-standardized incidence rate, with an EAPC of 1.23. The East Asia region showed the most prominent decrease in age-standardized mortality rate, with an EAPC of -1.77. Except for the stable situation in Western sub-Saharan Africa (EAPC of 0.08), the age-standardized DALY rate in most regions showed a significant decrease[Table 3]. 2. The correlation between ASR, EAPC, and SDI When exploring the multi-dimensional impact factors of the global burden of cancer in women in 2021, the Socio-demographic Index (SDI) emerged as a key indicator, revealing the complex associations between the comprehensive development level of medical care in various countries and cancer risk factors. The impact of low physical activity on the incidence of cancer in women increases with the rise of SDI, reaching a peak when SDI reaches 0.76, and then showing a decreasing trend. Similarly, the influence of tobacco and high body-mass index (BMI) on the incidence of cancer in women also follows a pattern of initial increase followed by a decrease, with the SDI critical point for tobacco being 0.78 and for high BMI being 0.76. It is noteworthy that the incidence of cancer in women in high-income North America is significantly affected by tobacco, far exceeding the global average; while women in Australia are more affected by high BMI. In contrast to the aforementioned trends, the impact of alcohol and dietary on the incidence of cancer in women shows a trend of decreasing, then increasing, and then decreasing again with the increase of SDI. The impact of alcohol reaches its lowest point at an SDI of about 0.45, and then reaches its highest point at an SDI of about 0.78; the impact of dietary reaches its lowest point at an SDI of about 0.4 and reaches its highest point at an SDI of about 0.76. In addition, the impact of high fasting plasma glucose on the incidence of cancer in women continues to rise with the increase of SDI, and no obvious turning point has been observed among different regions.[Figure3] 3.Attributable to comprehensive lifestyle-related risk factors of the burden of cancer in women. When analyzing the regional differences in DALYs and mortality rates for cancer among female patients worldwide, we observe significant disparities in the impact of specific health risk factors across regions with different income levels. In high-income areas, such as high-income North America, Western Europe, and Australalia, high body-mass index (BMI), alcohol, tobacco, and dietary are the primary health risk factors, leading to longer DALYs and relatively higher mortality rates for female cancer patients. For instance, in high-income North America and Western Europe, the impact of low physical activity on female cancer patients is substantial, with DALYs and mortality rates relatively high, at 2% and 2.3%, respectively, and 1.8% and 2.3%. In contrast, in low-income regions, such as Southern sub-Saharan Africa, while DALYs and mortality rates for certain health risk factors like low physical activity and high fasting plasma glucose are lower, these areas may face greater challenges in addressing other risk factors. Middle-income regions, such as Southeast Asia and Latin America, exhibit varying trends under the influence of different health risk factors. The impact of alcohol is particularly significant among female cancer patients in high-income regions, with generally higher DALYs and mortality rates, such as in Australasia (DALYs at 3.7%, mortality rate at 3.1%) and Western Europe (DALYs at 3.2%, mortality rate at 2.8%). In low-income areas, such as East Asia and Central Latin America, DALYs and mortality rates are relatively lower, ranging from 0.8–1.2%. The influence of high BMI on DALYs and mortality rates is higher in high-income regions and certain specific areas, while it is relatively lower in some low-income regions, showing a clear polarization. Eastern Europe has the highest DALYs and mortality rates at 9.9% and 10.2%, respectively. High-income North America and Central Europe also have relatively high DALYs at 8.1% and 8.2%, with mortality rates at 8.3% and 7.9%. The impact of high fasting plasma glucose on cancer among women varies by region, with a global DALYs percentage of 2.9% and a mortality rate of 3.6%. High-income North America has the highest DALYs percentage at 5.4% and the highest mortality rate percentage at 6.1%. In low-income areas like East sub-Saharan Africa, DALYs are lower at 0.7%, and mortality rates are also lower at 1.1%. Tobacco has a significant regionalized impact on women with cancer, particularly in high-income North America, where DALYs are at 18%, and the mortality rate is 18.2%, the highest among all regions. Other specific areas, such as Western Europe with DALYs at 13.6% and a mortality rate of 12%, and Eastern Europe with DALYs at 10.9% and a mortality rate of 13.2%, also have relatively high levels. Dietary has a substantial impact on women with cancer globally, with a generally higher trend. The global average DALYs are at 7.2%, and the mortality rate is 7.8%. Eastern Europe has the highest DALYs and mortality rates at 9.6% and 10.4%, respectively, while South Asia has the lowest DALYs and mortality rates at 5.2% and 5.7%, the lowest among all regions. Most other regions have DALYs and mortality rates between 6% and 9%.[Figure4] 4.Age-group differences in the burden of cancer among women In the global disease analysis for the year 2021, it has been observed that the number of women with cancer deaths attributed to various risk factors and their percentage of total deaths both show an increasing trend with age. In the 45–49 age group, deaths and death percentages are close to each other due to high body-mass index (BMI), high fasting plasma glucose, and alcohol. By the 50–54 age group, deaths attributed to different risk factors begin to rise significantly, with a particularly marked increase after the age of 60. In most age groups, dietary and tobacco result in a higher number of deaths and death percentages. The impact of high BMI on Disability-Adjusted Life Years (DALYs) begins to increase significantly during middle age (approximately 45–54 years), with the number of cancer among women deaths and death percentages starting to rise notably after the age of 49. This increase reaches a small peak in the 50–54 age group, after which the rate of increase slows down. Further research into the impact of high BMI on middle-aged women (40–60 years) reveals that, among all age groups, uterine cancer has the highest percentage of DALYs, especially in the 55–59 age group, where DALYs approach 0.3% and remain relatively stable. Kidney cancer has the second-highest percentage of DALYs across all age groups and remains relatively stable at around 0.1%. While most cancers maintain a relatively stable level of DALYs in this age range, breast cancer DALYs show a significant increase, rising from 0 in the 45–49 age group to 0.1 in the 50–54 age group, after which it stabilizes around 0.1.[Figure5] These findings emphasize the increasing health risks that women face from cancer as they age, particularly during middle age, where the impact of high BMI on women's health is particularly significant. These trends are important for the development of targeted preventive measures and public health policies to mitigate the burden of women with cancer and improve their quality of life. Discussion In our in-depth research analyzing the correlation between women's health and cancer risk, we adopted a comprehensive analysis of multiple key factors, including low physical activity, alcohol, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI), especially the association between high BMI and different cancer types in middle-aged women. These factors, intertwined, create a complex pattern of cancer risk among women. The research findings reveal that the incidence of cancer among women is influenced by a combination of lifestyle-related factors, and this influence exhibits significant regional variations. Notably, in high-income North America and Western Europe, the disparities in disability-adjusted life years (DALYs) and mortality percentages attributed to various risk factors stand out prominently when compared to low-income areas such as Southern and Eastern Sub-Saharan Africa. Our research has delved into the correlation between low physical activity and cancer risk, uncovering a significant relationship between the two. Studies published in the Journal of the American Medical Association (JAMA) further confirm that low physical activity is a key factor in cancer mortality risk [ 8 ] . Specifically, prolonged sedentary behavior, exemplified by extended periods of television viewing, has been intimately associated with obesity, cardiovascular diseases, multiple types of cancer, diabetes mellitus, and an elevated overall mortality rate [ 9 ] . Based on these findings, the American Cancer Society(ACS), in its American Cancer Society nutrition and physical activity guideline for cancer survivors, recommends that adults engage in at least 150 to 300 minutes of moderate-intensity or 75 to 150 minutes of vigorous-intensity exercise per week, emphasizing that exceeding 300 minutes of activity can yield additional health benefits [ 10 ] . Furthermore, research consistently shows that increasing physical activity can significantly reduce the risk of liver cancer, lung cancer, endometrial cancer, and breast cancer among women [ 11 ] . However, the phenomenon of insufficient physical activity worldwide is alarming. According to the World Health Organization, the prevalence of physical inactivity in high-income Asia-Pacific regions and South Asia stands at 48% and 45% respectively, whereas this rate is 28% in high-income Western countries and 14% in Oceania. Our research specifically points out that in High-income North America and Western Europe, the influence of low physical activity is notably pronounced in the incidence of neoplasia among females. This may be associated with changes like work for women in these regions. The advancement of remote work technology, the variability in job nature, the intersectionality of work and socioeconomic status, as well as irregular and prolonged work patterns, collectively impact women's health and exacerbate the influence of low physical activity on their cancer risk [ 12 ] . Similarly, dietary habits have a non-negligible impact on women's risk of developing tumors. Existing research has revealed a close connection between dietary habits and endometriosis [ 13 ][ 14 ] . By implementing dietary management strategies, such as reducing the fat content in meals and increasing the intake of dietary fiber, it is feasible to effectively decrease the levels of circulating estrogens in the body, thereby yielding positive therapeutic outcomes for patients with endometriosis. Notably, a prospective study conducted by the "Nurses' Health Study II" (NHSII) revealed a striking finding: Women who adopted a "Western dietary pattern" characterized by high intake of red meat, processed meat, refined grains, and desserts had a 27% increased risk of developing tumors. Conversely, women who followed a healthy dietary pattern had a 13% reduced risk of tumors [ 15 ] . Despite investigating the relationship between dietary patterns and the risk of pancreatic cancer, no significant association has been found. It is worth noting that women with type 2 diabetes seem to face a higher risk of pancreatic cancer, which may be closely related to their higher fasting glucose [ 16 ] . The study conducted by Pan et al. on 22,837 postmenopausal women further confirms this point: High fasting glucose and insulin resistance are not only associated with an increased incidence of breast cancer but also with poorer prognosis for patients [ 17 ] . Our research also indicates that in 2021, the general impact of dietary habits on the cancer risk of women globally remained significant, particularly in Eastern Europe, where this phenomenon may be closely related to the widespread adoption of Western dietary habits among women in the region. Given that high plasma glucose is often accompanied by insulin resistance and has a close association with hormone-related cancers, we strongly recommend that women minimize their intake of red meat, processed meat, refined grains, and desserts, to actively advocate and practice healthy dietary habits to safeguard their health and well-being. The impact of tobacco and alcohol on the cancer risk of women exhibits notable variations across different regions, particularly in High-SDI areas where the trend shows a marked increase. According to the World Health Organization (WHO), the prevalence of smoking among women in high-income countries attained 21% in 2010, with projections indicating a slight decline to 15% by 2030. Of particular concern is the smoking prevalence among women in Europe, which stood at 18% in 2022, ranking it the highest globally. This elevated prevalence is consonant with our research findings, which indicate a persistent trend of higher disability-adjusted life years (DALYs) and a greater percentage of deaths attributable to smoking among women in high-income regions. The WHO report further underscores that the tobacco industry targets women through marketing strategies that associate smoking with freedom, and liberation, and even mistakenly portray it as a means of maintaining a slim physique. Such marketing tactics have contributed to an escalation in smoking rates among women in certain regions, particularly in Europe, where the smoking rate among women exceeds double the global average and is decreasing at a much slower pace compared to other regions. Given that smoking is a significant risk factor for lung cancer, which remains one of the major threats to women's health, we urge women to reduce smoking and avoid passive smoking [ 18 ] . This requires strengthening women's self-protection awareness and enhancing society's overall awareness of smoking cessation and tobacco control, particularly in Europe. There is a clear causal relationship between alcohol use and a variety of cancers, especially oral cancer, pharyngeal cancer, laryngeal cancer, esophageal cancer, liver cancer, colorectal cancer, and breast cancer, which is particularly susceptible to women [ 19 ] . Among women, the close association between alcohol and the risk of breast cancer cannot be ignored, despite some women having inadequate awareness of this hazard [ 20 ] . As society progresses, attitudes towards women drinking alcohol have become increasingly open, but this has also quietly led to an increase in negative inducements, such as using alcohol to cope with emotional distress, stress, anxiety, and depression. This trend has led to an increasing consumption of alcohol among women, particularly in high-income regions. In response to this significant shift in women's drinking behaviors, there is an urgent necessity to elevate public awareness regarding the harms associated with alcohol consumption, with a particular emphasis on its unique risks for women, including the elevation of breast cancer risk [ 21 ] . It is imperative to enhance women's comprehension of the perils of alcohol and motivate them to make healthier lifestyle choices, as these actions are pivotal in mitigating the risk of alcohol-related cancers. High Body-Mass Index (BMI) is closely associated with an increased risk of various types of cancer. Epidemiological data from the International Agency for Research on Cancer (IARC) indicate that High BMI is linked to an elevated risk of cancers of the colon and rectum, liver, gallbladder, pancreas, kidney, thyroid, postmenopausal breast, endometrium, ovary, esophagus (adenocarcinoma), and gastric cardia, as well as gliomas and multiple myeloma. From 1975 to 2016, the obesity rate among women (BMI ≥ 30 kg/m²) doubled, from 7–16% [ 22 ] . Notably, the most significant increases in obesity rates among women and girls were observed in Central Asia, North Africa and Middle East, primarily due to changes in the global food system that promote the consumption of energy-dense, nutritionally imbalanced foods and reduce opportunities for physical activity. Our research further reveals that in Eastern Europe, High BMI is also a significant risk factor for cancer among women. High BMI is recognized as a risk factor for both the incidence and mortality of breast cancer [ 23 ] . Although some views suggest that high BMI may be associated with a reduced risk of breast cancer among premenopausal women [ 24 ] , our study found that among perimenopausal women (aged 40–60 years), a significant increase in BMI is accompanied by an increase in DALYs (Disability-Adjusted Life Years) and the percentage of deaths due to breast cancer. The increasing risk of adverse outcomes in breast cancer among women may be linked to the intricate hormonal fluctuations during the perimenopausal period. These changes include a decline in estrogen and progesterone levels, an elevation in follicle-stimulating hormone levels, and other dynamic hormonal shifts. Given the correlation between high Body Mass Index (BMI) and cancer risk, it is crucial to adopt more proactive measures to mitigate this risk. This involves improving the global food system, advocating for healthy diets, and encouraging increased physical activity among women. These strategies are essential in reducing the likelihood of developing BMI-related cancers. Overall, the risk of cancer among women globally is significantly influenced by various lifestyle factors. Due to economic levels and social divisions of labor, women may have easier access to risk factors such as unhealthy diets, lack of physical activity, tobacco use, and alcohol consumption, thereby facing a higher risk of cancer in high-income North America and Western Europe. Conversely, low-income regions may confront different health challenges due to socioeconomic factors. Research published in The Lancet highlights significant disparities in the prevention, early detection, treatment, and survival outcomes of cancers among women between high-income and low-income countries [ 25 ] . Alarmingly, projections indicate that by 2030, three-quarters of cancer deaths globally will occur in low-income and middle-income countries. However, these countries face numerous challenges, including funding shortages and sociocultural barriers, which limit their participation in cancer research to less than 5%. This situation exacerbates the existing inequality in global cancer prevention and control efforts. Conclusions Public health strategies must tailor and implement effective health promotion programs according to the characteristics of each region, promoting healthy lifestyles and thereby reducing the incidence of cancers among women. This is particularly crucial for middle-aged and elderly women, where efforts in health promotion and disease prevention are of utmost importance. By controlling risk factors for cancer, advocating for healthy diets, increasing physical activity, and reducing the use of tobacco and alcohol, we can effectively decrease the incidence and mortality rates of malignant tumors. These lifestyle modifications can also alleviate the burden of disease that women bear due to cancer. By focusing on these areas, we can make significant strides in improving the health outcomes for women globally. Declarations Ethics approval and consent to participate: For GBD studies, the Institutional Review Board of the University of Washington reviewed and approved a waiver of informed consent (https://www.healthdata.org/research-analysis/gbd). Consent for publication: Agree to publish. Availability of data and materials: These data can be accessed through the GBD 2021 Data Sources tool, which provides detailed information on data sources on the website of the Institute for Health Metrics and Evaluation ([GBD 2021 Data Sources](https://ghdx.healthdata.org/gbd-2021/sources)). Competing interests: All authors declare no competing interests. Funding: This work was funded by: The Science and Technology Project of Shaanxi Province(2022SF-496) The Funds of the Second Affiliated Hospital of Xi’an Jiaotong University for Scientists. No.RC(GG201807) The Funds of the Second Affiliated Hospital of Xi’an Jiaotong University. No.YJ(ZYTS)2019012/No.2020YJ(ZYTS)226 The Xinrui Cancer Research Support Program(cphcf-2022-231) The Medical Research Developing Funds(KM228009) Authors' contributions: YZ and DL conceived and designed the study and completed the article. QD, YM, NX, YL conducted the statistical analysis and explained the results of the study. YZ, QD, YM, NX, YL were responsible for creating figures and tables of the study. DL scrutinized the whole process of this study and reviewed the initial manuscript. All authors contributed to the article and approved the submitted version. Acknowledgements: We express our gratitude to all the scholars who have contributed to the GBD research. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. 10.3322/caac.21834 . Ginsburg O, Vanderpuye V, Beddoe AM, et al. Women, power, and cancer: a Lancet Commission. Lancet. 2023;402(10417):2113–66. 10.1016/S0140-6736(23)01701-4 . Islami F, Ward EM, Sung H et al. Annual Report to the Nation on the Status of Cancer, Part 1: National Cancer Statistics. J Natl Cancer Inst. 2021;113(12):1648–1669. 10.1093/jnci/djab131 Morgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 2023;72(2):338–44. 10.1136/gutjnl-2022-327736 . Dyba T, Randi G, Bray F, et al. The European cancer burden in 2020: Incidence and mortality estimates for 40 countries and 25 major cancers. Eur J Cancer. 2021;157:308–47. 10.1016/j.ejca.2021.07.039 . Li X, Chang Z, Wang J et al. Unhealthy lifestyle factors and the risk of colorectal cancer: a Mendelian randomization study. Sci Rep. 2024;14(1):13825. Published 2024 Jun 15. 10.1038/s41598-024-64813-y Global incidence. prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2133–61. Gilchrist SC, Howard VJ, Akinyemiju T, et al. Association of Sedentary Behavior With Cancer Mortality in Middle-aged and Older US Adults. JAMA Oncol. 2020;6(8):1210–7. 10.1001/jamaoncol.2020.2045 . Yang L, Cao C, Kantor ED, et al. Trends in Sedentary Behavior Among the US Population, 2001–2016. JAMA. 2019;321(16):1587–97. 10.1001/jama.2019.3636 . Rock CL, Thomson CA, Sullivan KR, et al. American Cancer Society nutrition and physical activity guideline for cancer survivors. CA Cancer J Clin. 2022;72(3):230–62. 10.3322/caac.21719 . Patel AV, Friedenreich CM, Moore SC, Hayes SC, Silver JK, Campbell KL, Winters-Stone K, Gerber LH, George SM, Fulton JE, Denlinger C, Morris GS, Hue T, Schmitz KH, Matthews CE. American College of Sports Medicine Roundtable Report on Physical Activity, Sedentary Behavior, and Cancer Prevention and Control. Med Sci Sports Exerc. 2019;51(11):2391–402. 10.1249/MSS.0000000000002117 . PMID: 31626056; PMCID: PMC6814265. Frank J, Mustard C, Smith P, Siddiqi A, Cheng Y, Burdorf A, Rugulies R. Work as a social determinant of health in high-income countries: past, present, and future. Lancet. 2023;402(10410):1357–1367. 10.1016/S0140-6736(23)00871-1 . PMID: 37838441. Barnard ND, Holtz DN, Schmidt N, Kolipaka S, Hata E, Sutton M, Znayenko-Miller T, Hazen ND, Cobb C, Kahleova H. Nutrition in the prevention and treatment of endometriosis: A review. Front Nutr. 2023;10:1089891. 10.3389/fnut.2023.1089891 . PMID: 36875844; PMCID: PMC9983692. Armour M, Middleton A, Lim S, Sinclair J, Varjabedian D, Smith CA. Dietary Practices of Women with Endometriosis: A Cross-Sectional Survey. J Altern Complement Med. 2021;27(9):771–7. 10.1089/acm.2021.0068 . Epub 2021 Jun 23. PMID: 34161144. Dougan MM, Fest S, Cushing-Haugen K, Farland LV, Chavarro J, Harris HR, Missmer SA. A prospective study of dietary patterns and the incidence of endometriosis diagnosis. Am J Obstet Gynecol. 2024;231(4):443. 10.1016/j.ajog.2024.04.030 . .e1-443.e10 . Epub 2024 Apr 29. PMID: 38692470; PMCID: PMC11410522. Shyam S, Greenwood DC, Mai CW, Tan SS, Yusof BM, Moy FM, Cade JE. Major dietary patterns in the United Kingdom Women's Cohort Study showed no evidence of prospective association with pancreatic cancer risk. Nutr Res. 2023;118:41–51. Epub 2023 Jul 25. PMID: 37562156. Pan K, Chlebowski RT, Mortimer JE, Gunter MJ, Rohan T, Vitolins MZ, Adams-Campbell LL, Ho GYF, Cheng TD, Nelson RA. Insulin resistance and breast cancer incidence and mortality in postmenopausal women in the Women's Health Initiative. Cancer. 2020;126(16):3638–3647. doi: 10.1002/cncr.33002. Epub 2020 Jun 12. Erratum in: Cancer. 2020 Oct 20. 10.1002/cncr.33178 . PMID: 32530506. Proctor RN. Tobacco and the global lung cancer epidemic. Nat Rev Cancer. 2001;1(1):82 – 6. 10.1038/35094091 . PMID: 11900255. Boffetta P, Hashibe M. Alcohol and cancer. Lancet Oncol. 2006;7(2):149 – 56. doi: 10.1016/S1470-2045(06)70577-0. PMID: 16455479. Doyle A, O'Dwyer C, Mongan D, Millar SR, Galvin B. Factors associated with public awareness of the relationship between alcohol use and breast cancer risk. BMC Public Health. 2023;23(1):577. 10.1186/s12889-023-15455-8 . PMID: 36978036; PMCID: PMC10044731. Roche AM, Bowden J. Women, alcohol, and breast cancer: opportunities for promoting better health and reducing risk. Med J Aust. 2023;218(11):509–10. 10.5694/mja2.51984 . Epub 2023 May 27. PMID: 37244646. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, Goding Sauer A, Shuval K, Gapstur SM, Jacobs EJ, Giovannucci EL, Jemal A. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin. 2019;69(2):88–112. Epub 2018 Dec 12. PMID: 30548482. Goodwin PJ. Obesity, insulin resistance and breast cancer outcomes. Breast. 2015;24(Suppl 2):S56–9. 10.1016/j.breast.2015.07.014 . Epub 2015 Aug 15. PMID: 26283600. Premenopausal Breast Cancer Collaborative Group, Schoemaker MJ, Nichols HB, Wright LB, Brook MN, Jones ME, O'Brien KM, Adami HO, Baglietto L, Bernstein L, Bertrand KA, Boutron-Ruault MC, Braaten T, Chen Y, Connor AE, Dorronsoro M, Dossus L, Eliassen AH, Giles GG, Hankinson SE, Kaaks R, Key TJ, Kirsh VA, Kitahara CM, Koh WP, Larsson SC, Linet MS, Ma H, Masala G, Merritt MA, Milne RL, Overvad K, Ozasa K, Palmer JR, Peeters PH, Riboli E, Rohan TE, Sadakane A, Sund M, Tamimi RM, Trichopoulou A, Ursin G, Vatten L, Visvanathan K, Weiderpass E, Willett WC, Wolk A, Yuan JM, Zeleniuch-Jacquotte A, Sandler DP, Swerdlow AJ. Association of Body Mass Index and Age With Subsequent Breast Cancer Risk in Premenopausal Women. JAMA Oncol. 2018;4(11):e181771. 10.1001/jamaoncol.2018.1771 . Epub 2018 Nov 8. PMID: 29931120; PMCID: PMC6248078. Kyrgiou M, Bowden S, Denny L, Fagotti A, Abu-Rustum NR, Ramirez PT, Querleu D. Innovation in gynaecological cancer: highlighting global disparities. Lancet Oncol. 2024;25(4):425–430. doi: 10.1016/S1470-2045(24)00137-2. Epub 2024 Mar 7. PMID: 38461833. Tables Table1 Incidence of women with cancer in 1990 and 2021 location Num_1990 ASR_1990 Num_2021 ASR_2021 EAPC_CI Global 22165069(18685016-26237173) 921.58(779.52-1081.22) 39416539(33872888.9590652-45407653) 923.44(790.49-1072.74) 0.01 (-0.05-0.07) Low SDI 706820(596541-844208) 399.01(338.82-467.26) 1693917(1416632.39703802-2032377) 407.58(346.65-475.23) 0.1 (0.05-0.14) Low-middle SDI 2429534(2011702-2971667) 520.11(436.65-626.4) 5009162(4148260.48238419-6041436) 550.35(458.31-657.84) 0.27 (0.22-0.31) Middle SDI 5254990(4314030-6471646) 697.36(582.13-828.73) 9451119(7919660.86468957-11165503) 709.78(597.92-837.45) 0.09 (0.05-0.13) High-middle SDI 5719245(4766214-6824101) 1040.16(866.46-1237.19) 8045870(6854255.85163976-9341013) 1019.35(861.55-1203.18) -0.06 (-0.08--0.03) High SDI 8030118(6911120-9220751) 1546.32(1320.13-1797.16) 15185287(13351221.4546543-17205705) 1981.31(1724.82-2258.8) 0.74 (0.59-0.88) Andean Latin America 77106(66675-90126) 545.46(472.34-635.11) 195031(168467.812226081-224823) 595.85(514.6-683.29) 0.28 (0.25-0.31) Australasia 101126(89997-112944) 869.16(766.62-976.81) 182856(162439.493382015-203716) 833.38(734.79-936.87) -0.13 (-0.19--0.08) Caribbean 76321(66833-86565) 499.06(436.86-565.44) 138753(122750.803260829-158014) 519.94(459.35-592.85) 0.17 (0.15-0.18) Central Asia 295577(241375-362501) 896.79(740.75-1087.89) 427891(351494.928496137-514379) 873.82(720.24-1060.29) -0.05 (-0.06--0.04) Central Europe 1026612(863137-1220530) 1489.19(1249.97-1777.34) 1173471(1011746.86499761-1356416) 1600.16(1368.85-1864.27) 0.29 (0.22-0.36) Central Latin America 326188(282327-381814) 541.24(471.33-621.52) 808180(702145.499307344-936110) 589.43(513.33-679.75) 0.32 (0.22-0.43) Central Sub-Saharan Africa 60143(50729-72535) 330(278.89-384.26) 156695(132879.135919693-186360) 338.54(285.2-395.37) 0.07 (0.04-0.1) East Asia 5248542(4227258-6562798) 903.88(739.83-1088.99) 8330726(6884414.68652594-9872709) 1008.71(828.17-1218.52) 0.38 (0.33-0.42) Eastern Europe 1822950(1509528-2158390) 1361.8(1123.28-1628.74) 1870740(1572481.02912042-2226561) 1393.86(1162.09-1666.47) 0.05 (0.03-0.07) Eastern Sub-Saharan Africa 183219(160528-211594) 318.81(279.33-361.32) 429707(372467.439762035-498252) 314.03(276.45-357.96) -0.19 (-0.24--0.13) High-income Asia Pacific 1668022(1394489-1997992) 1744.8(1446.17-2091.21) 1984849(1715460.83729049-2279702) 1728.45(1455.68-2065.28) -0.09 (-0.12--0.06) High-income North America 3491725(3035562-3996076) 2043.54(1755.88-2351.5) 9262219(8156069.71713985-10499141) 3484.32(3057.74-3942.96) 1.52 (1.24-1.81) North Africa and Middle East 618172(508515-753325) 486.19(405.31-584.18) 1481961(1244156.03434634-1775776) 524.18(441.79-620.18) 0.42 (0.33-0.5) Oceania 14606(11896-18355) 572.68(476.92-695.65) 33694(27530.7146834257-41643) 571.33(477.76-689.49) -0.02 (-0.03--0.01) South Asia 2428889(1983916-3014708) 545.57(452.92-665.8) 5562386(4482371.16383241-6833857) 617.95(502.58-746.18) 0.54 (0.44-0.64) Southeast Asia 1137541(950867-1391483) 562.52(475.23-668.5) 2154649(1831138.515807-2566580) 587.07(501.7-694.65) 0.12 (0.11-0.13) Southern Latin America 213264(183983-246040) 850.61(733.92-989.74) 339243(300329.782954053-384451) 838.05(740.12-954.48) -0.26 (-0.34--0.18) Southern Sub-Saharan Africa 150814(124762-185183) 707.84(585.1-850.35) 296071(246576.620707796-353615) 763.42(640.76-902.44) 0.32 (0.25-0.39) Tropical Latin America 322476(284346-369030) 510.83(458.39-569.34) 661795(583960.238646799-748576) 491.88(434.31-556.85) -0.02 (-0.08-0.03) Western Europe 2697339(2332988-3069274) 1074.31(920.71-1235.63) 3365282(2988963.83569517-3784496) 1027.47(898.1-1166.91) -0.09 (-0.16--0.03) Western Sub-Saharan Africa 204438(172841-243051) 316.62(267.04-372.66) 560339(469282.322484562-668409) 332.85(281.49-389.63) 0.18 (0.16-0.19) Table2 Death of women with cancer in 1990 and 2021 location Num_1990 ASR_1990 Num_2021 ASR_2021 EAPC_CI Global 2506728(2338857-2647286) 118.08(109.84-124.68) 4286993(3887875-4623753) 93.6(85.02-100.92) -0.75 (-0.79--0.71) Low SDI 122570(109128-138285) 97.91(85.86-110.69) 252242(220278-284369) 89.7(78.66-100.71) -0.36 (-0.41--0.3) Low-middle SDI 253912(228511-277155) 77.31(68.93-84.86) 601698(549136-649839) 78.62(71.75-84.77) 0.04 (0.01-0.07) Middle SDI 598350(541093-658708) 109.99(99.14-120.66) 1178031(1057364-1318606) 84.77(75.88-94.92) -0.94 (-0.99--0.9) High-middle SDI 687278(636396-734754) 124.67(115.15-133.27) 1055947(930213-1179761) 97.74(86.49-109.21) -0.74 (-0.81--0.68) High SDI 841615(782335-871234) 131.9(123.68-136.08) 1194470(1031897-1286296) 99.08(88.4-104.98) -0.81 (-0.86--0.77) Central Asia 30782(29515-32021) 109.47(104.77-114.03) 35886(32601-39103) 77.81(70.98-84.52) -0.96 (-1.01--0.91) Central Europe 108506(104705-111172) 130.64(125.91-133.9) 153959(141366-164481) 121.02(111.45-129.12) -0.16 (-0.22--0.09) Central Sub-Saharan Africa 12544(10362-15204) 97.08(80.38-119.55) 30617(23543-38735) 95.55(73.25-121.11) -0.08 (-0.11--0.05) Eastern Europe 199035(193048-203133) 114.38(111.09-116.67) 206552(186982-227765) 96.91(87.21-107.33) -0.47 (-0.58--0.36) Eastern Sub-Saharan Africa 55827(49454-62815) 129.71(113.52-145.49) 112528(95738-132247) 115.38(99.3-133.86) -0.47 (-0.53--0.42) South Asia 217955(190506-241796) 70.75(60.96-78.91) 535425(479882-598920) 69.41(62.07-77.88) -0.16 (-0.25--0.08) Southern Sub-Saharan Africa 15403(13756-17682) 97.11(85.81-112.24) 42347(38374-46123) 126.63(115.36-137.25) 0.8 (0.59-1.02) Western Europe 449961(418261-465921) 132.83(124.87-136.86) 545278(467910-585826) 100.01(89.26-105.67) -0.79 (-0.82--0.75) Western Sub-Saharan Africa 35820(29107-43222) 75.75(61.48-91.5) 91479(69741-111811) 81.64(65.06-97.56) 0.26 (0.23-0.29) Central Latin America 50431(48987-51290) 113.04(108.84-115.4) 119136(104524-133238) 88.21(77.41-98.51) -1.01 (-1.07--0.95) Tropical Latin America 51608(49256-52973) 104.78(98.66-108.07) 135661(124227-142791) 96.34(88.32-101.34) -0.26 (-0.3--0.23) Southern Latin America 35588(33938-36669) 140(133.32-144.33) 53747(49192-56486) 109.02(100.85-114.2) -0.49 (-0.56--0.41) Andean Latin America 14178(13025-15401) 128.68(118.2-140.27) 35109(28933-42443) 113.13(93.25-136.56) -0.51 (-0.59--0.43) Caribbean 15907(14982-16714) 116.77(110.22-122.4) 30843(27276-34591) 108.11(95.52-121.11) -0.17 (-0.21--0.13) High-income Asia Pacific 121513(112003-126793) 108.89(100.32-113.71) 235757(183213-266580) 78.3(65.76-85.59) -1.08 (-1.11--1.06) High-income North America 279543(257700-290595) 138.94(129.88-143.66) 378523(334912-401891) 103.95(93.62-109.54) -0.71 (-0.82--0.6) East Asia 601538(503172-707685) 136.46(114.97-159.82) 1045921(834566-1282130) 93.7(74.86-114.78) -1.35 (-1.43--1.28) Southeast Asia 126534(109195-141594) 87.66(75.63-97.63) 304566(260893-350318) 87.1(74.61-99.89) 0.09 (0.01-0.17) Oceania 1416(1122-1783) 91.97(75.11-115.09) 3473(2847-4318) 88.25(73.5-109.11) -0.1 (-0.13--0.07) North Africa and Middle East 65521(60347-71856) 74.04(67.87-81.2) 162279(144397-181571) 73.03(64.7-81.29) 0.04 (-0.04-0.12) Australasia 17117(16045-17743) 132.72(125.08-137.3) 27907(23991-30078) 94.51(83.4-100.71) -0.86 (-0.95--0.78) Table3 DALYs of women with cancer in 1990 and 2021 location Num_1990 ASR_1990 Num_2021 ASR_2021 EAPC_CI Global 73721144(69286568-77877766) 3285.33(3089.49-3466.82) 111646997(103569823-119654830) 2507.59(2327.14-2686.76) -0.98 (-1.03--0.94) Low SDI 4661270(4114244-5258559) 3019.11(2681.12-3408.03) 9029146(7744081-10331533) 2614.57(2276.57-2952.18) -0.6 (-0.69--0.51) Low-middle SDI 9330674(8419223-10174575) 2388.57(2155.1-2604.1) 19443734(17698324-21063672) 2329.99(2122.84-2520.53) -0.08 (-0.13--0.02) Middle SDI 19888332(18039086-21959531) 3174.59(2876.25-3502.67) 32932863(29743469-36639903) 2331.58(2108.9-2587.23) -1.17 (-1.25--1.08) High-middle SDI 19550534(18100980-21022077) 3550.71(3288.06-3819.32) 25583425(22806766-28571773) 2561.56(2290.45-2858.6) -1.22 (-1.29--1.15) High SDI 20206370(19276891-20760493) 3483.48(3346.75-3571.9) 24542829(22292884-25907052) 2465.22(2295.32-2576.99) -1.15 (-1.17--1.12) Central Asia 970572(939608-1005090) 3326.37(3214.83-3443.09) 1115645(1010281-1230534) 2288.06(2077.65-2517.65) -1.17 (-1.21--1.13) Central Europe 2886503(2808386-2950553) 3621.84(3530.72-3700.77) 3417995(3162447-3648866) 3115.7(2885.9-3333.14) -0.55 (-0.62--0.48) Central Sub-Saharan Africa 454198(373229-550451) 2863.71(2363.49-3475.04) 1067023(816964-1335977) 2747.49(2100.98-3476.31) -0.15 (-0.2--0.09) Eastern Europe 5496415(5373320-5608538) 3424.13(3352.73-3491.04) 5109161(4598781-5671081) 2719.37(2444.89-3027.99) -1.05 (-1.14--0.95) Eastern Sub-Saharan Africa 2170024(1897983-2448998) 3949.21(3495.69-4447.04) 4091714(3414031-4857405) 3307.57(2815.02-3899.34) -0.77 (-0.87--0.68) South Asia 8209260(7220534-9108556) 2249.44(1977.07-2494.5) 17351240(15533327-19433332) 2087.6(1866.2-2337.35) -0.32 (-0.47--0.18) Southern Sub-Saharan Africa 506031(456926-569728) 2815.82(2529.64-3211.47) 1296196(1160144-1435059) 3552.51(3190.61-3908.22) 1.23 (0.9-1.55) Western Europe 10158239(9698965-10432637) 3456.92(3336.33-3536.44) 10781759(9712543-11361930) 2480.9(2306.55-2583.05) -1 (-1.05--0.96) Western Sub-Saharan Africa 1294442(1046837-1555585) 2252.94(1829.56-2720.27) 3238005(2399039-4096823) 2285.17(1734.6-2811.19) 0.08 (0.03-0.13) Central Latin America 1657908(1625061-1684970) 3095.2(3020.66-3145.89) 3442586(3011508-3887918) 2511.49(2199.12-2835.68) -0.78 (-0.88--0.69) Tropical Latin America 1644734(1595925-1680172) 2922.17(2815-2991.69) 3730400(3514341-3894911) 2699.45(2546.38-2817.96) -0.38 (-0.43--0.33) Southern Latin America 942683(912144-965110) 3731.21(3611.25-3819.98) 1283815(1210036-1338521) 2859.85(2714.53-2976.53) -0.71 (-0.79--0.62) Andean Latin America 459758(418922-501195) 3590.31(3275.52-3893.32) 966163(788612-1173273) 3036.04(2480.2-3683.72) -0.72 (-0.83--0.61) Caribbean 488767(451760-522956) 3346.87(3117.39-3566.08) 844142(738483-958447) 3096.67(2715.39-3524.4) -0.11 (-0.15--0.07) High-income Asia Pacific 3081532(2921905-3184201) 2836.09(2690.89-2926.47) 4097258(3431149-4492928) 1910.81(1717.81-2029.97) -1.31 (-1.34--1.28) High-income North America 6851051(6506042-7077078) 3781.33(3634.45-3890.17) 8398054(7785213-8795350) 2640.51(2492.29-2749.63) -1.25 (-1.31--1.19) East Asia 19140081(16010143-22661456) 3893.2(3260.52-4589.79) 26289762(20872671-32595359) 2443.36(1951.2-3016.25) -1.77 (-1.88--1.65) Southeast Asia 4440754(3790711-4996104) 2646.25(2280.3-2961.58) 9263070(7963832-10782970) 2497.48(2149.24-2897.98) -0.32 (-0.4--0.24) Oceania 52939(41079-66732) 2743.36(2166.2-3450.13) 126361(101544-157018) 2623.04(2146.35-3247.69) -0.14 (-0.18--0.1) North Africa and Middle East 2388138(2176427-2637436) 2166.15(1992.74-2374.55) 5140121(4542089-5820244) 2013.08(1783.58-2267.1) -0.07 (-0.16-0.01) Australasia 427115(409365-440964) 3541.57(3412.39-3648.32) 596528(538151-631941) 2354.4(2166.29-2474.57) -1.34 (-1.37--1.3) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6238383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434986427,"identity":"63989f75-ae97-438c-8c0c-5df9be94d52b","order_by":0,"name":"Yuxuan Zhu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuxuan","middleName":"","lastName":"Zhu","suffix":""},{"id":434986428,"identity":"47b37e4c-6e5f-4424-b71c-f399627bd6ea","order_by":1,"name":"Qin DU","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"DU","suffix":""},{"id":434986429,"identity":"a3a07a99-45e6-49b2-98a9-c28a04d11e41","order_by":2,"name":"Yiyang Ma","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"Ma","suffix":""},{"id":434986430,"identity":"a4647c15-e2f1-4486-8450-ea6dec7fd246","order_by":3,"name":"Nanxin Xu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Nanxin","middleName":"","lastName":"Xu","suffix":""},{"id":434986431,"identity":"04d3de9d-60f0-4951-9638-bb263e739e25","order_by":4,"name":"Yuhang Luo","email":"","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Luo","suffix":""},{"id":434986432,"identity":"c763f498-eecb-4da9-a27a-8acc74d16471","order_by":5,"name":"Di Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDCCAxBKhuF4Y+ODDxUScvxEaGFsAFI8DGcONxvOOGNhLNlAtJYb7m3SnG0ViRsIaeE73vz8wcc9DDx8NxgbpBnnSTBuYGB++OgGHi2SZ44ZNs54xsAjebuxwbhwmwSzOQObsXEOHi0GN3IYm3kOMPAY3DnYkDxzmwSbZQMPmzRxWm4kNhzmnSPBY3CABC2NzbwNEhIEtYD8MnMGUIvkmYPNjDOOSRhINhPwCzDEHnz4cIBBju94+/MfH2rq6vvZmx8+xqcFCv4jsZkJKx8Fo2AUjIJRQAAAAB8FUyZpR65mAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Di","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-16 15:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6238383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6238383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79582946,"identity":"c371cb1b-9f53-4f99-9a71-6f4e4ed9c4e7","added_by":"auto","created_at":"2025-03-31 12:08:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68284,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized incidence rate of cancers in women per 100,000 population 2021\u003c/p\u003e","description":"","filename":"floatimage17.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6238383/v1/a0f4f527c25ebce1d7b4314c.jpeg"},{"id":79584256,"identity":"190e93e5-d265-40a5-8814-43caf05fb32e","added_by":"auto","created_at":"2025-03-31 12:16:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76483,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardized DALYs rate of cancers in women per 100,000 population 2021\u003c/p\u003e","description":"","filename":"floatimage29.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6238383/v1/82e32bd8bfb392753455e855.jpeg"},{"id":79584255,"identity":"3721ac08-346d-4687-b969-41b33dcd0fc7","added_by":"auto","created_at":"2025-03-31 12:16:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":678933,"visible":true,"origin":"","legend":"\u003cp\u003eAge-standardised rates of incidence and DALYs of women with cancer, globally and for 21 GBD regions, by SDI (2021), from 1990 to 2021.a.Age-standardised DALYs for women cancer by Socio-demographic Index. b.Age-standardised incidence for women cancer by Socio-demographic Index.The impact of different factors on women with cancer (according to age-standardized DALYs) c. Dietary.d. Alcohol. e.High body-mass. f.High fasting plasma glucose. g.Low physical activity. h.Tobacco.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6238383/v1/4547a3cce53b771c9d385e1b.png"},{"id":79584258,"identity":"dcf88e3b-43fa-4ddf-9b5d-d753395795f6","added_by":"auto","created_at":"2025-03-31 12:16:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":332580,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive lifestyle-related risk factors attributed to women with cancer burden (mortality rate compared to DALYs), a.Low physical activity. b. Alcohol. c. High body-mass.d.High fasting plasma glucose. e.Tobacco. f.Dietary.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6238383/v1/45d15a89b015f6f2ec5e666c.png"},{"id":79584272,"identity":"efcf9e42-26dc-411a-86a3-52581d884175","added_by":"auto","created_at":"2025-03-31 12:16:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169187,"visible":true,"origin":"","legend":"\u003cp\u003eAge-group differences in the burden of cancer among women.Women Deaths by Risk Type and Age in 2021 a.Number b.Percent.Women BMI by CancerType Deaths in 2021 c.Number d.Percent.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6238383/v1/3ac26f359245207d2da15b6f.png"},{"id":79812313,"identity":"d56831bc-ddef-45d5-b93d-a51e93bfde89","added_by":"auto","created_at":"2025-04-03 06:54:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1994001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6238383/v1/0c0ceee5-3e07-4724-9892-f178732ae27c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Burden of Female Cancers Attributable to Lifestyle Risk Factors: A 1990-2021 Analysis from the Global Burden of Disease Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer has become a leading global health threat, contributing significantly to female mortality and disability worldwide. According to data from the American Cancer Society(ACS), between 2015 and 2019, the annual incidence rates of breast, pancreatic, and corpus uteri cancers increased by 0.6\u0026ndash;1%, while liver cancer rates in women surged by 2\u0026ndash;3% annually. The GLOBOCAN project by the International Agency for Research on Cancer (IARC) predicts there will be nearly 20\u0026nbsp;million new cancer cases and 9.7\u0026nbsp;million cancer-related deaths globally in 2022, with women facing a 20% lifetime cancer risk and 12% cancer mortality probability. Breast cancer stands not only as the most prevalent cancer type among women but also as the foremost contributor to cancer-related deaths, surpassed only by lung and colorectal cancers in overall mortality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to data from The Lancet in 2023, breast, colorectal, lung, cervical, and thyroid cancers are the five most common types of cancer among women, accounting for 53.7% of all female cancer cases\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite medical advancements in early detection and targeted therapies, women worldwide face persistent barriers to timely cancer diagnosis and quality care. Social determinants including caregiving responsibilities, geographic constraints, and ethnic disparities frequently delay medical intervention. This reality underscores the urgency for better understanding the risk factors for cancer in female populations. With the continuous rise in cancer incidence, medical technology dedicated to conquering cancer is also advancing, especially in the improvement of early prevention and treatment methods, due to the innovation of early diagnosis and treatment technologies, including targeted therapies and immunotherapies. As cancer research deepens, researchers have gradually come to recognize that lifestyle factors are closely related to the risk of women developing tumors. Globally, the incidence and mortality rates of lung cancer among women remain alarmingly high. According to 2022 data, there were approximately 2.5\u0026nbsp;million new cases and more than 1.8\u0026nbsp;million deaths worldwide, representing 12.4% of all cancer diagnoses and 18.7% of cancer-related deaths. The accelerated decline in lung cancer mortality rates observed between 2014 and 2018 is strongly associated with the reduction in smoking prevalence\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. While tobacco use remains the primary risk factor for lung cancer, a substantial proportion of cases occur among non-smokers, particularly among East Asian women. This phenomenon may be attributed to environmental factors such as exposure to air pollution and the use of solid fuels for household energy. Similarly, colorectal cancer, the third most common cancer globally\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, shows strong association with unhealthy lifestyle factors, such as unhealthy diet, smoking, alcohol consumption, obesity, and lack of exercise. Moreover, geographical factors also affect the occurrence of cancer in women, including the implementation of organized and opportunistic screening programs, as well as variations in the prevalence and distribution of major risk factors, such as parity and age at first birth\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. These findings further emphasize the close link between lifestyle factors and the risk of women developing cancers, highlighting the importance of adopting preventive measures and a healthy lifestyle\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Globally, the incidence and mortality rates of cancer among women are on the rise, particularly in low-income and middle-income countries. Changes in lifestyle, including unhealthy diets, lack of exercise, smoking, and alcohol consumption, are regarded as the primary factors driving the increase in the cancer burden. Given the aging global population and the westernization of lifestyles, it is anticipated that the burden of cancer among women will continue to grow over the coming decades.\u003c/p\u003e \u003cp\u003eThis study conducts a systematic analysis of the Global Burden of Disease (GBD) 2021 data, aiming to quantify the contribution of comprehensive lifestyle factors to the burden of female cancer at global, regional, and national levels from 1990 to 2021. The research analyze six lifestyle factors: low physical activity, alcohol, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI). By analyzing the data from 21 GBD regions and 5 Socio-demographic Index (SDI) regions, the study reveals the trends in the burden of cancer among women influenced by various risk factors, as well as the differences across different regions and at different levels of the Socio-demographic Index (SDI).\u003c/p\u003e \u003cp\u003eWe hope to provide a comprehensive perspective on how lifestyle factors impact the global burden of cancer among women and to offer scientific evidence for the development of effective public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn this study, we analyzed data from the Global Burden of Disease Study 2021 (GBD 2021), with a particular focus on the impact of lifestyle-related risk factors on the incidence of cancer among women. GBD 2021 covers six lifestyle factors, including low physical activity, alcohol, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI), which are further used to estimate the burden of cancer in women. These data can be accessed through the GBD 2021 Data Sources tool, which provides detailed information on data sources on the website of the Institute for Health Metrics and Evaluation ([GBD 2021 Data Sources](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org/gbd-2021/sources\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org/gbd-2021/sources\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eGBD 2021 is a global collaborative project supported by more than 11,500 contributors from 164 countries. Through extensive data collection, review, and analysis, GBD 2021 systematically assesses global health status and disease burden. This information can be found on the website of the Institute for Health Metrics and Evaluation (IHME). The disease models for cancers in women are detailed in the GBD 2021 Methods Appendices, which can be accessed via the following link: [GBD Methods Appendices 2021 Cancers](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdata.org/gbd/methods-appendices-2021/cancers\u003c/span\u003e\u003cspan address=\"https://www.healthdata.org/gbd/methods-appendices-2021/cancers\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing the GBD Results Tool, we extracted data on the incidence, mortality, and disability-adjusted life years (DALYs) for cancers in women from GBD 2021 ([GBD Results Tool](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)). The world is divided into 21 GBD regions and 5 Socio-Demographic Index (SDI) regions. Age-Standardized Rates (ASR) and Estimated Annual Percentage Change (EAPC) are used to measure trends in the incidence and mortality of tumors caused by different risk factors in women. ASR trends are used to accurately depict changes in disease patterns in populations, while EAPC is used to quantify trends in ASR over time across different populations.\u003c/p\u003e \u003cp\u003eSocio-Demographic Index:\u003c/p\u003e \u003cp\u003eThe Socio-Demographic Index (SDI) is a composite development status indicator closely related to health outcomes, calculated based on the geometric mean of three dimensions: the fertility rate of women under the age of 25, the average years of education for individuals aged 15, and above, and lagged distribution of per capita income. For GBD 2021, the final SDI values are multiplied by 100 for reporting, facilitating comparison and analysis. SDI values range from 0 (the theoretical minimum level of development related to health) to 100 (the theoretical maximum level). A recent GBD 2021 vertex paper describes the assembly method of SDI and divides 204 countries into five quintiles (low, low-middle, middle, high-middle, and high) based on the 2021 national-level SDI estimates\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eUsing data from the United Nations' standard projection dataset for the year 2019, age-standardized incidence rates (ASR), such as age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized disability-adjusted life year rates (ASDR), were calculated. This dataset provides population data in five-year age groups from 1990 to 2030 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://population.un.org/wpp/Download/Standard/Population)(https://population.un.org/wpp/Download/Standard/Population/\u003c/span\u003e\u003cspan address=\"https://population.un.org/wpp/Download/Standard/Population)(https://population.un.org/wpp/Download/Standard/Population/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The rates are derived based on the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ASR=\\frac{\\sum\\:_{i=1}^{A}{a}_{i}{w}_{i}}{\\sum\\:_{i=1}^{A}{w}_{i}}\\times\\:\\text{100,000}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eA\u003c/em\u003e represents the number of age groups, \u003cem\u003ei\u003c/em\u003e denotes the \u003cem\u003ei\u003c/em\u003eth age group, a\u003csub\u003ei\u003c/sub\u003e is the rate to be standardized, and w\u003csub\u003ei\u003c/sub\u003e is the population size of the standard population for the same age group.\u003c/p\u003e \u003cp\u003eTo assess changes in the disease burden of cancer among women caused by different risk factors, we introduced the Estimated Annual Percentage Change (EAPC) indicator. EAPC is calculated based on a regression model by fitting the natural logarithm of ASR to the calendar year, which is a widely adopted summary measure used to assess trends in ASR over a specific time interval. It is assumed that there is a linear relationship between the natural logarithm of ASR and time. The EAPC of ASR and its corresponding confidence interval (CI) can be calculated to illustrate the temporal pattern of ASR changes from 1990 to 2019: EAPC\u0026thinsp;=\u0026thinsp;100 \u0026times; (exp(β) \u0026minus;\u0026thinsp;1). EAPC is expressed on a scale of -1 to 1. An EAPC\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates an increase in ASR, while an EAPC\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates a decrease in ASR.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in open-source software R (version 4.4.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.Global, Regional, and National Burden of Cancer in Women\u003c/h2\u003e \u003cp\u003eIn 2021, the incidence of cancer among women worldwide demonstrated significant geographical disparities[Figure1、Figure2]. It is estimated that there were 39,416,539 new cases of cancer among women globally, with an age-standardized incidence rate of 923.4 per 100,000 population[Table\u0026nbsp;1]. Among these cases, the number of deaths was approximately 4,286,993, with an age-standardized mortality rate of 93.6 per 100,000 population[Table\u0026nbsp;2]. Furthermore, the global burden of disability-adjusted life years (DALYs) attributed to cancer in women was 111,646,997, with an age-standardized DALY rate of 2,507.59 per 100,000 population[Table\u0026nbsp;3]. Among the 21 Global Burden of Disease (GBD) regions, High-income North America had the highest age-standardized incidence rate, reaching 3,484.32 per 100,000 population, while the southern sub-Saharan Africa had the highest age-standardized DALY rate, at 3,552.51 per 100,000 population.\u003c/p\u003e \u003cp\u003eFrom 1990 to 2021, the age-standardized incidence rate of cancer among women worldwide showed a slight increasing trend, with an annual percentage change (EAPC) of 0.01. Meanwhile, the global age-standardized mortality rate and DALY rate showed a decreasing trend, with EAPC of -0.75 and \u0026minus;\u0026thinsp;0.98, respectively. Among the 5 Sociodemographic Index (SDI) regions and 21 GBD regions, Southern sub-Saharan Africa had the most significant increase in age-standardized incidence rate, with an EAPC of 1.23. The East Asia region showed the most prominent decrease in age-standardized mortality rate, with an EAPC of -1.77. Except for the stable situation in Western sub-Saharan Africa (EAPC of 0.08), the age-standardized DALY rate in most regions showed a significant decrease[Table\u0026nbsp;3].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. The correlation between ASR, EAPC, and SDI\u003c/h3\u003e\n\u003cp\u003eWhen exploring the multi-dimensional impact factors of the global burden of cancer in women in 2021, the Socio-demographic Index (SDI) emerged as a key indicator, revealing the complex associations between the comprehensive development level of medical care in various countries and cancer risk factors. The impact of low physical activity on the incidence of cancer in women increases with the rise of SDI, reaching a peak when SDI reaches 0.76, and then showing a decreasing trend. Similarly, the influence of tobacco and high body-mass index (BMI) on the incidence of cancer in women also follows a pattern of initial increase followed by a decrease, with the SDI critical point for tobacco being 0.78 and for high BMI being 0.76. It is noteworthy that the incidence of cancer in women in high-income North America is significantly affected by tobacco, far exceeding the global average; while women in Australia are more affected by high BMI. In contrast to the aforementioned trends, the impact of alcohol and dietary on the incidence of cancer in women shows a trend of decreasing, then increasing, and then decreasing again with the increase of SDI. The impact of alcohol reaches its lowest point at an SDI of about 0.45, and then reaches its highest point at an SDI of about 0.78; the impact of dietary reaches its lowest point at an SDI of about 0.4 and reaches its highest point at an SDI of about 0.76. In addition, the impact of high fasting plasma glucose on the incidence of cancer in women continues to rise with the increase of SDI, and no obvious turning point has been observed among different regions.[Figure3]\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.Attributable to comprehensive lifestyle-related risk factors of the burden of cancer in women.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhen analyzing the regional differences in DALYs and mortality rates for cancer among female patients worldwide, we observe significant disparities in the impact of specific health risk factors across regions with different income levels. In high-income areas, such as high-income North America, Western Europe, and Australalia, high body-mass index (BMI), alcohol, tobacco, and dietary are the primary health risk factors, leading to longer DALYs and relatively higher mortality rates for female cancer patients. For instance, in high-income North America and Western Europe, the impact of low physical activity on female cancer patients is substantial, with DALYs and mortality rates relatively high, at 2% and 2.3%, respectively, and 1.8% and 2.3%.\u003c/p\u003e \u003cp\u003eIn contrast, in low-income regions, such as Southern sub-Saharan Africa, while DALYs and mortality rates for certain health risk factors like low physical activity and high fasting plasma glucose are lower, these areas may face greater challenges in addressing other risk factors. Middle-income regions, such as Southeast Asia and Latin America, exhibit varying trends under the influence of different health risk factors. The impact of alcohol is particularly significant among female cancer patients in high-income regions, with generally higher DALYs and mortality rates, such as in Australasia (DALYs at 3.7%, mortality rate at 3.1%) and Western Europe (DALYs at 3.2%, mortality rate at 2.8%). In low-income areas, such as East Asia and Central Latin America, DALYs and mortality rates are relatively lower, ranging from 0.8\u0026ndash;1.2%.\u003c/p\u003e \u003cp\u003eThe influence of high BMI on DALYs and mortality rates is higher in high-income regions and certain specific areas, while it is relatively lower in some low-income regions, showing a clear polarization. Eastern Europe has the highest DALYs and mortality rates at 9.9% and 10.2%, respectively. High-income North America and Central Europe also have relatively high DALYs at 8.1% and 8.2%, with mortality rates at 8.3% and 7.9%. The impact of high fasting plasma glucose on cancer among women varies by region, with a global DALYs percentage of 2.9% and a mortality rate of 3.6%. High-income North America has the highest DALYs percentage at 5.4% and the highest mortality rate percentage at 6.1%. In low-income areas like East sub-Saharan Africa, DALYs are lower at 0.7%, and mortality rates are also lower at 1.1%.\u003c/p\u003e \u003cp\u003eTobacco has a significant regionalized impact on women with cancer, particularly in high-income North America, where DALYs are at 18%, and the mortality rate is 18.2%, the highest among all regions. Other specific areas, such as Western Europe with DALYs at 13.6% and a mortality rate of 12%, and Eastern Europe with DALYs at 10.9% and a mortality rate of 13.2%, also have relatively high levels. Dietary has a substantial impact on women with cancer globally, with a generally higher trend. The global average DALYs are at 7.2%, and the mortality rate is 7.8%. Eastern Europe has the highest DALYs and mortality rates at 9.6% and 10.4%, respectively, while South Asia has the lowest DALYs and mortality rates at 5.2% and 5.7%, the lowest among all regions. Most other regions have DALYs and mortality rates between 6% and 9%.[Figure4]\u003c/p\u003e \n\u003ch3\u003e4.Age-group differences in the burden of cancer among women\u003c/h3\u003e\n \u003cp\u003eIn the global disease analysis for the year 2021, it has been observed that the number of women with cancer deaths attributed to various risk factors and their percentage of total deaths both show an increasing trend with age. In the 45\u0026ndash;49 age group, deaths and death percentages are close to each other due to high body-mass index (BMI), high fasting plasma glucose, and alcohol. By the 50\u0026ndash;54 age group, deaths attributed to different risk factors begin to rise significantly, with a particularly marked increase after the age of 60. In most age groups, dietary and tobacco result in a higher number of deaths and death percentages. The impact of high BMI on Disability-Adjusted Life Years (DALYs) begins to increase significantly during middle age (approximately 45\u0026ndash;54 years), with the number of cancer among women deaths and death percentages starting to rise notably after the age of 49. This increase reaches a small peak in the 50\u0026ndash;54 age group, after which the rate of increase slows down.\u003c/p\u003e \u003cp\u003eFurther research into the impact of high BMI on middle-aged women (40\u0026ndash;60 years) reveals that, among all age groups, uterine cancer has the highest percentage of DALYs, especially in the 55\u0026ndash;59 age group, where DALYs approach 0.3% and remain relatively stable. Kidney cancer has the second-highest percentage of DALYs across all age groups and remains relatively stable at around 0.1%. While most cancers maintain a relatively stable level of DALYs in this age range, breast cancer DALYs show a significant increase, rising from 0 in the 45\u0026ndash;49 age group to 0.1 in the 50\u0026ndash;54 age group, after which it stabilizes around 0.1.[Figure5]\u003c/p\u003e \u003cp\u003eThese findings emphasize the increasing health risks that women face from cancer as they age, particularly during middle age, where the impact of high BMI on women's health is particularly significant. These trends are important for the development of targeted preventive measures and public health policies to mitigate the burden of women with cancer and improve their quality of life.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eIn our in-depth research analyzing the correlation between women's health and cancer risk, we adopted a comprehensive analysis of multiple key factors, including low physical activity, alcohol, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI), especially the association between high BMI and different cancer types in middle-aged women. These factors, intertwined, create a complex pattern of cancer risk among women. The research findings reveal that the incidence of cancer among women is influenced by a combination of lifestyle-related factors, and this influence exhibits significant regional variations. Notably, in high-income North America and Western Europe, the disparities in disability-adjusted life years (DALYs) and mortality percentages attributed to various risk factors stand out prominently when compared to low-income areas such as Southern and Eastern Sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eOur research has delved into the correlation between low physical activity and cancer risk, uncovering a significant relationship between the two. Studies published in the Journal of the American Medical Association (JAMA) further confirm that low physical activity is a key factor in cancer mortality risk\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Specifically, prolonged sedentary behavior, exemplified by extended periods of television viewing, has been intimately associated with obesity, cardiovascular diseases, multiple types of cancer, diabetes mellitus, and an elevated overall mortality rate\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Based on these findings, the American Cancer Society(ACS), in its American Cancer Society nutrition and physical activity guideline for cancer survivors, recommends that adults engage in at least 150 to 300 minutes of moderate-intensity or 75 to 150 minutes of vigorous-intensity exercise per week, emphasizing that exceeding 300 minutes of activity can yield additional health benefits\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Furthermore, research consistently shows that increasing physical activity can significantly reduce the risk of liver cancer, lung cancer, endometrial cancer, and breast cancer among women\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, the phenomenon of insufficient physical activity worldwide is alarming. According to the World Health Organization, the prevalence of physical inactivity in high-income Asia-Pacific regions and South Asia stands at 48% and 45% respectively, whereas this rate is 28% in high-income Western countries and 14% in Oceania. Our research specifically points out that in High-income North America and Western Europe, the influence of low physical activity is notably pronounced in the incidence of neoplasia among females. This may be associated with changes like work for women in these regions. The advancement of remote work technology, the variability in job nature, the intersectionality of work and socioeconomic status, as well as irregular and prolonged work patterns, collectively impact women's health and exacerbate the influence of low physical activity on their cancer risk\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, dietary habits have a non-negligible impact on women's risk of developing tumors. Existing research has revealed a close connection between dietary habits and endometriosis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. By implementing dietary management strategies, such as reducing the fat content in meals and increasing the intake of dietary fiber, it is feasible to effectively decrease the levels of circulating estrogens in the body, thereby yielding positive therapeutic outcomes for patients with endometriosis. Notably, a prospective study conducted by the \"Nurses' Health Study II\" (NHSII) revealed a striking finding: Women who adopted a \"Western dietary pattern\" characterized by high intake of red meat, processed meat, refined grains, and desserts had a 27% increased risk of developing tumors. Conversely, women who followed a healthy dietary pattern had a 13% reduced risk of tumors\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Despite investigating the relationship between dietary patterns and the risk of pancreatic cancer, no significant association has been found. It is worth noting that women with type 2 diabetes seem to face a higher risk of pancreatic cancer, which may be closely related to their higher fasting glucose\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The study conducted by Pan et al. on 22,837 postmenopausal women further confirms this point: High fasting glucose and insulin resistance are not only associated with an increased incidence of breast cancer but also with poorer prognosis for patients\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Our research also indicates that in 2021, the general impact of dietary habits on the cancer risk of women globally remained significant, particularly in Eastern Europe, where this phenomenon may be closely related to the widespread adoption of Western dietary habits among women in the region. Given that high plasma glucose is often accompanied by insulin resistance and has a close association with hormone-related cancers, we strongly recommend that women minimize their intake of red meat, processed meat, refined grains, and desserts, to actively advocate and practice healthy dietary habits to safeguard their health and well-being.\u003c/p\u003e \u003cp\u003eThe impact of tobacco and alcohol on the cancer risk of women exhibits notable variations across different regions, particularly in High-SDI areas where the trend shows a marked increase. According to the World Health Organization (WHO), the prevalence of smoking among women in high-income countries attained 21% in 2010, with projections indicating a slight decline to 15% by 2030. Of particular concern is the smoking prevalence among women in Europe, which stood at 18% in 2022, ranking it the highest globally. This elevated prevalence is consonant with our research findings, which indicate a persistent trend of higher disability-adjusted life years (DALYs) and a greater percentage of deaths attributable to smoking among women in high-income regions. The WHO report further underscores that the tobacco industry targets women through marketing strategies that associate smoking with freedom, and liberation, and even mistakenly portray it as a means of maintaining a slim physique. Such marketing tactics have contributed to an escalation in smoking rates among women in certain regions, particularly in Europe, where the smoking rate among women exceeds double the global average and is decreasing at a much slower pace compared to other regions. Given that smoking is a significant risk factor for lung cancer, which remains one of the major threats to women's health, we urge women to reduce smoking and avoid passive smoking\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This requires strengthening women's self-protection awareness and enhancing society's overall awareness of smoking cessation and tobacco control, particularly in Europe.\u003c/p\u003e \u003cp\u003eThere is a clear causal relationship between alcohol use and a variety of cancers, especially oral cancer, pharyngeal cancer, laryngeal cancer, esophageal cancer, liver cancer, colorectal cancer, and breast cancer, which is particularly susceptible to women\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Among women, the close association between alcohol and the risk of breast cancer cannot be ignored, despite some women having inadequate awareness of this hazard\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. As society progresses, attitudes towards women drinking alcohol have become increasingly open, but this has also quietly led to an increase in negative inducements, such as using alcohol to cope with emotional distress, stress, anxiety, and depression. This trend has led to an increasing consumption of alcohol among women, particularly in high-income regions. In response to this significant shift in women's drinking behaviors, there is an urgent necessity to elevate public awareness regarding the harms associated with alcohol consumption, with a particular emphasis on its unique risks for women, including the elevation of breast cancer risk\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. It is imperative to enhance women's comprehension of the perils of alcohol and motivate them to make healthier lifestyle choices, as these actions are pivotal in mitigating the risk of alcohol-related cancers.\u003c/p\u003e \u003cp\u003eHigh Body-Mass Index (BMI) is closely associated with an increased risk of various types of cancer. Epidemiological data from the International Agency for Research on Cancer (IARC) indicate that High BMI is linked to an elevated risk of cancers of the colon and rectum, liver, gallbladder, pancreas, kidney, thyroid, postmenopausal breast, endometrium, ovary, esophagus (adenocarcinoma), and gastric cardia, as well as gliomas and multiple myeloma. From 1975 to 2016, the obesity rate among women (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) doubled, from 7\u0026ndash;16%\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Notably, the most significant increases in obesity rates among women and girls were observed in Central Asia, North Africa and Middle East, primarily due to changes in the global food system that promote the consumption of energy-dense, nutritionally imbalanced foods and reduce opportunities for physical activity. Our research further reveals that in Eastern Europe, High BMI is also a significant risk factor for cancer among women. High BMI is recognized as a risk factor for both the incidence and mortality of breast cancer\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Although some views suggest that high BMI may be associated with a reduced risk of breast cancer among premenopausal women\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, our study found that among perimenopausal women (aged 40\u0026ndash;60 years), a significant increase in BMI is accompanied by an increase in DALYs (Disability-Adjusted Life Years) and the percentage of deaths due to breast cancer. The increasing risk of adverse outcomes in breast cancer among women may be linked to the intricate hormonal fluctuations during the perimenopausal period. These changes include a decline in estrogen and progesterone levels, an elevation in follicle-stimulating hormone levels, and other dynamic hormonal shifts. Given the correlation between high Body Mass Index (BMI) and cancer risk, it is crucial to adopt more proactive measures to mitigate this risk. This involves improving the global food system, advocating for healthy diets, and encouraging increased physical activity among women. These strategies are essential in reducing the likelihood of developing BMI-related cancers.\u003c/p\u003e \u003cp\u003eOverall, the risk of cancer among women globally is significantly influenced by various lifestyle factors. Due to economic levels and social divisions of labor, women may have easier access to risk factors such as unhealthy diets, lack of physical activity, tobacco use, and alcohol consumption, thereby facing a higher risk of cancer in high-income North America and Western Europe. Conversely, low-income regions may confront different health challenges due to socioeconomic factors. Research published in The Lancet highlights significant disparities in the prevention, early detection, treatment, and survival outcomes of cancers among women between high-income and low-income countries\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Alarmingly, projections indicate that by 2030, three-quarters of cancer deaths globally will occur in low-income and middle-income countries. However, these countries face numerous challenges, including funding shortages and sociocultural barriers, which limit their participation in cancer research to less than 5%. This situation exacerbates the existing inequality in global cancer prevention and control efforts.\u003c/p\u003e"},{"header":"Conclusions","content":" \u003cp\u003ePublic health strategies must tailor and implement effective health promotion programs according to the characteristics of each region, promoting healthy lifestyles and thereby reducing the incidence of cancers among women. This is particularly crucial for middle-aged and elderly women, where efforts in health promotion and disease prevention are of utmost importance. By controlling risk factors for cancer, advocating for healthy diets, increasing physical activity, and reducing the use of tobacco and alcohol, we can effectively decrease the incidence and mortality rates of malignant tumors. These lifestyle modifications can also alleviate the burden of disease that women bear due to cancer. By focusing on these areas, we can make significant strides in improving the health outcomes for women globally.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003cp\u003eFor GBD studies, the Institutional Review Board of the University of Washington reviewed and approved a waiver of informed consent (https://www.healthdata.org/research-analysis/gbd).\u003c/p\u003e\n\u003cp\u003eConsent for publication:\u003c/p\u003e\n\u003cp\u003eAgree to publish.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials:\u003c/p\u003e\n\u003cp\u003eThese data can be accessed through the GBD 2021 Data Sources tool, which provides detailed information on data sources on the website of the Institute for Health Metrics and Evaluation ([GBD 2021 Data Sources](https://ghdx.healthdata.org/gbd-2021/sources)).\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis work was funded by:\u003c/p\u003e\n\u003cp\u003eThe Science and Technology Project of Shaanxi Province(2022SF-496)\u003c/p\u003e\n\u003cp\u003eThe Funds of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University for Scientists. No.RC(GG201807)\u003c/p\u003e\n\u003cp\u003eThe Funds of the Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University. No.YJ(ZYTS)2019012/No.2020YJ(ZYTS)226\u003c/p\u003e\n\u003cp\u003eThe Xinrui Cancer Research Support Program(cphcf-2022-231)\u003c/p\u003e\n\u003cp\u003eThe Medical Research Developing Funds(KM228009)\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions:\u003c/p\u003e\n\u003cp\u003eYZ and DL conceived and designed the study and completed the article. QD, YM, NX, YL conducted the statistical analysis and explained the results of the study. YZ, QD, YM, NX, YL were responsible for creating figures and tables of the study. DL scrutinized the whole process of this study and reviewed the initial manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eAcknowledgements:\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all the scholars who have contributed to the GBD research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21834\u003c/span\u003e\u003cspan address=\"10.3322/caac.21834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGinsburg O, Vanderpuye V, Beddoe AM, et al. Women, power, and cancer: a Lancet Commission. Lancet. 2023;402(10417):2113\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)01701-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)01701-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslami F, Ward EM, Sung H et al. Annual Report to the Nation on the Status of Cancer, Part 1: National Cancer Statistics. J Natl Cancer Inst. 2021;113(12):1648\u0026ndash;1669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jnci/djab131\u003c/span\u003e\u003cspan address=\"10.1093/jnci/djab131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 2023;72(2):338\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2022-327736\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2022-327736\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDyba T, Randi G, Bray F, et al. The European cancer burden in 2020: Incidence and mortality estimates for 40 countries and 25 major cancers. Eur J Cancer. 2021;157:308\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejca.2021.07.039\u003c/span\u003e\u003cspan address=\"10.1016/j.ejca.2021.07.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Chang Z, Wang J et al. Unhealthy lifestyle factors and the risk of colorectal cancer: a Mendelian randomization study. Sci Rep. 2024;14(1):13825. Published 2024 Jun 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-64813-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-64813-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal incidence. prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2133\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilchrist SC, Howard VJ, Akinyemiju T, et al. Association of Sedentary Behavior With Cancer Mortality in Middle-aged and Older US Adults. JAMA Oncol. 2020;6(8):1210\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2020.2045\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2020.2045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Cao C, Kantor ED, et al. Trends in Sedentary Behavior Among the US Population, 2001\u0026ndash;2016. JAMA. 2019;321(16):1587\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2019.3636\u003c/span\u003e\u003cspan address=\"10.1001/jama.2019.3636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRock CL, Thomson CA, Sullivan KR, et al. American Cancer Society nutrition and physical activity guideline for cancer survivors. CA Cancer J Clin. 2022;72(3):230\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21719\u003c/span\u003e\u003cspan address=\"10.3322/caac.21719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel AV, Friedenreich CM, Moore SC, Hayes SC, Silver JK, Campbell KL, Winters-Stone K, Gerber LH, George SM, Fulton JE, Denlinger C, Morris GS, Hue T, Schmitz KH, Matthews CE. American College of Sports Medicine Roundtable Report on Physical Activity, Sedentary Behavior, and Cancer Prevention and Control. Med Sci Sports Exerc. 2019;51(11):2391\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1249/MSS.0000000000002117\u003c/span\u003e\u003cspan address=\"10.1249/MSS.0000000000002117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31626056; PMCID: PMC6814265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank J, Mustard C, Smith P, Siddiqi A, Cheng Y, Burdorf A, Rugulies R. Work as a social determinant of health in high-income countries: past, present, and future. Lancet. 2023;402(10410):1357\u0026ndash;1367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)00871-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)00871-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37838441.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnard ND, Holtz DN, Schmidt N, Kolipaka S, Hata E, Sutton M, Znayenko-Miller T, Hazen ND, Cobb C, Kahleova H. Nutrition in the prevention and treatment of endometriosis: A review. Front Nutr. 2023;10:1089891. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2023.1089891\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2023.1089891\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36875844; PMCID: PMC9983692.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmour M, Middleton A, Lim S, Sinclair J, Varjabedian D, Smith CA. Dietary Practices of Women with Endometriosis: A Cross-Sectional Survey. J Altern Complement Med. 2021;27(9):771\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/acm.2021.0068\u003c/span\u003e\u003cspan address=\"10.1089/acm.2021.0068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Jun 23. PMID: 34161144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDougan MM, Fest S, Cushing-Haugen K, Farland LV, Chavarro J, Harris HR, Missmer SA. A prospective study of dietary patterns and the incidence of endometriosis diagnosis. Am J Obstet Gynecol. 2024;231(4):443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajog.2024.04.030\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2024.04.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e1-443.e10\u003c/span\u003e\u003cspan address=\"http://.e1-443.e10\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Apr 29. PMID: 38692470; PMCID: PMC11410522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShyam S, Greenwood DC, Mai CW, Tan SS, Yusof BM, Moy FM, Cade JE. Major dietary patterns in the United Kingdom Women's Cohort Study showed no evidence of prospective association with pancreatic cancer risk. Nutr Res. 2023;118:41\u0026ndash;51. Epub 2023 Jul 25. PMID: 37562156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan K, Chlebowski RT, Mortimer JE, Gunter MJ, Rohan T, Vitolins MZ, Adams-Campbell LL, Ho GYF, Cheng TD, Nelson RA. Insulin resistance and breast cancer incidence and mortality in postmenopausal women in the Women's Health Initiative. Cancer. 2020;126(16):3638\u0026ndash;3647. doi: 10.1002/cncr.33002. Epub 2020 Jun 12. Erratum in: Cancer. 2020 Oct 20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.33178\u003c/span\u003e\u003cspan address=\"10.1002/cncr.33178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32530506.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProctor RN. Tobacco and the global lung cancer epidemic. Nat Rev Cancer. 2001;1(1):82\u0026thinsp;\u0026ndash;\u0026thinsp;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/35094091\u003c/span\u003e\u003cspan address=\"10.1038/35094091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 11900255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoffetta P, Hashibe M. Alcohol and cancer. Lancet Oncol. 2006;7(2):149\u0026thinsp;\u0026ndash;\u0026thinsp;56. doi: 10.1016/S1470-2045(06)70577-0. PMID: 16455479.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoyle A, O'Dwyer C, Mongan D, Millar SR, Galvin B. Factors associated with public awareness of the relationship between alcohol use and breast cancer risk. BMC Public Health. 2023;23(1):577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-023-15455-8\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-15455-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36978036; PMCID: PMC10044731.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoche AM, Bowden J. Women, alcohol, and breast cancer: opportunities for promoting better health and reducing risk. Med J Aust. 2023;218(11):509\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5694/mja2.51984\u003c/span\u003e\u003cspan address=\"10.5694/mja2.51984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 May 27. PMID: 37244646.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, Goding Sauer A, Shuval K, Gapstur SM, Jacobs EJ, Giovannucci EL, Jemal A. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin. 2019;69(2):88\u0026ndash;112. Epub 2018 Dec 12. PMID: 30548482.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodwin PJ. Obesity, insulin resistance and breast cancer outcomes. Breast. 2015;24(Suppl 2):S56\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.breast.2015.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.breast.2015.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2015 Aug 15. PMID: 26283600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremenopausal Breast Cancer Collaborative Group, Schoemaker MJ, Nichols HB, Wright LB, Brook MN, Jones ME, O'Brien KM, Adami HO, Baglietto L, Bernstein L, Bertrand KA, Boutron-Ruault MC, Braaten T, Chen Y, Connor AE, Dorronsoro M, Dossus L, Eliassen AH, Giles GG, Hankinson SE, Kaaks R, Key TJ, Kirsh VA, Kitahara CM, Koh WP, Larsson SC, Linet MS, Ma H, Masala G, Merritt MA, Milne RL, Overvad K, Ozasa K, Palmer JR, Peeters PH, Riboli E, Rohan TE, Sadakane A, Sund M, Tamimi RM, Trichopoulou A, Ursin G, Vatten L, Visvanathan K, Weiderpass E, Willett WC, Wolk A, Yuan JM, Zeleniuch-Jacquotte A, Sandler DP, Swerdlow AJ. Association of Body Mass Index and Age With Subsequent Breast Cancer Risk in Premenopausal Women. JAMA Oncol. 2018;4(11):e181771. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2018.1771\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2018.1771\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2018 Nov 8. PMID: 29931120; PMCID: PMC6248078.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyrgiou M, Bowden S, Denny L, Fagotti A, Abu-Rustum NR, Ramirez PT, Querleu D. Innovation in gynaecological cancer: highlighting global disparities. Lancet Oncol. 2024;25(4):425\u0026ndash;430. doi: 10.1016/S1470-2045(24)00137-2. Epub 2024 Mar 7. PMID: 38461833.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"124%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTable1\u0026nbsp;Incidence of women with cancer in 1990 and 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003elocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003eNum_1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003eASR_1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003eNum_2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003eASR_2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003eEAPC_CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e22165069(18685016-26237173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e921.58(779.52-1081.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e39416539(33872888.9590652-45407653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e923.44(790.49-1072.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.01 (-0.05-0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e706820(596541-844208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e399.01(338.82-467.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e1693917(1416632.39703802-2032377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e407.58(346.65-475.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.1 (0.05-0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e2429534(2011702-2971667)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e520.11(436.65-626.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e5009162(4148260.48238419-6041436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e550.35(458.31-657.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.27 (0.22-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e5254990(4314030-6471646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e697.36(582.13-828.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e9451119(7919660.86468957-11165503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e709.78(597.92-837.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.09 (0.05-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e5719245(4766214-6824101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1040.16(866.46-1237.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e8045870(6854255.85163976-9341013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1019.35(861.55-1203.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.06 (-0.08--0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e8030118(6911120-9220751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1546.32(1320.13-1797.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e15185287(13351221.4546543-17205705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1981.31(1724.82-2258.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.74 (0.59-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e77106(66675-90126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e545.46(472.34-635.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e195031(168467.812226081-224823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e595.85(514.6-683.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.28 (0.25-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e101126(89997-112944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e869.16(766.62-976.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e182856(162439.493382015-203716)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e833.38(734.79-936.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.13 (-0.19--0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e76321(66833-86565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e499.06(436.86-565.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e138753(122750.803260829-158014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e519.94(459.35-592.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.17 (0.15-0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e295577(241375-362501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e896.79(740.75-1087.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e427891(351494.928496137-514379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e873.82(720.24-1060.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.05 (-0.06--0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e1026612(863137-1220530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1489.19(1249.97-1777.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e1173471(1011746.86499761-1356416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1600.16(1368.85-1864.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.29 (0.22-0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e326188(282327-381814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e541.24(471.33-621.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e808180(702145.499307344-936110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e589.43(513.33-679.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.32 (0.22-0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e60143(50729-72535)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e330(278.89-384.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e156695(132879.135919693-186360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e338.54(285.2-395.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.07 (0.04-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e5248542(4227258-6562798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e903.88(739.83-1088.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e8330726(6884414.68652594-9872709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1008.71(828.17-1218.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.38 (0.33-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e1822950(1509528-2158390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1361.8(1123.28-1628.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e1870740(1572481.02912042-2226561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1393.86(1162.09-1666.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.05 (0.03-0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e183219(160528-211594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e318.81(279.33-361.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e429707(372467.439762035-498252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e314.03(276.45-357.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.19 (-0.24--0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e1668022(1394489-1997992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1744.8(1446.17-2091.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e1984849(1715460.83729049-2279702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1728.45(1455.68-2065.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.09 (-0.12--0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e3491725(3035562-3996076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e2043.54(1755.88-2351.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e9262219(8156069.71713985-10499141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e3484.32(3057.74-3942.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e1.52 (1.24-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e618172(508515-753325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e486.19(405.31-584.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e1481961(1244156.03434634-1775776)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e524.18(441.79-620.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.42 (0.33-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e14606(11896-18355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e572.68(476.92-695.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e33694(27530.7146834257-41643)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e571.33(477.76-689.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.02 (-0.03--0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e2428889(1983916-3014708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e545.57(452.92-665.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e5562386(4482371.16383241-6833857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e617.95(502.58-746.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.54 (0.44-0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e1137541(950867-1391483)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e562.52(475.23-668.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e2154649(1831138.515807-2566580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e587.07(501.7-694.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.12 (0.11-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e213264(183983-246040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e850.61(733.92-989.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e339243(300329.782954053-384451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e838.05(740.12-954.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.26 (-0.34--0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e150814(124762-185183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e707.84(585.1-850.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e296071(246576.620707796-353615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e763.42(640.76-902.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.32 (0.25-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e322476(284346-369030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e510.83(458.39-569.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e661795(583960.238646799-748576)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e491.88(434.31-556.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.02 (-0.08-0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e2697339(2332988-3069274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1074.31(920.71-1235.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e3365282(2988963.83569517-3784496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e1027.47(898.1-1166.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e-0.09 (-0.16--0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.9731%;\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.1047%;\"\u003e\n \u003cp\u003e204438(172841-243051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e316.62(267.04-372.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.075%;\"\u003e\n \u003cp\u003e560339(469282.322484562-668409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8515%;\"\u003e\n \u003cp\u003e332.85(281.49-389.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1443%;\"\u003e\n \u003cp\u003e0.18 (0.16-0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cbr\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"124%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 709px;\"\u003e\n \u003cp\u003eTable2\u0026nbsp;Death of women with cancer in 1990 and 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003elocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eNum_1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eASR_1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eNum_2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eASR_2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eEAPC_CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e2506728(2338857-2647286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e118.08(109.84-124.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e4286993(3887875-4623753)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e93.6(85.02-100.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.75 (-0.79--0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e122570(109128-138285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e97.91(85.86-110.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e252242(220278-284369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e89.7(78.66-100.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.36 (-0.41--0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e253912(228511-277155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e77.31(68.93-84.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e601698(549136-649839)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e78.62(71.75-84.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.04 (0.01-0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e598350(541093-658708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e109.99(99.14-120.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1178031(1057364-1318606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e84.77(75.88-94.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.94 (-0.99--0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e687278(636396-734754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e124.67(115.15-133.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1055947(930213-1179761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e97.74(86.49-109.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.74 (-0.81--0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e841615(782335-871234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e131.9(123.68-136.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1194470(1031897-1286296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e99.08(88.4-104.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.81 (-0.86--0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e30782(29515-32021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e109.47(104.77-114.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e35886(32601-39103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e77.81(70.98-84.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.96 (-1.01--0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e108506(104705-111172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e130.64(125.91-133.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e153959(141366-164481)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e121.02(111.45-129.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.16 (-0.22--0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e12544(10362-15204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e97.08(80.38-119.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e30617(23543-38735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e95.55(73.25-121.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.08 (-0.11--0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e199035(193048-203133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e114.38(111.09-116.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e206552(186982-227765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e96.91(87.21-107.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.47 (-0.58--0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e55827(49454-62815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e129.71(113.52-145.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e112528(95738-132247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e115.38(99.3-133.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.47 (-0.53--0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e217955(190506-241796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e70.75(60.96-78.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e535425(479882-598920)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e69.41(62.07-77.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.16 (-0.25--0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e15403(13756-17682)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e97.11(85.81-112.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e42347(38374-46123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e126.63(115.36-137.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.8 (0.59-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e449961(418261-465921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e132.83(124.87-136.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e545278(467910-585826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e100.01(89.26-105.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.79 (-0.82--0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e35820(29107-43222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e75.75(61.48-91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e91479(69741-111811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e81.64(65.06-97.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.26 (0.23-0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e50431(48987-51290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e113.04(108.84-115.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e119136(104524-133238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e88.21(77.41-98.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.01 (-1.07--0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e51608(49256-52973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e104.78(98.66-108.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e135661(124227-142791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e96.34(88.32-101.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.26 (-0.3--0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e35588(33938-36669)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e140(133.32-144.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e53747(49192-56486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e109.02(100.85-114.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.49 (-0.56--0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e14178(13025-15401)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e128.68(118.2-140.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e35109(28933-42443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e113.13(93.25-136.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.51 (-0.59--0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e15907(14982-16714)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e116.77(110.22-122.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e30843(27276-34591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e108.11(95.52-121.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.17 (-0.21--0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e121513(112003-126793)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e108.89(100.32-113.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e235757(183213-266580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e78.3(65.76-85.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.08 (-1.11--1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e279543(257700-290595)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e138.94(129.88-143.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e378523(334912-401891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e103.95(93.62-109.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.71 (-0.82--0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e601538(503172-707685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e136.46(114.97-159.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e1045921(834566-1282130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e93.7(74.86-114.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.35 (-1.43--1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e126534(109195-141594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e87.66(75.63-97.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e304566(260893-350318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e87.1(74.61-99.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.09 (0.01-0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1416(1122-1783)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e91.97(75.11-115.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e3473(2847-4318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e88.25(73.5-109.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.1 (-0.13--0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e65521(60347-71856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e74.04(67.87-81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e162279(144397-181571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e73.03(64.7-81.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.04 (-0.04-0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e17117(16045-17743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e132.72(125.08-137.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e27907(23991-30078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e94.51(83.4-100.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.86 (-0.95--0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 709px;\"\u003e\n \u003cp\u003eTable3 DALYs of women with cancer in 1990 and 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003elocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eNum_1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eASR_1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eNum_2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eASR_2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eEAPC_CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eGlobal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e73721144(69286568-77877766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3285.33(3089.49-3466.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e111646997(103569823-119654830)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2507.59(2327.14-2686.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.98 (-1.03--0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4661270(4114244-5258559)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3019.11(2681.12-3408.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e9029146(7744081-10331533)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2614.57(2276.57-2952.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.6 (-0.69--0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e9330674(8419223-10174575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2388.57(2155.1-2604.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e19443734(17698324-21063672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2329.99(2122.84-2520.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.08 (-0.13--0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e19888332(18039086-21959531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3174.59(2876.25-3502.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e32932863(29743469-36639903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2331.58(2108.9-2587.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.17 (-1.25--1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e19550534(18100980-21022077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3550.71(3288.06-3819.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e25583425(22806766-28571773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2561.56(2290.45-2858.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.22 (-1.29--1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e20206370(19276891-20760493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3483.48(3346.75-3571.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e24542829(22292884-25907052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2465.22(2295.32-2576.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.15 (-1.17--1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e970572(939608-1005090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3326.37(3214.83-3443.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1115645(1010281-1230534)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2288.06(2077.65-2517.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.17 (-1.21--1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2886503(2808386-2950553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3621.84(3530.72-3700.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3417995(3162447-3648866)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3115.7(2885.9-3333.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.55 (-0.62--0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e454198(373229-550451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2863.71(2363.49-3475.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1067023(816964-1335977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2747.49(2100.98-3476.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.15 (-0.2--0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e5496415(5373320-5608538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3424.13(3352.73-3491.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e5109161(4598781-5671081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2719.37(2444.89-3027.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.05 (-1.14--0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2170024(1897983-2448998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3949.21(3495.69-4447.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e4091714(3414031-4857405)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3307.57(2815.02-3899.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.77 (-0.87--0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e8209260(7220534-9108556)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2249.44(1977.07-2494.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e17351240(15533327-19433332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2087.6(1866.2-2337.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.32 (-0.47--0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e506031(456926-569728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2815.82(2529.64-3211.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1296196(1160144-1435059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3552.51(3190.61-3908.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e1.23 (0.9-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e10158239(9698965-10432637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3456.92(3336.33-3536.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e10781759(9712543-11361930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2480.9(2306.55-2583.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1 (-1.05--0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1294442(1046837-1555585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2252.94(1829.56-2720.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3238005(2399039-4096823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2285.17(1734.6-2811.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.08 (0.03-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1657908(1625061-1684970)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3095.2(3020.66-3145.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3442586(3011508-3887918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2511.49(2199.12-2835.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.78 (-0.88--0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1644734(1595925-1680172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2922.17(2815-2991.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3730400(3514341-3894911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2699.45(2546.38-2817.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.38 (-0.43--0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e942683(912144-965110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3731.21(3611.25-3819.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1283815(1210036-1338521)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2859.85(2714.53-2976.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.71 (-0.79--0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e459758(418922-501195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3590.31(3275.52-3893.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e966163(788612-1173273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3036.04(2480.2-3683.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.72 (-0.83--0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e488767(451760-522956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3346.87(3117.39-3566.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e844142(738483-958447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e3096.67(2715.39-3524.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.11 (-0.15--0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3081532(2921905-3184201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2836.09(2690.89-2926.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e4097258(3431149-4492928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1910.81(1717.81-2029.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.31 (-1.34--1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e6851051(6506042-7077078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3781.33(3634.45-3890.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e8398054(7785213-8795350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2640.51(2492.29-2749.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.25 (-1.31--1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e19140081(16010143-22661456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3893.2(3260.52-4589.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e26289762(20872671-32595359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2443.36(1951.2-3016.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.77 (-1.88--1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4440754(3790711-4996104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2646.25(2280.3-2961.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e9263070(7963832-10782970)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2497.48(2149.24-2897.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.32 (-0.4--0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e52939(41079-66732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2743.36(2166.2-3450.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e126361(101544-157018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2623.04(2146.35-3247.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.14 (-0.18--0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e2388138(2176427-2637436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2166.15(1992.74-2374.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e5140121(4542089-5820244)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2013.08(1783.58-2267.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.07 (-0.16-0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e427115(409365-440964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3541.57(3412.39-3648.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e596528(538151-631941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2354.4(2166.29-2474.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e-1.34 (-1.37--1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Women, Cancer, Lifestyle Risk Factors, Global Burden of Disease, Public Health Interventions","lastPublishedDoi":"10.21203/rs.3.rs-6238383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6238383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Cancer remains a leading cause of female mortality worldwide, especially in low- and middle-income countries. While lifestyle contributes to cancer causation, the extent of its burden on women across different populations remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethod: We conducted a study to investigate the role of six lifestyle factors—low physical activity, alcohol consumption, high fasting plasma glucose, dietary, tobacco, and high body-mass index (BMI)—in the cancer burden among women globally, regionally, and nationally from 1990 to 2021.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResult: Research shows that disability-adjusted life years (DALYs) and age-standardized mortality rates (ASRs) linked to health risks like high BMI, alcohol, tobacco, and poor diet are higher in high-SDI regions. Eastern Europe has the highest DALYs from high BMI (9.9%) and unhealthy diets (10.4%). Australia leads in alcohol-related DALYs (3.7%) and mortality (3.1%), while high-income North America has the highest tobacco-related DALYs (18%) and mortality (18.2%). Female cancer mortality rises with age, especially among women aged 40-60, with breast cancer DALYs increasing significantly. These insights aid in targeted cancer prevention strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: These findings highlight the critical role of lifestyle factors in shaping global cancer epidemiology among women, providing a basis for developing targeted preventive measures and public health policies to reduce the disease burden on women.\u003c/p\u003e","manuscriptTitle":"Global Burden of Female Cancers Attributable to Lifestyle Risk Factors: A 1990-2021 Analysis from the Global Burden of Disease Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 12:08:46","doi":"10.21203/rs.3.rs-6238383/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c680ecb4-052c-4fa3-9be3-fe67d5701de4","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-03T06:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 12:08:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6238383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6238383","identity":"rs-6238383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}