Educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women

Educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women | 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 Article Educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women Flaviane G. Santana, M. S. Branco, Kássio S. Cunha, Leandro F. M. Rezende, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7236382/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 This study aimed to describe educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women using data from the 2019 Brazilian National Health Survey. The sample included women aged 50 to 69 years. The outcomes were breast cancer diagnosis, mammography screening, and clinical breast examination (CBE), with skin color, area of residence, and educational level as equity stratifiers. Simple and complex measures of inequality were applied, including the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII). Inequality measures indicated that access to mammography referrals, screening, and CBE was higher among White women, urban residents, and those with higher education levels. Women with higher education who identified as White (2.6%), Brown (3.4%), or lived in urban areas (3.1%) had a higher prevalence of breast cancer diagnosis compared to Black women (2.3%) and rural residents (1.9%) with the same education level. These findings suggest that breast cancer screening and diagnosis disproportionately benefit White, highly educated, and urban-dwelling women. The results underscore the need for equitable screening programs, awareness campaigns, and public health policies targeting structurally disadvantaged groups to reduce persistent disparities in breast cancer outcomes. Biological sciences/Cancer Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Oncology Breast Cancer Health Inequalities Social Determinants of Health Figures Figure 1 Introduction Globally, female breast cancer is the most common type, accounting for 2.3 million new cases and 666 thousand deaths in 2022 ( 1 , 2 ). In Brazil, approximately 95 thousand cases and 22 thousand deaths occurred in 2022 ( 3 ). The Brazilian Strategic Action Plan for Addressing Chronic Non-Communicable Diseases (2021–2030) sets a target to reduce premature mortality from breast cancer by 10% ( 4 ). Achieving this goal requires a deeper understanding of social disparities that affect diagnosis and access to healthcare services, especially in vulnerable populations. Social inequalities influence exposure to breast cancer risk factors, prevention, diagnosis, and timely treatment initiation ( 5 ). Socially vulnerable populations face inequities in accessing early cancer diagnosis and effective, timely treatment, leading to higher mortality rates and lower survival rates ( 6 – 9 ). For instance, Black and Brown women have shown higher prevalence rates of advanced-stage breast cancer diagnosis, whereas White women and those with higher education levels exhibit lower prevalence rates ( 10 – 12 ), highlighting the disparities in healthcare service access and availability. In addition, women with higher income and education levels ( 7 , 13 ), as well as White women ( 13 ), have shown higher screening rates in Brazil. While these studies provide valuable insights into local contexts, studies using a nationally representative sample and incorporating additional dimensions of inequality are lacking. In addition to race/skin color and educational level, considering place of residence—distinguishing between urban and rural areas—may contribute to a more comprehensive and robust analysis of disparities in breast cancer diagnosis and screening across Brazil. National guidelines recommend biennial mammography screening for women aged 50 to 69 years, which aligns with the target population of this study. In this study, we aimed to describe social inequalities—related to education, skin color, and place of residence—in breast cancer diagnosis, mammography screening, and clinical breast examinations among women aged 50 to 69 years. Methods Data Source and Sampling We retrieved cross-sectional data from the 2019 National Health Survey (PNS – Acronym in Portuguese), a nationally representative population survey covering individuals residing in private households across Brazil. The target population of the PNS consisted of individuals aged 15 years or older living in permanent private households, excluding those located in special census sectors or sparsely populated areas ( 14 ). The 2019 PNS was approved by the National Research Ethics Commission (CONEP), under the National Health Council (CNS), with approval number 3.529.376. The sampling design followed a three-stage cluster approach with stratification of Primary Sampling Units (PSUs), which correspond to census sectors or groups of census sectors. Households served as Secondary Sampling Units (SSUs), while the residents selected to answer the specific questionnaire comprised the Tertiary Sampling Units (TSUs). The PNS sample included responses from 94,114 participants ( 14 ). In this study, we analyzed data from 15,016 women aged 50 to 69 years who responded to questions about breast cancer diagnosis. The data are publicly available on the Brazilian Institute of Geography and Statistics (IBGE) website and do not allow participant identification. Assessment of breast cancer screening and diagnosis The outcomes of this study included whether a CBE was conducted by a healthcare professional, whether a mammography was completed, and whether a breast cancer diagnosis had been reported. For CBE, the analysis considered the time since the last examination and whether it had been performed. Regarding mammography, the survey question assessed whether a doctor had requested the exam, whether the exam was conducted, the time since the last mammogram, and whether it was paid for or provided by the Brazilian Unified Health System (SUS). Women diagnosed with breast cancer responded “yes” to the question: “Have you ever been diagnosed with any type of cancer?” and then specified the type of cancer, including breast cancer as an option. Only cases where breast cancer was explicitly selected were considered as diagnosed cases. Equity Stratifiers Individuals were stratified by skin color (White, Black, Brown, Yellow, and Indigenous), area of residence (urban or rural), and educational level. Educational level has been used as a proxy for socioeconomic position due to its greater stability in the health context compared to other socioeconomic indicators ( 15 , 16 ). Four categories of educational level were considered: incomplete primary education (including no formal education and incomplete primary education), complete primary education (comprising completed primary and incomplete secondary education), complete secondary education (including completed secondary and incomplete higher education), and complete higher education (covering individuals with a university degree or higher). Statistical Analysis Descriptive analyses included prevalence of breast cancer screening and diagnosis, along with their 95% confidence intervals (95% CI). Inequalities in breast cancer diagnosis and access to CBE and mammography in 2019, stratified by educational level, skin color, and area of residence, were visually represented using equiplots ( www.equidade.org/equiplot ). Since educational level is an ordinal variable, individuals were ranked from the most to the least educated. Measures of inequality were assessed using absolute differences and ratios, with binomial linear regression applied to estimate confidence intervals for differences and log-binomial regression for ratio confidence intervals. Complex inequality measures were also applied, including the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII). Both indices were estimated using logistic regression, offering a more detailed perspective on health disparities. SII quantifies absolute inequality, measuring the predicted difference in an outcome between the most and least advantaged groups, while RII captures relative inequality by comparing the likelihood of an outcome across different socioeconomic strata. Due to the small sample size, Yellow and Indigenous women were analyzed exclusively through equiplots, as their low representation limited the feasibility of more complex statistical analyses. The SII values range from negative to positive, where negative values indicate a higher prevalence of the analyzed outcome (breast cancer diagnosis and screening) among individuals with lower education levels, while positive values indicate greater prevalence among those with higher education levels. A value of zero represents the absence of inequality. To enhance interpretability, SII results were expressed as percentages, simulating a population of 100 individuals. Similarly. RII values range from 1. Values below 1 indicate a higher prevalence of the outcome among less-educated individuals, while values above 1 suggest a greater prevalence among more-educated individuals. A value of 1 signifies no inequality. Due to the small sample sizes of Yellow and Indigenous women (n = 1.0% (CI: 0.7; 1.4) and n = 0.4% (CI: 0.3; 0.5), respectively), these groups were analyzed exclusively through equiplots. Their limited representation in the sample did not provide sufficient statistical power to estimate complex measures of inequality, such as the SII and RII, particularly for breast cancer diagnosis. Including them in regression-based analyses could result in unstable or biased estimates. Therefore, to preserve the robustness of our findings, these subgroups were excluded from analyses involving complex modeling. All statistical analyses were conducted using STATA/SE® 17, accounting for the complex sampling design of the PNS. Results The prevalence of breast cancer diagnosis 2.0% (95% CI: 1.6–2.5). Women with breast cancer diagnosis were more likely White (52.7% [95% CI: 47.0–58.4]), did not complete primary education (34.7% [95% CI: 30.5–39.1]), and lived in urban areas (87.2% [95% CI: 84.9–89.2]) (Table 1 ). Table 1 – Sociodemographic characteristics of women aged 50 to 69 years according to breast cancer screening and diagnosis. PNS. Brazil. 2019. SOCIODEMOGRAPHIC CHARACTERISTICS N TOTAL SAMPLE % (CI 95%) CBE (a) % (CI 95%) MAMOGRAPH (b) % (CI 95%) BREAST CANCER (c) % (CI 95%) N - 15.016 12.651 10.739 235 % (CI 95%) 100 87.0 (86.0; 87.9) 75.6 (74.5; 76.7) 2.0 (1.6; 2.5) Mean Age (SE) - 58.3 (0.04) 58.2 (0.04) 58.2 (0.05) 58.7 (0.16) Skin Color White 5,983 47.7 (45.8; 48.4) 48.2 (46.9; 49.6) 47.8 (46.3; 49.3) 52.7 (47.0; 58.4) Black 1,688 11.1 (10.3; 12.0) 10.7 (9.8; 11.5) 10.7 (9.8; 11.7) 12.2 (10.7; 13.8) Yellow 111 1.0 (0.7; 1.4) 1.0 (0.7; 1.5) 1.1 (0.8; 1.6) 1.0 (0.1; 8.9) Brown 7,124 40.4 (39.1; 41.7) 39.6 (38.3; 41.0) 39.9 (38.5; 41.4) 33.4 (28.6; 38.6) Indigenous 109 0.4 (0.3; 0.5) 0.4 (0.3; 0.6) 0.4 (0.3; 0.6) 0.6 (0.4; 1.0) Educational Level Incomplete Primary Education 7,568 48.6 (47.14; 50.0) 45.8 (44.3; 47.2) 44.9 (43.3; 46.4) 34.7 (30.5; 39.1) Complete Primary Education 1,737 12.5 (11.6; 13.5) 12.6 (11.7; 13.7) 12.3 (11.3; 13.4) 16.1 (11.3; 22.3) Complete Secondary Education 3,338 22.8 (21.6; 24.0) 24.1 (22.8; 25.4) 24.4 (23.1; 25.8) 24.0 (18.9; 29.9) Complete Higher Education 2,373 16.1 (15.1; 17.2) 17.1 (16.4; 18.7) 18.4 (17.1; 19.7) 25.3 (20.3; 31.0) Area of Residence Urban 12,373 88.3 (87.7; 88.8) 89.1 (88.6; 89.7) 89.6 (89.0; 90.1) 87.2 (84.9; 89.2) Rural 2,643 11.7 (11.2; 12.2) 10.9 (10.3; 11.4) 10.4 (9.9; 11.0) 12.8 (10.8; 15.1) (a) Underwent a clinical breast examination (CBE) at least once with a healthcare professional. (b) Underwent mammography at least once. (c) Diagnosed with breast cancer . %: Prevalence 95% CI: 95% Confidence Interval SE: Standard Error Regarding access to CBE and mammography, 9.7% of women had never undergone either examination (95% CI: 9.0–10.4). Among the 87% of women who had undergone a CBE, nearly half (45.8% [95% CI: 44.5–47.2]) reported having their last examination one year or more ago. Approximately 80% of women had received a physician’s referral for a mammogram (79.2% [95% CI: 78.2–80.2]). Regarding the timing of the last mammogram, 76.3% (95% CI: 75.0–77.6) had undergone the exam within the past two years, with nearly 54% of these exams performed in the public healthcare system (53.7% [95% CI: 52.0–55.5]) (Table 2 ). Table 2 – Access, Timing, and Use of Public Healthcare Services for Clinical Breast Examination and Mammography. PNS 2019. EXAMS TOTAL SAMPLE % (CI 95%) N 15,016 Had both exams 72.3 (71.1; 73.4) Had neither exam 9.7 (9.0; 10.4) Only CBE 14.7 (13.8; 15.6) Only mammography 3.3 (2.7; 3.9) Had CBE 87.0 (86.0; 87.9) Time Since Last CBE Less than 1 year 41.1 (39.7; 42.5) 1 year or more 45.8 (44.5; 47.2) Never had 13.0 (12.1; 14.0) Received a physician’s referral for mammography 79.2 (78.2; 80.2) Had a mammogram 75.6 (74.5; 76.7) Never had Time Since Last Mammogram Less than 1 year 52.0 (50.5; 53.5) Less than 2 years 76.3 (75.0; 77.6) 2 years or more 23.7 (22.4; 25.0) Paid for the last mammogram 23.4 (22.1; 24.8) Last mammogram performed through SUS 53.7 (52.0; 55.5) %: Prevalence 95% CI: 95% Confidence Interval IPE = Incomplete Primary Education; CPE = Complete Primary Education; CSE = Complete Secondary Education; CHE = Complete Higher Education. The estimates and the graph were generated using STATA/SE® 17 software. Breast cancer diagnosis, mammography referral, and mammography completion were most prevalent among women who self-identified as Yellow. Women who self-identified as White had the highest prevalence of CBE, while those who had never undergone any screening examination were predominantly Black and Brown-skinned women (Fig. 1 a), Women residing in urban areas showed higher prevalence in breast cancer diagnosis, CBE, and mammography completion (Fig. 1 b). Women who did not complete primary education had the highest prevalence of never undergoing any screening examination (Fig. 1 c). Regarding educational disparities, women with higher education levels reported a 2.6 percentage points (p.p.) higher prevalence of breast cancer diagnosis compared to those who did not complete primary education (SII: 2.6; 95% CI: 0.8;4.3). When examining the intersection of skin color and education, despite an educational gradient—where more educated women reported higher rates of breast cancer diagnosis—this disparity was less pronounced among Brown-skinned women (SII: 1.5; 95% CI: -0.3; 4.3); RII: 1.72; 95% CI: 0.65; 2.79) compared to other skin color categories. Interaction analyses between area of residence and education showed that among urban residents, highly educated women were 2.6 times more likely to report a breast cancer diagnosis compared to those with lower education levels (SII: 2.7; 95% CI: 0.8; 4.6; RII: 2.34; 95% CI: 1.08; 3.60) (Table 3 ). Table 3 – Analysis of Educational Inequality in Breast Cancer Diagnosis (n = 235), CBE, and Mammography (n = 15,016), PNS, Brazil, 2019. POPULATION EDUCATIONAL LEVEL SIMPLE MEASURE COMPLEX MEASURE IPE % CPE % CSE % CHE % D (CI 95%) R (CI 95%) SII (CI 95%) RII (CI 95%) With Breast Cancer 1.3 2.9 2.4 3.1 1.8 (0.2; 3.3) 2.35 (1.29; 4.27) 2.6 (0.8; 4.3) 2.27 (1.15; 3.40) Skin Color White 1.1 2.8 1.5 2.6 1.5 (-0.1; 3.1) 2.35 (1.03; 5.38) 2.9 (-0.1; 5.9) 2.29 (0.50; 4.08) Black 1.3 0.4 1.8 2.3 0.9 (-2.7; 4.6) 1.69 (0.32; 8.95) 2.1 (-1.6; 5.9) 2.36 (-1.01; 5.72) Brown 1.9 3.8 1.4 3.4 1.5 (-1.2; 4.2) 1.79 (0.75; 4.31) 1.5 (-0.3; 3.4) 1.72 (0.65; 2.79) Area of Residence Urban 1.2 2.9 2.4 3.1 1.9 (0.3; 3.5) 2.59 (1.37; 4.88) 2.7 (0.8; 4.6) 2.34 (1.08; 3.60) Rural 1.5 0.3 1.8 1.9 0.4 (-1.8; 2.6) 1.27 (0.37; 4.33) -0.3 (-2.8; 2.2) 0.89 (-1.90; 1.80) Had CBE 80.2 88.7 93.6 96.6 16.4 (14.5; 18.3) 1.20 (1.18; 1.23) 0.25 (0.21; 0.28) 1.34 (1.28; 1.40) Mammography Referral 72.2 79.3 86.0 90.9 18.7 (16.3; 21.1) 1.26 (1.22; 1.30) 25.4 (22.1; 28.7) 1.40 (1.33; 1.45) Had a Mammogram 67.8 75.6 82.6 89.2 21.4 (18.9; 23.9) 1.31 (1.27; 1.36) 28.3 (24.8; 31.7) 1.47 (1.39; 1.54) Had Both Exams 84.9 90.8 95.9 98.0 13.07 (11.7; 14.4) 1.15 (1.14; 1.17) 19.7 (17.1; 22.2) 1.25 (1.21; 1.29) Had No Exam 15.1 9.1 4.1 2.0 -13.07 (-14.4; -11.7) 0.13 (0.09; 0.18) -19.7 (-22.2; -17.1) 0.12 (0.08; 0.15) 95% CI: 95% Confidence Interval IPE: Incomplete Primary Education (lowest education level) CHE: Complete Higher Education (highest education level) CPE: complete primary education CSE: complete secondary education D: Absolute difference between extreme education levels in percentage points R: Ratio between extreme education levels SII: Absolute Index of Educational Inequality in percentage points RII: Relative Index of Educational Inequality Both screening exams were more prevalent among women wight higher education compared to those with lower education levels, with a 20% higher prevalence for CBE (SII: 0.25; 95% CI: 0.21; 0.28; RII: 1.34; 95% CI: 1.28; 1.40) and a 31% higher prevalence for mammography (SII: 28.3 95% CI: 24.8; 31.7; RII: 1.47 95% CI: 1.39; 1.54). Discussion Our findings indicate that White women, those with higher education levels, and urban residents had a higher prevalence of breast cancer diagnosis, mammography referrals, and completion of both mammography and clinical breast examination. Breast cancer screening is affected by social, economic, educational, cultural, racial, and geographical inequalities ( 12 , 17 – 21 ), creating barriers to healthcare services access. In this study, we observed that Black women have lower access to screening exams and a lower prevalence of breast cancer diagnosis ( 10 – 12 ). The literature shows that Black women faces a higher risk of advanced disease staging and delays in treatment initiation ( 22 , 23 ), late-stage breast cancer diagnosis in this group may contribute to lower survival rates ( 11 , 24 – 28 ). Thus, our results highlight the persistence of racial health disparities, often linked to institutional racism within the healthcare system ( 10 , 29 – 31 ) and structural obstacles that limit access to essential goods and services. Although the principle of equity is guaranteed by the national health system in Brazil, SUS ( 32 ), and by the National Policy for Comprehensive Health Care of the Black Population ( 20 ), in practice, equitable participation of Black individuals still faces significant barriers. Factors such as lower educational attainment, lower income, restricted access to information, and inadequacies in healthcare services further exacerbate these disparities ( 23 , 33 , 34 ). A previous study indicates that Black women in vulnerable situations receive lower-quality healthcare and are more likely to experience discrimination, negligence, and disrespectful treatment by healthcare professionals ( 7 ). Additionally, while high-income countries have improved breast cancer survival rates, mortality remains high in low-income countries ( 35 ), indicating that social inequalities not only influence cancer incidence but also worsen health outcomes ( 8 , 13 , 36 – 39 ). However, this finding should not be interpreted as a protective factor against the disease. Existing literature underscores a cycle of structural inequalities, in which socially disadvantaged groups tend to have lower educational attainment, resulting in reduced access to information and lower income levels. This interplay of structural disadvantages, known as intersectionality, further exacerbates health disparities ( 40 ), ultimately limiting access to preventive services ( 13 ), delaying diagnosis and treatment, increasing mortality risk, and reducing breast cancer survival rates ( 41 , 42 ). Specifically, among Black women and those residing in rural areas, individuals with higher education levels exhibited a lower prevalence of breast cancer diagnosis when compared to White women and urban residents with similar levels of education. Regardless of cancer type, previous studies highlight significant disparities in healthcare access, particularly among Black individuals ( 30 , 36 ), those with low educational attainment ( 34 ), and rural residents ( 43 – 45 ). These inequalities contribute directly to unequal access to healthcare services ( 22 , 34 ), reinforcing structural disparities in cancer prevention and treatment. Education is a key determinant of health inequality, as it shapes individuals’ ability to make informed health decisions ( 13 ) and influences women’s perceptions of mammographic screening and their concerns following a breast cancer diagnosis ( 7 , 19 ). Lower education levels are strongly linked to lower socioeconomic status, which in turn limits access to preventive healthcare services. As a result, economically disadvantaged groups tend to have lower cancer screening rates, further exacerbating disparities ( 46 ). Consistent with the findings of this study, research conducted both in Brazil and internationally ( 47 ) shows that women with higher income and education levels ( 7 , 13 , 47 – 49 ), as well as those who identify as White ( 13 ), have higher rates of breast cancer screening, highlighting persistent healthcare privileges among socially advantaged groups. Regarding area of residence, our findings indicate that women living in urban areas were more frequently diagnosed with breast cancer and had higher rates of CBE and mammography screenings. This disparity may be attributed to the multiple barriers rural residents face in accessing healthcare services, including long travel distances, high transportation costs, and communication challenges with healthcare professionals ( 43 – 45 ). Healthcare-seeking behavior and adherence to routine screenings tend to be lower in rural areas, where populations have less access to private health insurance and a poorer perception of their own health compared to urban residents. Additionally, in rural settings, healthcare utilization increases with higher educational attainment ( 43 ), as education is directly linked to health literacy and awareness ( 49 ). This may explain why rural women with higher education levels had lower breast cancer diagnosis rates, given that the number of highly educated individuals in rural areas remains low ( 50 ). Studies analyzing breast cancer screening rates have consistently shown that individuals living in more deprived and rural areas are less likely to undergo recommended cancer screenings, underscoring the urgent need for targeted intervention strategies ( 51 , 52 ). Therefore, enhancing patient engagement and accessibility could play a crucial role in improving screening rates among high-risk populations. In our study, we included only women aged 50 to 69 years, in accordance with the Brazilian Ministry of Health’s recommendations, which prioritize this age group for breast cancer screening. This guideline is based on evidence that breast cancer incidence increases with age, and within this range, the benefits of screening outweigh the risks. However, the effectiveness of mammographic screening remains a subject of debate in the scientific community ( 53 , 54 ). The Cochrane Review on breast cancer screening raises concerns about its impact on reducing mortality, emphasizing issues such as overdiagnosis and overtreatment, which can lead to unnecessary psychological distress and medical interventions ( 53 ). Similarly, the editorial advocating for abandoning early detection screening argues that large-scale population screening offers minimal benefits while exposing women to potential harms, including false positives, radiation exposure, and unnecessary procedures ( 54 ). These findings highlight the need for a critical reassessment of breast cancer screening policies, ensuring that strategies are evidence-based, carefully targeted, and tailored to the specific needs of the population to maximize benefits while minimizing potential risks. Furthermore, a recent national survey in the United States found that when women in their 40s were informed about the potential risks and benefits of mammography, many preferred to delay screening ( 55 ). This underscores the importance of shared decision-making between patients and healthcare providers, ensuring that women make informed choices about when to initiate breast cancer screening. Therefore, primary prevention strategies such as reducing alcohol consumption, promoting physical activity, and maintaining a healthy body weight should play a more prominent role in breast cancer prevention efforts. Several limitations should be considered while interpreting our findings. Self-reported breast cancer diagnosis primarily reflects access to diagnosis rather than the actual disease prevalence, which may have led to underestimation in subgroups with lower healthcare access. Indigenous and Yellow women had very small sample sizes, limiting the statistical power to estimate complex inequality measures such as SII and RII. These groups were therefore excluded from regression-based analyses. However, our study also has strengths. The sampling structure of the PNS ensured representativeness at the national level. During data collection, confidentiality and anonymity were maintained through the use of mobile data collection devices, minimizing response bias due to social desirability. Moreover, the use of multiple inequality measures across subgroups provided a more comprehensive understanding of disparities, effectively identifying the most vulnerable populations. Conclusions We conclude that educational, racial, and geographic disparities persist in breast cancer diagnosis, mammography screening, and CBE. White, highly educated, and urban-dwelling women had higher prevalence rates of breast cancer diagnosis and greater access to screening exams, highlighting significant inequities in healthcare access. To effectively address these disparities, increasing access to preventive screenings and early diagnosis among Black and Brown women, rural residents, and those with lower educational levels is necessary. This requires strengthening awareness campaigns, implementing targeted screening programs, and ensuring equitable access to diagnostic exams, regardless of socioeconomic status. Although identifying and addressing social disparities is a complex challenge, it is crucial to develop effective strategies that promote health equity, reduce inequalities, and ensure that all women receive timely and adequate breast cancer care. Declarations Funding This study was financially supported by the National Council for Scientific and Technological Development (CNPq – Grant N. 407933/2021-2, CMA; Grant 313491/2021-6, CMA; Grant N. 311109/2023-3, LFMR) and by the Coordination for the Improvement of Higher Education Personnel (CAPES – Grant N. 88887.816284/2023-00, KSC; CAPES – Grant N. 88887.008165/2024-00, FMSB) Author Contribution F.G.S. contributed to the study's development, writing, analysis, interpretation, and conclusions. F.M.S.B. assisted with writing, technical editing, and revision. K.S.C. provided substantial contributions to the study's conception, as well as data analysis and interpretation. L.F.M.R. played a key role in the initial project that led to this article, contributing substantially to both the methodology and the interpretation of the study results. C.M.A., as the corresponding author, participated in the technical editing of the manuscript and approved the final version for publication. Acknowledgement This study was financially supported by the National Council for Scientific and Technological Development (CNPq – Grant N. 407933/2021-2, CMA; Grant 313491/2021-6, CMA; Grant N. 311109/2023-3, LFMR) and by the Coordination for the Improvement of Higher Education Personnel (CAPES – Grant N. 88887.816284/2023-00, KSC; CAPES – Grant N. 88887.008165/2024-00) References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. maio de 2021;71(3):209–49. World Health Organization. CANCER TODAY. 2022 [citado 22 de junho de 2025]. Mortality, Females, in 2022. Disponível em: https://gco.iarc.fr/today/en/dataviz/tables?mode=population& sexes=2&cancers=20&types=1 World Health Organization. GLOBAL CANCER OBSERVATORY. 2022 [citado 22 de junho de 2025]. Incidence, Mortality and Prevalence by cancer site. Disponível em: https://gco.iarc.who.int/media/globocan/factsheets/populations/76-brazil-fact-sheet.pdf Brasil. Ministério da Saúde. Plano de Ações Estratégicas para o Enfrentamento das Doenças Crônicas e Agravos não Transmissíveis no Brasil 2021–2030 [Internet]. Brasília, DF: Ministério da Saúde; 2021 [citado 12 de março de 2024]. Disponível em: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/doencas-cronicas-nao-transmissiveis-dcnt/09-plano-de-dant-2022_2030.pdf Vaccarella S, Lortet-Tieulent J, Saracci R, Fidler MM, Conway DI, Vilahur N, et al. Reducing Social Inequalities in Cancer: Setting Priorities for Research. CA Cancer J Clin. setembro de 2018;68(5):324–6. Instituto Nacional de Câncer (Brasil). Estimativa 2023: incidência de câncer no Brasil. Rio de Janeiro, RJ: Instituto Nacional De Câncer; 2023. 160 p. Cabral ALLV, Giatti L, Martínez-Hernáez Á, Cherchiglia ML. Inequality in breast cancer care in a Brazilian capital city: a comparative analysis of narratives. Int J Equity Health. dezembro de 2019;18(1):88. Ferreira MDC, Arroyave I, Barros MBDA. Social inequalities in male cancer in a metropolis in the Southeast region of Brazil. Rev Saúde Pública. 22 de junho de 2023;57(1):38. Niessen LW, Mohan D, Akuoku JK, Mirelman AJ, Ahmed S, Koehlmoos TP, et al. Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. The Lancet. maio de 2018;391(10134):2036–46. Santos TBD, Borges AKDM, Ferreira JD, Meira KC, Souza MCD, Guimarães RM, et al. Prevalence and factors associated to advanced stage breast cancer diagnosis. Ciênc Saúde Coletiva. fevereiro de 2022;27(2):471–82. Santos-Silva I, Stavola BL, Renna NL, Nogueira MC, Aquino EML, Bustamante-Teixeira MT, et al. Ethnoracial and social trends in breast cancer staging at diagnosis in Brazil, 2001–14: a case only analysis. Lancet Glob Health. junho de 2019;7(6):e784–97. Oliveira NPD, De Camargo Cancela M, Martins LFL, De Souza DLB. A multilevel assessment of the social determinants associated with the late stage diagnosis of breast cancer. Sci Rep. 1 o de fevereiro de 2021;11(1):2712. Buranello MC, Meirelles MCCC, Walsh IAPD, Pereira GDA, Castro SSD. Breast cancer screening practice and associated factors: Women’s Health Survey in Uberaba MG Brazil, 2014. Ciênc Saúde Coletiva. agosto de 2018;23(8):2661–70. Stopa SR, Szwarcwald CL, Oliveira MMD, Gouvea EDCDP, Vieira MLFP, Freitas MPSD, et al. Pesquisa Nacional de Saúde 2019: histórico, métodos e perspectivas. Epidemiol E Serviços Saúde. 2020;29(5):e2020315. Borràs JM, Guillen M, Sanchez V, Juncà S, Vicente R. Educational level, voluntary private health insurance and opportunistic cancer screening among women in Catalonia (Spain): Eur J Cancer Prev. outubro de 1999;8(5):427–34. White K, Haas JS, Williams DR. Elucidating the Role of Place in Health Care Disparities: The Example of Racial/Ethnic Residential Segregation. Health Serv Res. junho de 2012;47(3pt2):1278–99. Oliveira SB, Soares DA. Access to breast cancer care in a municipality in Bahia: the perspective of users, workers and managers. Saúde Em Debate. março de 2020;44(124):169–81. Choi E, Lee YY, Suh M, Lee EY, Mai TTX, Ki M, et al. Socioeconomic Inequalities in Cervical and Breast Cancer Screening among Women in Korea, 2005–2015. Yonsei Med J. 2018;59(9):1026. Nabaa’ HA, Shelleh N. Barriers preventing Palestinian women from having a mammogram: a qualitative study. The Lancet. fevereiro de 2018;391:S16. Brasil. Ministério da Saúde. Política nacional de saúde integral da população negra: uma política para o SUS [Internet]. Ministério da Saúde; 2016. Disponível em: https://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_saude_populacao_negra_3d.pdf Silva MTAD, Silva Júnior VBD, Mangueira JDO, Gurgel Junior GD, Leal EMM. Distribution of mammograms and mammography offering in relation to the parametric care of the Public Health Care System in Pernambuco. Rev Bras Saúde Materno Infant. setembro de 2018;18(3):609–18. Cabral ALLV, Giatti L, Casale C, Cherchiglia ML. Social vulnerability and breast cancer: differentials in the interval between diagnosis and treatment of women with different sociodemographic profiles. Ciênc Saúde Coletiva. fevereiro de 2019;24(2):613–22. Barros ÂF, Araújo JMD, Murta-Nascimento C, Dias A. Clinical pathways of breast cancer patients treated in the Federal District, Brazil. Rev Saúde Pública. 30 de janeiro de 2019;53:14. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. janeiro de 2019;69(1):7–34. Ren JX, Gong Y, Ling H, Hu X, Shao ZM. Racial/ethnic differences in the outcomes of patients with metastatic breast cancer: contributions of demographic, socioeconomic, tumor and metastatic characteristics. Breast Cancer Res Treat. janeiro de 2019;173(1):225–37. Ellis L, Canchola AJ, Spiegel D, Ladabaum U, Haile R, Gomez SL. Racial and Ethnic Disparities in Cancer Survival: The Contribution of Tumor, Sociodemographic, Institutional, and Neighborhood Characteristics. J Clin Oncol. 1 o de janeiro de 2018;36(1):25–33. Nogueira MC, Guerra MR, Cintra JRD, Corrêa CSL, Fayer VA, Bustamante-Teixeira MT. Racial disparity in 10-year breast cancer survival: a mediation analysis using potential responses approach. Cad Saúde Pública [Internet]. 6 de setembro de 2018 [citado 13 de março de 2024];34(9). Disponível em: http://www.scielo.br/scielo.php?script=sci_arttext &pid=S0102-311X2018000905007&lng=pt&tlng=pt Lemos LLP, Souza MC, Guerra AA, Piazza T, Araújo RM, Cherchiglia ML. Racial disparities in breast cancer survival after treatment initiation in Brazil: a nationwide cohort study. Lancet Glob Health. fevereiro de 2024;12(2):e292–305. Williams D, Cooper L. Reducing Racial Inequities in Health: Using What We Already Know to Take Action. Int J Environ Res Public Health. 19 de fevereiro de 2019;16(4):606. Paller CJ, Wang L, Brawley OW. Racial Inequality in Prostate Cancer Outcomes—Socioeconomics, Not Biology. JAMA Oncol. 1 o de julho de 2019;5(7):983. Lopes VAS, Ribeiro JM. Cervical cancer control limiting factors and facilitators: a literature review. Ciênc Saúde Coletiva. setembro de 2019;24(9):3431–42. Brasil. Lei N o 8.080, de 19 de setembro de 1990. [Internet]. Lei N o 8.080, de 19 de setembro de 1990. 1990. Disponível em: https://www.planalto.gov.br/ccivil_03/leis/l8080.htm Constante HM, Bastos JL. Mapping the Margins in Health Services Research: How Does Race Intersect With Gender and Socioeconomic Status to Shape Difficulty Accessing HealthCare Among Unequal Brazilian States? Int J Health Serv. abril de 2021;51(2):155–66. Lazar M, Davenport L. Barriers to Health Care Access for Low Income Families: A Review of Literature. J Community Health Nurs. 2 de janeiro de 2018;35(1):28–37. Amato O, Guarneri V, Girardi F. Epidemiology trends and progress in breast cancer survival: earlier diagnosis, new therapeutics. Curr Opin Oncol. novembro de 2023;35(6):612–9. International Federation of Medical Students’ Associations. IFMSA Policy Document Ethnicity and Health [Internet]. 2018. Disponível em: https://ifmsa.org/wp-content/uploads/2021/02/IFMSA-policy-document-on-Ethnicity-and-Health-AM18.pdf Sengupta A, Bodini C, Franco S. Struggles for Health: An Emancipatory Approach in the Era of Neoliberal Globalization. Development. dezembro de 2018;61(1–4):101–7. Coughlin SS. Social determinants of colorectal cancer risk, stage, and survival: a systematic review. Int J Colorectal Dis. junho de 2020;35(6):985–95. Nogueira MC, Fayer VA, Corrêa CSL, Guerra MR, Stavola BD, dos-Santos-Silva I, et al. Inequities in access to mammographic screening in Brazil. Cad Saúde Pública. 2019;35(6):e00099817. Oliveira FD, Bastos JL, Moretti-Pires RO. Intersectionality, discrimination, and quality of life in the adult population in Florianópolis, Southern Brazil. Cad Saúde Pública. 2021;37(11):e00042320. Baeyens-Fernández JA, Molina-Portillo E, Pollán M, Rodríguez-Barranco M, Del Moral R, Arribas-Mir L, et al. Trends in incidence, mortality and survival in women with breast cancer from 1985 to 2012 in Granada, Spain: a population-based study. BMC Cancer. dezembro de 2018;18(1):781. Rodrigues GM, Carmo CND, Bergmann A, Mattos IE. Racial inequalities in advanced clinical staging in women with breast cancer treated at a referral hospital in Rio de Janeiro, Brazil. Saúde E Soc. 2021;30(3):e200813. Arruda NM, Maia AG, Alves LC. Inequality in access to health services between urban and rural areas in Brazil: a disaggregation of factors from 1998 to 2008. Cad Saúde Pública [Internet]. 21 de junho de 2018 [citado 13 de março de 2024];34(6). Disponível em: http://www.scielo.br/scielo.php?script=sci_arttext &pid=S0102-311X2018000605003&lng=pt&tlng=pt Dasgupta P, Baade PD, Aitken JF, Ralph N, Chambers SK, Dunn J. Geographical Variations in Prostate Cancer Outcomes: A Systematic Review of International Evidence. Front Oncol. 8 de abril de 2019;9:238. Galvão JR, Almeida PFD, Santos AMD, Fernandes NFS. Care trajectories of users through PHC in a health region: free traffic, slow points and stops. Physis Rev Saúde Coletiva. 2019;29(4):e290404. Peroni FMA, Lindelow M, Souza DOD, Sjoblom M. Realizing the right to health in Brazil’s Unified Health System through the lens of breast and cervical cancer. Int J Equity Health. dezembro de 2019;18(1):39. Soofi M, Karami-Matin B, Najafi F, Naghshbandi P, Soltani S. Decomposing socioeconomic disparity in the utilization of screening mammography: A cross-sectional analysis from the RaNCD cohort study. Health Care Women Int. 2 de setembro de 2023;44(9):1092–105. Malta DC, Prates EJS, Silva AGD, Santos FMD, Oliveira GDC, Vasconcelos NMD, et al. Inequalities in mammography and Papanicolaou test coverage: a time-series study. Sao Paulo Med J. dezembro de 2020;138(6):475–82. Schäfer AA, Santos LP, Miranda VIA, Tomasi CD, Soratto J, Quadra MR, et al. Regional and social inequalities in mammography and Papanicolaou tests in Brazilian state capitals in 2019: a cross-sectional study. Epidemiol E Serviços Saúde. 2021;30(4):e2021172. Pereira CN, Castro CND. TD 2632 - Educação no Meio Rural: diferenciais entre o rural e o urbano. Texto Para Discussão. 1 o de março de 2021;1–64. Kurani SS, McCoy RG, Lampman MA, Doubeni CA, Finney Rutten LJ, Inselman JW, et al. Association of Neighborhood Measures of Social Determinants of Health With Breast, Cervical, and Colorectal Cancer Screening Rates in the US Midwest. JAMA Netw Open. 9 de março de 2020;3(3):e200618. Antabe R, Kansanga M, Sano Y, Kyeremeh E, Galaa Y. Utilization of breast cancer screening in Kenya: what are the determinants? BMC Health Serv Res. dezembro de 2020;20(1):228. Adami H, Kalager M, Valdimarsdottir U, Bretthauer M, Ioannidis JPA. Time to abandon early detection cancer screening. Eur J Clin Invest. março de 2019;49(3):e13062. Gøtzsche PC, Jørgensen KJ. Screening for breast cancer with mammography. Cochrane Breast Cancer Group, organizador. Cochrane Database Syst Rev [Internet]. 4 de junho de 2013 [citado 23 de abril de 2025];2013(6). Disponível em: http://doi.wiley.com/ 10.1002/14651858.CD001877.pub5 Woloshin S, Jørgensen KJ, Hwang S, Welch HG. The New USPSTF Mammography Recommendations — A Dissenting View. N Engl J Med. 21 de setembro de 2023;389(12):1061–4. 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-7236382","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":500144124,"identity":"7ff300c1-0baa-425e-b367-c7195b563e6a","order_by":0,"name":"Flaviane G. Santana","email":"","orcid":"","institution":"Federal University of Uberlandia (UFU)","correspondingAuthor":false,"prefix":"","firstName":"Flaviane","middleName":"G.","lastName":"Santana","suffix":""},{"id":500144126,"identity":"70425c65-7f59-421c-9f86-b97a8679cd46","order_by":1,"name":"M. S. Branco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC9nYwdUDOAEwbWBDWwnMYosXYgIEZpEWCeC2JG8BaGIjRwsz8gOnGnzvp29n7j274USDBwN/enUBAC5sBc27bs9ydPYfZbvYAHSZx5uwGvFrsmRnMf+c2HM7dcCOZ7QYPUIuBRC5+LTzM7B+Yc/4cTjcAarn5hzgtPAbMOWyHE0BabhNpC08ByC+GG84cNrstYyDBQ9AvPOztG4AOuyNvcLzx2c03f2zk+Nt78WvBNIM05aNgFIyCUTAKsAIAmLRDvx/jdI0AAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Uberlandia (UFU)","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"S.","lastName":"Branco","suffix":""},{"id":500144128,"identity":"72c83b93-e9a1-4a35-b918-a5d8fe1fb12a","order_by":2,"name":"Kássio S. Cunha","email":"","orcid":"","institution":"Fiocruz Brasília, Fundação Oswaldo Cruz","correspondingAuthor":false,"prefix":"","firstName":"Kássio","middleName":"S.","lastName":"Cunha","suffix":""},{"id":500144130,"identity":"b7c969f2-73ae-4486-b009-7baa4279265a","order_by":3,"name":"Leandro F. M. Rezende","email":"","orcid":"","institution":"Escola Paulista de Medicina, Universidade Federal de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Leandro","middleName":"F. M.","lastName":"Rezende","suffix":""},{"id":500144132,"identity":"cc07f791-19ae-43a6-a551-aab4c471bf26","order_by":4,"name":"Catarina M. Azeredo","email":"","orcid":"","institution":"Federal University of Uberlandia (UFU)","correspondingAuthor":false,"prefix":"","firstName":"Catarina","middleName":"M.","lastName":"Azeredo","suffix":""}],"badges":[],"createdAt":"2025-07-28 17:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7236382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7236382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89397408,"identity":"b73b4bb2-7929-4ca1-84e0-7384c7501d86","added_by":"auto","created_at":"2025-08-19 13:47:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83100,"visible":true,"origin":"","legend":"\u003cp\u003eEquiplots on Racial, Residential, and Educational Inequality in Breast Cancer Diagnosis and Screening Exams, PNS, Brazil, 2019.\u003c/p\u003e\n\u003cp\u003eIPE = Incomplete Primary Education; CPE = Complete Primary Education; CSE = Complete Secondary Education; CHE = Complete Higher Education. The estimates and the graph were generated using STATA/SE® 17 software.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7236382/v1/1d24cb8fe71273dac1243e23.png"},{"id":90818423,"identity":"99b4921f-c36f-4bf6-a7da-fbd2383d6fbb","added_by":"auto","created_at":"2025-09-08 13:38:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1062157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7236382/v1/8fcc7b45-a113-4393-b4b7-e62d02be1d02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, female breast cancer is the most common type, accounting for 2.3\u0026nbsp;million new cases and 666 thousand deaths in 2022 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Brazil, approximately 95 thousand cases and 22 thousand deaths occurred in 2022 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The Brazilian Strategic Action Plan for Addressing Chronic Non-Communicable Diseases (2021–2030) sets a target to reduce premature mortality from breast cancer by 10% (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Achieving this goal requires a deeper understanding of social disparities that affect diagnosis and access to healthcare services, especially in vulnerable populations.\u003c/p\u003e\u003cp\u003eSocial inequalities influence exposure to breast cancer risk factors, prevention, diagnosis, and timely treatment initiation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Socially vulnerable populations face inequities in accessing early cancer diagnosis and effective, timely treatment, leading to higher mortality rates and lower survival rates (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For instance, Black and Brown women have shown higher prevalence rates of advanced-stage breast cancer diagnosis, whereas White women and those with higher education levels exhibit lower prevalence rates (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), highlighting the disparities in healthcare service access and availability. In addition, women with higher income and education levels (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), as well as White women (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), have shown higher screening rates in Brazil.\u003c/p\u003e\u003cp\u003eWhile these studies provide valuable insights into local contexts, studies using a nationally representative sample and incorporating additional dimensions of inequality are lacking. In addition to race/skin color and educational level, considering place of residence—distinguishing between urban and rural areas—may contribute to a more comprehensive and robust analysis of disparities in breast cancer diagnosis and screening across Brazil. National guidelines recommend biennial mammography screening for women aged 50 to 69 years, which aligns with the target population of this study.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to describe social inequalities—related to education, skin color, and place of residence—in breast cancer diagnosis, mammography screening, and clinical breast examinations among women aged 50 to 69 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData Source and Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrieved cross-sectional data from the 2019 National Health Survey (PNS \u0026ndash; Acronym in Portuguese), a nationally representative population survey covering individuals residing in private households across Brazil. The target population of the PNS consisted of individuals aged 15 years or older living in permanent private households, excluding those located in special census sectors or sparsely populated areas (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). The 2019 PNS was approved by the National Research Ethics Commission (CONEP), under the National Health Council (CNS), with approval number 3.529.376.\u003c/p\u003e\n\u003cp\u003eThe sampling design followed a three-stage cluster approach with stratification of Primary Sampling Units (PSUs), which correspond to census sectors or groups of census sectors. Households served as Secondary Sampling Units (SSUs), while the residents selected to answer the specific questionnaire comprised the Tertiary Sampling Units (TSUs). The PNS sample included responses from 94,114 participants (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). In this study, we analyzed data from 15,016 women aged 50 to 69 years who responded to questions about breast cancer diagnosis. The data are publicly available on the Brazilian Institute of Geography and Statistics (IBGE) website and do not allow participant identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of breast cancer screening and diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcomes of this study included whether a CBE was conducted by a healthcare professional, whether a mammography was completed, and whether a breast cancer diagnosis had been reported. For CBE, the analysis considered the time since the last examination and whether it had been performed. Regarding mammography, the survey question assessed whether a doctor had requested the exam, whether the exam was conducted, the time since the last mammogram, and whether it was paid for or provided by the Brazilian Unified Health System (SUS). Women diagnosed with breast cancer responded \u0026ldquo;yes\u0026rdquo; to the question: \u0026ldquo;Have you ever been diagnosed with any type of cancer?\u0026rdquo; and then specified the type of cancer, including breast cancer as an option. Only cases where breast cancer was explicitly selected were considered as diagnosed cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEquity Stratifiers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals were stratified by skin color (White, Black, Brown, Yellow, and Indigenous), area of residence (urban or rural), and educational level. Educational level has been used as a proxy for socioeconomic position due to its greater stability in the health context compared to other socioeconomic indicators (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). Four categories of educational level were considered: incomplete primary education (including no formal education and incomplete primary education), complete primary education (comprising completed primary and incomplete secondary education), complete secondary education (including completed secondary and incomplete higher education), and complete higher education (covering individuals with a university degree or higher).\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eDescriptive analyses included prevalence of breast cancer screening and diagnosis, along with their 95% confidence intervals (95% CI). Inequalities in breast cancer diagnosis and access to CBE and mammography in 2019, stratified by educational level, skin color, and area of residence, were visually represented using equiplots (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.equidade.org/equiplot\u003c/span\u003e\u003c/span\u003e). Since educational level is an ordinal variable, individuals were ranked from the most to the least educated. Measures of inequality were assessed using absolute differences and ratios, with binomial linear regression applied to estimate confidence intervals for differences and log-binomial regression for ratio confidence intervals.\u003c/p\u003e\n\u003cp\u003eComplex inequality measures were also applied, including the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII). Both indices were estimated using logistic regression, offering a more detailed perspective on health disparities. SII quantifies absolute inequality, measuring the predicted difference in an outcome between the most and least advantaged groups, while RII captures relative inequality by comparing the likelihood of an outcome across different socioeconomic strata. Due to the small sample size, Yellow and Indigenous women were analyzed exclusively through equiplots, as their low representation limited the feasibility of more complex statistical analyses. The SII values range from negative to positive, where negative values indicate a higher prevalence of the analyzed outcome (breast cancer diagnosis and screening) among individuals with lower education levels, while positive values indicate greater prevalence among those with higher education levels. A value of zero represents the absence of inequality. To enhance interpretability, SII results were expressed as percentages, simulating a population of 100 individuals. Similarly. RII values range from \u0026lt;\u0026thinsp;1 to \u0026gt;\u0026thinsp;1. Values below 1 indicate a higher prevalence of the outcome among less-educated individuals, while values above 1 suggest a greater prevalence among more-educated individuals. A value of 1 signifies no inequality. Due to the small sample sizes of Yellow and Indigenous women (n\u0026thinsp;=\u0026thinsp;1.0% (CI: 0.7; 1.4) and n\u0026thinsp;=\u0026thinsp;0.4% (CI: 0.3; 0.5), respectively), these groups were analyzed exclusively through equiplots. Their limited representation in the sample did not provide sufficient statistical power to estimate complex measures of inequality, such as the SII and RII, particularly for breast cancer diagnosis. Including them in regression-based analyses could result in unstable or biased estimates. Therefore, to preserve the robustness of our findings, these subgroups were excluded from analyses involving complex modeling. All statistical analyses were conducted using STATA/SE\u0026reg; 17, accounting for the complex sampling design of the PNS.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe prevalence of breast cancer diagnosis 2.0% (95% CI: 1.6\u0026ndash;2.5). Women with breast cancer diagnosis were more likely White (52.7% [95% CI: 47.0\u0026ndash;58.4]), did not complete primary education (34.7% [95% CI: 30.5\u0026ndash;39.1]), and lived in urban areas (87.2% [95% CI: 84.9\u0026ndash;89.2]) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Sociodemographic characteristics of women aged 50 to 69 years according to breast cancer screening and diagnosis. PNS. Brazil. 2019.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOCIODEMOGRAPHIC CHARACTERISTICS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTOTAL SAMPLE\u003c/p\u003e\u003cp\u003e%\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCBE \u003csup\u003e(a)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e%\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAMOGRAPH\u003csup\u003e(b)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e%\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBREAST CANCER\u003csup\u003e(c)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e%\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e% (CI 95%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0\u003c/p\u003e\u003cp\u003e(86.0; 87.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003cp\u003e(74.5; 76.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003cp\u003e(1.6; 2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Age (SE)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.3 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.2 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.2 (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.7 (0.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkin Color\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003cp\u003e(45.8; 48.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.2\u003c/p\u003e\u003cp\u003e(46.9; 49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.8\u003c/p\u003e\u003cp\u003e(46.3; 49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.7\u003c/p\u003e\u003cp\u003e(47.0; 58.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003cp\u003e(10.3; 12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003cp\u003e(9.8; 11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003cp\u003e(9.8; 11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003cp\u003e(10.7; 13.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003cp\u003e(0.7; 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003cp\u003e(0.7; 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003cp\u003e(0.8; 1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003cp\u003e(0.1; 8.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.4\u003c/p\u003e\u003cp\u003e(39.1; 41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.6\u003c/p\u003e\u003cp\u003e(38.3; 41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.9\u003c/p\u003e\u003cp\u003e(38.5; 41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.4\u003c/p\u003e\u003cp\u003e(28.6; 38.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndigenous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003cp\u003e(0.3; 0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003cp\u003e(0.3; 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003cp\u003e(0.3; 0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003cp\u003e(0.4; 1.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncomplete Primary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003cp\u003e(47.14; 50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.8\u003c/p\u003e\u003cp\u003e(44.3; 47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.9\u003c/p\u003e\u003cp\u003e(43.3; 46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.7\u003c/p\u003e\u003cp\u003e(30.5; 39.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete Primary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003cp\u003e(11.6; 13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003cp\u003e(11.7; 13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003cp\u003e(11.3; 13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003cp\u003e(11.3; 22.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete Secondary Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.8\u003c/p\u003e\u003cp\u003e(21.6; 24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1\u003c/p\u003e\u003cp\u003e(22.8; 25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003cp\u003e(23.1; 25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003cp\u003e(18.9; 29.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplete Higher Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003cp\u003e(15.1; 17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003cp\u003e(16.4; 18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003cp\u003e(17.1; 19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.3\u003c/p\u003e\u003cp\u003e(20.3; 31.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea of Residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12,373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.3\u003c/p\u003e\u003cp\u003e(87.7; 88.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.1\u003c/p\u003e\u003cp\u003e(88.6; 89.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.6\u003c/p\u003e\u003cp\u003e(89.0; 90.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.2\u003c/p\u003e\u003cp\u003e(84.9; 89.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003cp\u003e(11.2; 12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003cp\u003e(10.3; 11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003cp\u003e(9.9; 11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003cp\u003e(10.8; 15.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e(a) Underwent a clinical breast examination (CBE) at least once with a healthcare professional.\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e(b) Underwent mammography at least once.\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e(c) Diagnosed with breast cancer\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003csup\u003e%: Prevalence\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e95% CI: 95% Confidence Interval\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003eSE: Standard Error\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding access to CBE and mammography, 9.7% of women had never undergone either examination (95% CI: 9.0\u0026ndash;10.4). Among the 87% of women who had undergone a CBE, nearly half (45.8% [95% CI: 44.5\u0026ndash;47.2]) reported having their last examination one year or more ago. Approximately 80% of women had received a physician\u0026rsquo;s referral for a mammogram (79.2% [95% CI: 78.2\u0026ndash;80.2]). Regarding the timing of the last mammogram, 76.3% (95% CI: 75.0\u0026ndash;77.6) had undergone the exam within the past two years, with nearly 54% of these exams performed in the public healthcare system (53.7% [95% CI: 52.0\u0026ndash;55.5]) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Access, Timing, and Use of Public Healthcare Services for Clinical Breast Examination and Mammography. PNS 2019.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEXAMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTOTAL SAMPLE\u003c/p\u003e\u003cp\u003e% (CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad both exams\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.3 (71.1; 73.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHad neither exam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.7 (9.0; 10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly CBE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.7 (13.8; 15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly mammography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003cp\u003e(2.7; 3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad CBE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.0 (86.0; 87.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Since Last CBE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.1 (39.7; 42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 year or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.8 (44.5; 47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever had\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.0 (12.1; 14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReceived a physician\u0026rsquo;s referral for mammography\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.2 (78.2; 80.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad a mammogram\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.6 (74.5; 76.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever had\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime Since Last Mammogram\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.0 (50.5; 53.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 2 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.3 (75.0; 77.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 years or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.7 (22.4; 25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePaid for the last mammogram\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.4 (22.1; 24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLast mammogram performed through SUS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.7 (52.0; 55.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e%: Prevalence\u003c/p\u003e\u003cp\u003e95% CI: 95% Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eIPE\u0026thinsp;=\u0026thinsp;Incomplete Primary Education; CPE\u0026thinsp;=\u0026thinsp;Complete Primary Education; CSE\u0026thinsp;=\u0026thinsp;Complete Secondary Education; CHE\u0026thinsp;=\u0026thinsp;Complete Higher Education. The estimates and the graph were generated using STATA/SE\u0026reg; 17 software.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBreast cancer diagnosis, mammography referral, and mammography completion were most prevalent among women who self-identified as Yellow. Women who self-identified as White had the highest prevalence of CBE, while those who had never undergone any screening examination were predominantly Black and Brown-skinned women (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), Women residing in urban areas showed higher prevalence in breast cancer diagnosis, CBE, and mammography completion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Women who did not complete primary education had the highest prevalence of never undergoing any screening examination (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding educational disparities, women with higher education levels reported a 2.6 percentage points (p.p.) higher prevalence of breast cancer diagnosis compared to those who did not complete primary education (SII: 2.6; 95% CI: 0.8;4.3). When examining the intersection of skin color and education, despite an educational gradient\u0026mdash;where more educated women reported higher rates of breast cancer diagnosis\u0026mdash;this disparity was less pronounced among Brown-skinned women (SII: 1.5; 95% CI: -0.3; 4.3); RII: 1.72; 95% CI: 0.65; 2.79) compared to other skin color categories.\u003c/p\u003e\u003cp\u003eInteraction analyses between area of residence and education showed that among urban residents, highly educated women were 2.6 times more likely to report a breast cancer diagnosis compared to those with lower education levels (SII: 2.7; 95% CI: 0.8; 4.6; RII: 2.34; 95% CI: 1.08; 3.60) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026ndash; Analysis of Educational Inequality in Breast Cancer Diagnosis (n\u0026thinsp;=\u0026thinsp;235), CBE, and Mammography (n\u0026thinsp;=\u0026thinsp;15,016), PNS, Brazil, 2019.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePOPULATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eEDUCATIONAL LEVEL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSIMPLE MEASURE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eCOMPLEX MEASURE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIPE %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCPE %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCSE %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCHE\u003c/p\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eD\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eR\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRII\u003c/p\u003e\u003cp\u003e(CI 95%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWith Breast Cancer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003cp\u003e(0.2; 3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003cp\u003e(1.29; 4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003cp\u003e(0.8; 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003cp\u003e(1.15; 3.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkin Color\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003cp\u003e(-0.1; 3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003cp\u003e(1.03; 5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003cp\u003e(-0.1; 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003cp\u003e(0.50; 4.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003cp\u003e(-2.7; 4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003cp\u003e(0.32; 8.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003cp\u003e(-1.6; 5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003cp\u003e(-1.01; 5.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003cp\u003e(-1.2; 4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003cp\u003e(0.75; 4.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003cp\u003e(-0.3; 3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003cp\u003e(0.65; 2.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea of Residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003cp\u003e(0.3; 3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003cp\u003e(1.37; 4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003cp\u003e(0.8; 4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003cp\u003e(1.08; 3.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003cp\u003e(-1.8; 2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003cp\u003e(0.37; 4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003cp\u003e(-2.8; 2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003cp\u003e(-1.90; 1.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad CBE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003cp\u003e(14.5; 18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003cp\u003e(1.18; 1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e(0.21; 0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003cp\u003e(1.28; 1.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMammography Referral\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003cp\u003e(16.3; 21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003cp\u003e(1.22; 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.4\u003c/p\u003e\u003cp\u003e(22.1; 28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003cp\u003e(1.33; 1.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad a Mammogram\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003cp\u003e(18.9; 23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003cp\u003e(1.27; 1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.3\u003c/p\u003e\u003cp\u003e(24.8; 31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003cp\u003e(1.39; 1.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad Both Exams\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.07\u003c/p\u003e\u003cp\u003e(11.7; 14.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003cp\u003e(1.14; 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.7\u003c/p\u003e\u003cp\u003e(17.1; 22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003cp\u003e(1.21; 1.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHad No Exam\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-13.07\u003c/p\u003e\u003cp\u003e(-14.4; -11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003cp\u003e(0.09; 0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-19.7\u003c/p\u003e\u003cp\u003e(-22.2; -17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003cp\u003e(0.08; 0.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e95% CI: 95% Confidence Interval\u003c/p\u003e\u003cp\u003eIPE: Incomplete Primary Education (lowest education level)\u003c/p\u003e\u003cp\u003eCHE: Complete Higher Education (highest education level)\u003c/p\u003e\u003cp\u003eCPE: complete primary education\u003c/p\u003e\u003cp\u003eCSE: complete secondary education\u003c/p\u003e\u003cp\u003eD: Absolute difference between extreme education levels in percentage points\u003c/p\u003e\u003cp\u003eR: Ratio between extreme education levels\u003c/p\u003e\u003cp\u003eSII: Absolute Index of Educational Inequality in percentage points\u003c/p\u003e\u003cp\u003eRII: Relative Index of Educational Inequality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBoth screening exams were more prevalent among women wight higher education compared to those with lower education levels, with a 20% higher prevalence for CBE (SII: 0.25; 95% CI: 0.21; 0.28; RII: 1.34; 95% CI: 1.28; 1.40) and a 31% higher prevalence for mammography (SII: 28.3 95% CI: 24.8; 31.7; RII: 1.47 95% CI: 1.39; 1.54).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings indicate that White women, those with higher education levels, and urban residents had a higher prevalence of breast cancer diagnosis, mammography referrals, and completion of both mammography and clinical breast examination.\u003c/p\u003e\u003cp\u003eBreast cancer screening is affected by social, economic, educational, cultural, racial, and geographical inequalities (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), creating barriers to healthcare services access. In this study, we observed that Black women have lower access to screening exams and a lower prevalence of breast cancer diagnosis (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The literature shows that Black women faces a higher risk of advanced disease staging and delays in treatment initiation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), late-stage breast cancer diagnosis in this group may contribute to lower survival rates (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Thus, our results highlight the persistence of racial health disparities, often linked to institutional racism within the healthcare system (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and structural obstacles that limit access to essential goods and services. Although the principle of equity is guaranteed by the national health system in Brazil, SUS (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and by the National Policy for Comprehensive Health Care of the Black Population (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), in practice, equitable participation of Black individuals still faces significant barriers. Factors such as lower educational attainment, lower income, restricted access to information, and inadequacies in healthcare services further exacerbate these disparities (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A previous study indicates that Black women in vulnerable situations receive lower-quality healthcare and are more likely to experience discrimination, negligence, and disrespectful treatment by healthcare professionals (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, while high-income countries have improved breast cancer survival rates, mortality remains high in low-income countries (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), indicating that social inequalities not only influence cancer incidence but also worsen health outcomes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, this finding should not be interpreted as a protective factor against the disease. Existing literature underscores a cycle of structural inequalities, in which socially disadvantaged groups tend to have lower educational attainment, resulting in reduced access to information and lower income levels. This interplay of structural disadvantages, known as intersectionality, further exacerbates health disparities (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), ultimately limiting access to preventive services (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), delaying diagnosis and treatment, increasing mortality risk, and reducing breast cancer survival rates (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Specifically, among Black women and those residing in rural areas, individuals with higher education levels exhibited a lower prevalence of breast cancer diagnosis when compared to White women and urban residents with similar levels of education.\u003c/p\u003e\u003cp\u003eRegardless of cancer type, previous studies highlight significant disparities in healthcare access, particularly among Black individuals (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), those with low educational attainment (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and rural residents (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). These inequalities contribute directly to unequal access to healthcare services (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), reinforcing structural disparities in cancer prevention and treatment. Education is a key determinant of health inequality, as it shapes individuals\u0026rsquo; ability to make informed health decisions (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and influences women\u0026rsquo;s perceptions of mammographic screening and their concerns following a breast cancer diagnosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Lower education levels are strongly linked to lower socioeconomic status, which in turn limits access to preventive healthcare services. As a result, economically disadvantaged groups tend to have lower cancer screening rates, further exacerbating disparities (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Consistent with the findings of this study, research conducted both in Brazil and internationally (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) shows that women with higher income and education levels (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), as well as those who identify as White (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), have higher rates of breast cancer screening, highlighting persistent healthcare privileges among socially advantaged groups.\u003c/p\u003e\u003cp\u003eRegarding area of residence, our findings indicate that women living in urban areas were more frequently diagnosed with breast cancer and had higher rates of CBE and mammography screenings. This disparity may be attributed to the multiple barriers rural residents face in accessing healthcare services, including long travel distances, high transportation costs, and communication challenges with healthcare professionals (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Healthcare-seeking behavior and adherence to routine screenings tend to be lower in rural areas, where populations have less access to private health insurance and a poorer perception of their own health compared to urban residents. Additionally, in rural settings, healthcare utilization increases with higher educational attainment (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), as education is directly linked to health literacy and awareness (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). This may explain why rural women with higher education levels had lower breast cancer diagnosis rates, given that the number of highly educated individuals in rural areas remains low (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Studies analyzing breast cancer screening rates have consistently shown that individuals living in more deprived and rural areas are less likely to undergo recommended cancer screenings, underscoring the urgent need for targeted intervention strategies (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Therefore, enhancing patient engagement and accessibility could play a crucial role in improving screening rates among high-risk populations.\u003c/p\u003e\u003cp\u003eIn our study, we included only women aged 50 to 69 years, in accordance with the Brazilian Ministry of Health\u0026rsquo;s recommendations, which prioritize this age group for breast cancer screening. This guideline is based on evidence that breast cancer incidence increases with age, and within this range, the benefits of screening outweigh the risks. However, the effectiveness of mammographic screening remains a subject of debate in the scientific community (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The Cochrane Review on breast cancer screening raises concerns about its impact on reducing mortality, emphasizing issues such as overdiagnosis and overtreatment, which can lead to unnecessary psychological distress and medical interventions (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Similarly, the editorial advocating for abandoning early detection screening argues that large-scale population screening offers minimal benefits while exposing women to potential harms, including false positives, radiation exposure, and unnecessary procedures (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). These findings highlight the need for a critical reassessment of breast cancer screening policies, ensuring that strategies are evidence-based, carefully targeted, and tailored to the specific needs of the population to maximize benefits while minimizing potential risks. Furthermore, a recent national survey in the United States found that when women in their 40s were informed about the potential risks and benefits of mammography, many preferred to delay screening (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). This underscores the importance of shared decision-making between patients and healthcare providers, ensuring that women make informed choices about when to initiate breast cancer screening. Therefore, primary prevention strategies such as reducing alcohol consumption, promoting physical activity, and maintaining a healthy body weight should play a more prominent role in breast cancer prevention efforts.\u003c/p\u003e\u003cp\u003eSeveral limitations should be considered while interpreting our findings. Self-reported breast cancer diagnosis primarily reflects access to diagnosis rather than the actual disease prevalence, which may have led to underestimation in subgroups with lower healthcare access. Indigenous and Yellow women had very small sample sizes, limiting the statistical power to estimate complex inequality measures such as SII and RII. These groups were therefore excluded from regression-based analyses. However, our study also has strengths. The sampling structure of the PNS ensured representativeness at the national level. During data collection, confidentiality and anonymity were maintained through the use of mobile data collection devices, minimizing response bias due to social desirability. Moreover, the use of multiple inequality measures across subgroups provided a more comprehensive understanding of disparities, effectively identifying the most vulnerable populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe conclude that educational, racial, and geographic disparities persist in breast cancer diagnosis, mammography screening, and CBE. White, highly educated, and urban-dwelling women had higher prevalence rates of breast cancer diagnosis and greater access to screening exams, highlighting significant inequities in healthcare access. To effectively address these disparities, increasing access to preventive screenings and early diagnosis among Black and Brown women, rural residents, and those with lower educational levels is necessary. This requires strengthening awareness campaigns, implementing targeted screening programs, and ensuring equitable access to diagnostic exams, regardless of socioeconomic status. Although identifying and addressing social disparities is a complex challenge, it is crucial to develop effective strategies that promote health equity, reduce inequalities, and ensure that all women receive timely and adequate breast cancer care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was financially supported by the National Council for Scientific and Technological Development (CNPq \u0026ndash; Grant N. 407933/2021-2, CMA; Grant 313491/2021-6, CMA; Grant N. 311109/2023-3, LFMR) and by the Coordination for the Improvement of Higher Education Personnel (CAPES \u0026ndash; Grant N. 88887.816284/2023-00, KSC; CAPES \u0026ndash; Grant N. 88887.008165/2024-00, FMSB)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.G.S. contributed to the study's development, writing, analysis, interpretation, and conclusions. F.M.S.B. assisted with writing, technical editing, and revision. K.S.C. provided substantial contributions to the study's conception, as well as data analysis and interpretation. L.F.M.R. played a key role in the initial project that led to this article, contributing substantially to both the methodology and the interpretation of the study results. C.M.A., as the corresponding author, participated in the technical editing of the manuscript and approved the final version for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was financially supported by the National Council for Scientific and Technological Development (CNPq \u0026ndash; Grant N. 407933/2021-2, CMA; Grant 313491/2021-6, CMA; Grant N. 311109/2023-3, LFMR) and by the Coordination for the Improvement of Higher Education Personnel (CAPES \u0026ndash; Grant N. 88887.816284/2023-00, KSC; CAPES \u0026ndash; Grant N. 88887.008165/2024-00)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. maio de 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. CANCER TODAY. 2022 [citado 22 de junho de 2025]. Mortality, Females, in 2022. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/today/en/dataviz/tables?mode=population\u0026amp;\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/today/en/dataviz/tables?mode=population\u0026amp;\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003esexes=2\u0026amp;cancers=20\u0026amp;types=1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. GLOBAL CANCER OBSERVATORY. 2022 [citado 22 de junho de 2025]. Incidence, Mortality and Prevalence by cancer site. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.who.int/media/globocan/factsheets/populations/76-brazil-fact-sheet.pdf\u003c/span\u003e\u003cspan address=\"https://gco.iarc.who.int/media/globocan/factsheets/populations/76-brazil-fact-sheet.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Plano de A\u0026ccedil;\u0026otilde;es Estrat\u0026eacute;gicas para o Enfrentamento das Doen\u0026ccedil;as Cr\u0026ocirc;nicas e Agravos n\u0026atilde;o Transmiss\u0026iacute;veis no Brasil 2021\u0026ndash;2030 [Internet]. Bras\u0026iacute;lia, DF: Minist\u0026eacute;rio da Sa\u0026uacute;de; 2021 [citado 12 de mar\u0026ccedil;o de 2024]. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/doencas-cronicas-nao-transmissiveis-dcnt/09-plano-de-dant-2022_2030.pdf\u003c/span\u003e\u003cspan address=\"https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/doencas-cronicas-nao-transmissiveis-dcnt/09-plano-de-dant-2022_2030.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaccarella S, Lortet-Tieulent J, Saracci R, Fidler MM, Conway DI, Vilahur N, et al. Reducing Social Inequalities in Cancer: Setting Priorities for Research. CA Cancer J Clin. setembro de 2018;68(5):324\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInstituto Nacional de C\u0026acirc;ncer (Brasil). Estimativa 2023: incid\u0026ecirc;ncia de c\u0026acirc;ncer no Brasil. Rio de Janeiro, RJ: Instituto Nacional De C\u0026acirc;ncer; 2023. 160 p.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCabral ALLV, Giatti L, Mart\u0026iacute;nez-Hern\u0026aacute;ez \u0026Aacute;, Cherchiglia ML. Inequality in breast cancer care in a Brazilian capital city: a comparative analysis of narratives. Int J Equity Health. dezembro de 2019;18(1):88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira MDC, Arroyave I, Barros MBDA. Social inequalities in male cancer in a metropolis in the Southeast region of Brazil. Rev Sa\u0026uacute;de P\u0026uacute;blica. 22 de junho de 2023;57(1):38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiessen LW, Mohan D, Akuoku JK, Mirelman AJ, Ahmed S, Koehlmoos TP, et al. Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the Sustainable Development agenda. The Lancet. maio de 2018;391(10134):2036\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos TBD, Borges AKDM, Ferreira JD, Meira KC, Souza MCD, Guimar\u0026atilde;es RM, et al. Prevalence and factors associated to advanced stage breast cancer diagnosis. Ci\u0026ecirc;nc Sa\u0026uacute;de Coletiva. fevereiro de 2022;27(2):471\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos-Silva I, Stavola BL, Renna NL, Nogueira MC, Aquino EML, Bustamante-Teixeira MT, et al. Ethnoracial and social trends in breast cancer staging at diagnosis in Brazil, 2001\u0026ndash;14: a case only analysis. Lancet Glob Health. junho de 2019;7(6):e784\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira NPD, De Camargo Cancela M, Martins LFL, De Souza DLB. A multilevel assessment of the social determinants associated with the late stage diagnosis of breast cancer. Sci Rep. 1\u003csup\u003eo\u003c/sup\u003e de fevereiro de 2021;11(1):2712.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuranello MC, Meirelles MCCC, Walsh IAPD, Pereira GDA, Castro SSD. Breast cancer screening practice and associated factors: Women\u0026rsquo;s Health Survey in Uberaba MG Brazil, 2014. Ci\u0026ecirc;nc Sa\u0026uacute;de Coletiva. agosto de 2018;23(8):2661\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStopa SR, Szwarcwald CL, Oliveira MMD, Gouvea EDCDP, Vieira MLFP, Freitas MPSD, et al. Pesquisa Nacional de Sa\u0026uacute;de 2019: hist\u0026oacute;rico, m\u0026eacute;todos e perspectivas. Epidemiol E Servi\u0026ccedil;os Sa\u0026uacute;de. 2020;29(5):e2020315.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorr\u0026agrave;s JM, Guillen M, Sanchez V, Junc\u0026agrave; S, Vicente R. Educational level, voluntary private health insurance and opportunistic cancer screening among women in Catalonia (Spain): Eur J Cancer Prev. outubro de 1999;8(5):427\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite K, Haas JS, Williams DR. Elucidating the Role of Place in Health Care Disparities: The Example of Racial/Ethnic Residential Segregation. Health Serv Res. junho de 2012;47(3pt2):1278\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira SB, Soares DA. Access to breast cancer care in a municipality in Bahia: the perspective of users, workers and managers. Sa\u0026uacute;de Em Debate. mar\u0026ccedil;o de 2020;44(124):169\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi E, Lee YY, Suh M, Lee EY, Mai TTX, Ki M, et al. Socioeconomic Inequalities in Cervical and Breast Cancer Screening among Women in Korea, 2005\u0026ndash;2015. Yonsei Med J. 2018;59(9):1026.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNabaa\u0026rsquo; HA, Shelleh N. Barriers preventing Palestinian women from having a mammogram: a qualitative study. The Lancet. fevereiro de 2018;391:S16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Pol\u0026iacute;tica nacional de sa\u0026uacute;de integral da popula\u0026ccedil;\u0026atilde;o negra: uma pol\u0026iacute;tica para o SUS [Internet]. Minist\u0026eacute;rio da Sa\u0026uacute;de; 2016. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_saude_populacao_negra_3d.pdf\u003c/span\u003e\u003cspan address=\"https://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_saude_populacao_negra_3d.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilva MTAD, Silva J\u0026uacute;nior VBD, Mangueira JDO, Gurgel Junior GD, Leal EMM. Distribution of mammograms and mammography offering in relation to the parametric care of the Public Health Care System in Pernambuco. Rev Bras Sa\u0026uacute;de Materno Infant. setembro de 2018;18(3):609\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCabral ALLV, Giatti L, Casale C, Cherchiglia ML. Social vulnerability and breast cancer: differentials in the interval between diagnosis and treatment of women with different sociodemographic profiles. Ci\u0026ecirc;nc Sa\u0026uacute;de Coletiva. fevereiro de 2019;24(2):613\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarros \u0026Acirc;F, Ara\u0026uacute;jo JMD, Murta-Nascimento C, Dias A. Clinical pathways of breast cancer patients treated in the Federal District, Brazil. Rev Sa\u0026uacute;de P\u0026uacute;blica. 30 de janeiro de 2019;53:14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. janeiro de 2019;69(1):7\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen JX, Gong Y, Ling H, Hu X, Shao ZM. Racial/ethnic differences in the outcomes of patients with metastatic breast cancer: contributions of demographic, socioeconomic, tumor and metastatic characteristics. Breast Cancer Res Treat. janeiro de 2019;173(1):225\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllis L, Canchola AJ, Spiegel D, Ladabaum U, Haile R, Gomez SL. Racial and Ethnic Disparities in Cancer Survival: The Contribution of Tumor, Sociodemographic, Institutional, and Neighborhood Characteristics. J Clin Oncol. 1\u003csup\u003eo\u003c/sup\u003e de janeiro de 2018;36(1):25\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNogueira MC, Guerra MR, Cintra JRD, Corr\u0026ecirc;a CSL, Fayer VA, Bustamante-Teixeira MT. Racial disparity in 10-year breast cancer survival: a mediation analysis using potential responses approach. Cad Sa\u0026uacute;de P\u0026uacute;blica [Internet]. 6 de setembro de 2018 [citado 13 de mar\u0026ccedil;o de 2024];34(9). Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.scielo.br/scielo.php?script=sci_arttext\u003c/span\u003e\u003cspan address=\"http://www.scielo.br/scielo.php?script=sci_arttext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026amp;pid=S0102-311X2018000905007\u0026amp;lng=pt\u0026amp;tlng=pt\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLemos LLP, Souza MC, Guerra AA, Piazza T, Ara\u0026uacute;jo RM, Cherchiglia ML. Racial disparities in breast cancer survival after treatment initiation in Brazil: a nationwide cohort study. Lancet Glob Health. fevereiro de 2024;12(2):e292\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams D, Cooper L. Reducing Racial Inequities in Health: Using What We Already Know to Take Action. Int J Environ Res Public Health. 19 de fevereiro de 2019;16(4):606.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaller CJ, Wang L, Brawley OW. Racial Inequality in Prostate Cancer Outcomes\u0026mdash;Socioeconomics, Not Biology. JAMA Oncol. 1\u003csup\u003eo\u003c/sup\u003e de julho de 2019;5(7):983.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLopes VAS, Ribeiro JM. Cervical cancer control limiting factors and facilitators: a literature review. Ci\u0026ecirc;nc Sa\u0026uacute;de Coletiva. setembro de 2019;24(9):3431\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrasil. Lei N\u003csup\u003eo\u003c/sup\u003e 8.080, de 19 de setembro de 1990. [Internet]. Lei N\u003csup\u003eo\u003c/sup\u003e 8.080, de 19 de setembro de 1990. 1990. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.planalto.gov.br/ccivil_03/leis/l8080.htm\u003c/span\u003e\u003cspan address=\"https://www.planalto.gov.br/ccivil_03/leis/l8080.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConstante HM, Bastos JL. Mapping the Margins in Health Services Research: How Does Race Intersect With Gender and Socioeconomic Status to Shape Difficulty Accessing HealthCare Among Unequal Brazilian States? Int J Health Serv. abril de 2021;51(2):155\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLazar M, Davenport L. Barriers to Health Care Access for Low Income Families: A Review of Literature. J Community Health Nurs. 2 de janeiro de 2018;35(1):28\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmato O, Guarneri V, Girardi F. Epidemiology trends and progress in breast cancer survival: earlier diagnosis, new therapeutics. Curr Opin Oncol. novembro de 2023;35(6):612\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Federation of Medical Students\u0026rsquo; Associations. IFMSA Policy Document Ethnicity and Health [Internet]. 2018. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ifmsa.org/wp-content/uploads/2021/02/IFMSA-policy-document-on-Ethnicity-and-Health-AM18.pdf\u003c/span\u003e\u003cspan address=\"https://ifmsa.org/wp-content/uploads/2021/02/IFMSA-policy-document-on-Ethnicity-and-Health-AM18.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSengupta A, Bodini C, Franco S. Struggles for Health: An Emancipatory Approach in the Era of Neoliberal Globalization. Development. dezembro de 2018;61(1\u0026ndash;4):101\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoughlin SS. Social determinants of colorectal cancer risk, stage, and survival: a systematic review. Int J Colorectal Dis. junho de 2020;35(6):985\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNogueira MC, Fayer VA, Corr\u0026ecirc;a CSL, Guerra MR, Stavola BD, dos-Santos-Silva I, et al. Inequities in access to mammographic screening in Brazil. Cad Sa\u0026uacute;de P\u0026uacute;blica. 2019;35(6):e00099817.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira FD, Bastos JL, Moretti-Pires RO. Intersectionality, discrimination, and quality of life in the adult population in Florian\u0026oacute;polis, Southern Brazil. Cad Sa\u0026uacute;de P\u0026uacute;blica. 2021;37(11):e00042320.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaeyens-Fern\u0026aacute;ndez JA, Molina-Portillo E, Poll\u0026aacute;n M, Rodr\u0026iacute;guez-Barranco M, Del Moral R, Arribas-Mir L, et al. Trends in incidence, mortality and survival in women with breast cancer from 1985 to 2012 in Granada, Spain: a population-based study. BMC Cancer. dezembro de 2018;18(1):781.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodrigues GM, Carmo CND, Bergmann A, Mattos IE. Racial inequalities in advanced clinical staging in women with breast cancer treated at a referral hospital in Rio de Janeiro, Brazil. Sa\u0026uacute;de E Soc. 2021;30(3):e200813.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArruda NM, Maia AG, Alves LC. Inequality in access to health services between urban and rural areas in Brazil: a disaggregation of factors from 1998 to 2008. Cad Sa\u0026uacute;de P\u0026uacute;blica [Internet]. 21 de junho de 2018 [citado 13 de mar\u0026ccedil;o de 2024];34(6). Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.scielo.br/scielo.php?script=sci_arttext\u003c/span\u003e\u003cspan address=\"http://www.scielo.br/scielo.php?script=sci_arttext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026amp;pid=S0102-311X2018000605003\u0026amp;lng=pt\u0026amp;tlng=pt\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDasgupta P, Baade PD, Aitken JF, Ralph N, Chambers SK, Dunn J. Geographical Variations in Prostate Cancer Outcomes: A Systematic Review of International Evidence. Front Oncol. 8 de abril de 2019;9:238.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalv\u0026atilde;o JR, Almeida PFD, Santos AMD, Fernandes NFS. Care trajectories of users through PHC in a health region: free traffic, slow points and stops. Physis Rev Sa\u0026uacute;de Coletiva. 2019;29(4):e290404.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeroni FMA, Lindelow M, Souza DOD, Sjoblom M. Realizing the right to health in Brazil\u0026rsquo;s Unified Health System through the lens of breast and cervical cancer. Int J Equity Health. dezembro de 2019;18(1):39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoofi M, Karami-Matin B, Najafi F, Naghshbandi P, Soltani S. Decomposing socioeconomic disparity in the utilization of screening mammography: A cross-sectional analysis from the RaNCD cohort study. Health Care Women Int. 2 de setembro de 2023;44(9):1092\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalta DC, Prates EJS, Silva AGD, Santos FMD, Oliveira GDC, Vasconcelos NMD, et al. Inequalities in mammography and Papanicolaou test coverage: a time-series study. Sao Paulo Med J. dezembro de 2020;138(6):475\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026auml;fer AA, Santos LP, Miranda VIA, Tomasi CD, Soratto J, Quadra MR, et al. Regional and social inequalities in mammography and Papanicolaou tests in Brazilian state capitals in 2019: a cross-sectional study. Epidemiol E Servi\u0026ccedil;os Sa\u0026uacute;de. 2021;30(4):e2021172.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePereira CN, Castro CND. TD 2632 - Educa\u0026ccedil;\u0026atilde;o no Meio Rural: diferenciais entre o rural e o urbano. Texto Para Discuss\u0026atilde;o. 1\u003csup\u003eo\u003c/sup\u003e de mar\u0026ccedil;o de 2021;1\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurani SS, McCoy RG, Lampman MA, Doubeni CA, Finney Rutten LJ, Inselman JW, et al. Association of Neighborhood Measures of Social Determinants of Health With Breast, Cervical, and Colorectal Cancer Screening Rates in the US Midwest. JAMA Netw Open. 9 de mar\u0026ccedil;o de 2020;3(3):e200618.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntabe R, Kansanga M, Sano Y, Kyeremeh E, Galaa Y. Utilization of breast cancer screening in Kenya: what are the determinants? BMC Health Serv Res. dezembro de 2020;20(1):228.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdami H, Kalager M, Valdimarsdottir U, Bretthauer M, Ioannidis JPA. Time to abandon early detection cancer screening. Eur J Clin Invest. mar\u0026ccedil;o de 2019;49(3):e13062.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026oslash;tzsche PC, J\u0026oslash;rgensen KJ. Screening for breast cancer with mammography. Cochrane Breast Cancer Group, organizador. Cochrane Database Syst Rev [Internet]. 4 de junho de 2013 [citado 23 de abril de 2025];2013(6). Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.wiley.com/\u003c/span\u003e\u003cspan address=\"http://doi.wiley.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD001877.pub5\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD001877.pub5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoloshin S, J\u0026oslash;rgensen KJ, Hwang S, Welch HG. The New USPSTF Mammography Recommendations \u0026mdash; A Dissenting View. N Engl J Med. 21 de setembro de 2023;389(12):1061\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Breast Cancer, Health Inequalities, Social Determinants of Health","lastPublishedDoi":"10.21203/rs.3.rs-7236382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7236382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to describe educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women using data from the 2019 Brazilian National Health Survey. The sample included women aged 50 to 69 years. The outcomes were breast cancer diagnosis, mammography screening, and clinical breast examination (CBE), with skin color, area of residence, and educational level as equity stratifiers. Simple and complex measures of inequality were applied, including the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII). Inequality measures indicated that access to mammography referrals, screening, and CBE was higher among White women, urban residents, and those with higher education levels. Women with higher education who identified as White (2.6%), Brown (3.4%), or lived in urban areas (3.1%) had a higher prevalence of breast cancer diagnosis compared to Black women (2.3%) and rural residents (1.9%) with the same education level. These findings suggest that breast cancer screening and diagnosis disproportionately benefit White, highly educated, and urban-dwelling women. The results underscore the need for equitable screening programs, awareness campaigns, and public health policies targeting structurally disadvantaged groups to reduce persistent disparities in breast cancer outcomes.\u003c/p\u003e","manuscriptTitle":"Educational, racial, and geographic inequalities in breast cancer screening and diagnosis among Brazilian women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:47:16","doi":"10.21203/rs.3.rs-7236382/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":"4dd6831e-deb7-4f14-8c37-60098bbbfd4c","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53131722,"name":"Biological sciences/Cancer"},{"id":53131723,"name":"Health sciences/Diseases"},{"id":53131724,"name":"Health sciences/Health care"},{"id":53131725,"name":"Health sciences/Medical research"},{"id":53131726,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-09-08T13:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 13:47:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7236382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7236382","identity":"rs-7236382","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}