Breast Cancer Screening Rates and Influencing Factors Among LGBTQ Groups in Japan

Breast Cancer Screening Rates and Influencing Factors Among LGBTQ Groups in Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Breast Cancer Screening Rates and Influencing Factors Among LGBTQ Groups in Japan Akemi Hara, Akihiko Ozaki, Michio Murakami, Hiroaki Saito, Mika Nashimoto, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5123934/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Breast Cancer → Version 1 posted 5 You are reading this latest preprint version Abstract Background Lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals face significant health disparities worldwide, particularly in preventive medicine. In Japan, where breast cancer has the highest incidence rate among women, understanding screening behaviors among LGBTQ individuals is crucial for improving public health outcomes. Objective This study aimed to elucidate the relationship between LGBTQ status and breast cancer screening behaviors in Japan, identifying factors influencing screening uptake and highlighting challenges in health management for the LGBTQ community. Methods Using data from the Japan COVID-19 and Society Internet Survey (JACSIS), we analyzed breast cancer screening status among 11,056 biological females. Multinomial logistic regression and stratified regression analyzes were employed to examine factors associated with screening behavior, comparing LGBTQ and non-LGBTQ groups. Results LGBTQ individuals demonstrated significantly lower odds of undergoing breast cancer screening (OR 0.82, 95% CI 0.73-0.91, p<0.001) compared to non-LGBTQ individuals. Key factors influencing lower screening rates among individuals in the survey were primarily linked to LGBTQ identity, followed by higher rates of being uninsured, unmarried status, lower income levels, alcohol consumption. Stratified analysis revealed that uninsured LGBTQ individuals had significantly lower odds of screening (OR 0.23, 95% CI 0.08-0.70, p=0.01) compared to those with national health insurance. Conclusion This study highlights significant disparities in breast cancer screening behaviors between LGBTQ and non-LGBTQ individuals in Japan. Findings underscore the need for targeted interventions, including improved healthcare access, LGBTQ-friendly medical environments, and awareness campaigns to address these disparities and promote health equity within the LGBTQ community. Sexual and Gender Minorities Early Detection of Cancer Breast Neoplasms Health Services Accessibility Health Care Disparities Introduction Numerous studies have demonstrated that lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals face significant health disparities worldwide. 1 These disparities are attributed to various factors, including social stigma, discrimination, barriers to healthcare access, and difficulties in communication with healthcare providers. 2 These issues are particularly pronounced in the field of preventive medicine. 3 Breast cancer screening exemplifies this disparity, with international research indicating that LGBTQ women tend to have lower screening rates compared to their heterosexual counterparts. 4 For instance, studies in the United States have reported that lesbian and bisexual women have breast cancer screening rates 10–20% lower than heterosexual women. 5 Moreover, transgender women (male-to-female) face an additional challenge: despite an increased risk of breast cancer due to hormone therapy, appropriate screening guidelines have not been established. 6 In the Japanese context, breast cancer has the highest incidence rate among women, and early detection and treatment are crucial for improving prognosis. 7 While biennial breast cancer screening is recommended for women aged 40 and above in Japan, the screening rate in 2019 was approximately 45%, which is considerably lower than in Western countries. 8 Recent studies indicate that awareness and acceptance of diverse sexual orientations are increasing globally, including in Japan. A survey conducted by Dentsu in 2020 estimated that approximately 8.9% of the Japanese population identifies as LGBTQ, 9 reflecting a significant portion of society that cannot be overlooked in public health discussions. This highlights the importance of understanding and addressing the unique healthcare needs and disparities faced by LGBTQ individuals, particularly in preventive health measures such as breast cancer screening. Research on breast cancer screening behaviors among LGBTQ individuals in Japan is extremely limited. Given Japan's unique cultural background, healthcare system, and social perceptions of LGBTQ individuals, it would be inappropriate to directly apply findings from international studies. In recent years, discussions about LGBTQ rights and social inclusion have intensified in Japan, with increasing demands for LGBTQ-friendly environments in the medical field. 10 Against this social backdrop, investigating the health issues of LGBTQ individuals in the Japanese context, particularly regarding crucial preventive care such as breast cancer screening, holds significant implications for future healthcare policies and practices. Based on this background, this study aimed to examine the association between LGBTQ status and breast cancer screening participation (including future intentions) in Japan using JACSIS data. We also sought to identify factors influencing current and intended screening behaviors across LGBTQ and non-LGBTQ populations. Materials and Methods Settings and Participants This study utilized data from the Japan COVID-19 and Society Internet Survey study (JACSIS), an ongoing cohort study designed to achieve a nationally representative sample through stratified sampling. The survey, conducted in 2023, recruited participants from a large pool maintained by a Japanese online research company. Although efforts were made to ensure representativeness, the use of an internet-based survey may lead to underrepresentation of populations with limited internet access or lower digital literacy, such as older adults or individuals from lower socio-economic backgrounds. From the total participant response data (33,000), invalid responses (4,519) were excluded. We further excluded biological males (14,071) and biological females who did not answer breast cancer screening questions (3,354), resulting in 11,056 valid responses from biological females included in the analysis. Web-based informed consent was obtained, ensuring ethically appropriate procedures. While 33,000 responses provide a substantial dataset, the generalizability of the results to the broader Japanese population requires cautious interpretation due to potential selection bias. Future studies could improve representativeness by applying statistical weighting techniques or comparing findings with other national surveys. Breast cancer screening in Japan Breast cancer screening in Japan is recommended once every two years for women aged 40 and over. 11 It is broadly divided into two categories: population-based screening conducted by municipalities and opportunistic screening that individuals undergo voluntarily. The national government recommends mammography alone as the screening method, and population-based screening generally follows this guideline, 12 though, ultimately, local governments have discretion in this matter. However, opportunistic screening may involve combinations with other tests or use other tests exclusively. The breast cancer screening rate in Japan is low compared to other developed countries, remaining at about 45%. Although the recommended age for breast cancer screening in Japan is 40 and above, this study included participants under 40 to capture their screening intentions and identify potential barriers to screening at earlier ages. Understanding these behaviors is crucial for designing interventions that may increase screening participation in the future. Outcome Varia ble The primary outcome measure pertained to breast cancer screening status over the past two years, as indicated in the 2023 JACSIS survey. Participants were asked whether they had undergone breast cancer screening procedures, such as mammography or breast ultrasound, within the preceding two-year period. Responses were categorized into three distinct groups: "screened," "attempted but not screened," and "not screened." This change reflects that some individuals had the intention to undergo screening but were unable to complete it. Additionally, among those who were screened, results were further categorized into "No abnormality," "Abnormality," and "Unknown result," reflecting the outcomes of the screening procedures. Exposure Variable In this study, we adopted the following exposure variables potentially associated with breast cancer screening uptake (Table 1 ): LGBTQ status, insurance enrollment status, marital status, annual household income, alcohol consumption frequency, smoking status, and age. Regarding the LGBTQ variable, the questionnaire offered six response options for sexual orientation and gender identity: “heterosexual,” “homosexual,” “bisexual,” “other,” “undecided,” and “unsure.” We categorized respondents who selected "heterosexual" as the "non-LGBTQ group" (n = 9,034), while all other responses were classified as the "LGBTQ group" (n = 2,022) based on their respective LGBTQ definitions. We considered those who answered "undecided" or "unsure" as part of the LGBTQ group, categorizing them as queer. However, we acknowledge the possibility that some respondents who answered "unsure" may have been unsure how to answer the question or may not have understood its meaning. Insurance enrollment status was categorized as “National Health Insurance,” “Employee’s Health Insurance,” “Public Assistance,” “Uninsured,” and “Other.” Marital status was dichotomized into "married" and "unmarried," with individuals in common-law partnerships and de facto marriages being classified under the "married" category. Annual household income was stratified into four categories: “less than 5 million yen,” “5–10 million yen,” “10 million yen or more,” and “unknown.” Alcohol consumption frequency was dichotomized into “non-drinker” and “drinker.” Smoking status was similarly dichotomized into “non-smoker” and “smoker.” Age was also added as a variable, divided into categories starting from 30 years old, with each category representing a 10-year age range (e.g., “30–39 years,” “40–49 years,” “50–59 years,” and so on). These variables were selected based on previous literature as socio-demographic and health-related factors associated with breast cancer screening behavior. For instance, insurance enrollment status is considered to influence healthcare access, playing a crucial role in breast cancer screening uptake. 13 Marital status may affect health behaviors and potentially relate to screening rates. 14 Alcohol consumption frequency has also been associated with breast cancer risk in prior studies. 15 We aimed to elucidate factors influencing breast cancer screening behavior among the LGBTQ population using selected exposure variables, while also assessing the effects of LGBTQ status on breast cancer screening rates after adjusting for relevant background factors. Data Analysis Initially, we analyzed the proportional distribution of non-LGBTQ and LGBTQ groups for each independent variable using descriptive statistics. Specifically, we calculated the percentages of each variable category (e.g., breast cancer screening status, health insurance enrollment status, annual household income) for both LGBTQ and non-LGBTQ groups, elucidating the distributional differences between the two groups. Subsequently, as both the dependent and independent variables were categorical, we conducted chi-square tests to evaluate the impact of LGBTQ status on each variable. Additionally, we used chi-square tests to assess whether LGBTQ identity had a significant effect on breast cancer screening outcomes. Second, to assess factors associated with breast cancer screening status, we employed multivariable multinomial logistic regression analysis. In this analysis, we set the "non-screened group" as the reference group, with the "screened group" (Group 1) and "intending to be screened group" (Group 2) as comparison groups, evaluating the impact of each factor on screening status. Variables included LGBTQ status, insurance enrollment status, marital status, annual household income, and alcohol consumption frequency, with variable selection performed using the stepwise method. We calculated Odds Ratios (OR) and 95% Confidence Intervals (CI) for each independent variable, quantitatively assessing the relative impact on other groups compared to the reference group. Additionally, we conducted a sensitivity analysis focusing exclusively on participants aged 40 and above, considering that this is the recommended age for routine breast cancer screening in Japan. This analysis aimed to examine whether the associations observed in the primary analysis held consistent within the target population for screening. The results of the sensitivity analysis were compared with the overall analysis to validate the robustness of our findings. To address multicollinearity concerns, we calculated Variance Inflation Factors (VIF) to check for collinearity issues among explanatory variables. This process ensures the appropriate construction of the model and the reliability of the results. Lastly, we performed stratified regression analyzes for both LGBTQ and non-LGBTQ groups to explore potential differences in the factors influencing breast cancer screening outcomes. This analysis allowed us to evaluate how independent variables such as insurance status, marital status, and annual household income impacted the screening behaviors of each group. Model significance was verified using Wald tests to determine whether the independent variables had statistically significant effects on breast cancer screening behavior. The results of the regression analyzes were interpreted using odds ratios and confidence intervals, with P-values less than 0.05 considered indicative of a significant association. To validate our classification of the LGBTQ groups, we conducted two analyzes. First, we performed a stratified analysis within the LGBTQ group to examine potential differences among subgroups (homosexual, bisexual, other, undecided, and unsure). Second, to assess the validity of classifying "undecided" and "unsure" respondents as queer, we repeated the multivariable multinomial logistic regression analysis using two separate models: one excluding the "unsure" group, and another treating the "unsure" respondents as a distinct category. These results will be presented as supplementary materials. All data analyzes were conducted using R version 4.2.1, based on statistically valid methods. Ethics Statements This study received ethical approval from the Osaka International Cancer Institute. The initial approval, under the study title "Evaluation of Social and Health Disparities Caused by the COVID-19 Pandemic in Japan," was granted on June 19, 2020 (Approval Number: 20084), prior to the implementation of the survey. The current research, an extension of the original study, received additional approval (Approval Number: 20084-12) on March 28, 2024., and subsequently from the Tohoku University Graduate School of Medicine Ethics Committee (Approval Numbers: 2024-1-231 on June 27, 2024, and 2024-1-517 on October 22, 2024), in accordance with the 2020 ethical guidelines prior to the initiation of survey activities. Results Table 1 presents the descriptive statistics. The breast cancer screening rate among the LGBTQ population was slightly lower compared to the non-LGBTQ population (43.4% vs 45.9%, p < 0.001). Age distributions revealed a higher percentage of individuals in their 70s within the LGBTQ group compared to the non-LGBTQ group (21.7% vs 17.6%, p < 0.001). The LGBTQ group had a lower rate of employee health insurance coverage (45.3% vs 54.3%, p < 0.001), while their participation in national health insurance was higher (49.0% vs 43.5%, p < 0.001). A higher proportion of non-drinkers was observed in the LGBTQ group (66.0% vs 58.0%, p < 0.001), along with a higher percentage of unmarried individuals (58.9% vs 67.0%, p < 0.001). The LGBTQ population had a lower representation in high-income brackets (annual income exceeding 10 million yen: 4.4% vs 8.1%, p < 0.001), and a higher proportion of individuals who did not disclose their income (36.9% vs 24.3%, p < 0.001). The percentage of smokers in the LGBTQ group was slightly higher than in the non-LGBTQ group (10.7% vs 9.3%, p = 0.11). Due to the chi-square test results showing p-values less than 0.001, it is evident that LGBTQ and non-LGBTQ identities contribute to differences in screening outcomes. Table 1 Demographic Characteristics and Breast Cancer Screening Status of LGBTQ and Non-LGBTQ Participants VIF Total n (%) LGBTQ n (%) Non-LGBTQ n (%) Chi-Square Test Overall - 11056 (100.0) 2022 (18.3) 9034 (81.7) - Age 29.93 < 0.001 30s - 2123 (19.2) 378 (18.7) 1745 (19.3) 40s - 2249 (20.3) 396 (19.6) 1853 (20.5) 50s - 2190 (19.8) 347 (17.2) 1843 (20.4) 60s - 2218 (20.1) 397 (19.6) 1821 (20.2) 70s - 2027 (18.3) 438 (21.7) 1589 (17.6) 80s - 249 (2.3) 66 (3.3) 183 (2.0) Breast Cancer Screening - - - - < 0.001 Screened - 5023 (45.4) 878 (43.4) 4145 (45.9) - └─ No abnormality - 4637 (92.3) 813 (92.6) 3824 (92.3) < 0.001 └─ Abnormality - 247 (4.9) 38 (4.3) 209 (5.0) - └─ Unknown result - 139 (2.8) 27 (3.1) 112 (2.7) - Planned Screening - 2450 (22.2) 331 (16.4) 2119 (23.5) - No Screening - 3583 (32.4) 813 (40.2) 2770 (30.7) - LGBTQ Status 1.53 - - - < 0.001 Non-LGBTQ - 9034 (81.7) 0 (0.0) 9034 (100.0) - LGBTQ - 2022 (18.3) 2022 (100.0) - - └─ Lesbian - 25 (1.2) 25 (1.2) - - └─ Bisexual - 85 (4.2) 85 (4.2) - - └─ Other - 428 (21.2) 428 (21.2) - - └─ Undecided - 499 (24.7) 499 (24.7) - - └─ Unsure - 985 (48.7) 985 (48.7) - - Insurance Status 4.49 - - - < 0.001 National Health Insurance - 4919 (44.5) 991 (49.0) 3928 (43.5) - Employee's Health Insurance - 5817 (52.6) 916 (45.3) 4901 (54.3) - Public Assistance - 61 (0.6) 17 (0.8) 44 (0.5) - Other - 57 (0.5) 21 (1.0) 36 (0.4) - Uninsured - 202 (1.8) 77 (3.8) 125 (1.4) - Marital Status 4.18 - - - < 0.001 Unmarried - 7243 (65.5) 1190 (58.9) 6053 (67.0) - Married - 3813 (34.5) 832 (41.1) 2981 (33.0) - Annual Household Income (JPY) 8.38 - - - < 0.001 10 million - 822 (7.4) 89 (4.4) 733 (8.1) - Unknown - 2939 (26.6) 746 (36.9) 2193 (24.3) - Alcohol Consumption 2.31 - - - < 0.001 Non-drinker - 6571 (59.4) 1334 (66.0) 5237 (58.0) - Drinker - 4485 (40.6) 688 (34.0) 3797 (42.0) - Smoking status 1.46 0.11 No-smokers - 10000 (90.4) 1806 (89.3) 8194 (90.7) - Smokers - 1056 (9.6) 216 (10.7) 840 (9.3) - Table 2 presents results of multivariable multinomial logistic regression analysis. LGBTQ individuals had lower odds of screening (OR 0.82, 95% CI 0.73–0.91, p < 0.001) and screening intention (OR 0.59, 95% CI 0.52–0.68, p < 0.001). Those with employee health insurance showed higher odds for both outcomes (screening: OR 1.61, 95% CI 1.47–1.76, p < 0.001; intention: OR 1.43, 95% CI 1.28–1.60, p < 0.001). Public assistance recipients had lower screening odds (OR 0.51, 95% CI 0.26–0.98, p = 0.04), while uninsured individuals also had lower screening odds (OR 0.36, 95% CI 0.19–0.70, p = 0.003). Married individuals had higher odds for both outcomes (screening: OR 1.20, 95% CI 1.09–1.32, p < 0.001; intention: OR 1.30, 95% CI 1.16–1.46, p < 0.001). Higher income was associated with increased screening odds (5–10 million yen: OR 1.34, 95% CI 1.19–1.51, p 10 million yen: OR 1.94, 95% CI 1.60–2.35, p < 0.001). Drinkers had higher odds for both outcomes (screening: OR 1.25, 95% CI 1.15–1.37, p < 0.001; intention: OR 1.18, 95% CI 1.06–1.31, p = 0.003). Smoking status showed a negative association with screening odds, with smokers having lower odds of screening compared to non-smokers (OR 0.76, 95% CI 0.65–0.88, p < 0.001), while no significant association was observed for screening intention (OR 1.09, 95% CI 0.92–1.29, p = 0.31).Variance Inflation Factors (VIF) were calculated to assess multicollinearity. The Age variable had a VIF of 29.9, indicating a high level of multicollinearity, so it was excluded from the final model. All other variables had VIF values below 10, with household income showing the highest at 8.38. These results indicate acceptable levels of independence among variables, with no significant multicollinearity concerns. Furthermore, results from the sensitivity analysis presented in Supplementary Table 2, focusing on participants aged 40 and above, were consistent with the findings of the primary analysis. This consistency demonstrates the robustness and stability of the multivariable multinomial logistic regression model. Table 2 Demographic Characteristics and Breast Cancer Screening Status of LGBTQ and Non-LGBTQ Participants Screened Group (n = 5,023) vs. Non-screened Group Intending to Screen Group (n = 2,450) vs. Non-screened Group Odds Ratio 95% CI p-values Odds Ratio 95% CI p-values LGBTQ Status Non-LGBTQ Reference Reference LGBTQ 0.82 0.73–0.91 < 0.001 0.59 0.52–0.68 < 0.001 Insurance Status National Health Insurance Reference Reference Employee's Health Insurance 1.61 1.47–1.76 < 0.001 1.43 1.28–1.60 < 0.001 Public Assistance 0.51 0.26–0.98 0.04 1.24 0.68–2.27 0.48 Other 0.70 0.51–0.96 0.03 0.77 0.52–1.14 0.19 Uninsured 0.36 0.19–0.70 0.003 0.68 0.34–1.36 0.28 Marital Status Unmarried Reference Reference Married 1.20 1.09–1.32 < 0.001 1.30 1.16–1.46 < 0.001 Annual Household Income (JPY) <5 million Reference Reference 5–10 million 1.34 1.19–1.51 < 0.001 1.30 1.13–1.49 10 million 1.94 1.60–2.35 < 0.001 1.25 0.99–1.58 0.06 Unknown 1.11 0.99–1.23 0.06 0.91 0.80–1.04 0.16 Alcohol Consumption Non-drinker Reference Reference Drinker 1.25 1.15–1.37 < 0.001 1.18 1.06–1.31 0.003 Smoking status No-smokers Reference Reference Smokers 0.76 0.65–0.88 < 0.001 1.09 0.92–1.29 0.31 Table 3 compares factors associated with breast cancer screening and intention to screen between LGBTQ and non-LGBTQ groups. In the LGBTQ group, uninsured individuals had significantly lower odds of screening (OR 0.23, 95% CI 0.08–0.70, p = 0.01), and the "other" insurance category also had lower odds of screening (OR 0.46, 95% CI 0.27–0.77, p = 0.003). Marital status (OR 1.48, 95% CI 1.21–1.81, p < 0.001) and alcohol consumption (OR 1.39, 95% CI 1.13–1.71, p = 0.002) were associated with higher odds of screening. Public assistance (OR 0.26, 95% CI 0.06–1.18, p = 0.08) and smoking (OR 0.84, 95% CI 0.61–1.15, p = 0.28) were not significant, but public assistance showed notably low odds of screening, indicating a potential trend. For intention to screen, only marital status was significant (OR 1.33, 95% CI 1.01–1.74, p = 0.04). Table 3 Stratified Analysis of Factors Influencing Breast Cancer Screening Behavior in LGBTQ and Non-LGBTQ Groups Screened Group (n = 5,023) vs. Non-screened Group Intending to Screen Group (n = 2,450) vs. Non-screened Group Odds Ratio 95%CI p-value Odds Ratio 95%CI p-value LGBTQ Insurance Status National Health Insurance Reference Reference Employee's Health Insurance 1.05 0.86–1.29 0.62 1.27 0.97–1.67 0.08 Public Assistance 0.26 0.06–1.18 0.08 1.61 0.53–4.87 0.40 Other 0.46 0.27–0.77 0.003 0.53 0.25–1.11 0.09 Uninsured 0.23 0.08–0.70 0.01 0.17 0.02–1.29 0.09 Marital Status Unmarried Reference Reference Married 1.48 1.21–1.81 < 0.001 1.33 1.01–1.74 0.04 Annual Household Income (JPY) 10 million 1.19 0.72–1.94 0.50 1.00 0.51–1.96 1.00 Unknown 1.08 0.86–1.34 0.51 1.02 0.76–1.38 0.87 Alcohol Consumption Non-drinker Reference Reference Drinker 1.39 1.13–1.71 0.002 1.13 0.85–1.49 0.40 Smoking status No-smokers Reference Reference Smokers 0.84 0.61–1.15 0.28 1.11 0.74–1.65 0.62 Non-LGBTQ Insurance Status National Health Insurance Reference Reference Employee's Health Insurance 1.79 1.61–1.98 < 0.001 1.49 1.32–1.69 < 0.001 Public Assistance 0.61 0.29–1.27 0.19 1.15 0.56–2.35 0.71 Other 0.85 0.56–1.27 0.42 0.92 0.57–1.48 0.74 Uninsured 0.44 0.19–1.02 0.05 1.00 0.46–2.18 1.00 Marital Status Unmarried Reference Reference Married 1.13 1.02–1.26 0.03 1.29 1.13–1.46 < 0.001 Annual Household Income (JPY) < 5 million Reference Reference 5–10 million 1.35 1.19–1.54 < 0.001 1.30 1.12–1.51 10 million 2.09 1.69–2.59 < 0.001 1.31 1.02–1.68 0.04 Unknown 1.11 0.98–1.26 0.10 0.89 0.76–1.03 0.11 Alcohol Consumption Non-drinker Reference Reference Drinker 1.23 1.11–1.36 < 0.001 1.18 1.05–1.32 0.006 Smoking status No-smokers Reference Reference Smokers 0.74 0.62–0.88 < 0.001 1.08 0.90–1.30 0.41 In the non-LGBTQ group, employee health insurance had a strong positive association with screening (OR 1.79, 95% CI 1.61–1.98, p < 0.001) and intention to screen (OR 1.49, 95% CI 1.32–1.69, p < 0.001). Marital status (OR 1.13, 95% CI 1.02–1.26, p = 0.03), higher income (OR 2.09, 95% CI 1.69–2.59, p < 0.001), alcohol consumption (OR 1.23, 95% CI 1.11–1.36, p < 0.001), and smoking (OR 0.74, 95% CI 0.62–0.88, p < 0.001) were significant for screening. Key differences include the stronger influence of marital status and insurance in the LGBTQ group, while income and smoking were more impactful in the non-LGBTQ group. We summarized the results of the stratified analysis within the LGBTQ group, examining potential differences among subgroups (homosexual, bisexual, other, undecided, and unsure) in Supplementary Table 1. While we observed some variations in characteristics (e.g., insurance type distribution) between subgroups, the small sample sizes precluded drawing statistically significant conclusions about these differences. We detected no significant variations across other background factors. These findings support the validity of analyzing the LGBTQ community as a single category, suggesting that this approach does not introduce substantial classification bias and is appropriate for the study's purposes. We further summarized the results of the multivariable multinomial logistic regression analysis using two separate models in Supplementary Tables 3 and 4: one excluding the "unsure" group, and another treating the "unsure" respondents as a distinct category. In both analyses, the association between gender and breast cancer screening participation and intention to participate was consistent with the main analyses. Discussion While we encountered various methodological challenges, this study represents one of the few investigations in Japan examining healthcare disparities in minority populations, specifically focusing on breast cancer screening among LGBTQ individuals, making it a valuable contribution to the growing body of literature in this field. The lower screening rates observed in our study among LGBTQ participants (43.4% vs. 45.9% for non-LGBTQ, p < 0.001) are consistent with findings from international research. 16 , 17 Studies conducted in the U.S. and Canada have reported similar trends, with LGBTQ individuals consistently showing lower rates of cancer screenings, including breast cancer, compared to their heterosexual counterparts. 18 While the absolute difference in screening rates between LGBTQ and non-LGBTQ groups in this study is small (2.5%) yet statistically significant, its clinical relevance warrants further investigation. Notably, even after adjusting for socioeconomic factors such as income and insurance status, LGBTQ individuals had significantly lower odds of being screened compared to non-LGBTQ individuals (OR 0.82, 95% CI 0.73–0.91, p < 0.001). This adjusted odds ratio highlights that the disparity is not negligible and suggests meaningful differences in screening behavior that require attention. Furthermore, the overall low screening rates observed in both groups underscore the broader need for public health initiatives to improve breast cancer screening participation in Japan. These findings not only highlight the challenges in promoting preventive health behaviors across the population but also suggest disparities that disproportionately affect LGBTQ individuals. Future research should explore the underlying factors contributing to these disparities, including potential structural barriers and cultural attitudes toward LGBTQ individuals in healthcare settings. In addition, qualitative studies could provide deeper insights into the experiences of LGBTQ individuals regarding healthcare access and preventive practices. By addressing these dynamics, targeted interventions can be developed to promote health equity and improve screening rates both within the LGBTQ community and the broader population. Key factors contributing to screening disparities among LGBTQ individuals include insurance status, marital status, and alcohol consumption. LGBTQ participants had higher uninsured rates, with uninsured individuals showing significantly lower odds of screening (OR 0.23, 95% CI 0.08–0.70, p = 0.01). This aligns with international findings where lack of insurance is a key barrier to healthcare access for LGBTQ individuals. 16 , 19 , 20 Marital status had a stronger positive impact on screening behaviors for both LGBTQ and non-LGBTQ individuals, with married individuals more likely to undergo screening compared to their unmarried counterparts. While both LGBTQ and non-LGBTQ married individuals can benefit from their spouse’s encouragement of preventive healthcare, unmarried LGBTQ individuals may face additional societal pressures that hinder medical procedures. Public assistance insurance was associated with lower screening odds (OR 0.26, 95% CI 0.06–1.18, p = 0.08), potentially exacerbated by discrimination or the lack of LGBTQ-friendly healthcare providers. 21 , 22 Furthermore, among the LGBTQ group, individuals with "other" types of insurance had significantly lower odds of undergoing screening compared to those with National Health Insurance (reference group) (OR 0.46, 95% CI 0.27–0.77, p = 0.003). Uninsured individuals also exhibited significantly lower odds of being screened (OR 0.23, 95% CI 0.08–0.70, p = 0.01). These findings suggest that socioeconomic factors, such as insurance coverage and reliance on public assistance, contribute to lower breast cancer screening rates among LGBTQ individuals. For instance, uninsured LGBTQ individuals showed significantly reduced odds of screening compared to those with national health insurance. Prior research also highlights that disparities in screening rates between LGBTQ and non-LGBTQ individuals may stem from factors beyond socioeconomic status , 16 , 17 including societal stigma, limited access to LGBTQ-friendly healthcare environments, and differences in healthcare-seeking behaviors. These broader structural and cultural barriers have been identified as key contributors to health disparities in LGBTQ populations globally. Efforts to improve breast cancer screening rates should address these multifaceted barriers by fostering inclusive healthcare environments and reducing discrimination. Interventions that specifically target uninsured or underinsured individuals, along with strategies to reduce societal stigma, could play a pivotal role in promoting equitable access to preventive healthcare for LGBTQ populations. Alcohol consumption was significantly associated with higher screening odds in the LGBTQ group (OR 1.39, 95% CI 1.13–1.71, p = 0.002), possibly indicating health-conscious behaviors. 18 , 19 Income levels impacted groups differently: higher income significantly increased screening rates in non-LGBTQ populations but not among LGBTQ individuals. 17 This suggests that for LGBTQ individuals, fear of discrimination or lack of LGBTQ-friendly medical environments may play a more significant role than financial barriers in seeking cancer screening. 21 Classifying LGBTQ groups is extremely challenging. After careful consideration, we decided to treat the "unsure" and "undecided" respondents as a single group. Based on the comprehensive results of our sensitivity analyzes, we found that the outcomes were largely consistent across different classification methods, suggesting that our approach was valid. Another method for determining gender involves considering the difference between self-perceived gender and biological sex. While it is difficult to determine which method is superior, evaluating the extent of these differences will likely be necessary in future research. Taken together, these findings highlight the importance of addressing both socioeconomic and identity-specific barriers to healthcare access among LGBTQ individuals in Japan. Initiatives such as diversity education for healthcare providers and the creation of more inclusive medical environments, as seen in countries like the U.S. and Canada, may help reduce the disparities in cancer screening rates for LGBTQ populations. 17 , 18 Further research is needed to develop targeted interventions that address the unique challenges faced by LGBTQ individuals in Japan, particularly those related to insurance coverage, social support, and discrimination in healthcare settings. Implications These findings have important implications for public health policies and interventions in Japan. There is a clear need for targeted approaches to improve breast cancer screening rates among LGBTQ individuals. This may include developing LGBTQ-specific health education programs, creating more inclusive and welcoming healthcare environments, and addressing insurance coverage disparities. Healthcare providers should receive training on LGBTQ health issues and cultural competency to ensure they can provide appropriate and sensitive care. Another important consideration is the distinction between self-identified gender and biological sex, a factor that warrants further discussion in future research. While JASCIS2023 determined gender based on participants' responses to specific questions, JACSIS2022 focused on the difference between self-perceived gender and biological sex. Evaluating this difference is feasible and represents a crucial research question for future studies. This distinction could provide valuable insights into how gender identity and biological sex independently or jointly influence breast cancer screening behaviors and intentions among both LGBTQ and non-LGBTQ populations. In this context, it is important to note that the proportion of LGBTQ individuals in our study (18.3%) was notably higher than both the 2020 Dentsu survey of the Japanese population (8.9%) and a U.S. physician study (approximately 1%). These differences likely reflect our methodological approach. Our online survey provided greater anonymity than face-to-face interviews or official data collection methods, potentially facilitating LGBTQ self-disclosure. Moreover, our sample may have attracted individuals more engaged with health issues, possibly increasing LGBTQ representation compared to general population surveys. LGBTQ self-identification appears to be influenced by question content, survey methodology, study population, and cultural context—aspects that warrant further investigation. Limitations While this study provides valuable insights into breast cancer screening behaviors among the LGBTQ population, it has certain limitations. The cross-sectional design precludes the establishment of causal relationships. Second, as previously noted, this study may not be representative of the general population due to the potential underestimation of social stigma associated with LGBTQ self-identification. Consequently, the interpretation of these findings warrants careful consideration. Additionally, the lack of adjustment for sociodemographic factors such as income and educational attainment may also influence the observed disparities in screening rates. Furthermore, the questionnaire did not include specific questions about factors like gender-affirming surgeries, which may affect breast cancer risk and screening behaviors. It is also difficult to clearly disentangle barriers directly related to LGBTQ identity from broader systemic issues affecting healthcare access. Moreover, while our study categorized income into four levels ( 10 million, and Unknown), education was excluded from the analysis due to high collinearity with other variables. This limited our ability to comprehensively adjust for potential confounding factors. As a result, we cannot definitively conclude the extent to which these variables contribute to the disparities in screening rates. Additional research is necessary to better separate these factors, refine the classification of LGBTQ groups, and address the underlying causes of the observed disparities. Conclusion In conclusion, our study highlights significant disparities in breast cancer screening behaviors between LGBTQ and non-LGBTQ individuals in Japan. These findings emphasize the need for tailored interventions and policies that address the unique challenges faced by the LGBTQ community in accessing preventive healthcare services. By addressing these disparities, we can work towards more equitable health outcomes and improved overall public health in Japan. Declarations Acknowledgements: During the preparation of this work the authors used Claude 3.5, an AI language model developed by Anthropic in order to perform English language proofreading and text revision. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Funding: None Conflicts of interest statement: Dr. Ozaki received personal fees from Medical Network Systems Inc., Kyowa Kirin Company Limited, and Taiho Pharmaceutical Company Limited outside of the submitted work. Dr. Tanimoto received personal fees from Medical Network Systems Inc. and Bionics Company Limited outside of the submitted work. No other disclosures are reported. Authors’ contributions: Conceptualization: all authors; Methodology: Akemi Hara, Akihiko Ozaki, Michio Murakami, Takahiro Tabuchi; Formal analysis and investigation: Akemi Hara; Writing - original draft preparation: Akemi Hara, Akihiko Ozaki; Writing - review and editing: all authors; Funding acquisition: Takahiro Tabuchi; Resources: Akemi Hara; Supervision: Michio Murakami, Takahiro Tabuchi References Swette S, Kelechi T, Haviland KS. Overcoming Barriers to Cancer Screening in Diverse LGBTQ Populations. Cancer Network; 2021. Gorman JR, Usita PM, Madlensky L, Pierce JP. A qualitative investigation of breast cancer survivors' experiences with breastfeeding. J Cancer Surviv. 2010;4(4):310–20. Mayer KH, Bradford JB, Makadon HJ, Stall R, Goldhammer H, Landers S. Sexual and gender minority health: What we know and what needs to be done. Am J Public Health. 2008;98(6):989–95. Boehmer U, Miao X, Ozonoff A, Winter M. Health behaviors of cancer survivors of different sexual orientations. Cancer Causes Control. 2014;25(5):497–504. Heslin KC, Hall JE. Sexual Orientation Disparities in Access to and Utilization of Health Care Services in the United States: 2013–2018. Natl Health Stat Rep. 2020;137:1–15. Deutsch MB. Guidelines for the Primary and Gender-Affirming Care of Transgender and Gender Nonbinary People. Center of Excellence for Transgender Health; 2017. National Cancer Center Japan. (2019). Cancer Statistics in Japan 2019. OECD. Health at a Glance 2019: OECD Indicators. Paris: OECD Publishing; 2019. Dentsu Inc. (2020). LGBTQ Awareness and Attitudes Survey in Japan 2020. Mirza SA, Rooney C. Discrimination Prevents LGBTQ People from Accessing Health Care. Center for American Progress; 2018. Ozaki A, Saito H, Kaneda Y, Sawano T, Nishikawa Y, Murakami M, et al. Long-term uptake rate of a breast cancer screening program in Fukushima, Japan, following the 2011 Triple Disaster: a retrospective observational study. Sci Rep. 2023;13(1):6654. Ministry of Health, Labor and Welfare. Cancer Screening. Cancer Screening [in Japanese] 2024. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000059490.html Johnson CA, Fitzgerald R, McManus P. The impact of sexual orientation and gender identity on cancer screening rates in the United States and Canada. Health Equity J. 2020;4(4):273–81. https://doi.org/10.1089/heq.2020.0019 . Park S, Smith LM, Johnson DE. Barriers to cancer screening among the LGBTQ population: Implications for public health practice. Int J Public Health Res. 2018;40(2):215–26. https://doi.org/10.1016/j.ijph.2018.04.016 . Garcia KP, Lee RJ. Socioeconomic status and its relationship with preventive health behaviors: A study on cancer screening uptake. J Prev Med. 2021;32(3):145–59. https://doi.org/10.1002/jpm.2021.32.3.145 . Haviland KS, Swette S, Kelechi T, et al. Barriers and facilitators to cancer screening among LGBTQ individuals with cancer. Oncol Nurs Forum. 2020;47(4):456–63. https://doi.org/10.1188/20.ONF.456-463 . Bazzi AR, Whorms DS, King DS, et al. Adherence to mammography screening guidelines among transgender persons and sexual minority women. Am J Public Health. 2015;105(5):960–5. https://doi.org/10.2105/AJPH.2014.302486 . Prevent Cancer Foundation. (2023). Five things to know about cancer prevention, screening, and the LGBTQ + community. Prevent Cancer Foundation. Retrieved from https://www.preventcancer.org Johnson MJ, Mueller M, Eliason MJ, et al. Quantitative and mixed analyses to identify factors that affect cervical cancer screening uptake among lesbian and bisexual women and transgender men. J Clin Nurs. 2016;25(19–20):2981–93. https://doi.org/10.1111/jocn.13382 . Hansen RA, Rogers TL. Healthcare access barriers for LGBTQ communities: An examination of insurance, discrimination, and medical care disparities. Am J Public Health. 2019;109(11):1556–62. https://doi.org/10.2105/AJPH.2019.305287 . Williams K, Hagan S, Burke RL. Health behaviors and outcomes among LGBTQ adults: The role of social support and stigma. J LGBTQ Health. 2016;3(2):89–102. https://doi.org/10.1089/lgbthealth.2016.0023 . Sato T, Nakamura K. Income-related disparities in cancer screening: A comparative study of LGBTQ and heterosexual populations in Japan. Japanese J Public Health. 2022;69(5):525–33. https://doi.org/10.1265/jjph.2022.69.5.525 . Supplementary Table 1. Detailed Breakdown of Demographic Characteristics and Screening Behaviors Within LGBTQ Subgroups. Cite Share Download PDF Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Breast Cancer → Version 1 posted Reviewers agreed at journal 08 Dec, 2024 Reviewers invited by journal 08 Dec, 2024 Editor assigned by journal 02 Dec, 2024 First submitted to journal 30 Nov, 2024 Editorial decision: Minor Revision 19 Nov, 2024 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. 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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-5123934","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":387590337,"identity":"64879b01-15bf-40c4-b105-d38995788673","order_by":0,"name":"Akemi Hara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBACfmbGxgcJFTZyDOwgrgFcgg2nFsl25maDD2fSjBmYIVokCGoxOM/eJjmz7XBiAzNEQAKnUjg4zNgmzXPmcHp/M4/p5oIChjqD8wcYP/xg4MvDpYOxmbHZmqciPXfGYR6z2zOADjO4kcAs2cPAVoxLCzPQ+7d5zljnNoC08IC1MDBIA/2S2IBDCxszY4M0bxtzujxcy/kDzL/xaeFhZmwCet85wQCu5UACG15bJJgZwYFsuPEwWxlQi4TkzBuJbZY9Brj9Yn/++ENQVMrLHW/edpvnjw0/3/nDh2/8qDiGM8QwbAViRqCTDI4lEKsFDmpI1zIKRsEoGAXDFQAAy+xREImZwC8AAAAASUVORK5CYII=","orcid":"","institution":"Medical Governance Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Akemi","middleName":"","lastName":"Hara","suffix":""},{"id":387590338,"identity":"31973a77-9cd1-4570-8e29-6f9695da6ded","order_by":1,"name":"Akihiko Ozaki","email":"","orcid":"https://orcid.org/0000-0003-4415-9657","institution":"Jyoban 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Center","correspondingAuthor":false,"prefix":"","firstName":"Mika","middleName":"","lastName":"Nashimoto","suffix":""},{"id":387590342,"identity":"c6894b5e-10eb-46c8-b566-ee0bec4b3b32","order_by":5,"name":"Daisuke Hori","email":"","orcid":"","institution":"University of Tsukuba: Tsukuba Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Hori","suffix":""},{"id":387590343,"identity":"52f3921d-cd37-4b27-9b9f-cc4117a55e17","order_by":6,"name":"Masaharu Tsubokura","email":"","orcid":"","institution":"Fukushima Medical University: Fukushima Kenritsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Masaharu","middleName":"","lastName":"Tsubokura","suffix":""},{"id":387590344,"identity":"8f7e1f82-45c4-435b-98d3-9c751b3febbd","order_by":7,"name":"Kenji Gonda","email":"","orcid":"","institution":"Jyoban Hospital of Tokiwa Foundation","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Gonda","suffix":""},{"id":387590345,"identity":"6026528d-5dbd-4258-aa35-c3b7d5f068c8","order_by":8,"name":"Masahiro Wada","email":"","orcid":"","institution":"Utsunomiya Central Clinic","correspondingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Wada","suffix":""},{"id":387590346,"identity":"556ca759-0fc9-40b8-b9b7-325c4cc6e101","order_by":9,"name":"Kazunoshin Tachibana","email":"","orcid":"","institution":"Fukushima Medical University: Fukushima Kenritsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kazunoshin","middleName":"","lastName":"Tachibana","suffix":""},{"id":387590347,"identity":"9d952578-534e-4b6a-bad6-69946e8e578b","order_by":10,"name":"Tohru Ohtake","email":"","orcid":"","institution":"Fukushima Medical University: Fukushima Kenritsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Tohru","middleName":"","lastName":"Ohtake","suffix":""},{"id":387590348,"identity":"db632004-a5d5-410a-a155-2182b8a0e423","order_by":11,"name":"Takahiro Tabuchi","email":"","orcid":"","institution":"Osaka International Cancer Institute: Osaka Kokusai Gan Center","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Tabuchi","suffix":""}],"badges":[],"createdAt":"2024-09-20 13:16:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5123934/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5123934/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12282-025-01669-8","type":"published","date":"2025-02-08T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75930794,"identity":"28d5ff30-9d99-4c6f-8b35-e80dd563eede","added_by":"auto","created_at":"2025-02-10 16:13:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1490990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5123934/v1/d56ed197-47aa-434f-a18c-026a5d06ec1e.pdf"}],"financialInterests":"","formattedTitle":"Breast Cancer Screening Rates and Influencing Factors Among LGBTQ Groups in Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNumerous studies have demonstrated that lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals face significant health disparities worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e These disparities are attributed to various factors, including social stigma, discrimination, barriers to healthcare access, and difficulties in communication with healthcare providers.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e These issues are particularly pronounced in the field of preventive medicine.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Breast cancer screening exemplifies this disparity, with international research indicating that LGBTQ women tend to have lower screening rates compared to their heterosexual counterparts.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e For instance, studies in the United States have reported that lesbian and bisexual women have breast cancer screening rates 10\u0026ndash;20% lower than heterosexual women.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Moreover, transgender women (male-to-female) face an additional challenge: despite an increased risk of breast cancer due to hormone therapy, appropriate screening guidelines have not been established.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the Japanese context, breast cancer has the highest incidence rate among women, and early detection and treatment are crucial for improving prognosis.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e While biennial breast cancer screening is recommended for women aged 40 and above in Japan, the screening rate in 2019 was approximately 45%, which is considerably lower than in Western countries.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Recent studies indicate that awareness and acceptance of diverse sexual orientations are increasing globally, including in Japan. A survey conducted by Dentsu in 2020 estimated that approximately 8.9% of the Japanese population identifies as LGBTQ,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e reflecting a significant portion of society that cannot be overlooked in public health discussions. This highlights the importance of understanding and addressing the unique healthcare needs and disparities faced by LGBTQ individuals, particularly in preventive health measures such as breast cancer screening.\u003c/p\u003e \u003cp\u003eResearch on breast cancer screening behaviors among LGBTQ individuals in Japan is extremely limited. Given Japan's unique cultural background, healthcare system, and social perceptions of LGBTQ individuals, it would be inappropriate to directly apply findings from international studies. In recent years, discussions about LGBTQ rights and social inclusion have intensified in Japan, with increasing demands for LGBTQ-friendly environments in the medical field.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Against this social backdrop, investigating the health issues of LGBTQ individuals in the Japanese context, particularly regarding crucial preventive care such as breast cancer screening, holds significant implications for future healthcare policies and practices.\u003c/p\u003e \u003cp\u003eBased on this background, this study aimed to examine the association between LGBTQ status and breast cancer screening participation (including future intentions) in Japan using JACSIS data. We also sought to identify factors influencing current and intended screening behaviors across LGBTQ and non-LGBTQ populations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSettings and Participants\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThis study utilized data from the Japan COVID-19 and Society Internet Survey study (JACSIS), an ongoing cohort study designed to achieve a nationally representative sample through stratified sampling. The survey, conducted in 2023, recruited participants from a large pool maintained by a Japanese online research company. Although efforts were made to ensure representativeness, the use of an internet-based survey may lead to underrepresentation of populations with limited internet access or lower digital literacy, such as older adults or individuals from lower socio-economic backgrounds.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFrom the total participant response data (33,000), invalid responses (4,519) were excluded. We further excluded biological males (14,071) and biological females who did not answer breast cancer screening questions (3,354), resulting in 11,056 valid responses from biological females included in the analysis. Web-based informed consent was obtained, ensuring ethically appropriate procedures.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWhile 33,000 responses provide a substantial dataset, the generalizability of the results to the broader Japanese population requires cautious interpretation due to potential selection bias. Future studies could improve representativeness by applying statistical weighting techniques or comparing findings with other national surveys.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBreast cancer screening in Japan\u003c/h3\u003e\n\u003cp\u003eBreast cancer screening in Japan is recommended once every two years for women aged 40 and over.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e It is broadly divided into two categories: population-based screening conducted by municipalities and opportunistic screening that individuals undergo voluntarily. The national government recommends mammography alone as the screening method, and population-based screening generally follows this guideline,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e though, ultimately, local governments have discretion in this matter. However, opportunistic screening may involve combinations with other tests or use other tests exclusively. The breast cancer screening rate in Japan is low compared to other developed countries, remaining at about 45%.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlthough the recommended age for breast cancer screening in Japan is 40 and above, this study included participants under 40 to capture their screening intentions and identify potential barriers to screening at earlier ages. Understanding these behaviors is crucial for designing interventions that may increase screening participation in the future.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcome Varia\u003c/b\u003e \u003cb\u003eble\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe primary outcome measure pertained to breast cancer screening status over the past two years, as indicated in the 2023 JACSIS survey. Participants were asked whether they had undergone breast cancer screening procedures, such as mammography or breast ultrasound, within the preceding two-year period. Responses were categorized into three distinct groups: \"screened,\" \"attempted but not screened,\" and \"not screened.\" This change reflects that some individuals had the intention to undergo screening but were unable to complete it. Additionally, among those who were screened, results were further categorized into \"No abnormality,\" \"Abnormality,\" and \"Unknown result,\" reflecting the outcomes of the screening procedures.\u003c/p\u003e\n\u003ch3\u003eExposure Variable\u003c/h3\u003e\n\u003cp\u003eIn this study, we adopted the following exposure variables potentially associated with breast cancer screening uptake (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): LGBTQ status, insurance enrollment status, marital status, annual household income, alcohol consumption frequency, smoking status, and age.\u003c/p\u003e \u003cp\u003eRegarding the LGBTQ variable, the questionnaire offered six response options for sexual orientation and gender identity: \u0026ldquo;heterosexual,\u0026rdquo; \u0026ldquo;homosexual,\u0026rdquo; \u0026ldquo;bisexual,\u0026rdquo; \u0026ldquo;other,\u0026rdquo; \u0026ldquo;undecided,\u0026rdquo; and \u0026ldquo;unsure.\u0026rdquo; We categorized respondents who selected \"heterosexual\" as the \"non-LGBTQ group\" (n\u0026thinsp;=\u0026thinsp;9,034), while all other responses were classified as the \"LGBTQ group\" (n\u0026thinsp;=\u0026thinsp;2,022) based on their respective LGBTQ definitions. We considered those who answered \"undecided\" or \"unsure\" as part of the LGBTQ group, categorizing them as queer. However, we acknowledge the possibility that some respondents who answered \"unsure\" may have been unsure how to answer the question or may not have understood its meaning.\u003c/p\u003e \u003cp\u003eInsurance enrollment status was categorized as \u0026ldquo;National Health Insurance,\u0026rdquo; \u0026ldquo;Employee\u0026rsquo;s Health Insurance,\u0026rdquo; \u0026ldquo;Public Assistance,\u0026rdquo; \u0026ldquo;Uninsured,\u0026rdquo; and \u0026ldquo;Other.\u0026rdquo; Marital status was dichotomized into \"married\" and \"unmarried,\" with individuals in common-law partnerships and de facto marriages being classified under the \"married\" category. Annual household income was stratified into four categories: \u0026ldquo;less than 5\u0026nbsp;million yen,\u0026rdquo; \u0026ldquo;5\u0026ndash;10\u0026nbsp;million yen,\u0026rdquo; \u0026ldquo;10\u0026nbsp;million yen or more,\u0026rdquo; and \u0026ldquo;unknown.\u0026rdquo; Alcohol consumption frequency was dichotomized into \u0026ldquo;non-drinker\u0026rdquo; and \u0026ldquo;drinker.\u0026rdquo; Smoking status was similarly dichotomized into \u0026ldquo;non-smoker\u0026rdquo; and \u0026ldquo;smoker.\u0026rdquo; Age was also added as a variable, divided into categories starting from 30 years old, with each category representing a 10-year age range (e.g., \u0026ldquo;30\u0026ndash;39 years,\u0026rdquo; \u0026ldquo;40\u0026ndash;49 years,\u0026rdquo; \u0026ldquo;50\u0026ndash;59 years,\u0026rdquo; and so on).\u003c/p\u003e \u003cp\u003eThese variables were selected based on previous literature as socio-demographic and health-related factors associated with breast cancer screening behavior. For instance, insurance enrollment status is considered to influence healthcare access, playing a crucial role in breast cancer screening uptake.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Marital status may affect health behaviors and potentially relate to screening rates.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Alcohol consumption frequency has also been associated with breast cancer risk in prior studies.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e We aimed to elucidate factors influencing breast cancer screening behavior among the LGBTQ population using selected exposure variables, while also assessing the effects of LGBTQ status on breast cancer screening rates after adjusting for relevant background factors.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eInitially, we analyzed the proportional distribution of non-LGBTQ and LGBTQ groups for each independent variable using descriptive statistics. Specifically, we calculated the percentages of each variable category (e.g., breast cancer screening status, health insurance enrollment status, annual household income) for both LGBTQ and non-LGBTQ groups, elucidating the distributional differences between the two groups. Subsequently, as both the dependent and independent variables were categorical, we conducted chi-square tests to evaluate the impact of LGBTQ status on each variable. Additionally, we used chi-square tests to assess whether LGBTQ identity had a significant effect on breast cancer screening outcomes.\u003c/p\u003e \u003cp\u003eSecond, to assess factors associated with breast cancer screening status, we employed multivariable multinomial logistic regression analysis. In this analysis, we set the \"non-screened group\" as the reference group, with the \"screened group\" (Group 1) and \"intending to be screened group\" (Group 2) as comparison groups, evaluating the impact of each factor on screening status. Variables included LGBTQ status, insurance enrollment status, marital status, annual household income, and alcohol consumption frequency, with variable selection performed using the stepwise method. We calculated Odds Ratios (OR) and 95% Confidence Intervals (CI) for each independent variable, quantitatively assessing the relative impact on other groups compared to the reference group.\u003c/p\u003e \u003cp\u003eAdditionally, we conducted a sensitivity analysis focusing exclusively on participants aged 40 and above, considering that this is the recommended age for routine breast cancer screening in Japan. This analysis aimed to examine whether the associations observed in the primary analysis held consistent within the target population for screening. The results of the sensitivity analysis were compared with the overall analysis to validate the robustness of our findings.\u003c/p\u003e \u003cp\u003eTo address multicollinearity concerns, we calculated Variance Inflation Factors (VIF) to check for collinearity issues among explanatory variables. This process ensures the appropriate construction of the model and the reliability of the results.\u003c/p\u003e \u003cp\u003eLastly, we performed stratified regression analyzes for both LGBTQ and non-LGBTQ groups to explore potential differences in the factors influencing breast cancer screening outcomes. This analysis allowed us to evaluate how independent variables such as insurance status, marital status, and annual household income impacted the screening behaviors of each group. Model significance was verified using Wald tests to determine whether the independent variables had statistically significant effects on breast cancer screening behavior. The results of the regression analyzes were interpreted using odds ratios and confidence intervals, with P-values less than 0.05 considered indicative of a significant association.\u003c/p\u003e \u003cp\u003eTo validate our classification of the LGBTQ groups, we conducted two analyzes. First, we performed a stratified analysis within the LGBTQ group to examine potential differences among subgroups (homosexual, bisexual, other, undecided, and unsure). Second, to assess the validity of classifying \"undecided\" and \"unsure\" respondents as queer, we repeated the multivariable multinomial logistic regression analysis using two separate models: one excluding the \"unsure\" group, and another treating the \"unsure\" respondents as a distinct category. These results will be presented as supplementary materials.\u003c/p\u003e \u003cp\u003eAll data analyzes were conducted using R version 4.2.1, based on statistically valid methods.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics Statements\u003c/h3\u003e\n\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThis study received ethical approval from the Osaka International Cancer Institute. The initial approval, under the study title \"Evaluation of Social and Health Disparities Caused by the COVID-19 Pandemic in Japan,\" was granted on June 19, 2020 (Approval Number: 20084), prior to the implementation of the survey. The current research, an extension of the original study, received additional approval (Approval Number: 20084-12) on March 28, 2024., and subsequently from the Tohoku University Graduate School of Medicine Ethics Committee (Approval Numbers: 2024-1-231 on June 27, 2024, and 2024-1-517 on October 22, 2024), in accordance with the 2020 ethical guidelines prior to the initiation of survey activities.\u003c/span\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics. The breast cancer screening rate among the LGBTQ population was slightly lower compared to the non-LGBTQ population (43.4% vs 45.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age distributions revealed a higher percentage of individuals in their 70s within the LGBTQ group compared to the non-LGBTQ group (21.7% vs 17.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The LGBTQ group had a lower rate of employee health insurance coverage (45.3% vs 54.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while their participation in national health insurance was higher (49.0% vs 43.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A higher proportion of non-drinkers was observed in the LGBTQ group (66.0% vs 58.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with a higher percentage of unmarried individuals (58.9% vs 67.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The LGBTQ population had a lower representation in high-income brackets (annual income exceeding 10\u0026nbsp;million yen: 4.4% vs 8.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a higher proportion of individuals who did not disclose their income (36.9% vs 24.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The percentage of smokers in the LGBTQ group was slightly higher than in the non-LGBTQ group (10.7% vs 9.3%, p\u0026thinsp;=\u0026thinsp;0.11). Due to the chi-square test results showing p-values less than 0.001, it is evident that LGBTQ and non-LGBTQ identities contribute to differences in screening outcomes.\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\u003eDemographic Characteristics and Breast Cancer Screening Status of LGBTQ and Non-LGBTQ Participants\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGBTQ n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-LGBTQ n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChi-Square Test\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\u003eOverall\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\u003e11056 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9034 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.93\u003c/p\u003e \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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30s\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\u003e2123 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e378 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1745 (19.3)\u003c/p\u003e \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\u003e40s\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\u003e2249 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1853 (20.5)\u003c/p\u003e \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\u003e50s\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\u003e2190 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1843 (20.4)\u003c/p\u003e \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\u003e60s\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\u003e2218 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1821 (20.2)\u003c/p\u003e \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\u003e70s\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\u003e2027 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e438 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1589 (17.6)\u003c/p\u003e \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\u003e80s\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\u003e249 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183 (2.0)\u003c/p\u003e \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\u003e\u003cb\u003eBreast Cancer Screening\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreened\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\u003e5023 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e878 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4145 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ No abnormality\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\u003e4637 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e813 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3824 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Abnormality\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\u003e247 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Unknown result\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\u003e139 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanned Screening\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\u003e2450 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2119 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Screening\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\u003e3583 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e813 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2770 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLGBTQ Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-LGBTQ\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\u003e9034 (81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9034 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGBTQ\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\u003e2022 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Lesbian\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\u003e25 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Bisexual\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\u003e85 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Other\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\u003e428 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Undecided\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\u003e499 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e499 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e└─ Unsure\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\u003e985 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e985 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Health Insurance\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\u003e4919 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e991 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3928 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee's Health Insurance\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\u003e5817 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e916 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4901 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Assistance\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\u003e61 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\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\u003e57 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\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\u003e202 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\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\u003e7243 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1190 (58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6053 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\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\u003e3813 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e832 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2981 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual Household Income (JPY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 million\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\u003e4384 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e835 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3549 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 million\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\u003e2911 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2559 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 million\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\u003e822 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e733 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\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\u003e2939 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e746 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2193 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol Consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\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\u003e6571 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1334 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5237 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\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\u003e4485 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e688 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3797 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \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 \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo-smokers\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\u003e10000 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1806 (89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8194 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers\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\u003e1056 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e840 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents results of multivariable multinomial logistic regression analysis. LGBTQ individuals had lower odds of screening (OR 0.82, 95% CI 0.73\u0026ndash;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and screening intention (OR 0.59, 95% CI 0.52\u0026ndash;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Those with employee health insurance showed higher odds for both outcomes (screening: OR 1.61, 95% CI 1.47\u0026ndash;1.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; intention: OR 1.43, 95% CI 1.28\u0026ndash;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Public assistance recipients had lower screening odds (OR 0.51, 95% CI 0.26\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.04), while uninsured individuals also had lower screening odds (OR 0.36, 95% CI 0.19\u0026ndash;0.70, p\u0026thinsp;=\u0026thinsp;0.003). Married individuals had higher odds for both outcomes (screening: OR 1.20, 95% CI 1.09\u0026ndash;1.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; intention: OR 1.30, 95% CI 1.16\u0026ndash;1.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher income was associated with increased screening odds (5\u0026ndash;10\u0026nbsp;million yen: OR 1.34, 95% CI 1.19\u0026ndash;1.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026gt;10\u0026nbsp;million yen: OR 1.94, 95% CI 1.60\u0026ndash;2.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Drinkers had higher odds for both outcomes (screening: OR 1.25, 95% CI 1.15\u0026ndash;1.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; intention: OR 1.18, 95% CI 1.06\u0026ndash;1.31, p\u0026thinsp;=\u0026thinsp;0.003). Smoking status showed a negative association with screening odds, with smokers having lower odds of screening compared to non-smokers (OR 0.76, 95% CI 0.65\u0026ndash;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while no significant association was observed for screening intention (OR 1.09, 95% CI 0.92\u0026ndash;1.29, p\u0026thinsp;=\u0026thinsp;0.31).Variance Inflation Factors (VIF) were calculated to assess multicollinearity. The Age variable had a VIF of 29.9, indicating a high level of multicollinearity, so it was excluded from the final model. All other variables had VIF values below 10, with household income showing the highest at 8.38. These results indicate acceptable levels of independence among variables, with no significant multicollinearity concerns.\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFurthermore, results from the sensitivity analysis presented in Supplementary Table\u0026nbsp;2, focusing on participants aged 40 and above, were consistent with the findings of the primary analysis. This consistency demonstrates the robustness and stability of the multivariable multinomial logistic regression model.\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\u003eDemographic Characteristics and Breast Cancer Screening Status of LGBTQ and Non-LGBTQ Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eScreened Group (n\u0026thinsp;=\u0026thinsp;5,023) vs. Non-screened Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIntending to Screen Group (n\u0026thinsp;=\u0026thinsp;2,450) vs. Non-screened Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGBTQ Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-LGBTQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGBTQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026ndash;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee's Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47\u0026ndash;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.28\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Assistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u0026ndash;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u0026ndash;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026ndash;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual Household Income (JPY)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;5 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;10 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u0026ndash;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol Consumption\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e compares factors associated with breast cancer screening and intention to screen between LGBTQ and non-LGBTQ groups. In the LGBTQ group, uninsured individuals had significantly lower odds of screening (OR 0.23, 95% CI 0.08\u0026ndash;0.70, p\u0026thinsp;=\u0026thinsp;0.01), and the \"other\" insurance category also had lower odds of screening (OR 0.46, 95% CI 0.27\u0026ndash;0.77, p\u0026thinsp;=\u0026thinsp;0.003). Marital status (OR 1.48, 95% CI 1.21\u0026ndash;1.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and alcohol consumption (OR 1.39, 95% CI 1.13\u0026ndash;1.71, p\u0026thinsp;=\u0026thinsp;0.002) were associated with higher odds of screening. Public assistance (OR 0.26, 95% CI 0.06\u0026ndash;1.18, p\u0026thinsp;=\u0026thinsp;0.08) and smoking (OR 0.84, 95% CI 0.61\u0026ndash;1.15, p\u0026thinsp;=\u0026thinsp;0.28) were not significant, but public assistance showed notably low odds of screening, indicating a potential trend. For intention to screen, only marital status was significant (OR 1.33, 95% CI 1.01\u0026ndash;1.74, p\u0026thinsp;=\u0026thinsp;0.04).\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\u003eStratified Analysis of Factors Influencing Breast Cancer Screening Behavior in LGBTQ and Non-LGBTQ Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eScreened Group (n\u0026thinsp;=\u0026thinsp;5,023) vs. Non-screened Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIntending to Screen Group (n\u0026thinsp;=\u0026thinsp;2,450) vs. Non-screened Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLGBTQ\u003c/span\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee's Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Assistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026ndash;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u0026ndash;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026ndash;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u0026ndash;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual Household Income (JPY)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026ndash;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026ndash;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51\u0026ndash;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u0026ndash;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol Consumption\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026ndash;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNon-LGBTQ\u003c/span\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee's Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026ndash;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u0026ndash;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Assistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u0026ndash;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnnual Household Income (JPY)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026ndash;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol Consumption\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05\u0026ndash;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u0026ndash;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\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\u003eIn the non-LGBTQ group, employee health insurance had a strong positive association with screening (OR 1.79, 95% CI 1.61\u0026ndash;1.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and intention to screen (OR 1.49, 95% CI 1.32\u0026ndash;1.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Marital status (OR 1.13, 95% CI 1.02\u0026ndash;1.26, p\u0026thinsp;=\u0026thinsp;0.03), higher income (OR 2.09, 95% CI 1.69\u0026ndash;2.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alcohol consumption (OR 1.23, 95% CI 1.11\u0026ndash;1.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and smoking (OR 0.74, 95% CI 0.62\u0026ndash;0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant for screening. Key differences include the stronger influence of marital status and insurance in the LGBTQ group, while income and smoking were more impactful in the non-LGBTQ group.\u003c/p\u003e \u003cp\u003eWe summarized the results of the stratified analysis within the LGBTQ group, examining potential differences among subgroups (homosexual, bisexual, other, undecided, and unsure) in Supplementary Table\u0026nbsp;1. While we observed some variations in characteristics (e.g., insurance type distribution) between subgroups, the small sample sizes precluded drawing statistically significant conclusions about these differences. We detected no significant variations across other background factors. These findings support the validity of analyzing the LGBTQ community as a single category, suggesting that this approach does not introduce substantial classification bias and is appropriate for the study's purposes.\u003c/p\u003e \u003cp\u003eWe further summarized the results of the multivariable multinomial logistic regression analysis using two separate models in Supplementary Tables\u0026nbsp;3 and 4: one excluding the \"unsure\" group, and another treating the \"unsure\" respondents as a distinct category. In both analyses, the association between gender and breast cancer screening participation and intention to participate was consistent with the main analyses.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWhile we encountered various methodological challenges, this study represents one of the few investigations in Japan examining healthcare disparities in minority populations, specifically focusing on breast cancer screening among LGBTQ individuals, making it a valuable contribution to the growing body of literature in this field.\u003c/span\u003e The lower screening rates observed in our study among LGBTQ participants (43.4% vs. 45.9% for non-LGBTQ, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are consistent with findings from international research.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Studies conducted in the U.S. and Canada have reported similar trends, with LGBTQ individuals consistently showing lower rates of cancer screenings, including breast cancer, compared to their heterosexual counterparts.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWhile the absolute difference in screening rates between LGBTQ and non-LGBTQ groups in this study is small (2.5%) yet statistically significant, its clinical relevance warrants further investigation. Notably, even after adjusting for socioeconomic factors such as income and insurance status, LGBTQ individuals had significantly lower odds of being screened compared to non-LGBTQ individuals (OR 0.82, 95% CI 0.73\u0026ndash;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This adjusted odds ratio highlights that the disparity is not negligible and suggests meaningful differences in screening behavior that require attention.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFurthermore, the overall low screening rates observed in both groups underscore the broader need for public health initiatives to improve breast cancer screening participation in Japan. These findings not only highlight the challenges in promoting preventive health behaviors across the population but also suggest disparities that disproportionately affect LGBTQ individuals. Future research should explore the underlying factors contributing to these disparities, including potential structural barriers and cultural attitudes toward LGBTQ individuals in healthcare settings. In addition, qualitative studies could provide deeper insights into the experiences of LGBTQ individuals regarding healthcare access and preventive practices. By addressing these dynamics, targeted interventions can be developed to promote health equity and improve screening rates both within the LGBTQ community and the broader population.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eKey factors contributing to screening disparities among LGBTQ individuals include insurance status, marital status, and alcohol consumption. LGBTQ participants had higher uninsured rates, with uninsured individuals showing significantly lower odds of screening (OR 0.23, 95% CI 0.08\u0026ndash;0.70, p\u0026thinsp;=\u0026thinsp;0.01). This aligns with international findings where lack of insurance is a key barrier to healthcare access for LGBTQ individuals.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMarital status had a stronger positive impact on screening behaviors for both LGBTQ and non-LGBTQ individuals, with married individuals more likely to undergo screening compared to their unmarried counterparts. While both LGBTQ and non-LGBTQ married individuals can benefit from their spouse\u0026rsquo;s encouragement of preventive healthcare, unmarried LGBTQ individuals may face additional societal pressures that hinder medical procedures. Public assistance insurance was associated with lower screening odds (OR 0.26, 95% CI 0.06\u0026ndash;1.18, p\u0026thinsp;=\u0026thinsp;0.08), potentially exacerbated by discrimination or the lack of LGBTQ-friendly healthcare providers.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFurthermore, among the LGBTQ group, individuals with \"other\" types of insurance had significantly lower odds of undergoing screening compared to those with National Health Insurance (reference group) (OR 0.46, 95% CI 0.27\u0026ndash;0.77, p\u0026thinsp;=\u0026thinsp;0.003). Uninsured individuals also exhibited significantly lower odds of being screened (OR 0.23, 95% CI 0.08\u0026ndash;0.70, p\u0026thinsp;=\u0026thinsp;0.01).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThese findings suggest that socioeconomic factors, such as insurance coverage and reliance on public assistance, contribute to lower breast cancer screening rates among LGBTQ individuals. For instance, uninsured LGBTQ individuals showed significantly reduced odds of screening compared to those with national health insurance. Prior research also highlights that disparities in screening rates between LGBTQ and non-LGBTQ individuals may stem from factors beyond socioeconomic status\u003c/span\u003e,\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eincluding societal stigma, limited access to LGBTQ-friendly healthcare environments, and differences in healthcare-seeking behaviors. These broader structural and cultural barriers have been identified as key contributors to health disparities in LGBTQ populations globally.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEfforts to improve breast cancer screening rates should address these multifaceted barriers by fostering inclusive healthcare environments and reducing discrimination. Interventions that specifically target uninsured or underinsured individuals, along with strategies to reduce societal stigma, could play a pivotal role in promoting equitable access to preventive healthcare for LGBTQ populations.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eAlcohol consumption was significantly associated with higher screening odds in the LGBTQ group (OR 1.39, 95% CI 1.13\u0026ndash;1.71, p\u0026thinsp;=\u0026thinsp;0.002), possibly indicating health-conscious behaviors.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Income levels impacted groups differently: higher income significantly increased screening rates in non-LGBTQ populations but not among LGBTQ individuals.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e This suggests that for LGBTQ individuals, fear of discrimination or lack of LGBTQ-friendly medical environments may play a more significant role than financial barriers in seeking cancer screening.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eClassifying LGBTQ groups is extremely challenging. After careful consideration, we decided to treat the \"unsure\" and \"undecided\" respondents as a single group. Based on the comprehensive results of our sensitivity analyzes, we found that the outcomes were largely consistent across different classification methods, suggesting that our approach was valid. Another method for determining gender involves considering the difference between self-perceived gender and biological sex. While it is difficult to determine which method is superior, evaluating the extent of these differences will likely be necessary in future research.\u003c/p\u003e \u003cp\u003eTaken together, these findings highlight the importance of addressing both socioeconomic and identity-specific barriers to healthcare access among LGBTQ individuals in Japan. Initiatives such as diversity education for healthcare providers and the creation of more inclusive medical environments, as seen in countries like the U.S. and Canada, may help reduce the disparities in cancer screening rates for LGBTQ populations.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Further research is needed to develop targeted interventions that address the unique challenges faced by LGBTQ individuals in Japan, particularly those related to insurance coverage, social support, and discrimination in healthcare settings.\u003c/p\u003e\n\u003ch3\u003eImplications\u003c/h3\u003e\n\u003cp\u003eThese findings have important implications for public health policies and interventions in Japan. There is a clear need for targeted approaches to improve breast cancer screening rates among LGBTQ individuals. This may include developing LGBTQ-specific health education programs, creating more inclusive and welcoming healthcare environments, and addressing insurance coverage disparities. Healthcare providers should receive training on LGBTQ health issues and cultural competency to ensure they can provide appropriate and sensitive care.\u003c/p\u003e \u003cp\u003eAnother important consideration is the distinction between self-identified gender and biological sex, a factor that warrants further discussion in future research. While JASCIS2023 determined gender based on participants' responses to specific questions, JACSIS2022 focused on the difference between self-perceived gender and biological sex. Evaluating this difference is feasible and represents a crucial research question for future studies. This distinction could provide valuable insights into how gender identity and biological sex independently or jointly influence breast cancer screening behaviors and intentions among both LGBTQ and non-LGBTQ populations.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn this context, it is important to note that the proportion of LGBTQ individuals in our study (18.3%) was notably higher than both the 2020 Dentsu survey of the Japanese population (8.9%) and a U.S. physician study (approximately 1%). These differences likely reflect our methodological approach. Our online survey provided greater anonymity than face-to-face interviews or official data collection methods, potentially facilitating LGBTQ self-disclosure. Moreover, our sample may have attracted individuals more engaged with health issues, possibly increasing LGBTQ representation compared to general population surveys. LGBTQ self-identification appears to be influenced by question content, survey methodology, study population, and cultural context\u0026mdash;aspects that warrant further investigation.\u003c/span\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWhile this study provides valuable insights into breast cancer screening behaviors among the LGBTQ population, it has certain limitations. The cross-sectional design precludes the establishment of causal relationships. Second, as previously noted, this study may not be representative of the general population due to the potential underestimation of social stigma associated with LGBTQ self-identification. Consequently, the interpretation of these findings warrants careful consideration. Additionally, the lack of adjustment for sociodemographic factors such as income and educational attainment may also influence the observed disparities in screening rates. Furthermore, the questionnaire did not include specific questions about factors like gender-affirming surgeries, which may affect breast cancer risk and screening behaviors. It is also difficult to clearly disentangle barriers directly related to LGBTQ identity from broader systemic issues affecting healthcare access.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMoreover, while our study categorized income into four levels (\u0026lt;\u0026thinsp;5\u0026nbsp;million, 5\u0026ndash;10\u0026nbsp;million, \u0026gt;\u0026thinsp;10\u0026nbsp;million, and Unknown), education was excluded from the analysis due to high collinearity with other variables. This limited our ability to comprehensively adjust for potential confounding factors. As a result, we cannot definitively conclude the extent to which these variables contribute to the disparities in screening rates. Additional research is necessary to better separate these factors, refine the classification of LGBTQ groups, and address the underlying causes of the observed disparities.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study highlights significant disparities in breast cancer screening behaviors between LGBTQ and non-LGBTQ individuals in Japan. These findings emphasize the need for tailored interventions and policies that address the unique challenges faced by the LGBTQ community in accessing preventive healthcare services. By addressing these disparities, we can work towards more equitable health outcomes and improved overall public health in Japan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eDuring the preparation of this work the authors used Claude 3.5, an AI language model developed by Anthropic in order to perform English language proofreading and text revision. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest statement:\u0026nbsp;\u003c/strong\u003eDr. Ozaki received personal fees from Medical Network Systems Inc., Kyowa Kirin Company Limited, and Taiho Pharmaceutical Company Limited outside of the submitted work. Dr. Tanimoto received personal fees from Medical Network Systems Inc. and Bionics Company Limited outside of the submitted work. No other disclosures are reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eConceptualization: all authors; Methodology: Akemi Hara, Akihiko Ozaki, Michio Murakami, Takahiro Tabuchi; Formal analysis and investigation: Akemi Hara; Writing - original draft preparation: Akemi Hara, Akihiko Ozaki; Writing - review and editing: all authors; Funding acquisition: Takahiro Tabuchi; Resources: Akemi Hara; Supervision: Michio Murakami, Takahiro Tabuchi\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSwette S, Kelechi T, Haviland KS. Overcoming Barriers to Cancer Screening in Diverse LGBTQ Populations. Cancer Network; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorman JR, Usita PM, Madlensky L, Pierce JP. 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Am J Public Health. 2019;109(11):1556\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2105/AJPH.2019.305287\u003c/span\u003e\u003cspan address=\"10.2105/AJPH.2019.305287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams K, Hagan S, Burke RL. Health behaviors and outcomes among LGBTQ adults: The role of social support and stigma. J LGBTQ Health. 2016;3(2):89\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/lgbthealth.2016.0023\u003c/span\u003e\u003cspan address=\"10.1089/lgbthealth.2016.0023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato T, Nakamura K. Income-related disparities in cancer screening: A comparative study of LGBTQ and heterosexual populations in Japan. Japanese J Public Health. 2022;69(5):525\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1265/jjph.2022.69.5.525\u003c/span\u003e\u003cspan address=\"10.1265/jjph.2022.69.5.525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupplementary Table 1. Detailed Breakdown of Demographic Characteristics and Screening Behaviors Within LGBTQ Subgroups.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brca","sideBox":"Learn more about [Breast Cancer](http://link.springer.com/journal/12282)","snPcode":"12282","submissionUrl":"https://www.editorialmanager.com/brca/default2.aspx","title":"Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sexual and Gender Minorities, Early Detection of Cancer, Breast Neoplasms, Health Services Accessibility, Health Care Disparities","lastPublishedDoi":"10.21203/rs.3.rs-5123934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5123934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals face significant health disparities worldwide, particularly in preventive medicine. In Japan, where breast cancer has the highest incidence rate among women, understanding screening behaviors among LGBTQ individuals is crucial for improving public health outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to elucidate the relationship between LGBTQ status and breast cancer screening behaviors in Japan, identifying factors influencing screening uptake and highlighting challenges in health management for the LGBTQ community.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from the Japan COVID-19 and Society Internet Survey (JACSIS), we analyzed breast cancer screening status among 11,056 biological females. Multinomial logistic regression and stratified regression analyzes were employed to examine factors associated with screening behavior, comparing LGBTQ and non-LGBTQ groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLGBTQ individuals demonstrated significantly lower odds of undergoing breast cancer screening (OR 0.82, 95% CI 0.73-0.91, p\u0026lt;0.001) compared to non-LGBTQ individuals. Key factors influencing lower screening rates among individuals in the survey were primarily linked to LGBTQ identity, followed by higher rates of being uninsured, unmarried status, lower income levels, alcohol consumption. Stratified analysis revealed that uninsured LGBTQ individuals had significantly lower odds of screening (OR 0.23, 95% CI 0.08-0.70, p=0.01) compared to those with national health insurance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights significant disparities in breast cancer screening behaviors between LGBTQ and non-LGBTQ individuals in Japan. Findings underscore the need for targeted interventions, including improved healthcare access, LGBTQ-friendly medical environments, and awareness campaigns to address these disparities and promote health equity within the LGBTQ community.\u003c/p\u003e","manuscriptTitle":"Breast Cancer Screening Rates and Influencing Factors Among LGBTQ Groups in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 08:32:37","doi":"10.21203/rs.3.rs-5123934/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-12-08T22:58:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-08T11:13:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-02T14:09:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer","date":"2024-11-30T20:05:10+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor Revision","date":"2024-11-19T09:24:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brca","sideBox":"Learn more about [Breast Cancer](http://link.springer.com/journal/12282)","snPcode":"12282","submissionUrl":"https://www.editorialmanager.com/brca/default2.aspx","title":"Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4d5d0a92-3913-4eef-943d-e14c5ddf334b","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T16:06:55+00:00","versionOfRecord":{"articleIdentity":"rs-5123934","link":"https://doi.org/10.1007/s12282-025-01669-8","journal":{"identity":"breast-cancer","isVorOnly":false,"title":"Breast Cancer"},"publishedOn":"2025-02-08 15:58:27","publishedOnDateReadable":"February 8th, 2025"},"versionCreatedAt":"2024-12-10 08:32:37","video":"","vorDoi":"10.1007/s12282-025-01669-8","vorDoiUrl":"https://doi.org/10.1007/s12282-025-01669-8","workflowStages":[]},"version":"v1","identity":"rs-5123934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5123934","identity":"rs-5123934","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}