Cancer survivorship among sexual and gender minority and cisgender heterosexual individuals

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McElroy, Ting Guan, Allison B. Anbari, Maria T. Brown This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7271773/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To evaluate predictors of being a cancer survivor among those aged 45–64 and 65–80 years and inclusive of sexual and gender minority (SGM) status using a national population-based study dataset. Methods Behavioral Risk Factor and Surveillance System (BRFSS) survey used 2021 data. Descriptive statistics describe the study population characteristics. Logistic regression models, adjusting for known or suspected risk factors, evaluate participant characteristics, including SGM status, stratified by male and females and by 2 age groups (45–64 years, 65 + years) associated with being a cancer survivor. Results BRFSS data comprise 88,839 females (12,400 female cancer survivors) and 72,389 males (8,558 male cancer survivors). Being older, reported poor health, having multiple chronic conditions were associated with increased odds and being Black or Hispanic was associated with lower odds of being a cancer survivor. In women being overweight/obese was associated with increased odds of being a cancer survivor, and for men binge drinking was associated with lower odds of being a cancer survivor. Those identifying as SGM had similar odds of being a cancer survivor. Conclusions These findings underscore the complex interplay of demographic and health-related factors in predicting cancer survivorship status, highlighting the need for targeted interventions that address differences across sex, ethnicity, and health behaviors. Cancer survivor BRFSS SGM Risk factors Cancer Introduction Over 18 million people are living with a history of cancer in the United States (U.S.), representing 5.4% of the population [ 1 ]. From 2022 to 2040, the projected number of persons living with cancer for 5 or more years is expected to be 19.2 million, an increase of 53% [ 1 ]. In addition, cancer mortality continues to decrease as indicated by a 32% reduction in cancer mortality rates from 1991 to 2019. The most impactful factor associated with this decrease is falling cigarette smoking rates; other factors include early detection through screening and newer treatments [ 2 ]. Data from well-established national population-based studies support that elevated health-related risk factors for a cancer diagnosis are disproportionally experienced by sexual and gender minority (SGM; aka: lesbian, gay, bisexual, transgender) individuals compared to cisgender heterosexual individuals [ 3 ]. Further, structural and systemic stigma faced by SGM individuals also have been cited as underlying factors in behavioral choices in seeking medical care or using psychoactive drugs [ 4 , 5 ]. As aging is a known predictor of a cancer diagnosis, concerns among older SGM individuals have also indicated a disparity in care [ 6 ], including timely access [ 7 ]. Using a community-based system dynamics approach, Gillani and colleagues identified key drivers of healthcare disparities within sexual and gender minority (SGM) populations, including societal and structural stigma, provider bias, and pathologization [ 8 – 10 ]. The strength of this complex systems methodology lies in its ability to account for dynamic feedback loops that influence individual health behaviors and outcomes. For example, the absence of inclusive healthcare environments may discourage routine medical check-ups, thereby leading to delayed cancer diagnoses and poorer prognoses. SGM individuals face compounding challenges related to both healthcare access and cancer risk. Barriers to timely care, often rooted in discrimination and systemic inequities, contribute to later-stage diagnoses and reduced survival rates. Simultaneously, elevated health-related risk factors within SGM groups may increase their likelihood of developing cancer. These opposing forces—reduced survival due to delayed care versus increased incidence from heightened risk—may ultimately result in comparable odds of being a cancer survivor between SGM and cisgender heterosexual survey respondents, despite entrenched differences between the two groups. This study examined predictors of cancer survivorship using a national population-based survey, adjusting for both suspected and established cancer risk factors. A note on terminology. Sexual and gender minority is a term that has been adopted by the National Institute of Health (NIH) to identify people who do not identify as cisgender as gender identity and/or heterosexual as sexual orientation. SGM is used in this manuscript as an inclusive term, albeit not commonly adopted within the SGM community. A long list of acronyms can be used, such as LGBTQQIP2SAA (lesbian, gay, bisexual, transgender, queer, questioning, intersex, pansexual, two-spirit (2S), androgynous, and asexual), but even this list does not fully encompass all possible descriptions of one’s identity. Another term that has differing views on its acceptability is ‘cancer survivor.’ This term has been defined by the NIH to mean anyone with a history of cancer from the time of diagnosis through the rest of their life. Although alternative terms include persons living with cancer, individuals with a history of cancer, thrivers, warriors, or persons on a cancer journey, the term cancer survivor will be used in this manuscript. Methods This cross-sectional study analyzed data from the 2021 wave of the Behavioral Risk Factor and Surveillance System (BRFSS) survey. The Centers for Disease Control and Prevention fielded the BRFSS survey with data on sexual orientation and gender identity using the optional Sexual Orientation and Gender Identity module in 33 of 39 states [ 11 ]. Participants in these 33 states also administered the health status module of the core interview, in which participants indicated whether they had received diagnoses of various chronic health conditions, including ever been diagnosed with cancer (yes, no). Respondents were included in the sample if they were 45 years of age or older and responded to both the sexual orientation and gender identity module and the health status module. Measures Cancer history was determined based on participant response to the health status question: “Has a doctor, nurse, or other health professional ever told you that you had … cancer.” A yes response was coded as cancer survivor. Skin cancer” was excluded because the ability to differentiate between a diagnosis of melanoma (a life-threatening, potentially metastatic disease) and nonmelanoma skin cancer (typically not life threatening) was not possible. Sexual minority status was determined using the following question: “Which of the following best represents how you think of yourself?” (six categories: gay; straight, that is, not gay; bisexual; something else; I don’t know the answer; refused). Any response other than ‘straight, that is, not gay’ was coded as sexual minority. Gender minority status was determined by one question: “Do you consider yourself to be transgender?” The five answer options were as follows: yes, transgender, male-to-female; yes, transgender, female-to-male; yes, transgender, gender nonconforming; no; don’t know. Any response other than ‘no’ to this gender identity question was coded as gender diverse (GD). SGM comprised any participant labeled as SM and/or GD. Poor health was measured using the general health item in the Health Status section of the questionnaire: “Would you say that in general your health is?” The available responses ranged from excellent (1) to poor (5), with higher values indicating poorer health. The responses to this ordinal variable were divided into a dichotomous variable, with the values of 1 (poor) and 0 (fair, good, very good, and excellent). Chronic health conditions were identified from the following question: “Has a doctor, nurse, or other health professional ever told you that you had any of the following?” These eight conditions were: history of heart attack (or myocardial infarction), heart disease (angina or coronary heart disease), history of stroke, diabetes (excluding prediabetes or borderline diabetes), kidney disease, chronic lung disease (chronic obstructive pulmonary disease, emphysema, or chronic bronchitis), some form of [arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia], and [currently have] asthma. The dichotomous variable for each of the eight chronic health conditions (positive response = 1) were summed into a continuous variable for number of chronic health conditions (range 0–8) and a dichotomous variable, with the values of 1 (two or more chronic health condition reported) and 0 (zero or one chronic health condition reported). Demographic characteristics included age (two groups: 45–64 years and 65–80 years and as continuous variable), race (four groups: White, Black, Hispanic, other (all other responses)), educational attainment (two groups: at least some college versus high school education or less). Health-related behaviors included current smoker (yes/no), binge drinker (yes/no) in the past 30 days, drank five or more drinks on one occasion (for males) or drank four or more drinks on one occasion (for females), and had a body mass index (BMI in kg/m 2 ) of 25 or higher (yes/no). Analytical plan Descriptive statistics characterize the sample. Bivariate analysis examined the relationship between SGM identity, cancer history, and health by performing t-tests to compare mean differences in continuous variables and chi-square tests for categorical variables. Binary logistic regression models evaluated the association between being cancer survivor (dependent variable) and participants’ characteristics (independent variables) including SGM status, self-reported poor health, two or more chronic health conditions, BMI of ≥ 25 kg/m 2 , smoking status, educational attainment, binge drinking status, race/ethnicity, and age by sex (male and female separately). This logistic regression model also stratified for those aged 45–54 years and aged 65–80 years by sex (female and male). Results Sample Characteristics This dataset was comprised of 88,839 females (with 3,291 identified as SGM, 3.7%) and 72,389 males (with 2,871 identified as SGM, 4.0%). For cancer survivors, 433 female SGM, 11,960 non-SGM and 330 male SGM, 8228 non-SGM were used in the analysis. SGM participants from the full dataset were significantly younger, Hispanic or some other race/ethnicity (other means not White, Black, or Hispanic), identified as a smoker, reported poor health, and reported having 2 or more chronic conditions compared to non-SGM participants. SGM females but not SGM males also had BMI ≥ 25 kg/m 2 , higher educational attainment, and qualified as a binge drinker compared to non-SGM peers. In contrast to the higher proportion of SGM females at BMI ≥ 25 kg/m 2 , fewer SGM males had BMI ≥ 25 kg/m 2 compared to their non-SGM peers. Reporting a history of cancer was similar between non-SGM and SGM participants. Stratifying by ages 45–64 years and 65–80 years, among the younger cohort, female SGM cancer survivors were significantly younger (by about one year) and reported having BMI ≥ 25 kg/m 2 more frequently when compared to non-SGM cancer survivors. No significant differences were observed between the older SGM and non-SGM females. The younger cohort of male (45–64 years) SGM cancer survivors was significantly different in being a current smoker, having 2 or more chronic conditions and identifying as Hispanic compared to non-SGM male cancer survivors. Among the older cohort, male SGM cancer survivors were significantly younger (by less than one year) and fewer reported having BMI ≥ 25 kg/m 2 compared to non-SGM cancer survivors. These two significant differences were not observed among the younger male cohort. See Table 2 for details. Multivariate Binary Logistic Regression Models Results from multivariate logistic regressions (Table 3 ) reveal that when controlling for age, race/ethnicity, education attainment, health status, and risky health-related behaviors (BMI ≥ 25 kg/m 2 , binge drinking and smoking), female SGM identity was not associated with greater odds being a cancer survivor (OR: 1.00; 95% CI: 0.90–1.11). The finding was similar for the male population (OR: 1.05; 95% CI: 0.93–1.19). For the entire sample, among the covariates in the model, being older, reporting poor health, and having multiple chronic conditions were associated with increased odds of being a cancer survivor. Being Hispanic (compared to White participants) was associated with lower odds of being a cancer survivor. Results stratified by age (45–64 years and 65–80 years) were also evaluated with most of the findings similar to the full sample. Five characteristics were consistent in women of both age groups: the odds of being a cancer survivor were lower for females identifying as Black or Hispanic and higher among females in poor health, having 2 + chronic conditions, or older age. The odds of being a cancer survivor were also greater for women aged 65–80 years with a BMI of ≥ 25 kg/m 2 ; this risk was not present in women aged 45–64 years old. Women aged 45–64 years who identified as ‘other’ race had lower odds of being a cancer survivor. There was more variation in the odds of being a cancer survivor between males in the two age groups and all but one characteristic (i.e., educational attainment) in the younger group was associated with having a higher odds of being a cancer survivor (i.e., older age, identifying as SGM, 2 + chronic conditions, reported poor health) or having a lower odds (Black, ‘other’, Hispanic, current smoker, binge drinker and BMI of ≥ 25). Males aged 65–80 years had lower odds of being a cancer survivor if they were self-identified as ‘other’ race or Hispanic or a current smoker. Their odds of being a cancer survivor was greater if they were older, reported poor health or 2 + chronic conditions. See Table 3 for details. Discussion This study aimed to describe predictors or cancer survivorship using a national population-based survey, adjusting for both suspected and established cancer risk factors. These results suggest that sexual and gender minority (SGM) adults have comparable odds of being cancer survivors as non-SGM adults, despite numerous studies indicating that SGM individuals exhibit a higher prevalence of cancer-related risk factors compared to their cisgender, heterosexual peers. Consistent with other research, we find that multiple chronic conditions [ 12 – 14 ], self-reported poor health [ 15 ], and aging are associated with greater odds of being a cancer survivor. Being Black or Hispanic is associated with lower odds of being a cancer survivor. In men aged 45–64 years binge drinking is associated with lower odds of being a cancer survivor. Our results also support current smokers being less likely to be a cancer survivor for the male population. Our findings add new evidence on the risk factors associated with being a cancer survivor among SGM and heterosexual cisgender peers. Findings on prevalence of cancer survivorship among SGM and heterosexual peers have inconsistent findings in other national probabilistic-based surveys, such as the National Health Interview Survey (NHIS). In an analysis of 2017 and 2021 surveys, gay men and lesbians were more likely to report being a cancer survivor (73% and 2.3 fold) but no difference was observed for bisexual men and women compared to heterosexual peers after adjusting for risk factors [ 16 ]. In contrast, using 2013–2016 NHIS data, gay men and bisexual women were more likely to being a cancer survivor but not lesbians or bisexual men, compared to heterosexual peers [ 17 ]. Our findings indicate that identifying as Black or Hispanic is associated with significantly lower odds of reporting cancer survivorship. In particular, Black women were notably less likely to be cancer survivors, which may reflect a survival bias rooted in disproportionately high cancer-related mortality within this population [ 18 , 19 ]. For breast cancer—a leading cancer type among women—the literature consistently demonstrates that Black women experience higher mortality rates, are diagnosed at younger ages, and are more likely to present with biologically aggressive subtypes such as triple-negative breast cancer, compared to their white counterparts [ 20 , 21 ]. These disparities suggest that the lower survivorship observed in our sample may not solely reflect lower incidence or access to post-treatment support, but rather a bias introduced by exclusion of individuals who did not survive long enough to be included in cross-sectional studies. Consequently, the observed trend underscores the importance of considering systemic factors and structural inequities that influence both cancer outcomes and data interpretation in population-level research. A similar pattern of reduced cancer survivorship was observed among Black males aged 45–64 years, but not among those aged 65–80 years, suggesting age-related variations in survivorship trends within racial subgroups. Across ethnic categories, Hispanic/Latinx populations exhibit lower incidence and mortality rates for several cancer types [ 18 , 22 ]. However, interpreting these findings requires caution due to the complexity and heterogeneity embedded within the "Hispanic" classification. This ethnic category encompasses individuals from diverse national origins—including Mexico (59%), Puerto Rico (9%), El Salvador (4%), Cuba (4%), the Dominican Republic (4%), and 13 other countries that each represent between 0.2% and 3% of the U.S. population [ 23 ]. Moreover, demographic distribution varies regionally, and approximately one-third of Hispanic individuals residing in the United States were foreign-born as of 2021 [ 24 ]. In our dataset, all individuals within this broad classification were grouped under a single Hispanic category, which—despite our models indicating a lower likelihood of living with cancer among Hispanic participants—restricts our ability to draw more nuanced, country-specific conclusions regarding cancer risk and survivorship Among the risky health-related behaviors, current behaviors were reported, not necessarily their behaviors prior to and at the time of cancer diagnosis. SGM women but not SGM men are much more likely to have BMI ≥ 25 kg/m 2 compared to non-SGM individuals. In a systematic review, obesity at the time of diagnosis has been associated with increased cancer-related mortality and recurrence, particularly for breast, colorectal, and prostate cancers [ 25 ]. Globally, obesity remains a well-established risk factor for cancer mortality [ 26 ]. However, an emerging body of research describes an “obesity paradox,” suggesting that a higher body mass index (BMI) at the time of diagnosis may correlate with improved cancer survival rates [ 27 – 29 ]. For instance, Zhang and colleagues analyzed NHANES data and classified cancer patients into five weight trajectories—long-term obesity, long-term overweight, recent weight gain, recent weight loss, and stable normal weight—based on self-reported weight over a 25-year span. Compared to those with stable normal weight, both long-term overweight and obesity were associated with a reduced cancer mortality risk (17% and 23%, respectively); while recent weight gain and loss were linked to increased mortality risk (19% and 40%) [ 30 ]. In another study, Boehmer and colleagues speculated that sexual minority women may be more likely than heterosexual women to embrace a cancer diagnosis as a ‘teachable moment’ and adopt healthy lifestyle choices including achieving a healthy weight subsequent to their breast cancer diagnosis [ 31 ]. Given the complexity of these patterns and the lack of longitudinal weight data in our cohort, drawing definitive conclusions about survivorship likelihood and weight status remains challenging. Alcohol consumption is established as a risk factor for multiple types of cancer, including those of the liver, pancreas, breast (in women), upper gastrointestinal tract, head-neck, and colorectal [ 32 – 34 ]. A pattern of early initiation and problematic use of alcohol among SGM individuals has been documented, though current data suggest sexual minority females but not males are at the greatest long-term risk of binge and heavy alcohol use [ 35 , 36 ]. Importantly, patterns of alcohol consumption may vary over the life course among SGM populations compared to non-SGM peers. For instance, Marshall and colleagues identified four alcohol use trajectories (infrequent drinkers, low-risk drinkers, potentially hazardous drinkers, and consistently hazardous drinkers) among sexual minority veterans (ages 39–52 years) over a seven years period. While alcohol use declined across all trajectories, approximately 12% of participants remained consistently hazardous drinkers, slightly above the 9% prevalence of binge drinking among SGM participants in our sample. Heavy alcohol consumption is further recognized as a significant risk factor for cancer mortality. Using NHANES (2005–2018) data, one study found both depression and heavy alcohol consumption were independently associated with a twofold cancer mortality risk; although, the synergistic effect of these two health-related behaviors did not reach statistical significance [ 37 ]. Additionally, cohort studies suggest that elevated alcohol intake following a cancer diagnosis correlates with increased risks of recurrence and mortality[ 38 – 40 ]. In our sample, binge drinking prevalence was higher among sexual and gender minority (SGM) females (9.5%) than non-SGM females (6.0%). Nevertheless, in logistic regression models stratified by age (45–64 and 65–80 years) and sex, binge drinking did not emerge as a statistically significant risk factor for cancer survivorship among females. These findings highlight the need for more nuanced research into the temporal and behavioral patterns of alcohol use among SGM populations and their potential impact on cancer outcomes. Smoking is associated with numerous cancers [ 41 – 43 ] and is causally linked to lung cancer [ 44 , 45 ]. Consistent with the existing literature, SGM individuals smoke more than non-SGM individuals [ 3 ]. In analyses stratified by age groups among cancer survivors, female smoking prevalence was comparable between SGM and non-SGM individuals. In contrast, male SGM cancer survivors demonstrated significantly higher smoking rates across both age groups. However, when evaluating cancer risk, SGM male survivors were notably less likely to smoke than non-SGM male survivors—a finding that may reflect differential behavioral responses post-diagnosis. Recent smoking cessation among those diagnosed with lung cancer showed significant improvement in survival [ 47 ]. This supports Boehmer’s proposition that a cancer diagnosis may serve as a “teachable moment” to adopt healthier lifestyle behaviors. SGM men, in particular, capitalize on this opportunity by quitting smoking. Strengths and Limitations This study is based on the nationally representative Behavioral Risk Factor Surveillance System (BRFSS), a probabilistic survey design that supports the generalizability of findings to the broader U.S. population. While generational alignment between BRFSS and other datasets is not exact, the representation of sexual and gender minority (SGM) individuals in BRFSS closely parallels estimates from the Pew Research Center [ 48 ]. Our study has limitations. Only a handful of states included the optional ‘Cancer Survivor’ module, which collected additional data on the specific type of cancer diagnosed, age at diagnosis, and number of different types of cancer, and current treatment status. The absence of these data restricts our ability to examine nuanced differences in outcomes—such as variations based on specific cancer types, distinctions between long-term and recent diagnoses, cumulative exposure to cancer therapies over the life course, identification of HIV-related malignancies, and the potential impact of receiving active treatment during survey participation. Due to the small number of states implementing this module, the sample size of sexual and gender minority (SGM) survivors was insufficient to support these stratified analyses. Future studies with broader geographic coverage and enriched survivor data are warranted to explore these critical dimensions. Conclusion A more nuanced understanding of how health-related risk factors influence survivorship across the cancer continuum is critical for advancing survivorship research. While prior studies have indicated that sexual and gender minority (SGM) individuals may be at elevated risk for cancer, our findings do not support a higher prevalence of cancer among those with an SGM identity [ 49 ]. Because SGM status in the BRFSS is not collected by all states, and because sexual orientation and gender identity data are neither required in cancer registries nor routinely gathered in outpatient clinics or hospital admissions, a greater emphasis on collecting these data is essential to effectively examine similarities and differences in health outcomes across the cancer survivorship between SGM and non-SGM patients. However, limitations in data collection remain a substantial barrier. The Behavioral Risk Factor Surveillance System (BRFSS) does not capture SGM status uniformly across all states, and sexual orientation and gender identity (SOGI) data are not routinely gathered in cancer registries, outpatient settings, or hospital admissions. To comprehensively assess disparities in cancer survivorship between SGM and non-SGM populations, greater emphasis must be placed on systematic and standardized collection of SOGI data. Only through such efforts can researchers and clinicians accurately identify and address potential inequities in cancer outcomes. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study analysis. Data acquisition and analysis were performed by [Maria T Brown]. The first draft of the manuscript was written by [Jane A McElroy] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The dataset analyzed for this study are available on the CDC website, https://www.cdc.gov/brfss/annual_data/annual_2021.html Ethics approval We used BRFSS public datasets (secondary data analysis of collected survey data) and our analysis did not require ethics approval. The Syracuse University Institutional Review Board deemed this “Not Human Subjects Research – The data does not meet the definition of human subjects research data because the researcher will not interact/communicate with participants and the data is not identifiable.” Consent to participate We used BRFSS public datasets (secondary data analysis of collected survey data). Informed consent was obtained from all individual participants in the study at the time of data collection. Consent to publish No identifiable information was included in the manuscript References Tonorezos E, Devasia T, Mariotto AB, Mollica MA, Gallicchio L, Green P et al (2024) Prevalence of cancer survivors in the United States. 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BMC Public Health 25(1):608. 10.1186/s12889-025-21863-9 Bener A, Öztürk AE, Dasdelen MF, Barisik CC, Dasdelen ZB, Agan AF et al (2024) Colorectal cancer and associated genetic, lifestyle, cigarette, nargileh-hookah use and alcohol consumption risk factors: a comprehensive case-control study. Oncol Rev 18:1449709. 10.3389/or.2024.1449709 Centers for Disease Control and Prevention (CDC). Health Effects of Cigarettes: Cancer (2024) https://www.cdc.gov/tobacco/about/cigarettes-and-cancer.html Accessed May 18 2025 Tang FH, Wong HYT, Tsang PSW, Yau M, Tam SY, Law L et al (2025) Recent advancements in lung cancer research: a narrative review. Transl Lung Cancer Res 14(3):975–990. 10.21037/tlcr-24-979 Zoschke IN, Bennis SL, Tang Y, Wilkerson JM, Stull CL, Nyitray AG et al (2025) The influence of tobacco use, hazardous drinking, and other risk factors on HPV-associated oropharyngeal cancer risk and screening perceptions among gay and bisexual men: a cross-sectional study. BMC Oral Health 25(1):462. 10.1186/s12903-025-05774-0 Yong PC, Sigel K, Rehmani S, Wisnivesky J, Kale MS (2020) Lung Cancer Screening Uptake in the United States. Chest 157(1):236–238. 10.1016/j.chest.2019.08.2176 Caini S, Del Riccio M, Vettori V, Scotti V, Martinoli C, Raimondi S et al (2022) Quitting Smoking At or Around Diagnosis Improves the Overall Survival of Lung Cancer Patients: A Systematic Review and Meta-Analysis. J Thorac Oncol 17(5):623–636. 10.1016/j.jtho.2021.12.005 Jones JM (2024) LGBTQ + Identification in U.S. Now at 7.6%. https://news.gallup.com/poll/611864/lgbtq-identification.aspx Accessed Jun 20 2025 Kratzer TB, Star J, Minihan AK, Bandi P, Scout NFN, Gary M et al (2024) Cancer in people who identify as lesbian, gay, bisexual, transgender, queer, or gender-nonconforming. Cancer 130(17):2948–2967. 10.1002/cncr.35355 Tables Table 1 Sample Characteristics, Females and Males Ages 45–80 Year Olds, BRFSS 2021 Characteristics Full Sample (N = 161,228) Females Males SGM (N = 3,291) Non-SGM (N = 85,548) Sig. SGM (N-2,871) Non-SGM (N = 69,518) Sig. History of Cancer 13.0% 13.2% 14.0% 11.5% 11.9% Demographics Sexual and Gender Minority 3.8% 100.0% 0.0% 100.0% 0.0% Age (mean, SD, in years) 64.2,10.5 61.8, 10.7 64.7, 10.5 *** 62.4, 10.3 63.8, 10.4 *** At least some college 72.2% 74.3% 70.5% *** 74.4% 74.2% Race Black 6.6% 6.3% 7.4% * 5.5% 5.7% Other 6.4% 8.0% 5.9% *** 9.0% 6.8% *** Hispanic 5.1% 6.8% 4.8% *** 8.6% 5.2% *** Health Behaviors Binge drinker 8.7% 9.5% 6.0% *** 11.7% 11.9% BMI ≥ 25 (in kg/m 2 ) 65.2% 62.1% 57.8% *** 69.1% 74.4% *** Current smoker 11.9% 15.4% 11.5% *** 17.8% 12.1% *** Health Outcomes Poor health 19.6% 24.6% 19.6% *** 22.4% 19.2% *** Number of chronic health conditions (mean, SD) 1.1, 1.2 1.2, 1.3 1.1, 1.2 *** 1.1, 1.3 1.0, 1.2 *** Two or more chronic conditions 26.6% 31.1% 26.9% *** 29.7% 25.9% *** Table 2 Sex and Age-Specific Characteristics of Cancer Survivors, BRFSS 2021 Females Males Ages 45–64 Ages 65–80 Ages 45–64 Ages 65–80 Characteristics SGM (N = 206) Non SGM (N = 3914) sig. SGM (N = 227) Non SGM (N = 8046) sig. SGM (N = 119) Non SGM (N = 1958) sig. SGM (N = 211) Non SGM (N = 6270) sig. Age (mean, SD) 55.5, 5.8 56.6, 5.5 ** 73.5, 5.0 74.1, 5.1 57.7, 4.8 57.9, 5.1 73.3, 5.2 74.2, 5.0 ** At least some college 70.4% 69.2% 76.2% 69.5% * 65.6% 73.4% 79.6% 73.2% * Race (%) White 80.6% 82.5% 87.7% 87.9% 74.8% 83.9% *** 86.7% 88.6% ** Black 4.4% 5.6% 7.1% 5.3% 6.7% 5.2% 5.7% 5.3% Other 9.2% 7.0% 4.0% 4.8% 6.7% 6.2% 3.8% 4.1% Hispanic 5.8% 4.9% 1.3% 2.1% 11.8% 4.7% *** 3.8% 2.0% Health Behaviors (%) Binge drinking 10.2% 8.6% 4.4% 2.5% 14.3% 12.7% 8.1% 5.2% BMI ≥ 25 (in kg/m 2 ) 74.8% 62.6% *** 55.5% 58.1% 71.4% 75.5% 63.0% 71.2% ** Current smoker 21.8% 18.6% 8.4% 7.7% 27.7% 15.0% *** 11.9% 7.2% * Health Outcomes (%) Poor health 39.3% 32.3% * 31.7% 29.3% 39.5% 34.3% 34.1% 30.6% Number of chronic conditions (mean, SD) 1.4, 1.4 1.2, 1.3 * 1.5, 1.4 1.4, 1.3 1.7, 1.7 1.2, 1.3 *** 1.6, 1.5 1.5, 1.3 Two or more chronic health conditions 38.4% 32.3% 41.0% 39.1% 43.7% 30.6% ** 44.6% 41.5% Table 3 Sex- and Age-Stratified Logistic Regression Models of Cancer Survivorship and Associated Risk Factors All ages 45–64 years 65–80 years All ages 45–64 years 65–80 years OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Age (in years) 1.04 1.04–1.04 1.04 1.03–1.05 1.03 1.03–1.03 1.07 1.07–1.08 1.09 1.08–1.10 1.06 1.06–1.07 Sexual and Gender minority (ref: non-SGM, cisgender heterosexual) 1.00 0.90–1.11 1.06 0.91–1.23 0.94 0.81–1.08 1.05 0.93–1.19 1.32 1.09–1.61 0.93 0.79–1.08 Race (ref: Non-Hispanic White) Black 0.62 0.57–0.67 0.56 0.49–0.65 0.65 0.59–0.73 0.94 0.85–1.05 0.67 0.55–0.82 1.09 0.96–1.23 Other 0.92 0.84-1.00 0.94 0.82 − 0.65 0.90 0.80–1.01 0.7 0.63–0.78 0.69 0.57–0.82 0.71 0.62–0.81 Hispanic 0.61 0.55–0.69 0.61 0.52–0.71 0.61 0.52–0.72 0.62 0.54–0.72 0.62 0.50–0.76 0.62 0.51–0.75 At least some college (ref: High School or less) 0.97 0.93–1.01 0.97 0.91–1.04 0.98 0.93–1.03 0.97 0.90-1.00 0.97 0.88–1.07 0.95 0.89–1.01 Current smoker (ref: Past or Never Smoker) 1.01 0.95–1.07 1.03 0.94–1.04 0.94 0.86–1.03 0.87 0.80–0.94 0.78 0.68–0.88 0.88 0.79–0.98 Binge drinking (ref: No Drinking or No Binge Drinking) 0.98 0.89–1.07 0.99 0.88–1.11 0.99 0.85–1.16 0.88 0.80–0.96 0.84 0.74–0.96 0.93 0.83–1.05 BMI ≥ 25 (in kg/m 2 ) (ref: BMI < 25) 1.08 1.04–1.13 1.06 0.99–1.13 1.09 1.03–1.14 0.97 0.92–1.03 0.87 0.78–0.97 1.00 0.94–1.06 2 + Chronic health conditions (ref: 0–1 chronic conditions) 1.26 1.20–1.31 1.35 1.25–1.46 1.21 1.15–1.28 1.22 1.16–1.28 1.41 1.26–1.57 1.16 1.09–1.23 Poor health (ref: Fair to Excellent Health) 1.80 1.71–1.88 2.12 1.96–2.30 1.64 1.55–1.74 1.87 1.77–1.97 2.40 2.16–2.67 1.69 1.59–1.81 Additional Declarations No competing interests reported. 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McElroy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHACxgNAgpmBvQFJjIeAHogWngNgjgTRWoBqE4jUott++MDhiorD7Pwz35hu/NlWV8cvdoDxwds23FrMzqQlHDxz5jCzxO0cs9u8bYclJGcnMBvOxaflBo/Bwca2w8wMIC2MbQckDG4nsEnz4tXC/wGsRf7mGbObQIdJ2N9OYP+NXwsPA1iLwQ0esxu8bcwSBtIJbMx4tZxJMzjYcCad2fBMWtltnnOHJWfcTmyWnHMOj5bjhx8+bKiwTpY7fnjbzR9ldfz8s5MPfnhThlsLFDQngylGNjDZQFA9ENTZQeg/xCgeBaNgFIyCkQYAufxYW6a82S0AAAAASUVORK5CYII=","orcid":"","institution":"University of Missouri Family and Community Medicine Department","correspondingAuthor":true,"prefix":"","firstName":"Jane","middleName":"A.","lastName":"McElroy","suffix":""},{"id":499624866,"identity":"7e971916-13da-47ed-b7a6-eedb0168d6d1","order_by":1,"name":"Ting Guan","email":"","orcid":"","institution":"Indiana University School of Social Work","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Guan","suffix":""},{"id":499624867,"identity":"9d94996d-40a0-49b1-8d7e-8bb03461c753","order_by":2,"name":"Allison B. Anbari","email":"","orcid":"","institution":"University of Missouri Sinclair School of Nursing","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"B.","lastName":"Anbari","suffix":""},{"id":499624868,"identity":"f74afd1b-1dbd-4fb6-8cb9-75831a669bdb","order_by":3,"name":"Maria T. Brown","email":"","orcid":"","institution":"Syracuse University School of Social Work","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"T.","lastName":"Brown","suffix":""}],"badges":[],"createdAt":"2025-08-01 13:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7271773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7271773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89318823,"identity":"1380262d-0863-41da-93ee-b82202a4ab26","added_by":"auto","created_at":"2025-08-18 17:46:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7271773/v1/828b7f31-ee0a-48b2-a674-69a2676d1467.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cancer survivorship among sexual and gender minority and cisgender heterosexual individuals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver 18\u0026nbsp;million people are living with a history of cancer in the United States (U.S.), representing 5.4% of the population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. From 2022 to 2040, the projected number of persons living with cancer for 5 or more years is expected to be 19.2\u0026nbsp;million, an increase of 53% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition, cancer mortality continues to decrease as indicated by a 32% reduction in cancer mortality rates from 1991 to 2019. The most impactful factor associated with this decrease is falling cigarette smoking rates; other factors include early detection through screening and newer treatments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Data from well-established national population-based studies support that elevated health-related risk factors for a cancer diagnosis are disproportionally experienced by sexual and gender minority (SGM; aka: lesbian, gay, bisexual, transgender) individuals compared to cisgender heterosexual individuals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Further, structural and systemic stigma faced by SGM individuals also have been cited as underlying factors in behavioral choices in seeking medical care or using psychoactive drugs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As aging is a known predictor of a cancer diagnosis, concerns among older SGM individuals have also indicated a disparity in care [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], including timely access [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUsing a community-based system dynamics approach, Gillani and colleagues identified key drivers of healthcare disparities within sexual and gender minority (SGM) populations, including societal and structural stigma, provider bias, and pathologization [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The strength of this complex systems methodology lies in its ability to account for dynamic feedback loops that influence individual health behaviors and outcomes. For example, the absence of inclusive healthcare environments may discourage routine medical check-ups, thereby leading to delayed cancer diagnoses and poorer prognoses. SGM individuals face compounding challenges related to both healthcare access and cancer risk. Barriers to timely care, often rooted in discrimination and systemic inequities, contribute to later-stage diagnoses and reduced survival rates. Simultaneously, elevated health-related risk factors within SGM groups may increase their likelihood of developing cancer. These opposing forces\u0026mdash;reduced survival due to delayed care versus increased incidence from heightened risk\u0026mdash;may ultimately result in comparable odds of being a cancer survivor between SGM and cisgender heterosexual survey respondents, despite entrenched differences between the two groups. This study examined predictors of cancer survivorship using a national population-based survey, adjusting for both suspected and established cancer risk factors.\u003c/p\u003e\u003cp\u003eA note on terminology.\u003c/p\u003e\u003cp\u003eSexual and gender minority is a term that has been adopted by the National Institute of Health (NIH) to identify people who do not identify as cisgender as gender identity and/or heterosexual as sexual orientation. SGM is used in this manuscript as an inclusive term, albeit not commonly adopted within the SGM community. A long list of acronyms can be used, such as LGBTQQIP2SAA (lesbian, gay, bisexual, transgender, queer, questioning, intersex, pansexual, two-spirit (2S), androgynous, and asexual), but even this list does not fully encompass all possible descriptions of one\u0026rsquo;s identity. Another term that has differing views on its acceptability is \u0026lsquo;cancer survivor.\u0026rsquo; This term has been defined by the NIH to mean anyone with a history of cancer from the time of diagnosis through the rest of their life. Although alternative terms include persons living with cancer, individuals with a history of cancer, thrivers, warriors, or persons on a cancer journey, the term cancer survivor will be used in this manuscript.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis cross-sectional study analyzed data from the 2021 wave of the Behavioral Risk Factor and Surveillance System (BRFSS) survey. The Centers for Disease Control and Prevention fielded the BRFSS survey with data on sexual orientation and gender identity using the optional Sexual Orientation and Gender Identity module in 33 of 39 states [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Participants in these 33 states also administered the health status module of the core interview, in which participants indicated whether they had received diagnoses of various chronic health conditions, including ever been diagnosed with cancer (yes, no). Respondents were included in the sample if they were 45 years of age or older and responded to both the sexual orientation and gender identity module and the health status module.\u003c/p\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003cp\u003eCancer history was determined based on participant response to the health status question: \u0026ldquo;Has a doctor, nurse, or other health professional ever told you that you had \u0026hellip; cancer.\u0026rdquo; A yes response was coded as cancer survivor. Skin cancer\u0026rdquo; was excluded because the ability to differentiate between a diagnosis of melanoma (a life-threatening, potentially metastatic disease) and nonmelanoma skin cancer (typically not life threatening) was not possible. Sexual minority status was determined using the following question: \u0026ldquo;Which of the following best represents how you think of yourself?\u0026rdquo; (six categories: gay; straight, that is, not gay; bisexual; something else; I don\u0026rsquo;t know the answer; refused). Any response other than \u0026lsquo;straight, that is, not gay\u0026rsquo; was coded as sexual minority. Gender minority status was determined by one question: \u0026ldquo;Do you consider yourself to be transgender?\u0026rdquo; The five answer options were as follows: yes, transgender, male-to-female; yes, transgender, female-to-male; yes, transgender, gender nonconforming; no; don\u0026rsquo;t know. Any response other than \u0026lsquo;no\u0026rsquo; to this gender identity question was coded as gender diverse (GD). SGM comprised any participant labeled as SM and/or GD.\u003c/p\u003e\u003cp\u003ePoor health was measured using the general health item in the Health Status section of the questionnaire: \u0026ldquo;Would you say that in general your health is?\u0026rdquo; The available responses ranged from excellent (1) to poor (5), with higher values indicating poorer health. The responses to this ordinal variable were divided into a dichotomous variable, with the values of 1 (poor) and 0 (fair, good, very good, and excellent). Chronic health conditions were identified from the following question: \u0026ldquo;Has a doctor, nurse, or other health professional ever told you that you had any of the following?\u0026rdquo; These eight conditions were: history of heart attack (or myocardial infarction), heart disease (angina or coronary heart disease), history of stroke, diabetes (excluding prediabetes or borderline diabetes), kidney disease, chronic lung disease (chronic obstructive pulmonary disease, emphysema, or chronic bronchitis), some form of [arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia], and [currently have] asthma. The dichotomous variable for each of the eight chronic health conditions (positive response\u0026thinsp;=\u0026thinsp;1) were summed into a continuous variable for number of chronic health conditions (range 0\u0026ndash;8) and a dichotomous variable, with the values of 1 (two or more chronic health condition reported) and 0 (zero or one chronic health condition reported). Demographic characteristics included age (two groups: 45\u0026ndash;64 years and 65\u0026ndash;80 years and as continuous variable), race (four groups: White, Black, Hispanic, other (all other responses)), educational attainment (two groups: at least some college versus high school education or less). Health-related behaviors included current smoker (yes/no), binge drinker (yes/no) in the past 30 days, drank five or more drinks on one occasion (for males) or drank four or more drinks on one occasion (for females), and had a body mass index (BMI in kg/m\u003csup\u003e2\u003c/sup\u003e) of 25 or higher (yes/no).\u003c/p\u003e\u003cp\u003eAnalytical plan\u003c/p\u003e\u003cp\u003eDescriptive statistics characterize the sample. Bivariate analysis examined the relationship between SGM identity, cancer history, and health by performing t-tests to compare mean differences in continuous variables and chi-square tests for categorical variables. Binary logistic regression models evaluated the association between being cancer survivor (dependent variable) and participants\u0026rsquo; characteristics (independent variables) including SGM status, self-reported poor health, two or more chronic health conditions, BMI of \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, smoking status, educational attainment, binge drinking status, race/ethnicity, and age by sex (male and female separately). This logistic regression model also stratified for those aged 45\u0026ndash;54 years and aged 65\u0026ndash;80 years by sex (female and male).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSample Characteristics\u003c/p\u003e\u003cp\u003eThis dataset was comprised of 88,839 females (with 3,291 identified as SGM, 3.7%) and 72,389 males (with 2,871 identified as SGM, 4.0%). For cancer survivors, 433 female SGM, 11,960 non-SGM and 330 male SGM, 8228 non-SGM were used in the analysis. SGM participants from the full dataset were significantly younger, Hispanic or some other race/ethnicity (other means not White, Black, or Hispanic), identified as a smoker, reported poor health, and reported having 2 or more chronic conditions compared to non-SGM participants. SGM females but not SGM males also had BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, higher educational attainment, and qualified as a binge drinker compared to non-SGM peers. In contrast to the higher proportion of SGM females at BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, fewer SGM males had BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e compared to their non-SGM peers. Reporting a history of cancer was similar between non-SGM and SGM participants.\u003c/p\u003e\u003cp\u003eStratifying by ages 45\u0026ndash;64 years and 65\u0026ndash;80 years, among the younger cohort, female SGM cancer survivors were significantly younger (by about one year) and reported having BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e more frequently when compared to non-SGM cancer survivors. No significant differences were observed between the older SGM and non-SGM females. The younger cohort of male (45\u0026ndash;64 years) SGM cancer survivors was significantly different in being a current smoker, having 2 or more chronic conditions and identifying as Hispanic compared to non-SGM male cancer survivors. Among the older cohort, male SGM cancer survivors were significantly younger (by less than one year) and fewer reported having BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e compared to non-SGM cancer survivors. These two significant differences were not observed among the younger male cohort. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\u003c/p\u003e\u003cp\u003eMultivariate Binary Logistic Regression Models\u003c/p\u003e\u003cp\u003eResults from multivariate logistic regressions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveal that when controlling for age, race/ethnicity, education attainment, health status, and risky health-related behaviors (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, binge drinking and smoking), female SGM identity was not associated with greater odds being a cancer survivor (OR: 1.00; 95% CI: 0.90\u0026ndash;1.11). The finding was similar for the male population (OR: 1.05; 95% CI: 0.93\u0026ndash;1.19). For the entire sample, among the covariates in the model, being older, reporting poor health, and having multiple chronic conditions were associated with increased odds of being a cancer survivor. Being Hispanic (compared to White participants) was associated with lower odds of being a cancer survivor. Results stratified by age (45\u0026ndash;64 years and 65\u0026ndash;80 years) were also evaluated with most of the findings similar to the full sample. Five characteristics were consistent in women of both age groups: the odds of being a cancer survivor were lower for females identifying as Black or Hispanic and higher among females in poor health, having 2\u0026thinsp;+\u0026thinsp;chronic conditions, or older age. The odds of being a cancer survivor were also greater for women aged 65\u0026ndash;80 years with a BMI of \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e; this risk was not present in women aged 45\u0026ndash;64 years old. Women aged 45\u0026ndash;64 years who identified as \u0026lsquo;other\u0026rsquo; race had lower odds of being a cancer survivor. There was more variation in the odds of being a cancer survivor between males in the two age groups and all but one characteristic (i.e., educational attainment) in the younger group was associated with having a higher odds of being a cancer survivor (i.e., older age, identifying as SGM, 2\u0026thinsp;+\u0026thinsp;chronic conditions, reported poor health) or having a lower odds (Black, \u0026lsquo;other\u0026rsquo;, Hispanic, current smoker, binge drinker and BMI of \u0026ge;\u0026thinsp;25). Males aged 65\u0026ndash;80 years had lower odds of being a cancer survivor if they were self-identified as \u0026lsquo;other\u0026rsquo; race or Hispanic or a current smoker. Their odds of being a cancer survivor was greater if they were older, reported poor health or 2\u0026thinsp;+\u0026thinsp;chronic conditions. See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to describe predictors or cancer survivorship using a national population-based survey, adjusting for both suspected and established cancer risk factors.\u003c/p\u003e\u003cp\u003eThese results suggest that sexual and gender minority (SGM) adults have comparable odds of being cancer survivors as non-SGM adults, despite numerous studies indicating that SGM individuals exhibit a higher prevalence of cancer-related risk factors compared to their cisgender, heterosexual peers. Consistent with other research, we find that multiple chronic conditions [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], self-reported poor health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and aging are associated with greater odds of being a cancer survivor. Being Black or Hispanic is associated with lower odds of being a cancer survivor. In men aged 45\u0026ndash;64 years binge drinking is associated with lower odds of being a cancer survivor. Our results also support current smokers being less likely to be a cancer survivor for the male population.\u003c/p\u003e\u003cp\u003eOur findings add new evidence on the risk factors associated with being a cancer survivor among SGM and heterosexual cisgender peers. Findings on prevalence of cancer survivorship among SGM and heterosexual peers have inconsistent findings in other national probabilistic-based surveys, such as the National Health Interview Survey (NHIS). In an analysis of 2017 and 2021 surveys, gay men and lesbians were more likely to report being a cancer survivor (73% and 2.3 fold) but no difference was observed for bisexual men and women compared to heterosexual peers after adjusting for risk factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, using 2013\u0026ndash;2016 NHIS data, gay men and bisexual women were more likely to being a cancer survivor but not lesbians or bisexual men, compared to heterosexual peers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings indicate that identifying as Black or Hispanic is associated with significantly lower odds of reporting cancer survivorship. In particular, Black women were notably less likely to be cancer survivors, which may reflect a survival bias rooted in disproportionately high cancer-related mortality within this population [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For breast cancer\u0026mdash;a leading cancer type among women\u0026mdash;the literature consistently demonstrates that Black women experience higher mortality rates, are diagnosed at younger ages, and are more likely to present with biologically aggressive subtypes such as triple-negative breast cancer, compared to their white counterparts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These disparities suggest that the lower survivorship observed in our sample may not solely reflect lower incidence or access to post-treatment support, but rather a bias introduced by exclusion of individuals who did not survive long enough to be included in cross-sectional studies. Consequently, the observed trend underscores the importance of considering systemic factors and structural inequities that influence both cancer outcomes and data interpretation in population-level research.\u003c/p\u003e\u003cp\u003eA similar pattern of reduced cancer survivorship was observed among Black males aged 45\u0026ndash;64 years, but not among those aged 65\u0026ndash;80 years, suggesting age-related variations in survivorship trends within racial subgroups. Across ethnic categories, Hispanic/Latinx populations exhibit lower incidence and mortality rates for several cancer types [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, interpreting these findings requires caution due to the complexity and heterogeneity embedded within the \"Hispanic\" classification. This ethnic category encompasses individuals from diverse national origins\u0026mdash;including Mexico (59%), Puerto Rico (9%), El Salvador (4%), Cuba (4%), the Dominican Republic (4%), and 13 other countries that each represent between 0.2% and 3% of the U.S. population [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, demographic distribution varies regionally, and approximately one-third of Hispanic individuals residing in the United States were foreign-born as of 2021 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our dataset, all individuals within this broad classification were grouped under a single Hispanic category, which\u0026mdash;despite our models indicating a lower likelihood of living with cancer among Hispanic participants\u0026mdash;restricts our ability to draw more nuanced, country-specific conclusions regarding cancer risk and survivorship\u003c/p\u003e\u003cp\u003eAmong the risky health-related behaviors, current behaviors were reported, not necessarily their behaviors prior to and at the time of cancer diagnosis. SGM women but not SGM men are much more likely to have BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e compared to non-SGM individuals. In a systematic review, obesity at the time of diagnosis has been associated with increased cancer-related mortality and recurrence, particularly for breast, colorectal, and prostate cancers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Globally, obesity remains a well-established risk factor for cancer mortality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, an emerging body of research describes an \u0026ldquo;obesity paradox,\u0026rdquo; suggesting that a higher body mass index (BMI) at the time of diagnosis may correlate with improved cancer survival rates [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For instance, Zhang and colleagues analyzed NHANES data and classified cancer patients into five weight trajectories\u0026mdash;long-term obesity, long-term overweight, recent weight gain, recent weight loss, and stable normal weight\u0026mdash;based on self-reported weight over a 25-year span. Compared to those with stable normal weight, both long-term overweight and obesity were associated with a reduced cancer mortality risk (17% and 23%, respectively); while recent weight gain and loss were linked to increased mortality risk (19% and 40%) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In another study, Boehmer and colleagues speculated that sexual minority women may be more likely than heterosexual women to embrace a cancer diagnosis as a \u0026lsquo;teachable moment\u0026rsquo; and adopt healthy lifestyle choices including achieving a healthy weight subsequent to their breast cancer diagnosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Given the complexity of these patterns and the lack of longitudinal weight data in our cohort, drawing definitive conclusions about survivorship likelihood and weight status remains challenging.\u003c/p\u003e\u003cp\u003eAlcohol consumption is established as a risk factor for multiple types of cancer, including those of the liver, pancreas, breast (in women), upper gastrointestinal tract, head-neck, and colorectal [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A pattern of early initiation and problematic use of alcohol among SGM individuals has been documented, though current data suggest sexual minority females but not males are at the greatest long-term risk of binge and heavy alcohol use [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Importantly, patterns of alcohol consumption may vary over the life course among SGM populations compared to non-SGM peers. For instance, Marshall and colleagues identified four alcohol use trajectories (infrequent drinkers, low-risk drinkers, potentially hazardous drinkers, and consistently hazardous drinkers) among sexual minority veterans (ages 39\u0026ndash;52 years) over a seven years period. While alcohol use declined across all trajectories, approximately 12% of participants remained consistently hazardous drinkers, slightly above the 9% prevalence of binge drinking among SGM participants in our sample.\u003c/p\u003e\u003cp\u003eHeavy alcohol consumption is further recognized as a significant risk factor for cancer mortality. Using NHANES (2005\u0026ndash;2018) data, one study found both depression and heavy alcohol consumption were independently associated with a twofold cancer mortality risk; although, the synergistic effect of these two health-related behaviors did not reach statistical significance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, cohort studies suggest that elevated alcohol intake following a cancer diagnosis correlates with increased risks of recurrence and mortality[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In our sample, binge drinking prevalence was higher among sexual and gender minority (SGM) females (9.5%) than non-SGM females (6.0%). Nevertheless, in logistic regression models stratified by age (45\u0026ndash;64 and 65\u0026ndash;80 years) and sex, binge drinking did not emerge as a statistically significant risk factor for cancer survivorship among females. These findings highlight the need for more nuanced research into the temporal and behavioral patterns of alcohol use among SGM populations and their potential impact on cancer outcomes.\u003c/p\u003e\u003cp\u003eSmoking is associated with numerous cancers [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and is causally linked to lung cancer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Consistent with the existing literature, SGM individuals smoke more than non-SGM individuals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In analyses stratified by age groups among cancer survivors, female smoking prevalence was comparable between SGM and non-SGM individuals. In contrast, male SGM cancer survivors demonstrated significantly higher smoking rates across both age groups. However, when evaluating cancer risk, SGM male survivors were notably less likely to smoke than non-SGM male survivors\u0026mdash;a finding that may reflect differential behavioral responses post-diagnosis. Recent smoking cessation among those diagnosed with lung cancer showed significant improvement in survival [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This supports Boehmer\u0026rsquo;s proposition that a cancer diagnosis may serve as a \u0026ldquo;teachable moment\u0026rdquo; to adopt healthier lifestyle behaviors. SGM men, in particular, capitalize on this opportunity by quitting smoking.\u003c/p\u003e\u003cp\u003eStrengths and Limitations\u003c/p\u003e\u003cp\u003eThis study is based on the nationally representative Behavioral Risk Factor Surveillance System (BRFSS), a probabilistic survey design that supports the generalizability of findings to the broader U.S. population. While generational alignment between BRFSS and other datasets is not exact, the representation of sexual and gender minority (SGM) individuals in BRFSS closely parallels estimates from the Pew Research Center [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study has limitations. Only a handful of states included the optional \u0026lsquo;Cancer Survivor\u0026rsquo; module, which collected additional data on the specific type of cancer diagnosed, age at diagnosis, and number of different types of cancer, and current treatment status. The absence of these data restricts our ability to examine nuanced differences in outcomes\u0026mdash;such as variations based on specific cancer types, distinctions between long-term and recent diagnoses, cumulative exposure to cancer therapies over the life course, identification of HIV-related malignancies, and the potential impact of receiving active treatment during survey participation. Due to the small number of states implementing this module, the sample size of sexual and gender minority (SGM) survivors was insufficient to support these stratified analyses. Future studies with broader geographic coverage and enriched survivor data are warranted to explore these critical dimensions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA more nuanced understanding of how health-related risk factors influence survivorship across the cancer continuum is critical for advancing survivorship research. While prior studies have indicated that sexual and gender minority (SGM) individuals may be at elevated risk for cancer, our findings do not support a higher prevalence of cancer among those with an SGM identity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Because SGM status in the BRFSS is not collected by all states, and because sexual orientation and gender identity data are neither required in cancer registries nor routinely gathered in outpatient clinics or hospital admissions, a greater emphasis on collecting these data is essential to effectively examine similarities and differences in health outcomes across the cancer survivorship between SGM and non-SGM patients. However, limitations in data collection remain a substantial barrier. The Behavioral Risk Factor Surveillance System (BRFSS) does not capture SGM status uniformly across all states, and sexual orientation and gender identity (SOGI) data are not routinely gathered in cancer registries, outpatient settings, or hospital admissions. To comprehensively assess disparities in cancer survivorship between SGM and non-SGM populations, greater emphasis must be placed on systematic and standardized collection of SOGI data. Only through such efforts can researchers and clinicians accurately identify and address potential inequities in cancer outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study analysis. Data acquisition and analysis were performed by [Maria T Brown]. The first draft of the manuscript was written by [Jane A McElroy] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed for this study are available on the CDC website, https://www.cdc.gov/brfss/annual_data/annual_2021.html\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used BRFSS public datasets \u0026nbsp;(secondary data analysis of collected survey data) and our analysis did not require ethics approval. \u0026nbsp;The Syracuse University Institutional Review Board deemed this \u0026ldquo;Not Human Subjects Research \u0026ndash; The data does not meet the definition of human subjects research data because the researcher will not interact/communicate with participants and the data is not identifiable.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used BRFSS public datasets \u0026nbsp;(secondary data analysis of collected survey data). \u0026nbsp;Informed consent was obtained from all individual participants in the study at the time of data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo identifiable information was included in the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTonorezos E, Devasia T, Mariotto AB, Mollica MA, Gallicchio L, Green P et al (2024) Prevalence of cancer survivors in the United States. 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J Thorac Oncol 17(5):623\u0026ndash;636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jtho.2021.12.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2021.12.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones JM (2024) LGBTQ\u0026thinsp;+\u0026thinsp;Identification in U.S. Now at 7.6%. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://news.gallup.com/poll/611864/lgbtq-identification.aspx\u003c/span\u003e\u003cspan address=\"https://news.gallup.com/poll/611864/lgbtq-identification.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed Jun 20 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKratzer TB, Star J, Minihan AK, Bandi P, Scout NFN, Gary M et al (2024) Cancer in people who identify as lesbian, gay, bisexual, transgender, queer, or gender-nonconforming. Cancer 130(17):2948\u0026ndash;2967. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.35355\u003c/span\u003e\u003cspan address=\"10.1002/cncr.35355\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\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\u003eSample Characteristics, Females and Males Ages 45\u0026ndash;80 Year Olds, BRFSS 2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFull Sample (N\u0026thinsp;=\u0026thinsp;161,228)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSGM\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;3,291)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-SGM\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;85,548)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSGM\u003c/p\u003e\u003cp\u003e(N-2,871)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-SGM\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;69,518)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSexual and Gender Minority\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean, SD, in years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.2,10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.8, 10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.7, 10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.4, 10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e63.8, 10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least some college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Behaviors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBinge drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (in kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e69.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of chronic health\u003c/p\u003e\u003cp\u003econditions (mean, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.1, 1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2, 1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1, 1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.1, 1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0, 1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo or more chronic\u003c/p\u003e\u003cp\u003econditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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\u003eSex and Age-Specific Characteristics of Cancer Survivors, BRFSS 2021\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eAges 45\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eAges 65\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eAges 45\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003eAges 65\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSGM (N\u0026thinsp;=\u0026thinsp;206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon SGM (N\u0026thinsp;=\u0026thinsp;3914)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003esig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSGM\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;227)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon SGM (N\u0026thinsp;=\u0026thinsp;8046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003esig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSGM (N\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNon SGM (N\u0026thinsp;=\u0026thinsp;1958)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003esig.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eSGM (N\u0026thinsp;=\u0026thinsp;211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eNon SGM (N\u0026thinsp;=\u0026thinsp;6270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003esig.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e55.5, 5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.6, 5.5\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\u003e73.5, 5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.1, 5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e57.7, 4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e57.9, 5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e73.3, 5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e74.2, 5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least some college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e70.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e65.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e73.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e79.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e73.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e80.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e87.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e74.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e83.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e86.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e88.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Behaviors (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBinge drinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (in kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e74.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.6%\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\u003e55.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e71.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e75.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e63.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e71.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e21.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Outcomes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e39.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3%\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\u003e31.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e34.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e34.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e30.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of chronic conditions (mean, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.4, 1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2, 1.3\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\u003e1.5, 1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.4, 1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.7, 1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.2, 1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.6, 1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.5, 1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo or more chronic health conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e38.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e43.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e30.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e44.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e41.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSex- and Age-Stratified Logistic Regression Models of Cancer Survivorship and Associated Risk Factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAll ages\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e45\u0026ndash;64 years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e65\u0026ndash;80 years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eAll ages\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e45\u0026ndash;64 years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003e65\u0026ndash;80 years\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (in years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.04\u0026ndash;1.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.03\u0026ndash;1.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.03\u0026ndash;1.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1.07\u0026ndash;1.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e1.08\u0026ndash;1.10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e1.06\u0026ndash;1.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSexual and Gender minority (ref: non-SGM, cisgender heterosexual)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u0026ndash;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u0026ndash;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81\u0026ndash;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.93\u0026ndash;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e1.09\u0026ndash;1.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.79\u0026ndash;1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace (ref: Non-Hispanic White)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.57\u0026ndash;0.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.49\u0026ndash;0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.59\u0026ndash;0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.85\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.55\u0026ndash;0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.96\u0026ndash;1.23\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\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.82\u0026thinsp;\u0026minus;\u0026thinsp;0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.80\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.63\u0026ndash;0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.57\u0026ndash;0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.62\u0026ndash;0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.55\u0026ndash;0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.52\u0026ndash;0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.52\u0026ndash;0.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.54\u0026ndash;0.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.50\u0026ndash;0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.51\u0026ndash;0.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt least some college (ref: High School or less)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u0026ndash;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.90-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.88\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.89\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker (ref: Past or Never Smoker)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u0026ndash;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.80\u0026ndash;0.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.68\u0026ndash;0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.79\u0026ndash;0.98\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBinge drinking (ref: No Drinking or No Binge Drinking)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u0026ndash;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85\u0026ndash;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.80\u0026ndash;0.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.74\u0026ndash;0.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.83\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (in kg/m\u003csup\u003e2\u003c/sup\u003e) \u003c/p\u003e\u003cp\u003e (ref: BMI\u0026thinsp;\u0026lt;\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.04\u0026ndash;1.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u0026ndash;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.03\u0026ndash;1.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.92\u0026ndash;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.78\u0026ndash;0.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.94\u0026ndash;1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026thinsp;+\u0026thinsp;Chronic health conditions (ref: 0\u0026ndash;1 chronic conditions)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.20\u0026ndash;1.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.25\u0026ndash;1.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.15\u0026ndash;1.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1.16\u0026ndash;1.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e1.26\u0026ndash;1.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e1.09\u0026ndash;1.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor health (ref: Fair to Excellent Health)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.71\u0026ndash;1.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.96\u0026ndash;2.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.55\u0026ndash;1.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1.77\u0026ndash;1.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e2.16\u0026ndash;2.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e1.59\u0026ndash;1.81\u003c/b\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"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer survivor, BRFSS, SGM, Risk factors, Cancer","lastPublishedDoi":"10.21203/rs.3.rs-7271773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7271773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo evaluate predictors of being a cancer survivor among those aged 45\u0026ndash;64 and 65\u0026ndash;80 years and inclusive of sexual and gender minority (SGM) status using a national population-based study dataset.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBehavioral Risk Factor and Surveillance System (BRFSS) survey used 2021 data. Descriptive statistics describe the study population characteristics. Logistic regression models, adjusting for known or suspected risk factors, evaluate participant characteristics, including SGM status, stratified by male and females and by 2 age groups (45\u0026ndash;64 years, 65\u0026thinsp;+\u0026thinsp;years) associated with being a cancer survivor.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBRFSS data comprise 88,839 females (12,400 female cancer survivors) and 72,389 males (8,558 male cancer survivors). Being older, reported poor health, having multiple chronic conditions were associated with increased odds and being Black or Hispanic was associated with lower odds of being a cancer survivor. In women being overweight/obese was associated with increased odds of being a cancer survivor, and for men binge drinking was associated with lower odds of being a cancer survivor. Those identifying as SGM had similar odds of being a cancer survivor.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings underscore the complex interplay of demographic and health-related factors in predicting cancer survivorship status, highlighting the need for targeted interventions that address differences across sex, ethnicity, and health behaviors.\u003c/p\u003e","manuscriptTitle":"Cancer survivorship among sexual and gender minority and cisgender heterosexual individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 14:46:06","doi":"10.21203/rs.3.rs-7271773/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"93c5b5c0-abea-48ad-a4fd-3b908c76c2fe","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T17:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 14:46:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7271773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7271773","identity":"rs-7271773","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}