Abstract
Objective
To examine long-term cardiovascular disease (CVD) risk disparities by sexual identity using a nationally representative sample of young adults in the United States.
Methods
Data include participants in wave 4 (2008/09; ages 24–34 years) of the National Longitudinal Study of Adolescent to Adult Health (7087 females; 6340 males). Sexual identity was self-reported (heterosexual, mostly heterosexual, bisexual, mostly homosexual, homosexual) and a Framingham-based prediction model was used to estimate participants’ risk of a CVD event over 30 years. Differences in CVD risk by sexual identity, relative to heterosexuals were calculated with linear regression models adjusted for age, race/ethnicity, education, and financial distress.
Results
Average 30-year CVD risk was 17.2% (95% CI: 16.7, 17.7) in males and 9.0% (95% CI: 8.6, 9.3) in females. Compared to heterosexual females, mostly heterosexual (0.8%; 95% CI: 0.2, 1.4) and mostly homosexual females (2.8%; 95% CI: 0.8, 4.9) had higher CVD risk. Bisexual and homosexual females had higher but not statistically significant CVD risk compared to heterosexuals. Among males, differences in CVD risk by sexual identity were not statistically significant.
Conclusion
Sexual identity was associated with CVD risk in sexual minority subgroups. Population- and clinic-based prevention strategies are needed to minimize disparities in subsequent disease.
Keywords: Cardiovascular diseases, Homosexuality, Health status disparities
Introduction
Given the estimated costs of cardiovascular disease (CVD) ($1 trillion by 2030) (Heidenreich et al., 2011) and growing evidence of the cost effectiveness of primary prevention (Weintraub et al., 2011), greater attention is being paid to the extent of and disparities in CVD risk in young adults (ages 18–35) in an effort to mitigate risk well in advance of disease onset. Disparities in young adult CVD risk by race/ethnicity and socioeconomic status are well described in the literature, yet critical information gaps remain regarding potentially increased risk among individuals with a sexual minority identity (i.e., those who identify as anything other than heterosexual, including gay, lesbian, bisexual, queer, or pansexual) in early adulthood (Institute of Medicine, 2011).
Most prior research examining sexual identity differences in individual CVD risk factors has found that young individuals with a sexual minority identity are at elevated risk of a number of CVD risk factors (e.g., smoking, obesity, hypertension, C-reactive protein) (Everett and Mollborn, 2013; Hatzenbuehler et al., 2013; Katz-Wise et al., 2014). However, by examining individual risk factors, studies have not accounted for the fact that risk factors cluster and differ in the strength of their relationship to CVD (Freedman et al., 1999). Studies that have accounted for these factors (Farmer et al., 2013a,b) have examined risk among older adults and utilized a short-term prediction function that has been shown to be a poorer predictor of subclinical (Berry et al., 2009) and clinical (Pencina et al., 2009) CVD than a longer-term risk prediction function, which also accounts for competing causes of death. This is of particular relevance to assessing CVD risk in young adults whose risk of having a CVD event increases steadily but minimally each year until middle age (Go et al., 2014).
This study addresses this gap by providing the first long-term CVD risk comparisons across sexual identity using data from a nationally representative sample of young adults. This information is of particular importance to developing primary prevention strategies targeted to this diverse segment of the population.
Methods
Sample
Data were from the National Longitudinal Study of Adolescent to Adult Health (Harris et al., 2009) and include non-Hispanic White, Black, Asian/Pacific Islander (API), and Hispanic respondents who participated in wave 4 (2008–09; response rate 80.3%; ages 24–34 years), were free of cancer and heart disease, and who had valid study weights and non-missing data (7087 females; 6340 males).
Variables
Age, race/ethnicity, sexual identity, education, financial stress (a positive response to any of 6 items indicating an inability to pay for basic needs), and CVD risk factors (BMI (Entzel et al., 2009), current smoking, systolic blood pressure (Entzel et al., 2009), use of antihypertensive medication (Tabor and Whitsel, 2010), and diabetes (considered present if the participant had a fasting glucose ≥126 mg/dl, a non-fasting glucose ≥200 mg/dl, HbA1c ≥6.5%, self-reported a health provider diagnosis of diabetes except during pregnancy, or used anti-diabetic medication in the prior four weeks (Whitsel et al., 2012)), were ascertained from interview data, anthropometric measurements, and biological specimens. Sexual identity was assessed through self-report and categorized as 1) heterosexual, 2) mostly heterosexual, 3) bisexual, 4) mostly homosexual, and 5) homosexual. Pansexual and queer sexual identities were not measured in the Add Health data. Participants provided written informed consent and the parent study was approved by the institutional review board of the University of North Carolina, Chapel Hill.
Statistical analyses
The risk of developing CVD over a 30-year period was calculated using a Framingham-based prediction model (Pencina et al., 2009). The function predicts the occurrence of a “general” composite CVD endpoint including coronary death, myocardial infarction, coronary insufficiency, angina pectoris, stroke, transient ischemic attack, intermittent claudication, and congestive heart failure (Pencina et al., 2009). Descriptive statistics were calculated by sex and sexual identity. Differences in 30-year CVD risk by sexual identity, relative to heterosexuals, were calculated with linear regression models adjusted for age, race/ethnicity, education, and financial distress, separately for males and females. Descriptive statistics and regression models were computed using SUDAAN 11 survey procedures. All analyses incorporated survey design and unequal probability of selection per Add Health user guidance (Chantala and Tabor, 2010). Statistical significance is defined as a p-value < 0.05.
Results
Mean age was 28.9 (95% CI: 28.6, 29.1) years; the sample was 69.5% non-Hispanic White (n = 7617), 15.5% Black (n = 2861), 3.4% API (n = 845), and 11.7% Hispanic (n = 2104). Table 1 indicates participant characteristics by sexual identity. Among females, 80.5% were heterosexual (n = 5713), 15.6% were mostly heterosexual (n = 1089), 2.3% were bisexual (n = 154), 0.8% were mostly homosexual (n = 60) and 0.9% (n = 71) were homosexual. Among males, the sample was 93.5% heterosexual (n = 5912), 3.5% mostly heterosexual (n = 213), 0.7% bisexual (n = 45), 0.6% mostly homosexual (n = 53), and 1.7% were homosexual (n = 117). Average 30-year CVD risk was 17.2% (95% CI: 16.7, 17.7) in males and 9.0% (95% CI: 8.6, 9.3) in females. Compared to heterosexuals, long-term CVD risk was higher among all subgroups of sexual minority females (SMF) and lower among mostly heterosexual and homosexual males.
Table 1.
Participant Characteristics in the National Longitudinal Study of Adolescent to Adult Health (2008–09) by Sexual Identity, Females and Males (N=13,427), weighted.
| FEMALES (N=7,087) | ||||||
|---|---|---|---|---|---|---|
| Total Women | Heterosexual (N=5713) | Mostly Heterosexual (N=1089) | Bisexual (N=154) | Mostly Homosexual (N=60) | Homosexual (N=71) | |
| Age, mean (95% CI) | 28.8 (28.5, 29.0) | 28.8 (28.6, 29.1) | 28.5 (28.2, 28.7) | 28.3 (27.9, 28.6) | 28.4 (27.8, 29.0) | 28.9 (28.3, 29.5) |
| Education, n (%) | ||||||
| < High School | 416 (7.1) | 316 (6.7) | 69 (7.1) | 22 (19.5) | 2 (3.5) | 7 (10.5) |
| High school graduate | 936 (13.7) | 761 (13.9) | 133 (11.7) | 22 (17.2) | 8 (24.1) | 12 (18.7) |
| Some college | 3139 (44.6) | 2481 (43.8) | 511 (48.2) | 77 (45.0) | 32 (47.4) | 38 (47.8) |
| College graduate + | 2596 (34.6) | 2155 (35.6) | 376 (33.0) | 33 (18.4) | 18 (25.0) | 14 (23.0) |
| Race, n (%) | ||||||
| Non-Hispanic White | 3959 (69.3) | 3111 (67.7) | 701 (77.5) | 84 (69.4) | 31 (73.2) | 32 (64.1) |
| Black | 1625 (16.1) | 1360 (17.3) | 187 (9.4) | 39 (16.6) | 14 (12.0) | 25 (26.0) |
| Asian/Pacific Islander | 409 (3.3) | 341 (3.4) | 58 (2.9) | 6 (1.8) | 1 (0.1) | 3 (1.1) |
| Hispanic | 1094 (11.4) | 901 (11.6) | 143 (10.2) | 25 (12.2) | 14 (14.7) | 11 (8.8) |
| Financial stress, n (%) | ||||||
| No | 5195 (72.9) | 4318 (75.1) | 710 (65.6) | 78 (45.0) | 41 (66.8) | 48 (72.9) |
| Yes | 1892 (27.1) | 1395 (24.9) | 379 (34.4) | 76 (55.0) | 19 (33.2) | 23 (27.1) |
| Body mass index, mean (95% CI) | 29.0 (28.6, 29.3) | 29.0 (28.6, 29.4) | 28.7 (27.9, 29.4) | 29.8 (28.1, 31.4) | 30.3 (27.2, 33.3) | 31.0 (28.5, 33.5) |
| Systolic blood pressure, mean (95% CI) | 120.0 (119.5, 120.4) | 119.9 (119.4, 120.4) | 119.7 (118.5, 121.0) | 121.6 (118.7, 124.6) | 123.1 (119.9, 126.3) | 122.4 (119.4, 125.4) |
| Hypertensive medication usage, n (%) | ||||||
| No | 6901 (97.3) | 5563 (97.4) | 1058 (96.8) | 150 (97.0) | 60 (100.0) | 70 (98.3) |
| Yes | 186 (2.7) | 150 (2.6) | 31 (3.2) | 4 (3.0) | 0 (0.0) | 1 (1.7) |
| Current smoker, n (%) | ||||||
| No | 4910 (65.9) | 4149 (69.3) | 610 (53.8) | 84 (46.0) | 34 (43.5) | 33 (44.7) |
| Yes | 2177 (34.1) | 1564 (30.7) | 479 (46.2) | 70 (54.0) | 26 (56.5) | 38 (55.3) |
| Diabetes, n (%) | ||||||
| No | 6593 (94.0) | 5318 (94.0) | 1012 (94.0) | 142 (93.2) | 53 (87.0) | 68 (98.1) |
| Yes | 494 (6.0) | 395 (6.0) | 77 (6.0) | 12 (6.8) | 7 (13.0) | 3 (1.9) |
| 30-year Cardiovascular Disease Risk | 9.0 (8.6, 9.3) | 8.8 (8.4, 9.1) | 9.4 (8.7, 10.0) | 10.4 (8.8, 12.1) | 11.6 (9.1, 14.1) | 10.5 (9.1, 11.9) |
| MALES (N=6,340) | ||||||
|---|---|---|---|---|---|---|
| Total Men | Heterosexual (N=5912) | Mostly Heterosexual (N=213) | Bisexual (N=45) | Mostly Homosexual (N=53) | Homosexual (N=117) | |
| Age, mean (95% CI) | 29.0 (28.7, 29.2) | 29.0 (28.7, 29.2) | 28.8 (28.3, 29.4) | 28.8 (28.1, 29.4) | 28.5 (27.8, 29.2) | 29.1 (28.6, 29.6) |
| Education, n (%) | ||||||
| < High School | 575 (10.2) | 545 (10.4) | 14 (6.5) | 8 (22.1) | 2 (3.6) | 6 (3.8) |
| High school graduate | 1163 (20.5) | 1114 (21.1) | 27 (12.0) | 7 (18.4) | 5 (7.8) | 10 (10.8) |
| Some college | 2811 (42.1) | 2636 (42.3) | 85 (37.7) | 18 (39.4) | 21 (38.2) | 51 (43.5) |
| College graduate + | 1791 (27.1) | 1617 (26.1) | 87 (43.9) | 12 (20.2) | 25 (50.5) | 50 (42.0) |
| Race, n (%) | ||||||
| Non-Hispanic White | 3658 (69.7) | 3403 (69.5) | 146 (77.4) | 25 (78.1) | 27 (67.4) | 57 (59.3) |
| Black | 1236 (14.9) | 1169 (15.2) | 26 (8.1) | 10 (12.9) | 12 (13.5) | 19 (12.7) |
| Asian/Pacific Islander | 436 (3.4) | 409 (3.4) | 12 (3.3) | 1 (1.1) | 2 (3.3) | 12 (5.4) |
| Hispanic | 1010 (12.0) | 931 (11.8) | 29 (11.3) | 9 (7.9) | 12 (15.8) | 29 (22.6) |
| Financial stress, n (%) | ||||||
| No | 4980 (77.5) | 4668 (78.0) | 151 (68.6) | 27 (53.0) | 41 (74.2) | 93 (78.3) |
| Yes | 1360 (22.5) | 1244 (22.0) | 62 (31.4) | 18(47.0) | 12 (25.8) | 24 (21.8) |
| Body mass index, mean (95% CI) | 28.8 (28.6, 29.1) | 28.9 (28.7, 29.2) | 27.3 (26.4, 28.3) | 29.1 (25.6, 32.6) | 29.1 (26.2, 32.0) | 26.8 (25.4, 28.2) |
| Systolic blood pressure, mean (95% CI) | 129.9 (129.4, 130.4) | 129.8 (129.3, 130.4) | 130.3 (127.8, 132.7) | 128.1 (122.6, 133.6) | 131.8 (127.5, 136.1) | 131.4 (127.9, 134.8) |
| Hypertensive medication usage, n (%) | ||||||
| No | 6153 (96.7) | 5736 (96.7) | 210 (99.0) | 45 (100.0) | 49 (90.1) | 113 (94.8) |
| Yes | 187 (3.3) | 176 (3.4) | 3 (1.0) | 0 (0.0) | 4 (9.9) | 4 (5.2) |
| Current smoker, n (%) | ||||||
| No | 3752 (56.8) | 3506 (56.7) | 126 (61.9) | 21 (36.3) | 30 (53.2) | 69 (56.4) |
| Yes | 2588 (43.3) | 2406 (43.3) | 87 (38.1) | 24 (63.7) | 23 (46.8) | 48 (43.7) |
| Diabetes, n (%) | ||||||
| No | 5944 (94.1) | 5546 (94.0) | 198 (95.6) | 42 (91.4) | 49 (90.1) | 109 (96.5) |
| Yes | 396 (5.9) | 366 (6.0) | 15 (4.4) | 3 (8.6) | 4 (9.9) | 8 (3.5) |
| 30-year Cardiovascular Disease Risk | 17.2 (16.7, 17.7) | 17.3 (16.8, 17.8) | 15.6 (14.0, 17.2) | 18.4 (15.4, 21.3) | 18.7 (15.1, 22.3) | 16.2 (14.3, 18.0) |
Table 2 presents estimated average differences in 30-year CVD risk by sexual identity compared to heterosexuals, adjusted for age, race/ethnicity, education, and financial stress. Black compared to White race, older age, and financial stress were associated with increased CVD risk; higher educational levels were associated with decreased CVD risk. API and Hispanic females had lower CVD risk than White females.
Table 2.
Thirty Year Cardiovascular Disease Risk for Sexual Minorities Compared to Heterosexuals in the National Longitudinal Study of Adolescent to Adult Health (2008–09), Males and Females (N=13,427), weighted.
| FEMALES (N=7,087) | MALES (N=6,340) | ||||
|---|---|---|---|---|---|
| Percent Difference (95% CI) Compared to Reference | p-value | Percent Difference (95% CI) Compared to Reference | p-value | ||
| Sexual Identity | Sexual Identity | ||||
| Heterosexual | ref | Ref | Heterosexual | Ref | ref |
| Mostly Heterosexual | 0.8 (0.2, 1.4) | <0.01 | Mostly Heterosexual | −0.9 (−2.4, 0.5) | 0.22 |
| Bisexual | 1.2 (−0.4, 2.8) | 0.13 | Bisexual | 0.7 (−2.6, 4.0) | 0.66 |
| Mostly Homosexual | 2.8 (0.8, 4.9) | <0.01 | Mostly Homosexual | 3.0 (−0.4, 6.4) | 0.09 |
| Homosexual | 1.2 (−0.1, 2.4) | 0.08 | Homosexual | −0.6 (−2.2, 1.0) | 0.46 |
| Age | 0.9 (0.8, 1.0) | <0.001 | Age | 1.5 (1.3, 1.6) | <0.001 |
| Financial Stress | 1.2 (0.7, 1.8) | <0.001 | Financial Stress | 0.9 (0.0, 1.7) | 0.04 |
| Education | Education | ||||
| < High School | ref | Ref | < High School | Ref | ref |
| High school graduate | −0.2 (−1.1, 0.7) | 0.60 | High school graduate | −1.6 (−3.0, −0.3) | 0.02 |
| Some college | −1.2 (−2.0, −0.5) | <0.01 | Some college | −2.4 (−3.8, −1.0) | <0.01 |
| College graduate + | −3.2 (−4.0, −2.5) | <0.001 | College graduate + | −5.2 (−6.5, −3.8) | <0.001 |
| Race | Race | ||||
| Non-Hispanic White | Ref | Ref | Non-Hispanic White | ref | ref |
| Black | 0.9 (0.4, 1.5) | <0.001 | Black | 1.0 (0.1, 1.9) | 0.03 |
| Asian/Pacific Islander | −1.4 (−2.2, −0.5) | <0.01 | Asian/Pacific Islander | −0.2 (−2.0, 1.6) | 0.79 |
| Hispanic | −0.9 (−1.5, −0.3) | <0.01 | Hispanic | −0.2 (−1.2, 0.8) | 0.65 |
Notes: Linear regression models were conducted separately by sex. Each model contains all of the variables listed.
Compared to heterosexual females, mostly heterosexual and mostly homosexual females had a 0.8% (95% CI: 0.2, 1.4) and 2.8% (95% CI: 0.8, 4.9) higher risk of CVD, respectively. Bisexual and homosexual females had a higher risk of CVD compared to heterosexuals, but these findings were not statistically significant. Among males, differences in CVD risk by sexual identity were not statistically significant, but demonstrate a pronounced elevated risk among mostly homosexual men compared to heterosexuals.
Discussion
This study provides further evidence for CVD risk disparities between subgroups of SMF compared to heterosexual females (Farmer et al., 2013b; Lick et al., 2013) and suggests that mostly homosexual males may additionally be at higher risk of CVD compared to heterosexual males. Study findings are bolstered by the use of a nationally representative sample of young adults, objectively measured CVD risk factors, and a longer-term risk prediction function which may more accurately predict CVD risk (Berry et al., 2009; Pencina et al., 2009), especially among young adults.
Compared to heterosexuals, mostly homosexual and mostly heterosexual females had significantly higher CVD risk. Mostly homosexual males also had higher CVD risk, than heterosexual males, although not at traditionally significant levels. While more research is needed to understand the mechanisms underlying the increased CVD risk among those who identify as mostly homosexual or mostly heterosexual, individuals in these groups might have discordant identities or are in the process of sexual orientation identity change. Both sexual orientation identity discordance (Gattis et al., 2012) and sexual orientation identity change (Everett, 2015) have been associated with psychological distress which increases risk for CVD though behavioral and physiologic pathways (Everson-Rose and Lewis, 2005).
To the best of our knowledge, the present study is the first to examine long-term CVD risk by sexual identity among young adults so comparisons across studies are limited. However, a 10-year Framingham risk prediction function used to determine the ratio of vascular to chronological age in a sample of adults 20–69 years of age showed higher risk among SMF and bisexual males (Farmer et al., 2013a,b). The researchers, however, grouped homosexual and bisexual females together for the analysis and did not distinguish among mostly heterosexual, mostly homosexual, and bisexual males. In those prior studies, homosexually experienced heterosexual males were shown to have lower CVD risk. Our findings cannot speak to CVD risk in this population because sexual behavior was not used to examine CVD risk in the present study. As recommended by the Institute of Medicine, standardization of sexual identity measures is needed to facilitate comparison across studies (Institute of Medicine, 2011).
Limitations
The current study is limited to an assessment of sexual identity, which does not always track with sexual attraction and behavior, especially among sexual minority males (Wolitski et al., 2006). Sample sizes of some sexual minority subgroups may have been too small to detect differences between groups. The Framingham risk score was developed on a predominately White cohort and may overestimate coronary heart disease risk in some racial/ethnic minorities (D’Agostino et al., 2001). Calibration is often warranted, but cannot be accomplished on such a young cohort. Validation of the risk function’s ability to accurately detect CVD risk across diverse racial and ethnic groups is needed in older cohorts where sufficient numbers of the participants have had a CVD event.
Conclusion
Our findings highlight an elevated long-term risk of CVD among young adults with a sexual minority identity, especially females. CVD prevention guidelines for women address risk disparities across a number of socio-demographic strata, and highlight the need for cultural competence, but do not acknowledge disparities by sexual identity (Mosca et al., 2011). This study and a growing body of health disparities’ literature begins to fill that gap to support the development of targeted clinical and population based strategies to minimize disparities in subsequent disease.
Acknowledgments
Funding sources
Dr. Cari Jo Clark was supported by the National Center for Advancing Translational Sciences of the NIH Award Number 8UL1TR000114-02/KL2TR000113 and grant number R03HD068045 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Additional support was provided by the Program in Health Disparities Research and the Applied Clinical Research Program at the University of Minnesota. Dr. Susan A. Everson-Rose was supported in part by grant 1P60MD003422 from the National Institute on Minority Health and Health Disparities and by the Program in Health Disparities Research and the Applied Clinical Research Program at the University of Minnesota. Julia M. Przedworski was additionally supported by the National Cancer Institute Cancer Education and Career Development Program under award number R25CA163184. Study sponsors had no role in study design; collection, analysis, or interpretation of data; writing the report; or the decision to submit the report for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.
Footnotes
Conflict of interest statement
The authors declare that there are no conflicts of interest.
Contributor Information
Iris W. Borowsky, Email: borow004@umn.edu.
John Salisbury, Email: john.salisbury@rainbowhealth.org.
Joann Usher, Email: Joann.usher@rainbowhealth.org.
Rachael A. Spencer, Email: ralennon@gmail.com.
Julia M. Przedworski, Email: prze0009@umn.edu.
Lynette M. Renner, Email: renn0042@umn.edu.
Colleen Fisher, Email: cfisher@umn.edu.
Susan A. Everson-Rose, Email: saer@umn.edu.
References
- Berry JD, Liu K, Folsom AR, Lewis CE, Carr JJ, Polak JF, Shea S, Sidney S, O’Leary DH, et al. Prevalence and progression of subclinical atherosclerosis in younger adults with low short-term but high lifetime estimated risk for cardiovascular disease: the coronary artery risk development in young adults study and multi-ethnic study of atherosclerosis. Circulation. 2009;119:382–389. doi: 10.1161/CIRCULATIONAHA.108.800235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chantala K, Tabor J. Strategies to Perform a Design-Based Analysis Using the Add Health Data. Carolina Population Center, University of North Carolina; Chapel Hill: 2010. p. 19. [Google Scholar]
- D’Agostino RB, Sr, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286:180–187. doi: 10.1001/jama.286.2.180. [DOI] [PubMed] [Google Scholar]
- Entzel P, Whitsel E, Richardson A, Tabor J, Hallquist S, Hussey J, Halpern C, Mullan Harris K. Add Health Wave IV Documentation Cardiovascular and Anthropometric Measures. Carolina Population Center, University of North Carolina at Chapel Hill; Chapel Hill, NC: 2009. [Google Scholar]
- Everett B. Sexual orientation identity change and depressive symptoms a longitudinal analysis. J Health Soc Behav. 2015;56:37–58. doi: 10.1177/0022146514568349. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Everett B, Mollborn S. Differences in hypertension by sexual orientation among U.S. young adults. J Community Health. 2013;38:588–596. doi: 10.1007/s10900-013-9655-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Everson-Rose SA, Lewis TT. Psychosocial factors and cardiovascular diseases. Annu Rev Public Health. 2005;26:469–500. doi: 10.1146/annurev.publhealth.26.021304.144542. [DOI] [PubMed] [Google Scholar]
- Farmer GW, Bucholz KK, Flick LH, Burroughs TE, Bowen DJ. CVD risk among men participating in the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2010: differences by sexual minority status. J Epidemiol Community Health. 2013a;67:772–778. doi: 10.1136/jech-2013-202658. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Farmer GW, Jabson JM, Bucholz KK, Bowen DJ. A population-based study of cardiovascular disease risk in sexual-minority women. Am J Public Health. 2013b;103:1845–1850. doi: 10.2105/AJPH.2013.301258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Freedman DS, Dietz WH, Srinivasan SR, Berenson GS. The relation of overweight to cardiovascular risk factors among children and adolescents: the Bogalusa Heart Study. Pediatrics. 1999;103:1175–1182. doi: 10.1542/peds.103.6.1175. [DOI] [PubMed] [Google Scholar]
- Gattis M, Sacco P, Cunningham-Williams R. Substance use and mental health disorders among heterosexual identified men and women who have same-sex partners or same-sex attraction: results from the national epidemiological survey on alcohol and related conditions. Arch Sex Behav. 2012;41:1185–1197. doi: 10.1007/s10508-012-9910-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, et al. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014:e28–e292. doi: 10.1161/01.cir.0000441139.02102.80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Harris KM, Halpern CT, Whitsel E, Hussey J, Tabor P, Entzel P, Udry JR. The National Longitudinal Study of Adolescent to Adult Health: Research Design [WWW document] 2009 [Google Scholar]
- Hatzenbuehler ML, McLaughlin KA, Slopen N. Sexual orientation disparities in cardiovascular biomarkers among young adults. Am J Prev Med. 2013;44:612–621. doi: 10.1016/j.amepre.2013.01.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, Finkelstein EA, Hong Y, Johnston SC, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123:933–944. doi: 10.1161/CIR.0b013e31820a55f5. [DOI] [PubMed] [Google Scholar]
- Institute of Medicine. The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding. The National Academies Press; Washington, DC: 2011. [PubMed] [Google Scholar]
- Katz-Wise SL, Blood EA, Milliren CE, Calzo JP, Richmond TK, Gooding HC, Austin SB. Sexual orientation disparities in BMI among US adolescents and young adults in three race/ethnicity groups. J Obes. 2014;2014:8. doi: 10.1155/2014/537242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lick DJ, Durso LE, Johnson KL. Minority stress and physical health among sexual minorities. Perspect Psychol Sci. 2013;8:521–548. doi: 10.1177/1745691613497965. [DOI] [PubMed] [Google Scholar]
- Mosca L, Benjamin EJ, Berra K, Bezanson JL, Dolor RJ, Lloyd-Jones DM, Newby LK, Pina IL, Roger VL, et al. Effectiveness-based guidelines for the prevention of cardiovascular disease in women—2011 update: a guideline from the american heart association. Circulation. 2011;123:1243–1262. doi: 10.1161/CIR.0b013e31820faaf8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pencina MJ, D’Agostino RB, Sr, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of cardiovascular disease: the Framingham heart study. Circulation. 2009;119:3078–3084. doi: 10.1161/CIRCULATIONAHA.108.816694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tabor J, Whitsel EA. Add Health Wave IV Documentation: Prescription Medication Use. Carolina Population Center, University of North Carolina at Chapel Hill; Chapel Hill, NC: 2010. [Google Scholar]
- Weintraub WS, Daniels SR, Burke LE, Franklin BA, Goff DC, Jr, Hayman LL, Lloyd-Jones D, Pandey DK, Sanchez EJ, et al. Value of primordial and primary prevention for cardiovascular disease: a policy statement from the American Heart Association. Circulation. 2011;124:967–990. doi: 10.1161/CIR.0b013e3182285a81. [DOI] [PubMed] [Google Scholar]
- Whitsel EA, Tabor JW, Nguyen QC, Cuthbertson CC, Wener MH, Potter AJ, Killeya-Jones LA, Mullan Harris K. Measures of Glucose Homeostasis: Add Health Wave IV Documentation. Carolina Population Center, University of North Carolina at Chapel Hill; Chapel Hill, NC: 2012. [Google Scholar]
- Wolitski RJ, Jones KT, Wasserman JL, Smith JC. Self-identification as “down low” among men who have sex with men (MSM) from 12 US cities. AIDS Behav. 2006;10:519–529. doi: 10.1007/s10461-006-9095-5. [DOI] [PubMed] [Google Scholar]