Abstract
Objectives. To assess the extent to which lesbian, gay, and bisexual (LGB) adults aged 18 to 64 years experience barriers to health care.
Methods. We used 2013 National Health Interview Survey data on 521 gay or lesbian (291 men, 230 women), 215 bisexual (66 men, 149 women), and 25 149 straight (11 525 men, 13 624 women) adults. Five barrier-to-care outcomes were assessed (delayed or did not receive care because of cost, did not receive specific services because of cost, delayed care for noncost reasons, trouble finding a provider, and no usual source of care).
Results. Relative to straight adults, gay or lesbian and bisexual adults had higher odds of delaying or not receiving care because of cost. Bisexual adults had higher odds of delaying care for noncost reasons, and gay men had higher odds than straight men of reporting trouble finding a provider. By contrast, gay or lesbian women had lower odds of delaying care for noncost reasons than straight women. Bisexual women had higher odds than gay or lesbian women of reporting 3 of the 5 barriers investigated.
Conclusions. Members of sexual minority groups, especially bisexual women, are more likely to encounter barriers to care than their straight counterparts.
Existing research on health care access and use suggests that lesbian, gay, and bisexual (LGB) individuals face multiple barriers to care at both the structural and individual levels. Lack of health insurance coverage was a major structural barrier prior to the Obergefell v. Hodges (135 S Ct 2584) landmark Supreme Court ruling, and it will likely continue to be for some time. Nonrecognition of same-sex partnerships in several states has precluded many LGB individuals from obtaining employer-sponsored health insurance coverage through their partners.1 In addition, several studies have shown that LGB individuals are more likely than heterosexuals to lack insurance,2,3 with disparities in coverage particularly pronounced among women.4–7
Another structural barrier is the limited number of culturally competent providers trained in LGB health needs,8–13 which may result in negative experiences with the health care system in the form of enacted, perceived, or internalized stigma (e.g., anticipation of disrespectful provider behaviors or refusal of treatment because of sexual orientation).4,14–17 As a result, LGB individuals may delay obtaining medical care. In fact, several studies have shown that LGB persons are more likely than their heterosexual counterparts to delay medical care, more likely to experience inadequate care, and less likely to have a usual place of care.2,5,6,14,18–21 These LGB-specific barriers to care often intersect with barriers associated with other sociodemographic characteristics (e.g., gender, race/ethnicity), further limiting the ability of LGB individuals to obtain high-quality, timely care.14
Much of the research just mentioned has been limited geographically or by the measure of sexual minority status used. Most studies focus on a single city, state, or set of states.3–5,18,19,22–24 In the few studies in which national data have been used, sexual minority status has primarily been defined with respect to membership in a same-sex couple.2,6,14 In addition to being error prone,25,26 this definition excludes members of sexual minority groups who were not in cohabiting relationships at the time the study was conducted. Very few studies have incorporated both a national probability sample and an identity-based measure of sexual minority status.21,27–29
We used data from a recent nationally representative sample of adults to examine whether men and women who self-identify as sexual minorities encounter more barriers to health care than their heterosexual counterparts. Focusing on both demand (e.g., cost) and supply (e.g., availability and access) barriers,30 we hypothesize that a sexual minority identity (gay, lesbian, or bisexual) is associated with a greater likelihood of reporting barriers to care, net of several sociodemographic, socioeconomic, and health status covariates.
METHODS
We used data collected from 25 885 adults (14 003 women and 11 882 men) aged 18 to 64 years who participated in the 2013 version of the National Health Interview Survey (NHIS). Based on a multistage area probability sample design, the NHIS is a multipurpose, nationally representative annual health survey of the civilian, noninstitutionalized US population conducted by the National Center for Health Statistics. US Census Bureau interviewers use the computer-assisted personal interviewing technique to administer the NHIS. Telephone interviewing is permitted to complete missing portions of interviews.31
We drew most of the variables included in our analyses from the NHIS Household Composition, Family Core, and Sample Adult Core modules. Demographic and relationship information on all household members was collected via the Household Composition module. The Family Core module collected self-reported and proxy-reported information on each selected household member, including disability status, health insurance coverage, and income. The Sample Adult Core module was administered to 1 adult (aged ≥ 18 years) randomly selected from each family. These individuals answered questions themselves unless they were mentally or physically unable to do so, in which case a knowledgeable family member served as a proxy respondent. The final sample adult response rate was 61.2%.31
Measures
Barriers to health care.
Five barrier-to-care outcomes were constructed as dichotomous composite variables of several interrelated items from the Family Core and Sample Adult Core modules. The first outcome, “delayed or did not receive care because of cost,” was based on responses to 2 Family Core questions. The family respondent (who may or may not be the sample adult) was asked whether anyone in the family, in the past year, needed medical care but did not get it because of cost or delayed care because of cost. If the family respondent answered yes to either question for the sample adult, the sample adult was defined as having delayed or forgone care as a result of cost.
The “did not receive specific services because of cost” outcome was a composite of 6 questions. It indicated whether the sample adult forwent 1 or more of the following because of cost in the past year: prescription medications, mental health care or counseling, dental care or check-ups, eyeglasses, a visit to a specialist, or follow-up care.
The third outcome, “delayed care for noncost reasons,” captured whether the sample adult had experienced 1 or more of the following in the past year: being unable to get through on the telephone or to get an appointment soon enough, waiting too long to see a doctor, not finding the clinic’s or doctor’s office open during a convenient time, or not having access to transportation to get to the care site.
The “trouble finding a provider” outcome was based on responses to 3 questions asking whether the sample adult, in the past year, had trouble finding a doctor or provider, was told that a clinic or doctor was not accepting new patients, or was told that his or her health care coverage was not accepted.
The final outcome, “no usual source of care,” was based on 2 questions. The first ascertained whether the sample adult had a place he or she usually went when they were sick or needed advice about their health, and the second asked about the place where they received care (e.g., clinic or health center, doctor’s office, hospital emergency room). Adults who did not have a place they usually visited when they were sick or in need of medical advice or who reported a hospital emergency room as their usual source of care were categorized as not having a usual source of care.
Note that these barriers to care are interrelated. Although an exploration of these interrelationships is beyond our scope here, a factor analysis of the items constituing the 5 outcomes produced 5 factors, consistent with the operational definitions outlined.
Sexual orientation.
Sexual orientation was ascertained with the following question: “Which of the following best represents how you think of yourself?” (Quality evaluations of this question and the resulting data have been discussed in detail elsewhere.32,33) For male respondents, the response options were “gay”; “straight, that is, not gay”; “bisexual”; “something else”; and “I don’t know the answer.” The response categories for female respondents were “lesbian or gay”; “straight, that is, not lesbian or gay”; “bisexual”; “something else”; and “I don’t know the answer.” Something else, I don’t know the answer, and refused responses, which accounted for 1.1% of the total, were considered as missing in the analyses.
Covariates.
Multivariate models included covariates empirically informed by the literature on disparities in health care access and use by sexual orientation. We organized these covariates into the following categories: sociodemographic characteristics, socioeconomic characteristics, and health status. Sociodemographic characteristics included age, race/ethnicity, marital status, nativity, neighborhood tenure, region of residence, and urbanicity.
The socioeconomic characteristics assessed were education, work status, poverty status, and health insurance coverage. Poverty status, calculated via the NHIS imputed income files, was defined according to the ratio of a family’s total income to the federal poverty threshold (FPT). Ratios were categorized as poor (< 100% of FPT), near poor (≥ 100% to < 200% of FPT), and not poor (≥ 200% of FPT).
Health status covariates included reported health status, activity limitation, and serious psychological distress. The reported health status and serious psychological distress variables have been described elsewhere.34 The dichotomous activity limitation variable was generated from responses to questions ascertaining whether the sample adult, as a result of physical, mental, or emotional problems, needs the help of other people to manage personal care or routine needs, is unable to work or limited in the kind or amount of work he or she does, has difficulty walking without special equipment, has difficulty remembering or experiences periods of confusion, or is limited in any way in any other activities. If the family respondent answered any of these items affirmatively, the sample adult was classified as having an activity limitation.
Statistical Analyses
Given the unique factors related to access to care among adults aged 65 years or older (e.g., Medicare), our analyses were limited to adults aged 18 to 64 years. All analyses were stratified by gender. We present descriptive statistics for all variables along with prevalence rates for each barrier-to-health care measure by sexual orientation. Two-tailed significance tests were conducted to determine whether there were significant differences (P < .05) by sexual orientation.
Because of the relatively small size of the LGB population and the resulting small samples of LGB individuals in the NHIS, our analyses yielded some prevalence estimates requiring cautious interpretation. Thus, we calculated the relative standard error for each estimate (i.e., we divided the standard error of the estimate by the estimate itself and multiplied by 100). Estimates with relative standard errors at or above 30% and below 50% are considered moderately unreliable, and estimates with relative standard errors of 50% or above are considered unreliable (as denoted in Table 2).
TABLE 2—
Prevalence of Barriers to Care among US Men and Women Aged 18–64 Years, by Sexual Orientation and Gender: National Health Interview Survey, 2013 (n = 25 885)
| Barrier and Group | Men, % (95% CI) | Women, % (95% CI) |
| Delayed/did not receive care because of cost | ||
| Straight | 11.7 (11.0, 12.5) | 14.1 (13.4, 14.9) |
| Gay/lesbian | 17.0a (11.8, 22.1) | 22.5a (15.9, 29.1) |
| Bisexual | 26.1a (12.4, 39.8) | 30.0b (20.1, 39.9) |
| Did not receive specific services because of cost | ||
| Straight | 17.1 (16.2, 18.2) | 22.8 (21.7, 23.8) |
| Gay/lesbian | 20.4 (14.6, 26.2) | 25.3 (18.4, 32.2) |
| Bisexual | 27.9 (14.8, 41.0) | 43.0c,d (32.4, 53.7) |
| Delayed care for noncost reasons | ||
| Straight | 7.7 (7.1, 8.3) | 11.5 (10.8, 12.2) |
| Gay/lesbian | 11.5 (6.6, 16.4) | 8.1 (4.2, 12.0) |
| Bisexual | 20.8e (6.2, 35.3) | 25.4b,d (15.4, 35.5) |
| Trouble finding a provider | ||
| Straight | 4.2 (3.7, 4.6) | 6.4 (5.9, 7.0) |
| Gay/lesbian | 8.5 (4.1, 13.0) | 5.1e (2.1, 8.2) |
| Bisexual | …f | 14.7a,g (7.6, 21.9) |
| No usual source of care when sick or need medical advice | ||
| Straight | 23.6 (22.6, 24.7) | 14.5 (13.7, 15.3) |
| Gay/lesbian | 18.8 (12.1, 25.4) | 24.4b (17.1, 31.8) |
| Bisexual | 25.5 (13.3, 37.7) | 28.4b (18.0, 38.7) |
Note. CI = confidence interval.
P < .05 for comparison of gay/lesbian with straight and bisexual with straight.
P < .01 for comparison of gay/lesbian with straight and bisexual with straight.
P < .001 for comparison of gay/lesbian with straight and bisexual with straight.
P < .01 for comparison of bisexual with gay/lesbian.
Estimate has a relative standard error ≥ 30% and < 50%, and should be interpreted with caution.
Estimate considered unreliable (relative standard error ≥ 50%).
P < .05 for comparison of bisexual with gay/lesbian.
Next, we fit a set of logistic regression models to assess the relationship between sexual orientation and each outcome net of the sociodemographic, socioeconomic, and health status covariates. The goal was to determine whether any of the differences by sexual orientation observed in the bivariate analyses could be explained by the covariates.
To account for the stratified, complex cluster sampling design of the NHIS, we used SAS-callable SUDAAN version 11.0 software (RTI International, Research Triangle Park, NC) in conducting our analyses. Final sample adult weights, adjusted for nonresponse and calibrated to population control totals, were used so that our results would be generalizable to the US adult civilian, noninstitutionalized population aged 18 to 64 years.
RESULTS
Table 1 presents gender-stratified descriptive statistics for barriers to health care, sexual orientation, and the sociodemographic, socioeconomic, and health status covariates. For 4 of the 5 outcomes, a higher percentage of women than men reported a barrier to care. For example, 14.4% of women and 11.9% of men delayed or did not receive care because of cost, and 23.0% of women and 17.2% of men did not receive specific health services because of cost. In addition, 11.6% of women and 7.8% of men reported delaying care for a noncost reason, and 6.5% of women and 4.3% of men reported having trouble finding a provider. Finally, only 14.8% of women reported no usual source of medical care, as compared with 23.5% of men.
TABLE 1—
Descriptive Estimates for Barriers to Care, Sexual Orientation, Sociodemographic and Socioeconomic Characteristics, and Health Status Among US Men and Women Aged 18–64 Years: National Health Interview Survey, 2013 (n = 25 885)
| Variable | Men (n = 11 882), % (95% CI) | Women (n = 14 003), % (95% CI) |
| Barriers to care | ||
| Delayed/did not receive care because of cost | 11.9 (11.2, 12.7) | 14.4 (13.7, 15.2) |
| Did not receive specific services because of cost | 17.2 (16.3, 18.2) | 23.0 (22.0, 24.1) |
| Delayed care for noncost reasons | 7.8 (7.2, 8.5) | 11.6 (10.9, 12.3) |
| Trouble finding a provider | 4.3 (3.8, 4.8) | 6.5 (6.0, 7.0) |
| No usual source of care when sick or need medical advice | 23.5 (22.5, 24.6) | 14.8 (14.0, 15.6) |
| Sexual orientation | ||
| Straight | 97.5 (97.1, 97.8) | 97.3 (96.9, 97.6) |
| Gay/lesbian | 2.0 (1.7, 2.4) | 1.7 (1.4, 2.0) |
| Bisexual | 0.5 (0.4, 0.7) | 1.1 (0.8, 1.3) |
| Sociodemographic characteristics | ||
| Age, y | ||
| 18–24 | 16.1 (15.0, 17.3) | 15.3 (14.4, 16.3) |
| 25–44 | 42.0 (40.7, 43.2) | 41.9 (40.8, 43.1) |
| 45–64 | 41.9 (40.6, 43.2) | 42.7 (41.6, 43.9) |
| Race/ethnicity | ||
| Hispanic | 17.3 (16.2, 18.4) | 16.2 (15.4, 17.1) |
| Non-Hispanic White | 63.8 (62.5, 65.1) | 62.9 (61.7, 64.0) |
| Non-Hispanic Black | 11.3 (10.6, 12.1) | 12.9 (12.1, 13.7) |
| Non-Hispanic other | 7.6 (6.9, 8.2) | 8.1 (7.4, 8.8) |
| Marital status | ||
| Never married | 27.9 (26.7, 29.2) | 24.3 (23.3, 25.4) |
| Married/cohabiting | 61.4 (60.2, 62.7) | 60.4 (59.3, 61.6) |
| Divorced/separated/widowed | 10.7 (10.0, 11.3) | 15.3 (14.6, 15.9) |
| Not US-born | 19.2 (18.2, 20.3) | 18.9 (18.0, 19.8) |
| Neighborhood tenure, y | ||
| ≤ 3 | 38.2 (36.9, 39.5) | 38.4 (37.2, 39.6) |
| 4–10 | 28.8 (27.7, 30.0) | 28.3 (27.3, 29.2) |
| 11–20 | 19.0 (18.0, 20.0) | 18.4 (17.5, 19.3) |
| ≥ 21 | 14.0 (13.1, 15.0) | 15.0 (14.1, 15.9) |
| US region of residence | ||
| Northeast | 17.2 (16.1, 18.3) | 17.1 (16.1, 18.2) |
| Midwest | 23.4 (22.2, 24.5) | 21.7 (20.7, 22.8) |
| South | 35.6 (34.3, 36.9) | 38.0 (36.8, 39.3) |
| West | 23.9 (22.8, 25.0) | 23.1 (22.1, 24.2) |
| Place of residence | ||
| Large MSA | 33.8 (32.1, 35.5) | 33.7 (32.0, 35.3) |
| Small MSA | 52.5 (50.6, 54.5) | 51.9 (50.0, 53.9) |
| Not in MSA | 13.7 (12.4, 15.2) | 14.4 (13.1, 15.8) |
| Socioeconomic characteristics | ||
| Education | ||
| < high school | 13.5 (12.6, 14.4) | 11.6 (10.9, 12.3) |
| High school or equivalent | 26.9 (25.8, 28.1) | 23.1 (22.1, 24.1) |
| Some college | 30.5 (29.3, 31.7) | 34.0 (32.9, 35.0) |
| ≥ bachelor’s degree | 29.1 (27.9, 30.4) | 31.4 (30.3, 32.5) |
| Currently working | 76.4 (75.2, 77.5) | 65.2 (64.2, 66.3) |
| Poverty status | ||
| Poor (< 100% of FPT) | 13.1 (12.2, 14.0) | 15.6 (14.7, 16.5) |
| Near poor (≥ 100% to < 200% of FPT) | 17.3 (16.4, 18.2) | 19.1 (18.2, 20.0) |
| Not poor (≥ 200% of FPT) | 69.7 (68.4, 70.9) | 65.4 (64.2, 66.5) |
| Health insurance coverage | ||
| Private coverage | 65.3 (64.1, 66.5) | 64.9 (63.7, 66.0) |
| Public/other coverage | 13.0 (12.2, 13.8) | 16.7 (15.8, 17.6) |
| Uninsured/no coverage | 21.8 (20.7, 22.8) | 18.5 (17.6, 19.4) |
| Health status | ||
| Reported health status | ||
| Poor/fair | 10.7 (10.0, 11.5) | 11.7 (11.0, 12.4) |
| Good | 24.4 (23.3, 25.4) | 25.3 (24.3, 26.3) |
| Very good/excellent | 65.0 (63.8, 66.1) | 63.0 (61.9, 64.1) |
| Activity limitation | 11.8 (11.1, 12.7) | 12.1 (11.4, 12.8) |
| Serious psychological distress | 3.3 (2.9, 3.8) | 4.6 (4.1, 5.1) |
Note. CI = confidence interval; FPT = federal poverty threshold; MSA = metropolitan statistical area. Percent distributions may not sum to 100% because of rounding.
With respect to sexual orientation, 1.7% of women self-identified as gay, 1.1% identified as bisexual, and 97.3% identified as straight. Among men, the corresponding figures were 2.0%, 0.5%, and 97.5%, respectively.
Bivariate associations between sexual orientation and each of the barrier-to-care measures among men and women are presented in Table 2. Significant differences were observed in comparisons of gay or lesbian and straight adults for only 2 of the 5 outcomes examined. Relative to their straight counterparts, a significantly higher percentage of both gay men (17.0% vs 11.7%) and gay or lesbian women (22.5% vs 14.1%) delayed or did not receive care because of cost in the preceding year, and a significantly higher percentage of gay or lesbian women (24.4%) than straight women (14.5%) reported no usual source of medical care.
Among bisexual women, by contrast, a consistent set of findings emerged across all outcomes. A significantly higher percentage of bisexual women than straight women reported delaying or not receiving care because of cost (30.0% vs 14.1%), not receiving specific health services because of cost (43.0% vs 22.8%), delaying care for noncost reasons (25.4% vs 11.5%), having trouble finding a medical provider (14.7% vs 6.4%), and having no usual source of care (28.4% vs 14.5%). This pattern remained in comparisons across sexual minority groups for 3 of the 5 outcomes. A significantly higher percentage of bisexual women than gay or lesbian women did not receive specific services as a result of cost (43.0% vs 25.3%), delayed care for noncost reasons (25.4% vs 8.1%), and had trouble finding a provider (14.7% vs 5.1%).
Table 3 presents adjusted odds ratios (AORs) and 95% confidence intervals (CIs) from logistic regression models for each of the barrier-to-health care measures. The gender-stratified models included all of the sociodemographic, socioeconomic, and health status covariates described earlier (and presented in Table 1).
TABLE 3—
Associations of Sexual Orientation and Barriers to Care Among US Men and Women Aged 18–64 Years: National Health Interview Survey, 2013 (n = 25 885)
| Barrier and Group | Men, AORa (95% CI) | Women, AORa (95% CI) |
| Delayed/did not receive care because of cost | ||
| Straight (Ref) | 1 | 1 |
| Gay/lesbian | 1.7 (1.1, 2.6) | 1.7 (1.1, 2.7) |
| Bisexual | 2.4 (1.2, 4.7) | 2.3 (1.2, 4.2) |
| Bisexual vs gay/lesbianb | 1.4 (0.6, 3.1) | 1.4 (0.6, 2.9) |
| Did not receive specific services because of cost | ||
| Straight (Ref) | 1 | 1 |
| Gay/lesbian | 1.4 (0.9, 2.0) | 1.0 (0.7, 1.6) |
| Bisexual | 1.7 (0.8, 3.3) | 2.2 (1.3, 3.7) |
| Bisexual vs gay/lesbianb | 1.2 (0.6, 2.7) | 2.2 (1.1, 4.2) |
| Delayed care for noncost reasons | ||
| Straight (Ref) | 1 | 1 |
| Gay/lesbian | 1.5 (0.9, 2.4) | 0.5 (0.3, 0.8) |
| Bisexual | 2.3 (1.1, 5.0) | 2.2 (1.2, 4.0) |
| Bisexual vs gay/lesbianb | 1.6 (0.7, 3.9) | 4.4 (2.0, 9.5) |
| Trouble finding a provider | ||
| Straight (Ref) | 1 | 1 |
| Gay/lesbian | 2.3 (1.3, 4.1) | 0.6 (0.3, 1.2) |
| Bisexual | 1.0 (0.3, 3.7) | 1.8 (1.0, 3.3) |
| Bisexual vs gay/lesbianb | 0.4 (0.1, 1.8) | 3.0 (1.2, 7.5) |
| No usual source of care when sick or need medical advice | ||
| Straight (Ref) | 1 | 1 |
| Gay/lesbian | 0.8 (0.5, 1.4) | 2.1 (1.3, 3.4) |
| Bisexual | 1.1 (0.5, 2.4) | 1.5 (0.7, 3.3) |
| Bisexual vs gay/lesbianb | 1.4 (0.6, 3.6) | 0.7 (0.3, 1.8) |
Note. AOR = adjusted odds ratio; CI = confidence interval.
The following covariates were included in the model: sociodemographic characteristics (age, race/ethnicity, marital status, not US born, neighborhood tenure, US region of residence, and place of residence), socioeconomic characteristics (education, work status, poverty status, and health insurance coverage), and health status measures (reported health status, activity limitation, and serious psychological distress).
AORs and 95% CIs for comparisons between the bisexual and gay/lesbian groups are based on the same model used in other analyses with gay/lesbian designated as the reference category.
Relative to the bivariate results, the multivariate logistic regression models reduced 2 significant associations to nonsignificance and gave rise to 2 previously undetected findings. Bisexual men (AOR = 2.4; 95% CI = 1.2, 4.7) and women (AOR = 2.3; 95% CI = 1.2, 4.2) had significantly higher odds than straight adults of delaying or not receiving care because of cost. Similarly, gay men (AOR = 1.7; 95% CI = 1.1, 2.6) and gay or lesbian women (AOR = 1.7; 95% CI = 1.1, 2.7) had significantly higher odds of delaying or not receiving care as a result of cost than their straight counterparts.
There were no significant findings among men with respect to not receiving specific services owing to cost. However, women who self-identified as bisexual had significantly higher odds than both their straight (AOR = 2.2; 95% CI = 1.3, 3.7) and gay or lesbian (AOR = 2.2; 95% CI = 1.1, 4.2) counterparts of not receiving services.
Bisexual men had more than twice the odds (AOR = 2.3; 95% CI = 1.1, 5.0) of delaying care for noncost reasons than straight men, and the result was nearly identical among bisexual women (AOR = 2.2; 95% CI = 1.2, 4.0) relative to straight women. Interestingly, gay or lesbian women had significantly lower odds (AOR = 0.5; 95% CI = 0.3, 0.8) of delaying care for noncost reasons than straight women. Comparisons of sexual minority groups showed that bisexual women had more than 4 times the odds (AOR = 4.4; 95% CI = 2.0, 9.5) of delaying care for noncost reasons than gay or lesbian women.
Gay men had significantly higher odds (AOR = 2.3; 95% CI = 1.3, 4.1) than straight men of having trouble finding a provider, and bisexual women had 3 times the odds (AOR = 3.0; 95% CI = 1.2, 7.5) relative to gay or lesbian women. Finally, gay or lesbian women had significantly higher odds than straight women of not having a usual source of care (AOR = 2.1; 95% CI = 1.3, 3.4).
DISCUSSION
In one of the few nationally representative examinations of LGB health, we investigated the associations between sexual orientation and barriers to health care. We identified a number of differences between sexual minority individuals and heterosexuals in the 5 barriers explored. For example, relative to straight adults, bisexual adults had more than twice the odds of delaying or not receiving care because of cost as well as delaying care for 1 or more noncost reasons. In addition, bisexual women had more than twice the odds of not receiving specific health services owing to cost than straight women. We also found that bisexual women tended to fare worse than gay or lesbian women, with bisexual women having higher odds of not receiving specific health services as a result of cost, delaying care for noncost reasons, and having trouble finding a provider. Contrary to suggestions that such differences may be attributable to the lower socioeconomic status of bisexual adults,18,35 our findings held after adjustment for poverty status and health insurance coverage.
Our findings for bisexual adults are largely consistent with previous research. Blosnich et al.23 found that bisexual adults had higher odds of not seeking medical care because of cost than straight adults, whereas Diamant et al.5 found that bisexual women were more likely than straight women to report that it was difficult or very difficult to obtain needed medical care. Likewise, Tjepkema36 found that both bisexual men and bisexual women were more likely than their heterosexual counterparts to report unmet health needs in the preceding year. With respect to specific services, studies have shown that bisexual women are more likely than straight women to report not receiving prescription medicines and mental health care owing to financial reasons5 and less likely to report receiving a dental cleaning in the preceding year.18 In addition, although little research has directly compared bisexual and gay or lesbian adults, our findings are consistent with a study showing that bisexual women are more likely than gay or lesbian women to report cost barriers to care. 22
By contrast, our results for gay or lesbian adults were somewhat mixed. Gay men and gay or lesbian women had higher odds of not receiving or delaying care because of cost than straight adults. This is consistent with research showing that same-sex couples in general1 and female same-sex couples5 have higher odds of reporting unmet medical needs than married, different-sex couples. Similarly, Everett and Mollborn21 found that sexual minority women were more likely to report unmet medical needs than heterosexual women. However, in our gender-stratified analyses, we found no differences between gay or lesbian and straight adults in terms of specific types of health services and no differences between gay and heterosexual men with regard to not having a usual source of care. The latter finding is consistent with several prior studies.37–39
In addition, relative to straight adults, gay or lesbian women had lower odds of delaying care for noncost reasons. Finally, gay men were no more or less likely than their straight counterparts to have a usual source of care, but they had more than twice the odds of having trouble finding a provider. The latter finding is consistent with previous research indicating that members of sexual minority groups may delay or fail to receive health care owing to concerns or fears over disclosing their sexual identity,20,40,41 a lack of culturally appropriate prevention services,40,41 or a lack of culturally competent medical providers versed in LGB needs.6,40,41
An obvious strength of this study is the nationally representative sample, allowing us to generalize our findings to all US civilian, noninstitutionalized men and women aged 18 to 64 years. Also, our study represents one of the few documented examinations of barriers to health care among members of sexual minority groups. Key limitations include the small sample of sexual minority individuals after stratification by gender, resulting in some unreliable estimates in the bivariate analyses and nonsignificant P values despite moderately sized associations in the multivariate analyses. In addition, we made multiple comparisons, increasing the possibility of type I errors. A final limitation is our inability to gather information on other sexual and gender minority groups, for example individuals identifying as transgender.
Given the disparities in access to care observed in this study, sustained research on health care access among LGB adults42 seems warranted. In addition, future research involving multiple years of NHIS data should consider both the direct and indirect effects, through socioeconomic and health status pathways, of sexual orientation on access to and use of health care, including use of health information technology.
ACKNOWEDGMENTS
The views expressed in this article are those of the authors and do not necessarily represent the official views of the National Center for Health Statistics, the Centers for Disease Control and Prevention, or the US Department of Health and Human Services.
HUMAN PARTICIPANT PROTECTION
The 2013 National Health Interview Survey (NHIS) was approved by the research ethics review board of the National Center for Health Statistics and by the US Office of Management and Budget. Written consent for participation in the 2013 NHIS was not received; instead, all respondents provided oral consent prior to participation.
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