Transgender identity and mental health functional impairment: analysis of the 2016 Behavioral Risk Factor Surveillance System
Introduction
Transgender people have a gender identity, behavioral, or gender expression that do not conform to the biological sex the person had or was identified as having at birth (1). They may identify themselves as heterosexual, homosexual, bisexual, asexual, or may decline to label their sexual orientation. The Institute of Medicine [2011] recognized that not only are lesbians, gay men, bisexual men and women, and transgender people all separate groups, but each of these groups encompasses subpopulations with their own unique health needs (2). However, data on the proportion of transgender subpopulations in the USA population are lacking and information on the mental health needs of transgender subpopulations is limited (2-4). The transgender population bears a disproportionate burden of mental health issues relative to the general population (5-11), and emerging evidence suggests that within the LGBT population, the odds of depression symptoms are higher among transgender individuals compared to non-transgender individuals (12-14).
In a heteronormative society, nontraditional gender identity and sexuality influence self-esteem and causes internalized transphobia, a discomfort with one’s own transgenderism as a result of internalizing society’s normative gender expectations. This can negatively affect physical and mental health outcomes among transgender people (15,16). Reports show that transgender individuals have a high prevalence of clinical depression (44.1%), anxiety (33.2%), and somatization disorders (27.5%) (16). After controlling for potential confounders, transgender identity was associated with higher odds of self-reported discrimination (OR: 2.63, P<0.01), depression (OR: 2.33, P<0.05), and suicide attempts (OR: 2.59, P<0.01) when compared with non-transgender individuals (1).
However, the data on mental health function disparities among transgender persons are not well-documented. Conron et al. [2012] found that 14.3% of transgender individuals reported more than 15 days of poor mental health in the past 30 days vs. 9.8% (95% CI: 9.2 to 10.4) of non-transgender individuals, although the difference was not statistically significant (P=0.54) (3).
We used CDC Behavioral Risk Factor Surveillance System (BRFSS) publicly available data to assess the socioeconomic status, behavioral factors, medical care access, comorbidities, and mental health functional impairments among transgender subpopulations (male to female, female to male, and non-conforming). We also tried to evaluate whether transgender status is independently associated with mental health functional impairments.
Methods
Sample and measures
We analyzed data from the 2016 BRFSS telephone survey for this study. The BRFSS is the largest continuously conducted health survey system in the world, with more than 400,000 adult interviews each year (17). In 2016, a total of 26 states participated in the optional Sexual Orientation and Gender Identity survey. The BRFSS collects data on USA residents regarding their demographic information, social economic status, chronic health conditions, including physical and mental dysfunction, use of preventive services, and health-related risk behaviors. Information from a total of 198,060 respondents with valid answers were collected and used in this study.
Statistical analysis
We used IBM Statistical Package for the Social Sciences (SSPS) version 22 to analyze the data. We restricted the analytic sample to 198,060 participants with valid responses to both sex orientation and transgender questionnaires. We employed chi-square test for bivariate analysis to assess the association of transgender subpopulations with background characteristics, and Pearson correlation to examine the correlation between mental health functional impairments and background characteristics. Variables with chi-square test P<0.05 and Pearson correlation R<0.5 were included in the regression models. Logistic and ordinal regression analyses controlling for demographic information, socioeconomic status, comorbidities, medical care access, and health-related risk behaviors were employed to test the association of transgender status and individual mental health morbidity.
Results
Frequency of self-reported sex orientation and transgender status
Table 1 presents the frequency of self-reported sex orientation and transgender status among 198,060 valid respondents. A total of 3,404 respondents (1.72%) self-reported as bisexual and 3,047 self-reported as either lesbian or gay (1.54%). Self-reported male-to-female transgender, female to male transgender, and non-conforming only count for 0.16%, 0.12%, and 0.07% of the total participants respectively.
Table 1
| Sex orientation and transgender status | Frequency | Percent |
|---|---|---|
| Sex orientation (P=0.000) | ||
| Bisexual | 3,404 | 1.72 |
| Lesbian or gay | 3,047 | 1.54 |
| Straight | 191,609 | 96.74 |
| Total | 198,060 | 100.00 |
| Transgender | ||
| Yes, male to female | 324 | 0.16 |
| Yes, female to male | 237 | 0.12 |
| Yes, non-conforming | 147 | 0.07 |
| Non-transgender | 197,352 | 99.65 |
| Total | 198,060 | 100.00 |
BAFSS, Behavioral Risk Factor Surveillance System.
Socioeconomic and demographic status
Sociodemographic characteristics of male-to-female, female to male, and non-conforming transgender respondents, and no transgender population are shown in Table 2. Among 196,436 non-transgender respondents, half of them were married and more than two-thirds own their own home. Percentages of marriage and home ownership were significantly lower among transgender subpopulation compared to non-transgender people. Less than half of non-conforming transgender persons own their house. Transgender persons were more likely to be young minorities, especially non-conforming transgender persons. Transgender participants were also significantly more likely to be less educated and to have less annual household income.
Table 2
| Socioeconomic status | Yes, male to female (%) | Yes, female to male (%) | Yes, non-conforming (%) | Non-transgender (%) |
|---|---|---|---|---|
| Marital status (P=0.000) | ||||
| Married | 131 (40.4) | 83 (35.3) | 52 (35.6) | 103,232 (52.6) |
| Not married | 193 (59.6) | 152 (64.7) | 94 (64.4) | 93,204 (47.4) |
| Total | 324 (100.0) | 235 (100.0) | 146 (100.0) | 196,436 (100.0) |
| Home owner (P=0.000) | ||||
| Yes | 192 (59.4) | 135 (57.0) | 72 (49.0) | 141,912 (72.2) |
| No | 131 (40.6) | 102 (43.0) | 75 (51.0) | 54,682 (27.8) |
| Total | 323 (100.0) | 237 (100.0) | 147 (100.0) | 196,594 (100.0) |
| Respondent race (P=0.000) | ||||
| Non-Hispanic white | 224 (70.2) | 161 (68.6) | 98 (67.6) | 154,072 (79.2) |
| Non-white or Hispanic | 95 (29.8) | 73 (31.2) | 47 (32.4) | 40,491 (20.8) |
| Total | 319 (100.0) | 234 (100.0) | 145 (100.0) | 94,563 (100.0) |
| Age categories (P=0.000) | ||||
| Age 18 to 34 | 64 (19.8) | 59 (24.9) | 55 (37.4) | 28,745 (14.6) |
| Age 35 to 44 | 28 (8.6) | 18 (7.6) | 13 (8.8) | 22,063 (11.2) |
| Age 45 to 54 | 55 (17.0) | 39 (16.5) | 20 (13.6) | 32,274 (16.4) |
| Age 55 to 64 | 86 (26.5) | 41 (17.3) | 26 (17.7) | 45,203 (22.9) |
| Age 65 or order | 91 (28.1) | 80 (33.8) | 33 (22.4) | 69,067 (35.0) |
| Total | 324 (100.0) | 237 (100.0) | 147 (100.0) | 197,352 (100.0) |
| Level of education completed (P=0.000) | ||||
| Did not graduate high school | 57 (17.8) | 33 (13.9) | 14 (9.5) | 13,433 (6.8) |
| Graduated high school | 127 (39.6) | 94 (39.7) | 48 (32.7) | 54,628 (27.2) |
| Attended college or tech school | 76 (23.7) | 63 (26.6) | 51 (34.7) | 53,466 (27.2) |
| Graduated from college or tech school | 61 (19.0) | 47 (19.8) | 34 (23.1) | 75,391 (38.3) |
| Total | 321 (100.0) | 237 (100.0) | 147 (100.0) | 196,918(100.0) |
| Annual household income (P=0.000) | ||||
| Less than $20,000 | 89 (31.4) | 75 (36.9) | 35 (28.2) | 28,052 (16.5) |
| $20,000 to less than $35,000 | 66 (23.3) | 50 (24.6) | 33 (26.6) | 32,480 (19.1) |
| $35,000 to less than $50,000 | 41 (14.5) | 25 (12.3) | 19 (15.3) | 24,537 (14.4) |
| $50,000 to less than $75,000 | 36 (12.7) | 25 (12.3) | 8 (6.5) | 28,093 (16.5) |
| $75,000 or more | 51 (18.0) | 28 (13.8) | 29 (23.4) | 57,222 (33.6) |
| Total | 283 (100.0) | 203 (100.0) | 124 (100.0) | 170,384(100.0) |
BAFSS, Behavioral Risk Factor Surveillance System.
Comorbidities
Compared to no-transgender respondents, transgender individuals were more likely to suffer from increased comorbidities (see Table 3), such as cardiovascular accidents (stroke or heart attack), diabetes, chronic obstructive pulmonary disease (COPD), and arthritis. The only exception is that the non-conforming transgender respondents report less arthritis (21.9%) and fewer strokes (2.7%) compared to other transgender subpopulations and non-transgender participants. Compared to non-conforming subpopulations, male to female and female to male transgender had significantly increased risk for myocardial infarction (MI) (by 26.4% and 16.8% respectively), stroke (by 151.8% and 181.5% respectively) and diabetes (by 48.6% and 59.0%).
Table 3
| Comorbidities | Yes, male to female (%) | Yes, female to male (%) | Yes, non-conforming (%) | Non-transgender (%) |
|---|---|---|---|---|
| Ever told have CHD or MI (P=0.000) | ||||
| Yes | 50 (15.8) | 34 (14.6) | 18 (12.5) | 18,053 (9.2) |
| No | 266 (84.2) | 199 (85.4) | 126 (87.5) | 177,894 (90.8) |
| Total | 316 (100.0) | 233 (100.0) | 144 (100.0) | 195,947 (100.0) |
| Ever told have a stroke (P=0.004) | ||||
| Yes | 22 (6.8) | 18 (7.6) | 4 (2.7) | 8,257 (4.2) |
| No | 300 (93.2) | 219 (92.4) | 143 (97.3) | 188,639 (95.8) |
| Total | 322 (100.0) | 237 (100.0) | 147 (100.0) | 196,896 (100.0) |
| Ever told have COPD (P=0.006) | ||||
| Yes | 41 (12.7) | 26 (11.0) | 17 (11.6) | 16,313 (8.3) |
| No | 282 (87.3) | 210 (89.0) | 129 (88.4) | 180,280 (91.7) |
| Total | 323 (100.0) | 236 (100.0) | 146 (100.0) | 196,593 (100.0) |
| Ever told have arthritis (P=0.003) | ||||
| Yes | 125 (38.9) | 89 (37.9) | 32 (21.9) | 69766 (35.5) |
| No | 196 (61.1) | 146 (62.1) | 114 (78.1) | 126676 (64.5) |
| Total | 321 (100.0) | 235 (100.0) | 146 (100.0) | 196442 (100.0) |
| Ever told have diabetes (P=0.000) | ||||
| Yes | 69 (21.4) | 52 (22.9) | 21 (14.4) | 2,667 (13.6) |
| No | 253 (78.6) | 175 (77.1) | 125 (85.6) | 168,889 (86.4) |
| Total | 322 (100.0) | 227 (100.0) | 146 (100.0) | 195,556 (100.0) |
BAFSS, Behavioral Risk Factor Surveillance System; CHD, coronary heart disease; MI, myocardial infarction; COPD, chronic obstructive pulmonary disease.
Behavioral factors
As shown in Table 4, 21.5% of transgender persons are current smokers and only 14.9% of non-transgender individuals are current smokers. Non-conforming transgender persons had a significantly higher risk of risky drinking behaviors (binge drinking or/and heavy drinking) compared to non-transgender, male to female, and female to male transgender individuals. Bisexual sex behavior was common among transgender population: 10.8% of male to female transgender persons, 14.3% of female to male transgender persons, and 32.0% of non-conforming transgender persons self-reported being bisexual, compared to only 1.7% of non-transgender persons. Approximately 10% of transgender persons self-reported being lesbian or gay compared to 1.5% of non-transgender persons.
Table 4
| Behavioral factors | Yes, male to female (%) | Yes, female to male (%) | Yes, non-conforming (%) | Non-transgender (%) |
|---|---|---|---|---|
| Current smokers (P=0.000) | ||||
| No | 250 (77.6) | 184 (78.3) | 116 (80.6) | 166,467 (85.1) |
| Yes | 72 (22.4) | 51 (21.7) | 28 (19.4) | 19,183 (14.9) |
| Total | 322 (100.0) | 235 (100.0) | 144 (100.0) | 195,650 (100.0) |
| Binge drinkers (P=0.001) | ||||
| No | 264 (84.1) | 207 (88.8) | 108 (75.5) | 167,452 (86.5) |
| Yes | 50 (15.9) | 26 (11.2) | 35 (24.5) | 26,158 (13.5) |
| Total | 314 (100.0) | 233 (100.0) | 143 (100.0) | 193,610 (100.0) |
| Heavy drinkers (P=0.000) | ||||
| No | 290 (92.9) | 218 (93.6) | 122 (84.7) | 181,355 (93.8) |
| Yes | 22 (7.1) | 15 (6.4) | 22 (15.3) | 12,044 (6.2) |
| Total | 312 (100.0) | 233 (100.0) | 144 (100.0) | 193,399 (100.0) |
| Sex orientation (P=0.000) | ||||
| Bisexual | 35 (10.8) | 34 (14.3) | 47 (32.0) | 3,288 (1.7) |
| Lesbian or gay | 28 (8.6) | 24 (10.1) | 19 (12.9) | 2,976 (1.5) |
| Straight | 261 (80.6) | 179 (75.5) | 81 (55.1) | 191,088 (96.8) |
| Total | 324 (100.0) | 237 (100.0) | 147 (100.0) | 197,352 (100.0) |
BAFSS, Behavioral Risk Factor Surveillance System.
Medical care access
As shown in Table 5, only 9.2% of non-transgender persons reported difficulty in seeing a health care provider due to cost in the past 12 months. However, these figures were 15.7%, 17.3%, and 16.3% for male to female, female to male, and non-conforming transgender persons, respectively. There were no statistically significant differences in yearly routine primary care visits and access to preventive medical services, such as yearly flu vaccination between the four groups (transgender subpopulations and non-transgender persons).
Table 5
| Medical care access | Yes, male to female (%) | Yes, female to male (%) | Yes, non-conforming (%) | Non-transgender (%) |
|---|---|---|---|---|
| Difficulty to see doctor due to cost in the past 12 months (P=0.000) | ||||
| Yes | 50 (15.7) | 41 (17.3) | 24 (16.3) | 18,085 (9.2) |
| No | 269 (84.3) | 196 (82.7) | 123 (83.7) | 178,898 (90.8) |
| Total | 319 (100.0) | 237 (100.0) | 147 (100.0) | 196,983 (100.0) |
| Visited a doctor for a routine checkup (P=0.505) | ||||
| Within past year | 242 (76.1) | 172 (75.1) | 105 (71.9) | 150,057 (76.8) |
| More than 1 year ago | 76 (23.9) | 57 (24.9) | 41 (28.1) | 45,436 (23.2) |
| Total | 318 (100.0) | 229 (100.0) | 146 (100.0) | 195,493 (100.0) |
| Had flu shot or vaccine in 12 months (P=0.087) | ||||
| Yes | 129 (40.2) | 96 (41.4) | 60 (41.4) | 89,323 (45.6) |
| No | 192 (59.8) | 136 (58.6) | 85 (58.6) | 106,393 (54.4) |
| Total | 321 (100.0) | 232 (100.0) | 145 (100.0) | 195,716 (100.0) |
BAFSS, Behavioral Risk Factor Surveillance System.
Functional impairments
Table 6 presents the functional impairments among three different transgender subpopulations and the non-transgender population. Physical health complaints were more common among three different transgender subpopulations compared to non-transgender population, especially for more serious physical health complain among male to female transgender persons and female to male transgender persons. Approximately, one in five male to female transgender persons or female to male transgender persons reported more serious physical dysfunction compared to one in six of non-conforming transgender persons and one in seven non-transgender persons. The patterns of mental health complain followed the same trend of physical health complain. Transgender persons were much more likely to report more serious mental distress compared to non-transgender persons. Only one in ten non-transgender individuals self-reported more serious mental distress in the past 30 days compared to one of three among non-conforming transgender persons and one in five among male to female and female to male transgender persons.
Table 6
| Functional impairments | Yes, male to female (%) | Yes, female to male (%) | Yes, non-conforming (%) | Non-transgender (%) |
|---|---|---|---|---|
| 3 level not good physical health status (P=0.000) | ||||
| Zero day not good in 30 days | 175 (56.3) | 122 (53.0) | 79 (54.1) | 122,125 (63.0) |
| 1–13 days not good in 30 days | 75 (24.1) | 57 (24.8) | 43 (29.5) | 45,457 (23.5) |
| 14 to 30 days not good in 30 days | 61 (19.6) | 51 (22.2) | 24 (16.4) | 26,248 (13.5) |
| Total | 311 (100.0) | 230 (100.0) | 146 (100.0) | 194,517 (100.0) |
| 3 level not good mental health status | ||||
| Zero day not good in 30 days | 200 (63.3) | 128 (56.6) | 63 (43.8) | 132,809 (68.2) |
| 1–13 days not good in 30 days | 57 (18.0) | 45 (19.9) | 39 (27.1) | 41,603 (21.4) |
| 14 to 30 days not good in 30 days | 59 (18.7) | 53 (23.5) | 42 (29.2) | 20,199 (10.4) |
| Total | 316 (100.0) | 226 (100.0) | 144 (100.0) | 194,611 (100.0) |
| Depressive disorder or dysthymia (P=0.000) | ||||
| Yes | 84 (26.3) | 79 (33.5) | 54 (36.7) | 34,096 (17.3) |
| No | 235 (73.7) | 157 (66.5) | 93 (63.3) | 162,598 (82.7) |
| Total | 319 (100.0) | 236 (100.0) | 147 (100.0) | 196,694 (100.0) |
| Serious difficulty concentrating or make decision (P=0.000) | ||||
| Yes | 63 (19.6) | 57 (24.4) | 43 (29.7) | 19,044 (9.7) |
| No | 259 (80.4) | 177 (75.6) | 102 (70.3) | 196,953 (90.3) |
| Total | 322 (100.0) | 234 (100.0) | 145 (100.0) | 195,997 (100.0) |
| Difficulty doing errands alone such as visiting a doctor (P=0.000) | ||||
| Yes | 46 (14.3) | 39 (16.7) | 24 (16.4) | 14253 (7.3) |
| No | 276 (85.7) | 195 (83.3) | 122 (83.6) | 182093 (92.7) |
| Total | 322 (100.0) | 234 (100.0) | 146 (100.0) | 196346 (100.0) |
| Sleep pattern (P=0.000) | ||||
| Adequate sleep | 273 (85.6) | 186 (80.2) | 114 (77.6) | 174835 (89.3) |
| Not adequate sleep | 46 (14.4) | 46 (19.8) | 33 (22.4) | 20912 (10.7) |
| Total | 319 (100.0) | 232 (100.0) | 147 (100.0) | 195747 (100.0) |
BAFSS, Behavioral Risk Factor Surveillance System.
Of 196,694 non-transgender individuals, 17.3% were diagnosed with a depressive disorder or dysthymia in the past, significantly less than the corresponding figures of 26.3%, 33.5%, and 36.7% of male to female, female to male, and non-conforming transgender persons, respectively. Transgender individuals were more likely to have mental health-related functional dysfunction as well.
Approximately one out of five male to female transgender persons, one out of four female to male transgender persons, and one out of three non-conforming transgender persons reported serious difficulty concentrating, remembering, or making decisions due to physical and emotional distress. However, only one out of ten non-transgender persons self-reported these problems. Similar disparities were observed for difficulty attending to minor social obligation (such as visiting doctor’s office or shopping) and poor sleep, with transgender persons having more disruption in the functional areas compared to non-transgender persons. An estimated 19.8% of female to male transgender persons and 22.4% of non-conforming transgender individuals reported poor sleep, compared to only 10.7% of non-transgender persons.
The independent effects of transgender on mental health dysfunction
After controlling for socioeconomic and demographic status, co-morbidities, behavioral factors, and medical care access, we found non-conforming transgender status to be independently associated with increased risk of self-reported mentally not good severity to the next higher level by 57.9% (parameter estimate: 0.579, 95% CI: 0.191, 0.966, P=0.003). However, compared to non-transgender individuals, all three transgender subpopulations were at increased risk of being diagnosed with depressive disorder or dysthymia compared to non-transgender persons (Table 7). Female to male transgender persons and non-conforming transgender persons had greater odds of having serious difficulty concentrating, remembering, or making decisions than non-transgender persons. No statistically significant differences in difficulty running errands alone were found between the 3 transgender groups and non-transgender respondents.
Table 7
| Independent correlates | Male to female transgender | Female to male transgender | Non-conforming |
|---|---|---|---|
| Mentally not good days in the past 30 days (0 days, 1 to 13 days, 14 to 30 days) | |||
| Estimate (P value) | 0.155 (P=0.271) | –0.006 (P=0.970) | 0.579 (P=0.003) |
| 95% CI | –0.121 to 0.431 | –0.342 to 0.329 | 0.191 to 0.966 |
| Have a depressive disorder, including dysthymia | |||
| OR (P value) | 1.431 (P=0.026) | 1.815 (P=0.001) | 2.488 (P=0.000) |
| 95% CI | 1.043 to 1.965 | 1.263 to 2.611 | 1.597 to 3.876 |
| Serious difficulty concentrating, remembering, or make decision | |||
| OR (P value) | 1.089 (P=0.665) | 1.835 (P=0.004) | 3.049 (P=0.000) |
| 95% CI | 0.739 to 1.608 | 1.212 to 2.770 | 1.852 to 5.025 |
| Difficulty doing errands alone such as visiting a doctor or shopping | |||
| OR (P value) | 1.383 (P=0.128) | 1.555 (P=0.073) | 1.570 (P=0.201) |
| 95% CI | 0.912 to 2.096 | 0.960 to 2.525 | 0.786 to 3.135 |
BAFSS, Behavioral Risk Factor Surveillance System.
Discussion
The BRFSS is the nation’s premier and largest system of health-related telephone surveys with a total 26 states participating in optional Sexual Orientation and Gender Identity survey in 2016 (17). Among 198,060 respondents, 0.35% self-identified as transgender persons, which is consistent with prior studies. Hafeez and Gates [2017] for example reported that about 0.3% of USA persons self-identify as transgender (18,19). Based on 12 USA surveys conducted between 2007 and 2015, Esther et al. [2017] reported that 0.4% of the USA population self-identified themselves as transgender (4). Our estimates are however, less than those reported by Crissman et al. [2017] (20,21). By using CDC 2014 BRFSS data, Crissman found that transgender individuals were found to comprise 0.53% of the total population. However, Crissman used weighted percentage, which increases the chances of having estimates that are greater than the raw data analysis.
Our findings regarding other sociodemographic characteristics are also consistent with other research findings (3,12,22). Transgender adults are more likely to be younger, homeless or live in shelters, Caucasian, and earn less household income. Findings regarding educational levels are more varied. Whereas our study indicated transgender persons are more likely to be less educated compared to non-transgender persons, others report that transgender adults are less educated or do not differ (1,3,12,22).
Researchers have found that risky health behaviors, such as smoking and substance abuse, may affect transgender population general health (3,21,23-25). However, little is known about other key health-related behaviors, such as risky drinking behaviors (binge drinking or heavy drinking), obesity, and lack of physical exercise (12). Although our study did not find either physical activities or obesity to be associated with transgender subpopulations, it confirms higher rates of current smoking among all three transgender subpopulations, which may contribute to the comorbidity disparities among these minority population [higher prevalence of coronary heart disease (CHD), MI, stroke, and COPD]. We also found only non-conforming transgender individuals to be more likely to engage in risky drinking behaviors (either binge drinking or heavy drinking), which may in part due to more serious mental health dysfunction among this transgender subpopulation because non-conforming transgender individuals may use alcohol to compensate their mental health dysfunction (26-28).
With an increased risk of physical and psychiatric or mental comorbidities, transgender people may not be able to access the care they need because they may avoid medical care for fear of being prejudged or due to financial difficulty. Our findings indicated that all three transgender subpopulations are at least 70% less likely to see health care provider due to financial costs in the past 12 months. At the same time, medical insurance may not cover needed transgender health services and some of the transgender people actually have been turned by health care providers (29). More importantly, transgender individuals share some of the risk factors, such as hormone replacement therapy, silicone injection, smoking, risky drinking behavioral, and mental health complaints. Transgender men who still have a uterus or breasts are at risk for cancer in these organs. However, they may unwilling to received cervical cancer or breast cancer screening. Our study also indicated that transgender persons are often at higher risk for heart disease, stroke, and diabetes which may also associated with some of these risk factors. However, the primary care may lack of the training on how to deal with specialized transgender health care issues.
We found important disparities in physical and mental functioning among transgender subpopulations and non-transgender persons. Compared to the gender non-conforming subpopulation and non-transgender individuals, male to female transgender and female to male transgender persons had greater prevalence of diabetes and cardiovascular disease, especially stroke. Importantly, these disparities exist cross all age groups (data not shown). The exact reasons for these disparities are not clear, however, some researchers hypothesize that exogenous hormone replacement may increase the risk for MI and stroke due to blood pressure elevation, insulin resistance, and lipid derangements (30). Transgender people have extremely high rates of smoking and drinking which also increase the risks for developing cardiovascular diseases (31). Researchers also reported that cross-sex hormone therapy is associated with increased risk of venous thrombosis, MI, cardiovascular disease, and diabetes (32-35). Prior to exogenous systemic hormone replacement therapy for transgender persons, the provider should therefore discuss the side effects of hormone therapy with patients, based on patient baseline risk factors.
We found that among all three transgender populations, non-conforming transgender adults were more likely to report poor mental health and functional impairment compared to male to female transgender and female to male transgender individuals. Sandfort et al. [2007] reported that compared with gender conforming gay and bisexual Latino men, gender non-conforming gay and bisexual Latinos were more likely to experience childhood sexual abuse, verbal/physical abuse, and rape by lovers or relatives (36). Being non-conforming transgender can be socially challenging. In addition, health care providers may not have adequate knowledge and capacity to provide unique care to this disadvantaged and underserved transgender population. Transgender persons have been turned away by health care providers or had other negative experiences. Some of them may avoid medical care because of these negative experiences. Transgender health services also currently are not covered by insurance, and some of the providers do not have the tools or skills to appropriately serve transgender persons. These challenges raise important legal, ethical, and medical considerations that are worthy of future study (37-39).
Our study is not without limitations. The BRFSS study is a telephone survey, and there is a risk of selection bias for persons with secure housing arrangements. It is estimated that 1 in 5 transgender people have unstable housing, or are at risk or in need of shelter services (3,12,38,39). Our respondent sample however, indicates that almost half of our transgender respondents own a home. There is also a chance that some of the information collected may not be accurate due to social desirability bias. For instance, respondents may deny transgender identity because of concerns about stigma, safety, privacy, or transphobia. Strengths of this study include the large sample size and our use of the largest continuously conducted health survey system in the world, in addition to robust statistical analytic approaches.
Conclusions
In summary, we found higher rates of self-reported physical/mental functional impairments, including depression and poor mental wellbeing among transgender groups compared to non-transgender persons. Our results will be useful in planning mental health services for this unique subpopulation.
Acknowledgments
Funding: None.
Footnote
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jphe.2019.12.02). XC serves as an unpaid editorial board member of Journal of Public Health and Emergency from Jan 2017 to Dec 2022. The other authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. We only used the 2016 CDC BRFSS publicly available data to assess social economic status, behavioral health-related factors, medical care access, co-morbidities, and mental health functional impairments among three transgender subpopulations. Ethics approval was not required.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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Cite this article as: Song J, Aliyu M, Chen XW. Transgender identity and mental health functional impairment: analysis of the 2016 Behavioral Risk Factor Surveillance System. J Public Health Emerg 2020;4:1.
