Mental Health and Risk Behaviors among Lesbian, Gay, Bisexual, Transgender, and Queer Adults Living in the United States, by State Policy Inclusivity per the Human Rights Campaign: A Secondary Analysis of the 2022 Behavioral Risk Factor Surveillance System Data
Author: Elle Elson
Committee Members: Dr. Amy Ferketich, PhD, MAS, MA & Dr. Joanne Patterson, PhD, MPH, MSW
Abstract
Background: Recent decades have seen the emergence of social and legal protections for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) individuals in the United States (US), yet a rapid increase in anti-LGBTQ+ legislation in 2022 highlights ongoing challenges. Discriminatory policies and societal stigma contribute to health disparities, particularly among LGBTQ+ young adults, with population-based implications for mental health, substance abuse, and access to health care.
Methods: The 2022 Behavioral Risk Factor Surveillance System (BRFSS) and 2022 Human Rights Campaign State Equality Index (SEI) were used to examine the relationships between state-level LGBTQ+ inclusivity (inclusive or restrictive) and mental health and risk behaviors (self-reported overall health, poor mental health days, depression diagnosis, cigarette use, e-cigarette use, binge drinking, heavy drinking, dual substance use of binge/heavy drinking and any nicotine use) among LGBTQ+ adults. Bivariate survey-weighted associations and multiple logistic regression models analyzed their disparate relationships.
Results: Restrictive states had greater relative odds of LGBTQ+ individuals not having health insurance (Adjusted Odds Ratio [AOR] 2.01, 95% Confidence Interval [CI] 1.61-2.53, p < 0.05), current smoking (AOR 1.22, 95% CI 1.02-1.45, p < 0.05), current use of e-cigarettes (AOR 1.22, 95% CI 1.03-1.45, p < 0.05), use of any type of nicotine (AOR 1.28, 95% CI 1.11-1.47, p < 0.05), and dual nicotine use (AOR 1.33, 95% CI 1.02-1.73, p < 0.05), compared to inclusive states.
Conclusion: LGBTQ+ adults living in restrictive policy states have increased odds of nicotine use and worse access to health care. State-level policy makers should consider their LGBTQ+ residents when proposing restrictive or discriminatory LGBTQ+ legislation and the negative health consequences for their constituents. Expanding access to low-cost, LGBTQ+ affirming health care with knowledge of the communities’ needs is critical for improving health outcomes in this marginalized population.
Current Study: The objective of this study was to examine how state-level LGBTQ+ policies impact mental health outcomes and risk behaviors among LGBTQ+ adults, comparing states with restrictive or inclusive LGBTQ+ policies and legislation. We hypothesized that states with more restrictive LGBTQ+ policies would evidence worse mental health outcomes and increased risk behaviors among LGBTQ+ adults. These relationships were analyzed in a sample of 17,988 LGBTQ+ adults who participated in the 2022 BRFSS. Our health indicators included prevalence of substance use, mental health diagnoses, access to health insurance, and overall health outcomes. We compared groups based on where they lived (restrictive vs. inclusive states with respect to state-level LGBTQ+ policies).
Methods
Data Source
The Behavioral Risk Factor Surveillance System (BRFSS) is a CDC-assisted health-data collection project conducted in partnership with state health departments to compromise all 50 states and the District of Columbia, Puerto Rico, Guam, and the US Virgin Islands (Behavioral Risk Factor Surveillance System 2022 Summary Data Quality Report, 2023). Data is collected from non-institutionalized adults (18 years old and greater) via landline telephone or cellular telephone interviews. In 2022, BRFSS had an overall response rate of 45.0% (BRFSS 2022 Summary Data Quality Report, 2023). The population of interest for this study consists of the states that included the optional module 26, which assessed participants sexual orientation and gender identity (SOGI) to determine LGBTQ+ status. A participant’s LGBTQ+ status was created from the 2022 BRFSS SOGI responses. For this categorization, participants were selected from the following variables of sexual orientation and gender minority from the 2022 BRFSS module. To determine sexual orientation, respondents were asked “Which of the following best represents how you think of yourself?”, with response options of “Gay; Straight, that is, not gay; Bisexual; Something else; I don’t know; Refused” (Center for Disease Control and Prevention [CDC] LLCP 2022: Codebook Report, 2022). For this analysis, sexual minority is defined as participants who answered “gay, bisexual, or something else” for sexual orientation. To determine gender minority status, respondents were asked, “Do you consider yourself to be transgender?”, with response options of, “Yes, Transgender, male-to-female; Yes, Transgender, female-to-male; Yes, gender nonconforming; No; Don’t know; Refused” (CDC, 2022). For this analysis, gender minority is defined as participants who answered “transgender male-to-female, female-to-male, or gender nonconforming” on the gender identity question. Henceforth, a participant was determined to be LGBTQ+ if they were a sexual minority and/or gender minority (SGM).
The 2022 Human Rights Campaign (HRC) State Equality Index (SEI) is a holistic assessment of statewide laws, policies, and court decisions that affect LGBTQ+ equality (Human Rights Campaign, 2022). Researchers for the SEI assessment included staff attorneys, pro bono attorneys, and law fellows who assessed the SEI for 52 markers of inclusivity (Human Rights Campaign, 2022). The SEI analyzed five categories of policies: Parenting laws and policies, Relationship recognition and religious refusal laws, Non-discrimination laws and policies, Hate crimes and criminal justice laws, and Youth-related laws and policies (Human Rights Campaign, 2022). States were then assigned one of four levels of LGBTQ+ equality: 1. High priority to achieve basic equality (lowest rating); 2. Building equality; 3. Solidifying equality; and 4. Working towards innovative equality (Human Rights Campaign, 2022). States were dichotomized into Inclusive and Restrictive states for statistical analysis, based on the statistical model from existing literature (White et al., 2023). The 31 included states are as follows: Alaska, Colorado, Connecticut, Delaware, Georgia, Hawaii, Illinois, Indiana, Iowa, Kansas, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, Nevada, New Mexico, North Carolina, North Dakota, Ohio, Pennsylvania, Rhode Island, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wisconsin.
LGBTQ+ inclusive states consisted of: Alaska, Colorado, Connecticut, Delaware, Hawaii, Illinois, Iowa, Maryland, Massachusetts, Minnesota, Nevada, New Mexico, Pennsylvania, Rhode Island, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin (n=20).
LGBTQ+ Restrictive states consisted of: Georgia, Indiana, Kansas, Louisiana, Michigan, Missouri, Montana, North Carolina, North Dakota, Ohio, Texas (n=11). The final analytical sample (n=31 states; N=17,988 adults) was determined by what States had Sexual Orientation and Gender Identity (SOGI) variables from an optional module from the BRFSS. States without SOGI variables were excluded from analysis.
Measures
Independent variable
State-level policy: After SGM participants were flagged in the BRFSS population, they were categorized under inclusive or restrictive state-level LGBTQ+ policy from the HRC SEI.
Dependent variables
Health insurance: A participant’s health insurance status was determined from the question, “What is the current primary source of your health insurance?”, with response options of, “A plan purchased through an employer or union; A private nongovernmental plan that you or another family members buys on your own; Medicare; Medigap; Medicaid; Military related; Indian Health Services; State sponsored health plan; Other government program; No coverage of any type; Don’t know; Refused” (CDC, 2022). Participants were coded with having health insurance for all response options, except “no coverage of any type,” “don’t know,” or “refused.”
General health: A participant’s self-reported general health status was determined from the question, “Would you say that in general your health is–“, with response options of, “Excellent, Very Good, Good, Fair, Poor” (CDC, 2022). For analysis, general health was categorized as “good or greater” and “fair or less.”
Mental health: A participant’s mental health status was determined from the question, “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”, with a response of the numbers of days, between 0 to 30 (CDC, 2022). For analysis, mental health was categorized into 0 to 13 poor mental health days or 14+ poor mental health days (CDC, 2004).
Depressive disorder: A participant’s depressive disorder status was determined from the question, “Has a medical provider ever told you had a depressive disorder (including depression, major depression, dysthymia, or minor depression?” with response options of, “Yes, No, don’t know, Refused” (CDC, 2022). For analysis, presence of a depressive disorder was binary coded with yes or no.
Smoking status: A participant’s current cigarette smoking status was determined from a two-step question. First, participants were asked, “Have you smoked at least 100 cigarettes in your entire lifetime?” with response options of, “Yes, No, don’t know, Refused” (CDC, 2022). Second, they responded to, “Do you now smoke cigarettes –” with response options of, “Every day, some days, not at all, don’t know, Refused” (CDC, 2022). For analysis, current cigarette smoking status was binary coded with yes as those who responded with “every day” or “some days” on the second question, and “not at all” from the second question or “no” from the first question, as not currently smoking cigarettes.
E-cigarettes: A participant’s current e-cigarette use was determined from the question, “Would you say you have never used e-cigarettes or other electronic vaping products in your entire life or now use them every day, use them some days, or used them in the past but do not currently use them at all?” with response options of, “Never used e-cigarettes in your entire life, Use them every day, Use them some days, Not at all, Don’t know, Refused” (CDC, 2022). For analysis, current e-cigarette use was binary coded with no as “never used e-cigarettes” or “not at all” and yes as “use them every day” or “use them some days.”
Any nicotine: A participant’s current use of any nicotine was a created variable, with a yes for either cigarette and/or e-cigarette use.
Dual nicotine: A participant’s status of current dual nicotine use was a created variable, with a yes for both cigarette and e-cigarette use.
Binge drinking: A participant’s status for binge drinking was a two-level question, with a positive response to drinking in the past 30 days and the appropriate rate of binge drinking based on participant’s sex (5 for men or 4 for women) on the question of, “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinking on an occasion?” (CDC, 2022). Any alcohol use was asked, “During the past 30 days, how many days per week or per month did you have at least one drink of any alcoholic beverage?”, with response options of, “Numbers of days per week, Numbers of days in past 30 days, not at all, don’t know, Refused” (CDC, 2022).
Heavy drinking: A participant’s status for heavy drinking was a three-level question, with a positive response to drinking in the past 30 days, and calculated amounts for heavy drinking based on participant’s sex from the maximum and average drinking questions (men having more than 14 drinks per week and women having more than seven drinks per week) (CDC, 2022). Maximum drinking was measured with, “During the past 30 days, what is the largest number of drinks you had on any occasion?” and average drinking was measured with, “During the past 30 days, on the days when you drank, about how many drinks did you drink on average?” (CDC, 2022).
Polysubstance use (binge drinking and any nicotine): A participant’s polysubstance use was defined as both binge drinking in the past 30 days and currently using any type of nicotine (cigarette or e-cigarette).
Polysubstance use (heavy drinking and any nicotine): A participant’s polysubstance use was defined as both heavy drinking in the past 30 days and currently using any type of nicotine (cigarette or e-cigarette).
Control variables
The control variables for analytical consideration were identified from previous research that informed this study question (White et al., 2023). The six categorical demographic variables were age, Race/Ethnicity, education, employment, income, and sex.
Age: Upon logistic regression analysis, age was split into two levels, 18-34 years old (young adults) and 35+ years old. With the greatest disparity in substance use compared to heterosexual peers, this variable was divided with young adults (18-34 years) and adults aged 35 years and older.
Race/Ethnicity: A participant’s race and ethnicity were BRFSS calculated variables from multiple race and ethnicity questions that are detailed in the “Calculated Variables in the 2022 BRFSS Data File” report (CDC, 2023). For logistic regression analysis, race/ethnicity were categorized into six levels: White, non-Hispanic; Black, non-Hispanic; American Indian and Alaska Native (AIAN) & Native Hawaiian and Other Pacific Islander (NHOPI); Asian, non-Hispanic; Multiracial, non-Hispanic; and Hispanic.
Education: A participant’s education level was calculated from the question, “What is the highest grade or year of school you completed?” with response options of “Never attended school or only kindergarten, Grades 1 through 8 (elementary), Grades 9 through 11 (some high school), Grade 12 or GED (High school graduate), College 1 year to 3 years (some college or technical school, College 4 years or more (College graduate), Refused” (CDC, 2022). For logistic regression analysis, education was collapsed into two levels, high school/GED or less and attended or graduated college/technical school.
Employment and income: Employment and income variables were not used in logistic regression analysis due to extensive missingness (10% missing for employment and 20% missing for income). As such, socio-economic status was supplemented by education level, depicted above.
Sex: A participant’s sex was not a reliable measure in this subpopulation as currently asked in BRFSS, “Are you male or female?” with response options of, “Male, Female, Non-binary (insert sex at birth module or terminate phone call, Don’t know/not sure (insert sex at birth module or terminate phone call, Refused (insert sex at birth module or terminate phone call)” (CDC, 2022). Appropriate examples of measuring sex and gender are detailed in “Measuring Sex, Gender, Identity, and Sexual Orientation” by The National Academies Press (2022).
Statistical Analyses
Survey-weighted descriptive statistics were used to categorize the analytic sample by state-level LGBTQ+ inclusivity. Survey-weighted and design adjusted chi-square analyses were performed to measure bivariate associations between state-level inclusivity status and each outcome variable (mental health and risk behaviors). Bivariate survey-weighted and design adjusted logistic regression models were performed to compare the intersectionality of risk among this population, without adjusting for demographic variables. Survey-weighted and design adjusted logistic regression models analyzed the outcome variables by state-level LGBTQ+ inclusivity, and controlled for age, race/ethnicity, and education. Other control variables were removed from the model due to missingness (income and employment) or for lack of statistical effect (sex).
Each logistic regression model was adjusted to remove missing data of demographic (Table 1) and individual outcome variable responses, to reduce bias with listwise deletion. All logistic regression models were adjusted for all control variables (age, race/ethnicity, and education), along with one individual outcome per model (self-reported overall health, poor mental health days, depression diagnosis, cigarette use, e-cigarette use, binge drinking, heavy drinking, dual substance use of binge/heavy drinking and any nicotine use). As a result, 12 unique survey-weighted and adjusted logistic regression models had varying sample sizes to ensure maximum representation in each subpopulation from varied missingness among outcome variables. During all analyses, the subpopulation command was implemented with Stata to ensure appropriate adjustment for weighted percentages (Table 1) and statistical analyses (Table 2, Table 3) [StataBE 18 was used for all analyses]. Survey-weighting for the 2022 BRFSS data involved using a specific weighting procedure for core data and the SOGI measurement model. In addition, design adjustments are calculated based on specific weighting of their survey design to accurately represent the national population, which can be found from the “Complex Sampling Weights and Preparing 2022 BRFSS Module Data for Analysis” from the CDC (2023). An alpha of .05 indicated statistically significant differences. As a secondary analysis of publicly available, non-identifiable data, this study was exempt from Institutional Review Board approval.
Results
The analytic sample consisted of 17,988 self-reported SGM participants in 31 US states (Table 1). After survey-weighting, the sample was evenly distributed across LGBTQ+ inclusive (51%) and restrictive states (49%) (Table 1, Figure 1). Sex was evenly distributed and not included in the final logistic regression model due to negligible differences when controlling for demographics. Additionally, sex is not a reliable measure in the LGBTQ+ population, as transgender and gender non-conforming individuals are not accurately measured with how this variable was written in the BRFSS survey. The majority of self-reported SGM young adults, 18 to 34 years, and were the primary age of interest for health-related disparities, particularly among risk behavior outcomes. White, non-Hispanic participants represented less than 60% in both state levels, with a higher proportion of Hispanic individuals living in restrictive states (20%) (Table 1). This was a well-educated sample, with less than 13% of participants who had less than a High School education (Table 1).
Primary outcomes were analyzed with Rao-Scott chi-square tests to show the disparity of LGBTQ+ participants living in restrictive policy states. A greater prevalence was found for being uninsured (χ2 <0.000), current smoking (χ2 = 0.002), current use e-cigarettes (χ2 = 0.026), use any type of nicotine (χ2 = 0.001), and dual nicotine use of cigarettes and e-cigarettes (χ2 = 0.027) (Table 2).
Unadjusted logistic regression models demonstrated statistically significant disparities in the same outcomes, not having health insurance state (Odds Ratio [OR] 2.12, 95% CI 1.72-2.62, p < 0.05), currently using cigarettes (OR 1.29, 95% CI 1.10-1.51, p < 0.05), current use of e-cigarettes (OR 1.20, 95% CI 1.02-1.41, p < 0.05), use of any type of nicotine (OR 1.26, 95% CI 1.10-1.44, p < 0.05), and dual nicotine use (OR 1.35, 95% CI 1.03-1.77, p < 0.05) (Table 3).
Adjusted logistic regressions models illustrated greater disparity of not having any health insurance in restrictive states (Adjusted Odds Ratio [AOR] 2.01, 95% Confidence Interval [CI] 1.61-2.53, p < 0.05), currently using cigarettes (AOR 1.22, 95% CI 1.02-1.45, p < 0.05), current use of e-cigarettes (AOR 1.22, 95% CI 1.03-1.45, p < 0.05), use of any type of nicotine (AOR 1.28, 95% CI 1.11-1.47, p < 0.05), and dual nicotine use (AOR 1.33, 95% CI 1.02-1.73, p < 0.05) (Table 3). All remaining outcome variables showed increased odds of poor mental and risk behavior outcomes, including worse self-reported health (AOR 1.09), having a depressive disorder (AOR 1.04), poor mental health in the last month (AOR 1.10), binge drinking in the last month (AOR 1.10), heavy drinking (AOR 1.09), and engaging in polysubstance use of nicotine and binge (AOR 1.09) or heavy drinking (AOR 1.06). However, these adjusted logistic regression models did not show statistically significant results compared to LGBTQ+ individuals in inclusive policy states.
Table 1. LGBTQ+ subpopulation demographics from 2022 Behavioral Risk Factor Surveillance System by state equality rating from 2022 Human Rights Campaign LGBTQ+ State Equality Index^
| |
Inclusive States |
Restrictive States |
| Variable |
Weighted % (unweighted N)^§ |
| Total Sample (17,988) |
51.2 (12,218) |
48.8 (5,770) |
| Sex†† |
|
|
| Male |
40.3 (5,065) |
37.2 (2,362) |
| Female |
59.7 (7,153) |
62.8 (3,408) |
| Age (in years)†† |
|
|
| 18-24 |
30.9 (2,160) |
32.3 (1,169) |
| 25-34 |
28.0 (2,860) |
27.3 (1,359) |
| 35-44 |
15.3 (2,103) |
14.7 (878) |
| 45-54 |
8.8 (1,453) |
9.9 (663) |
| 55-64 |
8.0 (1,535) |
7.2 (626) |
| 65 and older |
9.0 (2,107) |
8.6 (1,075) |
| Race/Ethnicity |
|
|
| White, non-Hispanic |
59.5 (8,334) |
53.8 (3,979) |
| Black, non-Hispanic |
9.3 (735) |
12.4 (518) |
| AIAN & NHOPI# |
2.4 (304) |
1.3 (114) |
| Asian, non-Hispanic |
5.4 (477) |
5.0 (126) |
| Multiracial, non-Hispanic |
5.9 (539) |
4.7 (193) |
| Hispanic |
14.6 (1,444) |
20.4 (657) |
| Missing |
(385) |
(183) |
| Education |
|
|
| Less than high school, High school, or GED |
40.0 (3,563) |
45.4 (2,010) |
| Attended college/technical school |
31.6 (3,224) |
31.0 (1,659) |
| Graduated college/technical school |
28.2 (5,397) |
23.3 (2,084) |
| Missing |
(34) |
(17) |
| Employment |
|
|
| Employed |
66.0 (7,309) |
64.5 (3,266) |
| Not employed |
25.2 (1,999) |
27.5 (911) |
| Retired |
8.9 (1,829) |
8.1 (884) |
| Missing |
(1,081) |
(709) |
| Income |
|
|
| Less than $15,000 |
5.2 (689) |
7.4 (427) |
| $15,000 – 24,999 |
8.5 (1,014) |
10.5 (609) |
| $25,000 – 34,999 |
10.5 (1,236) |
11.6 (709) |
| $35,000 – 49,999 |
10.7 (1,330) |
11.5 (727) |
| $50,000 – 99,999 |
22.5 (2,950) |
20.7 (1,283) |
| $100,000 – 199,999 |
14.8 (1,995) |
11.5 (690) |
| $200,000 and above |
4.5 (611) |
3.9 (175) |
| Missing |
(2,393) |
(1,150) |
| ^ Survey-weighting performed from CDC guidelines from 2022 BRFSS data guidelines; § Weighted % are column; †† No missing data; #American Indian, Alaska Native, Native Hawaiian, or other Pacific Islander, non-Hispanic |
|
|
|
Table 2. Descriptive results of mental health and risk behaviors from 2022 Behavioral Risk Factor Surveillance System by 2022 Human Rights Campaign LGBTQ+ State Equality Index^
| Variable |
Proportion for Inclusive States |
Proportion for Restrictive States |
χ2 |
| Self-reported overall health |
|
|
0.112 |
| Fair or poor health |
21.8 |
23.9 |
|
| Have any health insurance |
|
|
<0.000 |
| No |
7.6 |
14.9 |
|
| Have a depressive disorder |
|
|
0.684 |
| Yes |
45.9 |
45.3 |
|
| Poor mental health days in last month |
|
|
0.258 |
| 14+ days |
33.3 |
35.0 |
|
| Current cigarette smoker |
|
|
0.002 |
| Yes |
13.5 |
16.7 |
|
| Current e-cigarette use |
|
|
0.026 |
| Yes |
14.5 |
17.0 |
|
| Any nicotine use |
|
|
0.001 |
| Yes |
24.0 |
28.0 |
|
| Dual Nicotine Use |
|
|
0.027 |
| Yes |
3.8 |
5.1 |
|
| Binge drinking in last month |
|
|
0.225 |
| Yes |
22.2 |
23.8 |
|
| Heavy drinker |
|
|
0.570 |
| Yes |
8.2 |
8.7 |
|
| Polysubstance use |
|
|
0.363 |
| Binge drinking and current nicotine user |
10.4 |
11.3 |
|
| Polysubstance use |
|
|
0.687 |
| Heavy drinking and current nicotine user |
4.5 |
4.7 |
|
| ^ Survey-weighting performed from CDC guidelines from 2022 BRFSS data guidelines |
|
Table 3. Logistic regression analysis of mental health and risk behaviors among LGBTQ+ adults in the 2022 Behavioral Risk Factor Surveillance System Living in Restrictive States defined by the 2022 Human Rights Campaign LGBTQ+ State Equality Index^
| Mental Health and Risk Behaviors |
Odds Ratio (95% CI) |
p-value |
Adjusted Odds Ratio (95% CI) £ |
p-value |
| Self-reported overall healtha |
|
|
|
|
| Fair or poor health |
1.13 (0.97, 1.30) |
0.112 |
1.09 (0.94, 1.26) |
0.267 |
| Have any health insuranceb |
|
|
|
|
| No |
2.12 (1.72, 2.62) ** |
<0.000 |
2.01 (1.61, 2.53) ** |
<0.000 |
| Have a depressive disorderc |
|
|
|
|
| Yes |
0.98 (0.87, 1.10) |
0.684 |
1.04 (0.92, 1.64) |
0.565 |
| Poor mental health days in last monthd |
|
|
|
|
| 14+ days |
1.08 (0.95, 1.22) |
0.258 |
1.10 (0.96, 1.25) |
0.160 |
| Current cigarette smokere |
|
|
|
|
| Yes |
1.29 (1.10, 1.51) ** |
0.002 |
1.22 (1.02, 1.45) ** |
0.002 |
| Current e-cigarette userf |
|
|
|
|
| Yes |
1.20 (1.02, 1.41) ** |
0.026 |
1.22 (1.03, 1.45) ** |
0.021 |
| Any nicotineg |
|
|
|
|
| Yes |
1.26 (1.10, 1.44) ** |
0.001 |
1.28 (1.11, 1.47) ** |
0.001 |
| Dual nicotine useh |
|
|
|
|
| Yes |
1.35 (1.03, 1.77) ** |
0.027 |
1.33 (1.02, 1.73) ** |
0.033 |
| Binge drinking in last monthi |
|
|
|
|
| Yes |
1.10 (0.95, 1.27) |
0.225 |
1.10 (0.95, 1.27) |
0.181 |
| Heavy drinkerj |
|
|
|
|
| Yes |
1.06 (0.86, 1.31) |
0.181 |
1.09 (0.88, 1.35) |
0.570 |
| Polysubstance usek |
|
|
|
|
| Binge drinking and current nicotine user |
1.10 (0.90, 1.33) |
0.364 |
1.09 (0.89, 1.33) |
0.394 |
| Polysubstance usel |
|
|
|
|
| Heavy drinking and current nicotine user |
1.06 (0.81, 1.37) |
0.687 |
1.06 (0.81, 1.37) |
0.697 |
^ Survey-weighting performed from CDC guidelines from 2022 BRFSS data guidelines
£ Models adjusted for Income, Age, Education, and Race/Ethnicity
** indicates p < 0.05
aReferent (Ref) = Good or better health. bRef. = No health insurance. cRef. = No depressive disorder. dRef. = Zero-13 poor mental health days in last month. eRef. = No current cigarette use. fRef. = No current e-cigarette use. gRef. = Not a nicotine user. hRef. = No dual nicotine use. iRef. = Not a binge drinker. jRef. = Not a heavy drinker. kRef. = No binge drinking and nicotine use. lRef. = No heavy drinking and nicotine use.
Figure 1. State LGBTQ+ policy type from 2022 Human Rights Campaign LGBTQ+ State Equality Index
