Disproportionate use of polysubstance combinations varies by sexual identity among US adults

Luis M Mestre; Marney A White; Juhan Lee; Maria A Parker; Krysten W Bold

doi:10.1371/journal.pone.0340454

. 2026 Feb 18;21(2):e0340454. doi: 10.1371/journal.pone.0340454

Disproportionate use of polysubstance combinations varies by sexual identity among US adults

Luis M Mestre ^1,^*, Marney A White ^1,², Juhan Lee ³, Maria A Parker ⁴, Krysten W Bold ¹

Editor: Daniel Demant⁵

PMCID: PMC12915938 PMID: 41706684

Abstract

Polysubstance use is a major public health concern affecting lesbian, gay, and bisexual (LGB) adults, especially bisexual female adults. This study aims to identify the commonly used polysubstance combinations by LGB adults in the past 30 days and to determine whether these combinations differ by sexual identity and sex. Our analytic sample consisted of NSDUH 2021 and 2022 (n = 66,634 adults; 8.59% LGB adults). We used survey-weighted multinomial logistic regression models to assess the polysubstance combinations by sex. The most used substances were binge alcohol drinking, cannabis, cigarettes, and nicotine vape. Bisexual female adults used most of the assessed polysubstance combinations that included binge alcohol drinking, cannabis, or both, often involving three or four substances. Sex differences among the polysubstance combinations vary among heterosexual and bisexual adults but not among gay/lesbian adults. Public health strategies must consider the specific sexual identity, sex, and the types of substance combinations involved.

Introduction

Polysubstance use, the use of multiple substances, is a public health concern. Polysubstance use increases the risk of chronic diseases and mental health illnesses like depression and anxiety disorders, along with increased rates of preventable deaths [1–5]. Lesbian, gay, and bisexual (LGB) adults have a higher prevalence of substance use than heterosexual adults [6–15]. These differences are partly due to LGB adults engaging in substance use to cope with the social stress they experience [10,16–19]. Bisexual adults face more social stress than gay or lesbian adults because of bi-erasure and biphobia, which leads to discrimination [20–25]. Facing more social stress is especially common among bisexual female adults [20,21,25,26], who are more likely to use polysubstance combinations compared by sexual identity and sex [12,14,27].

Multiple studies have examined polysubstance use among LGB adults in the US, focusing on sex differences [9,10,12,14,27–33]. However, although these studies provide important insights, they have some limitations. A major limitation is that they do not use a representative sample of the US population. Many of these studies focus primarily on young adults [9,14,27,30–33] or LGB individuals from specific locations, like cities, community programs, or college campuses, which limits the external validity of the findings to all LGB adults in the US [9,14,27,28,32,33].

We identified two research gaps: (a) Not identifying the most used polysubstance combinations among LGB adults in the US. (b) Whether there are sex differences among LGB adults in the US regarding these polysubstance combinations. If such differences exist, we also aim to determine whether they vary by sexual identity among LGB adults nationwide. Addressing these research gaps can help inform clinical and public health initiatives, enabling us to customize substance use treatments and improve access for LGB adults based on their sex [34].

We hypothesized that sex differences in the polysubstance combinations will vary by sexual identity. We also hypothesized that bisexual female adults will have higher rates among all polysubstance combinations compared to all the other groups. We used data from the National Survey on Drug Use and Health (NSDUH) – a cross sectional study representative of the civilian, non-institutionalized US population, which is often used to assess polysubstance use among US adults [8,11–13]. Our representative sample includes LGB adults, our population of interest.

Methods

Study sample

The analytic sample from 2021 and 2022 of the NSDUH dataset was n = 66,634 adults, with 8.52% identifying as LGB [35]. The survey design changed in 2021, and earlier survey cycles had different survey collection procedures. Before 2021, NSDUH’s staff collected the data only in person, while since then, these data have been collected both in person and online [35]. We only included US adults using a complete case approach (i.e., with valid responses) in our analysis. This study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [36].

Ethical statement.

Ethical approval was not required. This study was waived from the Institutional Review Board (IRB) of the corresponding author’s institution oversight due to the nature of secondary analyses of publicly available, de-identified datasets. The data used in this study is secondary data. The IRB waived the need for ethics approval and patient consent for the collection, analysis, and publication of the de-identified data for this secondary data analysis.

Measures

Independent variable (exposure) – Sexual identity.

We used sexual identity as the independent variable in this study. The survey assessed sexual identity using the question, “Which of the following do you consider yourself to be?” Consistent with other studies, the participants self-identified either as heterosexual, gay/lesbian, bisexual, or do not know (i.e., Not Sure) [8,11,12].

Dependent variable (outcome) – Past 30 days used polysubstance combinations.

We defined polysubstance use as the different combinations of the four most used substances in the past 30 days: Binge Alcohol Drinking, Cannabis, Cigarettes, and Nicotine Vape – S1 Table. We used this approach to build upon earlier studies that looked at only two or three substances [14,17,37], as considering more options gives us better insight into the complex behavior of polysubstance use [1,4].

We used the imputed variables of past 30-day substances available in NSDUH 2021 and 2022 (S1 Table). Substances included binge alcohol drinking – five or more drinks at the same time or within a couple of hours of each other for male and four or more drinks for female adults; cannabis; cigarettes; cocaine; crack; hallucinogens (such as foxy, LSD – acid, ecstasy – molly, ketamine, PCP – angel dust, peyote, mescaline, psilocybin, dimethyltryptamine (i.e., DMT), alpha-methyltryptamine (i.e., AMT), and salvia divinorum); heroin; inhalants; methamphetamine; cannabis vape, flavor vape, and nicotine vape. Misused substances included: prescription pain relievers (including fentanyl); sedatives; stimulants, and tranquilizers.

Based on the four most used substances, we had 12 mutually exclusive combinations. The combinations comprised two, three, or four most used substances (Table 1). The mutually exclusive combinations were: (a) No use of any of the four most used substances – Binge Alcohol Drinking, Cannabis, Cigarettes, or Nicotine Vape (the reference group of this study). (b) Binge Alcohol Drinking + Cannabis. (c) Binge Alcohol Drinking + Cannabis + Cigarettes. (d) Binge Alcohol Drinking + Cannabis + Cigarettes + Nicotine Vape. (e) Binge Alcohol Drinking + Cannabis + Nicotine Vape. (f) Binge Alcohol Drinking + Cigarettes. (g) Binge Alcohol Drinking + Cigarettes + Nicotine Vape. (h) Binge Alcohol Drinking + Nicotine Vape. (i) Cannabis + Cigarettes. (j) Cannabis + Cigarettes + Nicotine Vape. (k) Cannabis + Nicotine Vape. (l) Cigarettes + Nicotine Vape.

Table 1. Descriptive Statistics by sexual identity, NSDUH 2021 and NSDUH 2022.

	n (weighted %^a; SE); p-value
Variables	Gay/Lesbian	Bisexual	Heterosexual	Not Sure^d	Total
	1,889 (2.44)	5,886 (5.50)	58,465 (91.48)	394 (0.58)	66,634	p-value ^b
Polysubstance Combinations
No use of any of the following – Binge Alcohol Drinking, Cannabis, Cigarettes, or Nicotine Vape	1,194 (66.67; 1.89); p < 0.001*^{,c, e}	3,173 (53.15; 1.26); p < 0.001*^{,c, e}	45,789 (81.00; 0.27)	340 (91.41; 1.82)	50,496 (79.18)	F(18, 33) = 18.80; p < 0.001*
Binge Alcohol Drinking + Cannabis	168 (9.18; 0.88); p < 0.001*^{,c, e}	570 (9.47; 0.81); p < 0.001*^{,c, e}	2,761 (3.84; 0.14)	8 (0.82; 0.39)	3,507 (4.27)
Binge Alcohol Drinking + Cannabis + Cigarettes	69 (3.19; 0.48); p < 0.001*^{,c, e}	268 (5.82; 0.48); p < 0.001*^{,c, e}	1,371 (2.31; 0.11)	5 (0.35; 0.18)	1,713 (2.52)
Binge Alcohol Drinking + Cannabis + Cigarettes + Nicotine vape	49 (1.59; 0.35); p < 0.001*^{,c, e}	242 (3.52; 0.40); p < 0.001*^{,c, e}	692 (0.76; 0.04)	2 (0.03; 0.02)	985 (0.93)
Binge Alcohol Drinking + Cannabis + Nicotine vape	59 (2.27; 0.87); p = 0.004*^,c;	311 (5.23; 0.43); p < 0.001*^{,c, e}	935 (1.01; 0.06)	3 (0.60; 0.45)	1,308 (1.27)
Binge Alcohol Drinking + Cigarettes	73 (4.21; 0.76); p < 0.001*^{,c, e}	187 (4.17; 0.58); p < 0.001*^{,c, e}	2,048 (4.00; 0.15)	11 (1.45; 0.74)	2,319 (4.00)
Binge Alcohol Drinking + Cigarettes + Nicotine vape	30 (0.99; 0.25); p < 0.001*^{,c, e}	137 (1.90; 0.28); p < 0.001*^{,c, e}	540 (0.72; 0.05)	4 (1.10; 0.79)	711 (0.79)
Binge Alcohol Drinking + Nicotine vape	46 (1.39; 0.34); p < 0.001*^{,c, e}	191 (2.51; 0.28); p < 0.001*^{,c, e}	1,132 (1.29; 0.07)	4 (0.24; 0.14)	1,373 (1.35)
Cannabis + Cigarettes	91 (5.64; 0.81); p < 0.001*^{,c, e}	231 (4.91; 0.54); p < 0.001*^{,c, ee}	1,403 (2.57; 0.15)	9 (1.92; 1.15)	1,734 (2.77)
Cannabis + Cigarettes + Nicotine vape	34 (1.75; 0.50); p < 0.001*^{,c, e}	151 (2.78; 0.36); p < 0.001*^{,c, e}	424 (0.55; 0.05)	2 (0.96; 0.68)	611 (0.70)
Cannabis + Nicotine vape	48 (1.50; 0.35); p < 0.001*^{,c, e}	285 (3.95; 0.41); p < 0.001*^{,c, e}	750 (0.95; 0.07)	5 (0.54; 0.30)	1,088 (1.13)
Cigarettes + Nicotine vape	28 (1.61; 0.47); p < 0.001*^{,c, e}	140 (2.58; 0.33); p < 0.001*^{,c, e}	620 (0.99; 0.07)	1 (0.58; 0.57)	789 (1.09)
Age Group
18-29 years	964 (28.93; 1.46)	4,114 (55.29; 1.33)	20,549 (17.71; 0.29)	215 (28.80; 3.29)	25,842 (20.11)	F(6, 45) = 106.46; p < 0.001*
30-49 years	646 (36.86; 1.92)	1,541 (32.67; 1.10)	22,231 (31.66; 0.32)	129 (37.98; 3.83)	24,547 (31.88)
$\geq$ 50 years	279 (34.21; 2.27)	231 (12.03; 1.26)	15,685 (50.63; 0.46)	50 (33.21; 4.96)	16,245 (49.09)
Sex
Male	917 (40.67; 2.07)	1,357 (29.05; 1.26)	26,803 (48.17; 0.34)	123 (34.66; 4.29)	29,200 (47.31)	F(3, 48) = 69.70; p < 0.001*
Female	972 (59.33; 2.07)	4,529 (70.95; 1.26)	31,662 (51.83; 0.34)	271 (65.34; 4.29)	37,434 (52.69)	F(3, 48) = 69.70; p < 0.001*
Race/Ethnicity
Hispanic	305 (16.84; 1.60)	975 (16.51; 1.22)	9,470 (16.86; 0.54)	110 (26.23; 3.93)	10,860 (16.90)	F(15, 36) = 4.36; p < 0.001*
Non-Hispanic Asian	89 (3.97; 1.03)	231 (4.49; 0.41)	3,763 (6.91; 0.31)	63 (20.88; 3.64)	4,146 (6.79)
Non-Hispanic Black	218 (13.11; 1.32)	533 (10.49; 0.85)	6,731 (12.23; 0.39)	52 (13.98; 3.03)	7,534 (12.17)
Non-Hispanic More than One Race	93 (2.87; 0.61)	379 (3.53; 0.50)	1,997 (1.70; 0.09)	19 (1.00; 0.35)	2,488 (1.82)
Non-Hispanic Native/Alaskan Native/ Hawaiian Native	36 (1.23; 0.61)	89 (1.16; 0.33)	847 (0.85; 0.07)	13 (2.44; 0.98)	985 (0.90)
Non-Hispanic White	1,148 (61.98; 1.95)	3,679 (63.82; 1.38)	35,657 (61.44; 0.74)	137 (35.47; 4.25)	40,621 (61.43)
Survey Year
2021	916 (44.31; 2.09)	2,670 (45.26; 1.27)	29,260 (49.60; 0.52)	185 (47.87)	33,031 (49.22)	F(3,48) = 4.51; p = 0.007*
2022	973 (55.69; 2.09)	3,216 (54.74; 1.27)	29,205 (50.40; 0.52)	209 (52.13)	33,603 (50.78)	F(3,48) = 4.51; p = 0.007*
Educational Attainment
Less than High School	426 (6.40; 0.84)	662 (10.11; 1.21)	5,543 (10.11; 0.27)	98 (26.57; 3.44)	6,446 (10.11)	F(9,42) = 13.09; p < 0.001*
High School Graduate	143 (23.07; 1.88)	1,609 (26.93; 1.26)	13,986 (27.04; 0.42)	129 (31.32; 3.50)	16,150 (26.96)
Some College/ Associate Degree	600 (31.04; 1.88)	2,118 (37.23; 0.81)	16,948 (29.35; 0.39)	94 (28.78; 3.85)	19,760 (29.83)
College Graduate	720 (39.49; 1.89)	1,497 (25.73; 1.42)	21,988 (33.50; 0.39)	73 (13.33; 2.80)	24,278 (33.10)
Household Income
< 20,000 USD	860 (38.14; 2.14)	3,547 (56.02; 1.44)	23,287 (35.78; 0.49)	280 (65.74; 4.50)	27,974 (37.13)	F(3,48) = 58.35; p < 0.001*
$\geq$ 20,000 USD	1,029 (61.86; 2.14)	2,339 (43.98; 1.44)	35,178 (64.22; 0.49)	114 (34.26; 4.50)	38,660 (62.87)	F(3,48) = 58.35; p < 0.001*
Urbanicity
Non-Metro	233 (7.62; 0.51)	937 (12.99; 1.14)	9,308 (14.02; 0.81)	59 (8.68; 1.96)	10,537 (13.78)	F(3,48) = 11.91; p < 0.001*
Metro	1,656 (92.38; 0.51)	4,949 (87.01; 1.14)	49,157 (85.98; 0.81)	335 (91.32; 1.96)	56,097 (86.22)	F(3,48) = 11.91; p < 0.001*
Medical Insurance
No	196 (8.48; 1.43)	712 (12.01; 0.86)	5,552 (8.60; 0.29)	76 (19.53; 2.89)	6,536 (8.85)	F(3,48) = 9.42; P < 0.001*
Yes	1,693 (91.52; 1.43)	5,174 (87.99; 0.86)	52,913 (91.40; 0.29)	318 (80.47; 2.89)	60,098 (91.15)	F(3,48) = 9.42; P < 0.001*
Employment status
No	196 (6.62; 1.07)	712 (8.67; 0.63)	5,552 (4.20; 0.16)	76 (5.25; 1.63)	6,536 (8.85)	F(3,48) = 15.26; p < 0.001*
Yes	1,693 (93.38; 1.07)	5,174 (91.33; 0.63)	52,913 (95.80; 0.16)	318 (94.75; 1.63)	60,098 (91.15)	F(3,48) = 15.26; p < 0.001*
Marital Status
No	1,524 (75.99; 1.70)	4,780 (73.31; 1.05)	31,746 (47.99; 0.42)	268 (56.51; 4.83)	38,318 (50.39)	F(3, 48) = 200.59; p < 0.001*
Yes	365 (24.01; 1.70)	1,106 (21.69; 1.05)	26,719 (52.01; 0.42)	126 (43.49; 4.83)	28,316 (49.61)	F(3, 48) = 200.59; p < 0.001*

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^a Some weighted percentages will not add to 100% due to rounding error ^b Survey-weighted Adjusted Wald Chi-Square Test.

“*” p-value < 0.05.

^cSurvey-Weighted t-test with 49 degrees of freedom (Reference group; heterosexual adults)

^dDue to low statistical power, we did not compared the proportions of the substance combinations of the “Not Sure” group with heterosexual adults

^eBonferroni Correction (p < 0.004). Correction for the multiple comparisons of the survey-weighted t-tests (12 comparisons in total, one per category).

Covariates.

We included other variables related to sexual identity and polysubstance use as done in previous studies [6,12,13,38–40]. These variables included age, survey cycle, race/ethnicity, sex, educational attainment, employment status, urbanicity, household income, marital status, and medical insurance status. Table 1 outlines how we operationalized these variables.

Statistical analysis

We used a complete cases approach to handle missing data, which removed 6,028 participants (6.39% of our sample). This small percentage of missing data is unlikely to bias our results [41]. We implemented NSDUH 2021 and 2022 survey weights; we divided the survey weights by two, the number of survey cycles included in the study [35]. We conducted a survey-weighted Adjusted Wald Chi-Square test to examine whether: (a) there were sex differences among all polysubstance combinations by sexual identity. (b) Associations between sexual identity and other variables. We also did survey-weighted t-tests to compare the estimated prevalence of: (a) the polysubstance combinations; (b) the substances included in the study to determine the polysubstance combinations; and (c) the male and female adults within sexual identity by each polysubstance combination.

We used a survey-weighted multinomial model to examine the association between sexual identity and the polysubstance combinations of the four most used substances – binge alcohol drinking, cannabis, cigarettes, and nicotine vape. Our model had “no use of any of the following – Binge Alcohol Drinking, Cannabis, Cigarettes, or Nicotine Vape” as the reference group, consistent with the reference for other studies [9,14]. The polysubstance combinations of the four most used substances, our outcome, consisted of 12 mutually exclusive categories. The multinomial model was adjusted by the covariates. We also included an interaction term of sexual identity and sex to compare female adults (lesbian, bisexual, and heterosexual) with male adults (gay, bisexual, and heterosexual) to determine whether there are sex differences by sexual identity. The independent variable of the multinomial model was the interaction between sexual identity and sex. We also did additional multinomial models with the same outcome, polysubstance combinations of the four most used substances, to compare lesbian and bisexual female adults with other groups such as gay male or bisexual male adults. The model diagnostics of each model are S2 in S1 Table.

As part of our sensitivity analyses, we implemented multiple imputations for sexual identity and medical insurance, variables that were not imputed by NSDUH. Then, we compared the imputed and observed distributions of these variables using a survey-weighted Rao-Scott Chi-Square goodness-of-fit test. We found no difference between the observed and imputed variables (S3 in S1 Table). We also did two sample proportions tests to determine whether there were differences in the estimated prevalence of the used substances ranked 4^th, our threshold, and 5^th by sexual identity. The family-wise error rate (i.e., the risk of false positives due to multiple testing) was 45.96% for 12 comparisons, 40.13% for ten, and 55.99% for 16 comparisons. Due to the risk of having at least one false positive was high in any of the of multiple testing we adjusted for multiple comparisons, we included a Bonferroni Correction in our sensitivity analyses for the survey-weighted t-tests and the multinomial models. The thresholds to determine a significant p-value with Bonferroni Corrections in these t-tests were 0.004, 0.005, or 0.003; 0.05 divided by the 12 mutually exclusive categories of the polysubstance combination variable, ten multinomial models to compare female adults by sexual identity with all the other sexual identity by sex groups, 16 for the substances that were considered to operationalize the polysubstance combinations variable, respectively. We did all the analyses in R Studio 3.16.

Results

Table 1 shows the descriptive statistics of the analytic sample, including the survey-weighted prevalence, by sexual identity, of the polysubstance combinations of two, three, or four substances. Bisexual adults had the lowest prevalence of having medical insurance, employment, an income of at least 20,000 USD, and a college degree compared to heterosexual and gay/lesbian adults (Table 1).

The most used substances in the past 30 days among gay/lesbian and bisexual adults were binge alcohol drinking, cannabis, cigarettes, and nicotine vape (S1 Table). Bisexual adults had the highest prevalence of past 30-day use of these four most common substances compared to any other sexual identity (S1 Table). We also found significant differences between the use of nicotine vape and cannabis vape by sexual identity groups; the substances ranked 4^th, our threshold, and 5^th, respectively, among heterosexual ( $χ^{2}$ (1)=1,620,361; p < 0.001), gay/lesbian ( $χ^{2}$ (1)=130,384; p < 0.001), and bisexual adults ( $χ^{2}$ (1)=5,288.4; p < 0.001)). Therefore, we did not lose significant information by using a cut-off of 4 substances.

Sex Differences in the Polysubstance Combinations vary by Sexual Identity

S4 in S1 Table shows the prevalence of these common polysubstance combinations among different sexual identities and sexes. We found that for all combinations there were sex differences within the sexual identities, heterosexual, gay/lesbian, and bisexual adults (S4 in S1 Table and Fig 1). Among heterosexual adults, male adults engaged more than female adults in most polysubstance combinations while among bisexual adults, female adults engaged more than male adults. Among gay/lesbian adults, although there were sex differences in all polysubstance combinations, they did not vary disproportionately more either among gay male or lesbian female adults.

We were able to confirm these sex differences patterns with the results from the Weighted Wald Adjusted Chi-Square Test. These findings indicate that sex was not associated with using polysubstance combinations among gay/lesbian adults (F(11,40) = 1.39; p = 0.216). However, sex was associated with these polysubstance combinations among heterosexual (F(11,40) = 17.29; p < 0.001) and bisexual adults (F(11,40) = 2.79; p < 0.001; S5 in S1 Table).

Bisexual female adults have higher rates among polysubstance combinations compared to heterosexual female, heterosexual male, gay male, and lesbian female adults

Lesbian female adults engaged more in polysubstance combinations than heterosexual female adults (Table 2). Lesbian female adults only engaged more in one combination than heterosexual male adults though they engaged less in one combination compared to bisexual and gay male adults. Bisexual female adults led in the use of most polysubstance combinations compared to all female adult groups and heterosexual male adults, while heterosexual female adults used them the least compared to all groups (Tables 3–4, and Table 5). Bisexual female adults used more polysubstance combinations that include binge alcohol drinking, cannabis, or both, often involving three or four substances (Table 3 and Table 4).

Table 2. Summary of Survey Weighted Multinomial Models with potential combinations by sexual identity and sex among lesbian female adults, NSDUH 2021 and 2022.

Polysubstance Combinations	Lesbian female vs. Heterosexual Male ^a			Lesbian Female vs. Heterosexual Female ^b			Lesbian Female vs. Bisexual Male ^c			Lesbian Female vs. Gay Male ^d
	PR	95% CI	p-value	PR	95% CI	p-value	PR	95% CI	p-value	PR	95% CI	p-value
Binge Alcohol Drinking + Cannabis	0.78	(0.41, 1.49)	0.452	1.54	(0.96, 2.46)	0.074	0.78	(0.44, 1.85)	0.507	0.45	(0.41, 1.22)	0.004*^{, e}
Binge Alcohol Drinking + Cannabis + Cigarettes	1.78	(0.77, 3.98)	0.157	1.94	(1.11, 3.40)	0.020*	1.65	(0.41, 3.74)	0.369	0.81	(0.38, 2.19)	0.631
Binge Alcohol Drinking + Cannabis + Cigarettes + Nicotine vape	1.31	(0.48, 3.56)	0.594	1.91	(1.02, 3.58)	0.043*	0.99	(0.37, 2.71)	0.984	0.61	(0.48, 2.09)	0.308
Binge Alcohol Drinking + Cannabis + Nicotine vape	0.73	(0.18, 2.99)	0.665	1.29	(0.71, 2.34)	0.411	0.26	(0.19, 1.65)	0.014*	0.56	(0.27, 2.25)	0.280
Binge Alcohol Drinking + Cigarettes	2.01	(0.88, 4.58)	0.099	1.75	(1.00, 3.05)	0.049*	1.3	(0.29, 4.39)	0.704	1.03	(0.50, 2.03)	0.935
Binge Alcohol Drinking + Cigarettes + Nicotine vape	1.51	(0.53, 4.27)	0.441	1.56	(0.75, 3.26)	0.236	0.33	(0.05, 7.04)	0.369	0.88	(0.36, 2.48)	0.790
Binge Alcohol Drinking + Nicotine vape	1.35	(0.47, 3.89)	0.582	0.99	(0.47, 2.06)	0.977	2.73	(0.49, 4.92)	0.087	1.55	(0.44, 3.34)	0.400
Cannabis + Cigarettes	1.77	(0.61, 5.10)	0.292	3.39	(1.80, 6.39)	<0.001*^{, e}	1.55	(0.16, 9.03)	0.670	0.81	(0.30, 2.83)	0.722
Cannabis + Cigarettes + Nicotine vape	5.53	(1.92, 15.94)	0.002*^,e	7.06	(3.26, 15.26)	<0.001*^{, e}	4.32	(0.41, 8.65)	0.059	2.74	(0.62, 3.87)	0.307
Cannabis + Nicotine vape	2.89	(1.00, 8.33)	0.048*	2.44	(1.11, 5.38)	0.028*	1.55	(0.34, 4.37)	0.500	1.01	(0.34, 3.00)	0.982
Cigarettes + Nicotine vape	1.65	(0.36, 7.61)	0.523	2.27	(0.85, 6.09)	0.104	1.36	(0.12, 11.27)	0.791	1.43	(0.25, 5.40)	0.649

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“*” p-value < 0.05.

PR = Prevalence Ratio.

CI = Confidence Interval.

NOTE: These are mutually exclusive categories. The reference group of the multinomial models was “No use”.

^aThe sample of this model was n = 66,634. We adjusted the model by sexual identity, age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. We also included an interaction term between sexual identity and sex on this model. The independent variable of this model was the interaction between sexual identity and sex.

^bOnly female adults were included in this analysis. The subsample for this model was n = 37,434. We adjusted the model by sexual identity, age group, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. The independent variable of this model was sexual identity.

^cOnly LGB adults were included in this analysis. The subsample for this model was n = 8,169. We adjusted the model by sexual identity, age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. We also included an interaction term between sexual identity and sex on this model. The independent variable of this model was the interaction between sexual identity and sex.

^dOnly gay/lesbian adults were included in this analysis. The subsample for this model was n = 1,889. We adjusted the model by age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. The independent variable of this model was sex.

^eBonferroni Correction (p < 0.005). Correction for the multiple comparisons of using multiple multinomial models (10 multinomial models in total, one per model).

Table 3. Summary of Survey Weighted Multinomial Models with potential combinations by sexual identity and sex among bisexual female adults versus male adults, NSDUH 2021 and 2022.

Polysubstance Combinations	Bisexual Female vs. Heterosexual Male ^a			Bisexual Female vs. Bisexual Male ^b			Bisexual Female vs. Gay Male ^c
	PR	95% CI	p-value	PR	95% CI	p-value	PR	95% CI	p-value
Binge Alcohol Drinking + Cannabis	2.04	(1.38, 3.02)	<0.001*^{, e}	0.97	(0.63, 1.97)	0.920	1.28	(0.54, 2.29)	0.507
Binge Alcohol Drinking + Cannabis + Cigarettes	1.84	(0.94, 3.58)	0.073	0.94	(0.32, 2.05)	0.899	0.60	(0.27, 2.42)	0.369
Binge Alcohol Drinking + Cannabis + Cigarettes + Nicotine vape	2.47	(1.35, 4.53)	0.003*^{, e}	1.09	(0.34, 3.01)	0.877	1.01	(0.37, 2.73)	0.984
Binge Alcohol Drinking + Cannabis + Nicotine vape	4.30	(2.44, 7.59)	<0.001*^{, e}	2.03	(0.59, 5.51)	0.212	3.91	(0.60, 5.42)	0.014*
Binge Alcohol Drinking + Cigarettes	2.42	(1.17, 5.00)	0.017*^{, e}	2.44	(0.13, 6.22)	0.366	0.77	(0.23, 3.49)	0.704
Binge Alcohol Drinking + Cigarettes + Nicotine vape	5.54	(2.12, 14.47)	<0.001*^{, e}	3.02	(0.21, 12.49)	0.288	3.04	(0.14, 18.50)	0.369
Binge Alcohol Drinking + Nicotine vape	1.23	(0.64, 2.35)	0.512	1.10	(0.03, 13.58)	0.950	0.37	(0.20, 2.05)	0.087
Cannabis + Cigarettes	1.8	(0.87, 3.72)	0.111	0.85	(0.33, 2.06)	0.733	0.65	(0.11, 6.18)	0.670
Cannabis + Cigarettes + Nicotine vape	2.71	(1.34, 5.49)	0.006*^{, e}	1.52	(0.09, 3.12)	0.641	0.23	(0.12, 2.43)	0.059
Cannabis + Nicotine vape	3.85	(2.14, 6.93)	<0.001*^{, e}	1.27	(0.26, 2.66)	0.687	0.64	(0.23, 2.98)	0.500
Cigarettes + Nicotine vape	2.01	(0.94, 4.32)	0.070	1.02	(0.09, 8.69)	0.940	0.73	(0.09, 8.62)	0.791

Open in a new tab

“*” p-value < 0.05.

PR = Prevalence Ratio.

CI = Confidence Interval.

NOTE: These are mutually exclusive categories. The reference group of the multinomial models was “No use”.

^bOnly bisexual adults were included in this analysis. The subsample for this model was n = 5,886. We adjusted the model by age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. The independent variable of this model was sex.

^cOnly LGB adults were included in this analysis. The subsample for this model was n = 8,169. We adjusted the model by sexual identity, age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. We also included an interaction term between sexual identity and sex on this model. The comparisons here were bisexual female vs. gay male adults and lesbian female adults vs. gay male adults. The independent variable of this model was the interaction between sexual identity and sex.

^e Bonferroni Correction (p < 0.005). Correction for the multiple comparisons of using multiple multinomial models (10 multinomial models in total, one per model).

Table 5. Summary of Survey Weighted Multinomial Models with potential combinations among heterosexual female adults versus heterosexual, bisexual and gay male adults, NSDUH 2021 and 2022.

Polysubstance Combinations	Heterosexual Male ^a			Bisexual Male ^b			Gay Male ^c
	PR	95% CI	p-value	PR	95% CI	p-value	PR	95% CI	p-value
Binge Alcohol Drinking + Cannabis	0.65	(0.55, 0.77)	<0.001*^{, e}	0.49	(0.33, 0.72)	<0.001*^{, e}	1.31	(0.69, 2.50)	0.411
Binge Alcohol Drinking + Cannabis + Cigarettes	0.49	(0.40, 0.61)	<0.001*^{, e}	0.54	(0.28, 1.05)	0.069	0.57	(0.25, 1.26)	0.165
Binge Alcohol Drinking + Cannabis + Cigarettes + Nicotine vape	0.50	(0.36, 0.71)	<0.001*^{, e}	0.40	(0.22, 0.74)	0.003*^{, e}	0.77	(0.28, 2.14)	0.616
Binge Alcohol Drinking + Cannabis + Nicotine vape	0.57	(0.44, 0.73)	<0.001*^{, e}	0.23	(0.13, 0.41)	<0.001*^{, e}	1.39	(0.34, 5.70)	0.649
Binge Alcohol Drinking + Cigarettes	0.61	(0.52, 0.70)	<0.001*^{, e}	0.41	(0.20, 0.85)	0.017*	0.51	(0.22, 1.17)	0.110
Binge Alcohol Drinking + Cigarettes + Nicotine vape	0.61	(0.45, 0.83)	0.001*^{, e}	0.18	(0.07, 0.47)	<0.001*^{, e}	0.66	(0.23, 1.91)	0.443
Binge Alcohol Drinking + Nicotine vape	0.95	(0.78, 1.15)	0.594	0.81	(0.42, 1.55)	0.521	0.74	(0.26, 2.13)	0.586
Cannabis + Cigarettes	0.48	(0.40, 0.59)	<0.001*^{, e}	0.56	(0.27, 1.15)	0.111	0.58	(0.20, 1.70)	0.316
Cannabis + Cigarettes + Nicotine vape	0.47	(0.33, 0.67)	<0.001*^{, e}	0.37	(0.18, 0.76)	0.007*	0.18	(0.06, 0.53)	0.002*^{, e}
Cannabis + Nicotine vape	0.36	(0.25, 0.51)	<0.001*^{, e}	0.26	(0.14, 0.47)	<0.001*^{, e}	0.34	(0.12, 0.99)	0.047*
Cigarettes + Nicotine vape	0.76	(0.59, 0.99)	0.043*	0.50	(0.23, 1.06)	0.069	0.61	(0.13, 2.83)	0.528

Open in a new tab

“*” p-value < 0.05.

PR = Prevalence Ratio.

CI = Confidence Interval.

NOTE: These are mutually exclusive categories. The reference group of the multinomial models was “No use”.

^aOnly heterosexual adults were included. The subsample for this analysis was n = 58,465. We adjusted the model by age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. The comparison was between heterosexual female vs. heterosexual male adults. The independent variable was sex.

^bOnly heterosexual and bisexual adults were included. The subsample for this analysis was n = 64,745. We adjusted the model by sexual identity, age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. We also included an interaction term between sexual identity and sex on this model. The comparison was heterosexual female vs. bisexual male adults. The independent variable was the interaction between sexual identity and sex.

^cOnly heterosexual and gay/lesbian adults were included. The subsample for this analysis was n = 60,748. We adjusted the model by sexual identity, age group, sex, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. We also included an interaction term between sexual identity and sex on this model. The comparison here was heterosexual female adults vs. gay male adults. The independent variable was the interaction between sexual identity and sex.

^eBonferroni Correction (p < 0.005). Correction for the multiple comparisons of using multiple multinomial models (10 multinomial models in total, one per model).

Table 4. Summary of Survey Weighted Multinomial Models with potential combinations among bisexual female adults versus heterosexual and lesbian female adults, NSDUH 2021 and 2022.

Polysubstance Combinations	Bisexual Female vs. Heterosexual Female ^a			Bisexual Female vs. Lesbian Female ^b
	PR	95% CI	p-value	PR	95% CI	p-value
Binge Alcohol Drinking + Cannabis	2.80	(2.09, 3.77)	<0.001*^{, e}	1.84	(1.06, 3.19)	0.029*
Binge Alcohol Drinking + Cannabis + Cigarettes	3.98	(3.00, 5.28)	<0.001*^{, e}	2.34	(1.23, 4.47)	0.010*
Binge Alcohol Drinking + Cannabis + Cigarettes + Nicotine vape	4.23	(2.79, 6.40)	<0.001*^{, e}	2.27	(1.21, 4.25)	0.011*
Binge Alcohol Drinking + Cannabis + Nicotine vape	4.24	(3.10, 5.81)	<0.001*^{, e}	3.35	(1.79, 6.28)	<0.001*^{, e}
Binge Alcohol Drinking + Cigarettes	2.12	(1.48, 3.03)	<0.001*^{, e}	1.38	(0.83, 2.29)	0.209
Binge Alcohol Drinking + Cigarettes + Nicotine vape	3.24	(2.16, 4.87)	<0.001*^{, e}	2.27	(0.98, 5.27)	0.058
Binge Alcohol Drinking + Nicotine vape	1.23	(0.88, 1.73)	0.223	1.22	(0.60, 2.47)	0.572
Cannabis + Cigarettes	3.10	(2.20, 4.36)	<0.001*^{, e}	1.09	(0.56, 2.13)	0.797
Cannabis + Cigarettes + Nicotine vape	7.03	(4.74, 10.44)	<0.001*^{, e}	1.09	(0.48, 2.49)	0.835
Cannabis + Nicotine vape	5.19	(3.55, 7.60)	<0.001*^{, e}	2.12	(0.97, 4.64)	0.057
Cigarettes + Nicotine vape	3.38	(2.23, 5.13)	<0.001*^{, e}	1.77	(0.72, 4.36)	0.218

Open in a new tab

“*” p-value < 0.05.

PR = Prevalence Ratio.

CI = Confidence Interval.

NOTE: These are mutually exclusive categories. The reference group of the multinomial models was “No use”

^aOnly female adults were included in this analysis. The subsample for this model was n = 37,434. We adjusted the model by sexual identity, age group, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. The comparisons here were bisexual female vs. heterosexual female adults and lesbian female adults vs. heterosexual female adults. The independent variable was sexual identity.

^bOnly LGB female adults were included in this analysis. The subsample for this model was n = 5,772. We adjusted the model by sexual identity, age group, race/ethnicity, survey cycle, educational attainment, household income, urbanicity, medical insurance, employment status, and marital status. The comparison here was bisexual female vs. lesbian female adults. The independent variable was sexual identity.

^eBonferroni Correction (p < 0.005). Correction for the multiple comparisons of using multiple multinomial models (10 multinomial models in total, one per model).

In the same model, we found that adults from 30–49 years and $\geq$ 50 years engaged more than 18–29 years adults in substance combinations that include cigarettes (S6 in S1 Table). Gay/lesbian and bisexual adults were mainly in the 18–29 years and 30–49 years groups (Table 1), indicating that some of these polysubstance combinations that were frequent among LGB adults are due to them being younger than heterosexual adults. The full multinomial model of the analytic sample size (n = 66,634) is in S7 in S1 Table.

Discussion

The key findings of this study are the following: (a) there were sex differences in polysubstance use among bisexual and heterosexual adults, as other studies have shown [9,10,12,14,28–31,33]; (b) bisexual female adults were at the highest risk for engaging in most of the used polysubstance combinations compared to heterosexual female adults, who were at the lowest risk, as seen in other studies [8,9,12,14,27,32,33]. This pattern holds even when looking at heterosexual or gay male adults and heterosexual or lesbian female adults as the reference group. (c) We also found significant differences based on sex and sexual identity in most polysubstance combinations, which was not done in previous studies. Heterosexual male adults engaged more in most polysubstance combinations than heterosexual female adults, while bisexual female adults engaged more in most polysubstance combinations than bisexual male adults. However, sex was not associated with using these polysubstance combinations disproportionately among gay/lesbian adults.

We hypothesized that sex differences in the polysubstance combinations will vary by sexual identity. We also hypothesized that bisexual female adults will have higher rates among all polysubstance combinations compared to all the other groups. The study’s findings partially support our hypotheses. We observed sex differences and how they vary though only among heterosexual and bisexual adults. We also found that bisexual female adults had higher rates in most of the polysubstance combinations than heterosexual female and male adults, lesbian female adults though not bisexual male adults.

Our findings support the claim that LGB groups engage in different polysubstance combinations. Therefore, substance use treatment and access to them should consider the challenges LGB groups experience and the polysubstance combinations they engage in. For example, bisexual female adults need improved access to substance use treatment, especially for binge drinking and cannabis use. Bisexual adults often have less insurance, higher unemployment, and lower education and income than gay, lesbian, or heterosexual individuals. These factors contribute to higher social stress [42–45]. Additionally, bisexual female adults face biphobia and bi-erasure, other sources of social stress [20,21,25,26]. One way to provide access for substance use treatment while considering the potential challenges LGB adults, particularly bisexual female adults, experienced is through digital interventions. Digital interventions have shown promising results in providing access to substance use treatment to populations who have less access to healthcare settings, or experience stigma due to components of their identities or engaging in substances [46]. Furthermore, given that, on average, bisexual adults are younger than their heterosexual peers, they are more likely to have access to a smartphone, facilitating access to digital interventions for this population.

We also found that the polysubstance combinations vary by age groups; the older the age group, the lower the likelihood of using most of the assessed polysubstance combinations. Particularly, we found that $\geq$ 50 years adults engage significantly less on combinations that include nicotine vape compared to 18–29 years adults. Given that, on average, LGB adults are younger than heterosexual adults, there is a plausibility that the disproportionate use of polysubstance combinations among bisexual female adults than heterosexual female adults can be partially due to age differences. Similarly, nicotine vape might be disproportionately more used among bisexual adults than heterosexual adults due to bisexual adults being, on average, a younger group. Future work should study whether the use of these substance combinations by sexual identity and sex varies by age.

Our study has multiple strengths. It is the first study to measure the past 30-day prevalence of polysubstance combinations among LGB US adults by sex in the US, focusing on the four most used substances. It also uses data from NSDUH 2021 and 2022, which are large and representative of the non-institutionalized and civilian US population, strengthening the external validity of the findings from this study. However, there are some limitations. NSDUH did not assess gender identities such as non-binary, other sexual identities like pansexual or asexual, nor sources of discrimination. In addition, NSDUH may have selection bias due to excluding people without homes and people in prison, who generally have higher rates of substance use [47,48]. There could be unmeasured confounders, such as childhood traumatic experiences, related to substance use that are more likely to occur among LGB individuals [49].

Conclusion

Bisexual female adults tend to use more combinations of these substances compared to heterosexual and gay/lesbian male or female adults. This study also found sex differences in the combinations of these substances, and they vary among heterosexual and bisexual adults but not among gay/lesbian adults. Future research should aim to examine why bisexual female adults engage in these polysubstance combinations to develop effective public health strategies to minimize their health risks and reduce polysubstance use among bisexual female adults. We also recommend future research that explores social, psychological, and structural drivers behind the disparities related to the disproportionate use of these polysubstance combinations. Furthermore, future work should include non-binary and transgender populations.

Supporting information

S1 Table. Weighted conditional percentages (%) of substances past 30 day use by sexual identity, NSDUH 2021 and NSDUH 2022 (without excluding missing data).

(ZIP)

pone.0340454.s001.zip^{(3.2MB, zip)}

Data Availability

The data underlying this study are the National Survey on Drug Use and Health (NSDUH) 2021 and 2022 data sets available in the following database: https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health/datafiles.

Funding Statement

This study was financially supported by the Intramural Research Program, National Institute on Drug Abuse in the form of a grant awarded to LMM (5T32DA019426-19). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.

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PLoS One. 2026 Feb 18;21(2):e0340454. doi: 10.1371/journal.pone.0340454.r001

Author response to Decision Letter 0

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19 May 2025

PLoS One. doi: 10.1371/journal.pone.0340454.r002

Decision Letter 0

Daniel Demant

21 Aug 2025

Dear Dr. Mestre,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewers' comments:

Reviewer's Responses to Questions

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1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The use of data is pertinent, the authors use data from 2021 to 2022 from NSDUH a large, nationally representative, and publicly available dataset, in cross-sectional and observational study , which is appropriate for the research questions posed. They used a sample of 66, 634 subjects and identifying 8,59% of them as LGB and focuses on the four most commonly used substances: binge alcohol drinking, cannabis, cigarettes, and nicotine vape. The methods sit in a survey-weighted multinomial logistic regression models, that we consider appropriate for analyzing categorical outcomes with multiple levels, it include relevant covariates (e.g., age, race/ethnicity, education, income, etc.) and interaction terms (e.g., sexual identity × sex) to explore nuanced differences.The use of Adjusted Wald Chi-Square tests to assess sex differences is also methodologically sound. Fortunately the study uses de-identified, publicly available data and was exempt from IRB review, which is clearly stated. About discussion for support conclusions, the obtained findings tells us that bisexual female adults show the highest prevalence of polysubstance use, particularly combinations involving binge drinking and cannabis. At the same time, the sex differences in polysubstance use are significant among heterosexual and bisexual adults, but not among gay/lesbian adults, and highlights socioeconomic disparities (e.g., lower insurance coverage, income, and education) among bisexual adults, which may contribute to higher substance use.. So, the evidence of results (e.g., Tables 2–4) providing prevalence ratios with confidence intervals and p-values, many of which are statistically significant and align with the narrative conclusions. The interaction effects between sex and sexual identity are clearly presented and interpreted on the study. However the same present limitations such as the exclusion of non-binary identities, potential selection bias, and unmeasured confounders (e.g., trauma history). Concluding, the statistical analysis is methodologically sound, well-executed, and appropriate for the research questions. It supports the conclusions drawn and reflects a high level of rigor. However, while the complete-case analysis is acceptable given the low missingness (6.39%), a sensitivity analysis using imputation could further strengthen the robustness, and the manuscript could benefit from model diagnostics or goodness-of-fit measures, though these are not always standard in survey-weighted multinomial models.

The topic of understanding enhanced risk for substance use amongst populations is important, but the authors don’t fully explain why specific polysubstances may be of greater risk or why understanding which different groups of polysubstances amongst groups may be important, beyond that public health strategies should consider these. Additionally, given the authors most recent publication, the analyses presented within this manuscript seem incomplete, as they don’t take into consideration their own work and findings of differences within age groups. While large datasets are useful, and several hypotheses can be tested, the analyses presented within this manuscript are incomplete, perhaps only incremental, and should be combined for a more complete analysis.

1. While the authors state that limitations to existing studies include the mixed definitions of polysubstance use, and mixed time of sampling (30 days vs 12 mo), the current study does not do anything different than what has already been done (30 days, and a definition of polysubstance use that includes 2, 3, or 4 substances). Thus, the contribution of this study is not removing any limitations.

2. The authors have a prior publication from 2024 in which they examined the impact of age of adult on distribution patterns of polysubstance use and show differing substances endorsed, as well as patterns of polysubstance use that is dependent on age. Given those findings, it is recommended that the authors include similar analyses with this data, as the findings presented here may in fact be different for the different age groups.

3. The statistics of the analyses, and descriptions of findings are problematic. Specific instances are listed here:

a. The first problem relates to Table 1, which is described as a descriptive statistics table. However, Table 1 only presents total numbers and percentages of each demographic or substance category. There are no statistics provided, there are no ranges of variability provided. Thus, this table is not descriptive statistic, and the written description of Table 1 should not suggest that one group is more or less than the other for any characteristic, without any statistical analyses being conducted.

b. Similarly, Supplemental Table 1 shows the raw data for endorsing use of a substance, but there are no statistics to show whether there’s an actual difference between what is ranked as number 4 or 5 substance. As such, this reduces the rationale for using these substances in the analyses.

c. Additionally, in the discussion of what Supplemental Table 2 shows, the authors use terms highest and lowest, higer and lower, suggesting these are statistically significant, but there are no official statistics used for these comparisons.

d. In the third paragraph of the results section, the authors provide statistics from Weighted Wald Adjusted Chi Square Test, but don’t show the data, or indicate where the readers could see these statistics in the tables provided.

e. It is unclear whether the authors make any adjustments for multiple comparisons, given the number of multinomial models they run.

f. The title of Table 4 is incomplete.

4. There are minor grammatical errors, primarily redundancy across paragraphs, within the introduction section.

5. The authors indicate that data is available via the public URL, however, they do not provide a link to their own data, including imputed data, missing data, cases included, etc.

6. There’s a paragraph in the Measures section that includes a definition of NSDUH misuse that doesn’t belong as it is not relevant to this manuscript.

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Reviewer #1: Yes: Sílvio Manuel da Rocha Brito

Reviewer #2: No

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PLoS One. 2026 Feb 18;21(2):e0340454. doi: 10.1371/journal.pone.0340454.r003

Author response to Decision Letter 1

6 Oct 2025

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The use of data is pertinent, the authors use data from 2021 to 2022 from NSDUH a large, nationally representative, and publicly available dataset, in cross-sectional and observational study , which is appropriate for the research questions posed. They used a sample of 66, 634 subjects and identifying 8,59% of them as LGB and focuses on the four most commonly used substances: binge alcohol drinking, cannabis, cigarettes, and nicotine vape. The methods sit in a survey-weighted multinomial logistic regression models, that we consider appropriate for analyzing categorical outcomes with multiple levels, it include relevant covariates (e.g., age, race/ethnicity, education, income, etc.) and interaction terms (e.g., sexual identity × sex) to explore nuanced differences. The use of Adjusted Wald Chi-Square tests to assess sex differences is also methodologically sound. Fortunately the study uses de-identified, publicly available data and was exempt from IRB review, which is clearly stated. About discussion for support conclusions, the obtained findings tells us that bisexual female adults show the highest prevalence of polysubstance use, particularly combinations involving binge drinking and cannabis. At the same time, the sex differences in polysubstance use are significant among heterosexual and bisexual adults, but not among gay/lesbian adults, and highlights socioeconomic disparities (e.g., lower insurance coverage, income, and education) among bisexual adults, which may contribute to higher substance use.. So, the evidence of results (e.g., Tables 2–4) providing prevalence ratios with confidence intervals and p-values, many of which are statistically significant and align with the narrative conclusions. The interaction effects between sex and sexual identity are clearly presented and interpreted on the study. However the same present limitations such as the exclusion of non-binary identities, potential selection bias, and unmeasured confounders (e.g., trauma history). Concluding, the statistical analysis is methodologically sound, well-executed, and appropriate for the research questions. It supports the conclusions drawn and reflects a high level of rigor.

However, while the complete-case analysis is acceptable given the low missingness (6.39%), a sensitivity analysis using imputation could further strengthen the robustness, and the manuscript could benefit from model diagnostics or goodness-of-fit measures, though these are not always standard in survey-weighted multinomial models.

Many thanks for your feedback. We did multiple imputations for variables that were not imputed by NSDUH. In our case, sexual identity and medical insurance. We found no significant differences in the survey-weighted observed and imputed distributions of sexual identity or medical insurance among adults 18 years or older who either engaged in polysubstance use or no substances at all. We compared observed vs. imputed distribution using a survey-weighted Chi-Square Goodness of Fit Test using the Rao Scott test statistic (Supplementary Table 3a and Supplementary Table 3b).

Supplementary Table 3a – Sensitivity Analysis for sexual identity observed and imputed distribution

Sexual Identity Observed

n (%) Imputed

n (%) Resultsa

Heterosexual 59,935 (91.44) 62,321 (91.38) χ^2 = 938.45,

df = 2.86,

p-value = 0.9864

Bisexual 1,933 (2.46) 2,044 (2.48)

Gay/Lesbian 6,031 (5.50) 6,316 (5.54)

Not Sure 417 (0.60) 435 (0.60)

Total 68,316 71,1116

a Survey-weighted Rao Scott Goodness of Fit Chi-Square Test

Supplementary Table 3b – Sensitivity Analysis for having medical insurance observed and imputed distribution

Medical Insurance Observed

n (%) Imputed

n (%) Resultsa

No 6,669 (8.92) 7,077 (8.94) χ^2 = 118.29,

df = 1.00,

p-value = 0.9325

Yes 60,752 (91.08) 64,039 (91.06)

Total 67,421 71,116

a Survey-weighted Rao Scott Goodness of Fit Chi-Square Test

The model diagnostics that were available for the model diagnostics are shown in Supplementary Table 2. We only included the degrees of freedom and residual deviance that were provided by the models’ object svy_vglm. All the multinomial models were adjusted by the covariates. However, only some included the interaction between sexual identity and sex, depending on the comparison of interest (Supplementary Table 2). However, given that some models had interactions and were survey-weighted, which use survey-weighted sample sizes instead of the unweighted (i.e., observed ones), the residual deviances and degrees of freedom were expected to be large due to non-constant variance and overdispersion. We included the following statement in the methods section:

“The model diagnostics of each model are in Supplementary Table 2.”

Supplementary Table 2 – Model Diagnostics for the Multinomial Models

Multinomial Models Model Diagnostics

Including all covariates? Including interaction between sexual identity and sex? Population of interest Reference Group Sample size Degrees of freedom and residual deviance

Yes Yes General population Heterosexual Male n = 66,634 df = 732,974

Residual Deviance = 363,956,198

Yes No Female Adults Heterosexual Female n = 37,434 df = 411,774

Residual Deviance = 140,279,290

Yes Yes LGB Adults Bisexual Male

Gay Male n = 8,169 df = 89,859

Residual Deviance = 526,842,142

Yes No Gay/Lesbian Adults Gay Male n = 1,889 df = 20,779

Residual Deviance = 9,846,903

Yes No Bisexual Adults Bisexual Male n = 5,886 df = 64,746

Residual Deviance = 350,862,927

Yes No LGB female Adults Lesbian Female n = 5,772 df = 63,492

Residual Deviance = 28,600,476

Yes No Heterosexual Adults Heterosexual Male n = 58,465 df = 643,115

Residual Deviance = 265,934,111

Yes Yes Heterosexual and Bisexual Adults Bisexual Male n = 64,745 df = 712,195

Residual Deviance = 300,052,683

Yes Yes Heterosexual and Gay/Lesbian Adults Gay Male n = 60,748 df = 668,228

Residual Deviance = 277,903,266

Reviewer #2: In this manuscript, “Disproportionate use of polysubstance combinations varies by sexual identity among US adults”, the authors probe the prevalence and differences in the variety of combinations of substances used within the last 30 days in lesbian, gay, and bisexual individuals from the NSDUH database. Authors provide analyses that revealed that bisexual female adults had the greatest use of polysubstances, and that there were sex differences between heterosexual and bisexual adults, but not gay or lesbian adults. The topic of understanding enhanced risk for substance use amongst populations is important, but the authors don’t fully explain why specific polysubstances may be of greater risk or why understanding which different groups of polysubstances amongst groups may be important, beyond that public health strategies should consider these. Additionally, given the authors most recent publication, the analyses presented within this manuscript seem incomplete, as they don’t take into consideration their own work and findings of differences within age groups. While large datasets are useful, and several hypotheses can be tested, the analyses presented within this manuscript are incomplete, perhaps only incremental, and should be combined for a more complete analysis.

While the authors state that limitations to existing studies include the mixed definitions of polysubstance use, and mixed time of sampling (30 days vs 12 mo), the current study does not do anything different than what has already been done (30 days, and a definition of polysubstance use that includes 2, 3, or 4 substances). Thus, the contribution of this study is not removing any limitations.

We appreciate the reviewer’s point and agree that our study does not fully resolve the challenges of varied polysubstance definitions and timeframes in the literature. Our intent was not to eliminate these limitations, but rather to use a nationally representative sample, unlike other studies, and to be transparent in our definition of polysubstance use, the timeframe, and the contribution of polysubstance combinations. We believe our approach provides a useful step toward greater consistency in future research and possible comparison to past research in other populations and samples. We removed the paragraphs in the introduction and discussion section that could’ve caused any confusion related to varied polysubstance definitions and timeframes in the literature.

We included the age group terms of the main multinomial model (sample size n = 66,634) in Supplementary Table 6.

We report these findings in the result section:

“In the same model, we found that adults ≥50 years and 30-49 years engaged more than 18-29 years adults in substance combinations that include cigarettes but not nicotine vape (≥50 years; Supplementary Table 6). Gay/lesbian and bisexual adults were mainly in the 18-29 years and 30-49 years groups (Table 1), indicating that some of these polysubstance combinations that were frequent among LGB adults are due to them being younger than heterosexual adults.”

We also stated in the discussion section the following:

“We also found that the polysubstance combinations vary by age groups; the older the age group, the lower the likelihood of using most of the assessed polysubstance combinations. Given that, on average, LGB adults are younger than heterosexual adults, there is a plausibility that the disproportionate use of polysubstance combinations among bisexual female adults than heterosexual female adults can be partially due to age differences. Future work should study whether the use of these substance combinations by sexual identity and sex varies by age.”

Supplementary Table 6 – Summary of Survey-Weighted Multinomial Model with potential combinations by age groups, NSDUH 2021 and 2022.

Reference Group 30-49 yearsa ≥50 yearsa

Polysubstance Combinations PR 95% CI p-value PR 95% CI p-value

Binge alcohol Drinking + Cannabis 0.74 (0.64, 0.86) <0.001*,b 0.22 (0.18, 0.28) <0.001*,b

Binge alcohol Drinking + Cannabis + Cigarettes 2.51 (2.04, 3.10) <0.001*,b 0.86 (0.67, 1.09) 0.206

Binge alcohol Drinking + Cannabis + Cigarettes + Nicotine vape 0.63 (0.44, 0.89) 0.009*,b 0.06 (0.03, 0.12) <0.001*,b

Binge alcohol Drinking + Cannabis + Nicotine vape 0.25 (0.18, 0.35) <0.001*,b 0.04 (0.02, 0.08) <0.001*,b

Binge alcohol Drinking + Cigarettes 3.50 (2.90, 4.22) <0.001*,b 1.80 (1.45, 2.22) <0.001*,b

Binge alcohol Drinking + Cigarettes + Nicotine vape 0.75 (0.54, 1.05) 0.097 0.15 (0.08, 0.26) <0.001*,b

Binge alcohol Drinking + Nicotine vape 0.44 (0.36, 0.54) <0.001*,b 0.05 (0.03, 0.08) <0.001*,b

Cannabis + Cigarettes 4.44 (3.40, 5.80) <0.001*,b 1.58 (1.13, 2.22) 0.008*,b

Cannabis + Cigarettes + Nicotine vape 1.91 (1.28, 2.86) <0.001*,b 0.16 (0.08, 0.31) <0.001*,b

Cannabis + Nicotine vape 0.54 (0.41, 0.70) <0.001*,b 0.12 (0.08, 0.18) <0.001*,b

Cigarettes + Nicotine vape 2.78 (2.05, 3.78) <0.001*,b 0.60 (0.40, 0.89) 0.011*,b

a Reference Group: 18-29 years

“*” p-value < 0.05

b Bonferroni Correction: p-value < 0.025

The model had a sample size of n = 66,634

3. The statistics of the analyses, and descriptions of findings are problematic. Specific instances are listed here:

Thank you for your comment. We included in Table 1 a survey-weighted chi-square test to determine whether the covariates are directly associated with sexual identity. We also compared the estimated prevalence of each substance combination using a survey-weighted t-test between gay/lesbian and heterosexual adults, and bisexual and heterosexual adults. Furthermore, we also included the SE of the weighted prevalence (%) to provide statistics related to variability.

We included in Supplementary Table 1 the p-values of the survey-weighted t tests that compared the estimated prevalence of each substance between gay/lesbian and heterosexual adults, and bisexual and heterosexual adults.

We also did additional analyses using two-sample proportion tests between the use of cannabis vape and nicotine vape by sexual Identity to compare their estimated prevalence. We wrote the following in the results section:

“The most used substances in the past 30 days among gay/lesbian and bisexual adults were binge alcohol drinking, cannabis, cigarettes, and nicotine vape (Supplementary Table 1). Bisexual adults had the highest prevalence of past 30-day use of these four most common substances compared to any other sexual identity (Supplementary Table 1). We also found significant differences between the use of nicotine vape and cannabis vape by sexual identity groups; the substances ranked 4th, our threshold, and 5th, respectively, among heterosexual (χ^2(1)=1,620,361; p <0.001), gay/lesbian (χ^2(1)=130,384; p <0.001), and bisexual adults (χ^2(1)=5,288.4; p <0.001)). Therefore, our cut-off at four substances did not lose significant information from commonly used substances ranked 5th or below.”

c. Additionally, in the discussion of what Supplemental Table 2 shows, the authors use terms highest and lowest, higher and lower, suggesting these are statistically significant, but there are no official statistics used for these comparisons.

We included in Supplementary Table 2, now Supplementary Table 4, survey-weighted t-tests to compare by sex the estimated prevalence of each substance combination within sexual identity. We included the following in the results section:

“Supplementary Table 4 shows the prevalence of these common polysubstance combinations among different sexual identities and sexes. We found that for all combinations there were sex differences within the sexual identities, heterosexual, gay/lesbian, and bisexual adults (Supplementary Table 4 and Figure 1). Among heterosexual adults, male adults engaged more than female adults in most polysubstance combinations while among bisexual adults, female adults engaged more than male adults. Among gay/lesbian adults, although there were sex differences in all polysubstance combinations, they did not vary disproportionately more either among gay male or lesbian female adults.”

We adjust accordingly the first paragraph of the discussion section:

“The key findings of this study are (a) there were sex differences in polysubstance use by all sexual identities, as other studies (Ehlke et al., 2023; Flentje et al., 2024; Goodwin et al., 2022; Jun et al., 2019; Kecojevic et al., 2017; McCabe et al., 2022; Moss et al., 2014; Schauer et al., 2013; Vogel et al., 2024). (b) Bisexual female adults were at the highest risk for engaging in most of the used polysubstance combinations compared to heterosexual female adults, who were at the lowest risk, as seen in other studies (Dermody, 2018; Ehlke et al., 2023; McCabe et al., 2004, 2022; Nguyen et al., 2021; Schauer et al., 2013; Vogel et al., 2024). This pattern holds even w

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PLoS One. doi: 10.1371/journal.pone.0340454.r004

Decision Letter 1

Daniel Demant

4 Nov 2025

Dear Dr. Mestre,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: No

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The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This study is important because addresses poly substance use, a major public health concern linked to chronic disease and mental health risks. It focuses on sexual identity and sex differences, highlighting disparities among LGB adults compared to heterosexual adults and uses a nationally representative dataset (NSDUH) for that, improving external validity over prior localized studies. At the same time, identifies specific substance combinations (e.g., binge drinking + cannabis) most prevalent among bisexual female adults, and provides evidence to guide targeted public health strategies and treatment access for vulnerable subgroups. The research has strong points: Large, nationally representative NSDUH dataset (66k+ adults) with focus on LGB adults, highlighting bisexual women’s higher risk. At the same time demonstrate rigorous methods: survey‑weighted multinomial models, interactions, sensitivity analyses with clear reporting of limitations, public dataset, supplementary diagnostics, showing novel findings on sex differences and age‑related patterns. Finally adds to public health knowledge with policy relevance. However, present us weak points as a non‑binary/transgender groups exclusion, limiting inclusive, a limited explanation of underlying social/psychological mechanisms, and a very large residual deviances reducing the model interpretability. Finally the imputed datasets/code were not fully shared as some presentation/grammar issues either. The methods are rigorous and seems correct with the investigation using valid representative instruments. So it are reliable and reproducible, though constrained by dataset definitions and exclusions. The results results support tailoring public health strategies by sexual identity, sex, and age to address disparities.The study concludes that bisexual female adults are at the highest risk for polysubstance use. The conclusions put evidence on sex differences between heterosexual and bisexual adults, but not among on gay and lesbian adults, and the age patterns showed that younger adults drive much of the disproportionate use. The findings emphasize the need for tailored public health strategies by sexual identity, sex, and age, we talked about it on contributions. In future, authors point future researches should include non‑binary and transgender populations for broader inclusivity, should explore social, psychological, and structural drivers behind these disparities.

Reviewer #2: The authors have addressed some of the original concerns, though some existing concerns remain.

1) Inconsistent expression of Bonferroni statistics in the tables. Some tables have markers indicating bonferroni significance for tests, some have marked only some, not all, of the tests as significant following Bonferroni, some just have the marker in the legend without making changes to the table, some have the marker in one column label, some don’t reference Bonferroni at all.

2) I don’t think Bonferroni corrections have been calculated properly. There are 12 permutations of drug combinations that are being compared across the 3 sexual identities and 2 sexes. Furthermore, there were 16 individual substances that were compared across the 3 sexual identities. The authors have only stated that they’ve adjusted for the sexual identities and sexes, but even that is inconsistent in the tables as listed above.

3) Theoretical comments:

a. The very first sentence of the abstract leads with “polysubstance is a major public health concern….especially LGB female adults.” However, the authors ultimate conclusion is this is only true for bisexual female adults, not lesbian, so, the leading statement should probably be revised.

b. The paper would be easier to follow if the authors either referenced the tables when they list the specific tests they conducted, OR, if in the results, they created subsections that were titled according to which experimental question they were probing, with the appropriate results within those subsections.

c. In the discussion section, the authors lean in on “access” to substance use treatments. However, the authors should consider the design of how access is provided, and not just access, as most likely services are available, but not specifically designed for different populations and different polysubstances.

4) Grammatical comments:

a. Second paragraph first sentence of introduction should read, “…have examined….”

b. First sentence of first paragraph of discussion should read, “…as other studies have shown”

c. The last sentence of the second paragraph of the results section is awkward. Could read, “therefore we did not lose significant information by using a cut-off of 4 substances….”

d. First sentence of the discussion is not a complete sentence.

5) In the Methods, in the second paragraph of the Dependent variable (outcome) section, the first two sentences are redundant. “we ranked all substances…” and “We used the imputed variables of past 30 day substances available in NSDUH 2021 and 2022”.

6) In the same paragraph as above, why did the authors list the substances in the text as they did? It implies to the reader that there is some ranking, and the list does not match the ranking provided in S1 Table.

7) The last sentence of that same paragraph is awkward, could read instead, “Misused substances included, 13) prescription pain relievers, ….”

8) Third paragraph of that same section indicates that the combinations of substances are shown in S1 Table, but they’re not. The combinations are shown in Table 1.

9) The first paragraph for Statistical analysis states that “as recommended, we implemented NSDUH 2021 and 2022 survey weights”. For what exactly? When edited, authors can combine this sentence with the following sentence to explain the weights were divided by 2.

10) In the first paragraph for Statistical analysis, the following doesn’t make sense, “we also did survey weighted t tests to compare the estimated prevalence of (b) the substances included in the study among gay/lesbian and heterosexual adults, and bisexual and heterosexual adults”

11) In the third paragraph of statistical analysis, authors state they did 2 sample proportion test, and reference S1 Table, but that’s not where the results for the 2 sample proportion test are reported.

12) Third paragraph of results section leads with “lesbian female adults engaged more in polysubstance combinations that heterosexual male and female adults but less than gay and bisexual male adults…”, There is only 1 combination for comparison with heterosexual males that lesbian females were greater, and there was 1 combination in which lesbian females were greater than bisexual males, and a different combination in which they were greater than gay males. Thus, this leading sentence needs to be revised to more accurately reflect the results.

13) In the fourth paragraph of the results section, first sentence, why is >50 years included in the parentheses?

14) The first sentence of the first paragraph of the discussion, and the second to last sentence of that same paragraph are making opposite claims. First sentence states sex differences by all sexual identities. Second to last sentence states sex not associated in gay/lesbian adults.

15) It would help the reader if the authors revised the last sentence in the first paragraph of the discussion to include what their original hypothesis was, and what was supported vs what was not supported by the current findings.

16) The first sentence of the second paragaraph of the discussion claims that the current “findings support the claim that LGB groups….experience different challenges”. The challenges that LGB groups experience was not directly tested by this study.

17) The paragraph in the discussion section describing the age discrepancies is incomplete in that it does not describe higher combinations that include nicotine for the older age group. This should be included.

18) When discussing the limitations to the study, the authors note that NSDUH did not assess a number of things. This section could be re-written to note that in 2023, NSDUH did expand survey response choices for sexual identify.

19) First sentence of conclusion notes the most used substances among LGB adults, but this was true for all adults.

20) Table 3a does not show difference between bisexual female adult vs gay male, except for 1 combination out of 12. The description of this in the results should be revised, as the comparison is not the same as to the heterosexual male, in which there are many more.

21) S1 Table needs more descriptions for the columns of data. It is unclear what the second column (Rank) belongs to, compared to the 4th column labeled Rank, etc. There are 4 ranked columns, 4 sexual identities, only 2 p-value columns?

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Reviewer #1: Yes: Sílvio Manuel da Rocha Brito

Reviewer #2: No

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PLoS One. 2026 Feb 18;21(2):e0340454. doi: 10.1371/journal.pone.0340454.r005

Author response to Decision Letter 2

12 Dec 2025

Finally the imputed datasets/code were not fully shared as some presentation/grammar issues either.

The methods are rigorous and seems correct with the investigation using valid representative instruments. So it are reliable and reproducible, though constrained by dataset definitions and exclusions. The results results support tailoring public health strategies by sexual identity, sex, and age to address disparities. The study concludes that bisexual female adults are at the highest risk for polysubstance use. The conclusions put evidence on sex differences between heterosexual and bisexual adults, but not among on gay and lesbian adults, and the age patterns showed that younger adults drive much of the disproportionate use. The findings emphasize the need for tailored public health strategies by sexual identity, sex, and age, we talked about it on contributions.

In future, authors point future researches should include non binary and transgender populations for broader inclusivity, should explore social, psychological, and structural drivers behind these disparities.

Thank you for your comment. We included the imputed datasets and code as a supplemental file. Furthermore, we included the recommended point for future research in the conclusion section: “Future research should aim to examine why bisexual female adults engage in these polysubstance combinations to develop effective public health strategies to minimize their health risks and reduce polysubstance use among bisexual female adults. We also recommend future research that explores social, psychological, and structural drivers behind the disparities in the disproportionate use of these polysubstance combinations. Furthermore, future work should include non-binary and transgender populations.”

Reviewer #2: The authors have addressed some of the original concerns, though some existing concersns remain.

Please refer to comment 2)

Thank you for the opportunity to clarify. We included a Bonferroni Correction in our sensitivity analyses for the survey-weighted t-tests and the multinomial models. The thresholds to determine a significant p-value with Bonferroni Corrections in these t-tests were 0.004, 0.005, or 0.003; 0.05 divided by the 12 mutually exclusive categories of the polysubstance combination variable, ten multinomial models to compare female adults by sexual identity with all the other sexual identity by sex groups, 16 for the substances that were considered to operationalized the polysubstance combinations variable, respectively. We included the Bonferroni corrections of 12 mutually exclusive categories in Table 1 and S4 Table (0.05/12). We used the Bonferroni Correction of ten comparisons (0.05/10) based on the number of multinomial models used to do multiple comparisons among sexual identity by sex used in Table 2, Table 3a, Table 3b, Table 4, and S6 Table. We used a Bonferroni correction of 0.05/16 when comparing the substances used to operationalize the polysubstance combinations variable, with 12 mutually exclusive categories of the most used four substances. This Bonferroni correction is in the S1 Table.

3) Theoretical comments:

We changed the sentence as “Polysubstance use is a major public health concern affecting lesbian, gay, and bisexual (LGB) adults, especially bisexual female adults.”

We added two subsections within the result section, as recommended by the reviewer. “Sex Differences in the Polysubstance Combinations vary by Sexual Identity” and “Bisexual Female Adults have Higher Rates among Polysubstance Combinations compared to Heterosexual Female, Heterosexual Male, Gay Male, and Lesbian Female adults.”

We added the following: “One way to provide access for substance use treatment while considering the potential challenges LGB adults, particularly bisexual female adults, experienced is through digital interventions. Digital interventions have shown promising results in providing access to substance use treatment to populations who have less access to healthcare settings, or experience stigma due to components of their identities or engaging in substances [46]. Furthermore, given that, on average, bisexual adults are younger than their heterosexual peers, they are more likely to have access to a smartphone, facilitating access to digital interventions for this population.”

4) Grammatical comments:

Thanks for the catches. We fixed these grammar issues.

a. Second paragraph first sentence of introduction should read, “…have examined….”

We changed it to “… have examined…”

b. First sentence of first paragraph of discussion should read, “…as other studies have shown”

We changed the first paragraph of the discussion section so it read “…as other studies have shown.”

c. The last sentence of the second paragraph of the results section is awkward. Could read, “therefore we did not lose significant information by using a cut-off of 4 substances….”

We included the reviewer’s recommendation.

d. First sentence of the discussion is not a complete sentence.

We changed the first sentence to ensure is a complete sentence as follows:

“The key findings of this study are the following: (a) there were sex differences in polysubstance use among bisexual and heterosexual adults, as other studies have shown [9,10,12,14,28–31,33]; (b) bisexual female adults were at the highest risk for engaging in most of the used polysubstance combinations compared to heterosexual female adults, who were at the lowest risk, as seen in other studies [8,9,12,14,27,32,33].”

We removed the sentence “We ranked all substances…”.

To avoid confusion, we removed the listing and just stated the substances.

7) The last sentence of that same paragraph is awkward, could read instead, “Misused substances included, 13) prescription pain relievers, ….”

We fixed the sentence as recommended.

8) Third paragraph of that same section indicates that the combinations of substances are shown in S1 Table, but they’re not. The combinations are shown in Table 1.

We changed it to Table 1.

We combined the sentences to avoid redundancy. The sentence now reads as: “We implemented NSDUH 2021 and 2022 survey weights; we divided the survey weights by two, the number of survey cycles included in the study.”

We changed it to the following: “(b) the substances included in the study to determine the polysubstance combinations”

We removed “S1 Table” as correctly stated by the reviewer.

We changed the sentence as follows: “Lesbian female adults engaged more in polysubstance combinations than heterosexual female adults (Table 2). Lesbian female adults only engaged more in one combination than heterosexual male adults, though they engaged less in one combination compared to bisexual and gay male adults.”

13) In the fourth paragraph of the results section, first sentence, why is >50 years included in the parentheses?

We removed the parentheses and the “>50” to avoid confusion.

We specified the following in the first sentence to have consistency: “The key findings of this study are the following: (a) there were sex differences in polysubstance use among bisexual and heterosexual adults, as other studies have shown.”

We added a small paragraph to bolster the last sentence and include the hypotheses.

“We hypothesized that sex differences in the polysubstance combinations will vary by sexual identity. We also hypothesized that bisexual female adults will have higher rates among all polysubstance combinations compared to all the other groups. The study’s findings partially support our hypotheses. We observed sex differences and how they vary, though only among heterosexual and gay/lesbian adults. We also found that bisexual female adults had higher rates in most of the polysubstance combinations than heterosexual female and male adults, lesbian female adults, though not bisexual male adults.”

We removed the “experience different challenges” of the sentence.

We changed the paragraph as the following to include information about nicotine vape and older ages.

“We also found that the polysubstance combinations vary by age groups; the older the age group, the lower the likelihood of using most of the assessed polysubstance combinations. Particularly, we found that ≥50 years adults engage significantly less on combinations that include nicotine vape compared to 18-29 years adults. Given that, on average, LGB adults are younger than heterosexual adults, there is a plausibility that the disproportionate use of polysubstance combinations among bisexual female adults than heterosexual female adults can be partially due to age differences. Similarly, nicotine vape might be disproportionately more used among bisexual adults than heterosexual adults due to bisexual adults being, on average, a younger group. Future work should study whether the use of these substance combinations by sexual identity and sex varies by age.”

NSDUH 2023 includes an additional response survey for sexual identity. However, those were “I use a different term” and “I am not sure about my sexual identity.” None of these options assessed specific sexual identities.

19) First sentence of conclusion notes the most used substances among LGB adults, but this was true for all adults.

We removed the first sentence.

We removed the gay male group from our statement in the results section to avoid any confusion.

There are only two p-value columns. One for gay/lesbian adul

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PLoS One. doi: 10.1371/journal.pone.0340454.r006

Decision Letter 2

Daniel Demant

21 Dec 2025

Disproportionate use of polysubstance combinations varies by sexual identity among US adults

PONE-D-25-25612R2

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PLoS One. doi: 10.1371/journal.pone.0340454.r007

Acceptance letter

Daniel Demant

PONE-D-25-25612R2

PLOS One

Dear Dr. Mestre,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Weighted conditional percentages (%) of substances past 30 day use by sexual identity, NSDUH 2021 and NSDUH 2022 (without excluding missing data).

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pone.0340454.s001.zip^{(3.2MB, zip)}

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pone.0340454.s004.docx^{(37.3KB, docx)}

Data Availability Statement

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PERMALINK

Disproportionate use of polysubstance combinations varies by sexual identity among US adults

Luis M Mestre

Marney A White

Juhan Lee

Maria A Parker

Krysten W Bold

Roles

Abstract

Introduction

Methods

Study sample

Ethical statement.

Measures

Independent variable (exposure) – Sexual identity.

Dependent variable (outcome) – Past 30 days used polysubstance combinations.

Table 1. Descriptive Statistics by sexual identity, NSDUH 2021 and NSDUH 2022.

Covariates.

Statistical analysis

Results

Sex Differences in the Polysubstance Combinations vary by Sexual Identity

Fig 1. Survey-weighted prevalence of polysubstance combinations by sexual identity and sex, NSDUH 2021-2022.

Bisexual female adults have higher rates among polysubstance combinations compared to heterosexual female, heterosexual male, gay male, and lesbian female adults

Table 2. Summary of Survey Weighted Multinomial Models with potential combinations by sexual identity and sex among lesbian female adults, NSDUH 2021 and 2022.

Table 3. Summary of Survey Weighted Multinomial Models with potential combinations by sexual identity and sex among bisexual female adults versus male adults, NSDUH 2021 and 2022.

Table 5. Summary of Survey Weighted Multinomial Models with potential combinations among heterosexual female adults versus heterosexual, bisexual and gay male adults, NSDUH 2021 and 2022.

Table 4. Summary of Survey Weighted Multinomial Models with potential combinations among bisexual female adults versus heterosexual and lesbian female adults, NSDUH 2021 and 2022.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Transfer Alert

Decision Letter 0

Daniel Demant

Roles

Author response to Decision Letter 1

Decision Letter 1

Daniel Demant

Roles

Author response to Decision Letter 2

Decision Letter 2

Daniel Demant

Roles

Acceptance letter

Daniel Demant

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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