Examining Daily Self-Efficacy, Minority Stressors, and Alcohol and Other Drug Use Among Trauma-Exposed Sexual Minority Women and Transgender and Gender-Diverse People

Jillian R Scheer; Ethan H Mereish; Amanda K Gilmore; Cory J Cascalheira; Emily C Helminen; Fatima Dobani; Kriti Behari; Sophia Pirog; Skyler D Jackson; Tami P Sullivan; Abigail W Batchelder

doi:10.1093/abm/kaae065

. 2024 Oct 14;58(12):832–844. doi: 10.1093/abm/kaae065

Examining Daily Self-Efficacy, Minority Stressors, and Alcohol and Other Drug Use Among Trauma-Exposed Sexual Minority Women and Transgender and Gender-Diverse People

Jillian R Scheer ^1,^✉, Ethan H Mereish ², Amanda K Gilmore ^3,⁴, Cory J Cascalheira ⁵, Emily C Helminen ⁶, Fatima Dobani ⁷, Kriti Behari ⁸, Sophia Pirog ⁹, Skyler D Jackson ¹⁰, Tami P Sullivan ¹¹, Abigail W Batchelder ^12,¹³

PMCID: PMC11568352 PMID: 39402938

Abstract

Background and Purpose

This study aimed to develop and test a novel model integrating social-learning and self-medication frameworks by examining the association between self-efficacy to resist alcohol and other drug (AOD) use and daily AOD use and unhealthy drinking risk among trauma-exposed sexual minority women (SMW) and transgender and gender-diverse (TGD) people. We examined whether minority stressors moderated these associations.

Methods

Data were from 57 trauma-exposed SMW and TGD people who participated in a 14-day daily diary study. Multilevel binary logistic models and ordinal logistic models were employed to examine associations between self-efficacy to resist AOD use and daily AOD use and unhealthy drinking risk at within- and between-person levels. We assessed same- and cross-level interactions between daily self-efficacy to resist AOD use and minority stressors in predicting AOD use and unhealthy drinking risk within the same 24-hour period (i.e., standardized as 6 pm to 6 pm; hereafter referred to as “same-day”).

Results

Self-efficacy to resist AOD use was associated with lower AOD use and unhealthy drinking risk. Minority stressors were associated with daily AOD use. Among those who experienced higher (vs. lower) average sexual minority stressors over the 2-week daily diary period, higher-than-usual self-efficacy to resist AOD use was less protective in decreasing risk of same-day unhealthy drinking.

Conclusions

Interventions aiming to mitigate AOD use and unhealthy drinking risk by bolstering self-efficacy to resist AOD use should consider the impact of recent cumulative exposure to sexual minority stressors in this population. Further, policy efforts are needed to reduce perpetuation of stigma.

Keywords: Sexual and gender minority, Alcohol and other drug use, Minority stressors, Self-efficacy, Intensive longitudinal designs

Due to stigma, self-efficacy is less effective in reducing daily substance use among sexual minority women and transgender and nonbinary people who have experienced trauma. Interventions aiming to strengthen self-efficacy as a mechanism of behavior change to reduce unhealthy substance use should consider the impact of chronic exposure to stigma in these populations.

Epidemiologic evidence demonstrates that compared to cisgender, heterosexual people, sexual minority cisgender women (SMW) and sexual minority transgender and gender-diverse (TGD) people—those who not only face heterosexism but also gender-related oppression—demonstrate a higher prevalence of alcohol and other drug (AOD) use and related outcomes, including drinking at unhealthy levels [1–3]. AOD use is especially common among SMW and TGD people with trauma exposure [4, 5]. Among SMW and TGD people, unhealthy alcohol use remains a leading cause of preventable death [6–8]. Compared to cisgender, heterosexual individuals, SMW and TGD people may face heightened vulnerability to AOD-attributable harm even when reporting similar or lower levels of AOD use [7, 9]. Thus, there has been an increasing demand for research on individual and contextual factors influencing AOD use patterns and unhealthy drinking risk among SMW and TGD people, including those with trauma [5–7].

AOD use and unhealthy drinking risk fluctuate daily and are driven partly by self-regulation skills, including self-efficacy, as well as contextual factors, such as stressful events [10–13]. Yet, little is known about whether and the conditions under which self-efficacy predicts daily AOD use and unhealthy drinking risk in trauma-exposed SMW and TGD people. Such findings could advance knowledge of mechanisms underlying daily AOD use patterns in these populations, inform the development and refinement of prevention strategies to minimize daily AOD use, and improve screening, intervention, and referral for specialized treatment among those drinking at unhealthy levels who may be at risk for alcohol use disorders [14].

Understanding Self-Efficacy as a Predictor of AOD Use and Unhealthy Drinking Risk

Social-learning theory [15] highlights the importance of cultivating self-efficacy, the belief in one’s ability to perform behaviors necessary for desired outcomes, such as decreasing AOD use [16]. Self-efficacy in resisting AOD use reliably predicts AOD use and AOD treatment outcomes in the general population [11]. Indeed, cognitive and behavioral treatments for unhealthy AOD use include components targeting self-efficacy (e.g., refusal skills training) [17].

Self-efficacy is a key predictor of lower AOD use among people who have experienced trauma [18]. For instance, one study found that greater self-efficacy to resist drinking attenuated the link between traumatic stress and alcohol use among veterans [19]. Similarly, among undergraduate students, results indicated that those with higher (vs. lower) trauma-specific coping self-efficacy demonstrated a weaker association between traumatic stress and drug use [20]. Likely due to their heightened exposure to adversity and minority stressors (e.g., discrimination) [21–24], data indicates that SMW and TGD people report lower self-efficacy than cisgender, heterosexual people [25, 26]. Thus, cultivating self-efficacy concerning AOD use control strategies is essential for trauma-exposed SMW and TGD people.

Cross-sectional research has identified self-efficacy as a determinant of AOD use among sexual and gender minority people [27, 28]. One study among a national sample of sexual and gender minority youth indicated that greater feelings of mastery over stressors were associated with a decreased likelihood of using alcohol, cannabis, and tobacco [29]. Other findings have been mixed, showing that self-efficacy was associated with declines in unhealthy drinking risk (e.g., binge drinking) but not drug use for sexual minority youth [26]. More granular information is needed to clarify the temporal associations between self-efficacy to resist AOD use and daily AOD use and unhealthy drinking risk among trauma-exposed SMW and TGD people.

Considering a Dynamic and Contextual Model of AOD Use and Unhealthy Drinking Risk

Self-efficacy and health-risk behaviors, such as AOD use and unhealthy drinking risk, fluctuate over time and across contexts [16, 19]. A systematic review of 91 studies revealed that daily fluctuations in self-efficacy predicted AOD use across addiction severity levels, from low to high risk of substance use disorders [30]. The influence of self-efficacy on initiating or maintaining risk behaviors may hinge on individuals’ need to manage environmental burdens, especially those escalating AOD use risk (i.e., high-risk situations) [11, 12]. Confidence in resisting AOD use may minimally influence behavior in the face of high-risk situations [12]. Further, the temporal, cumulative, and external nature of uncontrollable high-risk situations may impact the effectiveness of self-efficacy in mitigating AOD use and unhealthy drinking [31].

Cross-sectional studies consistently demonstrate that minority stressors (e.g., discrimination rooted in societal heterosexism or cissexism) represent unique contextual factors contributing to an increased prevalence of AOD use and unhealthy alcohol use among sexual and gender minority people [1, 21, 24, 27, 32]. Longitudinal and daily diary research further validates the influence of these stressors, such as rejection, on AOD use and unhealthy drinking risk in these populations [10, 33–35]. This research aligns with the self-medication model [36], positing that AOD use and unhealthy drinking may function to relieve distress stemming from minority stressors [10, 37]. SMW and TGD people facing cumulative life stress, including trauma and minority stressors, may be especially vulnerable to AOD use and unhealthy drinking [1, 5, 37].

The impact of self-efficacy in resisting AOD use on daily AOD use and unhealthy drinking risk may differ in the presence of minority stressors among trauma-exposed SMW and TGD people. High-risk situations, including minority stressors, may impact self-efficacy by posing adaptational demands through exposure to negative affect or depletion of coping resources [31]. Nevertheless, this study aims to provide a novel theoretical perspective on the conditional effects of self-efficacy, a modifiable mechanism of behavior change, on daily AOD use and unhealthy drinking risk in trauma-exposed SMW and TGD people. Specifically, this study aims to expand social-learning theory [15] and the self-medication model [36] by examining whether prior experiences of minority stressors (i.e., discrimination assessed at baseline), daily minority stressors (i.e., assessed at the within-person level), and the accumulation of recent minority stressors (i.e., average minority stressors assessed at the between-person level over the 2-week daily diary period) weaken the relationship between daily self-efficacy to resist AOD use and AOD use and unhealthy drinking risk. This approach has practical implications for developing contextualized self-efficacy training programs aimed at reducing daily AOD use and unhealthy drinking risk, especially for trauma-exposed SMW and TGD people whose self-efficacy is less effective in reducing daily substance use due to stigma.

The Present Study

Spanning 14 days, this daily diary study aimed to identify the extent to which there is situational and individual variability in self-efficacy to resist AOD use and minority stressors as well as their associations with daily AOD use and unhealthy drinking risk among trauma-exposed SMW and TGD people. Daily alcohol use and unhealthy drinking risk were examined as separate outcomes given evidence that daily fluctuations in self-efficacy predicted AOD use across addiction severity levels [30]. Further, this study aimed to enhance understanding of daily fluctuations in a range of drinking behaviors, from occasional or light drinking to heavy drinking and potentially harmful consumption patterns, within a nontreatment-seeking community sample of trauma-exposed SMW and TGD people. We also sought to examine minority stressors at baseline, at the day-level, and over a 2-week period as moderators of the associations between daily self-efficacy to resist AOD use and AOD use and unhealthy drinking risk.

We hypothesized that experiencing greater daily and average self-efficacy to resist AOD use will be associated with lower AOD use and unhealthy drinking risk within a standardized 24-hour period (i.e., 6 pm to 6 pm; hereafter referred to as “same-day”). Greater exposure to minority stressors reported at baseline, at the day-level, and over a recent 2-week period will be associated with greater AOD use and unhealthy drinking risk. Greater (vs. lower) exposure to minority stressors will weaken the relationship between daily self-efficacy to resist AOD use and AOD use and unhealthy drinking risk.

Methods

Participants and Procedures

Data are from a daily diary study of 57 trauma-exposed SMW and TGD people; data were collected between May 2021 and August 2022. Participants were recruited via social media, listservs, and organizations serving trauma survivors and sexual and gender minority people. We targeted the four most populous cities, 20 randomly selected small urban areas, and 20 randomly selected rural counties in the USA [38]. Eligible participants identified as sexual minority people; identified as cisgender or transgender women, transgender men, and/or as gender-diverse (e.g., nonbinary); were at least 18 years old; reported at least one Criterion A traumatic event [39]; spoke English; lived in the USA; could complete daily online surveys; had a working email address; and did not receive recent inpatient psychiatric support. Participants were emailed the first daily online survey within 2 weeks of completing an online baseline assessment. Links to daily online surveys were emailed at 6 pm Eastern time regardless of participants’ time zones; those with incomplete surveys were sent email reminders at 10 pm Eastern. Daily surveys expired at 5:59 pm Eastern the following day. Regardless of when the survey was completed, participants were instructed to report on the same standardized 24-hour period of 6 pm on day x to 6 pm on day y. Participants received $1 per daily survey, $5 for finishing all entries in the first week, and $6 bonus for completing all 14 entries. The Institutional Review Board at Syracuse University approved study procedures.

Measures

Baseline measures

Discrimination related to lesbian, gay, bisexual, transgender, or queer (LGBTQ+) status.

The 10-item everyday discrimination scale (EDS) [40, 41] was used to assess the frequency of exposure to interpersonal discrimination across 10 discriminatory experiences. Response options ranged from 0 (never) to 5 (almost everyday). Items were summed to create a total score, with higher scores indicating greater frequency of discriminatory experiences in daily life. Internal consistency was acceptable in the current study; α = .92. No specific timeframe in the instructions was used. The EDS has demonstrated adequate reliability with SMW and TGD people [42]. Participants who reported discrimination were asked to attribute this experience to various identities. Experiences attributed to one’s LGBTQ+ status were included in analyses.

Covariates.

Alcohol use severity and drug use severity were included as between-person covariates in all models given that a history of AOD use predicts current AOD use [43]. The 10-item Alcohol Use Disorders Identification Test (AUDIT) was used to assess alcohol use severity at baseline [44]. Higher scores reflect greater past-year alcohol use severity. Internal consistency was acceptable in the current study; α = .82. The AUDIT has demonstrated adequate reliability with SMW and TGD people [45, 46]. The 11-item drug use disorders identification test was used to assess drug use severity at baseline [47]. Higher scores reflect greater past-year drug use severity. Internal consistency was acceptable in the current study; α = .78.

Daily measures

Daily self-efficacy to resist AOD use.

Self-efficacy to prospectively resist AOD use was assessed using a single item [19]. Participants were asked: “How confident are you that you will not drink or use substances, including marijuana, nonprescribed medication/nonprescribed use of medication, club drugs, hallucinogens, cocaine, opiates, or methamphetamine, from 6 pm today to 6 pm tomorrow?” Response options ranged from 0 (not at all) to 3 (extremely). To ensure that self-efficacy was measured contemporaneously with daily AOD use and unhealthy drinking risk, a version of the self-efficacy to resist AOD use variable was created to represent “Day t − 1.” That is, for participants completing the Day 2 survey, where their self-efficacy is in reference to the period from 6 pm on Day 2 to 6 pm on Day 3, their self-efficacy rating was delayed by 1 day, corresponding to the period from 6 pm on Day 1 to 6 pm on Day 2. This adjustment ensured that the self-efficacy measurement timeframe aligned with the substance use reported on Day 2 (i.e., from 6 pm on Day 1 to 6 pm on Day 2).

Daily sexual minority stressors.

The 8-item daily sexual minority stressors scale [48] was used to assess daily sexual minority stressors. Items were adapted to measure stressors among sexual minority people who identify as transgender or as gender-diverse. For instance, instead of asking participants to describe something that happened because they identify as “sexual minority women,” we asked each participant, “For each of the following statements, rate how much this experience describes something that happened to you from 6 pm yesterday to 6 pm today because you identify as a sexual minority.” Response options ranged from 0 (not at all) to 6 (a lot). Responses using the 7-point scale were skewed. Therefore, each item was recoded as yes (1) or no (0) to reflect whether the participant endorsed the stressor or not [34]. The total number of minority stressors was then calculated with higher values reflecting greater total number of daily minority stressor event types. Three-level unconditional multilevel models were used to compute between-person reliability in the current study (α = .78) [49].

Daily alcohol use.

Participants were asked the number of standard alcoholic drinks they consumed from 6 pm on the previous day to 6 pm on the day of the survey. Participants could indicate the specific number of drinks consumed from 0 to 40. This daily drink count variable was recoded considering participants’ sex assigned at birth (male, female, or intersex), consistent with recommendations assumed to be based on physiological sex characteristics (e.g., physiological structures, body water content) which can impact processes of ethanol pharmacokinetics [50–52]. Specifically, an ordinal variable was created to reflect levels of daily alcohol consumption mapping onto sex-specific, risk-based recommendations for daily alcohol limits in the USA. Sex assigned at birth was coded 0 (female or intersex) or 1 (male) [50]. In line with previous studies [51] and guidelines for identifying risk of alcohol use disorders [52], we categorized daily unhealthy drinking risk as: ≤1 (female or intersex) and ≤2 (male) drinks/day were considered no-/low-risk (coded as 0), >1 ≤ 3 (female or intersex) and >2 ≤ 4 (male) drinks/day as moderate-risk (coded as 1), and ≥4 (female or intersex) and ≥5 (male) drinks/day as high-risk (coded as 2). Raw scores were also coded as 0 (no use) or 1 (any use) each day.

Daily drug use.

Participants were asked whether they used any of the following substances from 6 pm on the previous day to 6 pm on the day of the survey: cannabis, recreational use of prescribed/nonprescribed medication, club drugs, hallucinogens, cocaine, opiates, methamphetamine, or “other” [35]. Each item was coded 0 (no use) or 1 (any use); raw scores were combined and coded 0 (no use) or 1 (any use) to examine daily drug use occurrence.

Covariates.

Day of participation (1 = first day, 2 = second day, etc.), the total number of days participated in the daily diary study, and day of the week (0 = Monday through Wednesday, 1 = Thursday through Sunday) was assessed and included as covariates in all models [51].

Analytic Plan

Preliminary analyses were conducted using SPSS Version 27; multilevel models were conducted using Mplus Version 8.8. Primary analyses included several multilevel ordinal logistic models for daily unhealthy drinking risk and multilevel binary logistic models for daily alcohol use and daily drug use. Models that included daily alcohol use and daily drug use as outcome variables used binary logistic multilevel modeling given lack of overdispersion [53].

There were no missing data for any between-person variables (see Supplemental Table 1). Less than 1% of data were missing (0.2% to 0.3%) at the within-person level among those with daily surveys that were submitted. Data from all participants were included in the current study regardless of missing data given that participants were allowed to skip a diary day [54]. Missing data were handled using full information maximum likelihood estimation, which produces results similar to Bayesian methods [55] and is recommended for handling models with relatively small samples and nonskewed variables [56].

Our daily diary design produced data with two levels: Level 1 for measuring within-person, situational fluctuation, and Level 2 for assessing time-invariant individual or between-person differences. To facilitate interpretation of effect estimates, all continuous predictors and covariates were grand-mean centered and then split into a between-subject means component [57]. Continuous predictors and covariates at the day-level were further split into a within-subjects deviations from the means component [57]. Binary variables were left uncentered given the meaningful zero point of raw metrics. Covariates across Level 1 and 2 were included [58].

We conducted multilevel analyses in five steps, each of which corresponded to a specific model. In Model 0, the intraclass correlation (ICC) was calculated to measure the degree of variability in daily AOD use attributable to between- versus within-person differences [59]. In Model 1, control variables were entered as predictors at Level 1 and Level 2 to examine changes in explained variance as study predictors were added in subsequent models. Model 2 consisted of an adjusted random intercept analysis that modeled daily self-efficacy to resist AOD use and between-person mean self-efficacy to resist AOD use as fixed effect predictors of same-day daily AOD use. In Model 3, an adjusted random slope model in which daily self-efficacy to resist AOD use was entered as a random (in addition to fixed) effect predictor to test for significant variability in slopes predicting same-day daily AOD use. The Level 1 slope of daily self-efficacy to resist AOD use was allowed to vary randomly in all subsequent models. In Model 4, we entered minority stress variables as predictors of the random slopes of daily AOD use. Between- and within-person sexual minority stressors were modeled simultaneously. Model 5 tested whether sexual minority stressors moderated the link between daily self-efficacy to resist AOD use and daily AOD use and unhealthy drinking. Cross- and same-level interactions were assessed in separate models. Regression slopes one standard deviation above/below the mean of the moderator variables were compared [60]. Sensitivity analyses with participants who reported lifetime AOD use at baseline were conducted to reduce potential bias from including those with no prior AOD use, who may be less likely to engage in daily use. This approach provides a clearer picture of daily AOD use and unhealthy drinking patterns among those at higher risk, who may benefit more from interventions aimed at reducing daily AOD use and unhealthy drinking. Sensitivity analyses yielded similar results as those reported below (see Supplemental Material). Data and analysis code are available by emailing the corresponding author.

Results

Sample Characteristics

Participants were 57 SMW and TGD people (M_age = 28.42, SD = 10.10; 18 to 72 years; see Table 1). Over a third (35.1%) identified with multiple gender identity labels, followed by cisgender women (28.1%), transgender men or transmasculine (14.0%), nonbinary (8.8%), other identities (e.g., gender fluid; 8.8%), genderqueer (3.5%), and transgender women or transfeminine (1.7%). Nearly half (49.1%) identified with multiple sexual identity labels; 24.6% as plurisexual; fewer as lesbian or gay (14.0%) and queer (12.3%). Most (63.2%) identified as White, followed by Biracial or Multiracial (15.6%), Asian American (12.3%), Latine or Hispanic (5.3%), Black or African American (1.8%), and Native Hawaiian or other Pacific Islander (1.8%). Almost a third (31.6%) earned $10,000–$24,999 per year, followed by ≤$9,999 (22.8%), $25,000–$49,999 (19.3%), $50,000–$74,999 (12.3%), and ≥$75,000 (12.3%).

Table 1.

Baseline Sociodemographic Characteristics (N = 57)

Sociodemographic characteristics	n	%
Gender identity
Multiple gender identity labels^a	20	35.1
Cisgender woman	16	28.1
Transgender man or transmasculine	8	14.0
Nonbinary	5	8.8
Another identity (e.g., gender fluid, demi-girl)	5	8.8
Genderqueer	2	3.5
Transgender woman or transfeminine	1	1.7
Sex assigned at birth
Female	49	86.0
Male	7	12.3
Intersex	1	1.8
Sexual identity
Multiple sexual identity labels^b	28	49.1
Plurisexual^c	14	24.6
Lesbian or gay	8	14.0
Queer	7	12.3
Race/Ethnicity
White	36	63.2
Biracial or Multiracial	9	15.6
Asian American	7	12.3
Latine or Hispanic	3	5.3
Black or African American	1	1.8
Native Hawaiian or other Pacific Islander	1	1.8
Income
$10k–$24.9k	18	31.6
Under $9.9k	13	22.8
$25k–$49.9k	11	19.3
$50k–$74.9k	7	12.3
Above $75k	7	12.3
U.S. Region
West	20	35.1
Northeast	13	22.7
South	12	21.1
Midwest	12	21.1
Type of index trauma
Interpersonal (e.g., child abuse)	48	84.2
Noninterpersonal (e.g., accident)	9	15.8
Age (M, SD); range	28.42 (10.10); 18–72

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Note. Groups are listed by sample size.

^aMultiple gender identity labels include ≥ two gender identities (e.g., trans and nonbinary).

^bMultiple sexual identity labels include ≥ two sexual identities (e.g., queer, bi, and pansexual).

^cPlurisexual includes individuals who identify as bisexual and/or pansexual.

There was a total of 688 completed daily surveys from 57 participants. Most participants 47 (82.5%) completed all 14 daily surveys. An average of 12.80 (Mdn = 14, SD = 2.08) daily surveys were completed by those who completed at least one daily assessment [61]. Most participants (54.5%) completed daily surveys on the day they were administered. The average time for completing surveys on the day they were administered was 8:18 pm Eastern (range = 6:02 pm Eastern to 11:52 pm Eastern). Among those who completed daily surveys the following day, the average time of completion was 4:32 pm Eastern (range = 12:02 am Eastern to 5:59 pm Eastern). Participants could complete more than one survey in the same day (the prior day’s survey and the current day’s survey), though the prior day’s survey link expired before participants could access the current day’s survey link. As a reminder, links to daily online surveys were emailed at 6 pm Eastern time, irrespective of participants’ time zones. Regardless of when they completed the daily surveys (i.e., on the day of the survey or on the following day), participants were instructed to report on the same standardized 24-hour period from 6 pm on day x to 6 pm on day y. ICC for daily alcohol use was .570, .439 for daily unhealthy drinking risk, and .698 for daily drug use (see Supplemental Table 1).

Most participants (n = 49; 86%) reported lifetime alcohol use at baseline. Over the 2-week period (688 days total), there were 173 (25.1%) drinking days. In total, 36 (63.2%) out of 57 participants reported consuming alcohol over the 2-week period. On average, participants had 3.04 (SD = 3.85, range = 0–14) drinking days over 2 weeks, with those who drank (n = 36) averaging 4.81 (SD = 3.87, range = 1–14) drinking days. On drinking days, the average number of standard drinks consumed was 2.36 (range = 1–10, SD = 1.71).

Half of participants (n = 29, 50.1%) reported lifetime drug use at baseline. There were 128 (18.6%) drug use days across 688 days. Over the 2-week period (688 days total), participants reported using cannabis most often (177 days, 17.0%), followed by recreational use of prescribed/nonprescribed medication (4 days, 0.6%), methamphetamine (3 days, 0.4%), hallucinogens (2 days, 0.3%), and club drugs (1 day, 0.1%). At the between-person level, 26 (45.6%) participants reported drug use over 2 weeks. Participants averaged 2.25 (SD = 4.12, range = 0–14) drug use days over 2 weeks, and among those who used drugs, the average was 4.92 (SD = 4.93, range = 1–14) drug use days.

Multilevel Models Predicting Same-Day Unhealthy Drinking Risk

On days when participants reported higher self-efficacy to resist AOD use than their own average, they had a decreased risk of engaging in unhealthy drinking on the same day (see Supplemental Table 2). Participants with higher average self-efficacy to resist AOD use were at a reduced risk of unhealthy drinking on any given day. While sexual minority stressors were not associated with unhealthy drinking risk, the interaction between daily self-efficacy to resist AOD use and between-person average sexual minority stressors over 2 weeks was associated with unhealthy drinking risk (see Table 2). Among those with fewer between-person average sexual minority stressors over the 2 weeks, higher self-efficacy to resist AOD use was associated with a decreased risk of same-day unhealthy drinking (b = −1.03, p = .008). For those with greater between-person average sexual minority stressors over 2 weeks, the link between daily self-efficacy to resist AOD use and same-day unhealthy drinking risk was nonsignificant (b = −0.16, p = .680). Daily minority stressors and between-person discrimination related to LGBTQ+ status at baseline did not moderate the relationship between daily self-efficacy to resist AOD use and unhealthy drinking risk.

Table 2.

Model Statistics for Multilevel Models Predicting Same-Day Unhealthy Drinking Risk from Mean and Daily Self-Efficacy to Resist AOD Use and Minority Stressors

Predictor	Moderator: between-person mean sexual minority stressors		Moderator: within-person daily sexual minority stressors	Moderator: between-person discrimination related to LGBTQ+ status at baseline
	Model 4a	Model 5a	Model 5b	Model 4c	Model 5c
	b (95% CI)	b (95% CI)	b (95% CI)	b (95% CI)	b (95% CI)
Level 1 (WP)
Daily self-efficacy to resist AOD use	−0.60 ^c (-1.13, -0.08)	−0.59 ^* (−1.06, −0.13)	−0.61^c (−1.13, 0.09)	−0.56^c (−1.07, 0.04)	−0.57^c (−1.12, 0.03)
Minority stress moderator	0.06 (−0.12, 0.31)	0.07 (−0.17, 0.32)	0.07 (−0.17, 0.32)	—	—
Weekday	0.31 (−0.24, 0.85)	0.33 (−0.22, 0.88)	0.31 (−0.23, 0.86)	0.30 (−0.24, 0.85)	0.30 (−0.24, 0.85)
Day	0.03 (−0.05, 0.10)	0.03 (−0.05, 0.09)	0.03 (−0.05, 0.10)	0.02 (−0.05, 0.09)	0.02 (−0.05, 0.09)
Within-level interaction
WP daily self-efficacy to resist AOD use × minority stress moderator	—	—	−0.09 (−0.31, 0.48)	—	—
Level 2 (BP)
Intercept	−3.78^* (−4.79, −2.78)**	−3.81^* (−4.81, −2.81)**	−3.78^* (−2.78, −4.78)**	−3.76^* (−4.76, −2.77)**	−3.77^* (−4.77, −2.77)**
Mean self-efficacy to resist AOD use	−0.85^ (−1.28, −0.41)**	−0.83^* (−1.25, −0.40)**	−0.85^* (−1.28, −0.42)**	−0.80^ (−1.23, −0.37)**	−0.80^ (−1.24, −0.37)**
Minority stress moderator	−0.30 (−0.72, 0.12)	−0.29 (−0.69, 0.11)	−0.31 (−0.73, 0.11)	−0.01 (−0.06, 0.03)	−0.01 (−0.06, 0.03)
Baseline alcohol use severity	0.35 ^* (0.24, 0.46)**	0.35 ^* (0.24, 0.47)**	0.35 ^* (0.23, 0.46)**	0.34 ^* (0.23, 0.46)**	0.34 ^* (0.23, 0.46)**
Number of days completed	0.04 (−0.15, 0.23)	0.02 (−0.12, 0.22)	0.04 (−0.15, 0.23)	0.04 (−0.15, 0.24)	0.04 (−0.15, 0.24)
Cross-level interaction
WP daily self-efficacy to resist AOD use × minority stress moderator	—	0.36 ^c (0.01, 0.71)	—	—	−0.01 (−0.05, 0.04)
Random effect variances
Level 2 (BP)
Intercept^b	1.69 ^* (0.40, 2.98)	1.66 ^* (0.40, 2.92)	1.69 ^* (0.39, 2.98)	1.71 ^* (0.42, 2.99)	1.72 ^* (0.41, 3.02)
Daily self-efficacy to resist AOD use slopes	0.40 (−0.27, 1.07)	0.19 (−0.34, 0.72)	0.41 (−0.26, 1.09)	0.37 (−0.28, 1.01)	0.37 (−0.28, 1.01)

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AOD alcohol and other drug; CI confidence interval; WP within-person; BP between-person; LGBTQ+ lesbian, gay, bisexual, transgender, or queer. Unstandardized coefficients are reported. The boldface coefficients were significantly different from zero at 95% CI. Model 4 represents an adjusted random slope model in which the between- and within-person minority stress moderators were entered as fixed effect predictors. Model 4 features all main effects with no interactions. Model 5 represents an adjusted same-level or cross-level interaction (depending on the moderator) between the minority stress moderator and within-person daily self-efficacy to resist AOD use to test whether same-day AOD use slopes differed significantly by the minority stress moderator. Model 4a between-person mean sexual minority stressors and Model 4a within-person daily sexual minority stressors are redundant; only Model 4a between-person mean sexual minority stressors is presented. In Models 4 and 5, daily self-efficacy slopes were allowed to vary randomly across participants.

^a0 = weekday (Monday through Wednesday), 1 = weekend day (Thursday through Sunday).

^bThis parameter estimate represents between-person variance regarding average AOD use (fixed intercept estimate).

^cEstimate reflects p <.10, though the 95% CI does not include the null value (i.e., zero effect), indicating statistical significance.

^* p <.05,

^** p <.01,

^*** p <.001.

Multilevel Models Predicting Same-Day Alcohol Use

Daily fluctuations in self-efficacy to resist AOD use, relative to individuals’ average levels over 2 weeks, were not associated with reduced odds of drinking alcohol the same day (see Supplemental Table 2). However, between-person effects showed that people with higher average self-efficacy to resist AOD use had a 58% decrease in the odds of alcohol consumption on any given day. On days when participants experienced more sexual minority stressors compared to their typical levels, they exhibited a 33% increase in the odds of consuming alcohol on the same day (see Table 3). Between-person sexual minority stressors were not associated with daily alcohol use; sexual minority stressor variables did not moderate the association between daily self-efficacy to resist AOD use and same-day alcohol use.

Table 3.

Model Statistics for Multilevel Models Predicting Same-Day Alcohol Use from Mean and Daily Self-Efficacy to Resist AOD Use and Minority Stressors

Predictor	Moderator: between-person mean sexual minority stressors		Moderator: within-person daily sexual minority stressors	Moderator: between-person discrimination related to LGBTQ+ status at baseline
	Model 4a	Model 5a	Model 5b	Model 4c	Model 5c
	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
Level 1 (WP)
Daily self-efficacy to resist AOD use	0.72 (0.50, 1.05)	0.69 ^c (0.48, 0.98)	0.73 (0.50, 1.06)	0.71 (0.50, 1.01)	0.73 (0.50, 1.08)
Minority stress moderator	1.33 ^* (1.07, 1.65)	1.32 ^* (1.07, 1.64)	1.32 ^* (1.06, 1.64)	—	—
Weekday	1.90 ^* (1.17, 3.07)	1.89 ^* (1.17, 3.06)	1.90 ^* (1.17, 3.07)	1.77 ^* (1.10, 2.83)	1.78 ^* (1.11, 2.85)
Day	0.93 ^c (0.88, 0.99)	0.94 ^c (0.88, 0.99)	0.93 ^c (0.88, 0.99)	0.93 ^c (0.88, 0.99)	0.93 ^c (0.88, 0.99)
Within-level interaction
WP daily self-efficacy to resist AOD use ×minority stress moderator	—	—	0.92 (0.69, 1.24)	—	—
Level 2 (BP)
Intercept	0.13 ^* (0.06, 0.32)**	0.13 ^* (0.05, 0.31)**	0.13 ^* (0.06, 0.31)**	0.14 ^* (0.06, 0.33)**	0.14 ^* (0.06, 0.32)**
Mean self-efficacy to resist AOD use	0.42 ^ (0.25, 0.69)**	0.41 ^ (0.24, 0.68)**	0.42 ^ (0.25, 0.69)**	0.44 ^ (0.27, 0.71)**	0.43 ^ (0.26, 0.70)**
Minority stress moderator	0.90 (0.60, 1.36)	0.84 (0.55, 1.29)	0.90 (0.60, 1.36)	0.97 (0.93, 1.02)	0.97 (0.92, 1.02)
Baseline alcohol use severity	1.48 ^* (1.27, 1.72)**	1.48 ^* (1.27, 1.73)**	1.48 ^* (1.27, 1.72)**	1.45 ^* (1.26, 1.67)**	1.46 ^* (1.26, 1.69)**
Number of days completed	1.26 ^c (1.00, 1.58)	1.27 ^c (1.00, 1.61)	1.25 (0.99, 1.57)	1.26 ^c (1.01, 1.58)	1.26 ^c (1.00, 1.58)
Cross-level interaction
WP daily self-efficacy to resist AOD use × minority stress moderator	—	1.23 (0.92, 1.62)	—	—	1.01 (0.98, 1.05)
Random effect variances
Level 2 (BP)
Intercept^b	51.11 ^ (5.45, 479.62)**	51.21 ^ (5.41, 484.44)**	50.70 ^ (5.44, 472.48)**	38.24 ^ (4.88, 299.77)**	41.89 ^ (4.99, 351.08)**
Daily self-efficacy to resist AOD use slopes	1.21 (0.85, 1.72)	1.11 (0.8 6, 1.43)	1.22 (0.86, 1.73)	1.19 (0.89, 1.60)	1.20 (0.88, 1.65)

Open in a new tab

^a0 = weekday (Monday through Wednesday), 1 = weekend day (Thursday through Sunday).

^bThis parameter estimate represents between-person variance regarding average AOD use (fixed intercept estimate).

^cEstimate reflects p <.10, though the 95% CI does not include the null value (i.e., zero effect), indicating statistical significance.

^* p <.05,

^** p <.01,

^*** p <.001.

Multilevel Models Predicting Same-Day Drug Use

Daily fluctuations in self-efficacy to resist AOD use were not associated with lower odds of same-day drug use (see Supplemental Table 2). Between-person effects showed that people with higher average self-efficacy to resist AOD use had an 82% decrease in the odds of using drugs on any given day. On days when participants experienced more sexual minority stressors than were typical for them, they had a 51% increase in the odds of using drugs on the same day (see Table 4). Between-person effects showed that people who experienced higher levels of between-person average sexual minority stressors over 2 weeks had a 53% decrease in the odds of using drugs on a given day; between-person discrimination related to LGBTQ+ status at baseline was not associated with drug use on any given day. Minority stressor variables did not moderate the link between daily self-efficacy to resist AOD use and odds of same-day drug use.

Table 4.

Model Statistics for Multilevel Models Predicting Same-Day Drug Use from Mean and Daily Self-Efficacy to Resist AOD Use and Minority Stressors

Predictor	Moderator: between-person mean sexual minority stressors		Moderator: within-person daily sexual minority stressors	Moderator: between-person discrimination related to LGBTQ+ status at baseline
	Model 4a	Model 5a	Model 5b	Model 4c	Model 5c
	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
Level 1 (WP)
Daily self-efficacy to resist AOD use	0.76 (0.45, 1.27)	0.79 (0.47, 1.33)	0.77 (0.46, 1.29)	0.79 (0.49, 1.28)	0.68 ^c (0.46, 0.99)
Minority stress moderator	1.51 ^ (1.12, 2.03)**	1.57 ^ (1.13, 2.06)**	1.48 ^* (1.09, 2.00)	—	—
Weekday	1.60 (0.81, 3.18)	1.61 (0.81, 3.20)	1.60 (0.80, 3.16)	1.51 (0.77, 2.97)	1.52 (0.77, 2.98)
Day	1.01 (0.92, 1.10)	1.01 (0.92, 1.10)	1.01 (0.92, 1.10)	0.99 (0.91, 1.09)	0.99 (0.91, 1.08)
Within-level interaction
WP daily self-efficacy to resist AOD use x minority stress moderator	—	—	0.89 (0.57, 1.37)	—	—
Level 2 (BP)
Intercept	0.03 ^* (0.01, 0.08)**	0.02 ^* (0.01, 0.08)**	0.02 ^* (0.01, 0.08)**	0.03 ^* (0.01, 0.09)**	0.03 ^* (0.01, 0.10)**
Mean self-efficacy to resist AOD use	0.17 ^* (0.09, 0.29)**	0.17 ^* (0.09, 0.29)**	0.17 ^* (0.10, 0.29)**	0.18 ^* (0.10, 0.32)**	0.18 ^* (0.10, 0.32)**
Minority stress moderator	0.47 ^* (0.25, 0.87)	0.47 ^* (0.25, 0.86)	0.46 ^* (0.25, 0.87)	0.99 (0.94, 1.05)	0.99 (0.94, 1.04)
Baseline drug use severity	1.28 ^* (1.16, 1.41)**	1.27 ^* (1.16, 1.40)**	1.28 ^* (1.16, 1.41)**	1.27 ^* (1.15, 1.40)**	1.26 ^* (1.14, 1.39)**
Number of days completed	0.89 (0.72, 1.09)	0.89 (0.72, 1.09)	0.89 (0.72, 1.10)	0.90 (0.73, 1.13)	0.92 (0.74, 1.14)
Cross-level interaction
WP daily self-efficacy to resist AOD use × minority stress moderator	—	1.25 (0.76, 2.04)	—	—	0.98 (0.94, 1.02)
Random effect variances
Level 2 (BP)
Intercept^b	9.74 ^* (1.51, 63.05)	9.73 ^* (1.51, 62.93)	9.99 ^* (1.49, 66.82)	17.13 ^* (1.85, 158.38)	*15.61 (1.83, 132.69)**
Daily self-efficacy to resist AOD use slopes	1.06 (0.77, 1.45)	1.04 (0.84, 1.28)	1.07 (0.74, 1.55)	1.09 (0.77, 1.53)	1.10 (0.96, 1.04)

Open in a new tab

^a0 = weekday (Monday through Wednesday), 1 = weekend day (Thursday through Sunday).

^bThis parameter estimate represents between-person variance regarding average AOD use (fixed intercept estimate).

^cEstimate reflects p <.10, though the 95% CI does not include the null value (i.e., zero effect), indicating statistical significance.

^* p <.05,

^** p <.01,

^*** p <.001.

Discussion

Advancing our understanding of the prospective effects of self-efficacy to resist AOD use, we found that situational variability in self-efficacy to resist AOD use—beyond average over 2 weeks—was linked to a lower risk of unhealthy drinking the same day among trauma-exposed SMW and TGD people. Elevated self-efficacy to resist AOD use was associated with decreased odds of AOD use on any given day. Daily and cumulative exposure to sexual minority stressors over a recent 2-week period, but not prior discrimination exposure, were associated with daily AOD use but not unhealthy drinking risk. Higher-than-usual self-efficacy to resist AOD use on 1 day was associated with reduced engagement in unhealthy drinking the same day only among those experiencing lower (vs. greater) sexual minority stressors over 2 weeks, on average. These findings suggest that self-efficacy to resist AOD use may reduce daily AOD use and unhealthy drinking risk among trauma-exposed SMW and TGD people, especially in social climates characterized by lower exposure to sexual minority stressors.

Aligned with prior research [10, 33–35], our findings highlight daily fluctuations in AOD use and unhealthy drinking risk within a community sample of trauma-exposed SMW and TGD people. Our study also advances social-learning theory [15] and prior research with sexual and gender minority people [27–29] by emphasizing the impact of situational fluctuations in self-efficacy to resist AOD use on alcohol use and unhealthy drinking, as well as the influence of individual differences in self-efficacy to resist AOD use on drug use among trauma-exposed SMW and TGD people. The consistency between our results and those observed in the general population across addiction severity levels [16, 19, 30] indicates the importance of enhancing self-efficacy in trauma-exposed SMW and TGD people.

Our findings are consistent with prior research that emphasizes the proximal influence of sexual minority stressors on daily AOD use and unhealthy drinking risk among SMW and TGD people [10, 33–35]. We found that daily AOD use and unhealthy drinking risk on any given day could be attributed to stigma-related experiences spanning 14 days and the very same day, highlighting the importance of assessing these variables at the day level. These findings suggest that daily AOD use and risk of drinking heavily may be shaped by chronic and acute exposure to minority stressors, possibly as a way to self-medicate with distress related to these experiences [22, 24, 36]. Among SMW and TGD people with a history of adversity, such as sexual abuse, immediate (i.e., daily) and cumulative (i.e., average over 2 weeks) exposure to minority stressors might trigger dysregulated physiological stress responses or cognitive appraisals. These appraisals could involve overestimating the likelihood of negative outcomes and underestimating one’s capacity to cope with such stressors, thereby increasing the risk of AOD use and drinking at unhealthy levels to cope [10, 62].

The influence of self-efficacy on behavior initiation or maintenance may depend on individuals’ efforts to navigate environmental stressors, particularly those amplifying AOD use risk or high-risk situations [11, 12]. As theorized in the social-learning [15] and self-medication literature [36], and consistent with previous research indicating that confidence in resisting AOD use may minimally impact actual AOD use in high-risk situations [12, 31], our study offers further support for the dynamic interplay between self-regulatory mechanisms and external stressors in determining unhealthy drinking risk among trauma-exposed SMW and TGD people. Higher-than-usual self-efficacy to resist AOD use was associated with reduced unhealthy drinking risk the same day only among those experiencing fewer versus greater recent and cumulative sexual minority stressors (i.e., average levels of minority stressors over the 2-week daily diary period). Prior research indicates that self-efficacy to resist cannabis use during periods of heightened emotional distress may deter coping-oriented motives and self-medication behavior among trauma-exposed individuals in the general population [18]. Nevertheless, our findings underscore that although self-efficacy plays a role in same-day unhealthy drinking risk among trauma-exposed SMW and TGD people, its effects as a cognitive coping strategy to reduce AOD use may diminish in the face of chronic stigma.

Clinical Implications

Cognitive-behavioral treatment (CBT) interventions aimed at strengthening self-efficacy to resist AOD use could be beneficial for trauma-exposed SMW and TGD. Prior findings also suggest that strengthening self-efficacy early in treatment may prevent return to use among those using substances at unhealthy levels [31]. Fostering self-efficacy to resist AOD use may involve enhancing coping and stress-management skills (e.g., problem-solving), promoting active participation in goal setting and treatment planning, and cultivating mindful awareness of past successes and present strengths [62, 63]. Training SMW and TGD people to self-monitor daily fluctuations in self-efficacy to resist AOD use and to adjust self-efficacy beliefs as needed through cognitive restructuring techniques could be beneficial [11].

Improving structural and social climates, for example, by increasing anti-discrimination protections and challenging heterosexist and cissexist norms, is needed given the prevalence of minority stressors reported by trauma-exposed SMW and TGD participants. That is, 87.7% reported experiencing at least 1 day marked by such stressors over the course of 14 days. Supporting trauma-exposed SMW and TGD people to locate the source of their distress in the context of oppression could help to reduce AOD use and unhealthy drinking risk [51, 62]. Personalized interventions aiming to improve self-efficacy during high-risk situations should be tailored for trauma-exposed SMW and TGD people living in hostile social climates given that self-efficacy to resist AOD use does not influence same-day unhealthy drinking risk in the presence of elevated exposure to stigma across 14 days. For example, digital health interventions may prompt trauma-exposed SMW and TGD people who face chronic minority stressors to seek support, modify outcome expectancies related to drinking heavily, and utilize distress tolerance skills as potential ways to bolster the influence of self-efficacy on drinking [11, 62].

Limitations and Future Directions

Findings from this study should be interpreted in the context of the following limitations. Generalizability to trauma-exposed SMW and TGD people reporting high levels of unhealthy AOD use or meeting criteria for substance use disorders is unclear. Applicability to nontrauma-exposed SMW and TGD people, as well as sexual minority men and heterosexual TGD people is also unknown. Despite nationwide recruitment, some gender identities (e.g., transgender women) and racial demographics (e.g., Black and Latine) were underrepresented or not represented (e.g., Native/Indigenous). This study did not account for structural stigma, policy differences, or geographic factors on daily AOD use and unhealthy drinking risk. The context of minority stressors, including where occurred and by whom, was not examined. Future studies should explore if minority stressors occur in contexts where AOD use is available (e.g., social events) and if minority stressors precede daily AOD use and unhealthy drinking risk. Additionally, brief daily measures, including a single item to assess daily self-efficacy to resist AOD use, were used.

Further, this study relied on one’s sex assigned at birth to categorize unhealthy drinking risk. Using sex assigned at birth as a proxy of current sex-based physiological differences in alcohol responses can lead to imprecision in predicting one’s response to alcohol [64]. Future studies should examine alcohol use and unhealthy drinking levels in relation to gender-specific factors (e.g., gender socialization). Studies might also consider assessing daily alcohol use outcomes that are not contingent on sex assigned at birth, current sex-based physiological characteristics, or gender identity, such as subjective assessments of intoxication, drinking in risky contexts, and negative drinking consequences, in trauma-exposed SMW and TGD people.

In this study, nonsignificant random fluctuations were observed in the association between within-person self-efficacy to resist AOD use and same-day AOD use and unhealthy drinking risk. Future studies should explore additional protective factors, such as cognitive reappraisal or acceptance of AOD use cravings, that may reduce daily AOD use and unhealthy drinking risk among trauma-exposed SMW and TGD people. Using ecological momentary assessment with multiple daily surveys could better capture self-efficacy to resist AOD use as an immediate antecedent of daily AOD use and unhealthy drinking risk. While we examined the influence of prior, daily, and recent cumulative exposure to sexual minority stressors, future studies should consider other high-risk situations, such as trauma cues, that may impact the association between self-efficacy to resist AOD use and daily AOD use and unhealthy drinking risk. Future research also should examine the impact of minority stressors on daily self-efficacy.

While the timeframes for daily measures remained consistent regardless of local time (i.e., 6 pm the prior day to 6 pm on the survey day), future research is needed to examine whether time zone variability could have affected patterns of results, completion rates, and timing of completion.

Conclusions

This study deepens our understanding of self-efficacy to resist AOD use as a potentially effective coping strategy for reducing daily AOD use and unhealthy drinking risk among trauma-exposed SMW and TGD people. Daily AOD use and unhealthy drinking risk may be shaped by recent exposure to acute and chronic sexual minority stressors. Self-efficacy to resist AOD use does not reduce same-day unhealthy drinking risk for those experiencing recent chronic sexual minority stressors. Policy efforts are needed to reduce stigma and exposure to such stressors. CBT interventions aiming to strengthen self-efficacy as a mechanism of behavior change to reduce unhealthy drinking risk should consider the impact of recent cumulative exposure to stigma-salient high-risk situations among trauma-exposed SMW and TGD people.

Supplementary Material

Supplementary material is available at Annals of Behavioral Medicine online.

kaae065_suppl_Supplementary_Tables_1-2

kaae065_suppl_supplementary_tables_1-2.docx^{(48.8KB, docx)}

Acknowledgments

Jillian Scheer and Ethan Mereish acknowledge support by the National Institute on Alcohol Abuse and Alcoholism (NIAAA; K01AA028239, PI: Scheer; R01AA029989, PI: Mereish). Cory Cascalheira acknowledges support as a National Institutes of Health RISE Fellow (R25GM061222). Emily Helminen acknowledges support by the National Institute on Drug Abuse (T32DA016184). Fatima Dobani acknowledges support by the NIAAA (F31AA031428). Skyler Jackson acknowledges support by the National Institute of Mental Health (K01MH12231601). Abigail Batchelder acknowledges support by the National Institute on Drug Abuse (K23DA043418; R01DA057298). We would like to express our gratitude to the study participants.

Contributor Information

Jillian R Scheer, Department of Psychology, University of Rhode Island, Kingston, RI, USA.

Ethan H Mereish, Department of Psychology, Lavender Lab, University of Maryland, College Park, MD, USA.

Amanda K Gilmore, Department of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA; National Center for Sexual Violence Prevention, Mark Chaffin Center for Healthy Development, School of Public Health, Georgia State University, Atlanta, GA, USA.

Cory J Cascalheira, PTSD Outpatient Clinic, VA Puget Sound Health Care System, Seattle, WA, USA.

Emily C Helminen, Center for Alcohol and Addiction Studies, Brown University School of Public Health, Providence, RI, USA.

Fatima Dobani, Department of Psychology, Syracuse University, Syracuse, NY, USA.

Kriti Behari, Department of Psychology, Syracuse University, Syracuse, NY, USA.

Sophia Pirog, Department of Psychology, Syracuse University, Syracuse, NY, USA.

Skyler D Jackson, Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, USA.

Tami P Sullivan, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.

Abigail W Batchelder, Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, MA, USA; The Fenway Institute, Fenway Health, Boston, MA, USA.

Declarations of Interest

The authors declare no conflicts of interest.

Author Contributions

Jillian R. Scheer, PhD; jillian.scheer@uri.edu; ORCID ID: 0000-0002-7311-5904; conceptualization, data curation, funding acquisition, investigation, methodology, project administration, resources, software, supervision, writing-original draft, writing-review & editing, Ethan H. Mereish, PhD; emereish@umd.edu; ORCID ID: 0000-0003-4655-0836; conceptualization, writing-original draft, writing-review & editing, Amanda K. Gilmore, PhD; agilmore12@gsu.edu; ORCID ID: 0000-0002-5376-9206; writing-review & editing, Cory J. Cascalheira, BA, LSAA; cory.cascalheira@va.gov; ORCID ID: 0000-0001-5780-3101; conceptualization, data curation, project administration, writing-review & editing, Emily C. Helminen, PhD; ehelmine@syr.edu; ORCID ID: 0000-0002-3884-9603; conceptualization, project administration, writing-review & editing, Fatima Dobani, MS; fdobani@syr.edu; writing-review & editing, Kriti Behari MA; kbehari@syr.edu; ORCID ID: 0000-0002-5751-266X; writing-review & editing, Sophia Pirog, MA; sapirog@syr.edu; ORCID ID: 0000-0003-3422-4304; writing-review & editing, Skyler D. Jackson, PhD; skyler.jackson@yale.edu; ORCID ID: 0000-0002-0353-7992; conceptualization, funding acquisition, writing-original draft, writing-review & editing, Tami P. Sullivan, PhD; tami.sullivan@yale.edu; ORCID ID: 0000-0002-4981-1168; conceptualization, funding acquisition, writing-original draft, writing-review & editing, and Abigail W. Batchelder, PhD, MPH; abigail.batchelder@bmc.org; ORCID ID: 0000-0001-7225-1675; conceptualization, funding acquisition, writing-original draft, writing-review & editing

Funding

This study was funded by the Yale University Women’s Faculty Forum Seed Grant and Yale University’s Fund for Lesbian and Gay Studies (PI: Scheer). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funders, including the National Institutes of Health.

Open Science Disclosure Statements

Study registration. This study was not formally registered.

Analytic plan pre-registration. The analysis plan was not formally pre-registered.

Data Availability

De-identified data from this study are not available in a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author.

Analytic code availability. Analytic code used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

Materials availability. Materials used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

Transparency Statements

Study registration. This study was not formally registered.

Analytic plan pre-registration. The analysis plan was not formally pre-registered.

Data availability. De-identified data from this study are not available in an a public archive. De-identified data from this study will be made available (as allowable according to institutional IRB standards) by emailing the corresponding author.

Analytic code availability. Analytic code used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

Materials availability. Materials used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

References

1. Hughes TL, Veldhuis CB, Drabble LA, Wilsnack SC.. Research on alcohol and other drug (AOD) use among sexual minority women: a global scoping review. PLoS One. 2020; 15(3): e0229869. doi: https://doi.org/ 10.1371/journal.pone.0229869 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Hughto JMW, Quinn EK, Dunbar MS, Rose AJ, Shireman TI, Jasuja GK.. Prevalence and co-occurrence of alcohol, nicotine, and other substance use disorder diagnoses among US transgender and cisgender adults. JAMA Netw Open 2021; 4(2): e2036512. doi: https://doi.org/ 10.1001/jamanetworkopen.2020.36512 [DOI] [PMC free article] [PubMed] [Google Scholar]
3. McCabe SE, Engstrom CW, Kcomt L, Evans-Polce R, West BT.. Trends in binge drinking, marijuana use, illicit drug use, and polysubstance use by sexual identity in the United States (2006–2017). Subst Abuse. 2022; 43(1): 194–203. doi: https://doi.org/ 10.1080/08897077.2021.1913696 [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Bochicchio L, Porsch L, Zollweg S, Matthews AK, Hughes TL.. Health outcomes of sexual minority women who have experienced adverse childhood experiences: a scoping review. Trauma Violence Abuse. 2023; 25(1): 764–794. doi: https://doi.org/ 10.1177/15248380231162973 [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Scheer JR, Helminen EC, Cascalheira CJ, et al. Probable PTSD, PTSD symptom severity, and comorbid PTSD and hazardous drinking among sexual minority women compared to heterosexual women: a meta-analysis. Clin Psychol Rev. 2023; 102: 102283. doi: https://doi.org/ 10.1016/j.cpr.2023.102283 [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Gilbert PA, Pass LE, Keuroghlian AS, Greenfield TK, Reisner SL.. Alcohol research with transgender populations: a systematic review and recommendations to strengthen future studies. Drug Alcohol Depend. 2018; 186: 138–146. doi: https://doi.org/ 10.1016/j.drugalcdep.2018.01.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Lynch KE, Livingston NA, Gatsby E, Shipherd JC, DuVall SL, Williams EC.. Alcohol-attributable deaths and years of potential life lost due to alcohol among veterans: overall and between persons with minoritized and non-minoritized sexual orientations. Drug Alcohol Depend. 2022; 237: 109534. doi: https://doi.org/ 10.1016/j.drugalcdep.2022.109534 [DOI] [PubMed] [Google Scholar]
8. Malhotra A, Kort S, Lauther T, Mann N, Skopicki HA, Parikh PB.. Prevalence and predictors of cardiovascular disease and risk factors in transgender persons in the United States. Crit Pathw Cardiol. 2022; 21(1): 42–46. doi: https://doi.org/ 10.1097/HPC.0000000000000271 [DOI] [PubMed] [Google Scholar]
9. Boyd J, Sexton O, Angus C, Meier P, Purshouse RC, Holmes J.. Causal mechanisms proposed for the alcohol harm paradox: a systematic review. Addiction. 2022; 117(1): 33–56. doi: https://doi.org/ 10.1111/add.15567 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Mereish EH, Treloar Padovano H, Parlette B, Miranda R.. Momentary associations among minority stress, craving, affect, and nicotine use among sexual minority youth. J Clin Child Adolesc Psychol. 2022; 51(6): 877–891. doi: https://doi.org/ 10.1080/15374416.2022.2093208 [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Roos CR, Witkiewitz K.. A contextual model of self-regulation change mechanisms among individuals with addictive disorders. Clin Psychol Rev. 2017; 57: 117–128. doi: https://doi.org/ 10.1016/j.cpr.2017.08.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Shiffman S. Ecological momentary assessment in studies of substance use. Psychol Assess. 2009; 21(4): 486–497. doi: https://doi.org/ 10.1037/a0017074 [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Sullivan TP, Armeli S, Tennen H, Weiss NH, Hansen NB.. Fluctuations in daily PTSD symptoms are related to proximal alcohol use: a micro-longitudinal study of women victims of intimate partner violence. Am J Drug Alcohol Abuse. 2020; 46(1): 98–108. doi: https://doi.org/ 10.1080/00952990.2019.1624765 [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Babor TF, McRee BG, Kassebaum PA, Grimaldi PL, Ahmed K, Bray J.. Screening, Brief Intervention, and Referral to Treatment (SBIRT). Subst Abuse. 2007; 28(3): 7–30. doi: https://doi.org/ 10.1300/J465v28n03_03 [DOI] [PubMed] [Google Scholar]
15. Bandura A. Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall; 1977. [Google Scholar]
16. Kuerbis A, Armeli S, Muench F, Morgenstern J.. Motivation and self-efficacy in the context of moderated drinking: global self-report and ecological momentary assessment. Psychol Addict Behav. 2013; 27(4): 934–943. doi: https://doi.org/ 10.1037/a0031194 [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Kruger ES, Serier KN, Pfund RA, McKay JR, Witkiewitz K.. Integrative data analysis of self-efficacy in 4 clinical trials for alcohol use disorder. Alcohol Clin Exp Res. 2021; 45(11): 2347–2356. doi: https://doi.org/ 10.1111/acer.14713 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Hurd JA, Powers TG, Benight CC.. Not how often but when: self-efficacy to control cannabis use and PTSS. Psychol Trauma. 2023; 15(6): 979–987. doi: https://doi.org/ 10.1037/tra0001347 [DOI] [PubMed] [Google Scholar]
19. Possemato K, Maisto SA, Wade M, et al. Ecological momentary assessment of PTSD symptoms and alcohol use in combat veterans. Psychol Addict Behav. 2015; 29(4): 894–905. doi: https://doi.org/ 10.1037/adb0000129 [DOI] [PubMed] [Google Scholar]
20. Mahoney CT, Cestodio V, Porter KJ, Marchant KM.. The moderating roles of emotion regulation and coping self-efficacy on the association between PTSD symptom severity and drug use among female sexual assault survivors. Psychol Trauma. 2023; 15(6): 1027–1031. doi: https://doi.org/ 10.1037/tra0001194 [DOI] [PubMed] [Google Scholar]
21. Brooks V. Minority Stress and Lesbian Women. Lexington, MA: Lexington Books; 1981. [Google Scholar]
22. Hatzenbuehler ML. How does sexual minority stigma “get under the skin?” A psychological mediation framework. Psychol Bull. 2009; 135(5): 707–730. doi: https://doi.org/ 10.1037/a0016441 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Krueger EA, Fish JN, Upchurch DM.. Sexual orientation disparities in substance use: investigating social stress mechanisms in a national sample. Am J Prev Med. 2020; 58(1): 59–68. doi: https://doi.org/ 10.1016/j.amepre.2019.08.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003; 129(5): 674–697. doi: https://doi.org/ 10.1037/0033-2909.129.5.674 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Tucker JS, Ellickson PL, Klein DJ.. Understanding differences in substance use among bisexual and heterosexual young women. Women Health Issues. 2008; 18(5): 387–398. doi: https://doi.org/ 10.1016/j.whi.2008.04.004 [DOI] [PubMed] [Google Scholar]
26. Tyler KA, Ray CM.. Risk and protective factors for substance use among youth experiencing homelessness. Child Youth Serv Rev. 2019; 107: 104548. doi: https://doi.org/ 10.1016/j.childyouth.2019.104548 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Mereish EH. Substance use and misuse among sexual and gender minority youth. Curr Opin Psychol. 2019; 30: 123–127. doi: https://doi.org/ 10.1016/j.copsyc.2019.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Starks TJ, Millar BM, Lassiter JM, Parsons JT.. Preintervention profiles of information, motivational, and behavioral self-efficacy for methamphetamine use and HIV medication adherence among gay and bisexual men. AIDS Patient Care STDS. 2017; 31(2): 78–86. doi: https://doi.org/ 10.1089/apc.2016.0196 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Scheer JR, Edwards KM, Sheinfil AZ, Dalton MR, Firkey MK, Watson RJ.. Interpersonal victimization, substance use, and mental health among sexual and gender minority youth: the role of self-concept factors. J Interpers Violence. 2021; 37(19-20): NP18104–NP18129. doi: https://doi.org/ 10.1177/08862605211035868 [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Serre F, Fatseas M, Swendsen J, Auriacombe M.. Ecological momentary assessment in the investigation of craving and substance use in daily life: a systematic review. Drug Alcohol Depend. 2015; 148: 1–20. doi: https://doi.org/ 10.1016/j.drugalcdep.2014.12.024 [DOI] [PubMed] [Google Scholar]
31. Tate SR, Wu J, McQuaid JR, et al. Comorbidity of substance dependence and depression: role of life stress and self-efficacy in sustaining abstinence. Psychol Addict Behav. 2008; 22(1): 47–57. doi: https://doi.org/ 10.1037/0893-164X.22.1.47 [DOI] [PubMed] [Google Scholar]
32. Batchelder AW, Foley JD, Stanton AM, Gorman KR, Morris JC, Scheer JR.. Facets of gender expression and discrimination in relation to alcohol and other drug use severity among sexual minority women and gender diverse individuals assigned female at birth. LGBT Health. 2022; 10(3): 191–201. doi: https://doi.org/ 10.1089/lgbt.2022.0138 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Dyar C, Sarno EL, Newcomb ME, Whitton SW.. Longitudinal associations between minority stress, internalizing symptoms, and substance use among sexual and gender minority individuals assigned female at birth. J Consult Clin Psychol. 2020; 88(5): 389–401. doi: https://doi.org/ 10.1037/ccp0000487 [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Lewis RJ, Romano KA, Ehlke SJ, et al. Minority stress and alcohol use in sexual minority women’s daily lives. Exp Clin Psychopharmacol. 2021; 29(5):501–510. doi: https://doi.org/ 10.1037/pha0000484 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Livingston NA, Flentje A, Heck NC, Szalda-Petree A, Cochran BN.. Ecological momentary assessment of daily discrimination experiences and nicotine, alcohol, and drug use among sexual and gender minority individuals. J Consult Clin Psychol. 2017; 85(12): 1131–1143. doi: https://doi.org/ 10.1037/ccp0000252 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Khantzian E. The self medication hypothesis of addictive disorders: focus on heroin and cocaine dependence. Am J Psychiatry. 1985; 142(11):1259–1264. doi: https://doi.org/ 10.1176/ajp.142.11.1259 [DOI] [PubMed] [Google Scholar]
37. Puckett JA, Dyar C, Maroney MR, Mustanski B, Newcomb ME.. Daily experiences of minority stress and mental health in transgender and gender-diverse individuals. J Psychopathol Clin Sci. 2023; 132(3): 340–350. doi: https://doi.org/ 10.1037/abn0000814.supp [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Burton CL, Clark KA, Pachankis JE.. Risk from within: intraminority gay community stress and sexual risk-taking among sexual minority men. Ann Behav Med. 2020; 54(9): 703–712. doi: https://doi.org/ 10.1093/abm/kaaa014 [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Foa EB, McLean CP, Zang Y, et al. Psychometric properties of the Posttraumatic Diagnostic Scale for DSM-5 (PDS-5). Psychol Assess. 2016; 28(10): 1166–1171. doi: https://doi.org/ 10.1037/pas0000258 [DOI] [PubMed] [Google Scholar]
40. Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM.. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med. 2005; 61(7): 1576–1596. doi: https://doi.org/ 10.1016/j.socscimed.2005.03.006 [DOI] [PubMed] [Google Scholar]
41. Williams DR, Yan Y, Jackson JS, Anderson NB.. Racial differences in physical and mental health: socio-economic status, stress and discrimination. J Health Psychol. 1997; 2(3): 335–351. doi: https://doi.org/ 10.1177/135910539700200305 [DOI] [PubMed] [Google Scholar]
42. Meyer IH, Russell ST, Hammack PL, Frost DM, Wilson BDM.. Minority stress, distress, and suicide attempts in three cohorts of sexual minority adults: a U.S. probability sample. PLoS One. 2021; 16(3): e0246827. doi: https://doi.org/ 10.1371/journal.pone.0246827 [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Papp LM, Kouros CD.. Effect of COVID-19 disruptions on young adults’ affect and substance use in daily life. Psychol Addict Behav. 2021; 35(4): 391–401. doi: https://doi.org/ 10.1037/adb0000748 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG.. AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care. Geneva, Switzerland: Department of Mental Health and Substance Dependence, World Health Organization; 2001. [Google Scholar]
45. Belloir JA, Kidd JD, Dworkin JD, Bockting WO.. Examining the role of problematic drug use in the relationship between discrimination and sleep disturbance in transgender and nonbinary individuals. Addict Behav. 2022; 135: 107459. doi: https://doi.org/ 10.1016/j.addbeh.2022.107459 [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Helminen E, Scheer JR, Jackson SD, et al. PTSD symptoms and hazardous drinking indicators among trauma-exposed sexual minority women during heightened societal stress. Behav Med. 2021; 49(2): 183–194. doi: https://doi.org/ 10.1080/08964289.2021.2006132 [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Berman AH, Bergman H, Palmstierna T, Schlyter F.. Evaluation of the Drug Use Disorders Identification Test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample. Eur Addict Res. 2005; 11(1): 22–31. doi: https://doi.org/ 10.1159/000081413 [DOI] [PubMed] [Google Scholar]
48. Heron KE, Braitman AL, Lewis RJ, Shappie AT, Hitson PT.. Measuring sexual minority stressors in lesbian women’s daily lives: initial scale development. Psychol Sex Orientat Gender Divers. 2018; 5(3): 387–395. doi: https://doi.org/ 10.1037/sgd0000287 [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Nezlek JB. A practical guide to understanding reliability in studies of within-person variability. J Res Personal. 2017; 69: 149–155. doi: https://doi.org/ 10.1016/j.jrp.2016.06.020 [DOI] [Google Scholar]
50. Flentje A, Barger BT, Capriotti MR, et al. Screening gender minority people for harmful alcohol use. PLoS One. 2020; 15(4):e0231022. doi: https://doi.org/ 10.1371/journal.pone.0231022 [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Liautaud MM, Rendina HJ, Berke DS.. Examining the impact of daily discrimination on alcohol use among racially diverse, trauma-exposed sexually minoritized adults: a pilot study. Psychol Trauma Theory Res Pract Policy. 2023; 16(6):913–921. doi: https://doi.org/ 10.1037/tra0001577.supp [DOI] [PubMed] [Google Scholar]
52. National Institutes on Alcohol Abuse and Alcoholism. Rethinking Drinking. 2021. Accessed August 1, 2024. https://www.niaaa.nih.gov/sites/default/files/publications/NIAAA_RethinkingDrinking.pdf [Google Scholar]
53. Cain MK, Zhang Z, Yuan KH.. Univariate and multivariate skewness and kurtosis for measuring nonnormality: prevalence, influence and estimation. Behav Res Methods. 2017; 49(5): 1716–1735. doi: https://doi.org/ 10.3758/s13428-016-0814-1 [DOI] [PubMed] [Google Scholar]
54. Kiekens WJ, Baams L, Veenstra R.. Differences by sex and gender in the association between minority stress and alcohol use among sexual and gender minority youth: a daily diary study. Soc Sci Med. 2022; 294: 114679. doi: https://doi.org/ 10.1016/j.socscimed.2021.114679 [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Asparouhov T, Muthén B.. Bayesian analysis of latent variable models using Mplus. Mplus Technical Reports. 2010. Accessed August 1, 2024. https://www.statmodel.com/download/BayesAdvantages18.pdf [Google Scholar]
56. Kim SY, Huh D, Zhou Z, Mun EY.. A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome. Int J Behav Dev. 2020; 44(5): 447–457. doi: https://doi.org/ 10.1177/0165025419894730 [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Bolger N, Laurenceau JP.. Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. New York, NY: Guilford Press; 2013. [Google Scholar]
58. Mathieu JE, Aguinis H, Culpepper SA, Chen G.. Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. J Appl Psychol. 2012; 97(5): 951–966. doi: https://doi.org/ 10.1037/a0028380 [DOI] [PubMed] [Google Scholar]
59. Hedeker D. Methods for multilevel ordinal data in prevention research. Prev Sci. 2015; 16(7): 997–1006. doi: https://doi.org/ 10.1007/s11121-014-0495-x [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Hayes AF, Matthes J.. Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behav Res Methods. 2009; 41(3): 924–936. [DOI] [PubMed] [Google Scholar]
61. Scheer JR, Cascalheira CJ, Helminen EC, et al. “I know myself again, which makes me motivated for life”: feasibility and acceptability of using experience sampling methods with trauma-exposed sexual minority women. J Interpers Violence. 2023; 38(13-14): 8692–8720. doi: https://doi.org/ 10.1177/08862605231153888 [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Scheer JR, Clark KA, McConocha E, Wang K, Pachankis JE.. Toward cognitive-behavioral therapy for sexual minority women: voices from stakeholders and community members. Cogn Behav Pract. 2022; 30(3):471–494. doi: https://doi.org/ 10.1016/j.cbpra.2022.02.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
63. Kadden RM, Litt MD.. The role of self-efficacy in the treatment of substance use disorders. Addict Behav. 2011; 36(12): 1120–1126. doi: https://doi.org/ 10.1016/j.addbeh.2011.07.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Maskell PD, Sang KJC, Heymsfield SB, Shapses S, Dekorompay A.. Forensic alcohol calculations in transgender individuals undergoing gender-affirming hormonal treatment. Toxicology. 2022; 67(4):1624–1631. doi: https://doi.org/ 10.1111/1556-4029.15052 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

kaae065_suppl_Supplementary_Tables_1-2

kaae065_suppl_supplementary_tables_1-2.docx^{(48.8KB, docx)}

Data Availability Statement

Analytic code availability. Analytic code used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

Materials availability. Materials used to conduct the analyses presented in this study are not available in a public archive. They may be available by emailing the corresponding author.

[CIT0001] 1. Hughes TL, Veldhuis CB, Drabble LA, Wilsnack SC.. Research on alcohol and other drug (AOD) use among sexual minority women: a global scoping review. PLoS One. 2020; 15(3): e0229869. doi: https://doi.org/ 10.1371/journal.pone.0229869 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0002] 2. Hughto JMW, Quinn EK, Dunbar MS, Rose AJ, Shireman TI, Jasuja GK.. Prevalence and co-occurrence of alcohol, nicotine, and other substance use disorder diagnoses among US transgender and cisgender adults. JAMA Netw Open 2021; 4(2): e2036512. doi: https://doi.org/ 10.1001/jamanetworkopen.2020.36512 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0003] 3. McCabe SE, Engstrom CW, Kcomt L, Evans-Polce R, West BT.. Trends in binge drinking, marijuana use, illicit drug use, and polysubstance use by sexual identity in the United States (2006–2017). Subst Abuse. 2022; 43(1): 194–203. doi: https://doi.org/ 10.1080/08897077.2021.1913696 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0004] 4. Bochicchio L, Porsch L, Zollweg S, Matthews AK, Hughes TL.. Health outcomes of sexual minority women who have experienced adverse childhood experiences: a scoping review. Trauma Violence Abuse. 2023; 25(1): 764–794. doi: https://doi.org/ 10.1177/15248380231162973 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0005] 5. Scheer JR, Helminen EC, Cascalheira CJ, et al. Probable PTSD, PTSD symptom severity, and comorbid PTSD and hazardous drinking among sexual minority women compared to heterosexual women: a meta-analysis. Clin Psychol Rev. 2023; 102: 102283. doi: https://doi.org/ 10.1016/j.cpr.2023.102283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0006] 6. Gilbert PA, Pass LE, Keuroghlian AS, Greenfield TK, Reisner SL.. Alcohol research with transgender populations: a systematic review and recommendations to strengthen future studies. Drug Alcohol Depend. 2018; 186: 138–146. doi: https://doi.org/ 10.1016/j.drugalcdep.2018.01.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0007] 7. Lynch KE, Livingston NA, Gatsby E, Shipherd JC, DuVall SL, Williams EC.. Alcohol-attributable deaths and years of potential life lost due to alcohol among veterans: overall and between persons with minoritized and non-minoritized sexual orientations. Drug Alcohol Depend. 2022; 237: 109534. doi: https://doi.org/ 10.1016/j.drugalcdep.2022.109534 [DOI] [PubMed] [Google Scholar]

[CIT0008] 8. Malhotra A, Kort S, Lauther T, Mann N, Skopicki HA, Parikh PB.. Prevalence and predictors of cardiovascular disease and risk factors in transgender persons in the United States. Crit Pathw Cardiol. 2022; 21(1): 42–46. doi: https://doi.org/ 10.1097/HPC.0000000000000271 [DOI] [PubMed] [Google Scholar]

[CIT0009] 9. Boyd J, Sexton O, Angus C, Meier P, Purshouse RC, Holmes J.. Causal mechanisms proposed for the alcohol harm paradox: a systematic review. Addiction. 2022; 117(1): 33–56. doi: https://doi.org/ 10.1111/add.15567 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0010] 10. Mereish EH, Treloar Padovano H, Parlette B, Miranda R.. Momentary associations among minority stress, craving, affect, and nicotine use among sexual minority youth. J Clin Child Adolesc Psychol. 2022; 51(6): 877–891. doi: https://doi.org/ 10.1080/15374416.2022.2093208 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0011] 11. Roos CR, Witkiewitz K.. A contextual model of self-regulation change mechanisms among individuals with addictive disorders. Clin Psychol Rev. 2017; 57: 117–128. doi: https://doi.org/ 10.1016/j.cpr.2017.08.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0012] 12. Shiffman S. Ecological momentary assessment in studies of substance use. Psychol Assess. 2009; 21(4): 486–497. doi: https://doi.org/ 10.1037/a0017074 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0013] 13. Sullivan TP, Armeli S, Tennen H, Weiss NH, Hansen NB.. Fluctuations in daily PTSD symptoms are related to proximal alcohol use: a micro-longitudinal study of women victims of intimate partner violence. Am J Drug Alcohol Abuse. 2020; 46(1): 98–108. doi: https://doi.org/ 10.1080/00952990.2019.1624765 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0014] 14. Babor TF, McRee BG, Kassebaum PA, Grimaldi PL, Ahmed K, Bray J.. Screening, Brief Intervention, and Referral to Treatment (SBIRT). Subst Abuse. 2007; 28(3): 7–30. doi: https://doi.org/ 10.1300/J465v28n03_03 [DOI] [PubMed] [Google Scholar]

[CIT0015] 15. Bandura A. Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall; 1977. [Google Scholar]

[CIT0016] 16. Kuerbis A, Armeli S, Muench F, Morgenstern J.. Motivation and self-efficacy in the context of moderated drinking: global self-report and ecological momentary assessment. Psychol Addict Behav. 2013; 27(4): 934–943. doi: https://doi.org/ 10.1037/a0031194 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0017] 17. Kruger ES, Serier KN, Pfund RA, McKay JR, Witkiewitz K.. Integrative data analysis of self-efficacy in 4 clinical trials for alcohol use disorder. Alcohol Clin Exp Res. 2021; 45(11): 2347–2356. doi: https://doi.org/ 10.1111/acer.14713 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0018] 18. Hurd JA, Powers TG, Benight CC.. Not how often but when: self-efficacy to control cannabis use and PTSS. Psychol Trauma. 2023; 15(6): 979–987. doi: https://doi.org/ 10.1037/tra0001347 [DOI] [PubMed] [Google Scholar]

[CIT0019] 19. Possemato K, Maisto SA, Wade M, et al. Ecological momentary assessment of PTSD symptoms and alcohol use in combat veterans. Psychol Addict Behav. 2015; 29(4): 894–905. doi: https://doi.org/ 10.1037/adb0000129 [DOI] [PubMed] [Google Scholar]

[CIT0020] 20. Mahoney CT, Cestodio V, Porter KJ, Marchant KM.. The moderating roles of emotion regulation and coping self-efficacy on the association between PTSD symptom severity and drug use among female sexual assault survivors. Psychol Trauma. 2023; 15(6): 1027–1031. doi: https://doi.org/ 10.1037/tra0001194 [DOI] [PubMed] [Google Scholar]

[CIT0021] 21. Brooks V. Minority Stress and Lesbian Women. Lexington, MA: Lexington Books; 1981. [Google Scholar]

[CIT0022] 22. Hatzenbuehler ML. How does sexual minority stigma “get under the skin?” A psychological mediation framework. Psychol Bull. 2009; 135(5): 707–730. doi: https://doi.org/ 10.1037/a0016441 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0023] 23. Krueger EA, Fish JN, Upchurch DM.. Sexual orientation disparities in substance use: investigating social stress mechanisms in a national sample. Am J Prev Med. 2020; 58(1): 59–68. doi: https://doi.org/ 10.1016/j.amepre.2019.08.034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0024] 24. Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003; 129(5): 674–697. doi: https://doi.org/ 10.1037/0033-2909.129.5.674 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0025] 25. Tucker JS, Ellickson PL, Klein DJ.. Understanding differences in substance use among bisexual and heterosexual young women. Women Health Issues. 2008; 18(5): 387–398. doi: https://doi.org/ 10.1016/j.whi.2008.04.004 [DOI] [PubMed] [Google Scholar]

[CIT0026] 26. Tyler KA, Ray CM.. Risk and protective factors for substance use among youth experiencing homelessness. Child Youth Serv Rev. 2019; 107: 104548. doi: https://doi.org/ 10.1016/j.childyouth.2019.104548 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0027] 27. Mereish EH. Substance use and misuse among sexual and gender minority youth. Curr Opin Psychol. 2019; 30: 123–127. doi: https://doi.org/ 10.1016/j.copsyc.2019.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0028] 28. Starks TJ, Millar BM, Lassiter JM, Parsons JT.. Preintervention profiles of information, motivational, and behavioral self-efficacy for methamphetamine use and HIV medication adherence among gay and bisexual men. AIDS Patient Care STDS. 2017; 31(2): 78–86. doi: https://doi.org/ 10.1089/apc.2016.0196 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0029] 29. Scheer JR, Edwards KM, Sheinfil AZ, Dalton MR, Firkey MK, Watson RJ.. Interpersonal victimization, substance use, and mental health among sexual and gender minority youth: the role of self-concept factors. J Interpers Violence. 2021; 37(19-20): NP18104–NP18129. doi: https://doi.org/ 10.1177/08862605211035868 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0030] 30. Serre F, Fatseas M, Swendsen J, Auriacombe M.. Ecological momentary assessment in the investigation of craving and substance use in daily life: a systematic review. Drug Alcohol Depend. 2015; 148: 1–20. doi: https://doi.org/ 10.1016/j.drugalcdep.2014.12.024 [DOI] [PubMed] [Google Scholar]

[CIT0031] 31. Tate SR, Wu J, McQuaid JR, et al. Comorbidity of substance dependence and depression: role of life stress and self-efficacy in sustaining abstinence. Psychol Addict Behav. 2008; 22(1): 47–57. doi: https://doi.org/ 10.1037/0893-164X.22.1.47 [DOI] [PubMed] [Google Scholar]

[CIT0032] 32. Batchelder AW, Foley JD, Stanton AM, Gorman KR, Morris JC, Scheer JR.. Facets of gender expression and discrimination in relation to alcohol and other drug use severity among sexual minority women and gender diverse individuals assigned female at birth. LGBT Health. 2022; 10(3): 191–201. doi: https://doi.org/ 10.1089/lgbt.2022.0138 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0033] 33. Dyar C, Sarno EL, Newcomb ME, Whitton SW.. Longitudinal associations between minority stress, internalizing symptoms, and substance use among sexual and gender minority individuals assigned female at birth. J Consult Clin Psychol. 2020; 88(5): 389–401. doi: https://doi.org/ 10.1037/ccp0000487 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0034] 34. Lewis RJ, Romano KA, Ehlke SJ, et al. Minority stress and alcohol use in sexual minority women’s daily lives. Exp Clin Psychopharmacol. 2021; 29(5):501–510. doi: https://doi.org/ 10.1037/pha0000484 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0035] 35. Livingston NA, Flentje A, Heck NC, Szalda-Petree A, Cochran BN.. Ecological momentary assessment of daily discrimination experiences and nicotine, alcohol, and drug use among sexual and gender minority individuals. J Consult Clin Psychol. 2017; 85(12): 1131–1143. doi: https://doi.org/ 10.1037/ccp0000252 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0036] 36. Khantzian E. The self medication hypothesis of addictive disorders: focus on heroin and cocaine dependence. Am J Psychiatry. 1985; 142(11):1259–1264. doi: https://doi.org/ 10.1176/ajp.142.11.1259 [DOI] [PubMed] [Google Scholar]

[CIT0037] 37. Puckett JA, Dyar C, Maroney MR, Mustanski B, Newcomb ME.. Daily experiences of minority stress and mental health in transgender and gender-diverse individuals. J Psychopathol Clin Sci. 2023; 132(3): 340–350. doi: https://doi.org/ 10.1037/abn0000814.supp [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0038] 38. Burton CL, Clark KA, Pachankis JE.. Risk from within: intraminority gay community stress and sexual risk-taking among sexual minority men. Ann Behav Med. 2020; 54(9): 703–712. doi: https://doi.org/ 10.1093/abm/kaaa014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0039] 39. Foa EB, McLean CP, Zang Y, et al. Psychometric properties of the Posttraumatic Diagnostic Scale for DSM-5 (PDS-5). Psychol Assess. 2016; 28(10): 1166–1171. doi: https://doi.org/ 10.1037/pas0000258 [DOI] [PubMed] [Google Scholar]

[CIT0040] 40. Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM.. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med. 2005; 61(7): 1576–1596. doi: https://doi.org/ 10.1016/j.socscimed.2005.03.006 [DOI] [PubMed] [Google Scholar]

[CIT0041] 41. Williams DR, Yan Y, Jackson JS, Anderson NB.. Racial differences in physical and mental health: socio-economic status, stress and discrimination. J Health Psychol. 1997; 2(3): 335–351. doi: https://doi.org/ 10.1177/135910539700200305 [DOI] [PubMed] [Google Scholar]

[CIT0042] 42. Meyer IH, Russell ST, Hammack PL, Frost DM, Wilson BDM.. Minority stress, distress, and suicide attempts in three cohorts of sexual minority adults: a U.S. probability sample. PLoS One. 2021; 16(3): e0246827. doi: https://doi.org/ 10.1371/journal.pone.0246827 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0043] 43. Papp LM, Kouros CD.. Effect of COVID-19 disruptions on young adults’ affect and substance use in daily life. Psychol Addict Behav. 2021; 35(4): 391–401. doi: https://doi.org/ 10.1037/adb0000748 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0044] 44. Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG.. AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care. Geneva, Switzerland: Department of Mental Health and Substance Dependence, World Health Organization; 2001. [Google Scholar]

[CIT0045] 45. Belloir JA, Kidd JD, Dworkin JD, Bockting WO.. Examining the role of problematic drug use in the relationship between discrimination and sleep disturbance in transgender and nonbinary individuals. Addict Behav. 2022; 135: 107459. doi: https://doi.org/ 10.1016/j.addbeh.2022.107459 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0046] 46. Helminen E, Scheer JR, Jackson SD, et al. PTSD symptoms and hazardous drinking indicators among trauma-exposed sexual minority women during heightened societal stress. Behav Med. 2021; 49(2): 183–194. doi: https://doi.org/ 10.1080/08964289.2021.2006132 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0047] 47. Berman AH, Bergman H, Palmstierna T, Schlyter F.. Evaluation of the Drug Use Disorders Identification Test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample. Eur Addict Res. 2005; 11(1): 22–31. doi: https://doi.org/ 10.1159/000081413 [DOI] [PubMed] [Google Scholar]

[CIT0048] 48. Heron KE, Braitman AL, Lewis RJ, Shappie AT, Hitson PT.. Measuring sexual minority stressors in lesbian women’s daily lives: initial scale development. Psychol Sex Orientat Gender Divers. 2018; 5(3): 387–395. doi: https://doi.org/ 10.1037/sgd0000287 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0049] 49. Nezlek JB. A practical guide to understanding reliability in studies of within-person variability. J Res Personal. 2017; 69: 149–155. doi: https://doi.org/ 10.1016/j.jrp.2016.06.020 [DOI] [Google Scholar]

[CIT0050] 50. Flentje A, Barger BT, Capriotti MR, et al. Screening gender minority people for harmful alcohol use. PLoS One. 2020; 15(4):e0231022. doi: https://doi.org/ 10.1371/journal.pone.0231022 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0051] 51. Liautaud MM, Rendina HJ, Berke DS.. Examining the impact of daily discrimination on alcohol use among racially diverse, trauma-exposed sexually minoritized adults: a pilot study. Psychol Trauma Theory Res Pract Policy. 2023; 16(6):913–921. doi: https://doi.org/ 10.1037/tra0001577.supp [DOI] [PubMed] [Google Scholar]

[CIT0052] 52. National Institutes on Alcohol Abuse and Alcoholism. Rethinking Drinking. 2021. Accessed August 1, 2024. https://www.niaaa.nih.gov/sites/default/files/publications/NIAAA_RethinkingDrinking.pdf [Google Scholar]

[CIT0053] 53. Cain MK, Zhang Z, Yuan KH.. Univariate and multivariate skewness and kurtosis for measuring nonnormality: prevalence, influence and estimation. Behav Res Methods. 2017; 49(5): 1716–1735. doi: https://doi.org/ 10.3758/s13428-016-0814-1 [DOI] [PubMed] [Google Scholar]

[CIT0054] 54. Kiekens WJ, Baams L, Veenstra R.. Differences by sex and gender in the association between minority stress and alcohol use among sexual and gender minority youth: a daily diary study. Soc Sci Med. 2022; 294: 114679. doi: https://doi.org/ 10.1016/j.socscimed.2021.114679 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0055] 55. Asparouhov T, Muthén B.. Bayesian analysis of latent variable models using Mplus. Mplus Technical Reports. 2010. Accessed August 1, 2024. https://www.statmodel.com/download/BayesAdvantages18.pdf [Google Scholar]

[CIT0056] 56. Kim SY, Huh D, Zhou Z, Mun EY.. A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome. Int J Behav Dev. 2020; 44(5): 447–457. doi: https://doi.org/ 10.1177/0165025419894730 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0057] 57. Bolger N, Laurenceau JP.. Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. New York, NY: Guilford Press; 2013. [Google Scholar]

[CIT0058] 58. Mathieu JE, Aguinis H, Culpepper SA, Chen G.. Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. J Appl Psychol. 2012; 97(5): 951–966. doi: https://doi.org/ 10.1037/a0028380 [DOI] [PubMed] [Google Scholar]

[CIT0059] 59. Hedeker D. Methods for multilevel ordinal data in prevention research. Prev Sci. 2015; 16(7): 997–1006. doi: https://doi.org/ 10.1007/s11121-014-0495-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0060] 60. Hayes AF, Matthes J.. Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behav Res Methods. 2009; 41(3): 924–936. [DOI] [PubMed] [Google Scholar]

[CIT0061] 61. Scheer JR, Cascalheira CJ, Helminen EC, et al. “I know myself again, which makes me motivated for life”: feasibility and acceptability of using experience sampling methods with trauma-exposed sexual minority women. J Interpers Violence. 2023; 38(13-14): 8692–8720. doi: https://doi.org/ 10.1177/08862605231153888 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0062] 62. Scheer JR, Clark KA, McConocha E, Wang K, Pachankis JE.. Toward cognitive-behavioral therapy for sexual minority women: voices from stakeholders and community members. Cogn Behav Pract. 2022; 30(3):471–494. doi: https://doi.org/ 10.1016/j.cbpra.2022.02.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0063] 63. Kadden RM, Litt MD.. The role of self-efficacy in the treatment of substance use disorders. Addict Behav. 2011; 36(12): 1120–1126. doi: https://doi.org/ 10.1016/j.addbeh.2011.07.032 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0064] 64. Maskell PD, Sang KJC, Heymsfield SB, Shapses S, Dekorompay A.. Forensic alcohol calculations in transgender individuals undergoing gender-affirming hormonal treatment. Toxicology. 2022; 67(4):1624–1631. doi: https://doi.org/ 10.1111/1556-4029.15052 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Examining Daily Self-Efficacy, Minority Stressors, and Alcohol and Other Drug Use Among Trauma-Exposed Sexual Minority Women and Transgender and Gender-Diverse People

Jillian R Scheer, PhD

Ethan H Mereish, PhD

Amanda K Gilmore, PhD

Cory J Cascalheira, BA, LSAA

Emily C Helminen, PhD

Fatima Dobani, MS

Kriti Behari, MA

Sophia Pirog, MA

Skyler D Jackson, PhD

Tami P Sullivan, PhD

Abigail W Batchelder, PhD, MPH

Abstract

Background and Purpose

Methods

Results

Conclusions

Understanding Self-Efficacy as a Predictor of AOD Use and Unhealthy Drinking Risk

Considering a Dynamic and Contextual Model of AOD Use and Unhealthy Drinking Risk

The Present Study

Methods

Participants and Procedures

Measures

Baseline measures

Discrimination related to lesbian, gay, bisexual, transgender, or queer (LGBTQ+) status.

Covariates.

Daily measures

Daily self-efficacy to resist AOD use.

Daily sexual minority stressors.

Daily alcohol use.

Daily drug use.

Covariates.

Analytic Plan

Results

Sample Characteristics

Table 1.

Multilevel Models Predicting Same-Day Unhealthy Drinking Risk

Table 2.

Multilevel Models Predicting Same-Day Alcohol Use

Table 3.

Multilevel Models Predicting Same-Day Drug Use

Table 4.

Discussion

Clinical Implications

Limitations and Future Directions

Conclusions

Supplementary Material

Acknowledgments

Contributor Information

Declarations of Interest

Author Contributions

Funding

Open Science Disclosure Statements

Data Availability

Transparency Statements

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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