Stimulant Use Associated With Psychosocial Factors, HIV Risk, and Concurrent Hazardous Alcohol Use Among US Adults: Exploratory Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Stimulant use is a major public health problem that contributes to morbidity and mortality among men who have sex with men (MSM) in the United States. To reduce the harms associated with stimulant use, there is a need to identify the factors associated with stimulant use to inform interventions. Additionally, there is a need to use large crowdsourcing platforms like Amazon Mechanical Turk (MTurk) to engage more individuals who use substances across the United States. OBJECTIVE: We identified the correlates of stimulant use among people who use alcohol or stimulants in the United States recruited using MTurk. METHODS: Participants who were aged ≥18 years in the United States and reported alcohol or stimulant (ie, cocaine, crack cocaine, and methamphetamine) use were deemed eligible and recruited via the web platform MTurk. Participants completed a baseline survey, which assessed sociodemographics, psychosocial (ie, depression, affect, self-esteem, and stress) factors, substance use, and sexual behaviors. Data were collected and analyzed with STATA (version 17; StataCorp). Stratifying by MSM status, bivariate and multivariable logistic regression models were built in STATA to examine the correlates of stimulant use. Multivariable models controlled for age, race, health insurance, and relationship status. RESULTS: Of 272 participants, 201 (73.9%) identified as male, 134 (49.2%) were MSM, 52 (19.1%) were from racial and ethnic minoritized communities, and 158 (58%) were in a relationship. The mean age was 36.10 (SD 10.3) years. A total of 40 (14.7%) participants reported stimulant use in the past 6 months. Factors significantly associated with stimulant use were being MSM (adjusted odds ratio [aOR] 4.61, 95% CI 1.97-10.81), a higher Alcohol Use Disorders Identification Test-Concise score (aOR 1.24, 95% CI 1.08-1.42), more intense cravings for alcohol in the past 24 hours (aOR 1.03, 95% CI 1.01-1.04), a higher depression score (aOR 1.06, 95% CI 1.01-1.12), a greater number of male partners in the last 6 months (aOR 1.32, 95% CI 1.08-1.61), a greater number of female partners in the last 6 months (aOR 1.42, 95% CI 1.04-1.92), and being diagnosed with a sexually transmitted infection (eg, syphilis, gonorrhea, chlamydia, herpes simplex virus, human papillomavirus, and other) in the last 6 months (aOR 14.61, 95% CI 3.45-61.87). Additionally, there was a significant additive interaction between MSM status and negative affect, such that the impact of negative affect on stimulant use was significantly greater among MSM compared with non-MSM (relative excess risk due to interaction 0.085, 95% CI 0.037-0.13). CONCLUSIONS: Interventions that address stimulant use should use evidence-based approaches that reduce negative affect, depression, and cravings for alcohol. Additionally, interventions should be customized for MSM populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".