Methamphetamine use and associated factors among people who inject drugs in Iran 2020
Bibliographic record
Abstract
BACKGROUND: Methamphetamine use is a pressing public health concern among people who inject drugs (PWID) worldwide. This study aimed to estimate the prevalence of methamphetamine use and its correlates in a nationwide survey among PWID in Iran in 2020. METHODS: We recruited 2,684 PWID in 11 major cities from July 2019 to March 2020 using respondent-driven sampling (RDS). Participants were eligible if they were ≥ 18 years old, self-reported drug injection in the last 12 months, resided in the city of the study, and provided a valid RDS coupon. Behavioral data was collected using a standard questionnaire via face-to-face interviews. 2,259 PWID (84.2%) with valid responses to the primary outcome (i.e., methamphetamine use (either injecting or non-injection use) in the last three months) were included in the analysis. We used RDS-Gile's SS weighted analysis for descriptive statistics and a survey package (svy) using linearized variance estimations and stratification by cities for bivariable and multivariable logistic regression analyses. RESULTS: The last 3-month prevalence of methamphetamine use among PWID was 47.0% (95% CI: 44.9, 49.1). The following factors were significantly associated with methamphetamine use in the last three months: history of homelessness in the last 12 months (aOR = 1.57; 1.77, 2.10), drug use onset before the age 18 (aOR = 1.40; 1.05, 1.87), injecting drug for more than ten years (aOR = 1.47; 1.11, 1.95), using non-injection (aOR = 7.18; 4.93, 10.47) and injecting illicit opioids (aOR = 2.98; 2.03, 4.36) in the last three months, as well as having multiple sex partners in the last 12 months (aOR = 1.60; 1.50, 2.73) and region (north: aOR = 5.42; 2.92 10.03; south: aOR = 2.95; 2.04, 4.27; east: aOR = 24.43; 15.62, 38.22). CONCLUSIONS: The frequency of methamphetamine use among PWID is considerable in Iran. Our findings underscore the importance of implementing tailored comprehensive harm reduction services for this sub-population of PWID. They also emphasize the urgent need for evidence-based interventions to address the harms associated with the increasing co-use of opioids and methamphetamine among PWID.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".