Latent patterns of polysubstance use among people who use opioids: A systematic review
Bibliographic record
Abstract
BACKGROUND: A mounting body of evidence suggests that polysubstance use (PSU) is common among people who use opioids (PWUO). Measuring PSU, however, is statistically and methodologically challenging. Person-centered analytical approaches (e.g., latent class analysis) provide a holistic understanding of individuals' substance use patterns and help understand PSU heterogeneities among PWUO and their specific needs in an inductive manner. We reviewed person-centered studies that characterized latent patterns of PSU among PWUO. METHODS: We searched MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Google Scholar from inception, through to June 15, 2020, for empirical peer-reviewed studies or gray literature that reported on latent classes of PSU among PWUO. Two independent reviewers completed the title, abstract, full-text screening, and data extraction. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale, and quality of reporting was evaluated using the Guidelines for Reporting on Latent Trajectory Studies checklist. Studies' findings were summarized and presented in a narrative fashion. RESULTS: Out of the 3372 initial unique studies identified, 30 were included. PSU operationalization varied substantially among the studies. We identified five distinct PSU latent classes frequently observed across the studies: Infrequent/low PSU, PSU primarily involving heroin use, PSU primarily involving heroin and stimulant use, PSU primarily involving stimulant use, and frequent PSU. Belonging to higher frequency or severity PSU classes were associated with frequent injection drug use, sharing needles and paraphernalia, high-risk sexual behaviours, as well as experiences of adversities, such as homelessness, incarceration, and poor mental health. CONCLUSION: PSU patterns vary significantly across different subgroups of PWUO. The substantial heterogeneities among PWUO need to be acknowledged in substance use clinical practices and policy developments. Findings call for comprehensive interventions that recognize these within-group diversities and address the varying needs of PWUO.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".