Examining non-AIDS mortality among people who inject drugs
Bibliographic record
Abstract
OBJECTIVE: To systematically review and analyse data from cohorts of people who inject drugs (PWID) to improve existing estimates of non-AIDS mortality used to calculate mortality among PWID in the Spectrum Estimates and Projection Package. DESIGN: Systematic review and meta-analysis. METHODS: We conducted an update of an earlier systematic review of mortality among PWID, searching specifically for studies providing data on non-AIDS-related deaths. Random-effects meta-analyses were performed to derive pooled estimates of non-AIDS crude mortality rates across cohorts disaggregated by sex, HIV status and periods in and out of opioid substitution therapy (OST). Within each cohort, ratios of non-AIDS CMRs were calculated and then pooled across studies for the following paired sub-groups: HIV-negative versus HIV-positive PWID; male versus female PWID; periods in OST versus out of OST. For each analysis, pooled estimates by country income group and by geographic region were also calculated. RESULTS: Thirty-seven eligible studies from high-income countries and five from low and middle-income countries were found. Non-AIDS mortality was significantly higher in low and middle-income countries [2.74 per 100 person-years; 95% confidence interval (CI) 1.76-3.72] than in high-income countries (1.56 per 100 person-years; 95% CI 1.38-1.74). Non-AIDS CMRs were 1.34 times greater among men than women (95% CI 1.14-1.57; N = 19 studies); 1.50 times greater among HIV-positive than HIV-negative PWID (95% CI 1.15, 1.96; N = 16 studies); and more than three times greater during periods out of OST than for periods on OST (N = 7 studies). CONCLUSIONS: A comprehensive response to injecting drug must include efforts to reduce the high levels of non-AIDS mortality among PWID. Due to limitations of currently available data, including substantial heterogeneity between studies, estimates of non-AIDS mortality specific to geographic regions, country income level, or the availability of OST should be interpreted with caution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".