Prevalence of respiratory conditions among people who use illicit opioids: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: There are growing concerns over the respiratory health of people who use illicit opioids due to high rates of opioid inhalation and tobacco smoking in this group. This study aimed to summarize the evidence relating illicit opioid use with poor respiratory health. METHODS: A systematic review of the literature on the association between illicit opioid use and respiratory health was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance (PROSPERO ID = CRD42017059953). Electronic searches of MEDLINE, Embase, PsycINFO, CINAHL and the Cochrane Library databases were undertaken (English language, published January 1980-November 2018). All study designs excluding case studies were considered. Studies were undertaken in community and hospital settings in the United States (n = 23), United Kingdom (n = 7), Australia (n = 7), the Netherlands (n = 2), Canada (n = 2), Ireland (n = 1), Spain (n = 1) and Iran (n = 1). Measurements of respiratory disease, including asthma and chronic obstructive pulmonary disease (COPD) and related symptoms were extracted. Data on respiratory-related deaths and hospital admissions were also extracted. Meta-analysis of prevalence data was undertaken using a random effects meta-analysis model with parameters estimated using Markov chain Monte Carlo simulation. RESULTS: Meta-analyses estimated prevalence of asthma in people who inject illicit opioids as 8.5% [95% predictive interval (PrI) = 0.2%, 74.0%] and as 20.2% (95% PrI = 4.2%, 59.2%) in people who inhale illicit opioids. Prevalence of COPD in people who inject illicit opioids was estimated as 2.7% (95% PrI = 0.0%, 50.4%) and as 17.9% (95% PrI = 0.6%, 89.5%) in people who inhale illicit opioids. There was evidence of moderate to extreme heterogeneity across studies. CONCLUSIONS: There is evidence of increased burden of respiratory diseases in people who use illicit opioids. Due to the heterogeneity of study design and samples, it is difficult to gain accurate estimates of the prevalence of respiratory disease in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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".