Global burden, socioeconomic disparities, and spatiotemporal dynamics of opioid use disorder mortality and disability: a comprehensive analysis from the global burden of disease study 2017-2021
Bibliographic record
Abstract
BACKGROUND: The escalating global burden of opioid use disorder (OUD) necessitates a nuanced understanding of its epidemiological patterns, socioeconomic determinants, and temporal trends. This study quantifies the global, regional, and national burden of OUD-related mortality and disability, evaluates policy correlates, and identifies critical disparities across demographic and socioeconomic strata. METHODS: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017–2021, we analyzed age-standardized mortality rates (ASMRs), disability-adjusted life years (DALYs), and their 95% uncertainty intervals (UIs) across 204 countries and territories. Joinpoint regression modeled temporal trends, while multivariable generalized estimating equations (GEEs) assessed socioeconomic gradients via the Socio-demographic Index (SDI). Geospatial clustering was evaluated using Moran’s I statistic. Robustness was confirmed through sensitivity analyses, counterfactual modeling, and cross-validation. RESULTS: Globally, OUD mortality increased by 12.4% between 2017 and 2021, with the Americas remaining the epicenter (ASMR: 5.72 per 100,000; +47.3% deaths). Europe demonstrated progress (mortality decline: -1.8%), while Asia and Africa faced rising chronic disability (DALYs: +3.0% and + 10.2%, respectively). National-level extremes ranged from 0.01 per 100,000 in Japan to 7.16 per 100,000 in Canada. A pronounced socioeconomic gradient emerged: high-SDI regions exhibited 17.8-fold higher mortality rates (0.641% vs. 0.036% in low-SDI regions) and 13.9-fold greater DALY rates. Sex disparities persisted (male-to-female mortality ratio: 4.7:1), with males experiencing biphasic trends (+ 3.8% annual percent change [APC] pre-2020, -6.5% post-2020). Geospatial analyses revealed diverging trajectories, including rising mortality in the Americas (+ 4.2% APC) and declines in Africa (-3.1% APC). CONCLUSION: This study highlights stark regional and socioeconomic disparities in the OUD burden, exacerbated by synthetic opioid proliferation in high-income settings and chronic disability in resource-limited regions. Evidence-based harm reduction policies in high-SDI nations mitigated disability, while low-SDI regions faced accelerating crises. Targeted interventions addressing socioeconomic inequities, gender-specific risks, and geospatial vulnerabilities are urgently needed to curb the global opioid epidemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".