The rising burden of drug use disorders in the Americas, 2000–2021
Bibliographic record
Abstract
Objectives: Drug use disorders - preventable and treatable conditions - are a challenging and growing public health threat in the Region of the Americas. This study aims to provide a comprehensive analysis of the levels and trends of the burden of these disorders across countries in the Americas. Methods: This study analyzed morbidity, mortality and disease burden from drug use disorders, including opioid, cocaine, amphetamine, cannabis and other drug use disorders, across 38 countries in the Americas from 2000 to 2021. Using estimates from the Global Burden of Disease Study in 2021, trends were assessed using the average annual percentage change, estimated through regression analysis. Results: In 2021, 17.7 million (95% uncertainty interval [UI]: 15.9 to 19.9 million) people in the Americas were living with these disorders, mainly opioid use disorders (42.7%) and cannabis use disorders (31.5%). Drug use disorders accounted for 77 717 deaths (95% UI: 70 414 to 86 270) or 6.9 deaths (95% UI: 6.3 to 7.6) per 100 000 population, which was higher than the global estimates. Rates of age-standardized disability-adjusted life years from drug use disorders increased annually by 4.95%, reaching 695.36 years (95% UI: 583.45 to 807.69) per 100 000 population, higher than the global estimate. The burden of these disorders was consistently higher among male young adults. Regionwide in 2021, 145 515 (95% UI: 132 710 to 159 080) all-cause deaths (1.6%, 95% UI: 1.4 to 1.7% of total deaths) were attributed to drug use, primarily deaths from opioid use disorders, cirrhosis and liver cancer. Conclusions: Drug use disorders are a major and growing public health challenge in the Americas, driven mainly by opioid use disorders in young adults and the rise in these disorders among women. Urgent, evidence-based responses are needed that target high-risk populations, expand treatment and harm reduction, and strengthen data systems. Tailored strategies informed by national contexts and global frameworks can reduce avoidable deaths and improve population health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".