Age-specific disparities in fatal drug overdoses highest among older black adults and American Indian/Alaska native individuals of all ages in the United States, 2015-2020
Bibliographic record
Abstract
INTRODUCTION: Increasing disparities within and between racial/ethnic groups in overdose deaths underscore the need to identify drivers and patterns to optimize overdose prevention strategies. We assess age-specific mortality rates (ASMR) in drug overdose deaths by race/ethnicity in 2015-2019 and 2020. METHODS: Data were from the CDC Wonder, and included information for N = 411,451 deceased individuals in the United States (2015-2020) with a drug overdose-attributed cause of death (ICD-10 codes: X40-X44, X60-X64, X85, Y10-Y14). We compiled overdose death counts by age, race/ethnicity, and population estimates to derive ASMRs, mortality rate ratios (MRR), and cohort effects. RESULTS: The ASMRs for Non-Hispanic Black adults (2015-2019) followed a different pattern than other racial/ethnic groups-low ASMRs among young individuals and peaking between 55-64 years-a pattern exacerbated in 2020. Younger Non-Hispanic Black individuals had lower MRRs than young Non-Hispanic White individuals, yet, older Non-Hispanic Black adults had much higher MRRs than older Non-Hispanic White adults (45-54yrs:126%, 55-64yrs:197%; 65-74yrs:314%; 75-84:148%) in 2020. American Indian/Alaska Native adults had higher MRRs than Non-Hispanic White adults in death counts compiled from pre-pandemic years (2015-2019); however, MRRs increased in 2020 (15-24yrs:134%, 25-34yrs:132%, 35-44yrs:124%, 45-54yrs:134%, 55-64yrs:118%). Cohort analyses suggested a bimodal distribution of increasing fatal overdose rates among Non-Hispanic Black individuals aged 15-24 and 65-74. CONCLUSIONS AND RELEVANCE: Overdose fatalities unprecedently impact older Non-Hispanic Black adults and American Indian/Alaska Native populations of all ages, deviating from the pattern found for Non-Hispanic White individuals. Findings highlight the need for targeted naloxone and low-threshold buprenorphine programs to reduce racial disparities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".