Racial/Ethnic and Regional Disparities in Opioid-Involved Overdose Deaths among Children and Adolescents in the United States
Bibliographic record
Abstract
BACKGROUND: Despite the opioid crisis being declared a national emergency in 2017, few studies have examined disparities in overdose mortality trends among children and adolescents. This study assessed trends in opioid-involved overdose mortality among U.S. individuals aged 0 to 19 years, categorized by age, sex, race/ethnicity, census region, opioid type (prescription, synthetic, and heroin), and county rural/urban designation, from 1999 to 2019. METHODS: Mortality data were sourced from the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research Database. Opioid-related deaths were identified using ICD-10 codes. Crude and age-adjusted mortality rates (AAMR) were assessed by age, sex, race/ethnicity, census region, opioid type, and county rural/urban designation. Temporal trends were analyzed using Joinpoint regression to estimate annual percentage changes (APC) and average APC. 95% confidence intervals were derived using the Empirical Quantile method and the Parametric Method. RESULTS: Between 1999 and 2019, 10,799 children and adolescents died from opioid overdoses (AAMR = 0.6 per 100,000; 95% CI: 0.6-0.6). From 2013-2019, overall mortality increased by 4.5% annually (95% CI: 0.91, 15.54). Mortality trends increased among Non-Hispanic Black (APC = 7.84; 95% CI: 5.12-10.56) and Hispanic individuals (APC = 5.29; 95% CI: 2.84-7.74) from 1999 to 2019, while remaining stable among Non-Hispanic White individuals from 2004 to 2019 (APC = -0.69; 95% CI: -2.09 to 0.58). Mortality rates also increased in the Northeast from 1999 to 2019 (APC = 4.23; 95% CI: 2.70-5.78) and in the West from 2015 to 2019 (APC = 21.96; 95% CI: 13.50-39.67), with a sharp increase in deaths involving synthetic opioids from 2014 to 2019 (APC = 43.37; 95% CI: 21.13-120.46). CONCLUSIONS: Opioid overdose mortality trends among US children and adolescents have increased in recent years. Contemporary rises are most pronounced among Non-Hispanic Black and Hispanic children, in the Northeastern and Western regions, and from synthetic opioids. The disparities in opioid-related deaths underscores the need for targeted interventions and continued research to inform public health strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".