ArticleDrug and alcohol dependence2021
Development and validation of a prediction model for opioid use disorder among youth.
Article in Drug and alcohol dependence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Forecasting drug-overdose mortality by age in the United States at the national and county levels.PNAS nexus · 2024Article
- Fatal overdose: Predicting to prevent.The International journal on drug policy · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundYouth are vulnerable to opioid use initiation and its complications. With growing rates of opioid overdose, strategies to identify youth at risk of opioid use disorder (OUD) to efficiently focus prevention interventions are needed. This study developed and validated a prediction model of OUD in youth aged 14-18 years.
methodsThe model was developed in a Colorado healthcare system (derivation site) using Cox proportional hazards regression analysis. Model predictors and outcomes were identified using electronic health record data. The model was externally validated in a separate Denver safety net health system (validation site). Youth were followed for up to 3.5 years. We evaluated internal and external validity using discrimination and calibration.
resultsThe derivation cohort included 76,603 youth, of whom 108 developed an OUD diagnosis. The model contained 3 predictors (smoking status, mental health diagnosis, and non-opioid substance use or disorder) and demonstrated good calibration (p = 0.90) and discrimination (bootstrap-corrected C-statistic = 0.76: 95 % CI = 0.70, 0.82). Sensitivity and specificity were 57 % and 84 % respectively with a positive predictive value (PPV) of 0.49 %. The validation cohort included 45,790 youth of whom, 74 developed an OUD diagnoses. The model demonstrated poorer calibration (p < 0.001) but good discrimination (C-statistic = 0.89; 95 % CI = 0.84, 0.95), sensitivity of 87.8 % specificity of 68.6 %, and PPV of 0.45 %.
conclusionsIn two Colorado healthcare systems, the prediction model identified 57-88 % of subsequent OUD diagnoses in youth. However, PPV < 1% suggests universal prevention strategies for opioid use in youth may be the best health system approach.
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