Black Joshua C, Czizik Annika M
Rocky Mountain Poison & Drug Safety, Denver Health and Hospital Authority, Denver, Colorado, United States of America.
PLoS One. 2025 Jul 29;20(7):e0328286. doi: 10.1371/journal.pone.0328286. eCollection 2025.
The opioid crisis in the United States is a complex issue with interconnected factors that lead to opioid misuse and opioid-involved mortality. This study assessed the relative importance of different risk factor domains in predicting fatal opioid-involved mortality that occurred after hospital encounters involving opioids.
A machine learning model was developed by integrating multiple data sources, including hospital records, death records, and societal data. The model allowed simultaneous examination of risk factors across individual drug and non-drug related factors, hospital factors, and societal factors.
429,005 patients with opioid-related encounters in 2014 were assessed, where 56.6% were female and the mean age was 44.98. Among deaths that had specific drugs listed for both the hospital encounter and the death, 51.7% of hospital encounters progressed to a more potent opioid at death. Community factors cumulatively had similar importance as individual drug-related factors in predicting opioid-involved deaths and were relatively more important in predicting opioid-involved mortality compared to non-drug involved mortality. In predicting opioid-involved mortality, non-drug related individual-level predictors accounted for 45.1% of the importance. Community factors accounted for 27.9% of the importance and drug-related individual factors accounted for 22.5%. In contrast, community factors accounted for only 16.5% of the importance when predicting non-opioid-involved mortality.
Rather than suggesting community factors outweigh individual factors, our results highlight individual vulnerability may be amplified or mitigated by broader environmental factors. Interventions targeting larger social determinants of health may be strongly influential in reducing drug-involved mortality. This study demonstrated a quantitative evaluation of the different domains of risk factors and highlighted the importance of considering societal and community factors in a holistic approach to preventing opioid-involved mortality.
美国的阿片类药物危机是一个复杂的问题，涉及相互关联的因素，这些因素导致阿片类药物滥用和与阿片类药物相关的死亡。本研究评估了不同风险因素领域在预测涉及阿片类药物的医院就诊后发生的致命阿片类药物相关死亡中的相对重要性。
通过整合多个数据源（包括医院记录、死亡记录和社会数据）开发了一个机器学习模型。该模型允许同时检查个体药物和非药物相关因素、医院因素和社会因素等风险因素。
对2014年429,005例有阿片类药物相关就诊的患者进行了评估，其中56.6%为女性，平均年龄为44.98岁。在医院就诊和死亡都列出了特定药物的死亡病例中，51.7%的医院就诊在死亡时进展为更强效的阿片类药物。在预测阿片类药物相关死亡方面，社区因素累积起来与个体药物相关因素具有相似的重要性，并且与非阿片类药物相关死亡相比，在预测阿片类药物相关死亡方面相对更重要。在预测阿片类药物相关死亡时，非药物相关的个体水平预测因素占重要性的45.1%。社区因素占27.9%，药物相关的个体因素占22.5%。相比之下，在预测非阿片类药物相关死亡时，社区因素仅占重要性的16.5%。
我们的研究结果并非表明社区因素比个体因素更重要，而是强调个体脆弱性可能会被更广泛的环境因素放大或减轻。针对更大的健康社会决定因素的干预措施可能对降低药物相关死亡率有很大影响。本研究展示了对不同风险因素领域的定量评估，并强调了在整体预防阿片类药物相关死亡方法中考虑社会和社区因素的重要性。
