用机器学习找出美国药瘾死亡高风险县，揭示隐藏危机区和治疗荒漠。
Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts
- 基于XGBoost与SHAP分析4大数据源，识别影响药瘾致死率的关键社会因素。
- 治疗资源匮乏县死亡率高出52.6%，143个隐匿高风险县被首次发现。
- 适合公共卫生政策制定者和基层医疗团队关注未显性危机区域。
背景：芬太尼过量致死率在美国持续上升，但尚不清楚哪些县级社会结构条件导致更高死亡率。健康的社会决定因素（如残疾率、治疗可及性、行为健康问题）可能在死亡恶化前识别脆弱地区。此前无研究使用可解释机器学习结合SHAP归因分析2022年CDC WONDER数据来研究治疗缺口与隐匿高风险县。方法：整合四个政府数据源共975个美国县的数据，包括CDC WONDER（2022年）过量致死数据、CDC社会脆弱指数（SVI）、CDC PLACES健康行为数据及区域卫生资源文件。采用XGBoost模型预测过量致死风险，以标准化死亡率比（SMR）为指标，五折交叉验证评估模型性能，并用SHAP值解析各因素对风险的贡献。结果：XGBoost优于所有对比模型（斯皮尔曼相关系数=0.67，决定系数R²=0.457，平均绝对误差MAE=0.409，高风险召回率71.1%）。主要预测因子为残疾率、高血压、吸烟率及无车辆获取能力。治疗荒漠县的过量致死率显著更高（SMR 1.786 vs 1.170；p<0.0001）。K均值聚类识别出143个隐匿高风险县。过量死亡呈空间聚集（莫兰指数Moran's I=0.505，p=0.001），共发现75个热点与136个冷点。被压制的县占全部WONDER县的58.2%，多数为农村（72%）且属治疗荒漠（65%）。结论：县级社会健康决定因素可有效预测过量致死率，尤其残疾、治疗可及性与行为健康负担。应优先向治疗荒漠县扩展药物辅助治疗（MOUD），并对隐匿高风险县实施早期干预。
原文摘要 · Abstract (English)
Background: Fentanyl overdose deaths are still increasing across the U.S. We do not fully understand which county-level social and structural conditions lead to higher overdose death rates. Social determinants of health, including disability, treatment access, and behavioral health issues, may help identify vulnerable counties before deaths become severe. No earlier study has used explainable machine learning with SHAP attribution on 2022 CDC WONDER data to study treatment access gaps and silent risk counties. Methods: We combined data from four government sources for 975 U.S. counties, including CDC WONDER (2022) overdose mortality data, CDC Social Vulnerability Index (SVI), CDC PLACES health behavior data, and Area Health Resources Files. An XGBoost model was used to predict overdose mortality risk using Standardized Mortality Ratio (SMR). Five-fold cross-validation was used to test model accuracy, and SHAP values were used to show which factors increase or decrease risk. Results: XGBoost outperformed all tested models (Spearman rho=0.67, R2=0.457, MAE=0.409, high-risk recall=71.1%). Top predictors were disability rate, hypertension, smoking, and lack of vehicle access. Treatment desert counties had 52.6% higher overdose mortality (SMR 1.786 vs 1.170; p<0.0001). K-means identified 143 silent risk counties. Overdose deaths were spatially clustered (Moran's I=0.505, p=0.001) with 75 hotspots and 136 coldspots. Suppressed counties were 58.2% of WONDER counties, mostly rural (72%) and treatment deserts (65%). Conclusions: County-level SDOH factors predict overdose deaths, especially disability, treatment access, and behavioral health burden. MOUD expansion should prioritize treatment desert counties, and silent risk counties need early intervention before mortality worsens.
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