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.

翻译：背景：美国芬太尼过量死亡人数仍在上升。我们尚未完全了解哪些县级社会与结构性条件导致更高的过量死亡率。健康社会决定因素（包括残疾状况、治疗可及性和行为健康问题）有助于在死亡问题恶化前识别脆弱县。此前尚无研究利用2022年CDC WONDER数据结合可解释机器学习与SHAP归因方法分析治疗可及性缺口和无声风险县。方法：我们整合了来自四个政府来源的975个美国县数据，包括CDC WONDER（2022年）过量死亡率数据、CDC社会脆弱性指数（SVI）、CDC PLACES健康行为数据以及区域健康资源文件。采用XGBoost模型基于标准化死亡率比（SMR）预测过量死亡风险，通过五折交叉验证评估模型准确性，并使用SHAP值揭示影响风险增减的关键因素。结果：XGBoost优于所有测试模型（Spearman相关系数=0.67，R²=0.457，MAE=0.409，高风险召回率=71.1%）。首要预测因子为残疾率、高血压、吸烟和缺乏车辆使用。治疗荒漠县的过量死亡率高出52.6%（SMR 1.786 vs 1.170；p<0.0001）。K-means聚类识别出143个无声风险县。过量死亡呈现空间聚集性（Moran's I=0.505，p=0.001），包含75个热点区和136个冷点区。WONDER数据库中58.2%的县数据被压制，其中多为农村县（72%）和治疗荒漠县（65%）。结论：县级健康社会决定因素可有效预测过量死亡，尤其以残疾状况、治疗可及性和行为健康负担为关键。MOUD扩展应优先考虑治疗荒漠县，无声风险县需在死亡率恶化前实施早期干预。