Opioid overdose and related diseases remain a growing public health crisis in the USA. Identifying sociostructural and other contextual factors associated with adverse health outcomes is needed to improve prediction models to inform policy and interventions. We aimed to identify high-risk communities for targeted delivery of screening and prevention interventions for opioid use disorder and hepatitis C virus (HCV). In this ecological and modelling study, we fit mixed-effects negative binomial regression models to identify factors associated with, and predict, opioid-related and HCV-related hospitalisations for ZIP code tabulation areas (ZCTAs) in South Carolina, USA. All individuals aged 18 years or older living in South Carolina from Jan 1, 2016, to Dec 31, 2021, were included. Data on opioid-related and HCV-related hospitalisations, as well as data on additional individual-level variables, were collected from medical claims records, which were obtained from the South Carolina Revenue and Fiscal Affairs Office. Demographic and socioeconomic variables were obtained from the United States Census Bureau (American Community Survey, 2021) with additional structural health-care barrier data obtained from South Carolina's Center for Rural and Primary Health Care, and the American Hospital Directory. Between Jan 1, 2016, and Dec 31, 2021, 41 691 individuals were hospitalised for opioid misuse and 26 860 were hospitalised for HCV. There were a median of 80 (IQR 24–213) opioid-related hospitalisations and 61 (21–196) HCV-related hospitalisations per ZCTA. A standard deviation increase in ZCTA-level uninsured rate (relative risk 1·24 [95% CI 1·17–1·31]), poverty rate (1·24 [1·17–1·31]), mortality (1·18 [1·12–1·25]), and social vulnerability index (1·17 [1·10–1·24]) was significantly associated with increased combined opioid-related and HCV-related hospitalisation rates. A standard deviation increase in ZCTA-level income (0·79 [0·75–0·84]) and unemployment rate (0·87 [0·82–0·93]) was significantly associated with decreased combined opioid-related and HCV-related hospitalisations. Using 2016–20 hospitalisations as training data, our models predicted ZCTA-level opioid-related hospitalisations in 2021 with a median of 80·4% (IQR 66·8–91·1) accuracy and HCV-related hospitalisations in 2021 with a median of 75·2% (61·2–87·7) accuracy. Several underserved high-risk ZCTAs were identified for delivery of targeted interventions. Our results suggest that individuals from economically disadvantaged and medically under-resourced communities are more likely to have an opioid-related or HCV-related hospitalisation. In conjunction with hospitalisation forecasts, our results could be used to identify and prioritise high-risk, underserved communities for delivery of field-level interventions. South Carolina Center for Rural and Primary Healthcare, National Institute on Drug Abuse, and National Library of Medicine.

中文翻译：

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确定美国邮政编码地区与阿片类药物相关和丙型肝炎病毒相关住院相关的因素：生态和模型研究


阿片类药物过量和相关疾病仍然是美国日益严重的公共卫生危机。需要确定与不良健康结果相关的社会结构和其他背景因素，以改进预测模型，为政策和干预措施提供信息。我们的目的是确定高风险社区，以便有针对性地针对阿片类药物使用障碍和丙型肝炎病毒 (HCV) 进行筛查和预防干预措施。在这项生态和建模研究中，我们采用混合效应负二项式回归模型来识别与美国南卡罗来纳州邮政编码列表区域 (ZCTA) 的阿片类药物相关和 HCV 相关住院治疗相关的因素并进行预测。 2016年1月1日至2021年12月31日期间居住在南卡罗来纳州的所有年满18岁或以上的个人均包含在内。阿片类药物相关和丙型肝炎相关住院的数据以及其他个人层面变量的数据是从南卡罗来纳州收入和财政事务办公室获得的医疗索赔记录中收集的。人口和社会经济变量来自美国人口普查局（美国社区调查，2021），其他结构性医疗障碍数据来自南卡罗来纳州农村和初级卫生保健中心以及美国医院名录。 2016年1月1日至2021年12月31日期间，有41,691人因滥用阿片类药物而住院，26,860人因丙肝病毒住院。每个 ZCTA 中平均有 80 例 (IQR 24-213) 例阿片类药物相关住院治疗和 61 例 (21-196) 例 HCV 相关住院治疗。 ZCTA 级别未保险率（相对风险 1·24 [95% CI 1·17–1·31]）、贫困率（1·24 [1·17–1·31]）、死亡率（1 ·18 [1·12–1·25]）和社会脆弱性指数（1·17 [1·10–1·24]）与阿片类药物相关和丙型肝炎相关住院率的增加显着相关。 ZCTA 水平收入 (0·79 [0·75–0·84]) 和失业率 (0·87 [0·82–0·93]) 的标准差增加与阿片类药物相关和阿片类药物联合使用的减少显着相关。 HCV 相关住院治疗。使用 2016-20 年住院治疗作为训练数据，我们的模型预测 2021 年 ZCTA 级别阿片类药物相关住院治疗的准确度中位数为 80·4% (IQR 66·8–91·1)，预测 2021 年 HCV 相关住院治疗的准确度中位数为 80·4% (IQR 66·8–91·1) 75·2% (61·2–87·7) 的准确度。确定了几个服务不足的高风险 ZCTA，以实施有针对性的干预措施。我们的研究结果表明，来自经济弱势和医疗资源匮乏社区的个人更有可能因阿片类药物或丙肝病毒相关住院治疗。结合住院预测，我们的结果可用于识别高风险、服务不足的社区并确定优先顺序，以提供现场干预措施。南卡罗来纳州农村和初级保健中心、国家药物滥用研究所和国家医学图书馆。