Department of Public Health Sciences, Clemson University, Clemson, SC, USA; Center for Public Health Modeling and Response, Clemson University, Clemson, SC, USA.
Clemson University School of Health Research, Clemson University, Clemson, SC, USA; Prisma Health-Upstate, Greenville, SC, USA; University of South Carolina School of Medicine Greenville, Greenville, SC, USA.
Lancet Public Health. 2024 Jun;9(6):e354-e364. doi: 10.1016/S2468-2667(24)00076-8.
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.
在美国，阿片类药物过量和相关疾病仍然是一个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危社区，以便有针对性地提供阿片类药物使用障碍和丙型肝炎病毒（HCV）的筛查和预防干预措施。
在这项生态和建模研究中，我们使用混合效应负二项回归模型来确定与邮政编码分区（ZCTA）相关的因素，并预测美国南卡罗来纳州的阿片类药物相关和 HCV 相关的住院治疗。所有年龄在 18 岁或以上、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人都包括在内。阿片类药物相关和 HCV 相关的住院治疗数据，以及其他个人层面变量的数据，都是从医疗索赔记录中收集的，这些记录是从南卡罗来纳州财政和税务办公室获得的。人口统计学和社会经济变量是从美国人口普查局（2021 年美国社区调查）获得的，额外的结构医疗保健障碍数据是从南卡罗来纳州农村和初级保健中心以及美国医院名录获得的。
在 2016 年 1 月 1 日至 2021 年 12 月 31 日期间，有 41691 人因阿片类药物滥用住院治疗，26860 人因 HCV 住院治疗。每个 ZCTA 的中位数分别有 80（IQR 24-213）例阿片类药物相关住院治疗和 61（21-196）例 HCV 相关住院治疗。ZCTA 级别的未参保率（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]）每增加一个标准差，与阿片类药物相关和 HCV 相关的联合住院治疗率显著增加。ZCTA 级别的收入（0·79 [0·75-0·84]）和失业率（0·87 [0·82-0·93]）每增加一个标准差，与阿片类药物相关和 HCV 相关的联合住院治疗率显著降低。使用 2016-20 年的住院治疗数据作为训练数据，我们的模型预测 2021 年 ZCTA 级别的阿片类药物相关住院治疗的中位数为 80·4%（IQR 66·8-91·1），预测 HCV 相关住院治疗的中位数为 75·2%（61·2-87·7）。确定了几个服务不足的高危 ZCTA，以便提供有针对性的干预措施。
我们的研究结果表明，来自经济贫困和医疗资源不足社区的个人更有可能发生阿片类药物相关或 HCV 相关的住院治疗。结合住院治疗预测，我们的研究结果可用于确定和优先考虑高危、服务不足的社区，以提供现场一级的干预措施。
南卡罗来纳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。
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