Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action.
Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action.
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DOI：
10.1016/j.trsl.2021.03.018
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Bórquez A
中科院分区：
文献类型：
--
作者：
Marks C;Carrasco-Escobar G;Carrasco-Hernández R;Johnson D;Ciccarone D;Strathdee SA;Smith D;Bórquez A
美国 (US) 的阿片类药物危机是由药物和地区特定的阿片类药物使用相关流行病 (ORE) 的浪潮所定义的，其中包括过量服用和血源性感染，以及一系列健康危害。如果能够识别存在此类矿石风险的地区，更好的是，能够预测哪些地区将遭受这些矿石，有可能进一步降低发病率和死亡率。本叙述性综述的目的是确定和描述旨在对美国矿石进行“风险评估”、“检测”或“预测”的定量方法。我们实施的 PubMed 搜索包括：1) 目标（例如预测）、2) 流行病学结果（例如爆发）、3) 根本原因（例如阿片类药物使用）、4) 健康结果（例如用药过量、艾滋病毒）、5) 地点（例如美国）。总共纳入了 46 项研究，并提取了以下信息：学科、目标、健康结果、药物/物质类型、地理区域/分析单位和数据源。研究依赖于临床、流行病学、行为和药品市场监测，并应用了一系列方法，包括统计回归、地理空间分析、动态建模、系统发育分析和机器学习。在国家/州/县和邮政编码级别预测用药过量死亡率的研究正在迅速兴起。地理空间方法越来越多地用于识别阿片类药物使用和过量的热点地区。在传染病 ORE 的背景下，对患者样本进行常规基因测序，通过系统发育方法识别不断增长的传播簇，可以提高早期检测能力。协调实施多种互补方法将提高我们成功预测疫情风险和先发制人应对的能力。我们提出了一个用于预测美国矿石的多学科框架，并反思了研究团队在实施此类策略和良好实践时将面临的挑战。
The opioid crisis in the United States (US) has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the “risk assessment”, “detection” or “prediction” of OREs in the US. We implemented a PubMed search composed of the: 1) objective (e.g. prediction), 2) epidemiologic outcome (e.g. outbreak), 3) underlying cause (i.e. opioid use), 4) health outcome (e.g. overdose, HIV), 5) location (i.e. U.S.). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.
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影响因子：
4.6
作者：
Hernandez, Andres;Branscum, Adam J.;Cuadros, Diego F.
通讯作者：
Cuadros, Diego F.
影响因子：
3.7
作者：
Falade-Nwulia O;Hackman J;Mehta SH;McCormick SD;Kirk GD;Sulkowski M;Thomas D;Latkin C;Laeyendecker O;Ray SC
通讯作者：
Ray SC
影响因子：
33.9
作者：
Golden, Matthew R.;Lechtenberg, Richard;Buskin, Susan E.
通讯作者：
Buskin, Susan E.
影响因子：
3.3
作者：
Friedman, Lee S.
通讯作者：
Friedman, Lee S.
DOI：
10.1126/science.aau1184
发表时间：
2018-09-21
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jalal H;Buchanich JM;Roberts MS;Balmert LC;Zhang K;Burke DS
通讯作者：
Burke DS