Spatio-temporal Methods for Surveillance of the Opioid Syndemic

阿片类药物综合征的时空监测方法

• 批准号: 10641958
• 负责人: Staci Hepler
• 金额: $ 36.22万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641958
• 关键词: Achievement; Address; Admission activity; Area; Biological Models; Characteristics; Collaborations; Communicable Diseases; Communities; County; Data; Data Sources; Development; Elements; Emergency medical service; Epidemic; Epidemiology; Equation; Goals; HIV; HIV Infections; HIV/HCV; Health; Health Policy; Hepatitis C; Human; Individual; Infection; Infrastructure; Link; Measurement; Measures; Methodology; Methods; Modeling; Ohio; Opioid; Outcome; Overdose; Policy Maker; Population; Population Surveillance; Prevalence; Property; Public Health; Resources; Risk; Science; Services; Source; Statistical Methods; Statistical Models; Structure; Surveillance Methods; Surveillance Modeling; System; Time; United States; United States Dept. of Health and Human Services; Work; epidemic potential; experience; improved; meetings; model development; multiple data sources; novel; opioid epidemic; opioid misuse; overdose death; social; spatiotemporal; surveillance data; syndemic

PROJECT SUMMARY/ABSTRACT The United States is in the midst of a public health crisis due to the ongoing opioid syndemic. The opioid syndemic consists of the inter-related epidemics of opioid misuse, fatal and non-fatal overdose, human immunodeficiency virus (HIV), and hepatitis C (HCV). The consequences of opioid misuse are particularly severe in Ohio as the state has experienced overdose rates that are double the national average as well as elevated risk for epidemic levels of HIV and HCV. A key need for addressing the syndemic is to improve surveillance science methodology to better measure community-levels of opioid misuse and be able to identify and target areas of emerging risk with resources. However, no single data source currently observed by the public health surveillance system fully characterizes opioid misuse at relevant spatial and temporal supports. Novel statistical methods are needed to better leverage existing data and appropriately integrate multiple imperfect surveillance outcomes across different spatial scales to comprehensively estimate levels of opioid misuse and model the syndemic over space and time. Doing so will enable estimation and inference at small areas that are relevant to local policymakers and public health officials while accounting for measurement error. There are several methodological challenges that will be overcome with achievement of the following aims: 1) develop and assess a spatio-temporal factor model that estimates a factor that can be meaningfully interpreted longitudinally, 2) develop and assess a spatial factor model that allows for outcomes to have different spatial supports, and 3) develop and assess a multivariate spatio- temporal model to estimate areal prevalence of latent opioid misuse. Successful development of a comprehensive model of the opioid syndemic will advance surveillance science and will produce estimates of opioid misuse that advance epidemiological understanding and provide valuable information to policymakers and public health officials.

项目概要/摘要 由于持续存在的阿片类药物综合症，美国正处于公共卫生危机之中。 包括阿片类药物滥用、致命和非致命过量、人体免疫缺陷等相互关联的流行病 病毒（HIV）和丙型肝炎（HCV）滥用阿片类药物的后果在俄亥俄州尤为严重。 该州的用药过量率是全国平均水平的两倍，而且流行病的风险也很高 解决该综合征的一个关键需求是改进监测科学方法。 更好地衡量社区层面的阿片类药物滥用情况，并能够识别和瞄准新出现风险的领域 然而，目前公共卫生监测系统还没有完全观察到的单一数据源。 需要新的统计方法来表征阿片类药物在相关空间和时间支持下的滥用。 更好地利用现有数据并适当整合不同领域的多个不完整监测结果 空间尺度可全面估计阿片类药物滥用水平并在空间和时间上对综合征进行建模。 这样做将能够在与当地政策制定者和公众相关的小范围内进行估计和推断 卫生官员在考虑测量误差时会遇到一些方法上的挑战。 通过实现以下目标来克服：1）开发和评估时空因素模型 估计可以有意义地纵向解释的因素，2）开发和评估空间因素 模型允许结果具有不同的空间支持，3）开发和评估多元空间 估计潜在阿片类药物滥用的地区流行率的时间模型成功开发了一个综合性的模型。 阿片类药物综合症模型将推进监测科学，并将产生阿片类药物滥用的估计 促进流行病学理解并为政策制定者和公共卫生提供有价值的信息 官方的。

项目成果

A Bayesian Spatio-temporal Model to Optimize Allocation of Buprenorphine in North Carolina.

用于优化北卡罗来纳州丁丙诺啡分配的贝叶斯时空模型。

• 发表时间: 2023
• 作者: Dong, Qianyu;Kline, David;Hepler, Staci A
• 通讯作者: Hepler, Staci A

A spatio-temporal hierarchical model to account for temporal misalignment in American Community Survey explanatory variables.

用于解释美国社区调查解释变量中时间错位的时空分层模型。

• 发表时间: 2023-08
• 作者: Kwon, Jihyeon;Kline, David M;Hepler, Staci A
• 通讯作者: Hepler, Staci A

An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio.

用于估计俄亥俄州县级阿片类药物滥用流行率的综合丰度模型。

• 发表时间: 2023-01
• 作者: Hepler, Staci A;Kline, David M;Bonny, Andrea;McKnight, Erin;Waller, Lance A
• 通讯作者: Waller, Lance A

A Dynamic Spatial Factor Model to Describe the Opioid Syndemic in Ohio.

描述俄亥俄州阿片类药物流行病的动态空间因素模型。

• 发表时间: 2023-07-01
• 作者: Kline, David;Waller, Lance A;McKnight, Erin;Bonny, Andrea;Miller, William C;Hepler, Staci A
• 通讯作者: Hepler, Staci A

Intersectional inequities and longitudinal prevalence estimates of opioid use disorder in Massachusetts 2014-2020: a multi-sample capture-recapture analysis.

2014-2020 年马萨诸塞州阿片类药物使用障碍的交叉不平等和纵向患病率估计：多样本捕获-再捕获分析。

• 发表时间: 2024-04
• 作者: Wang, Jianing;Bernson, Dana;Erdman, Elizabeth A;Villani, Jennifer;Chandler, Redonna;Kline, David;White, Laura F;Barocas, Joshua A
• 通讯作者: Barocas, Joshua A