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.

项目总结/摘要 由于持续的阿片类药物综合症，美国正处于公共卫生危机之中。阿片类综合征 包括阿片类药物滥用、致命和非致命过量、人类免疫缺陷等相互关联的流行病 病毒（HIV）和丙型肝炎（HCV）。阿片类药物滥用的后果在俄亥俄州尤为严重， 该州的过量服用率是全国平均水平的两倍，而且流行病的风险也很高。 HIV和HCV的水平。解决这一流行病的一个关键需要是改进监测科学方法 更好地衡量阿片类药物滥用的社区水平，并能够确定和瞄准新出现的风险领域 资源。然而，公共卫生监测系统目前没有充分观察到单一数据源， 在相关的空间和时间支持下表征阿片类药物滥用。需要新的统计方法， 更好地利用现有数据，并适当整合不同领域的多个不完善的监测结果 空间尺度，以全面估计阿片类药物滥用水平，并模拟空间和时间上的流行病。 这样做将有助于在与当地决策者和公众相关的小范围内进行估计和推断 健康，同时考虑到测量误差。有几个方法上的挑战， 通过实现以下目标来克服：1）开发和评估时空因素模型 估计一个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，允许结果有不同的空间支持，和3）开发和评估一个多变量空间， 估计潜在阿片类药物滥用地区流行率的时间模型。成功开发了一个全面的 阿片类药物综合征的模型将促进监测科学，并将产生阿片类药物滥用的估计 这有助于加深对流行病学了解，并为决策者和公共卫生部门提供宝贵信息 的官员。

项目成果

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。

• DOI: 10.1016/j.sste.2023.100593
• 发表时间: 2023
• 期刊: Spatial and spatio-temporal epidemiology
• 影响因子: 3.4
• 作者: Kwon,Jihyeon;Kline,DavidM;Hepler,StaciA
• 通讯作者: Hepler,StaciA

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。

• DOI: 10.1080/2330443x.2023.2218448
• 发表时间: 2023
• 期刊: Statistics and public policy (Philadelphia, Pa.)
• 作者: Dong,Qianyu;Kline,David;Hepler,StaciA
• 通讯作者: Hepler,StaciA

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。

• DOI: 10.1097/ede.0000000000001617
• 发表时间: 2023
• 期刊: Epidemiology (Cambridge, Mass.)
• 作者: Kline,David;Waller,LanceA;McKnight,Erin;Bonny,Andrea;Miller,WilliamC;Hepler,StaciA
• 通讯作者: Hepler,StaciA

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。

• DOI: 10.1016/j.lana.2024.100709
• 发表时间: 2024
• 期刊: Lancet regional health. Americas
• 作者: Wang,Jianing;Bernson,Dana;Erdman,ElizabethA;Villani,Jennifer;Chandler,Redonna;Kline,David;White,LauraF;Barocas,JoshuaA
• 通讯作者: Barocas,JoshuaA