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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.
期刊论文(5)
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科研奖励(0)
会议论文
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.)
影响因子: --
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.)
影响因子: --
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
影响因子: --
作者: [Wang,Jianing, Bernson,Dana, Erdman,ElizabethA, Villani,Jennifer, Chandler,Redonna, Kline,David, White,LauraF, Barocas,JoshuaA]
通讯作者: Barocas,JoshuaA
Spatio-temporal Methods for Surveillance of the Opioid Syndemic
Spatio-temporal Methods for Surveillance of the Opioid Syndemic
A Bayesian Spatio-Temporal Approach for Estimating County-Level Opioid Misuse Rates in Ohio
  • 批准号:
    9600216
  • 项目类别:
  • 资助金额:
    $23.22万
  • 财政年份:
    2018
  • 负责人:
    Staci Hepler
  • 依托单位:
海外基金