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用于整合和纠正来自被动和主动疾病监测系统的数据的空间分层模型。

A spatial hierarchical model for integrating and bias-correcting data from passive and active disease surveillance systems.

机构信息

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.

Division of Environmental Health Sciences, University of California, Berkeley, California, USA.

出版信息

Spat Spatiotemporal Epidemiol. 2020 Nov;35:100341. doi: 10.1016/j.sste.2020.100341. Epub 2020 Jun 10.

DOI:10.1016/j.sste.2020.100341
PMID:33138957
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7704115/
Abstract

Disease surveillance data are important for monitoring disease burden and occurrence, and for informing a wide range of efforts to improve population health. Surveillance for infectious diseases may be conducted passively, relying on reports from healthcare facilities, or actively, involving surveys of the population at risk. Passive surveillance typically provides wide spatial coverage, but is subject to biases arising from differences in care-seeking behavior, diagnostic practices, and under-reporting. Active surveillance minimizes these biases, but is typically constrained to small areas and subpopulations due to resource limitations. Methods based on linkage of individual records between passive and active surveillance datasets provide a means to estimate and correct for the biases of each system, leveraging the size and coverage of passive surveillance and the quality of data in active surveillance. We develop a spatial Bayesian hierarchical model for bias-correcting data from both systems to yield an improved estimate of disease measures after adjusting for under-ascertainment. We apply the framework to data from a passive and an active surveillance system for pulmonary tuberculosis (PTB) in Sichuan, China, and estimate the average sensitivity of the active surveillance system at 70% (95% credible interval: 62%, 78%), and the passive system at 30% (95% CI: 24%, 35%). Passive surveillance sensitivity exhibited considerable spatial variability, and was positively associated with a site's gross domestic product per capita. Bias-corrected estimates of county-level PTB prevalence in the province in 2010 identified regions in the southeast with the highest PTB burden, yielding different geographic priorities than previous reports.

摘要

疾病监测数据对于监测疾病负担和发生情况以及为改善人口健康的各项工作提供信息至关重要。传染病监测可以被动进行,依靠医疗机构的报告,也可以主动进行,对高危人群进行调查。被动监测通常具有广泛的空间覆盖范围,但由于寻求医疗服务行为、诊断实践和漏报的差异,存在偏差。主动监测可以最大限度地减少这些偏差,但由于资源限制,通常局限于小范围和亚人群。基于个体记录在被动和主动监测数据集中的链接方法,提供了一种估计和纠正每个系统偏差的方法,利用了被动监测的规模和覆盖范围以及主动监测数据的质量。我们开发了一个空间贝叶斯分层模型,用于对来自两个系统的数据进行偏差校正,以便在调整漏报率后,对疾病指标进行更准确的估计。我们将该框架应用于中国四川省的一个被动和一个主动肺结核(PTB)监测系统的数据中,估计主动监测系统的平均灵敏度为 70%(95%可信区间:62%,78%),而被动监测系统的灵敏度为 30%(95%可信区间:24%,35%)。被动监测的灵敏度表现出相当大的空间变异性,与一个地区的人均国内生产总值呈正相关。该省 2010 年县级结核病流行率的校正偏差估计值确定了东南部结核病负担最高的地区,与以前的报告相比,确定了不同的地理重点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/0aea7a1409a6/nihms-1646799-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/8bb33535c8d4/nihms-1646799-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/4b482be9fc90/nihms-1646799-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/bee71d779c4f/nihms-1646799-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/0aea7a1409a6/nihms-1646799-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/8bb33535c8d4/nihms-1646799-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/4b482be9fc90/nihms-1646799-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/bee71d779c4f/nihms-1646799-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9206/7704115/0aea7a1409a6/nihms-1646799-f0004.jpg

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