Inverse probability weighting for selection bias in a Delaware community health center electronic medical record study of community deprivation and hepatitis C prevalence.
Inverse probability weighting for selection bias in a Delaware community health center electronic medical record study of community deprivation and hepatitis C prevalence.
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DOI：
10.1016/j.annepidem.2021.04.011
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发表时间：
2021-08
影响因子：
5.6
通讯作者：
Burstyn I
中科院分区：
文献类型：
--
作者：
Goldstein ND;Kahal D;Testa K;Burstyn I
旨在证明选择医疗机构如何在基于电子病历的社区剥夺和慢性丙型肝炎病毒感染研究中引起偏差，以便更准确地识别当地风险因素和患病率。我们创建了一个流域模型，试图定义选择进入回顾性队列的概率。然后，使用该概率的倒数，我们比较了朴素的未加权和加权模型，以证明选择偏差的影响。该队列的邮政编码级生态图表明，存在社区剥夺、丙型肝炎结果和距健康中心的距离（位于流域内的直观代表）的模式。单纯的多水平分析发现，生活在贫困程度较高的地区，感染 HCV 的几率增加 1.25 倍（95% CI：1.06，1.48），而加权分析发现，由于选择偏差，这种效应的确定性较低。我们观察到，选择所研究的医疗机构的服务区域可能会使社区剥夺与丙型肝炎的关联产生偏差。这种情况可以通过逆概率加权来缓解。
To demonstrate how selection into a healthcare facility can induce bias in an electronic medical record-based study of community deprivation and chronic hepatitis C virus infection, in order to more accurately identify local risk factors and prevalence. We created a catchment model that attempted to define the probability of selection into a retrospective cohort. Then using the inverse of this probability, we compared naïve unweighted and weighted models to demonstrate the impact of selection bias. ZIP code-level ecological plots of the cohort demonstrated that there was a pattern of the community deprivation, hepatitis C outcome, and distance to the health center (an intuitive proxy for being within catchments). The naïve multilevel analysis found that living in an area with greater deprivation resulted in 1.25 times greater odds of HCV (95% CI: 1.06, 1.48), whereas the weighted analysis found less certainty of this effect due to a selection bias. We observed that selection into the catchment area of the studied healthcare facility may bias the association of community deprivation and hepatitis C. This may be mitigated through inverse probability weighting.
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