Drexel University Dornsife School of Public Health, Department of Epidemiology and Biostatistics, Philadelphia, Pennsylvania, USA.
William J. Holloway Community Program, ChristianaCare, Wilmington, Delaware, USA; Sydney Kimmel College of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Ann Epidemiol. 2021 Aug;60:1-7. doi: 10.1016/j.annepidem.2021.04.011. Epub 2021 Apr 29.
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.
展示在基于电子病历的社区贫困与慢性丙型肝炎病毒感染研究中，医疗机构选择偏倚如何导致偏差，以便更准确地确定当地的风险因素和流行率。
我们创建了一个集水区模型，试图定义选择回顾性队列的概率。然后，我们使用该概率的倒数来比较朴素的未加权和加权模型，以展示选择偏倚的影响。
队列的邮政编码水平生态图表明，社区贫困程度、丙型肝炎结局和到卫生中心的距离（这是一个直观的集水区内的代理变量）之间存在一种模式。朴素的多层次分析发现，生活在贫困程度较高的地区会导致丙型肝炎病毒感染的几率增加 1.25 倍（95%置信区间：1.06，1.48），而加权分析由于选择偏倚，对这种效果的确定性较低。
我们观察到，选择进入研究医疗机构的集水区可能会使社区贫困与丙型肝炎之间的关联产生偏差。通过逆概率加权，可以减轻这种偏差。
