Estimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan

Roberts, Elizabeth S.; Gu, Tian; Wagner, Abram L.; Mukherjee, Bhramar; Fritsche, Lars G.

doi:10.1016/j.focus.2022.100015

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…Covariates. Similar to our previous COVID-19 studies [13,34,[56][57][58], we considered the following key covariates to control for potential confounding factors. Age at the time of recruitment (UKB) or last observed visit (All of Us, MGI), whichever came last, was considered a continuous variable for each participant.…”

Section: Covid-19 Severity Polygenic Risk Score (Prs) Constructionmentioning

confidence: 99%

“…Building on the existing knowledge of genetic factors influencing COVID-19 severity and our previous work on COVID-19 outcomes [22,23,34], health-related exposure trait PRS [35], and PRS for cancer traits [36], our study aims to employ PRS-CS, a polygenic prediction method, and summary statistics from a GWAS on hospitalization of the COVID-19 Host Genetics Initiative to develop a genome-wide PRS for COVID-19 severity [37]. This approach seeks to address the limitations of conventional risk assessments, which are prone to biases and inconsistencies across health systems and biobanks [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

Fritsche,

Nam,

et al. 2023

PLoS Genet

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Objective To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity. Methods Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis. Results The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection. Conclusion By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

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Section: Covid-19 Severity Polygenic Risk Score (Prs) Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

Fritsche,

Nam,

et al. 2023

PLoS Genet

Self Cite

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A framework for understanding selection bias in real-world healthcare data

Kundu,

Shi,

Morrison

et al. 2024

Journal of the Royal Statistical Society Series A: Statistics in Society

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Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real-world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference.

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Estimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan

Cited by 2 publications

References 43 publications

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

A framework for understanding selection bias in real-world healthcare data

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