Analysis of Error-prone Electronic Health Records with Multi-wave Validation Sampling: Association of Maternal Weight Gain during Pregnancy with Childhood Outcomes

TL;DR

This study develops a multi-wave validation sampling method for electronic health records to accurately estimate the association between maternal weight gain during pregnancy and childhood obesity or asthma, addressing data quality challenges.

Contribution

It introduces a novel multi-wave validation sampling approach that adaptively updates sampling based on influence function estimates to improve accuracy in EHR-based research.

Findings

Validated 996 of 10,335 dyads across 6 waves.

Estimated associations differ significantly from naive unvalidated estimates.

Efficient validation sampling improves accuracy of EHR-derived associations.

Abstract

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multi-wave, two-phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multi-wave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re-estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother-child EHR dyads in 6 sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naive estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data.