Journal article
Y. Lee, Ankita Patil, Cheryl R. Clark, Monik, C. Botero, David W. Stein, Elizabeth Karlson
medRxiv, 2024
medRxiv, 2024
Semantic Scholar
DOI
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APA
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Lee, Y., Patil, A., Clark, C. R., Monik, Botero, C., Stein, D. W., & Karlson, E. (2024). Impact of Participation Bias on Disease Prevalence Estimation in the All of Us Research Program: A Case Study of Ischemic Heart Disease and Stroke. MedRxiv.
Chicago/Turabian
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Lee, Y., Ankita Patil, Cheryl R. Clark, Monik, C. Botero, David W. Stein, and Elizabeth Karlson. “Impact of Participation Bias on Disease Prevalence Estimation in the All of Us Research Program: A Case Study of Ischemic Heart Disease and Stroke.” medRxiv (2024).
MLA
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Lee, Y., et al. “Impact of Participation Bias on Disease Prevalence Estimation in the All of Us Research Program: A Case Study of Ischemic Heart Disease and Stroke.” MedRxiv, 2024.
BibTeX Click to copy
@article{y2024a,
title = {Impact of Participation Bias on Disease Prevalence Estimation in the All of Us Research Program: A Case Study of Ischemic Heart Disease and Stroke},
year = {2024},
journal = {medRxiv},
author = {Lee, Y. and Patil, Ankita and Clark, Cheryl R. and Monik and Botero, C. and Stein, David W. and Karlson, Elizabeth}
}
Abstract
Importance: Disease prevalence estimation is highly sensitive to sample characteristics shaped by recruitment and data collection strategies. Using follow-up study modules that require active participant engagement may introduce participation bias, affecting the accuracy of disease prevalence estimation. Objective: To estimate the prevalence of ischemic heart disease (IHD) and stroke using electronic health records (EHR) and the self-reported Personal Medical History (PMH) survey collected in the All of Us Research Program. Design and settings: Cross-sectional study aimed at estimating the prevalence of IHD and stroke among 266,472 participants with EHR in the latest release of the All of Us Registered Tier Curated Data Repository (R2022Q4R9). Main outcomes and measures: Primary outcomes were IHD and stroke, ascertained using expert-curated diagnostic and procedure codes recorded in EHR. Secondary outcomes were IHD and stroke, ascertained using responses from the PMH survey. To mitigate the impact of participation bias in the PMH survey responses, we applied poststratification weighting based on annual household income and education. Results: Of the 266,472 participants with EHR, 17,054 (6.4%) were identified as having IHD and 7,461 (2.8%) as having stroke based on the EHR definitions. Among PMH survey respondents, the EHR-based prevalence was lower at 5.6% (95% CI, 5.4-5.7) for IHD and 2.2% (95% CI, 2.1-2.3) for stroke, compared to 7.2% (95% CI, 7.0-7.3) for IHD and 3.3% (95% CI, 3.2-3.4) for stroke among non-respondents. The PMH survey-based prevalence among respondents was 5.9% (95% CI, 5.7-6.0) for IHD and 3.6% (95% CI, 3.5-3.7) for stroke, with higher estimates among non-Hispanic White participants after applying poststratification weights. Conclusion and relevance: Our findings suggest that while the current All of Us cohort with EHR reflects the general US population for IHD and stroke prevalence, participants completing the PMH survey are skewed toward higher socioeconomic status and medical literacy. Future research should refine bias mitigation strategies when using voluntary follow-up data to estimate disease prevalence in this cohort.