ArticleAnnals of epidemiology2024
Addressing bias in preterm birth research: The role of advanced imputation techniques for missing race and ethnicity in perinatal health data.
Article in Annals of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Patient Disability Status and the Use of Stigmatizing Language in Clinical Notes During Hospital Admission for Birth.Journal of obstetric, gynecologic, and neonatal nursing : JOGNN · 2025Article
- Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study.JMIR public health and surveillance · 2025Article
- Stigmatizing and Positive Language in Birth Clinical Notes Associated With Race and Ethnicity.JAMA network open · 2025Article
- Documentation of Stigmatizing Language in Electronic Health Records and Birth Outcomes.Health equity · 2025Article
- Post-COVID-19 conditions identified by ICD-10-CM code in a cohort of U.S. primary care patients, 2021 - 2023.Journal of multimorbidity and comorbidityArticle
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Authors and funding
5 authors.
Funding
Abstract
objectivesTo evaluate the effectiveness of Bayesian Improved Surname Geocoding (BISG) and Bayesian Improved First Name Surname Geocoding (BIFSG) in estimating race and ethnicity, and how they influence odds ratios for preterm birth.
methodsWe analyzed hospital birth admission electronic health records (EHR) data (N = 9985). We created two simulation sets with 40 % of race and ethnicity data missing randomly or more likely for non-Hispanic black birthing people who had preterm birth. We calculated C-statistics to evaluate how accurately BISG and BIFSG estimate race and ethnicity. We examined the association between race and ethnicity and preterm birth using logistic regression and reported odds ratios (OR).
resultsBISG and BIFSG showed high accuracy for most racial and ethnic categories (C-statistics = 0.94-0.97, 95 % confidence intervals [CI] = 0.92-0.97). When race and ethnicity were not missing at random, BISG (OR = 1.25, CI = 0.97-1.62) and BIFSG (OR = 1.38, CI = 1.08-1.76) resulted in positive estimates mirroring the true association (OR = 1.68, CI = 1.34-2.09) for Non-Hispanic Black birthing people, while traditional methods showed contrasting estimates (Complete case OR = 0.62, CI = 0.41-0.94; multiple imputation OR = 0.63, CI = 0.40-0.98).
conclusionsBISG and BIFSG accurately estimate missing race and ethnicity in perinatal EHR data, decreasing bias in preterm birth research, and are recommended over traditional methods to reduce potential bias.
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Registered trials
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