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Addressing bias in preterm birth research: The role of advanced imputation techniques for missing race and ethnicity in perinatal health data.
Scroggins, Jihye Kim; Hulchafo, Ismael Ibrahim; Topaz, Maxim; Cato, Kenrick; Barcelona, Veronica.
Afiliación
  • Scroggins JK; Columbia University School of Nursing, New York, NY, United States. Electronic address: jks2238@cumc.columbia.edu.
  • Hulchafo II; Columbia University School of Nursing, New York, NY, United States.
  • Topaz M; Columbia University School of Nursing, New York, NY, United States; Data Science Institute, Columbia University, New York, NY, United States; Center for Home Care Policy & Research, VNS Health, New York, NY, United States.
  • Cato K; University of Pennsylvania School of Nursing, Philadelphia, PA, United States.
  • Barcelona V; Columbia University School of Nursing, New York, NY, United States.
Ann Epidemiol ; 94: 120-126, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38734192
ABSTRACT

OBJECTIVES:

To 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.

METHODS:

We 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).

RESULTS:

BISG 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).

CONCLUSIONS:

BISG 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Etnicidad / Sesgo / Teorema de Bayes / Nacimiento Prematuro / Registros Electrónicos de Salud Límite: Adult / Female / Humans / Newborn / Pregnancy Idioma: En Revista: Ann Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Etnicidad / Sesgo / Teorema de Bayes / Nacimiento Prematuro / Registros Electrónicos de Salud Límite: Adult / Female / Humans / Newborn / Pregnancy Idioma: En Revista: Ann Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos