Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity.

ABSTRACT:

Context

To evaluate algorithmic fairness in low birthweight predictive models.

Study design

This study analyzed insurance claims (n = 9,990,990; 2013-2021) linked with birth certificates (n = 173,035; 2014-2021) from the Arkansas All Payers Claims Database (APCD).

Methods

Low birthweight (< 2500 g) predictive models included four approaches (logistic, elastic net, linear discriminate analysis, and gradient boosting machines [GMB]) with and without racial/ethnic information. Model performance was assessed overall, among Hispanic individuals, and among non-Hispanic White, Black, Native Hawaiian/Other Pacific Islander, and Asian individuals using multiple measures of predictive performance (i.e., AUC [area under the receiver operating characteristic curve] scores, calibr

SUBMITTER: Brown CC 

PROVIDER: S-EPMC12304234 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature