Evaluating precision medicine tools in cystic fibrosis for racial and ethnic fairness.

Introduction: Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities.

Methods: We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry.

Racial Fairness of Individual- and Community-Level Proxies of Socioeconomic Status Among Birthing Parent-Child Dyads.

Background: While precision medicine algorithms can be used to improve health outcomes, concerns have been raised about racial equity and unintentional harm from encoded biases. In this study, we evaluated the fairness of using common individual- and community-level proxies of pediatric socioeconomic status (SES) such as insurance status and community deprivation index often utilized in precision medicine algorithms.

Methods: Using 2012-2021 vital records obtained from the Ohio Department of Health, we geocoded and matched each residential birth address to a census tract to obtain community deprivation index.