Decoupling race/ethnicity from pediatric lung function metrics to investigate potential misclassification bias in environmental epidemiologic research

Background and aim: Electronic health records (EHR) are increasingly being used in environmental epidemiologic research. Respiratory studies of asthma commonly use derived spirometry metrics, such as percent predicted forced expiratory volume in one second (FEV1%predicted), that are calculated from age, height, sex, and – controversially – race/ethnicity. We examine potential misclassification bias introduced by historically race/ethnicity-adjusted pediatric lung function metrics. Methods: Spirometry results for children with asthma ages 5-17 years were obtained from a clinical EHR data repository at Boston Medical Center (BMC) from 2003 to 2019. BMC is New England’s largest safety net hospital, serving a racio-ethnically diverse pediatric population. Recorded lung function metrics included FEV1 (liters), forced vital capacity (liters), and previously calculated FEV1%predicted (%) that adjusted for race/ethnicity. We recalculated FEV1%predicted values by standardizing to a global, multiethnic population using the Global Lung Initiative equations and compared to original FEV1%predicted from the EHR data repository. Results: The EHR included 1141 children that had 2651 spirometry measurements. Mean FEV1%predicted was 91.6% from the EHR and 87.2% when recalculated. Original FEV1%predicted values were higher compared to recalculated values for Black children (mean=89.3% [standard error=0.56] vs. 82.5% [0.42] , n=1526, p<0.01), but not for Asian (93.8% [3.72] vs. 92.0% [3.59], n=32, p=0.72), Hispanic (96.5% [1.65] vs. 94.4% [1.56], n=129, p=0.37), Other (93.8% [3.55] vs. 91.3% [2.52], n=47, p=0.57), Not Recorded (94.7% [0.73] vs. 94.1% [0.66], n=720, p=0.25), and White (94.1% [1.72] vs. 94.1% [1.42], n=195, p=0.97). Conclusions: Race/ethnicity-adjustment of a commonly used "objective” lung function metric in EHR may artificially improve lung function test results, particularly for Black children with asthma, resulting in a potential source of misclassification bias in environmental epidemiologic research. Continued use of historically race/ethnicity-adjusted lung function metrics warrants critical reevaluation, especially in research aimed at disentangling the environmental drivers of health disparities driven by structural racism.