Abstract PR-13: Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening
Authors: Young Corey D. , Cheung Li C. , Berg Christine D. , Rivera Patricia , Robbins Hilary A. , Chaturvedi Anil K. , Katki Hormuzd A. , Landy Rebecca 
Published: January 2022

Background: Some uses of “race correction” in clinical algorithms and prediction models unfairly reduce access to care, resulting in calls to remove racial/ethnic variables from all models and algorithms. However, for models that are based on unbiased, high-quality, and plentiful data, removing racial/ethnic variables may reduce prediction accuracy for minorities. We model racial/ethnic disparities in screening eligibility from augmenting USPSTF-2021 guidelines (ages 50-80, ≥20 pack-years, ≤15 quit-years) to also include individuals selected by an NCCN-recommended risk model that includes race (PLCOM2012) versus the same model with race/ethnicity removed (PLCOM2012_NoRace). Methods: We used previously published methodology to model the performance of lung cancer screening using 6915 ever-smokers ages 50-80 from the US-representative 2015 National Health Interview Survey (NHIS). Individuals were considered eligible for screening if they are eligible by USPSTF-2021 guidelines or by PLCOM2012 (“USPSTF+PLCOM2012”), versus being eligible by USPSTF-2021 or PLCOM2012_NoRace (“USPSTF+PLCOM2012_NoRace”). Both models used the NCCN-recommended ≥1.3% 6-year risk-threshold for eligibility. We evaluated model accuracy (average percent over/under-estimation) by race/ethnicity, estimated the proportion of life-years gainable achieved by each eligible cohort (LYG), and evaluated the LYG disparity (difference in LYG between whites and each minority). Results: USPSTF+PLCOM2012 and USPSTF+PLCOM2012_NoRace identified similar numbers of minorities as eligible for screening (~2.7 million). However, USPSTF+PLCOM2012_NoRace selected 125% more Hispanic-Americans and 31% less African-Americans. LYG disparities decreased using USPSTF+PLCOM2012_NoRace versus USPSTF+PLCOM2012 for Hispanic Americans (LYG: 33% to 29%). However, LYG disparities for African Americans increased (LYG: 16% to 18%). PLCOM2012 underestimated lung cancer risk by 49% for Hispanic-Americans, whereas PLCOM2012_NoRace performed well (4% overestimation). However, PLCOM2012underestimated risk in African-Americans by only 6%, PLCOM2012_NoRace underestimated risk in African-Americans by 36%. Conclusion: The model that was most accurate for a minority group was projected to reduce disparities the most for that group. Removing race from the PLCOM2012 model substantially underestimated risk for African-Americans and may increase disparities. Inexplicably, PLCOM2012 substantially underestimated risk in Hispanic-Americans despite including race/ethnicity, which was alleviated by removing race/ethnicity. Great care must be taken when removing racial/ethnic variables from models, because this will assign minorities risk estimates that may be largely, or entirely, based on the majority population.

Citation Format: Corey D. Young, Li C. Cheung, Christine D. Berg, Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki, Rebecca Landy. Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-13.