Abstract PO-247: Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening

Abstract

Abstract Background: For the same age and smoking history as whites, minorities have substantially different lung-cancer risk. However, current US Preventive Services Task Force (USPSTF) lung-cancer screening recommendations make no allowance for race/ethnicity and may induce health disparities. Incorporating individualized prediction-models into USPSTF guidelines may reduce racial/ethnic disparities in lung-cancer screening eligibility. We examine whether expanding current USPSTF lung cancer screening eligibility to include ever-smokers whose risk (calculated by an individualized prediction model) exceeded a threshold would reduce racial/ethnic disparities induced by current USPSTF guidelines. Methods: We used the US- representative 2015 National Health Interview Survey to examine screening eligibility. We identified the thresholds for each of 5 models: lung-cancer risk (Bach, PLCOM2012 and LCRAT models), lung-cancer death risk (LCDRAT model), and life- years gained by attending screening (LYFS-CT model), which select the same number of ever-smokers aged 50-80yrs as USPSTF guidelines. We defined 5 cohorts of ever- smokers as eligible for screening if they were eligible by each screening model or USPSTF guidelines. Among each race/ethnicity, we calculated the number eligible for screening, proportion of preventable lung-cancer deaths prevented (LCD sensitivity), proportion of gainable life-years gained (LYG sensitivity) and screening effectiveness (the number needed to screen to prevent one lung-cancer death). Results: USPSTF criteria performed best for whites (20% eligible, preventing 55% of preventable lung- cancer deaths). Asian-Americans had the least effective screening (NNS=419), only 13% of African-Americans were eligible despite having the most effective screening (NNS=135), and Hispanic-Americans had the lowest percentages eligible (9%) and deaths preventable (30%). Augmenting USPSTF criteria with LCDRAT or LYFS-CT prediction-models nearly equalized the performance of screening for African- Americans with that of whites, doubling the number of African-Americans eligible and increasing the number of preventable deaths and life-years gained by nearly 80%, although at a 25% loss in effectiveness. Prediction-models improved all screening metrics for Asian-Americans and Hispanic-Americans. However models estimated risk more accurately for whites than minorities. Conclusions: Augmenting USPSTF criteria with the LCDRAT or LYFS-CT prediction-models nearly eliminated the white/African-American disparity. All screening metrics were substantially improved for Asian/Hispanic-Americans. Citation Format: Rebecca Landy, Corey D. Young, Martin Skarzynski, Li C. Cheung, Christine D. Berg, M. Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki. Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-247.