Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations partially ameliorate racial disparities in screening eligibility compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4% = 15.0% to 64.5%-48.5% = 16.0%; Asian Americans: 48.3%-35.6% = 12.7% to 64.5%-45.2% = 19.3%; Hispanic Americans: 48.3%-24.8% = 23.5% to 64.5%-37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.

Automated summary

The draft 2020 USPSTF guidelines increased eligibility for lung cancer screening among minorities and Whites by similar proportions compared to the 2013 guidelines. However, the relative disparity in gainable life-years from screening between eligible Whites and minorities actually increased with the 2020 guidelines. Using the LYFS-CT model could help reduce disparities in screening eligibility by identifying high-benefit individuals regardless of race and ethnicity.