Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines.
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
- Department of Microbiology, Biochemistry and Immunology, Morehouse School of Medicine, Atlanta, GA, USA.
- Division of Pulmonary and Critical Care Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- International Agency for Research on Cancer, Lyon, France.
We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung-cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared to 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/Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). Draft USPSTF-2020 guidelines increased the number of eligible minorities versus USPSTF-2013 but may inadvertently increase racial/ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit, regardless of race/ethnicity.
- NLST-626: Studies to use extended NLST follow-up data (Hormuzd Katki - 2020)