National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies Date
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 1 of 1 Research Studies DisplayedLandy R, Gomez I, Caverly TJ
Methods for using race and ethnicity in prediction models for lung cancer screening eligibility.
The purpose of this study was to compare eligibility for lung cancer screening in a representative United States population by refitting the life-years gained from screening-computed tomography (LYFS-CT) model to exclude race and ethnicity versus a counterfactual eligibility method that recalculates life expectancy for racial and ethnic minority individuals utilizing the same covariates but substitutes White race and utilizes the higher predicted life expectancy, preventing historically underserved groups from being penalized. The National Health Interview Survey (NHIS) 2015-2018 included 25,601 individuals aged 50 to 80 years who ever smoked. The study found that removing race and ethnicity from the submodels underestimated lung cancer death risk and all-cause mortality in African American individuals. It also overestimated mortality in Hispanic American and Asian American individuals. As a result, the LYFS-CT NoRace model increased Hispanic American and Asian American eligibility by 108% and 73%, respectively, while decreasing African American eligibility by 39%. Utilizing LYFS-CT with the counterfactual all-cause mortality model better maintained calibration across groups and increased African American eligibility by 13% without decreasing eligibility for Hispanic American and Asian American individuals.
AHRQ-funded; HS026198.
Citation: Landy R, Gomez I, Caverly TJ .
Methods for using race and ethnicity in prediction models for lung cancer screening eligibility.
JAMA Netw Open 2023 Sep; 6(9):e2331155. doi: 10.1001/jamanetworkopen.2023.31155..
Keywords: Racial and Ethnic Minorities, Cancer: Lung Cancer, Cancer, Screening, Prevention