# Equity Implications of Lung Cancer Screening Strategies for Population Health: a Distributional Cost-Effectiveness Analysis > **NIH NIH R21** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2024 · $215,546 ## Abstract PROJECT SUMMARY Low-dose computed tomography (LDCT) screening reduces lung cancer deaths. However, LDCT screening rates are extremely low in the U.S., particularly among populations experiencing health disparities, such as Black and low-income Americans. Critics argue that lung cancer screening guidelines do not adequately identify those who would benefit most from screening. In 2021, the U.S. Preventive Services Task Force (USPSTF) updated its guidelines to respond to these critiques and widen their eligibility criteria. Currently, the USPSTF recommends LDCT annually for individuals between the ages of 50 and 80 years with at least a 20 pack-year history of smoking, and who currently smoke or have quit smoking within the past 15 years. The American Cancer Society (ACS) expanded their LDCT screening guidelines in November 2023 to similarly widen their eligibility criteria. The ACS and USPSTF guidelines base screening eligibility on age and smoking history. Yet, screening strategies that use risk prediction models may more equitably identify individuals at high lung cancer risk because they consider more factors (e.g., family cancer history). Some programs are therefore launching efforts to assess lung cancer risk among all age-eligible individuals who have ever smoked using the PLCOm2012 risk prediction model. Prior studies suggest USPSTF guidelines and risk prediction models are cost- effective, but it is unclear whether health benefits are equitably distributed across subpopulations. Cost- effectiveness analysis (CEA) often guides policy decisions but traditionally does not consider equity. Current CEAs of lung cancer screening focus on overall health benefits without considering the distribution of benefits across groups. Many analyses also assume ideal yet unrealistic conditions (e.g., universal screening uptake). This project will use a novel Distributional Cost-Effectiveness Analyses (DCEA) framework, which adds an equity dimension to standard CEA, to address limitations of previous work. The study will evaluate the equity impact and cost-effectiveness of three real-world lung cancer screening strategies: the USPSTF guidelines, the ACS guidelines, and an approach based on the PLCOm2012 risk prediction model. The study will consider equity by explicitly modeling disparities in screening eligibility and uptake rates across different subpopulations experiencing health disparities (e.g., Black Americans). The proposal has three aims. Aim 1 will assess the equity impact and cost-effectiveness of the three lung cancer screening strategies using DCEA. Aim 2 will evaluate trade-offs between reducing inequity and maximizing cost-effectiveness in these screening programs. Aim 3 will create an analytical tool for decision-makers to compare the impact of various interventions on lung cancer screening uptake, especially among historically disadvantaged groups. Overall, this project will provide data-driven insights and tools to assist in the development... ## Key facts - **NIH application ID:** 11039111 - **Project number:** 1R21MD020158-01 - **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES - **Principal Investigator:** Meng-Yun Lin - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $215,546 - **Award type:** 1 - **Project period:** 2024-09-22 → 2026-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/11039111 ## Citation > US National Institutes of Health, RePORTER application 11039111, Equity Implications of Lung Cancer Screening Strategies for Population Health: a Distributional Cost-Effectiveness Analysis (1R21MD020158-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11039111. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*