Population-Based Models Increase Lung Cancer Screening Coverage

Lung cancer remains the primary cause of cancer-related mortality globally, accounting for nearly half of the total decline in cancer deaths in recent years due to improved treatment and earlier detection [1, 2]. While the overall cancer death rate has dropped by 33% since 1991, the rising incidence of various malignancies in women and younger adults continues to challenge clinical progress [3, 4]. Annual screening with low-dose computed tomography (LDCT) is established as an effective intervention to reduce mortality among high-risk individuals, specifically those with a significant smoking history [5, 6]. Despite these clear clinical benefits, implementing effective screening at scale is hindered by low participation rates and significant disparities in access among underserved populations [7, 8]. A new analysis now evaluates which specific recruitment frameworks most effectively bridge the gap between clinical guidelines and real-world screening uptake.