aceMachine Learning Identifies Community-Level Factors Associated with Lung Cancer Incidence
Background Lung cancer is the leading cause of cancer-related death in the United States. Individual-level risk factors such as tobacco use are well characterized, but the community-level environmental, social, and economic contributions to geographic variation in incidence remain incompletely understood. Methods We conducted an ecological analysis of 542 Medical Service Study Areas (MSSAs) in California, linking age-adjusted lung cancer incidence rates (AAIR, 2015-2019) to 27 indicators of pollution, population…
A machine learning model using community indicators identified high-incidence California areas better than smoking data alone, which could help locate areas of elevated lung cancer burden where registry rates are suppressed.
The model predicted Hispanic-specific incidence less accurately than White-specific incidence, exposing data gaps that could undermine equitable cancer prevention.
The authors state the associations are concurrent rather than etiologic and not causal, and predictive performance was lower for some subpopulations, indicating data gaps.
Evidence
- Peer-reviewedCancer Epidemiology, Biomarkers & Prevention2026-10-15
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Truvace Impact Record TRV-2026-1985, v1: “Machine Learning Identifies Community-Level Factors Associated with Lung Cancer Incidence.” Truvace, 2026-10-20. /record/TRV-2026-1985 (accessed at citation time). sha256 efc7c09b04fbdcb4…
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