ArticleJCO clinical cancer informatics2026
Application of the Population Health Institute Model of Health for Identifying Cancer Catchment Area Priorities.
Article in JCO clinical cancer informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe University of Wisconsin Population Health Institute (PHI) Model of Health, grounded in models developed over a decade ago, provides a framework for prioritizing health-related investments including setting agendas, implementing policies, and sharing resources for improving community health and health equity. The model includes multiple determinants of health and two broad health outcomes (length and quality of life). We adapted the PHI Model of Health to cancer outcomes.
methodsUsing county-level publicly available data, health factor summary measures were derived in three areas: health infrastructure including health promotion and clinical care, physical environment, and social and economic factors. A composite health factor z-score was calculated as the weighted (40%, 15%, and 45%, respectively) average of the summary measures for each county, and k-means clustering was used to create unequally sized county groups with lower (healthier) to higher (less healthy) z-scores. We fit age-adjusted negative binomial regression models to estimate rate ratios and 95% CI for cancer mortality in relation to county health factor cluster.
resultsAge-adjusted cancer mortality rates increased across the 10 county health factor clusters for all-cancers as well as for lung, colorectal, breast, and prostate cancers. Rate ratios generally increased across the 10 health factor clusters for all cancers combined and for specific cancer types. Compared with counties with the most favorable health factor conditions, the counties with the least favorable conditions had an all-cancer mortality rate ratio of 1.49 (95% CI, 1.39 to 1.60).
conclusionThe PHI model of health adapted to cancer outcomes provides an approach for linking community-specific conditions to the interventions that hold promise to directly address drivers of the cancer burden.
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