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February 8, 2026JCO Clinical Cancer Informatics0 citations

Application of the Population Health Institute Model of Health for Identifying Cancer Catchment Area Priorities

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ATA. Trentham-DietzTLThomas P. LawlerRGRonald E. Gangnon

Key Points

  • The aim is to apply the PHI Model of Health to identify and prioritize community health factors related to cancer outcomes.
  • Adapted the PHI Model of Health for cancer outcomes.
  • Derived health factor summary measures from county-level data in health infrastructure, physical environment, and social/economic factors.
  • Calculated a composite health factor z-score for each county.
  • Utilized k-means clustering to categorize counties based on health factor clusters.
  • Employed age-adjusted negative binomial regression to estimate cancer mortality rates in relation to health factor clusters.
  • Cancer mortality rates increased across the 10 health factor clusters for all cancers.
  • Specific increases were observed in lung, colorectal, breast, and prostate cancer mortality rates.
  • Counties with the least favorable health factors had a cancer mortality rate ratio of 1.49 compared to the most favorable counties.

Abstract

PURPOSE The 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. METHODS Using 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. RESULTS Age-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). CONCLUSION The 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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Cite This Study

Trentham-Dietz et al. (2026) studied this question.

synapsesocial.com/papers/698828b90fc35cd7a8848776https://doi.org/10.1200/cci-25-00126
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