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A Geospatial Analysis of the Lung Cancer Burden in Philadelphia, Using Pennsylvania Cancer Registry Data from 2008–2017

Author

Listed:
  • Russell K. McIntire

    (College of Health, Lehigh University, Bethlehem, PA 18015, USA)

  • Katherine Senter

    (College of Population Health, Thomas Jefferson University, Philadelphia, PA 19107, USA)

  • Christine Shusted

    (Division of Pulmonary and Critical Care Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA)

  • Rickisa Yearwood

    (Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA 19111, USA)

  • Julie Barta

    (Division of Pulmonary and Critical Care Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA)

  • Scott W. Keith

    (Division of Biostatistics and Bioinformatics, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA)

  • Charnita Zeigler-Johnson

    (Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA 19111, USA)

Abstract

(1) Background: Lung cancer is the deadliest and second most prevalent cancer in Pennsylvania (PA), and African American patients are disproportionately affected. Lung cancer morbidity and mortality in Philadelphia County are among the highest in PA. Geographic information systems (GIS) are useful to explore geospatial variations in the cancer burden and risk factors. Therefore, we used GIS to analyze the lung cancer burden in Philadelphia to assess which areas of the city have the highest morbidity and mortality, identify potential clusters, and determine which census tract-level characteristics were associated with higher tract-level cancer burden. (2) Methods: Using secondary data from the Pennsylvania Cancer Registry, age-adjusted standardized incidence and mortality ratios (SIR and SMR) were calculated by census tract, and choropleth maps were created to visualize geographic variations in the disease burden. Two geostatistical methods were used to determine the presence of lung cancer clusters. Multivariable regression analyses were performed to identify which census-tract level characteristics correlated with a higher lung cancer burden. (3) Results: Three distinct geographical lung cancer clusters were identified. After controlling for demographics and other covariates, adult smoking prevalence, prevalence of chronic obstructive pulmonary disease, and percentage of residential addresses vacant were positively associated with higher lung cancer SIR and SMR. (4) Conclusions: Our findings may inform cancer control efforts within the region and guide future municipal-level GIS analyses of the lung cancer burden.

Suggested Citation

  • Russell K. McIntire & Katherine Senter & Christine Shusted & Rickisa Yearwood & Julie Barta & Scott W. Keith & Charnita Zeigler-Johnson, 2025. "A Geospatial Analysis of the Lung Cancer Burden in Philadelphia, Using Pennsylvania Cancer Registry Data from 2008–2017," IJERPH, MDPI, vol. 22(3), pages 1-13, March.
  • Handle: RePEc:gam:jijerp:v:22:y:2025:i:3:p:455-:d:1616431
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