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

Russell K. McIntire (), Katherine Senter, Christine Shusted, Rickisa Yearwood, Julie Barta, Scott W. Keith and Charnita Zeigler-Johnson
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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

IJERPH, 2025, vol. 22, issue 3, 1-13

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.

Keywords: lung cancer; geographic information systems; Philadelphia; cancer cluster (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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