Geographic Differences in Lung Cancer Incidence: A Study of a Major Metropolitan Area within Southeastern Pennsylvania

Yaqian Zhu, Thomas P. McKeon, Vicky Tam, Anil Vachani, Trevor M. Penning and Wei-Ting Hwang
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Yaqian Zhu: Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Thomas P. McKeon: Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Vicky Tam: Cartographic Modeling Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Anil Vachani: Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Trevor M. Penning: Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Wei-Ting Hwang: Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA

IJERPH, 2020, vol. 17, issue 24, 1-14

Abstract: This study investigated the geographic variation and the clustering of lung cancer incidence rates in Philadelphia and the surrounding areas using addresses at the time of diagnosis. Using 60,844 cases from Pennsylvania Cancer Registry, we calculated and mapped the age-adjusted incidence rates for five Pennsylvania (PA) counties near Philadelphia between 1998–2007 and 2008–2017. We identified ZIP codes with significantly higher incidence rates than the state rates and examined their demographic and exposure characteristics. Further, we tested for spatial autocorrelation and identified spatial clusters using Moran’s I statistic. Our results showed that approximately one in four ZIP codes had an incidence rate that was significantly higher than the PA state rate in each period studied. Clusters of higher incidences were detected in the southeastern part of PA bordering New Jersey. These areas tended to be more populated, of lower socioeconomic status, and closer to manufacturing facilities and major highways. Possibly driven by the community and environmental factors, the observed differences in disease incidence suggest the importance of including residential location in risk assessment tools for lung cancer.

Keywords: lung cancer; incidence; clustering; spatial autocorrelation; environmental risk factor (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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