Document Type
Presentation
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Publication Date
3-19-2021
Abstract
Lung cancer is the deadliest and second most prevalent cancer in Pennsylvania (PA), where African American patients are disproportionately affected. Lung cancer morbidity and mortality in Philadelphia County are among the highest in PA (PA Department of Health, 2019). Geographic Information Systems (GIS) are useful to explore geospatial variations in cancer burden and risk factors. We therefore used GIS to analyze lung cancer burden in Philadelphia to assess which areas of the city have highest morbidity and mortality, to identify potential clusters, and to determine which census tract-level characteristics were associated with higher tract-level cancer burden. We calculated the age-adjusted standardized incidence and mortality ratios (SIR and SMR) by census tract using national lung cancer rates and created choropleth maps to visualize geographic variation in disease burden. We used two geostatistical methods to determine the presence of lung cancer clusters. Lastly, we performed multivariable regression analyses to identify which census-tract level characteristics correlated with higher lung cancer burden. We identified two distinct lung cancer clusters. After controlling for other variables, adult smoking prevalence and prevalence of chronic obstructive pulmonary disease were positively associated with higher lung cancer SIR, and percent residential addresses vacant was negatively associated. These variables were also associated with higher lung cancer SMR, in addition to percent of census tract population White; percent Asian; and percent obese, which were negatively associated. Our findings may inform cancer control efforts within the region and guide future municipal-level GIS analyses of lung cancer burden.
Recommended Citation
Senter, Katherine, "A Geospatial Analysis of Lung Cancer Burden in Philadelphia, PA" (2021). Master of Public Health Capstone Presentations. Presentation 371.
https://jdc.jefferson.edu/mphcapstone_presentation/371
Language
English
Comments
Presentation: 5:36
Poster attached as supplemental file below