Authors: Daniel Wiese*, Temple University, Shannon M. Lynch, Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, Kevin A. Henry, Department of Geography and Urban Studies, Temple University, Philadelphia, PA, Kristen Sorice, Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, Minhhuyen Nguyen, Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, Evelyn Gonzalez, Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA
Topics: Medical and Health Geography, Quantitative Methods, Spatial Analysis & Modeling
Keywords: Liver Cancer, Sensitivity Analysis, Geographic Disparities, Health Disparities, SaTScan, Scan Statistics
Session Type: Poster
Start / End Time: 3:05 PM / 4:45 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download
Liver cancer rates are rising, particularly in minority populations. Risk factors for liver cancer are generally modifiable through lifestyle interventions, vaccinations, and treatments. However, resources are often limited while strategies to prioritize geographic areas are needed. Recommendations exist to focus prevention efforts on groups with the highest rates of liver cancer (Hispanics, Blacks, and individuals born 1950-1959 who are at high risk for HCV). Pennsylvania (PA) Liver Cancer Registry data from 2007-2014 were linked to the 2010 U.S. Census data. Using the space-time scan statistic (SaTScan) and relative risk estimates from BayesX, we generated geographic clusters with significantly elevated rates of incident liver cancer. Among several census-based variables (e.g. degree of poverty, deprivation or segregation), we identified four most important. Sensitivity, specificity, and positive predictive value (PPV) of a census tract being located in a high risk cluster and/or testing positive or negative for at least one of the four neighborhood variables were calculated. There were 9460 cases of liver cancer diagnosed in PA. Of 3217 census tracts in PA, 402 were located in the 5 high risk clusters (relative risks 1.83-3.73, all p<0.05), whereas 1,295 were positive for at least one of 4 neighborhood demographic variables. While sensitivity was relatively high (93.8%), specificity (67.3%) and PPV (28.8%) were low. Coupling disease cluster with neighborhood demographic data identifies areas that carry the greatest burden of liver cancer and reduces intervention targets more than neighborhood demographics alone. However, additional analyses are needed to improve the sensitivity/specificity of this combined geospatial approach.