Towards precision public health: Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention

OBJECTIVES: Liver cancer (LC) continues to rise, partially due to limited resources for prevention. To test the precision public health (PPH) hypothesis that fewer areas in need of LC prevention could be identified by combining existing surveillance data, we compared the sensitivity/specificity of standard recommendations to target geographic areas using U.S. Census demographic data only (percent (%) Hispanic, Black, and those born 1950–1959) to an alternative approach that couples additional geospatial data, including neighborhood socioeconomic status (nSES), with LC disease statistics. METHODS: Pennsylvania Cancer Registry data from 2007-2014 were linked to 2010 U.S. Census data at the Census tract (CT) level. CTs in the top 80th percentile for 3 standard demographic variables, %Hispanic, %Black, %born 1950–1959, were identified. Spatial scan statistics (SatScan) identified CTs with significantly elevated incident LC rates (p-value<0.05), adjusting for age, gender, diagnosis year. Sensitivity, specificity, and positive predictive value (PPV) of a CT being located in an elevated risk cluster and/or testing positive/negative for at least one standard variable were calculated. nSES variables (deprivation, stability, segregation) significantly associated with LC in regression models (p < 0.05) were systematically evaluated for improvements in sensitivity/specificity. RESULTS: 9,460 LC cases were diagnosed across 3,217 CTs. 1,596 CTs were positive for at least one of 3 standard variables. 5 significant elevated risk clusters (CTs = 402) were identified. 324 CTs were positive for a high risk cluster AND standard variable (sensitivity = 92%; specificity = 37%; PPV = 17.4%). Incorporation of 3 new nSES variables with one standard variable (%Black) further improved sensitivity (93%), specificity (62.9%), and PPV (26.3%). CONCLUSIONS: We introduce a quantitative assessment of PPH by applying established sensitivity/specificity assessments to geospatial data. Coupling existing disease cluster and nSES data can more precisely identify intervention targets with a liver cancer burden than standard demographic variables. Thus, this approach may inform prioritization of limited resources for liver cancer prevention.