Amin Bemanian1,2, Laura D Cassidy3, Raphael Fraser3, Purushottam W Laud3, Kia Saeian4, Kirsten M M Beyer3. 1. Institute for Health & Equity, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. abemanian@mcw.edu. 2. Medical Scientist Training Program, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. abemanian@mcw.edu. 3. Institute for Health & Equity, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA. 4. Division of Gastroenterology and Hepatology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA.
Abstract
PURPOSE: To calculate tract-level estimates of liver cancer mortality in Wisconsin and identify relationships with racial and socioeconomic variables. METHODS: County-level standardized mortality ratios (SMRs) of liver cancer in Wisconsin were calculated using traditional indirect adjustment methods for cases from 2003 to 2012. Tract-level SMRs were calculated using adaptive spatial filtering (ASF). The tract-level SMRs were checked for correlations to a socioeconomic advantage index (SEA) and percent racial composition. Non-spatial and spatial regression analyses with tract-level SMR as the outcome were conducted. RESULTS: County-level SMR estimates were shown to mask much of the variance within counties across their tracts. Liver cancer mortality was strongly correlated with the percent of Black residents in a census tract and moderately associated with SEA. In the multivariate spatially-adjusted regression analysis, only Percent Black composition remained significantly associated with an increased liver cancer SMR. CONCLUSIONS: Using ASF, we developed a high-resolution map of liver cancer mortality in Wisconsin. This map provided details on the distribution of liver cancer that were inaccessible in the county-level map. These tract-level estimates were associated with several racial and socioeconomic variables.
PURPOSE: To calculate tract-level estimates of liver cancer mortality in Wisconsin and identify relationships with racial and socioeconomic variables. METHODS: County-level standardized mortality ratios (SMRs) of liver cancer in Wisconsin were calculated using traditional indirect adjustment methods for cases from 2003 to 2012. Tract-level SMRs were calculated using adaptive spatial filtering (ASF). The tract-level SMRs were checked for correlations to a socioeconomic advantage index (SEA) and percent racial composition. Non-spatial and spatial regression analyses with tract-level SMR as the outcome were conducted. RESULTS: County-level SMR estimates were shown to mask much of the variance within counties across their tracts. Liver cancer mortality was strongly correlated with the percent of Black residents in a census tract and moderately associated with SEA. In the multivariate spatially-adjusted regression analysis, only Percent Black composition remained significantly associated with an increased liver cancerSMR. CONCLUSIONS: Using ASF, we developed a high-resolution map of liver cancer mortality in Wisconsin. This map provided details on the distribution of liver cancer that were inaccessible in the county-level map. These tract-level estimates were associated with several racial and socioeconomic variables.
Entities:
Keywords:
Cancer epidemiology; Cancer mapping; Disparities; Liver cancer; Race
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