Vincent L Freeman1,2,3, Emma E Boylan4, Oksana Pugach4,5, Sara L Mclafferty6, Katherine Y Tossas-Milligan4,7, Karriem S Watson7, Robert A Winn7. 1. Division of Epidemiology and Biostatistics, University of Illinois at Chicago School of Public Health, 1603 W. Taylor St., Chicago, IL, 60612, USA. freem981@uic.edu. 2. University of Illinois at Chicago Cancer Center, University of Illinois Hospital and Health Sciences System, 914 S. Wood St., Chicago, IL, 60612, USA. freem981@uic.edu. 3. Institute for Health Research and Policy, University of Illinois School of Public Health, 1747 W. Roosevelt Road, Chicago, IL, 60612, USA. freem981@uic.edu. 4. Division of Epidemiology and Biostatistics, University of Illinois at Chicago School of Public Health, 1603 W. Taylor St., Chicago, IL, 60612, USA. 5. Institute for Health Research and Policy, University of Illinois School of Public Health, 1747 W. Roosevelt Road, Chicago, IL, 60612, USA. 6. Department of Geography and Geographic Information Science, School of Earth, Society, and Environment, University of Illinois at Urbana-Champaign, 605 E. Springfield Ave, Champaign, IL, 61820, USA. 7. University of Illinois at Chicago Cancer Center, University of Illinois Hospital and Health Sciences System, 914 S. Wood St., Chicago, IL, 60612, USA.
Abstract
PURPOSE: To address locally relevant cancer-related health issues, health departments frequently need data beyond that contained in standard census area-based statistics. We describe a geographic information system-based method for calculating age-standardized cancer incidence rates in non-census defined geographical areas using publically available data. METHODS: Aggregated records of cancer cases diagnosed from 2009 through 2013 in each of Chicago's 77 census-defined community areas were obtained from the Illinois State Cancer Registry. Areal interpolation through dasymetric mapping of census blocks was used to redistribute populations and case counts from community areas to Chicago's 50 politically defined aldermanic wards, and ward-level age-standardized 5-year cumulative incidence rates were calculated. RESULTS: Potential errors in redistributing populations between geographies were limited to <1.5% of the total population, and agreement between our ward population estimates and those from a frequently cited reference set of estimates was high (Pearson correlation r = 0.99, mean difference = -4 persons). A map overlay of safety-net primary care clinic locations and ward-level incidence rates for advanced-staged cancers revealed potential pathways for prevention. CONCLUSIONS: Areal interpolation through dasymetric mapping can estimate cancer rates in non-census defined geographies. This can address gaps in local cancer-related health data, inform health resource advocacy, and guide community-centered cancer prevention and control.
PURPOSE: To address locally relevant cancer-related health issues, health departments frequently need data beyond that contained in standard census area-based statistics. We describe a geographic information system-based method for calculating age-standardized cancer incidence rates in non-census defined geographical areas using publically available data. METHODS: Aggregated records of cancer cases diagnosed from 2009 through 2013 in each of Chicago's 77 census-defined community areas were obtained from the Illinois State Cancer Registry. Areal interpolation through dasymetric mapping of census blocks was used to redistribute populations and case counts from community areas to Chicago's 50 politically defined aldermanic wards, and ward-level age-standardized 5-year cumulative incidence rates were calculated. RESULTS: Potential errors in redistributing populations between geographies were limited to <1.5% of the total population, and agreement between our ward population estimates and those from a frequently cited reference set of estimates was high (Pearson correlation r = 0.99, mean difference = -4 persons). A map overlay of safety-net primary care clinic locations and ward-level incidence rates for advanced-staged cancers revealed potential pathways for prevention. CONCLUSIONS: Areal interpolation through dasymetric mapping can estimate cancer rates in non-census defined geographies. This can address gaps in local cancer-related health data, inform health resource advocacy, and guide community-centered cancer prevention and control.
Entities:
Keywords:
Areal interpolation through dasymetric mapping; Local cancer health information; Precision public health
Authors: Limin X Clegg; Eric J Feuer; Douglas N Midthune; Michael P Fay; Benjamin F Hankey Journal: J Natl Cancer Inst Date: 2002-10-16 Impact factor: 13.506
Authors: Xingyou Zhang; James B Holt; Shumei Yun; Hua Lu; Kurt J Greenlund; Janet B Croft Journal: Am J Epidemiol Date: 2015-05-07 Impact factor: 4.897