A geographic information system-based method for estimating cancer rates in non-census defined geographical areas

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.

Artificial Intelligence Comes of Age in the Fight Against Cancer

“…17,18 AI can integrate the impact of social determinants on cancer rates and treatment outcomes. 19 These data can help address gaps in health care through policy, preventative care programs, and target clinical intervention.…”

“…Contributing factors include socioeconomic status, access to health care and treatment, culture, genetic variants, and molecular differences in tumors 17,18. AI can integrate the impact of social determinants on cancer rates and treatment outcomes 19. These data can help address gaps in health care through policy, preventative care programs, and target clinical intervention.…”