ArticleCancer causes & control : CCC2017
A geographic information system-based method for estimating cancer rates in non-census defined geographical areas.
Article in Cancer causes & control : CCC, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeTo 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.
methodsAggregated 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.
resultsPotential 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.
conclusionsAreal 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.