Dataset Information

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.

SUBMITTER: Freeman VL

PROVIDER: S-EPMC5839327 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Freeman Vincent L VL Boylan Emma E EE Pugach Oksana O Mclafferty Sara L SL Tossas-Milligan Katherine Y KY Watson Karriem S KS Winn Robert A RA

Cancer causes & control : CCC 20170820 10

<h4>Purpose</h4>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.<h4>Methods</h4>Aggregated records of cancer cases diagnosed from 2009 through 2013 in each of Chicago's 77 census-defined community are ...[more]

PMID: 28825153

Similar Datasets

Project description:Background Rates of major lower extremity amputation in patients with peripheral artery disease are higher in rural communities with markers of low socioeconomic status, but most Americans live in metropolitan areas. Whether amputation rates vary within US metropolitan areas is unclear, as are characteristics of high amputation rate urban communities. Methods and Results We estimated rates of major lower extremity amputation per 100 000 Medicare beneficiaries between 2010 and 2018 at the ZIP code level among ZIP codes with ≥100 beneficiaries. We described demographic characteristics of high and low amputation ZIP codes, and the association between major amputation rate and 3 ZIP code-level markers of socioeconomic status-the proportion of patients with dual eligibility for Medicaid, median household income, and Distressed Communities Index score-for metropolitan, micropolitan, and rural ZIP code cohorts. Between 2010 and 2018, 188 995 Medicare fee-for-service patients living in 31 391 ZIP codes with ≥100 beneficiaries had a major lower extremity amputation. The median (interquartile range) ZIP code-level number of amputations per 100 000 beneficiaries was 262 (75-469). Though nonmetropolitan ZIP codes had higher rates of major amputation than metropolitan areas, 78.2% of patients undergoing major amputation lived in metropolitan areas. Compared with ZIP codes with lower amputation rates, top quartile amputation rate ZIP codes had a greater proportion of Black residents (4.4% versus 17.5%, P<0.001). In metropolitan areas, after adjusting for clinical comorbidities and demographics, every $10 000 lower median household income was associated with a 4.4% (95% CI, 3.9-4.8) higher amputation rate, and a 10-point higher Distressed Communities Index score was associated with a 3.8% (95% CI, 3.4%-4.2%) higher amputation rate; there was no association between the proportion of patients eligible for Medicaid and amputation rate. These findings were comparable to the associations identified across all ZIP codes. Conclusions In metropolitan areas, where most individuals undergoing lower extremity amputation live, markers of lower socioeconomic status and Black race were associated with higher rates of major lower extremity amputation. Development of community-based tools for peripheral artery disease diagnosis and management targeted to communities with high amputation rates in urban areas may help reduce inequities in peripheral artery disease outcomes.

Project description:BackgroundWhen modelling infectious diseases, accurately capturing the pattern of dissemination through space is key to providing optimal recommendations for control. Mathematical models of disease spread in livestock, such as for foot-and-mouth disease (FMD), have done this by incorporating a transmission kernel which describes the decay in transmission rate with increasing Euclidean distance from an infected premises (IP). However, this assumes a homogenous landscape, and is based on the distance between point locations of farms. Indeed, underlying the spatial pattern of spread are the contact networks involved in transmission. Accordingly, area-weighted tessellation around farm point locations has been used to approximate field-contiguity and simulate the effect of contiguous premises (CP) culling for FMD. Here, geographic data were used to determine contiguity based on distance between premises' fields and presence of landscape features for two sample areas in Scotland. Sensitivity, positive predictive value, and the True Skill Statistic (TSS) were calculated to determine how point distance measures and area-weighted tessellation compared to the 'gold standard' of the map-based measures in identifying CPs. In addition, the mean degree and density of the different contact networks were calculated.ResultsUtilising point distances <1 km and <5 km as a measure for contiguity resulted in poor discrimination between map-based CPs/non-CPs (TSS 0.279-0.344 and 0.385-0.400, respectively). Point distance <1 km missed a high proportion of map-based CPs; <5 km point distance picked up a high proportion of map-based non-CPs as CPs. Area-weighted tessellation performed best, with reasonable discrimination between map-based CPs/non-CPs (TSS 0.617-0.737) and comparable mean degree and density. Landscape features altered network properties considerably when taken into account.ConclusionThe farming landscape is not homogeneous. Basing contiguity on geographic locations of field boundaries and including landscape features known to affect transmission into FMD models are likely to improve individual farm-level accuracy of spatial predictions in the event of future outbreaks. If a substantial proportion of FMD transmission events are by contiguous spread, and CPs should be assigned an elevated relative transmission rate, the shape of the kernel could be significantly altered since ability to discriminate between map-based CPs and non-CPs is different over different Euclidean distances.

Project description:BackgroundA common disclosure control practice for health datasets is to identify small geographic areas and either suppress records from these small areas or aggregate them into larger ones. A recent study provided a method for deciding when an area is too small based on the uniqueness criterion. The uniqueness criterion stipulates that an the area is no longer too small when the proportion of unique individuals on the relevant variables (the quasi-identifiers) approaches zero. However, using a uniqueness value of zero is quite a stringent threshold, and is only suitable when the risks from data disclosure are quite high. Other uniqueness thresholds that have been proposed for health data are 5% and 20%.MethodsWe estimated uniqueness for urban Forward Sortation Areas (FSAs) by using the 2001 long form Canadian census data representing 20% of the population. We then constructed two logistic regression models to predict when the uniqueness is greater than the 5% and 20% thresholds, and validated their predictive accuracy using 10-fold cross-validation. Predictor variables included the population size of the FSA and the maximum number of possible values on the quasi-identifiers (the number of equivalence classes).ResultsAll model parameters were significant and the models had very high prediction accuracy, with specificity above 0.9, and sensitivity at 0.87 and 0.74 for the 5% and 20% threshold models respectively. The application of the models was illustrated with an analysis of the Ontario newborn registry and an emergency department dataset. At the higher thresholds considerably fewer records compared to the 0% threshold would be considered to be in small areas and therefore undergo disclosure control actions. We have also included concrete guidance for data custodians in deciding which one of the three uniqueness thresholds to use (0%, 5%, 20%), depending on the mitigating controls that the data recipients have in place, the potential invasion of privacy if the data is disclosed, and the motives and capacity of the data recipient to re-identify the data.ConclusionThe models we developed can be used to manage the re-identification risk from small geographic areas. Being able to choose among three possible thresholds, a data custodian can adjust the definition of "small geographic area" to the nature of the data and recipient.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets