Lumping or splitting: seeking the preferred areal unit for health geography studies

Gregorio, David I.; DeChello, Laurie M; Samociuk, Holly; Kulldorff, Martin

doi:10.1186/1476-072x-4-6

Cited by 52 publications

(14 citation statements)

References 25 publications

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“…Still, the lack of qualifying results from the point-based SaTScan in low-density RC regions, when all areal-based tests passed our qualification criteria, was unexpected. Gregorio et al (2005) found general agreement between a point version of SaTScan and one applied to censusbased areal units, which are much larger aggregation units than ours, and had a wider range of unit populations.…”

Section: Discussionsupporting

confidence: 73%

“…If the point-based test had not failed, Kern would have a Consensus Cluster and Far Northern would have a Potential Cluster. Spatial tests that are adjusted for population density are more powerful in urban than rural areas (Gregorio et al., 2005). Still, the lack of qualifying results from the point-based SaTScan in low-density RC regions, when all areal-based tests passed our qualification criteria, was unexpected.…”

Section: Discussionmentioning

confidence: 99%

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Geographic distribution of autism in California: A retrospective birth cohort analysis

et al. 2010

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Prenatal environmental exposures are among the risk factors being explored for associations with autism. We applied a new procedure combining multiple scan cluster detection tests to identify geographically defined areas of increased autism incidence. This procedure can serve as a first hypothesis-generating step aimed at localized environmental exposures, but would not be useful for assessing widely distributed exposures, such as household products, nor for exposures from non-point sources, such as traffic. Geocoded mothers' residences on 2,453,717 California birth records, 1996–2000, were analyzed including 9,900 autism cases recorded in the California Department of Developmental Services (DDS) database through February 2006 which were matched to their corresponding birth records. We analyzed each of the 21 DDS Regional Center (RC) catchment areas separately because of wide variation in diagnostic practices. Ten clusters of increased autism risk were identified in eight RC regions, and one potential cluster in each of two other RC regions. After determination of clusters, multiple mixed Poisson regression models were fit to assess differences in known demographic autism risk factors between births within and outside areas of elevated autism incidence, independent of case status. Adjusted for other covariates, the majority of areas of autism clustering were characterized by high parental education, e.g., relative risks >4 for collegegraduate versus non-high school graduate parents. This geographic association possibly occurs because RCs do not actively conduct case finding and parents with lower education are, for various reasons, less likely to successfully seek services.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Geographic distribution of autism in California: A retrospective birth cohort analysis

et al. 2010

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“…Our findings support that data spatially aggregated to the tract level lead to results that contribute to increased capacity to identify local clusters [60]. We also show that data stability and greater consistency in the significant spatial patterns can be obtained through a temporal windowing approach, which is an important achievement for strategic planning when rates are based on small numbers.…”

Section: Discussionsupporting

confidence: 76%

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

2019

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Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

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“…All three choropleth maps in Fig. 2 display different spatial patterns, which illustrates the modifiable areal unit problem (MAUP) whereby the interpretation of a geographical phenomenon within a map depends on the scale and partitioning of the areal units that are imposed on the map (Waller and Got way, 2004; Gregorio et al ., 2005; Meliker et al ., 2009). In particular, zones with higher rate of late-stage diagnosis seem to shift east as the size of geographical units decreases.…”

Section: Resultsmentioning

confidence: 99%

Geostatistical analysis of health data with different levels of spatial aggregation

Goovaerts

2012

Spatial and Spatio-temporal Epidemiology

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This paper presents a geostatistical approach to combine two geographical sets of area-based data into the mapping of disease risk, with an application to the rate of prostate cancer late-stage diagnosis in North Florida. This methodology is used to combine individual-level data assigned to census tracts for confidentiality reasons with individual-level data that were allocated to ZIP codes because of incomplete geocoding. This form of binomial kriging, which accounts for the population size and shape of each geographical unit, can generate choropleth or isopleth risk maps that are all coherent through spatial aggregation. Incorporation of both types of areal data reduces the loss of information associated with incomplete geocoding, leading to maps of risk estimates that are globally less smooth and with smaller prediction error variance.

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Lumping or splitting: seeking the preferred areal unit for health geography studies

Abstract: Background: Findings are compared on geographic variation of incident and late-stage cancers across Connecticut using different areal units for analysis.

Cited by 52 publications

References 25 publications

Geographic distribution of autism in California: A retrospective birth cohort analysis

Geographic distribution of autism in California: A retrospective birth cohort analysis

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Geostatistical analysis of health data with different levels of spatial aggregation

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