Spatial error in geocoding physician location data from the AMA Physician Masterfile: Implications for spatial accessibility analysis

McLafferty, Sara; Freeman, Vincent L.; Barrett, Richard; Luo, Lan; Shockley, Alisa

doi:10.1016/j.sste.2012.02.004

Cited by 48 publications

(43 citation statements)

References 40 publications

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“…Much of the research on geocoding has focused on the positional accuracy of different geocoding techniques (Bell et al 2012; Bonner et al 2003; Bow et al 2004; Cayo and Talbot 2003; Duncan et al 2011; Goldberg and Cockburn 2012; Healy and Gilliland 2012; Jacquez 2012; Krieger et al 2001; Ratcliffe 2001; Ward et al 2005). Errors in positional accuracy can lead to incorrect assignment of areal units such as Census tract or even county, leading to misclassification errors (Goldberg and Cockburn 2012; McLafferty et al 2012). Completeness of geocoding and matching algorithm criteria are difficult to compare across many studies due to a lack of detail describing the geocoding process in many papers (Robinson et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Edwards

Strauss

Miranda

2013

Transactions in GIS

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Using geographic information systems to link administrative databases with demographic, social, and environmental data allows researchers to use spatial approaches to explore relationships between exposures and health. Traditionally, spatial analysis in public health has focused on the county, zip code, or tract level because of limitations to geocoding at highly resolved scales. Using 2005 birth and death data from North Carolina, we examine our ability to geocode population-level datasets at three spatial resolutions – zip code, street, and parcel. We achieve high geocoding rates at all three resolutions, with statewide street geocoding rates of 88.0% for births and 93.2% for deaths. We observe differences in geocoding rates across demographics and health outcomes, with lower geocoding rates in disadvantaged populations and the most dramatic differences occurring across the urban-rural spectrum. Our results suggest highly resolved spatial data architectures for population-level datasets are viable through geocoding individual street addresses. We recommend routinely geocoding administrative datasets to the highest spatial resolution feasible, allowing public health researchers to choose the spatial resolution used in analysis based on an understanding of the spatial dimensions of the health outcomes and exposures being investigated. Such research, however, must acknowledge how disparate geocoding success across subpopulations may affect findings.

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Section: Introductionmentioning

confidence: 99%

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Edwards

Strauss

Miranda

2013

Transactions in GIS

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“…In regard to GP practice locations, the allocation to city district boundaries has limitations due to possible errors retrieving geo coordinates [44]. …”

Section: Discussionmentioning

confidence: 99%

General practitioners in German metropolitan areas – distribution patterns and their relationship with area level measures of the socioeconomic status

Bauer

Brueggmann

Ohlendorf

et al. 2016

BMC Health Serv Res

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BackgroundGeographical variation of the general practitioner (GP) workforce is known between rural and urban areas. However, data about the variation between and within urban areas are lacking.MethodWe analyzed distribution patterns of GP full time equivalents (FTE) in German cities with a population size of more than 500,000. We correlated their distribution with area measures of social deprivation in order to analyze preferences within neighborhood characteristics. For this purpose, we developed two area measures of deprivation: Geodemographic Index (GDI) and Cultureeconomic Index (CEI).ResultsIn total n = 9034.75 FTE were included in n = 14 cities with n = 171 districts. FTE were distributed equally on inter-city level (mean: 6.49; range: 5.12–7.20; SD: 0.51). However, on intra-city level, GP distribution was skewed (mean: 6.54; range: 1.80–43.98; SD: 3.62). Distribution patterns of FTE per 10^4 residents were significantly correlated with GDI (r = −0.49; p < 0.001) and CEI (r = −0.22; p = 0.005). Therefore, location choices of GPs were mainly positively correlated with 1) central location (r = −0.50; p < 0.001), 2) small household size of population (r = −0.50; p < 0.001) and 3) population density (r = 0.35; p < 0.001).ConclusionIntra-city distribution of GPs was skewed, which could affect the equality of access for the urban population. Furthermore, health services planners should be aware of GP location preferences. This could be helpful to better understand and plan delivery of health services. Within this process the presented Geodemographic Index (GDI) could be of use.

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“…KDE results, therefore, reflect the intensity of the disease at a particular location 14 15. One advantage of KDE is that it is not affected by size or shape of aggregation unit 16. We generated four different KDE results from the four STI histories being analysed in this study.…”

Section: Methodsmentioning

confidence: 99%

Modelling individual vulnerability to sexually transmitted infections to optimise intervention strategies: analysis of surveillance data from Kalamazoo County, Michigan, USA

et al. 2018

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Spatial error in geocoding physician location data from the AMA Physician Masterfile: Implications for spatial accessibility analysis

Cited by 48 publications

References 40 publications

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

General practitioners in German metropolitan areas – distribution patterns and their relationship with area level measures of the socioeconomic status

Modelling individual vulnerability to sexually transmitted infections to optimise intervention strategies: analysis of surveillance data from Kalamazoo County, Michigan, USA

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