A method for modelling GP practice level deprivation scores using GIS.
BACKGROUND
A measure of general practice level socioeconomic deprivation can be used to explore the association between deprivation and other practice characteristics. An area-based categorisation is commonly chosen as the basis for such a deprivation measure. Ideally a practice population-weighted area-based deprivation score would be calculated using individual level spatially referenced data. However, these data are often unavailable. One approach is to link the practice postcode to an area-based deprivation score, but this method has limitations. This study aimed to develop a Geographical Information Systems (GIS) based model that could better predict a practice population-weighted deprivation score in the absence of patient level data than simple practice postcode linkage.


RESULTS
We calculated predicted practice level Index of Multiple Deprivation (IMD) 2004 deprivation scores using two methods that did not require patient level data. Firstly we linked the practice postcode to an IMD 2004 score, and secondly we used a GIS model derived using data from Rotherham, UK. We compared our two sets of predicted scores to "gold standard" practice population-weighted scores for practices in Doncaster, Havering and Warrington. Overall, the practice postcode linkage method overestimated "gold standard" IMD scores by 2.54 points (95% CI 0.94, 4.14), whereas our modelling method showed no such bias (mean difference 0.36, 95% CI -0.30, 1.02). The postcode-linked method systematically underestimated the gold standard score in less deprived areas, and overestimated it in more deprived areas. Our modelling method showed a small underestimation in scores at higher levels of deprivation in Havering, but showed no bias in Doncaster or Warrington. The postcode-linked method showed more variability when predicting scores than did the GIS modelling method.


CONCLUSIONS
A GIS based model can be used to predict a practice population-weighted area-based deprivation measure in the absence of patient level data. Our modelled measure generally had better agreement with the population-weighted measure than did a postcode-linked measure. Our model may also avoid an underestimation of IMD scores in less deprived areas, and overestimation of scores in more deprived areas, seen when using postcode linked scores. The proposed method may be of use to researchers who do not have access to patient level spatially referenced data.
Strong
Mark
M
Rotherham Primary Care Trust, Oak House, Moorhead Way, Bramley, Rotherham, S66 1YY, UK. m.strong@sheffield.ac.uk
Maheswaran
Ravi
R
Pearson
Tim
T
Fryers
Paul
P
D002391:Catchment Area, Health / Q000706:statistics & numerical data; D003710:Demography; D005194:Family Practice / Q000191:economics / Q000706:statistics & numerical data*; D040362:Geographic Information Systems; D006297:Health Services Accessibility; D054625:Healthcare Disparities; D006801:Humans; D008507:Medically Underserved Area; D015233:Models, Statistical; D011204:Poverty Areas; D011320:Primary Health Care / Q000191:economics / Q000706:statistics & numerical data*; D017062:Small-Area Analysis; D013222:State Medicine; D006113:United Kingdom; D035862:Vulnerable Populations / Q000706:statistics & numerical data*