Abstract
BackgroundSpatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences.MethodsWe present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions.ResultsChoice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application.ConclusionsSelecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-072X-13-47) contains supplementary material, which is available to authorized users.
Highlights
Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease
SES was found to be the only variable associated with DM II relative risk (RR) based on the Poisson models and prevalence based on the Binomial models, from both univariate and multivariate models
Geographic variation Spatially smoothed relative risks (RR) and relative excess risks (RER) and corresponding standard deviations and 95% credible intervals were obtained from the Poisson generalised linear mixed models (GLMMs) with mean imputation and conditional autoregressive (CAR) priors fit to covariate data
Summary
Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. Many geographic risk factors are modifiable and amenable to health promotion programmes, spatial analysis can provide useful information to inform resource allocation and public policy decisions. Maps of spatial models have been useful for highlighting differential risk across regions. Bayesian models are well suited to spatial modelling since the Routinely collected survey data can provide useful information about the distribution of covariates at a regional level, but frequently a problem with such data is the presence of missing covariate information. Often the data are spatially correlated and/or there are correlations between covariates In these cases, imputation of missing data with plausible values allows inferences to be made about outcomes and covariates using statistical methods suited to complete data. Several methods of imputation are available and it is important to select the one best suited to a particular dataset
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