ArticleInternational journal of health geographics2014
Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes.
Article in International journal of health geographics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Spatiotemporal analysis of gastric cancer incidence in Iran from 2014 to 2018: a geospatial epidemiological study.BMC public health · 2025Article
- SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- A multi-constraint Monte Carlo Simulation approach to downscaling cancer data.Health & place · 2025Article
- Retrospective multidisciplinary analysis of human alveolar echinococcosis in Hungary using spatial epidemiology approaches.Scientific reports · 2024Article
- MODELING RACIAL/ETHNIC DIFFERENCES IN COVID-19 INCIDENCE WITH COVARIATES SUBJECT TO NON-RANDOM MISSINGNESS.The annals of applied statistics · 2023Article
- Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.Scientific reports · 2021Article
- Bayesian spatial modelling of early childhood development in Australian regions.International journal of health geographics · 2020Article
- Does body shame mediate the relationship between parental bonding, self-esteem, maladaptive perfectionism, body mass index and eating disorders? A structural equation model.Eating and weight disorders : EWD · 2020Article
- Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty.Journal of the American Statistical Association · 2020Article
- Evaluation of geoimputation strategies in a large case study.International journal of health geographics · 2018Article
- Estimating missing values in China's official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling.Scientific reports · 2018Article
- Racial differences in spatial patterns for poor glycemic control in the Southeastern United States.Annals of epidemiology · 2018Article
- Spatial modelling of type II diabetes outcomes: a systematic review of approaches used.Royal Society open science · 2015Article
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3 authors.
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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.
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