Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

摘要

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.