Spatiotemporal modelling of Health Management Information System data to quantify malaria treatment burdens in the Kenyan Government's formal health sector

Abstract

This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has developed geostatistical modelling frameworks for the prediction of the monthly tally of treatments for malaria (MC) at all facilities and months where this value is missing. Three different kriging methodologies were compared to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space-time prediction approach, and (b) the replacement of a globally-stationary with a locally-varying random function model. Space-time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than conventional spatial-only kriging. A modification of space-time kriging that allowed space-time variograms to be recalculated for every prediction location with a spatially-local neighbourhood resulted in a larger decrease in mean imprecision over ordinary kriging (18.3%) although mean bias was reduced less (87.5%). Because the MC variable included non-spatial variation caused by differences between individual facilities and their catchment populations, a series of studies were conducted to model catchment population size. These predictions require refined models that incorporated rich local data that were not available at the national level so directly estimated catchment population values were not available. An alternative approach was developed that incorporated data on the total number of outpatients seen at facilties each month as a proxy measure of catchment size. Two modelling frameworks were developed to implement this approach and the most accurate model was identified in a cross-validation exercise.

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