Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya.

Abstract

Background: Soil-transmitted helminthiasis (STH) are a parasitic infection that predominantly affects impoverished regions. Model-based geostatistics (MBG) has been established as a set of modern statistical methods that enable mapping of disease risk in a geographical area of interest. We investigate how the use of remotely sensed covariates can help to improve the predictive inferences on STH prevalence using MBG methods. In particular, we focus on how the covariates impact on the classification of areas into distinct class of STH prevalence.

Methods: This study uses secondary data obtained from a sample of 1551 schools in Kenya, gathered through a combination of longitudinal and cross-sectional surveys. We compare the performance of two geostatistical models: one that does not make use of any spatially referenced covariate; and a second model that uses remotely sensed covariates to assist STH prevalence prediction. We also carry out a simulation study in which we compare the performance of the two models in the classifications of areal units with varying sample sizes and prevalence levels.

Results: The model with covariates generated lower levels of uncertainty and was able to classify 88 more districts into prevalence classes than the model without covariates, which instead left those as "unclassified". The simulation study showed that the model with covariates also yielded a higher proportion of correct classification of at least 40% for all sub-counties.

Conclusion: Covariates can substantially reduce the uncertainty of the predictive inference generated from geostatistical models. Using covariates can thus contribute to the design of more effective STH control strategies by reducing sample sizes without compromising the predictive performance of geostatistical models.