Using structured additive regression models to estimate risk factors of malaria: analysis of 2010 Malawi malaria indicator survey data

Abstract

Background: After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions.

Methods: We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi.

Results: Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk.

Conclusions: The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities.

Figure 1. Observed malaria risk in children under five.

Observed malaria risk in children under five years at the 140 EAs across Malawi.

Figure 2. Variation in probability of malaria in children aged less than 5 years with potential risk factors.

Variation in probability of malaria in children aged less than 5 years with (a) age (b) wealth index (c) altitude (d) latitude. The vertical bars are CI.

Figure 3. Non linear effect of different continuous covariates on malaria risk (a) altitude (b) latitude (c) minimum temperature (d) rainfall.

Plots based on model estimates from the multivariate spatial model with splines (model M). The inner and outer dotted lines are the and CI respectively. The solid middle line is the posterior mean.

Figure 4. Risk map of malaria in children less than 5 years.

(a) Predictive risk map of malaria in children less than 5 years (b) Standard errors associated with the risk map. Green (brown) colours represent lower (higher) standard errors.