Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS)

Abstract

BackgroundThe Zambia Malaria Indicator Survey (ZMIS) of 2006 was the first nation-wide malaria survey, which combined parasitological data with other malaria indicators such as net use, indoor residual spraying and household related aspects. The survey was carried out by the Zambian Ministry of Health and partners with the objective of estimating the coverage of interventions and malaria related burden in children less than five years. In this study, the ZMIS data were analysed in order (i) to estimate an empirical high-resolution parasitological risk map in the country and (ii) to assess the relation between malaria interventions and parasitaemia risk after adjusting for environmental and socio-economic confounders.MethodsThe parasitological risk was predicted from Bayesian geostatistical and spatially independent models relating parasitaemia risk and environmental/climatic predictors of malaria. A number of models were fitted to capture the (potential) non-linearity in the malaria-environment relation and to identify the elapsing time between environmental effects and parasitaemia risk. These models included covariates (a) in categorical scales and (b) in penalized and basis splines terms. Different model validation methods were used to identify the best fitting model. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting model.ResultsModel validation indicated that linear environmental predictors were able to fit the data as well as or even better than more complex non-linear terms and that the data do not support spatial dependence. Overall the averaged population-adjusted parasitaemia risk was 20.0% in children less than five years with the highest risk predicted in the northern (38.3%) province. The odds of parasitaemia in children living in a household with at least one bed net decreases by 40% (CI: 12%, 61%) compared to those without bed nets.ConclusionsThe map of parasitaemia risk together with the prediction error and the population at risk give an important overview of the malaria situation in Zambia. These maps can assist to achieve better resource allocation, health management and to target additional interventions to reduce the burden of malaria in Zambia significantly. Repeated surveys will enable the evaluation of the effectiveness of on-going interventions.

Predictive Malaria Risk and Uncertainty Mapping in Nchelenge District, Zambia: Evidence of Widespread, Persistent Risk and Implications for Targeted Interventions

“…[13][14][15] Extrapolating policy and programmatic decisions to these unsampled areas may be negatively impacted by statistical uncertainty. Some but not all malaria risk maps are associated with measures of spatial risk uncertainty, which is critical to provide valuable information on model fit, accuracy, and interpretation.…”

A review of spatial technologies with applications for malaria transmission modelling and control in Africa

“…More recently, Riedel et al (2010) studied the geographic patterns of malaria risk in Zambia using environmental indicators of malaria transmission. These environmental indicators were extracted from MODIS satellite remote sensing data.…”

“…The development of spatial analytical techniques has created an avenue to evaluate environmental variables that are generated by remote sensing satellite sensors and captured by Geographic Information Systems (GIS) for spatial and temporal environmental analysis (Tanser and le Sueur, 2002; Thomas et al, 2002). These tech- nologies provide tools for the identification and quantification of the population at risk of parasite infections in endemic communities (Tanser and le Sueur, 2002;Riedel et al, 2010). In the case of malaria in Africa, these tools have been used to model and develop malaria risk maps at different spatial scales.…”

“…This facilitated detection of malaria risk areas and prediction of malaria transmissions both spatially and temporally, which in turn, provided guidance to malaria control managers in decision-making and policy planning for cost-effective resources allocation (Mills et al, 2008). In many studies in Africa, particular emphases have been given in mapping incidence and/or prevalence (Craig et al, 1999;Yeshiwondim et al, 2009), identification of mosquito habitats (Hayes et al, 1985;Hay et al, 2000), and spatio-temporal prediction/forecasting transmission risk in relation to environment (Mboera et al, 2010;Riedel et al, 2010;Stensgaard et al, 2011). The advent of new spatial data sources with spatial statistics and geo-statistics offered great potential to increase our understanding of the link between malaria disease and environmental variables.…”

“…In the case of malaria in Africa, these tools have been used to model and develop malaria risk maps at different spatial scales. These include continental level (MARA/ARMA, 2008) and national spatial model Riedel et al, 2010;Stensgaard et al, 2011) which were used to construct malaria risk maps using data from a sample of a population with interpolation to areas where malaria case and prevalence data were captured. This facilitated detection of malaria risk areas and prediction of malaria transmissions both spatially and temporally, which in turn, provided guidance to malaria control managers in decision-making and policy planning for cost-effective resources allocation (Mills et al, 2008).…”