Estimating the Burden of Malaria in Senegal: Bayesian Zero-Inflated Binomial Geostatistical Modeling of the MIS 2008 Data

Abstract

The Research Center for Human Development in Dakar (CRDH) with the technical assistance of ICF Macro and the National Malaria Control Programme (NMCP) conducted in 2008/2009 the Senegal Malaria Indicator Survey (SMIS), the first nationally representative household survey collecting parasitological data and malaria-related indicators. In this paper, we present spatially explicit parasitaemia risk estimates and number of infected children below 5 years. Geostatistical Zero-Inflated Binomial models (ZIB) were developed to take into account the large number of zero-prevalence survey locations (70%) in the data. Bayesian variable selection methods were incorporated within a geostatistical framework in order to choose the best set of environmental and climatic covariates associated with the parasitaemia risk. Model validation confirmed that the ZIB model had a better predictive ability than the standard Binomial analogue. Markov chain Monte Carlo (MCMC) methods were used for inference. Several insecticide treated nets (ITN) coverage indicators were calculated to assess the effectiveness of interventions. After adjusting for climatic and socio-economic factors, the presence of at least one ITN per every two household members and living in urban areas reduced the odds of parasitaemia by 86% and 81% respectively. Posterior estimates of the ORs related to the wealth index show a decreasing trend with the quintiles. Infection odds appear to be increasing with age. The population-adjusted prevalence ranges from 0.12% in Thillé-Boubacar to 13.1% in Dabo. Tambacounda has the highest population-adjusted predicted prevalence (8.08%) whereas the region with the highest estimated number of infected children under the age of 5 years is Kolda (13940). The contemporary map and estimates of malaria burden identify the priority areas for future control interventions and provide baseline information for monitoring and evaluation. Zero-Inflated formulations are more appropriate in modeling sparse geostatistical survey data, expected to arise more frequently as malaria research is focused on elimination.

Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda

“…In other regions, IRS coverage is still very low [5]. The low coverage of this intervention has also been reported in other high endemic countries, namely, Tanzania [9], Burkina Faso [12], Senegal [10], Angola and Mozambique [13]. This could be attributed to the negative campaign against the use of DDT [31].…”

“…The latter give reliable estimates because they take into account correlation of malaria prevalence in space arising from common exposures affecting neighbouring areas similarly. Bayesian geostatistical models have been used in mapping of malaria burden [6] and recently in the analysis of MIS data in high endemic countries of SSA, namely, Zambia [7], Angola [8], Tanzania [9], Senegal [10], Nigeria [11] and Burkina Faso [12]. Despite comparable malaria transmission intensities in these countries, findings showed varied effects of interventions on malaria prevalence among children less than 5 years.…”

“…Despite comparable malaria transmission intensities in these countries, findings showed varied effects of interventions on malaria prevalence among children less than 5 years. For instance, a protective and non-protective effects were reported for ITNs and IRS respectively in Zambia [7], Angola [8] and Senegal [10]. On the other hand, no effects were observed for the role of interventions in Nigeria [11], and Tanzania [9].…”

Modeling excess zeros and heterogeneity in count data from a complex survey design with application to the demographic health survey in sub-Saharan Africa

“…While it requires further simulation studies to understand the operational characteristic of these models, it can be inferred from our study that the scale weighted ZINB model can be applied in other areas of biomedical research in which responses are measured in clusters using a complex survey design, for example, in modeling of number of days of missed primary activities due to illness, 31 study of outpatient psychiatric service use, 32 and study of malaria infection. 33 …”

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

“…The strength of the relationship also varied between consecutive years. Giardina et al (2012) estimated the burden of malaria in Senegal using Bayesian zero-inflated binomial geo statistical modelling using malaria prevalence data from Senegal Malaria Indicator Survey (SMIS). These models were developed to cope with the large number of zero-prevalence locations in the data.…”