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Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical modeling of the MIS 2008 data.

Giardina F, Gosoniu L, Konate L, Diouf MB, Perry R, Gaye O, Faye O, Vounatsou P - PLoS ONE (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.

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.

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Related in: MedlinePlus

Model comparison and validation.Percentage of test locations with malaria prevalence falling in the highest posterior density intervals (HPDI) predicted from Binomial and Zero-Inflated Binomial models (bars). Lines indicate the corresponding HPDI length.
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pone-0032625-g003: Model comparison and validation.Percentage of test locations with malaria prevalence falling in the highest posterior density intervals (HPDI) predicted from Binomial and Zero-Inflated Binomial models (bars). Lines indicate the corresponding HPDI length.

Mentions: Posterior model probabilities obtained from MCMC runs of 100 000 iterations using the GVS are presented in Table 1. Similar results were obtained with the other two variable selection methods, SSVS and KM. As shown in the table, the set of covariates that defined the Binomial as well as the ZIB geostatistical models with the highest posterior probabilities consisted of night LST, NDVI and area type (urban/rural). The predictive performance of the selected models is shown in Figure 3. The proportion of test locations falling into the 50–95% HPDIs was constantly higher under the ZIB model. Furthermore, the latter model estimated narrower HPDIs. Based on the above results, the ZIB was adopted to predict the parasitaemia risk at high spatial resolution and to assess the effects of interventions on the infection risk.


Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical modeling of the MIS 2008 data.

Giardina F, Gosoniu L, Konate L, Diouf MB, Perry R, Gaye O, Faye O, Vounatsou P - PLoS ONE (2012)

Model comparison and validation.Percentage of test locations with malaria prevalence falling in the highest posterior density intervals (HPDI) predicted from Binomial and Zero-Inflated Binomial models (bars). Lines indicate the corresponding HPDI length.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3293829&req=5

pone-0032625-g003: Model comparison and validation.Percentage of test locations with malaria prevalence falling in the highest posterior density intervals (HPDI) predicted from Binomial and Zero-Inflated Binomial models (bars). Lines indicate the corresponding HPDI length.
Mentions: Posterior model probabilities obtained from MCMC runs of 100 000 iterations using the GVS are presented in Table 1. Similar results were obtained with the other two variable selection methods, SSVS and KM. As shown in the table, the set of covariates that defined the Binomial as well as the ZIB geostatistical models with the highest posterior probabilities consisted of night LST, NDVI and area type (urban/rural). The predictive performance of the selected models is shown in Figure 3. The proportion of test locations falling into the 50–95% HPDIs was constantly higher under the ZIB model. Furthermore, the latter model estimated narrower HPDIs. Based on the above results, the ZIB was adopted to predict the parasitaemia risk at high spatial resolution and to assess the effects of interventions on the infection risk.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.

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.

Show MeSH
Related in: MedlinePlus