Bayesian geostatistical modeling of Malaria Indicator Survey data in Angola.
The 2006-2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities.
Gosoniu
Laura
L
Swiss Tropical Institute, Basel, Switzerland. laura.gosoniu@unibas.ch
Veta
Andre Mia
AM
Vounatsou
Penelope
P
D000810:Angola; D000818:Animals; D000852:Anopheles / Q000378:metabolism; D001499:Bayes Theorem; D002675:Child, Preschool; D004777:Environment; D005843:Geography; D006801:Humans; D007223:Infant; D008288:Malaria / Q000175:diagnosis* / Q000453:epidemiology*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012306:Risk; D012923:Social Class