Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity

Abstract

BackgroundIn Uganda, malaria and lymphatic filariasis (causative agent Wuchereria bancrofti) are transmitted by the same vector species of Anopheles mosquitoes, and thus are likely to share common environmental risk factors and overlap in geographical space. In a comprehensive nationwide survey in 2000-2003 the geographical distribution of W. bancrofti was assessed by screening school-aged children for circulating filarial antigens (CFA). Concurrently, blood smears were examined for malaria parasites. In this study, the resultant malariological data are analysed for the first time and the CFA data re-analysed in order to identify risk factors, produce age-stratified prevalence maps for each infection, and to define the geographical patterns of Plasmodium sp. and W. bancrofti co-endemicity.MethodsLogistic regression models were fitted separately for Plasmodium sp. and W. bancrofti within a Bayesian framework. Models contained covariates representing individual-level demographic effects, school-level environmental effects and location-based random effects. Several models were fitted assuming different random effects to allow for spatial structuring and to capture potential non-linearity in the malaria- and filariasis-environment relation. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting models. Maps of predicted hyper-endemic malaria and filariasis were furthermore overlaid in order to define areas of co-endemicity.ResultsPlasmodium sp. parasitaemia was found to be highly endemic in most of Uganda, with an overall population adjusted parasitaemia risk of 47.2% in the highest risk age-sex group (boys 5-9 years). High W. bancrofti prevalence was predicted for a much more confined area in northern Uganda, with an overall population adjusted infection risk of 7.2% in the highest risk age-group (14-19 year olds). Observed overall prevalence of individual co-infection was 1.1%, and the two infections overlap geographically with an estimated number of 212,975 children aged 5 - 9 years living in hyper-co-endemic transmission areas.ConclusionsThe empirical map of malaria parasitaemia risk for Uganda presented in this paper is the first based on coherent, national survey data, and can serve as a baseline to guide and evaluate the continuous implementation of control activities. Furthermore, geographical areas of overlap with hyper-endemic W. bancrofti transmission have been identified to help provide a better informed platform for integrated control.

Filaria zoogeography in Africa: ecology, competitive exclusion, and public health relevance

“…This could be related to the mountain range dividing the two countries, the relatively high elevations, and lack of dense tropical rainforest suitable for Chrysops in Uganda [54,56]. The distribution of O. volvulus is also limited and most prominent in the northern Nile River region [61], which contrasts to recent distribution maps showing W. bancrofti in the northeast region [62] and M. perstans in the southern region of the country [15]. Interestingly, M. perstans has also been associated with a condition known as Kampala, or Ugandan eyeworm, which occurs when worms invade the eye and which can be confused with other filarial diseases such L. loa [60].…”

Multilevel and geo-statistical modeling of malaria risk in children of Burkina Faso

“…For example, environmental factors such as rainfall, altitude and temperature, that affect Anopheles sp . bionomics, are associated with malaria transmission in endemic countries [ 8 – 11 ].…”

How Well Are Malaria Maps Used to Design and Finance Malaria Control in Africa?

“…Advances in computing and geostatistical techniques have increased the ability to define the spatial and temporal risks of malaria endemicity using probabilistic approaches at high spatial resolutions and formed the basis of a variety of country-level maps of risk including South Africa [13], Mali [14], Malawi [15], Zambia [16], Somalia [17], Democratic Republic of Congo [18], Kenya [19], Botswana [20], Angola [21], Namibia [22], Uganda [23], Cote D'Ivoire [24], Tanzania [25] and Senegal [26].…”