Spatial epidemiology of Rhodesian sleeping sickness in recently affected areas of central and eastern Uganda

Abstract

The tsetse transmitted fatal disease of humans, sleeping sickness, is caused by two morphologically identical subspecies of the parasite T. brucei; T. b. rhodesiense and T. b. gambiense. Current distributions of the two forms of disease are not known to overlap in any area, and Uganda is the only country with transmission of both. The distribution of Rhodesian sleeping sickness in Uganda has expanded in recent years, with five districts newly affected since 1998. This movement has narrowed the gap between Rhodesian and Gambian sleeping sickness endemic areas, heightening concerns over a potential future overlap which would greatly complicate the diagnosis and treatment of the two diseases. An improved understanding of the social, environmental and climatic determinants of the distribution of Rhodesian sleeping sickness is required to allow more effective targeting of control measures and to prevent further spread and possible concurrence with Gambian sleeping sickness. The work presented in this thesis investigates the drivers of the distribution and spread of Rhodesian sleeping sickness in districts of central and eastern Uganda which form part of the recent disease focus extension. The spatial distribution of Rhodesian sleeping sickness was examined in Kaberamaido and Dokolo districts where the disease was first reported in 2004, using three different methodologies. A traditional one-step logistic regression analysis of disease prevalence was compared with a two-step hierarchical logistic regression analysis. The two-step method included the analysis of disease occurrence followed by the analysis of disease prevalence in areas with a high predicted probability of occurrence. These two methods were compared in terms of their predictive accuracy. The incorporation of a stochastic spatial effect to model the residual spatial autocorrelation was carried out using a Bayesian geostatistical approach. The geostatistical analysis was compared with the non-spatial models to assess the importance of spatial autocorrelation, to establish which method had the highest predictive accuracy and to establish which factors were the most significant in terms of the disease’s distribution. Links between Rhodesian sleeping sickness and landcover in Soroti district were also assessed using a matched case-control study design. Temporal trends in these relationships were observed using an annually stratified analysis to allow an exploration of the disease’s dispersion following its introduction to a previously unaffected area. This work expands on previous research that demonstrated the source of infection in this area to be the movement of untreated livestock from endemic areas through a local livestock market. With regards to the comparison of regression frameworks, the two-step regression compared favourably with the traditional one-step regression, but the Bayesian geostatistical analysis outperformed both in terms of predictive accuracy. Each of these regression methods highlighted the importance of distance to the closest livestock market on the distribution of Rhodesian sleeping sickness, indicating that the disease may have been introduced to this area via the movement of untreated cattle from endemic areas, despite the introduction of regulations requiring the treatment of livestock prior to sale. In addition, several other environmental and climatic variables were significantly associated with sleeping sickness occurrence and prevalence within the study area. The temporal stratification of the matched case-control analysis highlights the dispersion of sleeping sickness away from the point of introduction (livestock market) into more suitable areas; areas with higher proportions of seasonally flooding grassland, lower proportions of woodland and dense savannah and lower elevations. These findings relate to the habitat preferences of the predominant vector species in the study area; Glossina fuscipes fuscipes, which prefers riverine vegetation. The findings presented highlight the importance of the livestock reservoir as well as the climatic and environmental preferences of the tsetse fly vector for the introduction of Rhodesian sleeping sickness into previously unaffected areas, the subsequent spread of infection following an introduction and the equilibrium spatial distribution of the disease. By enhancing the knowledge base regarding the spatial determinants of the distribution of Rhodesian sleeping sickness within newly affected areas, future control efforts within Uganda may be better targeted to decrease prevalence and to prevent further spread of the disease.