Spatial epidemiology and the psychosocial impacts of lymphatic filariasis in Nigeria

Nigeria's National Programme to Eliminate Lymphatic Filariasis has concluded mapping to identify endemic districts, based on a binary classification of endemic or non-endemic. Although this is useful for the planning of control programmes, it does not portray heterogeneities of infection level that may exist within districts. Entire populations in endemic districts are targeted with chemotherapy which aims to treating and prevent new infections. For populations with advanced stages of disease, such as lymphedema and hydrocele, palliative care is given to alleviate suffering. Work done in this thesis described the ecology of lymphatic filariasis, mapping the geographical and environmental limits of infection in Nigeria. Here, climate, environmental and demographic factors and their influence on infection was investigated. To refine prevalence estimates, machine learning models have been used to map the distribution of lymphatic filariasis, predicting infection level for previously unsurveyed areas. Maps presented here uncover the spatial heterogeneity of prevalence levels that exists within districts. A field survey was also conducted to pilot a patient search strategy for estimating patient numbers and classifying their stage and degree of morbidity. Here the psychosocial and economic impacts of LF morbidity was characterized in a typical endemic rural setting. Overall, this thesis describes the ecological niche of lymphatic filariasis (LF) in Nigeria and provides an estimate of the population living in areas that are environmentally suitable for disease transmission. Work presented in Chapter 3 provides baseline prevalence estimates at country level as well as subnational estimates. A field survey aimed at estimating the burden and classifying the degree of morbidity within communities gave insights into methods for identifying chronic LF patients in rural communities and priorities for care. Findings demonstrate that patients are at high risk of mental health problems. Here we have suggested alternative strategies to deliver morbidity care at the community level. For the implementation of the mass drug administration programme, there is need for robust estimates of infection level. Prevalence levels are also a key benchmark for monitoring the progress of interventions and assessing the feasibility driving down infection thresholds, thus achieving elimination targets. For morbidity management programmes, robust estimates of patient numbers are essential for planning of care delivery strategies. In prioritizing morbidity care, characterising patient needs is important to maximizing programme efforts.