“There are many fevers”: Communities’ perception and management of Febrile illness and its relationship with human animal interactions in South-Western Uganda

Diagnosing the causative agent of febrile illness in resource-limited countries is a challenge in part due to lack of adequate diagnostic infrastructure to confirm cause of infection. Most febrile illnesses (>60%) are non-malarial, with a significant proportion being zoonotic and likely from animal origins. To better characterize the pathways for zoonotic disease transmission and control in vulnerable communities, adequate information on the communities’ experiences and lexicon describing fever, and their understanding and perceptions of risk pathways is required. We undertook an ethnographic study to understand behaviors, exposures, and attitudes toward fever at the community level. Our hope is to better elucidate areas of priority surveillance and diagnostic investment. A focused ethnography consisting of participant observation, informal conversations, 4 barazas (community meetings), and formal ethnographic interviews (13 Focus group discussions and 17 Key informant interviews) was conducted between April and November 2015 in Kasese and Hoima Districts in Uganda. Perception of illness and associated risk factors was heavily influenced by the predominant livelihood activity of the community. The term “fever” referred to multiple temperature elevating disease processes, recognized as distinct pathological occurrences. However, malaria was the illness often cited, treated, or diagnosed both at the health facilities and through self-diagnosis and treatment. As expected, fever is as an important health challenge affecting all ages. Recognition of malarial fever was consistent with a biomedical model of disease while non-malarial fevers were interpreted mainly through ethno etiological models of explanation. These models are currently being used to inform education and prevention strategies and treatment regimens toward the goal of improving patients’ outcomes and confidence in the health system. Development of treatment algorithms that consider social, cultural, and economic contexts, especially where human-animal interaction is prevalent, should factor animal exposure and zoonotic illnesses as important differentials.