Poor livestock keepers: Ecosystem–poverty–health interactions
Humans have never been healthier, wealthier or more numerous. Yet, present success may be at the cost of future prosperity and in some places, especially in sub-Saharan Africa, poverty persists. Livestock keepers, especially pastoralists, are over-represented among the poor. Poverty has been mainly attributed to a lack of access, whether to goods, education or enabling institutions. More recent insights suggest ecosystems may influence poverty and the self-reinforcing mechanisms that constitute poverty traps in more subtle ways. The plausibility of zoonoses as poverty traps is strengthened by landmark studies on disease burden in recent years. While in theory, endemic zoonoses are best controlled in the animal host, in practice, communities are often left to manage disease themselves, with the focus on treatment rather than prevention. We illustrate this with results from a survey on health costs in a pastoral ecosystem. Epidemic zoonoses are more likely to elicit official responses, but these can have unintended consequences that deepen poverty traps. In this context, a systems understanding of disease control can lead to more effective and pro-poor disease management. We illustrate this with an example of how a system dynamics model can help optimize responses to Rift Valley fever outbreaks in Kenya by giving decision makers real-time access to the costs of the delay in vaccinating. In conclusion, a broader, more ecological understanding of poverty and of the appropriate responses to the diseases of poverty can contribute to improved livelihoods for livestock keepers in Africa. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’.... [Show full abstract]
Keywordspoverty; livestock keepers; zoonoses; ecosystems; system dynamics; Evolutionary Biology; Animals; Humans; Rift Valley Fever; Disease Outbreaks; Animal Husbandry; Kenya; Livestock
Fields of Research0602 Ecology; 050102 Ecosystem Function; 070301 Agro-ecosystem Function and Prediction; 06 Biological Sciences; 11 Medical and Health Sciences
© 2017 The Authors.