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Lancet. 2017 Jul 29;390(10093):500-509. doi: 10.1016/S0140-6736(17)30572-X. Epub 2017 Jul 27.

Evolutionary public health: introducing the concept.

Author information

1
Childhood Nutrition Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK. Electronic address: jonathan.wells@ucl.ac.uk.
2
Centre for Evolution and Medicine, Arizona State University, Phoenix, AZ, USA.
3
London School of Hygiene & Tropical Medicine, London, UK.
4
Department of Zoology, University of Cambridge, Cambridge, UK.
5
Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.

Abstract

The emerging discipline of evolutionary medicine is breaking new ground in understanding why people become ill. However, the value of evolutionary analyses of human physiology and behaviour is only beginning to be recognised in the field of public health. Core principles come from life history theory, which analyses the allocation of finite amounts of energy between four competing functions-maintenance, growth, reproduction, and defence. A central tenet of evolutionary theory is that organisms are selected to allocate energy and time to maximise reproductive success, rather than health or longevity. Ecological interactions that influence mortality risk, nutrient availability, and pathogen burden shape energy allocation strategies throughout the life course, thereby affecting diverse health outcomes. Public health interventions could improve their own effectiveness by incorporating an evolutionary perspective. In particular, evolutionary approaches offer new opportunities to address the complex challenges of global health, in which populations are differentially exposed to the metabolic consequences of poverty, high fertility, infectious diseases, and rapid changes in nutrition and lifestyle. The effect of specific interventions is predicted to depend on broader factors shaping life expectancy. Among the important tools in this approach are mathematical models, which can explore probable benefits and limitations of interventions in silico, before their implementation in human populations.

PMID:
28792412
DOI:
10.1016/S0140-6736(17)30572-X
[Indexed for MEDLINE]

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