Socioeconomic status effects on health vary between rural and urban Turkana

Background and objectives: Understanding the social determinants of health is a major goal in evolutionary biology and human health research. Low socioeconomic status (often operationalized as absolute material wealth) is consistently associated with chronic stress, poor health, and premature death in high income countries. However, the degree to which wealth gradients in health are universal—or are instead made even steeper under contemporary, post-industrial conditions—remains poorly understood.

Methodology: We quantified absolute material wealth and several health outcomes among a population of traditional pastoralists, the Turkana of northwest Kenya, who are currently transitioning toward a more urban, market-integrated lifestyle. We assessed whether wealth associations with health differed in subsistence-level versus urban contexts. We also explored the causes and consequences of wealth-health associations by measuring serum cortisol, potential sociobehavioral mediators in early life and adulthood, and adult reproductive success (number of surviving offspring).

Results: Higher socioeconomic status and greater material wealth predicts better self-reported health and more offspring in traditional pastoralist Turkana, but worse cardiometabolic health and fewer offspring in urban Turkana. We do not find robust evidence for either direct biological mediators (i.e., cortisol) or indirect sociobehavioral mediators (e.g., adult diet or health behaviors, early life experiences) of wealth-health relationships in either context.

Conclusions and implications: While social gradients in health are well-established across a variety of primate species, and in humans, across a variety of socioecological contexts, we show that the relationship between wealth and health can vary within a single population. Our findings emphasize that changes in economic and societal circumstances may directly alter how, why, and under what conditions socioeconomic status is predictive of health.

Methods
The data were collected and processed as described in Lea et al. 2020, Science Advances and Lea et al. 2021, Evolution, Medicine, and Public Health.