Modeling Health Histories and the Dynamics of Social Inequality in a Nonhuman Primate Population

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Project Summary The broad goal of the proposed project is to increase understanding of the dynamics of socially driven health disparities by developing improved methods to forecast individual health histories using a new primate model exposed to different levels of social disadvantage. Health disparities across different subgroups are a crucial societal problem1-3 and thus, accurate models describing and forecasting individual health histories are a fundamental first step to identify strategies for intervention. Most current models4-5 assume that the observed gradual accumulation of health decline in human populations reflect the change in the health history of individuals across their lifespan. However, recent work on functional limitations6 suggests that individual health histories are better described as a punctuated equilibrium pattern where the individual may experience periods of long-term stability interrupted by sudden changes. This points towards discrepancies between models used to test hypotheses about the impact of cumulative disadvantage over the lifespan, and the actual health histories experienced by individuals. To narrow the health gap and thus foster healthy aging across all groups, the theoretical and practical limitations imposed by current health history forecast methods must be overcome. This project aims to improve analytical understanding of health disparity dynamics by employing a comparative approach using data from the Cayo Santiago rhesus macaque population. This population uniquely allows for integrative longitudinal studies of health in a naturalistic, socially complex population, and thus is an ideal primate model system to yield information about how different individuals transition between multiple states of health across the socially stratified adult lifespan. The first aim is to characterize transition rules between multiple states of health across the socially stratified adult lifespan using indexes of health comprising both psychological and physiological health. The second aim is to use this new empirical dataset to formulate and develop a multistate forecasting model to analyze changing patterns of health and their relation to an individual’s social environment. The third aim is to then forecast cumulative health penalties and divergent health outcomes in order to identify stable or changing gaps in health across subgroups exposed to different levels of social disadvantage, such as social status and social integration. These aims will allow the formulation of an accurate dynamic health history forecast model, the examination of whether and how individual sociality affects health state transitions across sex and age, and ultimately the refinement, modification, and adaptation of data collection and current model assumptions for accurate assessment of socially driven health disparities. Specifically, this study will contribute to the NIH Stage 0 of Intervention Development through basic science, opening space to identify critical ages to target in a follow-up longitudinal study that will provide a crucial bridge to future translational research with humans.