Agata Foryciarz: Will tomorrow’s American retirees be healthier than their parents generation? Life-course epidemiology approaches for understanding and modeling changes in population health of US seniors (HEPII Seminars
2:00 PM
Research Seminar
As more Americans reach older age, retirement and health care systems will need to adapt to support their needs. While it was long hoped that increases in life expectancy will translate to more disability-free years, and an ability to work longer, recent work suggests that many of today’s seniors will enter retirement at worse health and with fewer resources than their parents’ generation. Designing appropriate policy solutions requires 1) understanding the complex sources of this phenomenon and 2) ensuring that population health simulations appropriately model the effectiveness of potential interventions.
My work addresses both these aims by leveraging two social epidemiology frameworks: the life course theory, which considers how exposures accumulate across chronological life stages, and the ecosocial theory, which describes the mechanisms through which social stratification generates health inequities. I will present results from two projects: in the first, I examine employment status-specific declines in health and well-being of the middle-aged population across the income distribution over the last decade, to consider whether changes in the US labor market may be driving observed disparities. In the second, I propose extensions of the Future Adult Model that more closely align models disease processes with life course theory, by reducing the reliance of the Markov (memoryless) assumption.
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Agata Foryciarz is an interdisciplinary population health researcher and a postdoctoral fellow at the Stanford Department of Epidemiology and Population Health and the Stanford Center on Longevity. She completed a PhD in computer science with a focus on biostatistics at Stanford in 2025.
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Register for a one-on-one meeting with Agata: https://is.muni.cz/tudle/ojj
Dinner registration (max. 5 people): https://is.muni.cz/tudle/yfcz
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