Population Health

Detailed description

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Increasing longevity is both an impressive achievement and a major challenge for the developed world. Continued improvements in life expectancy inevitably contribute to population aging and will influence both the number of years people can expect to enjoy good health and the number of years they can expect to spend physically or cognitively disabled. To quantify the extent to which increasing longevity is good news at the individual and societal level, we seek to answer three key questions. First, are the extra years of life spent in good or in poor health? Second, what are the driving factors that impact health status, and how do they vary throughout the life course and in a sociodemographic and spatiotemporal context? Third, which methods, models, and measures are most suitable for tackling these issues, and are thus a necessary prerequisite for the analysis of population health?

The Laboratory of Population Health seeks to address these three key questions from different angles, combining demographic, sociological, economic, epidemiological, and statistical perspectives. With the near-linear increase in the length of life experienced by many countries, the questions of whether the extra years of life are healthy or unhealthy and whether they are equally distributed across sociodemographic subgroups have become key policy concerns. They are of critical importance for promoting individual well-being; setting funding levels; and, ultimately, ensuring the sustainability of health and social welfare systems.

We adopt a cross-sectional and a life-course perspective to identify and quantify how major demographic, behavioral, and structural conditions affect population health. We analyze the independent and interacting impacts on trends and differentials in health and mortality of, first, behavioral determinants, such as cigarette smoking, excessive alcohol consumption, and obesity, with a particular focus on the dynamic replacement of cigarette smoking with other risky behaviors; second, structural conditions such as living conditions, economic development, and access to health care; and, third, sociodemographic factors such as family composition, educational attainment, migrant status, and ethnicity. We use multidimensional measures of health based on information on physical and cognitive functioning, diagnosed conditions, medication use, and other factors, and we investigate how changes in health status interact with all of these determinants within and across populations, covering a large number of countries, birth cohorts, and time periods.

New insights into the determinants and future of health, morbidity, and mortality require methodological innovations. We devote a substantial part of our work to developing methods, models, and measures that can be used to analyze various aspects of health and to generate insights from new angles. These innovations include reconceptualizing the measurement of age, developing new methods for the analysis of population-level disability dynamics, advancing demographic decomposition techniques, and developing innovations in the visualization of demographic data and in the statistical analysis of causal relationships in demographic research. 

The analyses conducted in the Laboratory of Population Health help to identify the harmful and the beneficial demographic and behavioral profiles and structural conditions that lead to populations either achieving high levels of health and life expectancy or lagging behind the high achievers. Analyzing causal effects and pathways helps to identify efficient policy interventions for improving health and reducing health inequalities. The forecasting approaches we develop help us better understand the future of health and the disability burden, both with and without policy interventions or medical innovations.


Selected Publications

GOISIS, A., ÖZCAN, B., MYRSKYLÄ, M.: Decline in the negative association between low birth weight and cognitive ability
Proceedings of the National Academy of Sciences, 114(1), 84-88. (2017)

”German East-West mortality difference: two cross-overs driven by smoking”. Forthcoming, Demography (2017)

Income differences in life expectancy: the changing contribution of harmful consumption of alcohol and smoking
Epidemiology 25:2, 182-190 (2014).

Reversing East-West mortality difference among German women, and the role of smoking
International Journal of Epidemiology 42:2, 549-558 (2013).

The relative effects of shocks in early- and later-life conditions on mortality
Population and Development Review 36:4, 803-829 (2010).



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