Arbeitsbereich

Bevölkerungsdynamik und Nachhaltiges Wohlbefinden

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Forschungsfeld

Health and Survival

The Health and Survival research area within the Lab of Population Dynamics and Sustainable Well-Being examines how health, mortality, and longevity are shaped across populations, with particular attention being given to inequality, uncertainty, and change. In a world characterized by population aging, widening social disparities, and recurrent crises, this research area advances demography by combining methodological innovation with substantive insights into the determinants of survival.

A first signature aspect of this research is its focus on socioeconomic inequalities in health and mortality, including in settings where data are incomplete or imperfect. Building on what we have learned about inequalities in contexts with high-quality data, this work develops methods that enable meaningful comparisons across a broader range of countries and populations. By leveraging age-at-death distributions, refining measures of lifespan inequality, and explicitly addressing biases such as misreporting in socioeconomic variables, the research expands the empirical reach of demography. This approach not only improves the measurement of inequalities, but also reveals patterns that would otherwise remain hidden, thereby contributing to a more globally inclusive understanding of mortality disparities.

A second defining pillar is the assessment of the impact of shocks on health and mortality, including epidemics, armed conflicts, and other forms of large-scale disruption. These events are studied not only as short-term fluctuations, but also as processes with long-lasting consequences for population structures, health trajectories, and family dynamics. By adopting cohort and life course perspectives, the research captures how exposure to crises accumulates over time and interacts with existing inequalities. The integration of diverse data sources, ranging from surveys and administrative records to our own primary data collections, enables the study of populations that are often difficult to observe, particularly in contexts of conflict, crisis, or rapid change.

A third key dimension is the development of innovative approaches to predicting mortality at both the individual and aggregate levels. At the micro level, the research explores the extent to which individual lifespans can be predicted using a combination of traditional statistical models and machine learning techniques. This work not only evaluates predictive performance, but also uncovers inequalities in predictability itself, thus highlighting how uncertainty about the future is socially structured. At the macro level, the research advances mortality forecasting by introducing flexible models that improve accuracy and interpretability. By incorporating cohort perspectives and causes of death within a solid theoretical framework, these models address the limitations of established approaches and provide more robust tools for anticipating demographic change.

Inequalities, shocks, and predictability are not treated as separate topics, but rather as interconnected dimensions of health and survival. This integrated perspective allows for a more comprehensive understanding of how longevity is shaped by social conditions, disrupted by external events, and characterized by both regularities and uncertainties. By advancing methods, expanding data horizons, and deepening the analysis of inequality and risk, this research area contributes to the broader goal of understanding the sustainability of population dynamics, not only in terms of the length of lives, but also with respect to how health and survival are distributed, experienced, and shaped across diverse contexts.

Projekte dieses Forschungsfelds