Life course epidemiology tries to understand and establish the dynamic character of the association and changes in risk factors and exposures with disease outcome over a life time of a person. Concepts like accumulation of risk, critical and sensitive periods, trajectory and path analysis are important issues.

We focus on observational longitudinal studies.  A  high level of methodological knowledge and research is required in the field of longitudinal data analysis for studies that cover a large life span of patients or healthy individuals.

Ultimately we aim to:

  • Unravel the complex origins of disease with a clear view to primary prevention.
  • Enhance the quality of care for patients through investigating mechanisms in the course of the disease, thus contributing to secondary and tertiary prevention.

Identify relevant risk factors and predictors for Healthy Ageing

Our activities support prevention research. We investigate persons' risk factors for incidence of disease and disease complications, or absence of disease by following groups of unselected individuals or specific patients over the life span (cohorts). From this data, we identify relevant risk factors and predictors for Healthy Ageing. This unique knowledge offers interesting opportunities for developing prevention strategies.

We aim to develop, improve and disseminate advanced methodology and apply these methods in future research projects to data from large UMCG population based cohorts such as:

  • We seek to promote Life Course Epidemiology research at the UMCG through the development, improvement and dissemination of relevant advanced methodologies, as well as their applications to the available cohorts at the UMCG.

    To achieve the aim of our program we strive for methodological improvement with respect to challenges like:

    • the cluster structure of the data through repeated measurements,
    • prognostic tools considering the variability of predictors over time,
    • evaluating (preventive) interventions/ exposures with lacking randomization,
    • requiring specific methods for causal inference,
    • computational problems due to missing data,
    • high dimensionality of problems, occurring from the improvement of data collection opportunities in recent years (big data),
    • analyses of ecological momentary assessments, Thus we combine methodological experts from various disciplines,
    • sharing knowledge, experiences and project ideas.