Health Promotion and Population Health

The overall vision for the research unit is to generate evidence on how to deliver, support and evaluate health promotion and prevention of non-communicable diseases to the right populations at the right time in the most efficient way to support healthy living. The research unit follows three major paths in seeking to deliver interdisciplinary scientific contributions within public health science regarding intervention research and quantitative methodology. It is a main ambition of the research unit to achieve this by integration of scientific disciplines such as health promotion, demography, intervention methodology, biostatistics and health economics.

Projects

Demography, determinants of NCDs and health economics

This research explores needs for population based health promotion and prevention by developing models translating epidemiologically used measures of risk into population health measures by considering life expectancy and healthy life expectancy summary measures, and to explore economic, societal and health impact of demographic changes. 

Health Promotion and intervention research in prevention of NCDs

This research contributes new models for health promoting interventions in diverse socio-economic groups by using modern approaches to co-designing, health literacy responsiveness and implementation of complex interventions targeted different groups (e.g. vulnerable new families, new migrants) and settings (e.g. communities and cities). The research focus is on early NCD prevention, e.g. inactivity, obesity.

Methods

Outcome research, methods for evaluation and communication

Public health interventions pose a strong need for exploring and developing modern analytical strategies to detect and remove the effects of unobserved confounding (instrumental variables, negative control outcomes and exposures, propensity scores, etc.), as interventions cannot always be evaluated in a randomized design. Often, public health interventions are complex to evaluate as their effects may have many facets and be delayed before observed. For example, screening programs will advance time of diagnosis, which in an open cohort perspective may easily be mistaken for over-detection of the disease. Some keywords for this group is lead-time (advancement of time of diagnosis) and modeling of outcome measures and effects in e.g. screening and other preventive health programs.