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Abstract
Non-communicable diseases such as type 2 diabetes and heart disease are among the most important causes of death and disability around the world, and therefore a major public health challenge. These conditions are caused by a complicated combination of genetic and lifestyle factors and can develop slowly without symptoms, which makes their prevention, diagnosis and treatment challenging.
The road to disease is different for everyone, and individual characteristics play an important role in determining who becomes sick and when. At the same time, public health recommendations tend to be the same for everyone. Although these interventions have been very successful in reducing the impact of chronic diseases in the population, this progress seems to be slowing down.
A possible way forward is implementing precision medicine in public health. Precision medicine is a concept based on using individuals’ information to subgroup a population based on their characteristics. The aim is to be able to offer the best therapy for these subgroups. In public health, it entails designing and implementing preventive strategies according to individuals’ attributes. However, the use of precision medicine in public health has not yet been widely studied.
In this thesis, I explore the utility of methods that can be used to apply precision medicine to public health issues. These methods aim to group individuals based on how an exposure changes over time, how several factors combine at a single time point, or seek to estimate the importance of different causes of disease. The overall purpose is to provide examples and a discussion on how precision medicine can be useful to inform and improve public health practice.
