Cause-of-death statistics in public health and epidemiology: Exploring new applications

The value of using cause-of death data in medicine, epidemiology, and public health is well-known. Death is a clear endpoint in epidemiological studies, and mortality databases are strongly regulated, and have large coverage in both time and space. However, given the rise of ageing populations and the capacities of big data, there is still room for improvement in the applications of mortality data.

In this thesis, we aimed to illustrate new potential applications of mortality data for public health monitoring and research. We explored the use of underlying cause-of-death data to monitor trends over time in cause-specific mortality. We concluded that a large number of causes of death may be selected, including ones with a relatively low number of annual deaths, for the analysis of long-term mortality time-trends in European countries. We also used multiple cause-of-death data to accurately quantify the collective mortality burden of systemic autoimmune diseases and to identify a characteristic comorbidity pattern. Classification systems could be adapted to better encompass this disease group.

Furthermore, we investigated the use of cause-of-death data linked with multiple healthcare registries to assess the prevalence of severe chronic diseases at the end of life, with applications in diabetes mellitus and dementia. After exploring different combinations of data sources, we found that the current lifetime risk of diabetes is about one third, and of dementia is about one fourth for the Dutch population. It is a major challenge to arrange optimal care for these population groups.

Mitratza, M. (2021). Cause-of-death statistics in public health and epidemiology: Exploring new applications. Dissertation, University of Amsterdam, handle:11245.1/5a8a56a0-c67b-47a3-a1b3-3b65ce6df64f.