Having a family history of a health condition could increase your own risk of developing that health condition. Family health history is therefore an important tool for identifying people who have an increased risk of common, complex conditions.

Researchers are gaining deeper understanding of how chronic diseases cluster within families and how individuals are affected by health events amongst family members using population based data that has been routinely collected as patients come into contact with their health providers.

Routinely-collected electronic databases that contain data collected for non-research purposes, such as tracking life events (e.g., birth, death, change of address) and patient management, are a potentially powerful resource for researchers to conduct family health history studies for entire populations.

The study’s co-author Dr. Amani Hamad commented that “Family members share part of their genes and environmental exposures. Therefore, diseases with a genetic and/or environmental component tend to cluster within families. Linking family members across multiple generations allows us to study the effects of family health history on individual’s disease risk and better tease apart the roles of genetics and environmental factors in the development of diseases.”

There are several “ingredients” needed to conduct multigenerational health research using routinely-collected electronic databases. Data on family structures are needed to identify relationships within the population. Information on health conditions must also be available and is often obtained from hospitalizations and outpatient physician visits, as well as prescription drug records or electronic medical records. By linking these separate databases containing family structures and health conditions, researchers can produce objective measures of health across multiple generations.

The article, ‘Multigenerational Health Research using Population-Based Linked Databases: An International Review ‘ published in IJPDS, aimed to identify and compare the attributes of sites across the globe that can be used to conduct family health history studies using routinely-collected electronic databases.

The study found and describes three European sites (Denmark, Norway, Sweden) and three non-European sites (Canadian province of Manitoba, Taiwan, Australian state of Western Australia). It also presents the challenges associated with using the data from these sites for multigenerational health research such as being able to identify the difference between biological and legal relationships, establishing accurate family linkages over time, and accurately identifying health conditions.

European sites primarily identified family structures using population registries, whereas non-European sites used health insurance registries or linked data from multiple sources. Most of the sites could link up to three generations, identifying parents, offspring, siblings, and grandparents, and all of the sites had near complete coverage for family relationships within their geographic area.

It was also found that it is possible to link family structure data to multiple health databases and registries, and in some cases, social databases containing information on education, income, and use of social services.

These findings present many opportunities for new research, including studies that compare results across the multiple sites, developing new methods to conduct these studies, and validating data sources for identifying family structures.  

It is the hope of the research team that future collaboration will enable new innovative approaches to address the challenges of working with routinely-collected electronic databases for multigenerational health research.

Click here to read the full open access article

Naomi C. Hamm, Department of Community Health Sciences, University of Manitoba


Hamm, N. C., Hamad, A. F., Wall-Wieler, E., Roos, L. L., Plana-Ripoll , O. and Lix, L. M. (2021) “Multigenerational Health Research using Population-Based Linked Databases: An International Review ”, International Journal of Population Data Science, 6(1). doi: 10.23889/ijpds.v6i1.1686.