How Data Harmonization provides a unique opportunity to accelerate health research
Data harmonization is a promising new way to support the advancement of research into population health to improve health and well-being of people worldwide, and provides unique opportunities for research.
Data harmonization is a way to make multiple datasets comparable and enable data combination process. The combined dataset allows us to answer our research questions that could not be address using a single dataset alone.
Dr Kamala Adhikari commented that “Combinations of dissimilar datasets is harmful, similar to discrepancies in health across various population groups.”
The technique of combining data from various sources in this way has many advantages. It allows researchers to conduct their investigations more quickly and is far more cost effective than having to go through an often lengthy process of collecting primary data. In addition, large harmonized datasets provide the necessary study power that is not always feasible with single study datasets, and they are particularly valuable when researching rare risk factors or outcomes.
In this new study, Dr Kamala Adhikari of the Department of Community Health Sciences, University of Calgary, Canada and her team, aimed to improve our understanding of risk factors for preterm birth using data harmonization techniques. The team set out to
- develop a prediction model for preterm birth
- compare and asses how suitable certain anxiety scales that are used to measure anxiety during pregnancy are, and
- assess whether a woman’s socioeconomic status has an effect on anxiety and/or depression during pregnancy and preterm birth.
The findings have improved our understanding of psychosocial factors affecting preterm birth and have provided new directions for future research, and clinical practice, to improve prevention of preterm birth.
Dr Kamala Adhikari, Department of Community Health Sciences, University of Calgary, Calgary, Canada
Adhikari, K., Patten , S. B. ., Patel , A. B. ., Premji , S. ., Tough , S. ., Letourneau , N. ., Giesbrecht , G. . and Metcalfe , A. . (2021) “Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management”, International Journal of Population Data Science, 6(1). doi: 10.23889/ijpds.v6i1.1680.