The method of data linkage, imputation and statistical analysis used provides a basis for improved analysis of database linkage studies.
However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias.
This study underscores the importance of manual review of data linkages to monitor linkage quality and suggests the need for increased transparency and standardizations in linkage studies.
This study highlights the potential benefits of further data linkage.
All CVD end points were retrieved by data linkage with the Swedish Cause of Death Register and the Hospital Discharge Register National Board of Health and Welfaree, Sweden), which is an efficient validated alternative to revised hospital discharge notes and death certificates (16, 17).
Vital status of all individuals at the end of follow-up was updated by data linkage with the regional stroke register in Malmö, and with the regional and national myocardial infarction register [ 19, 21].
The key feature of best-practice data linkage processes involves separation of the various tasks required to achieve the linkage.
Data flows for cross-jurisdictional linkage comprise three distinct phases: Flow of data for linkage Provision of project specific linkage keys Extraction of research data Phase One of the data flow model is about the linkage process.
All end point events were retrieved by data linkage with the Swedish Cause of Death and Hospital Discharge Registers (National Board of Health and Welfare, Sweden), which is a reliable validated alternative to revised hospital discharge records and death certificates (20, 21).
The study strengths include the large and representative nature of the sample, the detailed nature of clinical and demographic assessment, and the completeness of ascertainment of the major clinical end points using a well-validated data linkage system.
