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Unlinked vital events in census-based longitudinal studies can bias subsequent analysis
Dermot O'Reilly1, Michael Rosato, Sheelah Connolly
1Department of Epidemiology and Public Health, Queen's University, Mulhouse Building, Royal Victoria Hospital, Grosvenor Road, Belfast BT12 6BJ, Northern Ireland, UK. d.oreilly@qub.ac.uk
Journal of Clinical Epidemiology
|March 4, 2008
Summary
Non-linkage of census and death records can bias longitudinal studies. Unlinked individuals may be more disadvantaged, potentially understating true social gradients in health outcomes.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health Research
Background:
- Longitudinal studies rely on linking diverse datasets, such as census and vital events.
- Non-linkage between these records can introduce systematic biases.
- Understanding these biases is crucial for accurate interpretation of study findings.
Purpose of the Study:
- To investigate potential biases stemming from the non-linkage of census and vital event records.
- To assess the characteristics of individuals whose death records could not be linked to census data.
Main Methods:
- Linked 56,396 Northern Ireland death records (2001-2005) to the 2001 Census.
- Compared characteristics of matched and non-matched death records using multivariate logistic regression.
- Utilized subject attributes as recorded on death certificates.
Main Results:
- 6.0% (3,392) of deaths could not be linked to census records.
- Lower linkage rates observed in young adults, males, unmarried individuals, and those in deprived areas.
- Exclusion of 20.2% of suicides and 19.7% of external cause deaths for individuals under 65.
Conclusions:
- Non-linkage results from both census non-enumeration and incomplete death record information.
- Unlinked individuals may represent more disadvantaged or socially isolated populations.
- Analyses based on linked data may underestimate true social inequalities in mortality.
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