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GUILD: GUidance for Information about Linking Data sets.
Ruth Gilbert1, Rosemary Lafferty1, Gareth Hagger-Johnson1
1Administrative Data Research Centre for England, University College London Great Ormond Street Institute of Child Health, London, UK.
Journal of Public Health (Oxford, England)
|April 4, 2017
Summary
Data linkage errors can bias results, disproportionately affecting disadvantaged groups. New guidance aims to improve transparency and accuracy in data linkage for better policy and public health evidence.
Area of Science:
- Data Science
- Public Health Research
- Administrative Data Analysis
Background:
- Record linkage of administrative and survey data is vital for policy and service evidence generation.
- Data processing errors during linkage can introduce biases, compromising research validity.
- Lack of transparency regarding linkage processes hinders bias assessment and mitigation.
Framework:
- Develop comprehensive guidance for data providers, linkers, and analysts on essential linkage information.
- Establish clear reporting standards for data linkage processes.
- Promote awareness of potential biases and their impact on disadvantaged populations.
Implementation:
- Collaborative effort involving researchers and government data experts.
- Guidance extends beyond research reports to cover the entire linkage pathway.
- Focus on improving information availability at each linkage step.
Implications:
- Enhance transparency, reproducibility, and accuracy of data linkage.
- Improve the validity of analyses and interpretation of results from linked data.
- Mitigate bias in evidence generation, particularly for public health initiatives affecting vulnerable groups.