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CPRD GOLD and linked ONS mortality records: Reconciling guidelines
Antonella Delmestri1, Daniel Prieto-Alhambra1
1Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, UK.
This study provides guidelines for linking Clinical Practice Research Datalink (CPRD) GOLD and Office for National Statistics (ONS) mortality data. An SQL algorithm helps researchers improve data quality and reproducibility in clinical studies.
Area of Science:
- Epidemiology
- Health Informatics
- Data Science
Background:
- The Clinical Practice Research Datalink (CPRD) GOLD is a key UK primary care dataset for epidemiological research.
- Office for National Statistics (ONS) death data for England is the gold standard for mortality data and can be linked to CPRD GOLD.
- While CPRD GOLD's death data accuracy is sufficient for most studies, challenges exist in managing linked ONS and CPRD GOLD datasets.
Purpose of the Study:
- To provide guidelines for reconciling mortality information from both CPRD GOLD and linked ONS datasets.
- To enhance the quality, reproducibility, transparency, and comparability of clinical research using linked mortality data.
- To address challenges including linkage coverage, correctness, regional limitations, and data discrepancies.
Main Methods:
- Developed recommendations for managing mortality data from CPRD GOLD and linked ONS datasets.
- Considered linkage coverage period, correctness likelihood, regional limitations, and data discrepancies.
- Implemented these guidelines in a practical SQL algorithm for researcher use.
Main Results:
- Provided detailed guidelines for reconciling mortality data between CPRD GOLD and linked ONS death datasets.
- Addressed the strengths and limitations of both datasets.
- Developed an SQL algorithm to facilitate the consistent application of these guidelines.
Conclusions:
- Detailed guidelines for reconciling CPRD GOLD and ONS linked death datasets have been established.
- The consistent application of these guidelines, supported by an SQL algorithm, can significantly improve clinical research.
- Potential improvements include enhanced data quality, reproducibility, transparency, and comparability in clinical research.
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