RT Journal Article
SR Electronic
T1 Validity of diagnostic coding within the General Practice Research Database: a systematic review
JF British Journal of General Practice
JO Br J Gen Pract
FD British Journal of General Practice
SP e128
OP e136
DO 10.3399/bjgp10X483562
VO 60
IS 572
A1 Nada F Khan
A1 Sian E Harrison
A1 Peter W Rose
YR 2010
UL http://bjgp.org/content/60/572/e128.abstract
AB Background The UK-based General Practice Research Database (GPRD) is a valuable source of longitudinal primary care records and is increasingly used for epidemiological research.Aim To conduct a systematic review of the literature on accuracy and completeness of diagnostic coding in the GPRD.Design of study Systematic review.Method Six electronic databases were searched using search terms relating to the GPRD, in association with terms synonymous with validity, accuracy, concordance, and recording. A positive predictive value was calculated for each diagnosis that considered a comparison with a gold standard. Studies were also considered that compared the GPRD with other databases and national statistics.Results A total of 49 papers are included in this review. Forty papers conducted validation of a clinical diagnosis in the GPRD. When assessed against a gold standard (validation using GP questionnaire, primary care medical records, or hospital correspondence), most of the diagnoses were accurately recorded in the patient electronic record. Acute conditions were not as well recorded, with positive predictive values lower than 50%. Twelve papers compared prevalence or consultation rates in the GPRD against other primary care databases or national statistics. Generally, there was good agreement between disease prevalence and consultation rates between the GPRD and other datasets; however, rates of diabetes and musculoskeletal conditions were underestimated in the GPRD.Conclusion Most of the diagnoses coded in the GPRD are well recorded. Researchers using the GPRD may want to consider how well the disease of interest is recorded before planning research, and consider how to optimise the identification of clinical events.