Pooling of primary care electronic health record (EHR) data on Huntington's disease (HD) and cancer: establishing comparability of two large UK databases

Abstract

Objectives: To explore whether UK primary care databases arising from two different software systems can be feasibly combined, by comparing rates of Huntington's disease (HD, which is rare) and 14 common cancers in the two databases, as well as characteristics of people with these conditions.

Design: Descriptive study.

Setting: Primary care electronic health records from Clinical Practice Research Datalink (CPRD) GOLD and CPRD Aurum databases, with linked hospital admission and death registration data.

Participants: 4986 patients with HD and 1 294 819 with an incident cancer between 1990 and 2019.

Primary and secondary outcome measures: Incidence and prevalence of HD by calendar period, age group and region, and annual age-standardised incidence of 14 common cancers in each database, and in a subset of 'overlapping' practices which contributed to both databases. Characteristics of patients with HD or incident cancer: medical history, recent prescribing, healthcare contacts and database follow-up.

Results: Incidence and prevalence of HD were slightly higher in CPRD GOLD than CPRD Aurum, but with similar trends over time. Cancer incidence in the two databases differed between 1990 and 2000, but converged and was very similar thereafter. Participants in each database were most similar in terms of medical history (median standardised difference, MSD 0.03 (IQR 0.01-0.03)), recent prescribing (MSD 0.06 (0.03-0.10)) and demographics and general health variables (MSD 0.05 (0.01-0.09)). Larger differences were seen for healthcare contacts (MSD 0.27 (0.10-0.41)), and database follow-up (MSD 0.39 (0.19-0.56)).

Conclusions: Differences in cancer incidence trends between 1990 and 2000 may relate to use of a practice-level data quality filter (the 'up-to-standard' date) in CPRD GOLD only. As well as the impact of data curation methods, differences in underlying data models can make it more challenging to define exactly equivalent clinical concepts in each database. Researchers should be aware of these potential sources of variability when planning combined database studies and interpreting results.

Keywords: EPIDEMIOLOGY; Epidemiology; Health informatics; PRIMARY CARE.