Prevalence of Down's Syndrome in England, 1998–2013: Comparison of linked surveillance data and electronic health records - Doidge, James C; Morris, Joan K; Harron, Katie L; Stevens, Sarah; Gilbert, Ruth (International Journal of Population Data Science, 2020)
Introduction Disease registers and electronic health records are valuable resources for disease surveillance and research but can be limited by variation in data quality over time. Quality may be limited in terms of the accuracy of clinical information, of the 'internal linkage' that supports person-based analysis of most administrative datasets, or by errors in linkage between multiple datasets. Objectives By linking the National Down Syndrome Cytogenetic Register (NDSCR) to Hospital Episode Statistics for England (HES), we aimed to assess the quality of each and establish a consistent approach for analysis of trends in prevalence of Down’s syndrome among live births in England. Methods Probabilistic record linkage of NDSCR to HES for the period 1998–2013, supported by linkage of babies to mothers within HES. Comparison of prevalence estimates in England using NDSCR only, HES data only, and linked data. Capture-recapture analysis and quantitative bias analysis were used to account for potential errors, including false positive diagnostic codes, unrecorded diagnoses, and linkage error. Results Analyses of single-source data indicated increasing live birth prevalence of Down’s Syndrome, particularly analysis of HES. Linked data indicated a contrastingly stable prevalence of 12.3 (plausible range: 11.6–12.7) cases per 10 000 live births. Conclusions Case ascertainment in NDSCR improved slightly over time, creating a picture of slowly increasing prevalence. The emerging epidemic suggested by HES primarily reflects improving linkage within HES (assignment of unique patient identifiers to hospital episodes). Administrative data are valuable but trends should be interpreted with caution, and with assessment of data quality over time. Data linkage with quantitative bias analysis can provide more robust estimation and, in this case, reassurance that prevalence is not increasing. Routine linkage of administrative and register data can enhance the value of each.