Identifying dementia cases with routinely collected health data: A systematic review
Tim Wilkinson , Amanda Ly, Christian Schnier, Kristiina Rannikmea , Kathryn Bush, Carol Brayne, Terence J. Quinnd , Cathie L. M. Sudlow, On behalf of the UK Biobank, Neurodegenerative Outcomes Group, Dementias Platform UK
Introduction: Prospective, population-based studies can be rich resources for dementia research. Follow-up in many such studies is through linkage to routinely collected, coded health-care data sets. We evaluated the accuracy of these data sets for dementia case identification. Methods: We systematically reviewed the literature for studies comparing dementia coding in routinely collected data sets to any expert-led reference standard. We recorded study characteristics and two accuracy measures—positive predictive value (PPV) and sensitivity. Results: We identified 27 eligible studies with 25 estimating PPV and eight estimating sensitivity. Study settings and methods varied widely. For all-cause dementia, PPVs ranged from 33%–100%, but 16/27 were .75%. Sensitivities ranged from 21% to 86%. PPVs for Alzheimer’s disease (range 57%–100%) were generally higher than those for vascular dementia (range 19%–91%). Discussion: Linkage to routine health-care data can achieve a high PPVand reasonable sensitivity in certain settings. Given the heterogeneity in accuracy estimates, cohorts should ideally conduct their own setting-specific validation.