Identifying dementia cases with routinely collected health data: A systematic review. Issue 8 (2nd April 2018) - Wilkinson, Tim; Ly, Amanda; Schnier, Christian; Rannikmäe, Kristiina; Bush, Kathryn; Brayne, Carol; Quinn, Terence J.; Sudlow, Cathie L.M.; ,
Abstract: 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 PPV and reasonable sensitivity in certain settings. Given the heterogeneity in accuracy estimates, cohorts should ideally conduct their own setting‐specific validation.