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Identifying dementia cases with routinely collected health data: A systematic review
Author(s) -
Wilkinson Tim,
Ly Amanda,
Schnier Christian,
Rannikmäe Kristiina,
Bush Kathryn,
Brayne Carol,
Quinn Terence J.,
Sudlow Cathie L.M.
Publication year - 2018
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2018.02.016
Subject(s) - dementia , medicine , linkage (software) , coding (social sciences) , predictive value , disease , gerontology , statistics , mathematics , biochemistry , chemistry , gene
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.