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Identifying dementia cases with routinely-collected health data: a systematic review

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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 healthcare datasets. We evaluated the accuracy of these datasets for dementia case identification.METHODS: We systematically reviewed the literature for studies comparing dementia coding in routinely-collected datasets 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 21-86%. PPVs for Alzheimer’s disease (range 57-100%) were generally higher than for vascular dementia (range 19-91%).DISCUSSION: Linkage to routine healthcare 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.

    Research areas

  • Key words: dementia; Alzheimer disease; dementia, vascular; clinical coding; epidemiology; prospective studies; cohort studies; sensitivity; positive predictive value; predictive value of tests

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ID: 55134836