Identifying dementia cases with routinely collected health data

This review finds that routinely collected health-care data sets have the potential to be a cost-effective and comprehensive method of identifying dementia cases in prospective studies | Alzheimer’s & Dementia

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.

Full reference: Wilkinson, T. et al. | Identifying dementia cases with routinely collected health data: A systematic review | Alzheimer’s & Dementia | August 2018 | Volume 14, Issue 8, Pages 1038–1051

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