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. 2018 Aug;14(8):1038-1051.
doi: 10.1016/j.jalz.2018.02.016. Epub 2018 Apr 3.

Identifying dementia cases with routinely collected health data: A systematic review

Tim Wilkinson  1 , Amanda Ly  2 , Christian Schnier  2 , Kristiina Rannikmäe  3 , Kathryn Bush  3 , Carol Brayne  4 , Terence J Quinn  5 , Cathie L M Sudlow  6 ; UK Biobank Neurodegenerative Outcomes Group and Dementias Platform UK
Affiliations

Identifying dementia cases with routinely collected health data: A systematic review

Tim Wilkinson et al. Alzheimers Dement. 2018 Aug.

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.

Keywords: Alzheimer's disease; Clinical coding; Cohort studies; Dementia; Epidemiology; Positive predictive value; Predictive value of tests; Prospective studies; Sensitivity; Vascular.

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Figures

Fig. 1
Fig. 1
Study selection process.
Fig. 2
Fig. 2
PPV estimates for routinely collected coded health data to identify all-cause dementia cases, stratified by type of routine data set. Study size: number of cases with ≥1 dementia codes in data set. *High risk of bias or applicability concerns in one or more areas. Abbreviations: PPV, positive predictive value; CI, confidence interval.
Fig. 3
Fig. 3
PPV estimates for routinely collected coded health data to identify dementia subtype cases, stratified by type of routine data set. Study size: number of cases with ≥1 dementia codes in data set. *High risk of bias or applicability concerns in one or more areas. Abbreviations: AD, Alzheimer's disease; VaD, vascular dementia; PPV, positive predictive value; CI, confidence interval.
Fig. 4
Fig. 4
Sensitivity estimates for routinely collected coded health data to identify all-cause dementia cases, stratified by type of routine data set. Study size: Number of known dementia cases for which a code was sought. Abbreviation: CI, confidence interval.
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