Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data

Abstract

Prospective, population-based studies that recruit participants in mid-life are valuable resources for dementia research. Follow-up in these studies is often through linkage to routinely-collected healthcare datasets. We investigated the accuracy of these datasets for dementia case ascertainment in a validation study using data from UK Biobank-an open access, population-based study of > 500,000 adults aged 40-69 years at recruitment in 2006-2010. From 17,198 UK Biobank participants recruited in Edinburgh, we identified those with ≥ 1 dementia code in their linked primary care, hospital admissions or mortality data and compared their coded diagnoses to clinical expert adjudication of their full-text medical record. We calculated the positive predictive value (PPV, the proportion of cases identified that were true positives) for all-cause dementia, Alzheimer's disease and vascular dementia for each dataset alone and in combination, and explored algorithmic code combinations to improve PPV. Among 120 participants, PPVs for all-cause dementia were 86.8%, 87.3% and 80.0% for primary care, hospital admissions and mortality data respectively and 82.5% across all datasets. We identified three algorithms that balanced a high PPV with reasonable case ascertainment. For Alzheimer's disease, PPVs were 74.1% for primary care, 68.2% for hospital admissions, 50.0% for mortality data and 71.4% in combination. PPV for vascular dementia was 43.8% across all sources. UK routinely-collected healthcare data can be used to identify all-cause dementia in prospective studies. PPVs for Alzheimer's disease and vascular dementia are lower. Further research is required to explore the geographic generalisability of these findings.

Keywords: Alzheimer disease; Cohort studies; Data accuracy; Dementia; Predictive value of tests; Validation studies.

Fig. 1

Flow diagram of participant selection

Fig. 2

Area proportional Euler diagram indicating the datasets from which dementia cases were identified (n = 120). Distribution of cases identified at any time until end of follow-up (September 2015)

Fig. 3

Adjudicator subtype diagnoses of the 99 cases adjudicated to have dementia. AD Alzheimer’s disease, VaD vascular dementia, Mixed mixed AD/VaD, DLB/PDD dementia with Lewy bodies and Parkinson’s disease dementia, FTD frontotemporal dementia

Fig. 4

Positive predictive values for datasets, alone and in combination, stratified by dementia subtype. FTD frontotemporal dementia, PDD Parkinson’s disease dementia, DLB dementia with Lewy bodies. PPVs displayed for datasets where n ≥ 5. FTD, PDD and DLB combined into ‘other dementias’ category due to small numbers for each disease alone

Fig. 5

Effect of additional criteria on positive predictive values and numbers of cases ascertained. FTD Frontotemporal dementia, PDD Parkinson’s disease dementia, DLB dementia with Lewy bodies. ≥ 10 and ≥ 20 Alzheimer’s disease codes not shown due to small numbers (< 5). *Any Alzheimer’s disease, vascular dementia, FTD, PDD or DLB code to identify dementia of any cause