RT Journal Article
SR Electronic
T1 Identifying Parkinson’s disease and parkinsonism cases using routinely-collected healthcare data: a systematic review
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 331652
DO 10.1101/331652
A1 Zoe Harding
A1 Tim Wilkinson
A1 Anna Stevenson
A1 Sophie Horrocks
A1 Amanda Ly
A1 Christian Schnier
A1 David P Breen
A1 Kristiina Rannikmäe
A1 Cathie LM Sudlow
A1 on behalf of Dementias Platform UK
YR 2018
UL http://biorxiv.org/content/early/2018/05/25/331652.abstract
AB Background Population-based, prospective studies can provide important insights into Parkinson’s disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely-collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.Methods We searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely-collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.Results We identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPVs ranged from 56-90% in hospital datasets, 53-87% in prescription datasets, 81-90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPVs ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivities ranged from 15-73% in single datasets for PD and 43-63% in single datasets for parkinsonism.Conclusions In many settings, routinely-collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.