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Identifying Parkinson's disease and parkinsonism cases using routinely collected healthcare data:A systematic review
- Source :
- Harding, Z, Wilkinson, T, Stevenson, A, Horrocks, S, Ly, A, Schnier, C, Breen, D P, Rannikmae, K & Sudlow, C 2019, ' Identifying Parkinson's disease and parkinsonism cases using routinely collected healthcare data : A systematic review ', PLoS ONE, vol. 14, no. 1, 0198736 . https://doi.org/10.1371/journal.pone.0198736, https://doi.org/10.1371/journal.pone.0198736, PLoS ONE, Vol 14, Iss 1, p e0198736 (2019), PLoS ONE
- Publication Year :
- 2019
-
Abstract
- 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, PPV 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, PPV ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivity 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. However, given the wide range of identified accuracy estimates, we recommend cohorts conduct their own context-specific validation studies if existing evidence is lacking. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.
- Subjects :
- Male
Databases, Factual
Patients
ASCERTAINMENT
Science
ACCURACY
CLINICAL-DIAGNOSIS
Geographical locations
Diagnostic Medicine
Outpatients
Medicine and Health Sciences
Electronic Health Records
Humans
COHORT
Prospective Studies
European Union
VALIDITY
RECORDS
Primary Care
Computational Neuroscience
Sweden
Inpatients
Coding Mechanisms
Movement Disorders
Primary Health Care
MORTALITY
ALGORITHMS
DEATH
Biology and Life Sciences
Computational Biology
Parkinson Disease
Neurodegenerative Diseases
Hospitals
PREVALENCE
Health Care
Europe
Neurology
Health Care Facilities
Medicine
Female
People and places
Algorithms
Research Article
Neuroscience
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Harding, Z, Wilkinson, T, Stevenson, A, Horrocks, S, Ly, A, Schnier, C, Breen, D P, Rannikmae, K & Sudlow, C 2019, ' Identifying Parkinson's disease and parkinsonism cases using routinely collected healthcare data : A systematic review ', PLoS ONE, vol. 14, no. 1, 0198736 . https://doi.org/10.1371/journal.pone.0198736, https://doi.org/10.1371/journal.pone.0198736, PLoS ONE, Vol 14, Iss 1, p e0198736 (2019), PLoS ONE
- Accession number :
- edsair.pmid.dedup....3f2aa099b34d14335938af5526bd177c