Dataset Information

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.

ABSTRACT:

Background

Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases.

Methods

We performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy-positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies.

Results

Thirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55-92% and sensitivities from 75-93%. The single (UK-based) study of primary care data reported a PPV of 85%.

Conclusions

Diagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation.

SUBMITTER: Horrocks S

PROVIDER: S-EPMC5330471 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.

Horrocks Sophie S Wilkinson Tim T Schnier Christian C Ly Amanda A Woodfield Rebecca R Rannikmäe Kristiina K Quinn Terence J TJ Sudlow Cathie L M CL

PloS one 20170228 2

<h4>Background</h4>Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases.<h4>Methods</h4>We performed an electro ...[more]

PMID: 28245254

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Dataset Information

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.

Background

Methods

Results

Conclusions

Publications

Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.

Similar Datasets

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