Evidence map›Paper›PMID 28245254›Full record

SynthesisPloS one2017

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

Sophie Horrocks, Tim Wilkinson, Christian Schnier, Amanda Ly, Rebecca Woodfield, Kristiina Rannikmäe, Terence J Quinn, Cathie L M Sudlow

Abstract readSystematic Review
In one paragraph

Synthesis in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Identifying dementia cases with routinely collected health data: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2018
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  19. Analysis of incidence of motor neuron disease in England 1998-2019: use of three linked datasets.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sophie HorrocksCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Tim WilkinsonCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID http://orcid.org/0000-0001-8952-0982
Christian SchnierCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Amanda LyCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Rebecca WoodfieldCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Kristiina RannikmäeCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Terence J QuinnInstitute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.
Cathie L M SudlowCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.

Funding

Medical Research Council MC_U137686860Medical Research Council MR/L023784/2Medical Research Council MR/P001823/1
6 · The paper itself

Abstract

backgroundMotor 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.

methodsWe 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.

resultsThirteen 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%.

conclusionsDiagnostic 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.

Indexed as

Databases, FactualData CollectionDelivery of Health CareHumansMotor Neuron Disease

Identifiers

PMID28245254
PMCPMC5330471

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.