title: 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.
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Sophie
authors.initials: S
authors.lastname: Horrocks
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Tim
authors.identifier: http://orcid.org/0000-0001-8952-0982
authors.initials: T
authors.lastname: Wilkinson
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Christian
authors.initials: C
authors.lastname: Schnier
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Amanda
authors.initials: A
authors.lastname: Ly
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Woodfield
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Kristiina
authors.initials: K
authors.lastname: Rannikmäe
authors.affiliation: Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Terence J
authors.initials: TJ
authors.lastname: Quinn
authors.affiliation: Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.|UK Biobank, Stockport, United Kingdom.
authors.forename: Cathie L M
authors.initials: CL
authors.lastname: Sudlow
mesh_terms: D003625:Data Collection / Q000592:standards*; D016208:Databases, Factual / Q000592:standards; D003695:Delivery of Health Care; D006801:Humans; D016472:Motor Neuron Disease / Q000175:diagnosis*