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.
Highlights
Motor neurone disease (MND) is a rare, rapidly progressive, neurodegenerative disease, which leads to muscle wasting, weakness and usually death within a few years of onset[1]
We systematically evaluated the accuracy of such datasets in identifying MND cases
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
Summary
Motor neurone disease (MND) is a rare, rapidly progressive, neurodegenerative disease, which leads to muscle wasting, weakness and usually death within a few years of onset[1]. Population-based, prospective studies involving bio-sampling, detailed phenotyping and genotyping are ideal for investigating the determinants of diseases of complex aetiology, including neurodegenerative diseases such as MND. Through identifying sufficiently large numbers of incident cases of disease, such studies can provide adequate statistical power to detect associations of environmental, lifestyle, biological and genetic exposures with disease outcomes. They can overcome the inherent limitations of retrospective case-control studies, including recall and reverse causation biases. Population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. We systematically evaluated the accuracy of such datasets in identifying MND cases
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