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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Project description:Introduction: Achieving equity in health care remains a challenge for health care systems worldwide and marked inequities in access and quality of care persist. Identifying health care equity indicators is an important first step in integrating the concept of equity into assessments of health care system performance, particularly in emergency care. Methods: We conducted a systematic review of administrative data-derived health care equity indicators and their association with socioeconomic determinants of health (SEDH) in emergency care settings. Following PRISMA-Equity reporting guidelines, Ovid MEDLINE, EMBASE, PubMed, and Web of Science were searched for relevant studies. The outcomes of interest were indicators of health care equity and the associated SEDH they examine. Results: Among 29 studies identified, 14 equity indicators were identified and grouped into four categories that reflect the patient emergency care pathway. Total emergency department (ED) visits and ambulatory care-sensitive condition-related ED visits were the two most frequently used equity indicators. The studies analyzed equity based on seven SEDH: social deprivation, income, education level, social class, insurance coverage, health literacy, and financial and nonfinancial barriers. Despite some conflicting results, all identified SEDH are associated with inequalities in access to and use of emergency care. Conclusion: The use of administrative data-derived indicators in combination with identified SEDH could improve the measurement of health care equity in emergency care settings across health care systems worldwide. Using a combination of indicators is likely to lead to a more comprehensive, well-rounded measurement of health care equity than using any one indicator in isolation. Although studies analyzed focused on emergency care settings, it seems possible to extrapolate these indicators to measure equity in other areas of the health care system. Further studies elucidating root causes of health inequities in and outside the health care system are needed.

Project description:OBJECTIVE:The positive deviance approach seeks to identify and learn from exceptional performers. Although a framework exists to apply positive deviance within healthcare organisations, there is limited guidance to support its implementation. The approach has also rarely explored exceptional performance on broad outcomes, been implemented at ward level, or applied within the UK. This study develops and critically appraises a pragmatic method for identifying positively deviant wards using a routinely collected, broad measure of patient safety. DESIGN:A two-phased observational study was conducted. During phase 1, cross-sectional and temporal analyses of Safety Thermometer data were conducted to identify a discrete group of positively deviant wards that consistently demonstrated exceptional levels of safety. A group of matched comparison wards with above average performances were also identified. During phase 2, multidisciplinary staff and patients on the positively deviant and comparison wards completed surveys to explore whether their perceptions of safety supported the identification of positively deviant wards. SETTING:34 elderly medical wards within a northern region of England, UK. PARTICIPANTS:Multidisciplinary staff (n=161) and patients (n=188) clustered within nine positively deviant and comparison wards. RESULTS:Phase 1: A combination of analyses identified five positively deviant wards that performed best in the region, outperformed their organisation and performed consistently well over 12 months. Five above average matched comparator wards were also identified. Phase 2: Staff and patient perceptions of safety generally supported the identification of positively deviant wards using Safety Thermometer data, although patient perceptions of safety were less concordant with the routinely collected data. CONCLUSIONS:This study tentatively supports a pragmatic method of using routinely collected data to identify positively deviant elderly medical wards; however, it also highlights the various challenges that are faced when conducting the first stage of the positive deviance approach. TRIAL REGISTRATION NUMBER:UK Clinical Research Network Portfolio (reference-18050).

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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