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Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014
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Dr Martin Bardsley: Use of Retrospective Matching Methods 30 June 2014

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Martin Bardsley, Director of Research, Nuffield Trust explores the use of retrospective matching methods to study health services and other sectors. …

Martin Bardsley, Director of Research, Nuffield Trust explores the use of retrospective matching methods to study health services and other sectors.
Dr Martin Bardsley spoke at the Nuffield Trust event: The future of the hospital, in June 2014.

Published in: Health & Medicine
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  • 1. © Nuffield Trust17 July 2014 Use of retrospective matching methods to study health services and other sectors Martin Bardsley Nuffield Trust
  • 2. © Nuffield Trust Tomb raiders? Adapted from slide by Iain Buchan Manchester University Audit and Quality Improvement Patient safety (e.g. monitoring drug side effects or surgical mortality rates) Public Health programmes (immunisation; monitoring cancer rates) Evaluate Services (are they effective and cost effective?) Planning services (e.g. ICU bed availability; pandemic flu plans; manage changing patterns of demand) Manage Performance (e.g. readmission targets; health outcomes indicators) Resource allocation Research
  • 3. © Nuffield Trust Health and social care timeline – an individual’s history
  • 4. © Nuffield Trust Evidence for evaluation Evidence exists in different forms Not everything can be a prospective randomised trial Observational data can look at how things work in real life Detailed linked data sets allow quite sophisticated ways to standardise for prior risk Aslam S et al Matching research design to clinical research questions. Indian J Sex Transm Dis 2012;33:49-53
  • 5. © Nuffield Trust Methods using matched controls Idea is to track changes over time in a study cohort before and after they receive a service. (eg emergency admissions) Identify people who didn’t receive the service but look almost the same. Match on an individual basis (1:1 or 1 to many) using propensity score matching (similarity at baseline) or prognostic matching (similar on expected outcomes). Balance across a wide range of variables. Com[are outcome measure between the two groups
  • 6. © Nuffield Trust IC collates and adds HES IDsSites collate patient lists Patient identifiers (e.g. NHS number) Trial information (e.g. start and end date) Non-patient identifiable keys (e.g. HES ID) Participating sites Information Centre Nuffield Trust Lining local site data to national data sets
  • 7. © Nuffield Trust Evaluation: The Marie Curie Nursing Service Intervention: • Nursing care support to people at end of life, in their homes Nuffield commissioned to evaluate impact: • Are recipients more likely to die at home? • Reduction in emergency hospital admissions at end of life? Methods: • Retrospective matched control study – use of already existing administrative data
  • 8. © Nuffield Trust 0% 10% 20% 30% 40% 50% Comorbidities 0% 5% 10% 15% 20% 25% 30% 35% Cancer diagnoses Control group – how well matched? Diagnostic history 0% 10% 20% 30% 40% 50% Comorbidities 0% 5% 10% 15% 20% 25% 30% 35% Cancer diagnoses Marie Curie Controls
  • 9. © Nuffield Trust
  • 10. © Nuffield Trust And for 3 virtual wards…
  • 11. © Nuffield Trust Impact of eight different interventions on hospital use
  • 12. © Nuffield Trust Results (case management interventions only) • Effects were more pronounced for the case management interventions. • We found evidence of imperfect matching of cases and controls for the case management pilots. • Sensitivity analysis showed that we cannot be sure that the pilots increased emergency admissions, but it is unlikely that they reduced them. Difference in difference analysis (individual patient level) Absolute difference (per head) Relative difference p-value Emergency admissions 0.046 9% 0.02 A&E attendance -0.016 -3% 0.40 Elective admissions -0.107 -21% <.01 Outpatient attendance -0.545 -22% <.01
  • 13. © Nuffield Trust Potential of retrospective matching methods • Powerful but not perfect. Two key weaknesses in terms of ‘hidden confounders’ being limited to outcome recorded on routine data • Quick and cheap compared to prospective data collection – but can still require quite intensive analysis? • Can we make these types of approaches more accessible to providers and commissioners?
  • 14. © Nuffield Trust17 July 2014 www.nuffieldtrust.org.uk Sign-up for our newsletter www.nuffieldtrust.org.uk/newsletter Follow us on Twitter: Twitter.com/NuffieldTrust © Nuffield Trust Ian.blunt@nuffieldtrust.org.uk Adam.steventon@nuffieldtrust.org.uk

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