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How to attribute causality in quality improvement: lessons from epidemiology
  1. Alan J Poots1,
  2. Julie E Reed1,
  3. Thomas Woodcock1,
  4. Derek Bell1,
  5. Don Goldmann2,3
  1. 1National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North West London (NWL), Imperial College London, London, UK
  2. 2Institute for Healthcare Improvement, Boston, Massachusetts, USA
  3. 3Harvard TH Chan School of Public Health and Harvard Medical School, Institute for Healthcare Improvement, Boston, Massachusetts, USA
  1. Correspondence to Dr Alan J Poots, National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North West London (NWL), Imperial College London, SW10 9NH, London, UK; a.poots{at}imperial.ac.uk

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Background

Quality improvement and implementation (QI&I) initiatives face critical challenges in an era of evidence-based, value-driven patient care. Whether front-line staff, large organisations or government bodies design and run QI&I, there is increasing need to demonstrate impact to justify investment of time and resources in implementing and scaling up an intervention.

Decisions about sustaining, scaling up and spreading an initiative can be informed by evidence of causation and the estimated attributable effect of an intervention on observed outcomes. Achieving this in healthcare can be challenging, where interventions often are multimodal and applied in complex systems.1 Where there is weak evidence of causation, credibility in the effectiveness of the intervention is reduced with a resultant reduced desire to replicate. The greater confidence of a causal relationship between QI&I interventions and observed results, the greater our confidence that improvement will result when the intervention occurs in different settings.

Guidance exists for design, conduct, evaluation and reporting of QI&I initiatives;2–4; the Standards for QUality Improvement Reporting Excellence (SQUIRE) and the Standards for Reporting Implementation Studies (STARI) guidelines were developed specifically for reporting QI&I initiatives.5 6 However, much of this guidance is targeted at larger formal evaluations, and may require levels of resource or expertise not available to all QI&I initiatives. This paper proposes QI&I initiatives, regardless of scope and resources, can be enhanced by applying epidemiological principles, adapted from those promulgated by Austin Bradford Hill.7

Applying Bradford Hill Criteria and QI&I methods to strengthen evidence

Hill proposed nine ‘aspects of association’ that could be considered before ‘…deciding that the most likely interpretation is causation’.7 His objective was to improve the ability to form scientific judgements about causality. The nine aspects, subsequently referred to as the ‘Bradford Hill Criteria’ (BHC), are considered in the following sections. With roots in causes of disease, the BHC have natural alignment with healthcare. …

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