Evidence map›Paper›PMID 29728447›Full record

ArticleBMJ quality & safety2018

Investigating the association of alerts from a national mortality surveillance system with subsequent hospital mortality in England: an interrupted time series analysis.

Elizabeth Cecil, Alex Bottle, Aneez Esmail, Samantha Wilkinson, Charles Vincent, Paul P Aylin

Abstract read
In one paragraph

Article in BMJ quality & safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. A framework to support risk assessment in hospitals.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2019
    Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Elizabeth CecilPrimary Care and Public Health, Imperial College London, London, UK.
Alex BottlePrimary Care and Public Health, Imperial College London, London, UK.ORCID 0000-0001-9978-2011
Aneez EsmailHealth Services Research & Primary Care, University of Manchester, Manchester, UK.
Samantha WilkinsonLondon School of Hygiene & Tropical Medicine, London, UK.
Charles VincentMedical Science Division, University of Oxford, London, Oxfordshire, UK.
Paul P AylinPrimary Care and Public Health, Imperial College London, London, UK.

Funding

Department of Health 12/178/22
6 · The paper itself

Abstract

objectiveTo investigate the association between alerts from a national hospital mortality surveillance system and subsequent trends in relative risk of mortality.

backgroundThere is increasing interest in performance monitoring in the NHS. Since 2007, Imperial College London has generated monthly mortality alerts, based on statistical process control charts and using routinely collected hospital administrative data, for all English acute NHS hospital trusts. The impact of this system has not yet been studied.

methodsWe investigated alerts sent to Acute National Health Service hospital trusts in England in 2011-2013. We examined risk-adjusted mortality (relative risk) for all monitored diagnosis and procedure groups at a hospital trust level for 12 months prior to an alert and 23 months post alert. We used an interrupted time series design with a 9-month lag to estimate a trend prior to a mortality alert and the change in trend after, using generalised estimating equations.

resultsOn average there was a 5% monthly increase in relative risk of mortality during the 12 months prior to an alert (95% CI 4% to 5%). Mortality risk fell, on average by 61% (95% CI 56% to 65%), during the 9-month period immediately following an alert, then levelled to a slow decline, reaching on average the level of expected mortality within 18 months of the alert.

conclusionsOur results suggest an association between an alert notification and a reduction in the risk of mortality, although with less lag time than expected. It is difficult to determine any causal association. A proportion of alerts may be triggered by random variation alone and subsequent falls could simply reflect regression to the mean. Findings could also indicate that some hospitals are monitoring their own mortality statistics or other performance information, taking action prior to alert notification.

Indexed as

Cause of DeathCohort StudiesEnglandHospitalizationHospital MortalityHumansInterrupted Time Series AnalysisLondonMortalityQuality ImprovementRetrospective StudiesRisk Assessmenthealthcare quality improvementhealth services researchstatistical process control

Identifiers

PMID29728447
PMCPMC6288695

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.