Evidence map›Paper›PMID 36494640›Full record

ArticleBMC infectious diseases2022

Estimation of the impact of hospital-onset SARS-CoV-2 infections on length of stay in English hospitals using causal inference.

James Stimson, Koen B Pouwels, Russell Hope, Ben S Cooper, Anne M Presanis, Julie V Robotham

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.5field-weighted citation impact, top 11% of its field
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

3 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. In Support of Universal Admission Testing for SARS-CoV-2 During Significant Community Transmission.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

James StimsonHCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK. james.stimson@ukhsa.gov.uk.ORCID http://orcid.org/0000-0003-1855-6229
Koen B PouwelsNIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with the UK Health Security Agency, Oxford, UK.
Russell HopeHCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK.
Ben S CooperOxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Anne M PresanisMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Julie V RobothamHCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK.
University of Oxford · GBEnvironment Agency · GBUK Health Security Agency · GBUniversity of Cambridge · GB

Funding

Medical Research Council MC_PC_19074Medical Research Council MC_UU_00002/11Medical Research Council MRC_MC_UU_00002/11National Institute for Health Research Health Protection Research Unit NIHR200915
6 · The paper itself

Abstract

backgroundFrom March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, but we are currently unaware of any published studies estimating this excess.

methodsWe implemented appropriate causal inference methods to determine the extent to which observed additional hospital stay is attributable to the infection rather than the characteristics of the patients. Hospital admissions records were linked to SARS-CoV-2 test data to establish the study population (7.5 million) of all non-COVID-19 admissions to English hospitals from 1st March 2020 to 31st August 2021 with a stay of at least two days. The excess LoS due to hospital-onset SARS-CoV-2 infection was estimated as the difference between the mean LoS observed and in the counterfactual where infections do not occur. We used inverse probability weighted Kaplan-Meier curves to estimate the mean survival time if all hospital-onset SARS-CoV-2 infections were to be prevented, the weights being based on the daily probability of acquiring an infection. The analysis was carried out for four time periods, reflecting phases of the pandemic differing with respect to overall case numbers, testing policies, vaccine rollout and prevalence of variants.

resultsThe observed mean LoS of hospital-onset cases was higher than for non-COVID-19 hospital patients by 16, 20, 13 and 19 days over the four phases, respectively. However, when the causal inference approach was used to appropriately adjust for time to infection and confounding, the estimated mean excess LoS caused by hospital-onset SARS-CoV-2 was: 2.0 [95% confidence interval 1.8-2.2] days (Mar-Jun 2020), 1.4 [1.2-1.6] days (Sep-Dec 2020); 0.9 [0.7-1.1] days (Jan-Apr 2021); 1.5 [1.1-1.9] days (May-Aug 2021).

conclusionsHospital-onset SARS-CoV-2 is associated with a small but notable excess LoS, equivalent to 130,000 bed days. The comparatively high LoS observed for hospital-onset COVID-19 patients is mostly explained by the timing of their infections relative to admission. Failing to account for confounding and time to infection leads to overestimates of additional length of stay and therefore overestimates costs of infections, leading to inaccurate evaluations of control strategies.

Indexed as

COVID-19HospitalsHumansLength of StayPandemicsSARS-CoV-2Causal inferenceCOVID-19Excess length of stayPublic health data

Identifiers

PMID36494640
PMCPMC9733355
OpenAlexW4311663579

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.