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

BMC Infectious Diseases

Research

James Stimson¹  Russell Hope¹  Julie V. Robotham²  Anne M. Presanis³  Koen B. Pouwels⁴  Ben S. Cooper⁵ 

[1] HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK;HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK;NIHR 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;MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK;Joint Modelling Team, UK Health Security Agency, London, UK;NIHR 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;Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK;Oxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK;

关键词: COVID-19;    Public health data;    Excess length of stay;    Causal inference;   

DOI  :  10.1186/s12879-022-07870-w

 received in 2022-07-05, accepted in 2022-11-10,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Crown 2022

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