Dataset Information

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia.

ABSTRACT:

Background

The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the 'length of stay') is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of hospitalised COVID-19 cases in New South Wales, Australia, comparing estimates between a period where Delta was the dominant variant and a subsequent period where Omicron was dominant.

Methods

Using data on the hospital stays of 19,574 individuals who tested positive to COVID-19 prior to admission, we performed a competing-risk survival analysis of COVID-19 clinical progression.

Results

During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0-39, 40-69 and 70 +, respectively, 2.16 (95% CI: 2.12-2.21), 3.93 (95% CI: 3.78-4.07) and 7.61 days (95% CI: 7.31-8.01), compared to 3.60 (95% CI: 3.48-3.81), 5.78 (95% CI: 5.59-5.99) and 12.31 days (95% CI: 11.75-12.95) across the preceding Delta epidemic (1 July 2021-15 December 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80-2.30), 2.92 (95% CI: 2.50-3.67) and 6.02 days (95% CI: 4.91-7.01) for the same age groups.

Conclusions

Hospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic, contributing to a lessened health system burden despite a greatly increased infection burden. Our results demonstrate the utility of survival analysis in producing real-time estimates of hospital length of stay for assisting in situational assessment and planning of the COVID-19 response.

SUBMITTER: Tobin RJ

PROVIDER: S-EPMC9844941 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia.

Tobin Ruarai J RJ Wood James G JG Jayasundara Duleepa D Sara Grant G Walker Camelia R CR Martin Genevieve E GE McCaw James M JM Shearer Freya M FM Price David J DJ

BMC infectious diseases 20230117 1

<h4>Background</h4>The distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the 'length of stay') is a key factor in determining how incident caseloads translate into health system burden. Robust estimation of length of stay in real-time requires the use of survival methods that can account for right-censoring induced by yet unobserved events in patient progression (e.g. discharge, death). In this study, we estimate in real-time the length of stay distributions of h ...[more]

PMID: 36650474

Similar Datasets

Project description:BackgroundLength of stay (LOS) is frequently employed as a performance metric for trauma care. Following the establishment of the trauma network worldwide, the assessment and prediction of LOS in different levels of trauma centres have been extensively studied. However, assessing the total patient length of stay from a whole trauma network perspective is unclear. The objective of this study was to systematically analyse the overall Length of Stay (LOS) pattern within the SWTN before its establishment and in the immediate time after its foundation and, secondly, to assess the association between relevant impact factors and LOS.MethodologyA retrospective secondary analysis based on the trauma admission dataset from Trauma Audit and Research Network(TARN) dataset was conducted. The studied sample covered around 18000 patients admitted to trauma centres from South Wales Major trauma network between January 2012 and October 2021. The primary outcome is the total length of stay in the trauma network. Statistical tests were applied to examine the difference between normal and outlier LOS. Data visualisation was utilised to demonstrate the LOS patterns and potential association between LOS and relevant demographic and clinical predictors.ResultsThe distribution of length of stay in SWTN follows a right-skewed distribution with a median of 10 (IQR, 5-18) and a mean of 15.92 days. There were 1520 patients with outliers for LOS. A significant difference (p¡ 0.05) was found between the normal and outlier groups of LOS based on demographic (age, gender and residential information) and clinical characteristics(ward type, maximum of anatomically-based injury severity score(AIS) and probability of survival). Age group, maximum AIS score on specific injured region, ward type and its interaction effect with the number of admissions may associated with the LOS. Specifically, patients admitted to the geriatric ward exhibited notably prolonged LOS, and individuals with more than 2 admissions to long-term care and recovery-related wards such as neurosurgical rehabilitation, spinal injuries and burns wards also displayed elevated LOS.ConclusionOur finding supports prior evidence indicating elderly people are vulnerable to longer stays. Moreover, concerning the types of admission wards, patients admitted to rehabilitation wards who underwent more than 2 hospitalisations also faced an increased risk of prolonged stay. Based on these results, policymakers and healthcare providers should contemplate expanding the allocation of medical resources to this demographic to mitigate the length of stay and optimise associated healthcare costs.

Dataset Information

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia.

Background

Methods

Results

Conclusions

Publications

Real-time analysis of hospital length of stay in a mixed SARS-CoV-2 Omicron and Delta epidemic in New South Wales, Australia.

Similar Datasets

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