Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
School of Population Health, University of New South Wales, Sydney, Australia.
BMC Infect Dis. 2023 Jan 17;23(1):28. doi: 10.1186/s12879-022-07971-6.
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.
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.
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.
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.
新冠病毒病例占用医院床位的时间分布（即“住院时间”）是决定病例数量如何转化为卫生系统负担的关键因素。实时、稳健地估计住院时间需要使用生存分析方法，该方法可以考虑到患者进展中尚未观察到的事件（例如出院、死亡）导致的右删失。在这项研究中，我们对澳大利亚新南威尔士州住院的新冠病毒病例的住院时间分布进行了实时估计，比较了德尔塔变体占主导地位的时期和随后奥密克戎变体占主导地位的时期之间的估计结果。
我们使用了 19574 名在入院前新冠病毒检测呈阳性的个体的住院数据，对新冠病毒临床进展进行了竞争风险生存分析。
在混合的奥密克戎-德尔塔疫情期间，我们发现，对于年龄组为 0-39 岁、40-69 岁和 70 岁及以上的个体，直接从病房出院而未入住 ICU 的个体的平均住院时间分别为 2.16 天（95%CI：2.12-2.21）、3.93 天（95%CI：3.78-4.07）和 7.61 天（95%CI：7.31-8.01），而在之前的德尔塔疫情（2021 年 7 月 1 日至 2021 年 12 月 15 日）期间，这一数字分别为 3.60 天（95%CI：3.48-3.81）、5.78 天（95%CI：5.59-5.99）和 12.31 天（95%CI：11.75-12.95）。我们还考虑了亨特新英格兰地方卫生区（Hunter New England Local Health District）内个体住院数据，该地区报告称奥密克戎是唯一传播的变体，我们发现，对于相同的年龄组，平均从病房到出院的住院时间分别为 2.05 天（95%CI：1.80-2.30）、2.92 天（95%CI：2.50-3.67）和 6.02 天（95%CI：4.91-7.01）。
与之前的德尔塔疫情相比，在奥密克戎-德尔塔混合疫情期间，所有临床路径的住院时间都大幅缩短，尽管感染负担大大增加，但仍有助于减轻卫生系统负担。我们的研究结果表明，生存分析在实时估计医院住院时间方面具有实用性，有助于对新冠病毒疫情进行现场评估和规划。
