Sickness absence rates in NHS England staff during the COVID-19 pandemic: Insights from multivariate regression and time series modelling.

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-07-29 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0323035
Ewan McTaggart, Itamar Megiddo, John Bowers, Adam Kleczkowski
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引用次数: 0

Abstract

The COVID-19 pandemic placed immense strain on healthcare systems worldwide, with NHS England facing substantial challenges in managing staff illness-related absences amid surging treatment demands. Understanding the impact of the pandemic on sickness absence rates among NHS England staff is crucial to developing effective workforce management strategies and ensuring the continued delivery of healthcare. In this study, we use publicly available data to investigate the impact of the COVID-19 pandemic on sickness absence rates among NHS England staff between June 2020 and 2022. We begin with a data analysis to indicate the temporal patterns of sickness absence in NHS England staff between January 2015 and September 2022 inclusive. We then develop multivariate linear regression models to estimate COVID-19-related sickness absences. Indicators of COVID-19 activity, such as positive tests, hospitalizations, and ONS incidence, were incorporated. Furthermore, we use Seasonal ARIMA time series models to analyse the impact of COVID-19 on mental health-related absence. Our analysis highlights increases in sickness absence rates which coincide with the arrival of COVID-19 in England, and continue to rise throughout the pandemic. High periods of COVID-19 activity strongly correlated with staff absence, and the main categories driving the dynamics were COVID-19-related or mental health absences. We demonstrate that sickness absences in these two categories can be estimated accurately using multivariate linear regression (F(2, 15) = 132.63, [Formula: see text], adj [Formula: see text] =93.9%) and Seasonal ARIMA time series models, respectively. Moreover, we show that additional indicators of COVID-19 activity (positive tests, hospitalisations, ONS incidence) contain helpful information about staff infection pathways. This study offers insights into the dynamics of healthcare staff absences during a pandemic, contributing to both practical workforce management and academic research. The findings highlight the need for tailored approaches to address both infectious disease-related and mental health-related absences in healthcare settings during future health crises and opens new avenues for research into healthcare system resilience during crises.

COVID-19大流行期间NHS英格兰员工的缺勤率:来自多变量回归和时间序列模型的见解
COVID-19大流行给全球医疗保健系统带来了巨大压力,在治疗需求激增的情况下,英国国家医疗服务体系在管理员工因病缺勤方面面临重大挑战。了解疫情对英国国家医疗服务体系员工缺勤率的影响,对于制定有效的劳动力管理战略和确保持续提供医疗服务至关重要。在这项研究中,我们使用公开数据调查了2020年6月至2022年期间COVID-19大流行对英国NHS员工缺勤率的影响。我们首先进行数据分析,以表明2015年1月至2022年9月期间英国国民保健服务(NHS)员工生病缺勤的时间模式。然后,我们开发了多元线性回归模型来估计covid -19相关疾病缺勤情况。纳入了COVID-19活动指标,如阳性检测、住院率和ONS发病率。此外,我们使用季节性ARIMA时间序列模型来分析COVID-19对心理健康相关缺勤的影响。我们的分析强调,缺勤率的上升与COVID-19在英格兰的到来相吻合,并在整个大流行期间继续上升。COVID-19活动期与员工缺勤密切相关,推动这一趋势的主要类别是与COVID-19相关的缺勤或精神健康缺勤。我们证明,这两个类别的病假缺勤可以分别使用多元线性回归(F(2,15) = 132.63,[公式:见文],adj[公式:见文]=93.9%)和季节性ARIMA时间序列模型准确估计。此外,我们表明,COVID-19活动的其他指标(阳性检测、住院、ONS发病率)包含有关工作人员感染途径的有用信息。这项研究深入了解了大流行期间医护人员缺勤的动态,有助于实际的劳动力管理和学术研究。研究结果强调,在未来的卫生危机期间,需要采取量身定制的方法来解决卫生保健机构中与传染病相关和与精神健康相关的缺位问题,并为研究危机期间卫生保健系统的复原力开辟了新的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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