Uwak Inyang, Johnson Natalie, Mustapha Toriq, Rahman Mariya, Tonpay Tanaya, Regan Annette K, Mendoza-Sanchez Itza
Department of Environmental & Occupational Health, Texas A&M University School of Public Health, College Station, TX, United States.
School of Nursing and Health Professions, University of San Francisco, San Francisco, CA, United States.
Front Pediatr. 2023 Mar 13;11:1124316. doi: 10.3389/fped.2023.1124316. eCollection 2023.
To quantify changes on RSV- associated hospitalizations during COVID-19 pandemic, among children four years of age or younger at the state and county levels of Texas using routinely acquired hospital admission records.
We used the Texas Public Use Data Files (PUDF) of the Department of State Human Services (DSHS) to obtain hospital admissions and healthcare outcomes from 2006 to 2021. We used the 2006-2019 period to estimate a long-term temporal trend and predict expected values for 2020-2021. Actual and predicted values were used to quantify changes in seasonal trends of the number of hospital admissions and mean length of hospital stay. Additionally, we calculated hospitalization rates and assessed their similarity to rates reported in the RSV Hospitalization Surveillance Network (RSV-NET).
An unusually low number of hospitalizations in 2020 was followed by an unusual peak in the third quarter of 2021. Hospital admissions in 2021 were approximately twice those in a typical year. The mean length of hospital stay typically followed a seasonal trend before COVID-19, but increased by a factor of ∼6.5 during the pandemic. Spatial distribution of hospitalization rates revealed localized healthcare infrastructure overburdens during COVID-19. RSV associated hospitalization rates were, on average, two times higher than those of RSV-NET.
Hospital admission data can be used to estimate long-term temporal and spatial trends and quantify changes during events that exacerbate healthcare systems, such as pandemics. Using the mean difference between hospital rates calculated with hospital admissions and hospital rates obtained from RSV-NET, we speculate that state-level hospitalization rates for 2022 could be at least twice those observed in the two previous years, and the highest in the last 17 years.
利用常规获取的医院入院记录,量化新冠疫情期间德克萨斯州及各县4岁及以下儿童呼吸道合胞病毒(RSV)相关住院情况的变化。
我们使用德克萨斯州公共卫生服务部(DSHS)的公共使用数据文件(PUDF)来获取2006年至2021年的医院入院情况和医疗结果。我们用2006 - 2019年期间的数据来估计长期时间趋势,并预测2020 - 2021年的预期值。实际值和预测值用于量化住院人数季节性趋势和平均住院时间的变化。此外,我们计算了住院率,并评估其与RSV住院监测网络(RSV - NET)报告的率的相似性。
2020年住院人数异常低,随后在2021年第三季度出现异常高峰。2021年的住院人数约为正常年份的两倍。在新冠疫情之前,平均住院时间通常遵循季节性趋势,但在疫情期间增加了约6.5倍。住院率的空间分布显示了新冠疫情期间局部医疗基础设施负担过重的情况。RSV相关住院率平均比RSV - NET的住院率高两倍。
医院入院数据可用于估计长期的时间和空间趋势,并量化在加剧医疗系统负担的事件(如大流行)期间的变化。利用通过医院入院情况计算的住院率与从RSV - NET获得的住院率之间的平均差异,我们推测2022年州级住院率可能至少是前两年观察到的住院率的两倍,且是过去17年中最高的。