Sikhosana Mpho L, Jassat Waasila, Makatini Zinhle
Department of Virology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Department of Public Health and Outbreak Response, National Institute of Communicable Diseases, Johannesburg, South Africa.
S Afr J Infect Dis. 2022 Sep 30;37(1):434. doi: 10.4102/sajid.v37i1.434. eCollection 2022.
Gauteng province (GP) was one of the most affected provinces in the country during the first two pandemic waves in South Africa. We aimed to describe the characteristics of coronavirus disease 2019 (COVID-19) patients admitted in one of the largest quaternary hospitals in GP during the first two waves.
Study objectives were to determine factors associated with hospital admission during the second wave and to describe factors associated with in-hospital COVID-19 mortality.
Data from a national hospital-based surveillance system of COVID-19 hospitalisations were used. Multivariable logistic regression models were conducted to compare patients hospitalised during wave 1 and wave 2, and to determine factors associated with in-hospital mortality.
The case fatality ratio was the highest (39.95%) during wave 2. Factors associated with hospitalisation included age groups 40-59 years (adjusted odds ratio [aOR]: 2.14, 95% confidence interval [CI]: 1.08-4.27), 60-79 years (aOR: 2.49, 95% CI: 1.23-5.02) and ≥ 80 years (aOR: 3.39, 95% CI: 1.35-8.49). Factors associated with in-hospital mortality included age groups 60-79 years (aOR: 2.55, 95% CI: 1.11-5.84) and ≥ 80 years (aOR: 5.66, 95% CI: 2.12-15.08); male sex (aOR: 1.56, 95% CI: 1.22-1.99); presence of an underlying comorbidity (aOR: 1.76, 95% CI: 1.37-2.26), as well as being admitted during post-wave 2 (aOR: 2.42, 95% CI: 1.33-4.42).
Compared to the recent omicron-driven pandemic waves characterised by lower admission rates and less disease severity among younger patients, COVID-19 in-hospital mortality during the earlier waves was associated with older age, being male and having an underlying comorbidity.
This study showed how an active surveillance system can contribute towards identifying changes in disease trends.
在南非的前两波疫情中,豪登省(GP)是该国受影响最严重的省份之一。我们旨在描述在前两波疫情期间,GP省最大的一家四级医院收治的2019冠状病毒病(COVID-19)患者的特征。
研究目的是确定第二波疫情期间与住院相关的因素,并描述与院内COVID-19死亡相关的因素。
使用来自全国基于医院的COVID-19住院监测系统的数据。进行多变量逻辑回归模型,以比较第一波和第二波疫情期间住院的患者,并确定与院内死亡率相关的因素。
第二波疫情期间的病死率最高(39.95%)。与住院相关的因素包括40 - 59岁年龄组(调整后的优势比[aOR]:2.14,95%置信区间[CI]:1.08 - 4.27)、60 - 79岁年龄组(aOR:2.49,95% CI:1.23 - 5.02)和≥80岁年龄组(aOR:3.39,95% CI:1.35 - 8.49)。与院内死亡相关的因素包括60 - 79岁年龄组(aOR:2.55,95% CI:1.11 - 5.84)和≥80岁年龄组(aOR:5.66,95% CI:2.12 - 15.08);男性(aOR:1.56,95% CI:1.22 - 1.99);存在基础合并症(aOR:1.76,95% CI:1.37 - 2.26),以及在第二波疫情后期入院(aOR:2.42,95% CI:1.33 - 4.42)。
与近期以较低入院率和年轻患者疾病严重程度较低为特征的奥密克戎驱动的疫情波相比,早期疫情波期间COVID-19的院内死亡率与年龄较大、男性以及存在基础合并症有关。
本研究展示了一个主动监测系统如何有助于识别疾病趋势的变化。