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利用广义结构加性回归模型确定与南非 COVID-19 医院死亡相关的因素和聚类。

Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa.

机构信息

Division of Epidemiology & Biostatistics, School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.

Division of Epidemiology & Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, Cape Town, South Africa.

出版信息

BMC Public Health. 2023 May 5;23(1):830. doi: 10.1186/s12889-023-15789-3.

Abstract

BACKGROUND

The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors.

METHODS

COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian.

RESULTS

The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts.

CONCLUSION

The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

摘要

背景

南非于 2020 年 3 月报告首例 COVID-19 病例,截至 2022 年 3 月,该国已记录超过 360 万例实验室确诊病例和 10 万例死亡。SARS-CoV-2 病毒的传播和感染以及一般因 COVID-19 而导致的死亡已被证明具有空间相关性,但南非尚未充分研究住院死亡的空间模式。本研究使用国家 COVID-19 住院数据,在调整已知死亡率风险因素后,研究医院死亡的空间影响。

方法

从国家传染病研究所(NICD)获取 COVID-19 住院数据和死亡数据。使用广义结构加性逻辑回归模型,在调整人口统计学和临床协变量后,评估 COVID-19 住院死亡的空间影响。连续协变量通过假设二阶随机游走先验进行建模,同时通过马尔可夫随机场先验指定空间自相关,并分别通过模糊先验指定固定效应。推理是完全贝叶斯的。

结果

COVID-19 住院死亡率随患者年龄的增加而增加,入住重症监护病房(ICU)(OR=4.16;95%可信区间:4.05-4.27)、吸氧(OR=1.49;95%可信区间:1.46-1.51)和有创机械通气(OR=3.74;95%可信区间:3.61-3.87)。在公立医院住院(OR=3.16;95%可信区间:3.10-3.21)也与死亡率显著相关。在感染激增后的几个月中,住院死亡风险增加,在连续几个月低感染后下降,突出了滞后于流行曲线的高峰和低谷。在控制了这些因素后,林波波省的 Vhembe、Capricorn 和 Mopani 地区以及东开普省的 Buffalo City、O.R. Tambo、Joe Gqabi 和 Chris Hani 地区的 COVID-19 住院死亡的几率仍然明显更高,这表明这些地区可能存在卫生系统挑战。

结论

结果表明,52 个地区的 COVID-19 住院死亡率存在很大差异。我们的分析提供了重要信息,可用于加强卫生政策和公共卫生系统,造福南非全体人民。了解空间上 COVID-19 住院死亡率的差异可以指导干预措施,以实现受影响地区的更好健康结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f53/10161425/45f5ef787417/12889_2023_15789_Fig1_HTML.jpg

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