Cluster for Health Services Research, Norwegian Institute of Public Health, Oslo, Norway.
Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.
Nat Commun. 2023 Sep 20;14(1):5839. doi: 10.1038/s41467-023-41541-x.
Whereas the nature of the post-COVID condition following mild acute COVID-19 is increasingly well described in the literature, knowledge of its risk factors, and whether it can be predicted, remains limited. This study, conducted in Norway, uses individual-level register data from 214,667 SARS-CoV-2 infected individuals covering a range of demographic, socioeconomic factors, as well as cause-specific healthcare utilization in the years prior to infection to assess the risk of post-COVID complaints ≥3 months after testing positive. We find that the risk of post-COVID was higher among individuals who prior to infection had been diagnosed with psychological (OR = 2.12, 95% CI 1.84-2.44), respiratory (OR = 2.03, 95% CI 1.78-2.32), or general and unspecified health problems (OR = 1.78, 95% CI 1.52-2.09). To assess the predictability of post-COVID after mild initial disease, we use machine learning methods and find that pre-infection characteristics, combined with information on the SARS-CoV-2 virus type and vaccine status, to a considerable extent (AUC = 0.79, 95% CI 0.75-0.81) could predict the occurrence of post-COVID complaints in our sample.
虽然轻度急性 COVID-19 后新冠后遗症(post-COVID condition)的性质在文献中越来越多地被描述,但对其危险因素及其是否可预测的了解仍然有限。本研究在挪威进行,使用了来自 214667 名 SARS-CoV-2 感染者的个体水平登记数据,这些数据涵盖了一系列人口统计学、社会经济学因素,以及感染前特定于病因的医疗保健利用情况,以评估阳性检测后 3 个月以上出现新冠后遗症的风险。我们发现,在感染前被诊断患有心理(OR=2.12,95%CI 1.84-2.44)、呼吸(OR=2.03,95%CI 1.78-2.32)或一般和未指定健康问题(OR=1.78,95%CI 1.52-2.09)的个体中,新冠后遗症的风险更高。为了评估轻度初始疾病后新冠后遗症的可预测性,我们使用机器学习方法发现,感染前的特征,结合 SARS-CoV-2 病毒类型和疫苗接种状况的信息,在很大程度上(AUC=0.79,95%CI 0.75-0.81)可以预测我们样本中新冠后遗症的发生。