Golpour Monireh, Jalali Hossein, Alizadeh-Navaei Reza, Talarposhti Masoumeh Rezaei, Mousavi Tahoora, Ghara Ali Asghar Nadi
Cancer Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
Thalassemia Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran.
BMC Infect Dis. 2025 Jan 31;25(1):145. doi: 10.1186/s12879-025-10521-5.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) is a public health problem and may result in co-infection with other pathogens such as influenza virus. This review investigates the co-infection of SARS-CoV-2 and influenza A/B among patients with COVID-19.
This meta- analysis included 38 primary studies investigating co-infection of SARS-CoV-2 with influenza in confirmed cases of COVID-19. The global online databases were used to identify relevant studies published between December 2019 and July 2024. Data analysis was performed using STATA Ver. 17 software, and standard errors of prevalence were calculated using the binomial distribution formula. Heterogeneity of study results was evaluated using the I-square and Q index, and publication bias was examined using the Begg's and Egger's tests, as well as funnel plot. A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. In addition, sensitivity analyses were performed to check the impact of each primary study on the overall estimate.
The analysis found that the prevalence of influenza in co-infected patients at 95% confidence interval using a random effect model was 14% (95% CI: 8-20%). Significant heterogeneity was observed in the random-effects model for influenza A, 11% (95% CI: 5-18%) and B, 4% (95% CI: 2-7%) in co-infected patients. The highest prevalence of influenza A/B (21%), influenza A (17%) and influenza B (20%) was shown in Asia and Europe respectively. Subgroup analysis by study year showed that the co-prevalence of COVID-19 and influenza A/B was similar in the pre-2021 and post-2021 time periods, at 14% (95% CI: 5-23%) for pre-2021 and 6-22% for 2021 and post-2021. Also, the overall prevalence of influenza A and B in COVID-19 patients is 11% and 4%, and there was no significant difference between the time periods before and after 2021. Meta-regression with a random-effects model showed that the variables location, year group, and total patients showed only 2.71% of very high heterogeneity (I² = 99.92%), and none of these variables had a significant effect on the co-prevalence of COVID-19 and influenza A/B (p > 0.05). Also, meta-regression results showed that these variables had no significant effect on influenza A and B prevalence (p > 0.05) and showed only a small proportion of the very high heterogeneity (I² = 99.72%), (I² = 68.78%). In our study, Egger's test indicated that there was publication bias or small study effects in this meta-analysis (p = 0.0000).
The combination of SARS-CoV-2 with influenza and other respiratory viruses requires the best treatment protocols to reduce the severity of the disease. In this approach, high vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic.
导致2019冠状病毒病（COVID-19）的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）是一个公共卫生问题，可能导致与其他病原体如流感病毒的合并感染。本综述调查了COVID-19患者中SARS-CoV-2与甲型/乙型流感的合并感染情况。
这项荟萃分析纳入了38项关于COVID-19确诊病例中SARS-CoV-2与流感合并感染情况的初步研究。使用全球在线数据库来识别2019年12月至2024年7月期间发表的相关研究。使用STATA 17软件进行数据分析，并使用二项分布公式计算患病率的标准误差。使用I²和Q指数评估研究结果的异质性，并使用Begg检验、Egger检验以及漏斗图来检查发表偏倚。采用随机效应模型确定患病率，并使用森林图展示结果及其95%置信区间。此外，进行敏感性分析以检查每项初步研究对总体估计值的影响。
分析发现，采用随机效应模型，合并感染患者中流感的患病率在95%置信区间为14%（95%CI：8-20%）。在随机效应模型中，合并感染患者中甲型流感的患病率为11%（95%CI：5-18%），乙型流感的患病率为4%（95%CI：2-7%），观察到显著的异质性。甲型/乙型流感、甲型流感和乙型流感的患病率在亚洲和欧洲分别达到最高，分别为21%、17%和20%。按研究年份进行的亚组分析显示，2021年之前和2021年之后COVID-19与甲型/乙型流感的合并患病率相似，2021年之前为14%（95%CI：5-23%），2021年及之后为6-22%。此外，COVID-19患者中甲型和乙型流感的总体患病率分别为11%和4%，2021年前后各时间段之间无显著差异。随机效应模型的Meta回归显示，变量地点、年份组和患者总数仅显示出2.71%的极高异质性（I² = 99.92%），且这些变量均对COVID-19与甲型/乙型流感的合并患病率无显著影响（p>0.05）。此外，Meta回归结果显示，这些变量对甲型和乙型流感患病率无显著影响（p>0.05），仅显示出一小部分极高的异质性（I² = 99.72%），（I² = 68.78%）。在我们的研究中，Egger检验表明该荟萃分析存在发表偏倚或小型研究效应（p = 0.0000）。
SARS-CoV-2与流感及其他呼吸道病毒的合并感染需要最佳治疗方案以降低疾病严重程度。通过这种方式，针对季节性流感和SARS-CoV-2的高疫苗接种覆盖率可降低近期大流行中合并感染的风险。
