https://www.cidrap.umn.edu/covid-19/meta-analysis-estimates-14-global-covid-flu-coinfection-rate
Meta-analysis estimates 14% global COVID-flu coinfection rate
Mary Van Beusekom, MS
Today at 2:42 p.m.
COVID-19
Influenza, General
For the meta-analysis, researchers in Iran evaluated studies published from December 2019 to July 2024 on coinfections with the two viruses.
"Multiple pathogens can cause immune overload, with the body's immune response having an insufficient response to either virus when faced with these co-infections," the researchers wrote. "In patients coinfected with both SARS-CoV-2 and influenza A virus, this phenomenon can worsen respiratory symptoms such as pneumonia, sinus infection, bronchitis, and cardiovascular disease and increase the risk for severe respiratory failure."
Viruses can also worsen the effects of other viruses, complicating clinical interpretation and leading to more hospitalizations and higher death rates, they said. And because both COVID-19 and flu have similar symptoms, and diagnostic assays may not be able to distinguish between them, diagnosis and treatment can be challenging.
Similar rates before and after 2021
The estimated rate of COVID-flu coinfections was 14% (95% confidence interval [CI], 8% to 20%). Significant heterogeneity was seen in the random-effects model for influenza A (11%; 95% CI, 5% to 18%) and B (4%; 95% CI, 2% to 7%) in co-infected patients, which could limit generalizability of the findings.
The highest rates of influenza A/B (21%), influenza A (17%), and influenza B (20%) were observed in Asia and Europe. The co-prevalence of COVID-19 and influenza A/B was similar in the pre- and post-2021 periods (14% [95% CI, 5% to 23%] for pre-2021 and 6% to 22% for 2021 and after), a subgroup analysis by year showed.
The overall prevalence of influenza A and B in COVID-19 patients was 11% and 4%, respectively, with no significant difference between the periods before and after 2021, but heterogeneity was very high in both time periods (99.83% and 99.97%, respectively), indicating wide differences among studies.
Significant heterogeneity among studies
"The analysis shows significant heterogeneity between studies, as indicated by an I² statistic of 99.97%," the researchers wrote. "This value indicates that almost all the observed variability is due to differences between studies and cannot be attributed to random chance. This high heterogeneity is likely due to a variety of reasons, including methodological differences, geographic variation, temporal variation in study periods, and differences in diagnostic criteria."
High vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic.
Meta-regression with a random-effects model revealed that 2.71% of location, year group, and total patients were very highly heterogeneous, and none of these variables had a significant effect on the rate of COVID–influenza A/B co-infection. But the remaining heterogeneity was significant, which the researchers said suggests that factors such as methodologic or demographic factors may have affected the results.
"The combination of SARS-CoV-2 with influenza and other respiratory viruses requires the best treatment protocols to reduce the severity of the disease," the study authors concluded. "In this approach, high vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic."
Meta-analysis estimates 14% global COVID-flu coinfection rate
Mary Van Beusekom, MS
Today at 2:42 p.m.
COVID-19
Influenza, General
The global prevalence of COVID-flu coinfection is 14%, with Asia and Europe having the highest rates of influenza A/B coinfection, but with wide variation among studies, estimates a meta-analysis of 38 studies published late last week in BMC Infectious Diseases.For the meta-analysis, researchers in Iran evaluated studies published from December 2019 to July 2024 on coinfections with the two viruses.
"Multiple pathogens can cause immune overload, with the body's immune response having an insufficient response to either virus when faced with these co-infections," the researchers wrote. "In patients coinfected with both SARS-CoV-2 and influenza A virus, this phenomenon can worsen respiratory symptoms such as pneumonia, sinus infection, bronchitis, and cardiovascular disease and increase the risk for severe respiratory failure."
Viruses can also worsen the effects of other viruses, complicating clinical interpretation and leading to more hospitalizations and higher death rates, they said. And because both COVID-19 and flu have similar symptoms, and diagnostic assays may not be able to distinguish between them, diagnosis and treatment can be challenging.
Similar rates before and after 2021
The estimated rate of COVID-flu coinfections was 14% (95% confidence interval [CI], 8% to 20%). Significant heterogeneity was seen in the random-effects model for influenza A (11%; 95% CI, 5% to 18%) and B (4%; 95% CI, 2% to 7%) in co-infected patients, which could limit generalizability of the findings.
The highest rates of influenza A/B (21%), influenza A (17%), and influenza B (20%) were observed in Asia and Europe. The co-prevalence of COVID-19 and influenza A/B was similar in the pre- and post-2021 periods (14% [95% CI, 5% to 23%] for pre-2021 and 6% to 22% for 2021 and after), a subgroup analysis by year showed.
The overall prevalence of influenza A and B in COVID-19 patients was 11% and 4%, respectively, with no significant difference between the periods before and after 2021, but heterogeneity was very high in both time periods (99.83% and 99.97%, respectively), indicating wide differences among studies.
Significant heterogeneity among studies
"The analysis shows significant heterogeneity between studies, as indicated by an I² statistic of 99.97%," the researchers wrote. "This value indicates that almost all the observed variability is due to differences between studies and cannot be attributed to random chance. This high heterogeneity is likely due to a variety of reasons, including methodological differences, geographic variation, temporal variation in study periods, and differences in diagnostic criteria."
High vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic.
Meta-regression with a random-effects model revealed that 2.71% of location, year group, and total patients were very highly heterogeneous, and none of these variables had a significant effect on the rate of COVID–influenza A/B co-infection. But the remaining heterogeneity was significant, which the researchers said suggests that factors such as methodologic or demographic factors may have affected the results.
"The combination of SARS-CoV-2 with influenza and other respiratory viruses requires the best treatment protocols to reduce the severity of the disease," the study authors concluded. "In this approach, high vaccination coverage against seasonal influenza and SARS-CoV-2 could reduce the risk of co-infection in the recent pandemic."