key: cord-0986946-hwqqexqo authors: Sabeena, S.; Ravishankar, N.; Robin, S. title: The impact of COVID-19 pandemic on influenza surveillance: a systematic review and meta-analysis date: 2022-04-01 journal: nan DOI: 10.1101/2022.03.31.22273236 sha: 152000222930ac79f4bcefa323475ba227e5a47a doc_id: 986946 cord_uid: hwqqexqo Influenza activity was reported to be below the seasonal levels during the COVID-19 pandemic globally. However, during the SARS-CoV-2 outbreak, the routine real-time surveillance of influenza like illness (ILI) and acute respiratory infection (ARI) was adversely affected due to the changes in priorities, economic constraints, repurposing of hospitals for COVID care and closure of outpatient services. A systematic review and meta-analysis were carried out to assess the pooled proportion of symptomatic cases tested for influenza virus before the COVID-19 pandemic in 2019 and during the pandemic in 2020/2021. The study was designed based on PRISMA guidelines and the meta-analysis was performed to synthesise the pooled proportion of patients sampled for influenza surveillance before the COVID-19 pandemic in 2019 and during the pandemic in 2020/21 with 95% confidence interval (CI). The overall pooled proportion of symptomatic cases undergone influenza surveillance before and during the pandemic was 2.38% (95% CI 2.08%-2.67%) and 4.18% (95% CI 3.8%-4.52%) respectively. However, the pooled proportion of samples tested for influenza before the pandemic was 0.69% (95% CI 0.45-0.92%) and during the pandemic was 0.48% (95% CI 0.28-0.68%) when studies from Canada were excluded. The meta-analysis concludes that globally there was a decline in influenza surveillance during the COVID-19 pandemic except in Canada. Severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) virus is genetically linked to deadly SARS coronavirus, which emerged in 2002 and disappeared within eighteen months. SARS-CoV-2 and influenza viruses affecting the respiratory systems have the same transmission route, and various control measures to combat the current pandemic mitigate the spread of other respiratory viruses such as influenza and respiratory syncytial virus 1 . The diagnosis of influenza cases was 99% lower in 2020 than in the previous influenza seasons. 2, 3 Influenza is a systematically monitored viral disease due to the ongoing threat of epidemics and pandemics. A network of laboratories known as Global Influenza Surveillance and Response System (GISRS) was established in 1952 to update the circulating influenza strain variation information. This system facilitates the prompt identification and implementation of preventive measures against impending influenza activity in the community. The changing priorities in infectious disease surveillance and resource constraints during the Coronavirus disease-2019 (COVID-19) pandemic have negatively impacted the surveillance. Many countries have witnessed a short-term interruption or delayed influenza data reporting. Meanwhile, a study from Canada reported a drastic fall in the number of other seasonal non-SARS-CoV-2 respiratory viral infections, including influenza and respiratory syncytial virus (RSV) 4 . The surveillance data from the United States and Australia reported low influenza activity during 2020/2021. 5, 6 Various public health measures such as hand sanitisation, face masking, social distancing, travel restrictions, working from home, school closures and changes in health-seeking behaviours contribute to in low influenza activity. During the early months of the pandemic, outpatient services and elective procedures were deferred worldwide in hospitals. Even after the relaxation of lockdowns, fewer patients with acute respiratory All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint infections attended the hospitals mainly due to the fear of getting infected with SARS-CoV-2 and transmitting it to elderly family members. Surveillance is mandatory to identify and respond to early Influenza outbreaks of pandemic potential, and sentinel surveillance among a defined population provides data with the highest quality 7 . A steep drop in the number of influenza cases during the current pandemic was attributed to non-pharmaceutical public health measures, modified health care seeking behaviours and testing priorities. However, reduced population exposure might result in low immunity to the influenza virus resulting in a rebound activity in the coming seasons. This systematic review and meta-analysis aimed to evaluate the pooled proportion of patients sampled for influenza before and during the COVID-19 pandemic. This quantitative metaanalysis emphasises the importance of maintaining influenza surveillance during challenging times. This systematic review and meta-analysis were started after excluding registered or ongoing systematic reviews regarding the impact of the COVID-19 pandemic on influenza surveillance in the PROSPERO database. The study protocol was registered in PROSPERO database (CRD42021296702) and can be accessed at www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42021296702. An electronic search of PubMed/MEDLINE, Scopus and Google Scholar was carried out for the articles in English concerning the impact of the COVID-19 pandemic on Influenza surveillance among humans between January 2020 and December 2021. The study protocol was designed based on Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines updated in May 2020 8 . The meta-analysis component was modified appropriately to synthesise the pooled proportion of patients sampled for influenza All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint surveillance before the COVID-19 pandemic in 2019 and during the pandemic among the catchment population. Influenza-like illness (ILI): A case is defined as ILI if the symptoms begin within the past ten days with a measured fever of 38˚C or more, cough (respiratory infection) 9 . Acute Respiratory Infection (ARI): Some countries use ARI instead of ILI for surveillance of respiratory viruses, including influenza in humans. A case is defined as SARI if the symptoms begin within the past ten days with fever (measured≥ 38 ) and respiratory infection (cough) necessitating hospitalization 9 . An electronic search of PubMed/MEDLINE, Scopus and Google Scholar was carried out for all the articles published between January 2020 and December 2021 concerning the impact of the COVID-19 pandemic on Influenza surveillance. The relevant articles in English involving human subjects were identified using search terms such as "impact" AND "COVID-19" OR "SARS-CoV-2" AND "influenza surveillance" NOT "vaccination". Original research articles published in peer-reviewed scientific journals reporting the number of ILI/ARI/SARI cases sampled for influenza among the catchment population, as part of influenza surveillance at the sentinel, non-sentinel and diagnostic surveillance units before the COVID-19 pandemic in 2019 and during the pandemic were included. Observational studies such as cross-sectional and cohort studies reporting the number of patients screened for influenza among the target population were included. The studies reporting only the influenza positivity were excluded. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. A validated proforma including the first author, year of publication, region, study design, number of ILI/ARI/SARI cases attending the influenza surveillance centres before the pandemic in 2019, symptomatic cases during the pandemic, and the target population of the study area was prepared. A three-stage selection process was carried out for the final inclusion of the studies. One reviewer assessed the titles from the records for the relevance for inclusion in the study (n=5384). Studies applicable to the review were moved to the second stage after excluding irrelevant topics and duplicates (n=155). In the second stage, the abstracts of the studies were obtained and were independently analysed by two reviewers (n=94). After reviewing the abstracts, full texts of studies were retrieved, which were scrutinised by two reviewers independently (n=55). The corresponding authors were contacted electronically if further interpretation was needed. Manual library searches for articles in peer-reviewed journals were carried out, and references of retrieved articles were reviewed to increase the search sensitivity. The study selection process was depicted in the PRISMA chart (Figure 1 ). The last date of the search was on January 31, 2022. To assess the risk of bias in individual studies (quality assessment), chosen after the abstract and content review, the National Institutes of Health checklist for observational, cohort and cross-sectional studies was used 10 . The studies with a minimum score of eight or above, seven, or five or less than five "Yes responses" were considered good, fair, and poor quality, respectively. For cross-sectional studies, question numbers 1, 2, 3, 4, 5 and 11 were applicable. The responses to the remaining eight questions (6-10, 12, 13, 14) were marked as not applicable (NA). The studies with six "Yes" responses were considered good, and those with four /five were taken as fair. The studies with less than four "Yes responses" were All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint considered poor quality. The quality of the studies was assessed independently by two reviewers. The meta-analysis was accomplished in STATA version 13.0 (College Station, Texas 77, 845 USA). The forest plots were constructed using metaprop package in STATA. A substantial amount of heterogeneity across the studies was expected as the included studies were observational. The pooled proportion of symptomatic cases of influenza-like illness, acute respiratory illness or severe acute respiratory illness presented to surveillance centres out of the catchment population before the pandemic in 2019 and during the pandemic was reported with 95% CI along with Chi-square statistic (Q statistic) and I² index to quantify the heterogeneity. The I² value ranging between 0% to 24% specifies consistency. I² values of 25%-49% imply low heterogeneity, and 50-74% point toward moderate heterogeneity. The I² value varying between 75%-100% is indicative of high heterogeneity. The egger's test was used to analyse the publication bias. Weighted linear regression with standardised effect estimate and precision were considered the dependent and independent variable, respectively. In the present study, the log e proportion of patients under surveillance and precision were considered the effect estimate and 1/standard error of log e proportion rate, respectively. Weights were allotted using the inverse variance approach (1/variance of the effect estimate). A statistically significant bias coefficient is the evidence for publication bias. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The nine qualified studies for the meta-analysis were from the WHO-European region, Canada, Japan, Germany, Italy, Spain, South Africa and the United States, comprising 448,423 symptomatic cases sampled before the pandemic in 2019 and 1,270,518 cases sampled during the pandemic in 2020-2021 2,7,11-17 . Except for one study from Spain, which did not specify the months during which cases were sampled, all the included studies were qualified as good 15 . As per the US-CDC influenza surveillance data, approximately 8-9% of the US population is under routine influenza sentinel-surveillance 18 Table 1 . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Compared to the pre-pandemic period, the two qualified studies from Canada tested more than double the number of ILI cases for influenza as part of sentinel hospital and laboratorybased studies 11 Our meta-analysis included data from sentinel, non-sentinel and hospital-based surveillance centres in the context of an ongoing pandemic. As in Table 1 , the number of symptomatic cases sampled from hospitals was less than sentinel surveillance samples. Three studies enrolled cases as part of non-sentinel surveillance 12, 14, 16 . Even though all age groups were included in seven qualified studies, there was a discrepancy in the age groups enrolled for the surveillance. The figure 2 presents the overall pooled proportion of symptomatic cases undergone influenza testing before the pandemic in 2019 was 2.38% (95% CI 2.08%-2.67%). The figure 3 depicts the pooled proportion of cases sampled during the pandemic in 2020/2021, which was 4.18% (95% CI 3.84%-4.52%). However, the pooled proportions of samples tested before the pandemic in 2019 and during the pandemic were 0.69% (CI 0.45%-0.92%) and 0.48% (CI 0.28%-0.68%), respectively, when the studies from Canada were excluded (figures 4&5). As the I 2value was>90%, high heterogeneity was observed between the studies and random-effects model was used for pooling 21 . All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. There was no publication bias as the p-value for the bias coefficient was not statistically significant as shown Supplementary Table 1 (Table S1 ). Our systematic review observed a lack of data from the Middle East, South East Asia, (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Countries in the northern hemisphere reported a fall in influenza cases during the influenza season spanning between October and April in 2020 22, [27] [28] [29] . During the regular influenza season in the southern hemisphere from April to September Australia, New Zealand, South Africa, and Chile also observed low influenza activity 14, 30, 31 . Upon systematic review, the studies from China, Taiwan, Korea, Singapore, Japan and Bangladesh reported low influenza activity during the COVID-19 pandemic 27, 29, [32] [33] [34] [35] . Meagre test positivity rates for all respiratory viruses, including influenza and respiratory syncytial virus (RSV), were reported from Canada. 4 There was a significant disparity in the number of weeks during which the samples were procured for surveillance. Some studies included data from influenza season and few retrieved data from late winter. The data were based on sentinel, non-sentinel and hospital- 1 3 based surveillance in the context of an ongoing pandemic. Even though all age groups were included in seven qualified studies, there was a discrepancy in the age groups enrolled for the surveillance. Five of the nine qualified studies included data from surveillance centres with a defined denominator. Meanwhile, there were no well-defined catchment population for hospital or facility-based surveillance. The number of symptomatic cases sampled from hospitals was less than that of sentinel surveillance samples. Even during challenging times, sustained surveillance of influenza-like illness is essential to evaluate the geographical extent and sudden changes in circulating influenza strains globally. This is the first meta-analysis reporting the impact of the COVID-19 pandemic on influenza surveillance. Even though we limited the review to articles in English, very few studies were excluded for that reason. We could not include studies from Asia and South America because of incomplete data of cases tested in 2019, even after contacting the corresponding authors. Another limitation was the inaccuracy in the catchment population for hospital-based and All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint non-sentinel surveillances. Due to a lack of studies enrolling all age groups from Japan, one hospital-based study among paediatric cases had to be included for the quantitative synthesis. Globally there was a decline in influenza surveillance during the COVID-19 pandemic except in Canada. A steep decline in the seasonal influenza activity in both northern and southern hemispheres was observed. There is a need to have resilient ILI/SARI surveillance even during pandemics like COVID19 to recognise outbreaks by novel respiratory pathogens of pandemic potential. Conflicts of interest: Authors declare that there are no conflicts of interest. The author states that this manuscript does not involve any misconduct such as plagiarism, forgery, tampering, improper signature, multiple submission, repeated publication, split publication, etc. The systematic review and meta analysis used published data in indexed journals. As such, ethical approval to conduct the analysis was not sought as this was a secondary data analysis which requires no approval. No data with personal identifier was used The data that support the findings of this study are available from the corresponding author upon reasonable request (Dr S Sabeena, sabeenauthradam@gmail.com). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Figure 1 : PRISMA 2020 flow diagram for updated systematic reviews which included searches of databases, registers and other sources 8 . The flow diagram illustrates the number of studies identified, screened, abstracts/full-text articles included/excluded for the systematic review and meta-analysis. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint Figure 2 : The Forest Plot of the summary effect size (Proportion of symptomatic cases tested for influenza among the catchment population before the COVID-19 pandemic) using random effects model amongst the target population. Squares indicate the effect size of individual studies and the extended lines denote 95% confidence intervals (CI). Sizes of squares imply the weight of studies based on sample size using a random effects analysis. The diamond data indicates pooled prevalence. Test of heterogeneity: I 2= 100%, p-value=0.00. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint Figure 3 : The Forest Plot of the summary effect size (Proportion of symptomatic cases screened for influenza among the catchment population during the COVID-19 pandemic) using random effects model. Squares indicate the effect size of individual studies and the extended lines denote 95% confidence intervals (CI). Sizes of squares imply the weight of studies based on sample size using a random effects analysis. The diamond data indicates pooled prevalence. Test of heterogeneity: I 2= 100 %, p=0.00. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint Figure 4 : The Forest Plot depicting the summary effect size (Proportion of symptomatic cases tested for influenza among the catchment population before the COVID-19 pandemic) using random effects model amongst the target population after eliminating the studies from Canada. Test of heterogeneity: I 2= 100.00%, p-value=0.00 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted April 1, 2022. ; https://doi.org/10.1101/2022.03.31.22273236 doi: medRxiv preprint Figure 5 : The Forest Plot depicting the summary effect size (Proportion of symptomatic cases tested for influenza among the catchment population during the COVID-19 pandemic) using random effects model amongst the target population after eliminating the studies from Canada. Test of heterogeneity: I 2= 100.00%, p-value=0.00 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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