15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… JMIR Public Health Surveill. 2022 Nov; 8(11): e36712. PMCID: PMC9662290 Published online 2022 Nov 11. doi: 10.2196/36712: 10.2196/36712 PMID: 36265160 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Primary Health Care Electronic Records Monitoring Editor: Amaryllis Mavragani and Gunther Eysenbach Reviewed by Natalia Shcherbakova and Alex Cook #1,2 Maria Giner-Soriano, PharmD, PhD, Vanessa de Dios, MD,#3 Dan Ouchi, MSc,#1,2 Carles Vilaplana-Carnerero, PharmD, PhD,#1,2 Mònica Monteagudo, MD, PhD,#1,2 and Rosa Morros, MD, PhD#1,2,4,5 1 Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain 2 Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Spain 3 Department of Clinical Pharmacology, Medicines Area, Hospital Clinic of Barcelona, Barcelona, Spain 4 Plataforma Spanish Clinical Research Network, Unidad de Investigación Clínica, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain 5 Institut Català de la Salut, Barcelona, Spain Maria Giner-Soriano, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Gran Via de les Corts Catalanes 587, àtic, Barcelona, 08007, Spain, Phone: 34 934824110, Fax: 34 934824174, Email: mginer@idiapjgol.info. Corresponding author. # Contributed equally. Corresponding Author: Maria Giner-Soriano mginer@idiapjgol.info Received 2022 Jan 25; Revisions requested 2022 May 3; Revised 2022 May 11; Accepted 2022 Oct 18. Copyright ©Maria Giner-Soriano, Vanessa de Dios, Dan Ouchi, Carles Vilaplana-Carnerero, Mònica Monteagudo, Rosa Morros. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 11.11.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included. Abstract Background A possible link between influenza immunization and susceptibility to the complications of COVID-19 infection has been previously suggested owing to a boost in the immunity against SARS-CoV-2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 1/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Objective This study aimed to investigate whether individuals with COVID-19 could have benefited from vaccination against influenza. We hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2. Methods We performed a population-based cohort study including all patients with COVID-19 with reg‐ istered entries in the primary health care (PHC) electronic records during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) in Catalonia, Spain. We compared individuals who took an influenza vaccine before being infected with COVID-19, with those who had not taken one. Data were obtained from Information System for Research in Primary Care, captur‐ ing PHC information of 5.8 million people from Catalonia. The main outcomes assessed during follow-up were a diagnosis of pneumonia, hospital admission, and mortality. Results We included 309,039 individuals with COVID-19 and compared them on the basis of their in‐ fluenza immunization status, with 114,181 (36.9%) having been vaccinated at least once and 194,858 (63.1%) having never been vaccinated. In total, 21,721 (19%) vaccinated individuals and 11,000 (5.7%) unvaccinated individuals had at least one of their outcomes assessed. Those vaccinated against influenza at any time (odds ratio [OR] 1.14, 95% CI 1.10-1.19), recently (OR 1.13, 95% CI 1.10-1.18), or recurrently (OR 1.10, 95% CI 1.05-1.15) before being infected with COVID-19 had a higher risk of presenting at least one of the outcomes than did unvaccinated individuals. When we excluded people living in long-term care facilities, the results were similar. Conclusions We could not establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection, as the risk of COVID-19 complications was higher in vacci‐ nated than in unvaccinated individuals. Our results correspond to the first wave of the COVID- 19 pandemic, where more complications and mortalities due to COVID-19 had occurred. Despite that, our study adds more evidence for the analysis of a possible link between the quality of immunity and COVID-19 outcomes, particularly in the PHC setting. Keywords: SARS-CoV-2, COVID-19, influenza vaccines, pneumonia, electronic health records, primary health care, vaccination, public health, cohort study, epidemiology, eHeatlh, health outcome, mortality Introduction COVID-19 is caused by SARS-CoV-2, a novel coronavirus that emerged in China in 2019, which became the primary agent of a new pandemic that rapidly spread worldwide [1], with an aver‐ age global infection fatality rate of approximately 0.15%, depending on the data analyzed [2]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 2/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… SARS-CoV-2 mainly affects the respiratory tract and uses surface proteins in order to infect the host [3]. Although new variants of SARS-CoV-2 have emerged since December 2020, the coronavirus’ genome is composed of RNA and depends on the RNA polymerase to generate its proteins, with a mechanism of error correction that results in a lower mutation rate than the influenza virus [4]. This low mutation rate may suggest that the vaccines developed against SARS-CoV-2, as well as the immunity generated in those patients who were infected, could represent a long- lasting immunity [5,6]. COVID-19, similar to influenza A and B, is caused by RNA virus and produce similar symptoms. The influenza virus needs the hemagglutinin and neuraminidase surface proteins to infect cells, whereas SARS-CoV-2 needs the S protein [5]. Previous in vitro and animal studies suggest an induction pathway of indirect etiological immunity between the influenza vaccine and SARS- CoV-2. Animal models suggest that some influenza subtypes might lead to regulation of the an‐ giotensin-converting enzyme-2, with protective properties against SARS [7]. An unspecific ef‐ fect of infection and vaccination on the immune system and susceptibility to other infections has also been reported, albeit with discordant data [8-10]. Some modeling studies have sug‐ gested a possible association between influenza immunization and COVID-19 [11-14]. A study conducted in Australia assessed the cellular and humoral immune responses during and after disease occurrence in a patient with a mild COVID-19 infection. They found that the immune response in different cell types is associated with clinical recovery. These results are coincident with similar findings among patients with influenza reported by the same authors [15,16]. Other studies observed differences in the susceptibility to COVID-19 in children of different ages with a lower infection rate than that in adults and older individuals [17]. Although the mechanism underlying these differences in severity and susceptibility is unclear, a possible ex‐ planation might be the difference in the quantity and quality of the immune function deter‐ mined by the history of infections and the recent vaccines administered [18]. Consequently, a link between the quality of the immunity and recovery from COVID-19 may ex‐ ist. Thus, we hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2, and we aimed to investigate whether in‐ dividuals with COVID-19 could have benefited from vaccination against influenza. Methods Study Design We performed a population-based cohort study including all adults with COVID-19 in Catalonia, Spain, who were registered as confirmed cases (through the polymerase chain reac‐ tion [PCR]) or as probable cases (not confirmed through PCR but with International Classification of Diseases (ICD)-10 codes registered that are compatible with COVID-19) in the primary health care (PHC) system. All individuals with COVID-19 were diagnosed from the pandemic’s onset (March 2020) to June 30, 2020. Participants were compared on the basis of https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 3/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… their influenza vaccination status between those having received the influenza vaccine before having COVID-19 (vaccinated in the previous influenza seasonal campaign in 2019-2020 or be‐ fore) [19] with those who were not vaccinated. Data Source Our data source is the Information System for Research in Primary Care [20], which captures clinical information of approximately 5.8 million people from Catalonia, Spain (approximately 80% of the Catalan population). This information is pseudonymized, having originated from different data sources: (1) electronic health records in PHC system of the Catalan Health Institute, including sociodemographic characteristics, residents in nursing homes or long-term care facilities (LTCFs), comorbidities registered as ICD-10 codes [21], specialist referrals, clini‐ cal parameters, toxic habits, sickness leave, date of death, laboratory test data, and drug pre‐ scriptions issued in the PHC system, registered in accordance with the anatomical therapeutic chemical classification system [22]; (2) pharmacy invoice data corresponding to the PHC drug prescriptions; (3) database of diagnoses upon hospital discharge [23]; and (4) COVID-19 data from the Catalan Agency of Health Quality and Evaluation (AQuAS) [24]. COVID-19 Classification Participants were classified in accordance with the following criteria: confirmed cases are those with a confirmed COVID-19 diagnosis record, positive PCR outcome, or a positive serology test result. Those with an unconfirmed diagnosis or test (possible or unclear) along with any indi‐ vidual with a record of hospitalization, pneumonia, or death related to COVID-19 were consid‐ ered probable cases. During the first wave of the COVID-19 pandemic in Catalonia, PCR tests were not routinely conducted for all individuals with compatible symptoms owing to the un‐ availability of laboratory kits to carry out the tests. Thus, we needed to capture those patients with a possible diagnosis of COVID-19, such as those admitted to hospital with pneumonia or other COVID-19 symptoms, who were not tested. We designed an algorithm to classify patients as “COVID possible” when a test result was unavailable along with registered entries from dif‐ ferent databases: PCR tests or serology tests conducted in different settings, discharge diag‐ noses of pneumonia from Catalan hospitals or from emergency departments, and ICD-10 diag‐ noses related to COVID-19 coded in PHC. The date of COVID-19 diagnosis was set to be the first of all records used per patient. To guarantee that our algorithm is not far from the Catalan population, the resulting cohort was compared to the official COVID-19 cases reported by the AQuAS during the pandemic [24]. Influenza Immunization Patients were classified as having taken the influenza vaccine if they had been vaccinated at any time before having COVID-19, and grouped in accordance with the seasonal vaccination campaign: the immediate previous campaign (2019-2020) or other vaccination campaigns (2018-2019 and before) [19,25]. Variables https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 4/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… At baseline, the following variables were captured: sex, age, geographical area, MEDEA (Mortalidad en á reas pequeñ as Españ olas y Desigualdades Econó micas y Ambientales [Mortality in small Spanish areas and economic and environmental inequalities]) socioeco‐ nomic index (deprivation index based on 5 indicators of socioeconomic position; it helps ana‐ lyze health inequity, and higher the MEDEA socioeconomic index, worse the deprivation) [26], BMI, residence in nursing homes, smoking habits, comorbidities, and taking influenza vaccines and pneumococcal and tuberculosis vaccines. The main outcomes assessed during follow-up (up to June 2020) were at least one of the fol‐ lowing variables: diagnosis of pneumonia, hospital admission, and mortality. The risk of these events was analyzed in those people who had been vaccinated against influenza at any time be‐ fore having COVID-19, in those who were recently vaccinated (campaign of 2019-2020), and in those systematically vaccinated (who had been vaccinated at least during 3 different cam‐ paigns). We analyzed the same outcomes excluding those of people living in LTCFs, where vac‐ cination is nearly universal in our country [27]. Statistical Analysis Quantitative variables were described as mean (SD) values, whereas categorical variables were described as the proportion of vaccinated and unvaccinated individuals. Univariate analyses were based on the Student t test or chi-square test depending on the variable. For each outcome, we fitted a logistic regression model to estimate an odds ratio (OR) compar‐ ing the prevalence of each outcome among individuals given the influenza vaccine to that of unvaccinated individuals. The logistic model was fitted along with other covariables such as smoking habits, age, comorbidities (asthma, autoimmune disorders, prior cerebrovascular dis‐ ease, chronic kidney disease, chronic pulmonary obstructive disease, diabetes, heart failure, hypertension, ischemic heart disease, mental-behavioral disorders, obesity, organ transplant, and other respiratory diseases), concomitant drugs, and previous vaccines (pneumococcal and tuberculosis). As a sensitivity analysis, we conducted the same analysis on a matched popula‐ tion. Individuals vaccinated against influenza and unvaccinated controls were matched 1:2 in accordance with their age and gender at the time of infection or on an index date, and the re‐ ported ORs were obtained by fitting a conditional logistic regression model (clogit) accounting for matched pairs and adjusted using the same covariables as in the logistic model. We used the Wald test on the fitted coefficient to determine whether the log-odds were significantly dif‐ ferent from 0 at a threshold of .05. All analyses were performed in R (version 4.1.0 or above; The R Foundation). Ethical Considerations The study protocol was approved by the Research Ethics Committee of Institut Universitari d’Investigació en Atenció Primà ria (June 3, 2020). This is a database research study that has been conducted in accordance with the tenets of the Declaration of Helsinki (Fortaleza, Brazil 2013) and does not require consent from the study participants for the purpose of publica‐ tion. The need for consent was waived by the Research Ethics Committee of Institut Universitari d’Investigació en Atenció Primà ria as it is deemed unnecessary according to the European legislation (Regulation [EU] 2016/679). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 5/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Results We included 309,039 individuals with COVID-19 during the first wave of the pandemic in ac‐ cordance with their influenza immunization status (Table 1, Multimedia Appendix 1); 114,181 (36.9%) participants had received the influenza vaccine at least once before having COVID-19 and 194,858 (63.1%) had not been vaccinated, with more women in both groups, especially in the vaccinated cohort (61.0% women vs 39.0% men). The mean age was higher for vaccinated individuals (64.3 years, with 52.3% of them being older than 65 years). Vaccinated individuals had more comorbidities than unvaccinated individuals. Of those who received the influenza vaccine, 66,611 (58.3%) had been recently vaccinated (2019-2020) and 75,311 (66%) had been systematically vaccinated against influenza at least during 3 different years (Table 2). Of the participants with COVID-19, 11,000 (5.7%) unvaccinated and 21,721 (19%) vaccinated participants presented at least one of the following events: hospital admission, pneumonia, or death. For those who received the influenza vaccine at any time before having COVID-19, the risks of hospitalization (adjusted OR 1.14, 95% CI 1.10-1.19) and death (OR 1.32, 1.23-1.42) were higher than those among unvaccinated participants. For the recently vaccinated partici‐ pants, the risk was higher for hospitalization (OR 1.16, 95% CI 1.1-1.23), pneumonia (OR 1.12, 95% CI 1.02-1.23), and death (OR 1.14, 95% CI 1.04-1.24). For people with recurrent vaccina‐ tion, the risk of the 3 outcomes was also higher that among unvaccinated participants (OR 1.07, 1.16, and 1.24, respectively; Table 3). We have also analyzed the results in a matched popula‐ tion of vaccinated versus unvaccinated participants, revealing a higher risk of pneumonia and mortality, with an adjusted OR of 1.11 (95% CI 1.01-1.23) and 1.28 (95% CI 1.07-1.53), respec‐ tively (Multimedia Appendix 2). The risks of the outcomes based on influenza vaccination status and excluding those patients living in LTCFs are shown in Figure 1. For non-LTCF residents, the results are similar to those for the whole population, except that there was no significant increase in mortality (OR 0.93, 95% CI 0.85-1.03). Discussion Principal Findings We analyzed the negative outcomes among people with COVID-19 (N=309,039) and compared those who had received the influenza vaccine with those who were never vaccinated. Those who received the vaccine any time before having COVID-19 were at a higher risk of complica‐ tions than those who were unvaccinated. We obtained similar results for those who were re‐ cently vaccinated (2019-2020 campaign) and for those who were systematically vaccinated (at least 3 years), and the same comparisons were carried out after excluding individuals living in LTCFs. We also obtained similar results on matching vaccinated and unvaccinated individuals. Thus, we did not find a possible link between receiving the influenza vaccine and presenting better clinical outcomes after a COVID-19 infection. Comparison With Prior Work https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 6/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Some researchers have studied this possible association. Massoudi and Mohit [28] conducted a study in a hospital in Iran including health care workers, with 80 of them COVID-19 cases confirmed through PCR or on the basis of their symptoms, and 181 of them were controls. They concluded that individuals who were confirmed cases were less likely to have received the 2019 influenza vaccine (OR 0.04, 95% CI 0.01-0.14), suggesting a protective association be‐ tween the influenza vaccine and COVID-19. Their study had several limitations, such as the lack of availability of COVID-19 test kits or the samples limited to the workers of a single hospital [28]. Candelli et al [29] assessed 602 patients with COVID-19 enrolled at the emergency department in a hospital in Italy, of whom 24.9% had been previously vaccinated against influenza. They found that influenza immunization was independently associated with a lower risk of death at 60 days (OR 0.20, 95% CI 0.08-0.51), but not with a reduced need of endotracheal intubation (OR 0.73, 95% CI 0.35-1.56) [29]. A study conducted in Brazil [30] included 92,664 confirmed cases of COVID-19, of whom 31.1% had been recently vaccinated against influenza. They found that the vaccinated individu‐ als were at a lower risk of needing intensive care for COVID-19 (OR 0.92, 95% CI 0.86-0.99), a lower risk of needing respiratory support (OR 0.81, 95% CI 0.74-0.88), and lower odds of mor‐ tality (OR 0.82, 95% CI 0.75-0.89) [30]. In a systematic review [31] including 12 studies, the authors examined whether influenza vac‐ cination affects the risk of being infected with SARS-CoV-2 and the risk of complicated illness or poor outcomes among patients with COVID-19, all of whom having been confirmed cases through PCR testing. They concluded that influenza vaccination is unlikely to be associated with an increase in the risk of COVID-19 infection or severity and the risk of associated death [31]. There are reports from some countries with high influenza vaccination rates and high inci‐ dences of COVID-19 and mortality [32,33]. For instance, Kline et al [33] compared people vac‐ cinated against influenza with unvaccinated individuals admitted to hospital for COVID-19, and they found no differences in the rate of admission to the intensive care unit, intubation, or other complications [33]. Our results follow these same trends in a cohort of the general popu‐ lation attended to in the PHC system and not only hospitalized patients. Limitations We need to consider that our results correspond to the first wave of the COVID-19 pandemic, when there were more negative outcomes and mortalities due to COVID-19 than in the subse‐ quent waves in our setting; thus, this higher statistical power allowed us to detect differences. Furthermore, in subsequent waves, more confounders might have been present, such as COVID-19 vaccination or effects of the different SARS-CoV-2 variants, making it more difficult to manage their potential effect in the analysis of the outcomes of the infection. We also need to bear in mind that the target population for the influenza vaccine in our coun‐ try are people older than 60 years, individuals with chronic comorbidities or immunodefi‐ ciency, and health care workers among others [34], some of them being at a high risk of COVID-19 complications, which is why confounding variables were used to adjust the logistic regression model [35]. Nevertheless, estimates of the effectiveness of the influenza vaccine https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 7/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… have been frequently confounded, indicating that a different approach should be used with al‐ ternative study designs, different from the typical methods used to study drug exposure [36- 38]. Among other limitations of our study is the reliability of the COVID-19 diagnoses; we included individuals without a confirmed result, as during the first wave of the pandemic in our setting, PCR tests were not always performed. This limitation has been described in other studies in‐ cluding those conducted at the beginning of the pandemic when diagnostic tests for COVID-19 were not widely available and clinical algorithms were used to assess COVID-19 diagnoses [39]. We compared our number of COVID-19 cases with the official COVID-19 case numbers provided by the AQuAS during the pandemic [24]. Another limitation is the lack of hospital in‐ formation: we could not capture ICU admissions, ventilation, or treatments administered upon admission, which clearly have an influence on the prognosis and outcomes of COVID-19. Finally, we have not conducted any subgroup analysis that could have indicated any condition potentially resulting in any benefit or harm from influenza vaccination. Conclusions In conclusion, we were not able to establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection. Nonetheless, our study adds more evidence to the analysis of the possible link between the quality of the conferred immunity and outcomes of COVID-19 infection, and it has some strengths, such as the large cohort size, its representativeness with respect to the general population, and the completeness of its so‐ ciodemographic data. We have already highlighted that our cohort comprises individuals who received care from the PHC system; hence, we have estimated the risk of complications for a different population from the hospitalized individuals who are usually assessed in multiple studies. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 8/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Abbreviations AQuAS Catalan Agency of Health Quality and Evaluation ICD-10 International Classification of Diseases, version 10 LTCF long-term care facility Mortalidad en á reas pequeñ as Españ olas y Desigualdades Econó micas y MEDEA Ambientales (Mortality in small Spanish areas and economic and environmental inequalities) OR odds ratio PCR polymerase chain reaction PHC primary health care Multimedia Appendix 1 Baseline characteristics of the population included by gender. Multimedia Appendix 2 Conditional logistic regression model for the age and gender matched population. Matching performed in patients ≤ 65 years old to correct for age distribution. Footnotes Conflicts of Interest: None declared. References 1. Coronavirus disease 2019 (‎COVID-19)‎: situation report, 51. World Health Organization. 2020. [2022-10-31]. https://apps.who.int/iris/handle/10665/331475 . 2. Ioannidis JPA. Reconciling estimates of global spread and infection fatality rates of COVID-19: an overview of systematic evaluations. Eur J Clin Invest. 2021 May;51(5):e13554. doi: 10.1111/eci.13554. https://europepmc.org/abstract/MED/33768536 . [PMCID: PMC8250317] [PubMed: 33768536] [CrossRef: 10.1111/eci.13554] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 9/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… 3. Jackson CB, Farzan M, Chen B, Choe H. Mechanisms of SARS-CoV-2 entry into cells. Nat Rev Mol Cell Biol. 2022 Jan 05;23(1):3–20. doi: 10.1038/s41580-021-00418-x. https://europepmc.org/abstract/MED/34611326 .10.1038/s41580- 021-00418-x [PMCID: PMC8491763] [PubMed: 34611326] [CrossRef: 10.1038/s41580-021-00418-x] 4. Boehm E, Kronig I, Neher R, Eckerle I, Vetter P, Kaiser L, Geneva Centre for Emerging Viral Diseases Novel SARS-CoV-2 variants: the pandemics within the pandemic. Clin Microbiol Infect. 2021 Aug;27(8):1109–1117. doi: 10.1016/j.cmi.2021.05.022. https://linkinghub.elsevier.com/retrieve/pii/S1198-743X(21)00262-7 .S1198- 743X(21)00262-7 [PMCID: PMC8127517] [PubMed: 34015535] [CrossRef: 10.1016/j.cmi.2021.05.022] 5. Manzanares-Meza LD, Medina-Contreras O. SARS-CoV-2 and influenza: a comparative overview and treatment implications. Bol Med Hosp Infant Mex. 2020;77(5):262–273. doi: 10.24875/BMHIM.20000183. http://www.bmhim.com/frame_esp.php?id=177 .j77/5/262 [PubMed: 33064680] [CrossRef: 10.24875/BMHIM.20000183] 6. Bar-On YM, Flamholz A, Phillips R, Milo R. SARS-CoV-2 (COVID-19) by the numbers. Elife. 2020 Apr 02;9 doi: 10.7554/eLife.57309. doi: 10.7554/eLife.57309.57309 [PMCID: PMC7224694] [PubMed: 32228860] [CrossRef: 10.7554/eLife.57309] [CrossRef: 10.7554/eLife.57309] 7. Yang P, Gu H, Zhao Z, Wang W, Cao B, Lai C, Yang X, Zhang L, Duan Y, Zhang S, Chen W, Zhen W, Cai M, Penninger JM, Jiang C, Wang X. Angiotensin-converting enzyme 2 (ACE2) mediates influenza H7N9 virus-induced acute lung injury. Sci Rep. 2014 Dec 13;4:7027. doi: 10.1038/srep07027. doi: 10.1038/srep07027.srep07027 [PMCID: PMC4229671] [PubMed: 25391767] [CrossRef: 10.1038/srep07027] [CrossRef: 10.1038/srep07027] 8. Cowling B, Fang V, Nishiura H, Chan K, Ng S, Ip D, Chiu S, Leung G, Peiris J. Increased risk of noninfluenza respiratory virus infections associated with receipt of inactivated influenza vaccine. Clin Infect Dis. 2012 Jul;54(12):1778–1783. doi: 10.1093/cid/cis307. https://europepmc.org/abstract/MED/22423139 .cis307 [PMCID: PMC3404712] [PubMed: 22423139] [CrossRef: 10.1093/cid/cis307] 9. Feng S, Fowlkes AL, Steffens A, Finelli L, Cowling BJ. Assessment of virus interference in a test-negative study of influenza vaccine effectiveness. Epidemiology. 2017 Jul;28(4):514–524. doi: 10.1097/EDE.0000000000000670. https://europepmc.org/abstract/MED/28362642 . [PMCID: PMC5535302] [PubMed: 28362642] [CrossRef: 10.1097/EDE.0000000000000670] 10. Jehi L, Ji X, Milinovich A, Erzurum S, Rubin BP, Gordon S, Young JB, Kattan MW. Individualizing risk prediction for positive coronavirus disease 2019 testing: results from 11,672 patients. Chest. 2020 Oct;158(4):1364–1375. doi: 10.1016/j.chest.2020.05.580. https://europepmc.org/abstract/MED/32533957 .S0012-3692(20)31654-8 [PMCID: PMC7286244] [PubMed: 32533957] [CrossRef: 10.1016/j.chest.2020.05.580] 11. Gurwitz D. Angiotensin receptor blockers as tentative SARS-CoV-2 therapeutics. Drug Dev Res. 2020 Aug 04;81(5):537–540. doi: 10.1002/ddr.21656. https://europepmc.org/abstract/MED/32129518 . [PMCID: PMC7228359] [PubMed: 32129518] [CrossRef: 10.1002/ddr.21656] 12. Koo J, Cook A, Park M, Sun Y, Sun H, Lim J, Tam C, Dickens B. Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. Lancet Infect Dis. 2020 Jun;20(6):678–688. doi: 10.1016/s1473-3099(20)30162-6. [PMCID: PMC7158571] [PubMed: 32213332] [CrossRef: 10.1016/s1473-3099(20)30162-6] 13. Li Q, Tang B, Bragazzi NL, Xiao Y, Wu J. Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability. Math Biosci. 2020 Jul;325:108378. doi: 10.1016/j.mbs.2020.108378. https://europepmc.org/abstract/MED/32507746 .S0025-5564(20)30063-8 [PMCID: PMC7229764] [PubMed: 32507746] [CrossRef: 10.1016/j.mbs.2020.108378] 14. Salem ML, El-Hennawy D. The possible beneficial adjuvant effect of influenza vaccine to minimize the severity of COVID-19. Med Hypotheses. 2020 May 22;140:109752. doi: 10.1016/j.mehy.2020.109752. https://europepmc.org/abstract/MED/32361099 .S0306-9877(20)30606-X [PMCID: PMC7194943] [PubMed: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 10/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… 32361099] [CrossRef: 10.1016/j.mehy.2020.109752] 15. Andersen KG, Rambaut A, Lipkin WI, Holmes EC, Garry RF. The proximal origin of SARS-CoV-2. Nat Med. 2020 Apr;26(4):450–452. doi: 10.1038/s41591-020-0820-9. https://europepmc.org/abstract/MED/32284615 .10.1038/s41591-020-0820-9 [PMCID: PMC7095063] [PubMed: 32284615] [CrossRef: 10.1038/s41591-020-0820-9] 16. Koutsakos M, Wheatley AK, Loh L, Clemens EB, Sant S, Nü ssing S, Fox A, Chung AW, Laurie KL, Hurt AC, Rockman S, Lappas M, Loudovaris T, Mannering SI, Westall GP, Elliot M, Tangye SG, Wakim LM, Kent SJ, Nguyen THO, Kedzierska K. Circulating T cells, serological memory, and tissue compartmentalization shape human influenza-specific B cell immunity. Sci Transl Med. 2018 Feb 14;10(428) doi: 10.1126/scitranslmed.aan8405.10/428/eaan8405 [PubMed: 29444980] [CrossRef: 10.1126/scitranslmed.aan8405] 17. Mehta N, Mytton O, Mullins E, Fowler T, Falconer C, Murphy O, Langenberg C, Jayatunga W, Eddy D, Nguyen-Van- Tam JS. SARS-CoV-2 (COVID-19): what do we know about children? A systematic review. SSRN. doi: 10.2139/ssrn.3558015. Preprint posted online April 1, 2020. [PMCID: PMC7239259] [PubMed: 32392337] [CrossRef: 10.2139/ssrn.3558015] 18. Zheng J, Perlman S. Immune responses in influenza A virus and human coronavirus infections: an ongoing battle between the virus and host. Curr Opin Virol. 2018 Feb;28:43–52. doi: 10.1016/j.coviro.2017.11.002. https://europepmc.org/abstract/MED/29172107 .S1879-6257(17)30119-0 [PMCID: PMC5835172] [PubMed: 29172107] [CrossRef: 10.1016/j.coviro.2017.11.002] 19. Departament DSGDC. Pla d’informació de les infeccions respiratò ries agudes a Catalunya. Balanç temporada gripal18-20. [2022-11-03]. https://canalsalut.gencat.cat/ca/professionals/vigilancia-epidemiologica/pla-dinformacio- de-les-infeccions-respirato ries-agudes-a-catalunya-pidirac/ 20. Information system for the development of research in primary care. SIDIAP. 2022. [2022-10-31]. http://www.sidiap.org/index.php/en . 21. International Statistical Classification of Diseases and Related Health Problems 10th Revision. ICD-10 Version:2019. [2022-10-31]. https://icd.who.int/browse10/2019/en . 22. ATC/DDD Index 2022. WHO Collaborating Centre for Drug Statistics Methodology. [2022-10-31]. https://www.whocc.no/atc_ddd _index/ 23. Conjunt mínim bàsic de dades (CMBD) CatSalut. Servei Català de la Salut. [2022-10-31]. http://catsalut.gencat.cat/ca/proveidors -professionals/registres-catalegs/registres/cmbd/ 24. COVID-19 - Diagnò stics AP. Generalitat de Catalunya. [2022-10-31]. https://aquas.gencat.cat/ca/actualitat/ultimes- dades-coronavirus/index.html#googtrans(ca%7Cen) 25. Pla d’informació de les infeccions respiratò ries agudes a Catalunya (PIDIRAC) 2019-2020. Generalitat de Catalunya. Agència de Salut Pública de Catalunya. 2019. [2022-10-31]. https://scientiasalut.gencat.cat/bitstream/handle/11351/4318/pla_infor macio_infeccions_respiratories_agudes_catalunya_2019_2020.pdf?sequence=1&isAllowed=y . 26. Domínguez-Berjó n MF, Borrell C, Cano-Serral G, Esnaola S, Nolasco A, Pasarín MI, Ramis R, Saurina C, Escolar- Pujolar A. [Constructing a deprivation index based on census data in large Spanish cities (the MEDEA project)] Gac Sanit. 2008;22(3):179–187. doi: 10.1157/13123961. http://www.elsevier.es/en/linksolver/ft/ivp/0213-9111/22/179 .13123961 [PubMed: 18579042] [CrossRef: 10.1157/13123961] 27. Vacunas y Programa de Vacunació n. Ministerio de Sanidad. [2022-10-31]. https://www.mscbs.gob.es/profesionales/saludPublica/prevPromocion/vacunaciones/programasDeVacunacion/grip e/home.htm . https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 11/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… 28. Massoudi N, Mohit B. A case-control study of the 2019 influenza vaccine and incidence of COVID-19 among healthcare workers. J Clin Immunol. 2021 Feb 26;41(2):324–334. doi: 10.1007/s10875-020-00925-0. https://europepmc.org/abstract/MED/33244671 .10.1007/s10875-020-00925-0 [PMCID: PMC7690338] [PubMed: 33244671] [CrossRef: 10.1007/s10875-020-00925-0] 29. Candelli M, Pignataro G, Torelli E, Gullì A, Nista EC, Petrucci M, Saviano A, Marchesini D, Covino M, Ojetti V, Antonelli M, Gasbarrini A, Franceschi F. Effect of influenza vaccine on COVID-19 mortality: a retrospective study. Intern Emerg Med. 2021 Oct;16(7):1849–1855. doi: 10.1007/s11739-021-02702-2. https://europepmc.org/abstract/MED/33743150 .10.1007/s11739-021-02702-2 [PMCID: PMC7980752] [PubMed: 33743150] [CrossRef: 10.1007/s11739-021-02702-2] 30. Fink G, Orlova-Fink N, Schindler T, Grisi S, Ferrer APS, Daubenberger C, Brentani A. Inactivated trivalent influenza vaccination is associated with lower mortality among patients with COVID-19 in Brazil. BMJ Evid Based Med. 2020 Dec 11; doi: 10.1136/bmjebm-2020-111549. https://europepmc.org/abstract/MED/33310766 .bmjebm-2020-111549 [PubMed: 33310766] [CrossRef: 10.1136/bmjebm-2020-111549] 31. Del Riccio M, Lorini C, Bonaccorsi G, Paget J, Caini S. The association between influenza vaccination and the risk of SARS-CoV-2 infection, severe illness, and death: a systematic review of the literature. Int J Environ Res Public Health. 2020 Oct 27;17(21):7870. doi: 10.3390/ijerph17217870. https://www.mdpi.com/resolver?pii=ijerph17217870 .ijerph17217870 [PMCID: PMC7672609] [PubMed: 33121070] [CrossRef: 10.3390/ijerph17217870] 32. Influenza vaccination rates. Organisation for Economic Co-operation and Development. [2022-10-31]. https://data.oecd.org/health care/influenza-vaccination-rates.htm . 33. Kline A, Trinh LN, Hussein MH, Elshazli RM, Toraih EA, Duchesne J, Fawzy MS, Kandil E. Annual flu shot: does it help patients with COVID-19? Int J Clin Pract. 2021 Dec 27;75(12):e14901. doi: 10.1111/ijcp.14901. https://europepmc.org/abstract/MED/34547161 . [PMCID: PMC8646810] [PubMed: 34547161] [CrossRef: 10.1111/ijcp.14901] 34. Agència de Salut Pú blica de Catalunya (ASPCAT) Campanya de vacunació antigripal 2021-2022. Vacunació antigripal. [2022-11-03]. https://canalsalut.gencat.cat/ca/professionals/vigilancia-epidemiologica/pla-dinformacio-de-les- infeccions-respiratories-agudes-a-catalunya-pidirac/ 35. Rothman K, Greenland S, Lash T. Modern Epidemiology (3rd edition) Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2008. 36. Fireman B, Lee J, Lewis N, Bembom O, van der Laan M, Baxter R. Influenza vaccination and mortality: differentiating vaccine effects from bias. Am J Epidemiol. 2009 Sep 01;170(5):650–656. doi: 10.1093/aje/kwp173. https://europepmc.org/abstract/MED/19625341 .kwp173 [PMCID: PMC2728831] [PubMed: 19625341] [CrossRef: 10.1093/aje/kwp173] 37. McGrath LJ, Kshirsagar AV, Cole SR, Wang L, Weber DJ, Stü rmer T, Brookhart MA. Influenza vaccine effectiveness in patients on hemodialysis: an analysis of a natural experiment. Arch Intern Med. 2012 Apr 09;172(7):548–554. doi: 10.1001/archinternmed.2011.2238. https://europepmc.org/abstract/MED/22493462 .172/7/548 [PMCID: PMC4082376] [PubMed: 22493462] [CrossRef: 10.1001/archinternmed.2011.2238] 38. McGrath LJ, Ellis AR, Brookhart MA. Controlling time-dependent confounding by health status and frailty: restriction versus statistical adjustment. Am J Epidemiol. 2015 Jul 01;182(1):17–25. doi: 10.1093/aje/kwu485. https://europepmc.org/abstract/MED/25868551 .kwu485 [PMCID: PMC4479111] [PubMed: 25868551] [CrossRef: 10.1093/aje/kwu485] 39. Almalki YE, Qayyum A, Irfan M, Haider N, Glowacz A, Alshehri FM, Alduraibi SK, Alshamrani K, Alkhalik Basha MA, Alduraibi A, Saeed MK, Rahman S. A novel method for COVID-19 diagnosis using artificial intelligence in chest x-ray images. Healthcare (Basel) 2021 Apr 29;9(5) doi: 10.3390/healthcare9050522. https://www.mdpi.com/resolver? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 12/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… pii=healthcare9050522 .healthcare9050522 [PMCID: PMC8145061] [PubMed: 33946809] [CrossRef: 10.3390/healthcare9050522] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 13/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Figures and Tables Table 1 Sociodemographic and clinical characteristics of the study population (N=309,039). Characteristics Overall Not vaccinated against influenza V (n=194,858) C COVID-19 status, n (%) Confirmed 164,557 (53.2) 105,788 (54.3) 58,769 Possible 144,482 (46.8) 89,070 (45.7) 55,412 Gender, n (%) Female 173,071 (56.0) 103,413 (53.1) 69,658 Male 135,968 (44.0) 91,445 (46.9) 44,523 Age (years), mean (SD) 49.3 (22.3) 40.6 (17.5) 6 Age groups (years), n (%) ≤40 108,950 (35.3) 90,894 (46.6) 18,056 41-65 129,576 (41.9) 93,116 (47.8) 36,460 >65 70,513 (22.8) 10,848 (5.6) 59,665 Smoker status, n (%) 119,554 (38.7) 72,806 (37.4) 4 Having obesity, n (%) 78,882 (25.5) 36,973 (19.0) 4 Residents of long-term care facilities, n (%) 28,360 (9.2) 3146 (1.6) 2 Geographical information (MEDEA) Unknown 278 (0.1) 201 (0.1) 77 (0.1) Urban 252,014 (81.5) 159,859 (82.0) 92,155 Rural 56,747 (18.4) 34,798 (17.9) 21,949 Comorbidities, n (%) Asthma 22,734 (7.4) 9029 (4.6) 13,705 Autoimmune disorders 30,783 (10.0) 14,005 (7.2) 16,778 Cancer 23,600 (7.6) 6832 (3.5) 16,768 Cerebrovascular disease 6937 (2.2) 1053 (0.5) 5884 (5 Chronic kidney disease 18,450 (6.0) 2088 (1.1) 16,362 Chronic obstructive pulmonary 21,771 (7.0) 6155 (3.2) 15,616 disease Diabetes 30,513 (9.9) 5886 (3.0) 24,627 Heart failure 8307 (2.7) 693 (0.4) 7614 (6 Hypertension 75,346 (24.4) 21,624 (11.1) 53,722 I h i h di 10 049 (3 3) 1837 (0 9) 8212 (7 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 14/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Table 2 Taking influenza vaccines before having COVID-19. Vaccinated before h Campaign of 2019-2020 (recent immunization), n (%) 66,611 (58.3) Days from vaccination to infection, median (IQR) 146.0 (127.0-169.0 Campaign of 2018-2019, n (%) 60,161 (52.7) Days from vaccination to infection, median (IQR) 515.0 (495.0-539.0 Campaign of 2017-2018 or before, n (%) 102,235 (89.5) Days from vaccination to infection, median (IQR) 931.0 (875.0-2018 Campaigns during which participants were vaccinated before having COVID-19, n (%) 1 26,786 (23.5) 2 12,084 (10.6) ≥3 (recurrent immunization) 75,311 (66.0) 3 7931 (6.9) 4-5 11,146 (9.8) 6-10 18,945 (16.6) >10 37,289 (32.7) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 15/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Table 3 Logistic regression model of COVID-19 outcomes based on influenza immunization status. Any vaccination Influenza immunization status prior to having COVID-19, n (%) Unvaccinated (n=194,858) Vaccinated (n=114,181) ≥1 outcome 11,000 (5.7) 21,721 (19.0) Hospitalization 7848 (4.0) 10,592 (9.3) Pneumonia 3011 (1.6) 2740 (2.4) Death 1899 (0.97) 11,835 (10.4) Recent vaccination (with 66,611 vaccinated participants) ≥1 outcome 11,000 (5.7) 15,129 (22.7) Hospitalization 7848 (4.0) 7009 (10.5) Pneumonia 3011 (1.6) 1731 (2.6) Death 1899 (0.97) 8800 (13.2) Recurrent vaccination (with 75,311 vaccinated participants) ≥1 outcome 11,000 (5.7) 17,798 (23.6) Hospitalization 7848 (4.0) 8122 (10.8) Pneumonia 3011 (1.6) 1942 (2.6) Death 1899 (0.97) 10,561 (14.0) a A logistic regression model adjusted with the following relevant covariables was fitted: smoking habits, age, comor‐ bidities (asthma, autoimmune disorders, prior cerebrovascular disease, chronic kidney disease, chronic pulmonary obstructive disease, diabetes, heart failure, hypertension, ischemic heart disease, mental-behavioral disorders, obesity, organ transplant, and other respiratory diseases), co-medication, and previous vaccines (pneumococcal and tuberculosis). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 16/17 15/9/23 10:56 Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Prim… Figure 1 Risk of death and of combined COVID-19 complications in all the vaccinated population and excluding people living in long-term care facilities (LTCF). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662290/?report=printable 17/17