The effect of vaccination on post-COVID-19 major acute cardiac events and mortality: a target trial emulation

The effect of vaccination on post-COVID-19 major acute cardiac events and mortality: a target trial emulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The effect of vaccination on post-COVID-19 major acute cardiac events and mortality: a target trial emulation Tatjana Meister, Ülo Maiväli, Kaur Tenson, Anna Tisler, Ruth Kalda, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6319577/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The COVID-19 pandemic presents significant health challenges, including increased risk of mortality and long-term complications. While vaccination has proven remarkably effective in mitigating severe disease and mortality associated with acute COVID-19 infection, the long-term implications of vaccination, particularly its influence on post-COVID cardiovascular events and the temporal dynamics of such effects, remain poorly understood. This target trial emulation study utilizes real-world electronic medical record data from April 2021 to March 2023 to address this gap. We evaluate the effect of pre-infection COVID-19 vaccination on the risk of major acute cardiovascular events (MACE) and all-cause mortality in individuals aged 40–85 years during one year after SARS-CoV-2 infection. Among individuals with COVID-19 (n = 18,223 vaccinated, n = 15,331 not vaccinated), vaccination provided a significant protective effect against MACE (weighted incidence rate ratio [wIRR] 0.71, 95% CI 0.58–0.84) and all-cause mortality (wIRR 0.32, 95% CI 0.28–0.36). This effect persisted for approximately three months post-acute infection. These findings underscore the importance of COVID-19 vaccination in reducing both short-term and long-term health risks associated with the infection. Health sciences/Medical research/Epidemiology Health sciences/Diseases/Infectious diseases/Viral infection COVID-19 vaccination death mortality long COVID PACS post-acute covid syndrome MACE cardiac events target trial emulation DAG. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The world has now passed the peak of the COVID-19 pandemic. Yet, post-acute sequelae of SARS-CoV-2 infection are still a major concern. 1 There is a link between SARS-CoV-2 infection and an increased risk of long-term mortality and cardiovascular disease in those who survived the infection. 2 Observational studies have revealed an increased number of major acute cardiovascular events (MACE), both in the acute phase of COVID-19 and in the post-acute period. 3 4 2 The increased risk of MACE is not exclusive to severe COVID-19, as evidenced by increased MACE incidence in individuals with mild or moderate SARS-CoV-2 infections. 4 The benefits of vaccination might not be limited to the direct prophylaxis against targeted pathogens or mitigation of disease severity. There is a wide range of indirect effects, including herd immunity, reduced use of antimicrobials (limiting the development and spread of antibiotic resistance), and a reduced risk of certain diseases (incl. cancers). 10 Following the rollout of large-scale COVID-19 vaccination programs, several observational studies have investigated the impact of vaccination on post-acute sequelae of SARS-CoV-2 infection, including symptom burden and long-term mortality among COVID-19 survivors. 5 6 7 8 9 Few studies have specifically focused on the impact of COVID-19 vaccination on the long-haul burden of acute cardiovascular events in COVID-19 survivals as major contributors to mortality, and there is sparse evidence of time-dependence of these outcomes. 11 12 13 14 Furthermore, it remains unclear whether the benefit of vaccination varies across different subgroups, whether this benefit is mediated solely by reduced COVID-19 severity in vaccine recipients, or whether there is a possibility of some other mechanisms shaping the vaccine’s ability to attenuate long-term negative health consequences (i.e. less immune dysregulation in breakthrough infections and faster viral clearance). 15 16 This study investigates the time-varying impact of pre-infection COVID-19 vaccination on the incidence of acute cardiovascular events and all-cause mortality in individuals aged 40 to 85 years within 365 days following COVID-19 infection. RESULTS To achieve our study goals, we emulated a target trial using real-world electronic medical record data from April 2021 to March 2023 (Table S1 ; supplementary Discussion). To obtain causal effect estimates, we employed DAG-based inverse probability of treatment weighting and a marginal structural Poisson survival modelling with flexible time functions to adjust for confounding and capture time-varying effects on outcomes. Table 1 presents the baseline characteristics of study participants. A total of 15,331 unvaccinated and 18,223 vaccinated individuals, aged 40–85 years, who tested positive for SARS-CoV-2 between April 2021 and May 2022, were included in our TTE analysis. There was a substantial imbalance (SMD > 0.1) between the fully vaccinated and the non-vaccinated groups for language, education and county of residence (Table 1 ). After applying IPTW weights, all covariates were well-balanced (SMD < 0.1) between the vaccinated and the unvaccinated study groups (Supplementary Fig. 3a,b). In total, 33,554 individuals aged 40–85 years who got infected with SARs-CoV-2 were included in our cohort. Of them, 18 223 were fully vaccinated and 15 331 unvaccinated prior to their first positive SARS-CoV-2 test. A substantially higher proportion of unvaccinated individuals (2.8%) developed severe COVID-19 compared to vaccinated individuals (0.5%; p < 0.001). Table 1 Baseline characteristics of the study population by COVID-19 vaccination status. Not vaccinated (n,%)* (N = 15331) Vaccinated (n,%)* (N = 18223 ) Standardized mean difference (SMD) Sex Male 6472 (42.2) 7684 (42.2) 0.001 Female 8859 (57.8) 10539 (57.8) -0.001 Age, mean (sd) 54 (11.0) 55 (11.3) -0.080 Age groups (years) 40–70 13915 (90.8) 16149 (88.6) -0.071 71–85 1416 (9.2) 2074 (11.4) 0.071 Education primary 2105 (13.7) 1717 (9.4) 0.135 secondary 9744 (63.6) 9346 (51.3) 0.249 higher 3482 (22.7) 7160 (39.3) -0.361 Comorbidities Diabetes 1067 (7) 1461 (8) -0.040 Lung disease 1513 (9.9) 1824 (10) -0.005 Heart disease 5504 (35.9) 7206 (39.5) -0.075 *Data are presented as the number and percentage of individuals unless stated otherwise. During the follow-up period, 321 incident MACE cases were observed: 170 cases among vaccinated individuals and 151 among those unvaccinated. Among vaccinated individuals, the crude incidence rate (cIR) was 0.95 (95% CI 0.82–1.10) per 100 person-years. This varied by sex, with a cIR of 1.37 (95% CI 1.13–1.66) in males and 0.64 (95% CI 0.51–0.82) in females per 100 person-years. In unvaccinated, the cIR for MACE was 1.01 (95% CI 0.86–1.19), with 1.21 (95% CI 0.97–1.52) in males and 0.87 (95% CI 0.69–1.09) in females per 100 person-years. Forty-one percent of the MACE cases were diagnosed during the first 90 days after baseline (T 0 ), with the median time to MACE being 123 days. A sex-specific effect was observed in MACE incidence. In females, vaccination was associated with a lower cumulative incidence of MACE in both age groups (≤ 70, > 70). Conversely, in males, the vaccinated group aged > 70 years exhibited a higher cumulative MACE incidence compared to their unvaccinated counterparts (Fig. 1 b). In contrast, in males, the ≤ 70-year-old vaccinated group had a lowered cumulative MACE rate. During the follow-up, there were 640 all-cause deaths: 253 among vaccinated individuals (cIR 1.41, 95% CI 1.25–1.59 per 100 person-years) and 387 among unvaccinated individuals (cIR 2.59, 95% CI 2.34–2.85). This pattern was consistent across both sexes. Vaccinated males had a cIR of 2.16 (95% CI 1.85–2.51), while unvaccinated males had a cIR of 3.13 (95% CI 2.72–3.58). Similarly, vaccinated females had a cIR of 0.87 (95% CI 0.71–1.07) compared to 2.29 (95% CI 1.91–2.53) in unvaccinated females. Most deaths (60%) were diagnosed within 90 days from T 0 , with a median of 53 days until death. The all-cause death rates were higher for the unvaccinated of both sexes and all age groups (Supplementary, Fig. 1 a). Causal Inference of Vaccination on Post-COVID-19 MACE Vaccination conferred overall protection against MACE (weighted incidence rate ratio [wIRR] 0.71, 95% CI 0.58–0.84; Fig. 2 A, Table S5). This protective effect lasted approximately three months after the primary vaccination. In subgroup analysis, we saw a clear positive effect of vaccination against MACE in females (Fig. 1 c, e; for ≤ 70-year-olds females wIRR 0.46 (95%CI 0.27–0.64), and for > 70 females wIRR 0.60 (95%CI 0.39–0.82). A similar effect was observed for ≤ 70-year-old males (Fig. 1 b; wIRR 0.70 (95%CI 0.50–0.91). Unlike females and younger males, men older than 70 showed no protective benefit from vaccination, and indeed had a higher incidence of MACE (Fig. 1 d; wIRR 1.66, 95% CI 0.95–2.37), although this is not statistically significant. The weighted incidence rates of MACE were about three-fold higher amongst the unvaccinated females and the younger unvaccinated males immediately after a positive SARS-CoV-2 test, then decreased during the first three months to converge with that of the vaccinated cohort. In contrast, for older males, while at T 0 we see no significant difference in the MACE rates, the MACE rates for the vaccinated then quickly increase to reach a peak of about a three-fold difference from the unvaccinated at about two months post-infection, followed by a quick decrease and a subsequent convergence at about four months post-T 0 (Fig. 1 d). Amongst individuals with non-severe COVID-19, the pattern of effect of vaccination against MACE was similar to the whole cohort (wIRR 0.73, 95%CI 0.59–0.87), with definite effect observed for females (wIRR 0.55, 95%CI 0.40–0.70), but not for males (wIRR 1.09, 95%CI 0.84–1.35) (Supplementary, Fig. 4, Table 4). The limited number of MACE cases (n = 8) within the severe COVID-19 group precluded a robust analysis of MACE risk in this group. Causal Inference of Vaccination on Post-COVID-19 all-cause mortality Our findings indicate that vaccination reduces the risk of all-cause death among vaccinated individuals (wIRR of 0.32, 95% CI 0.28–0.36) (Fig. 2 a). Among the non-vaccinated, the initially increased death rates decreased exponentially, reaching the level of the vaccinated individuals at about 90 to 100 days in both females and males (Fig. 2 b.-e.). Although the overall death rates were more than twofold higher in unvaccinated males over 70 years old compared to unvaccinated females of the same age group, vaccination conferred a 5- to 10-fold reduction in the initial death rate. The protective effect of vaccination against mortality remained consistent across sexes and age groups. Females exhibited a wIRR of 0.36 (95% CI 0.30–0.43), with a more pronounced effect in those ≤ 70 years (wIRR 0.18, 95%CI 0.09–0.27)) compared to those over 70 (wIRR 0.33, 95% CI 0.33 (95% CI 0.25–0.42). Similarly, males demonstrated a wIRR of 0.44 (95% CI 0.38–0.50), with a slightly stronger effect in those over 70 years (wIRR 0.31, (95% CI 0.24–0.39) compared to those ≤ 70 (wIRR 0.48, 95% CI 0.36–0.60). Among individuals with non-severe COVID-19, the pattern of the effect of vaccination on all-cause mortality was similar to that observed in the entire cohort, with wIRR 0.35 (95% CI 0.27–0.43) for females and wIRR 0.45 (95%CI 0.37–0.53) for males (Supplementary file, Fig. 4, Table 4). The limited number of deaths in vaccinated individuals with severe C19 (n = 86) did not allow us to do a meaningful analysis comparable to the full study population. DISCUSSION This target trial emulation analysis investigated the impact of COVID-19 vaccination on post-COVID-19 infection sequelae, specifically focusing on MACE and all-cause mortality. Our findings demonstrate a 30% reduction in the risk of MACE and a 70% reduction in mortality during the year after infection with SARS-CoV-2 amongst individuals vaccinated against COVID-19 in comparison to those unvaccinated. The protective effect of vaccination is most pronounced during the three months post-acute infection, attenuating over time and becoming indistinguishable from those not vaccinated after about three months of follow-up. Our study advanced the current understanding of age- and sex-specific differences in MACE rates following COVID-19 infection. Vaccination provided a protective effect against MACE in females aged 40 to 85 and men younger than 70. However, this protective effect was not evident in males over 70, who exhibited an elevated incidence of MACE, with a peak observed approximately 60 days post-infection. The increased risk in males over 70 may be speculatively linked to pre-existing and undiagnosed cardiovascular disease or undetected (in our study) risk factors (i.e. subclinical or „silent” atherosclerosis) in older males. 18 19 20 Additionally, residual pathophysiological processes, potentially mediated by the downregulation of angiotensin-converting enzyme 2 (ACE2) receptors and endothelial impairment in severely ill COVID-19 survivors, may play a role. 21 Thus, while vaccination may prevent imminent COVID-related death, it could also unmask a later wave of cardiovascular events (incl. MACE) in survivors, particularly in older males with increased frailty and concomitant risk profile. Despite the established effectiveness of vaccines in reducing acute COVID-19 disease burden, the precise degree to which they attenuate the development of post-acute sequelae following SARS-CoV-2 infection is subject to ongoing investigation. A small number of studies that compared vaccinated and unvaccinated individuals with COVID-19, have also demonstrated a reduction in the risk of MACE and mortality among vaccinated individuals. 17 14 13 12 A nationwide Korean study using health databases found that COVID-19 vaccination reduced the risk of myocardial infarction and ischemic stroke in vaccinated individuals during the four-month follow-up period after COVID-19, except in those with severe COVID-19. 17 US national cohort-based research showed protection against MACE, especially in males, older adults, and those with prior cardiovascular events, lasting 180 days. 14 A UK cohort study observed a reduced risk of cardiovascular thrombotic events, strongest in the first 1–4 weeks post-vaccination, diminishing but persisting for up to 28 weeks. 13 The effect of COVID-19 vaccination on the development of post-acute sequelae and all-cause mortality remains poorly understood, particularly in individuals with differing severities of acute COVID-19. Our study demonstrated a clear difference in these adverse outcomes between vaccinated and unvaccinated individuals with non-severe COVID-19, suggesting that the effect of COVID-19 vaccines on adverse outcomes is not solely mediated by reducing disease severity. A few observational studies have shown reduced rates of all-cause mortality among vaccinated COVID-19 survivors beyond the post-acute phase (30 days of initial infection). 8 232627 This suggest that vaccination may confer a survival advantage extending beyond the initial protection against severe COVID-19. Vaccination has been shown to mitigate the risk of post-acute sequelae of SARS-CoV-2 infection. 28 Whether or not vaccines may play a role in mitigating persistent COVID-19 related health consequences (known as „long-covid“), which can otherwise lead to fatal outcomes, 29 30 31 warrants further examinations. The mechanisms through which COVID-19 vaccines affect post-COVID-19 adverse outcomes remain unclear. Aljadah et al . highlights a significant knowledge gap concerning the impact of vaccination on SARS-CoV-2's ability to impair endothelial function during and after infection. 21 COVID-19 vaccination may provide protection through various mechanisms, extending beyond just reducing the severity of the disease. These include reducing viral load and direct cell damage caused by virus entry and potentially counteracting the release of inflammatory mediators and endothelium damage (including the cardiovascular system) caused by an excessive and prolonged inflammatory response in the COVID-19 post-acute period. 32 33 There is also evidence that vaccination accelerates viral clearance and interfere immunological response in infected individuals, counteracting the virus persistence and long-COVID symptoms in infected individuals. 6 34 35 These multifaceted protective mechanisms are consistent with the observed patterns in our study, where vaccination conferred a significant but time-limited reduction in adverse cardiovascular outcomes and in all-cause mortality. The observation that vaccination may reduce cardiovascular mortality extends beyond the context of COVID-19. A recent systematic review demonstrated that influenza vaccination is associated with a decreased risk of major adverse cardiovascular events, particularly myocardial infarction, and cardiovascular death. 36 This suggests a broader protective effect of vaccination on cardiovascular health, potentially through mechanisms such as reducing inflammation and improving immune response, and support the notion that vaccination can provide benefits beyond protection against the targeted disease. This study has some strengths. The major strengths of our study lie in our population-based cohort and analysis of more than 30 000 individuals, and our use of a formal framework for causal inference. The real-world health data capture data from diverse patient populations, increasing the generalizability of findings, and actual clinical practice, increasing the external validity of our findings. The robust methodology, employing target trial emulation and a DAG-based study design, facilitates approximation to causal inference. Previous studies have generally employed conventional Cox regression models with time as an independent variable. 14 8 17 13 However, the Cox regression with its ubiquitous proportional hazards assumption, is plainly insufficient for capturing the observed non-linear time-dependent effects. 37 Our survival modelling strategy avoids reliance on the proportional hazard assumptions and linearity of age and follow-up time effects. Using penalized splines in interaction with the treatment (vaccination status), we identified a time-dependent, non-linear association between vaccination status and adverse outcomes (MACE, all-cause mortality). The observed dynamic and non-linear association likely reflects the true time window of the benefit of COVID-19 vaccination. Our study also has some limitations, which have been carefully considered and mitigated throughout our analysis, where possible. Observational studies of vaccination outcomes are inherently susceptible to healthy vaccinee bias. To mitigate this, we employed inverse probability of treatment weighting (IPTW) to adjust for confounding. There is a possibility of unobserved confounding as in any nonrandomized evaluation. Secondary health data sources lack certain types of information, such as social determinants of health (e.g., poverty, health- and risk behavior, tobacco use, treatment compliance) and specific parameters of cardiometabolic profile (i.e. arterial stiffness, c-reactive protein, lipid profile), which are known to influence health outcomes. Hence, we need to interpret our findings with caution because we cannot exclude the possibility of inaccurately addressing causal relationships due to bias arising from unmeasured confounding factors, particularly in older males. 38 Our analysis did not differentiate between specific vaccine types. However, it is possible that different COVID-19 vaccines may have varying efficacy in preventing cardiovascular events. This could be attributed to potential differences in their effects on endothelial function and their potential to induce cardiovascular adverse effects. 39 40 This comprehensive analysis of a large population-based cohort revealed that COVID-19 vaccination significantly reduced the risk of incident cardiovascular events and all-cause mortality following SARS-CoV-2 infection, particularly within the first three months after the acute infection. However, this protective effect was not observed in males over 70, who experienced an elevated incidence of MACE, highlighting the need for further research into age- and sex-specific responses to vaccination. Our findings underscore the complex interplay between COVID-19 vaccination, post-acute sequelae, and long-term health outcomes, emphasizing the importance of vaccination in mitigating the adverse consequences of SARS-CoV-2 infection.2 METHODS Design To compare the risks of death and incident MACE, such as myocardial infarction or stroke, within 365 days following COVID-19 between individuals fully vaccinated against SARS-CoV-2 and unvaccinated individuals, we specified and emulated a target trial (Supplementary, Table 1 ). 41 Setting and Data Sources We used observational data from the nationwide electronic health databases in Estonia. The study accrual period spanned from April 2021 to March 2023. In Estonia, COVID-19 vaccination started in January 2021 with a cumulative vaccination uptake of primary vaccination series about 70% among adult population by June 2022. The accrual of COVID-19 cases for the study period coincides with the epidemics of COVID-19 variants Alpha, Delta, and Omicron. 42 The Estonian Health Insurance Fund (EHIF) At the end of 2021, universal public health insurance covered 95.2% of Estonia's population of 1.3 million. 43 The EHIF maintains a complete record of the health care services provided. Diagnoses are defined according to the International Classification of Diseases, tenth revision (ICD-10). The EHIF database records sex, date of birth, and health care utilization information (incl. dates of service, diagnoses, treatment type: in- or outpatient). Estonian national health information system (ENHIS) Data on COVID-19 vaccination (dates, the type of vaccine), SARS-CoV-2 testing (dates) and laboratory confirmed (real-time polymerase chain reaction (PCR) or antigen testing) cases of SARS-CoV-2 infections (dates) were retrieved from ENHIS 44 , 45 According to law, all health-care providers and laboratories in Estonia are obligated to report health claims data to ENHIS. Thus, the expected coverage is 100%. The Population Register Estonian citizens and foreign nationals living in Estonia are listed in the Population Register held by the Ministry of the Interior based on their residence permit. 46 This register provided data on study subjects’ education, native language, and ethnicity. Estonian Causes of Death Registry We used data obtained from The Estonian Causes of Death Registry to identify the dates of deaths. The Estonian Causes of Death Registry gathers information on all deaths that are recorded on Estonian territory and in foreign missions. 47 The unique personal identification code assigned to all Estonian residents (at birth or immigration) was used to link data between health databases. Study population A study cohort was drawn from a random sample of 389,288 individuals covered by the EHIF. The study's eligibility criteria included individuals aged 40 to 85 years, having a first positive SARS-CoV-2 test between April 1, 2021, and May 31, 2022 (baseline, T 0 ), and having no COVID-19 vaccination prior to index date. Individuals with diagnoses of acute cardiovascular events (see Supplementary, Table 2 for clinical diagnoses included in MACE codes, as classified by International Classification of Disease, 10th Revision (ICD-10)) during the five years preceding the index date, or a positive SARS-CoV-2 test 180 days after COVID-19 vaccination, were also excluded. Only the first positive SARS-CoV-2 test was considered, regardless of possible reinfections (Fig. 3 ). Exposure We considered COVID-19 vaccination status as treatment (exposure) in our study. The vaccinated cohort consisted of individuals who completed primary COVID-19 vaccine series (two doses of BNT162b2 (Pfizer/BioNTech), mRNA-1273 (Moderna), AZD1222 (Oxford/AstraZeneca) or one dose of Ad26.COV2 (Janssen/Johnson & Johnson) 14–180 days before SARS-CoV-2 infection). Supplementary vaccine doses administered after the primary series were not considered in this analysis. The unvaccinated cohort included individuals who were not unvaccinated prior to their first SARS-CoV-2 infection. Outcomes Primary outcome: incident MACE MACE (major acute cardiovascular events) is a composite of clinical events previously described in various clinical trials aimed at evaluating the effectiveness and safety of cardiovascular interventions. 48 , 49 We used a two-point MACE outcome, defined as a composite of acute myocardial infarction (AMI) and stroke occurring 0-365 days after the 1st positive SARS-CoV-2 test, as the primary outcome. Our focus on two-point MACE in COVID-19 sequelae research is driven by their significant impact on individuals and health systems, potentially causing long-term disability, reduced quality of life, and premature death. We defined MACE components based on the ICD-10 codes in EHIF data (MACE diagnosis codes are presented in Supplementary, Table 2). Secondary outcome: All-cause mortality We defined all-cause mortality as any death occurring 0-365 days after the 1st positive SARS-CoV-2 test. Follow-up period Follow-up began at baseline (T 0 or index date; the date of the 1st positive SARS-CoV-2 test) and continued until the occurrence of outcome (MACE, all-cause death), a competing event (all-cause death for analysis of MACE only), or 365 days post-baseline, whichever occurred first. Thus, we incorporate all symptomatic and asymptomatic COVID-19 cases, as well as outcomes resulting in the acute and post-acute phases of COVID-19. Covariates Sociodemographic characteristics The analysis included the age (in years), sex (male, female), and education level of the study participants. Study participants' education levels were categorized into three groups: primary (basic education or below), secondary (general secondary or vocational education), and higher education (higher or tertiary education). 50 Pre-COVID-19 comorbidities We obtained data about diabetes (type 1 and 2), chronic pulmonary diseases, and cardiovascular diseases, recognizing their potential to increase the risk of contracting COVID-19 and experiencing severe disease course, as well as the potential of these diseases to increase the risk of MACE and mortality both during and after the acute phase of infection. Comorbidities were identified using ICD-10 codes (see detailed description in Supplementary, Table 3), as any primary or secondary diagnosis code in the claim or diagnosis of any type (hospital or outpatient) health care claims during the five years preceding the index date. Severity of COVID-19 Severe COVID-19 was defined as hospitalization occurring within 3 days before to 14 days after a positive SARS-CoV-2 test. We set additional criteria for COVID-19-related hospitalization to avoid misclassification of COVID-19 cases identified through routine screening during hospitalization for other reasons (e.g. childbirth). The diagnoses associated with hospitalization had to include an ICD-10-based COVID-19 diagnosis (U07.1, U07.2) and at least one additional diagnosis indicating a COVID-19-related condition, such as acute upper or lower respiratory tract infection or acute respiratory failure (Supplementary, Table 4). 51 Causal inference strategies This study was designed in the target trial emulation framework, as described in the supplementary material (Supplementary, Table 1 , and Supplementary discussion). 41 52 Briefly, we developed a protocol for a hypothetical target trial where the treatment is vaccination against COVID-19 or no vaccination; the outcomes are MACE and all-cause death. Our target trial investigates the average causal effect of COVID-19 vaccination on one-year risk of MACE and all-cause mortality in people who have had a SARS-CoV-2 infection. Statistical Analysis Participant characteristics are presented as means with standard deviation (SD) for continuous variables and frequencies and percentages for categorical variables. We estimated the crude cumulative incidence (cIR) of death to assess the influence of age group and sex on outcomes following COVID-19 diagnosis, stratified by vaccination status. A competing risks model was employed to evaluate the cumulative incidence of MACE over 365 days 53 54 (Supplementary, Fig. 1 ). As individuals were not randomly allocated to the different exposure groups, causal inferences from exposure to outcomes can be biased. Inverse probability of treatment weighting (IPTW) was used to adjust for confounding. 55 IPTW is a propensity score-based method, with the propensity scores reflecting their probability of belonging to the fully vaccinated group, which were calculated from logistic regression models that included the minimally sufficient set of covariates: calendar time, sex, age, education (primary, secondary, higher), native language (Estonian, Russian/Ukrainian, other), previous diabetes, cardiovascular disease and pulmonary disease (Supplementary, Fig. 2 for the DAG and Table 3 for the definitions of comorbidities). These covariates were derived from a directed acyclic graph (DAG) constructed in daggity.net. 56 IPTW creates a pseudo-population in which the exposure is independent of measured confounders. 57 We used the IP weights in a marginal structural Poisson survival model where the time from T 0 and the vaccination status were the sole predictors, which were given interaction in the spline model (see code in Supplement 2 for details). Weighted standardized mean differences (SMD) were calculated with the tidysmd package v. 0.2.0 and used to assess the balance of covariates between groups, with SMD ≤ 0.1 regarded as a sufficient balance. 58 59 The occurrence of main outcomes was assessed using incidence rates for 100 person-years (IR) and the treatment effects were evaluated using incidence rate ratios averaged over the entire one-year follow-up period (IRR). We employed a marginal structural model with IPT weights to estimate adjusted incidence rates (wIRs) per 100 person-years and incidence rate ratios (wIRRs, averaged over the one-year follow-up period) (Table S5). The model utilized Poisson regression with an unequally split timescale (0, 10, 30, 50, 75, 100, 150, 200, 300, 365 days) and modelled the follow-up time with penalized splines from the R::mgcv package, incorporating an interaction with vaccination status. The adjusted models were used to estimate IRs and IRRs with their corresponding 95% confidence intervals, which were calculated using the delta method by the marginal effects package. 60 The results were stratified by sex and age group (< 70 and ≥ 70 years). We then analyzed the results for individuals with severe and non-severe COVID-19 separately to control for the effect of disease severity on the main outcomes. Missing data (education for 1003 individuals) were addressed through single imputation using the R::mice package. 61 All statistical analyses were done in R v.4.3.2. 62 The R code for reproducing the figures, including the modelling code, as well as datasets (slightly modified for privacy reasons) that can be used to run the code, can be found at https://datadoi.ee/handle/33/667 (DOI: https://doi.org/10.23673/re-505 ). All research was carried out in accordance with relevant guidelines and regulations. The Research Ethics Committee of the University of Tartu approved the study by the 15th of March 2021 (No. 337/M-27). The need for informed consent was waived by the Ethics committee of the University of Tartu due to the retrospective nature of the study. Data sharing According to legislative regulation and data protection law in Estonia, the authors cannot publicly release the raw data received from the health data registers in Estonia. However, data are available from the corresponding author ( [email protected] ) upon reasonable request and with permission of EHIF, ENHIS, The Causes of Death Registry, and Population Registry. Declarations Author`s contributions AU and ÜM had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. Study concept and design - AU, ÜM, and TM. Acquisition, analysis, or interpretation of the data - AU, ÜM, TM, KS, AT, and KT. Drafting the manuscript - TM and AU. Critical revision of the manuscript - AU, ÜM, TM, KS, RK, AT, RK, and KT. Statistical analysis - ÜM, KT, TM. Model development and visualization – ÜM. Obtaining funding – AU. Reading and agreeing to the final version of the manuscript AU, ÜM, TM, KS, RK, AT, and KT. All authors meet the International Committee of Medical Journal Editors criteria for authorship and have confirmed their contributions. Conflict of interest The authors declared no conflicts of interest. Funding The research was carried out with the support of the Estonian Research Council grant PRG2218. Ethics committee approval The study was approved by the Research Ethics Committee of the University of Tartu (protocol number 323/T-28, 21.09.2020). References Woodrow, M. et al. Systematic Review of the Prevalence of Long COVID. Open. Forum Infect. Dis. 10 , ofad233 (2023). Reyes, L. F. et al. Major adverse cardiovascular events (MACE) in patients with severe COVID-19 registered in the ISARIC WHO clinical characterization protocol: A prospective, multinational, observational study. J. Crit. Care . 77 , 154318 (2023). Golchin Vafa, R. et al. The long-term effects of the Covid-19 infection on cardiac symptoms. BMC Cardiovasc. Disord . 23 , 286 (2023). Xie, Y., Xu, E. & Bowe, B. Al-Aly, Z. Long-term cardiovascular outcomes of COVID-19. Nat. Med. 28 , 583–590 (2022). Ceban, F. et al. 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Xu, S. et al. A safety study evaluating non-COVID-19 mortality risk following COVID-19 vaccination. Vaccine 41 , 844–854 (2023). Chan, Y. J. et al. The Effectiveness of COVID-19 Vaccination on Post-Acute Sequelae of SARS-CoV-2 Infection Among Geriatric Patients. J. Med. Virol. 96 , e70119 (2024). More & Than 3,500 Americans Have Died from Long COVID-Related Illness in the First 30 Months of the Pandemic. (2022). https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2022/20221214.htm Spartalis, M., Zweiker, D., Spartalis, E., Iliopoulos, D. C. & Siasos, G. Long COVID-19 Syndrome and Sudden Cardiac Death: The Phantom Menace. Curr. Med. Chem. 31 , 2–6 . DeVries, A., Shambhu, S., Sloop, S. & Overhage, J. M. One-Year Adverse Outcomes Among US Adults With Post–COVID-19 Condition vs Those Without COVID-19 in a Large Commercial Insurance Database. JAMA Health Forum . 4 , e230010 (2023). Zhu, X. et al. Dynamics of inflammatory responses after SARS-CoV-2 infection by vaccination status in the USA: a prospective cohort study. Lancet Microbe . 4 , e692–e703 (2023). Lei, Y. et al. SARS-CoV-2 Spike Protein Impairs Endothelial Function via Downregulation of ACE 2. Circ. Res. 128 , 1323–1326 (2021). Li, H. et al. Vaccination reduces viral load and accelerates viral clearance in SARS-CoV-2 Delta variant-infected patients. Ann. Med. 55 , 419 (2023). Fischer, C. et al. SARS-CoV-2 vaccination may mitigate dysregulation of IL-1/IL-18 and gastrointestinal symptoms of the post-COVID-19 condition. npj Vaccines . 9 , 1–11 (2024). Omidi, F., Zangiabadian, M., Shahidi Bonjar, A. H., Nasiri, M. J. & Sarmastzadeh, T. Influenza vaccination and major cardiovascular risk: a systematic review and meta-analysis of clinical trials studies. Sci. Rep. 13 , 20235 (2023). Wynant, W. & Abrahamowicz, M. Impact of the model-building strategy on inference about nonlinear and time-dependent covariate effects in survival analysis. Stat. Med. 33 , 3318–3337 (2014). Bonovas, S. & Piovani, D. Simpson’s Paradox in Clinical Research: A Cautionary Tale. J. Clin. Med. 12 , 1633 (2023). Yamaji, T. et al. Effects of BNT162b2 mRNA Covid-19 vaccine on vascular function. PLoS One . 19 , e0302512 (2024). Buoninfante, A., Andeweg, A., Genov, G. & Cavaleri, M. Myocarditis associated with COVID-19 vaccination. npj Vaccines . 9 , 1–8 (2024). Target Trial Emulation to Improve Causal Inference from Observational Data. What, Why, and How? J. Am. Soc. Nephrol. 34 , 1305 (2023). Päll, T. et al. SARS-CoV-2 clade dynamics and their associations with hospitalisations during the first two years of the COVID-19 pandemic. PLoS One . 19 , e0303176 (2024). Estonian Health Board. (2022). https://www.tervisekassa.ee/kindlustatud-isikute-andmed About. TEHIK https://www.tehik.ee/en/about About. TEHIK https://www.tehik.ee/en/about Population Register | Siseministeerium. https://www.siseministeerium.ee/en/activities/population-procedures/population-register Causes of Death Register | Tervise Arengu Instituut. https://www.tai.ee/en/statistika-ja-registrid/causes-death-register Kip, K. E., Hollabaugh, K., Marroquin, O. C. & Williams, D. O. The Problem With Composite End Points in Cardiovascular Studies: The Story of Major Adverse Cardiac Events and Percutaneous Coronary Intervention. J. Am. Coll. Cardiol. 51 , 701–707 (2008). Bosco, E., Hsueh, L., McConeghy, K. W., Gravenstein, S. & Saade, E. Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review. BMC Med. Res. Methodol. 21 , 241 (2021). International Standard Classification of Education (ISCED). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=International_Standard_Classification_of_Education_(ISCED ). Kluberg, S. A. et al. Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data. Pharmacoepidemiol Drug Saf. 31 , 476–480 (2022). Scola, G. et al. Implementation of the trial emulation approach in medical research: a scoping review. BMC Med. Res. Methodol. 23 , 186 (2023). Austin, P. C. & Fine, J. P. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat. Med. 36 , 4391–4400 (2017). Competing Risks Estimation. https://mskcc-epi-bio.github.io/tidycmprsk/ Austin, P. C. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivar. Behav. Res. 46 , 399–424 (2011). Byeon, S. & Lee, W. Directed acyclic graphs for clinical research: a tutorial. J. Minim. Invasive Surg. 26 , 97–107 (2023). Austin, P. C. The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Stat. Med. 29 , 2137–2148 (2010). Austin, P. C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28 , 3083–3107 (2009). Schneeweiss, S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 15 , 291–303 (2006). Marginal Effects Zoo. https://marginaleffects.com/. van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. J. Stat. Softw. 45 , 1–67 (2011). R: The R Project for Statistical Computing. https://www.r-project.org/ Additional Declarations No competing interests reported. Supplementary Files Supplementaryfinal.docx Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 30 May, 2025 Reviews received at journal 22 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 08 May, 2025 Editor invited by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6319577","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":457299280,"identity":"e7a4140b-6f6b-41b3-8a5c-53b8494f0404","order_by":0,"name":"Tatjana Meister","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJACAwjF2AAi5Qgq52FgRtJygIHBmIGNCC0IANSS2EBIiz37+QMFP3fYMei2Nzd//lBxL33D/QbGBx/w2cKTzGDYeyaZwezMwTaJA2eKczccY2A2nIHXYckMBrxtzAxmNxLbGA62JYC0sEnz4NPC/5jB8G9bPYPZ/YfNHw7+S0g3OMbA/vsPPi0SyQzGvG2HgbYwNkgcbEhIAGphY8ajg4HnxmMDY9m24zxmZxLbJM4cSzCceSyxWbIHjxb2/sRnhm/bquXMjh9//KGiJkGe7/Dhgx9+4LOGgYENFJXI/oUkA3yA+QEhFaNgFIyCUTDCAQC3ok6D37i2SgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tartu","correspondingAuthor":true,"prefix":"","firstName":"Tatjana","middleName":"","lastName":"Meister","suffix":""},{"id":457299281,"identity":"55502abe-1bfa-4948-966c-d8d711191983","order_by":1,"name":"Ülo Maiväli","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Ülo","middleName":"","lastName":"Maiväli","suffix":""},{"id":457299282,"identity":"7fde7fe8-3d54-4f0e-97be-868745cc7c06","order_by":2,"name":"Kaur Tenson","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Kaur","middleName":"","lastName":"Tenson","suffix":""},{"id":457299283,"identity":"a4a12195-9a47-406a-948b-1d07dbc86970","order_by":3,"name":"Anna Tisler","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Tisler","suffix":""},{"id":457299284,"identity":"9d11a247-b3f6-4a9d-89ee-95403ecca6aa","order_by":4,"name":"Ruth Kalda","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Kalda","suffix":""},{"id":457299286,"identity":"0e80466d-4d15-46b4-936e-629279069fa8","order_by":5,"name":"Kadri Suija","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Kadri","middleName":"","lastName":"Suija","suffix":""},{"id":457299289,"identity":"29db7c26-3f51-4244-8f74-cca9b2cdcf0c","order_by":6,"name":"Anneli Uusküla","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Anneli","middleName":"","lastName":"Uusküla","suffix":""}],"badges":[],"createdAt":"2025-03-27 10:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6319577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6319577/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-13043-x","type":"published","date":"2025-07-29T16:39:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82922732,"identity":"19cad673-a4fb-4a03-8041-777099ecedc9","added_by":"auto","created_at":"2025-05-16 18:15:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":349172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMACE weighted incidence rates (wIR) for the vaccinated and the unvaccinated over the study follow-up period. The vaccinated rates are denoted with green dotted lines, and the unvaccinated rates are in continuous red lines. Shaded areas represent 95% confidence intervals. A. MACE rates for the full study population. B.-E. MACE rates for populations stratified by sex and age. B. \u0026lt; 70-year-old males; C. \u0026lt; 70-year-old females; D.\u0026gt; 70males; E. \u0026gt; 70 females. The X-axis shows time from baseline (T0) in days, and the Y-axis plots the incidence rate per 100 person-years.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6319577/v1/428825f82a75bf11d3225e33.png"},{"id":82922416,"identity":"4e7e36dd-0421-4f1e-b5eb-675475aea9b1","added_by":"auto","created_at":"2025-05-16 18:07:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":332091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAll-cause death rates for the vaccinated and the unvaccinated over the study follow-up period. The vaccinated rates are denoted with green dotted lines and the unvaccinated are in continuous red lines. Shaded areas represent 95% confidence intervals. A. MACE race for the full study population. B.-E. MACE rates for populations stratified by sex and age. B. \u0026lt; 70-year-old males; C. \u0026lt; 70-year-old females; D. \u0026gt;70 males; E. 70+ females. The X-axis shows time from baseline (T0) in days, and the Y-axis plots the incidence rate per 100 person-years.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6319577/v1/1f93786cd648014f870645cd.png"},{"id":82923116,"identity":"391f8baf-66f5-486b-af93-05b34335ade0","added_by":"auto","created_at":"2025-05-16 18:23:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":474612,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart of cohort construction.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6319577/v1/ba22aa5f18bfd6483247f5e3.png"},{"id":88268607,"identity":"c12819d9-7972-470c-8fb2-d344ae8cff63","added_by":"auto","created_at":"2025-08-04 16:52:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2025488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6319577/v1/3174a96a-e893-4f1e-b3d0-bbdec1300878.pdf"},{"id":82923117,"identity":"7d8d6c93-8eb4-4110-8d2a-635f2e611ac0","added_by":"auto","created_at":"2025-05-16 18:23:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":547981,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6319577/v1/3afe21609878315222381798.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe effect of vaccination on post-COVID-19 major acute cardiac events and mortality: a target trial emulation\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe world has now passed the peak of the COVID-19 pandemic. Yet, post-acute sequelae of SARS-CoV-2 infection are still a major concern. \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e There is a link between SARS-CoV-2 infection and an increased risk of long-term mortality and cardiovascular disease in those who survived the infection. \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eObservational studies have revealed an increased number of major acute cardiovascular events (MACE), both in the acute phase of COVID-19 and in the post-acute period. \u003csup\u003e3 4 2\u003c/sup\u003e The increased risk of MACE is not exclusive to severe COVID-19, as evidenced by increased MACE incidence in individuals with mild or moderate SARS-CoV-2 infections. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe benefits of vaccination might not be limited to the direct prophylaxis against targeted pathogens or mitigation of disease severity. There is a wide range of indirect effects, including herd immunity, reduced use of antimicrobials (limiting the development and spread of antibiotic resistance), and a reduced risk of certain diseases (incl. cancers).\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Following the rollout of large-scale COVID-19 vaccination programs, several observational studies have investigated the impact of vaccination on post-acute sequelae of SARS-CoV-2 infection, including symptom burden and long-term mortality among COVID-19 survivors.\u003csup\u003e5 6 7 8 9\u003c/sup\u003e Few studies have specifically focused on the impact of COVID-19 vaccination on the long-haul burden of acute cardiovascular events in COVID-19 survivals as major contributors to mortality, and there is sparse evidence of time-dependence of these outcomes. \u003csup\u003e11 12 13 14\u003c/sup\u003e Furthermore, it remains unclear whether the benefit of vaccination varies across different subgroups, whether this benefit is mediated solely by reduced COVID-19 severity in vaccine recipients, or whether there is a possibility of some other mechanisms shaping the vaccine\u0026rsquo;s ability to attenuate long-term negative health consequences (i.e. less immune dysregulation in breakthrough infections and faster viral clearance). \u003csup\u003e15 16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study investigates the time-varying impact of pre-infection COVID-19 vaccination on the incidence of acute cardiovascular events and all-cause mortality in individuals aged 40 to 85 years within 365 days following COVID-19 infection.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTo achieve our study goals, we emulated a target trial using real-world electronic medical record data from April 2021 to March 2023 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; supplementary Discussion). To obtain causal effect estimates, we employed DAG-based inverse probability of treatment weighting and a marginal structural Poisson survival modelling with flexible time functions to adjust for confounding and capture time-varying effects on outcomes.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of study participants. A total of 15,331 unvaccinated and 18,223 vaccinated individuals, aged 40\u0026ndash;85 years, who tested positive for SARS-CoV-2 between April 2021 and May 2022, were included in our TTE analysis. There was a substantial imbalance (SMD\u0026thinsp;\u0026gt;\u0026thinsp;0.1) between the fully vaccinated and the non-vaccinated groups for language, education and county of residence (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After applying IPTW weights, all covariates were well-balanced (SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.1) between the vaccinated and the unvaccinated study groups (Supplementary Fig.\u0026nbsp;3a,b).\u003c/p\u003e \u003cp\u003eIn total, 33,554 individuals aged 40\u0026ndash;85 years who got infected with SARs-CoV-2 were included in our cohort. Of them, 18 223 were fully vaccinated and 15 331 unvaccinated prior to their first positive SARS-CoV-2 test. A substantially higher proportion of unvaccinated individuals (2.8%) developed severe COVID-19 compared to vaccinated individuals (0.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population by COVID-19 vaccination status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot vaccinated (n,%)*\u003c/p\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u003c/em\u003e\u0026thinsp;15331)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaccinated (n,%)*\u003c/p\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u003c/em\u003e\u0026thinsp;18223\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized mean difference (SMD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6472 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7684 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8859 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10539 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge groups (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13915 (90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16149 (88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e71\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1416 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2074 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2105 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1717 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9744 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9346 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3482 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7160 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1067 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1461 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1513 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1824 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5504 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7206 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Data are presented as the number and percentage of individuals unless stated otherwise.\u003c/p\u003e \u003cp\u003eDuring the follow-up period, 321 incident MACE cases were observed: 170 cases among vaccinated individuals and 151 among those unvaccinated. Among vaccinated individuals, the crude incidence rate (cIR) was 0.95 (95% CI 0.82\u0026ndash;1.10) per 100 person-years. This varied by sex, with a cIR of 1.37 (95% CI 1.13\u0026ndash;1.66) in males and 0.64 (95% CI 0.51\u0026ndash;0.82) in females per 100 person-years. In unvaccinated, the cIR for MACE was 1.01 (95% CI 0.86\u0026ndash;1.19), with 1.21 (95% CI 0.97\u0026ndash;1.52) in males and 0.87 (95% CI 0.69\u0026ndash;1.09) in females per 100 person-years.\u003c/p\u003e \u003cp\u003eForty-one percent of the MACE cases were diagnosed during the first 90 days after baseline (T\u003csub\u003e0\u003c/sub\u003e), with the median time to MACE being 123 days. A sex-specific effect was observed in MACE incidence. In females, vaccination was associated with a lower cumulative incidence of MACE in both age groups (\u0026le;\u0026thinsp;70, \u0026gt;\u0026thinsp;70). Conversely, in males, the vaccinated group aged\u0026thinsp;\u0026gt;\u0026thinsp;70 years exhibited a higher cumulative MACE incidence compared to their unvaccinated counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In contrast, in males, the \u0026le;\u0026thinsp;70-year-old vaccinated group had a lowered cumulative MACE rate.\u003c/p\u003e \u003cp\u003eDuring the follow-up, there were 640 all-cause deaths: 253 among vaccinated individuals (cIR 1.41, 95% CI 1.25\u0026ndash;1.59 per 100 person-years) and 387 among unvaccinated individuals (cIR 2.59, 95% CI 2.34\u0026ndash;2.85). This pattern was consistent across both sexes. Vaccinated males had a cIR of 2.16 (95% CI 1.85\u0026ndash;2.51), while unvaccinated males had a cIR of 3.13 (95% CI 2.72\u0026ndash;3.58). Similarly, vaccinated females had a cIR of 0.87 (95% CI 0.71\u0026ndash;1.07) compared to 2.29 (95% CI 1.91\u0026ndash;2.53) in unvaccinated females.\u003c/p\u003e \u003cp\u003eMost deaths (60%) were diagnosed within 90 days from T\u003csub\u003e0\u003c/sub\u003e, with a median of 53 days until death. The all-cause death rates were higher for the unvaccinated of both sexes and all age groups (Supplementary, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCausal Inference of Vaccination on Post-COVID-19 MACE\u003c/h2\u003e \u003cp\u003eVaccination conferred overall protection against MACE (weighted incidence rate ratio [wIRR] 0.71, 95% CI 0.58\u0026ndash;0.84; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Table S5). This protective effect lasted approximately three months after the primary vaccination. In subgroup analysis, we saw a clear positive effect of vaccination against MACE in females (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, e; for \u0026le;\u0026thinsp;70-year-olds females wIRR 0.46 (95%CI 0.27\u0026ndash;0.64), and for \u0026gt;\u0026thinsp;70 females wIRR 0.60 (95%CI 0.39\u0026ndash;0.82). A similar effect was observed for \u0026le;\u0026thinsp;70-year-old males (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; wIRR 0.70 (95%CI 0.50\u0026ndash;0.91). Unlike females and younger males, men older than 70 showed no protective benefit from vaccination, and indeed had a higher incidence of MACE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed; wIRR 1.66, 95% CI 0.95\u0026ndash;2.37), although this is not statistically significant.\u003c/p\u003e \u003cp\u003eThe weighted incidence rates of MACE were about three-fold higher amongst the unvaccinated females and the younger unvaccinated males immediately after a positive SARS-CoV-2 test, then decreased during the first three months to converge with that of the vaccinated cohort. In contrast, for older males, while at T\u003csub\u003e0\u003c/sub\u003e we see no significant difference in the MACE rates, the MACE rates for the vaccinated then quickly increase to reach a peak of about a three-fold difference from the unvaccinated at about two months post-infection, followed by a quick decrease and a subsequent convergence at about four months post-T\u003csub\u003e0\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eAmongst individuals with non-severe COVID-19, the pattern of effect of vaccination against MACE was similar to the whole cohort (wIRR 0.73, 95%CI 0.59\u0026ndash;0.87), with definite effect observed for females (wIRR 0.55, 95%CI 0.40\u0026ndash;0.70), but not for males (wIRR 1.09, 95%CI 0.84\u0026ndash;1.35) (Supplementary, Fig.\u0026nbsp;4, Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eThe limited number of MACE cases (n\u0026thinsp;=\u0026thinsp;8) within the severe COVID-19 group precluded a robust analysis of MACE risk in this group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCausal Inference of Vaccination on Post-COVID-19 all-cause mortality\u003c/h3\u003e\n\u003cp\u003eOur findings indicate that vaccination reduces the risk of all-cause death among vaccinated individuals (wIRR of 0.32, 95% CI 0.28\u0026ndash;0.36) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among the non-vaccinated, the initially increased death rates decreased exponentially, reaching the level of the vaccinated individuals at about 90 to 100 days in both females and males (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.-e.). Although the overall death rates were more than twofold higher in unvaccinated males over 70 years old compared to unvaccinated females of the same age group, vaccination conferred a 5- to 10-fold reduction in the initial death rate.\u003c/p\u003e \u003cp\u003eThe protective effect of vaccination against mortality remained consistent across sexes and age groups. Females exhibited a wIRR of 0.36 (95% CI 0.30\u0026ndash;0.43), with a more pronounced effect in those\u0026thinsp;\u0026le;\u0026thinsp;70 years (wIRR 0.18, 95%CI 0.09\u0026ndash;0.27)) compared to those over 70 (wIRR 0.33, 95% CI 0.33 (95% CI 0.25\u0026ndash;0.42). Similarly, males demonstrated a wIRR of 0.44 (95% CI 0.38\u0026ndash;0.50), with a slightly stronger effect in those over 70 years (wIRR 0.31, (95% CI 0.24\u0026ndash;0.39) compared to those\u0026thinsp;\u0026le;\u0026thinsp;70 (wIRR 0.48, 95% CI 0.36\u0026ndash;0.60).\u003c/p\u003e \u003cp\u003eAmong individuals with non-severe COVID-19, the pattern of the effect of vaccination on all-cause mortality was similar to that observed in the entire cohort, with wIRR 0.35 (95% CI 0.27\u0026ndash;0.43) for females and wIRR 0.45 (95%CI 0.37\u0026ndash;0.53) for males (Supplementary file, Fig.\u0026nbsp;4, Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eThe limited number of deaths in vaccinated individuals with severe C19 (n\u0026thinsp;=\u0026thinsp;86) did not allow us to do a meaningful analysis comparable to the full study population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis target trial emulation analysis investigated the impact of COVID-19 vaccination on post-COVID-19 infection sequelae, specifically focusing on MACE and all-cause mortality. Our findings demonstrate a 30% reduction in the risk of MACE and a 70% reduction in mortality during the year after infection with SARS-CoV-2 amongst individuals vaccinated against COVID-19 in comparison to those unvaccinated. The protective effect of vaccination is most pronounced during the three months post-acute infection, attenuating over time and becoming indistinguishable from those not vaccinated after about three months of follow-up.\u003c/p\u003e \u003cp\u003eOur study advanced the current understanding of age- and sex-specific differences in MACE rates following COVID-19 infection. Vaccination provided a protective effect against MACE in females aged 40 to 85 and men younger than 70. However, this protective effect was not evident in males over 70, who exhibited an elevated incidence of MACE, with a peak observed approximately 60 days post-infection. The increased risk in males over 70 may be speculatively linked to pre-existing and undiagnosed cardiovascular disease or undetected (in our study) risk factors (i.e. subclinical or \u0026bdquo;silent\u0026rdquo; atherosclerosis) in older males. \u003csup\u003e18 19 20\u003c/sup\u003e Additionally, residual pathophysiological processes, potentially mediated by the downregulation of angiotensin-converting enzyme 2 (ACE2) receptors and endothelial impairment in severely ill COVID-19 survivors, may play a role. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Thus, while vaccination may prevent imminent COVID-related death, it could also unmask a later wave of cardiovascular events (incl. MACE) in survivors, particularly in older males with increased frailty and concomitant risk profile.\u003c/p\u003e \u003cp\u003eDespite the established effectiveness of vaccines in reducing acute COVID-19 disease burden, the precise degree to which they attenuate the development of post-acute sequelae following SARS-CoV-2 infection is subject to ongoing investigation. A small number of studies that compared vaccinated and unvaccinated individuals with COVID-19, have also demonstrated a reduction in the risk of MACE and mortality among vaccinated individuals. \u003csup\u003e17 14 13 12\u003c/sup\u003e A nationwide Korean study using health databases found that COVID-19 vaccination reduced the risk of myocardial infarction and ischemic stroke in vaccinated individuals during the four-month follow-up period after COVID-19, except in those with severe COVID-19. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e US national cohort-based research showed protection against MACE, especially in males, older adults, and those with prior cardiovascular events, lasting 180 days. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e A UK cohort study observed a reduced risk of cardiovascular thrombotic events, strongest in the first 1\u0026ndash;4 weeks post-vaccination, diminishing but persisting for up to 28 weeks.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe effect of COVID-19 vaccination on the development of post-acute sequelae and all-cause mortality remains poorly understood, particularly in individuals with differing severities of acute COVID-19. Our study demonstrated a clear difference in these adverse outcomes between vaccinated and unvaccinated individuals with non-severe COVID-19, suggesting that the effect of COVID-19 vaccines on adverse outcomes is not solely mediated by reducing disease severity. A few observational studies have shown reduced rates of all-cause mortality among vaccinated COVID-19 survivors beyond the post-acute phase (30 days of initial infection). \u003csup\u003e8 232627\u003c/sup\u003e This suggest that vaccination may confer a survival advantage extending beyond the initial protection against severe COVID-19.\u003c/p\u003e \u003cp\u003eVaccination has been shown to mitigate the risk of post-acute sequelae of SARS-CoV-2 infection. \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Whether or not vaccines may play a role in mitigating persistent COVID-19 related health consequences (known as \u0026bdquo;long-covid\u0026ldquo;), which can otherwise lead to fatal outcomes, \u003csup\u003e29 30 31\u003c/sup\u003e warrants further examinations.\u003c/p\u003e \u003cp\u003eThe mechanisms through which COVID-19 vaccines affect post-COVID-19 adverse outcomes remain unclear. Aljadah \u003cem\u003eet al\u003c/em\u003e. highlights a significant knowledge gap concerning the impact of vaccination on SARS-CoV-2's ability to impair endothelial function during and after infection.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e COVID-19 vaccination may provide protection through various mechanisms, extending beyond just reducing the severity of the disease. These include reducing viral load and direct cell damage caused by virus entry and potentially counteracting the release of inflammatory mediators and endothelium damage (including the cardiovascular system) caused by an excessive and prolonged inflammatory response in the COVID-19 post-acute period. \u003csup\u003e32 33\u003c/sup\u003e There is also evidence that vaccination accelerates viral clearance and interfere immunological response in infected individuals, counteracting the virus persistence and long-COVID symptoms in infected individuals. \u003csup\u003e6 34 35\u003c/sup\u003e These multifaceted protective mechanisms are consistent with the observed patterns in our study, where vaccination conferred a significant but time-limited reduction in adverse cardiovascular outcomes and in all-cause mortality.\u003c/p\u003e \u003cp\u003eThe observation that vaccination may reduce cardiovascular mortality extends beyond the context of COVID-19. A recent systematic review demonstrated that influenza vaccination is associated with a decreased risk of major adverse cardiovascular events, particularly myocardial infarction, and cardiovascular death. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e This suggests a broader protective effect of vaccination on cardiovascular health, potentially through mechanisms such as reducing inflammation and improving immune response, and support the notion that vaccination can provide benefits beyond protection against the targeted disease.\u003c/p\u003e \u003cp\u003eThis study has some strengths. The major strengths of our study lie in our population-based cohort and analysis of more than 30 000 individuals, and our use of a formal framework for causal inference. The real-world health data capture data from diverse patient populations, increasing the generalizability of findings, and actual clinical practice, increasing the external validity of our findings. The robust methodology, employing target trial emulation and a DAG-based study design, facilitates approximation to causal inference. Previous studies have generally employed conventional Cox regression models with time as an independent variable. \u003csup\u003e14 8 17 13\u003c/sup\u003e However, the Cox regression with its ubiquitous proportional hazards assumption, is plainly insufficient for capturing the observed non-linear time-dependent effects. \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Our survival modelling strategy avoids reliance on the proportional hazard assumptions and linearity of age and follow-up time effects. Using penalized splines in interaction with the treatment (vaccination status), we identified a time-dependent, non-linear association between vaccination status and adverse outcomes (MACE, all-cause mortality). The observed dynamic and non-linear association likely reflects the true time window of the benefit of COVID-19 vaccination.\u003c/p\u003e \u003cp\u003eOur study also has some limitations, which have been carefully considered and mitigated throughout our analysis, where possible. Observational studies of vaccination outcomes are inherently susceptible to healthy vaccinee bias. To mitigate this, we employed inverse probability of treatment weighting (IPTW) to adjust for confounding. There is a possibility of unobserved confounding as in any nonrandomized evaluation. Secondary health data sources lack certain types of information, such as social determinants of health (e.g., poverty, health- and risk behavior, tobacco use, treatment compliance) and specific parameters of cardiometabolic profile (i.e. arterial stiffness, c-reactive protein, lipid profile), which are known to influence health outcomes. Hence, we need to interpret our findings with caution because we cannot exclude the possibility of inaccurately addressing causal relationships due to bias arising from unmeasured confounding factors, particularly in older males. \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur analysis did not differentiate between specific vaccine types. However, it is possible that different COVID-19 vaccines may have varying efficacy in preventing cardiovascular events. This could be attributed to potential differences in their effects on endothelial function and their potential to induce cardiovascular adverse effects. \u003csup\u003e39 40\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis comprehensive analysis of a large population-based cohort revealed that COVID-19 vaccination significantly reduced the risk of incident cardiovascular events and all-cause mortality following SARS-CoV-2 infection, particularly within the first three months after the acute infection. However, this protective effect was not observed in males over 70, who experienced an elevated incidence of MACE, highlighting the need for further research into age- and sex-specific responses to vaccination. Our findings underscore the complex interplay between COVID-19 vaccination, post-acute sequelae, and long-term health outcomes, emphasizing the importance of vaccination in mitigating the adverse consequences of SARS-CoV-2 infection.2\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eTo compare the risks of death and incident MACE, such as myocardial infarction or stroke, within 365 days following COVID-19 between individuals fully vaccinated against SARS-CoV-2 and unvaccinated individuals, we specified and emulated a target trial (Supplementary, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSetting and Data Sources\u003c/h2\u003e \u003cp\u003eWe used observational data from the nationwide electronic health databases in Estonia. The study accrual period spanned from April 2021 to March 2023. In Estonia, COVID-19 vaccination started in January 2021 with a cumulative vaccination uptake of primary vaccination series about 70% among adult population by June 2022. The accrual of COVID-19 cases for the study period coincides with the epidemics of COVID-19 variants Alpha, Delta, and Omicron. \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Estonian Health Insurance Fund (EHIF)\u003c/h3\u003e\n\u003cp\u003eAt the end of 2021, universal public health insurance covered 95.2% of Estonia's population of 1.3\u0026nbsp;million. \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e The EHIF maintains a complete record of the health care services provided. Diagnoses are defined according to the International Classification of Diseases, tenth revision (ICD-10). The EHIF database records sex, date of birth, and health care utilization information (incl. dates of service, diagnoses, treatment type: in- or outpatient).\u003c/p\u003e\n\u003ch3\u003eEstonian national health information system (ENHIS)\u003c/h3\u003e\n\u003cp\u003eData on COVID-19 vaccination (dates, the type of vaccine), SARS-CoV-2 testing (dates) and laboratory confirmed (real-time polymerase chain reaction (PCR) or antigen testing) cases of SARS-CoV-2 infections (dates) were retrieved from ENHIS \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e According to law, all health-care providers and laboratories in Estonia are obligated to report health claims data to ENHIS. Thus, the expected coverage is 100%.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe Population Register\u003c/h2\u003e \u003cp\u003eEstonian citizens and foreign nationals living in Estonia are listed in the Population Register held by the Ministry of the Interior based on their residence permit.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e This register provided data on study subjects\u0026rsquo; education, native language, and ethnicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEstonian Causes of Death Registry\u003c/h2\u003e \u003cp\u003eWe used data obtained from The Estonian Causes of Death Registry to identify the dates of deaths. The Estonian Causes of Death Registry gathers information on all deaths that are recorded on Estonian territory and in foreign missions.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe unique personal identification code assigned to all Estonian residents (at birth or immigration) was used to link data between health databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA study cohort was drawn from a random sample of 389,288 individuals covered by the EHIF. The study's eligibility criteria included individuals aged 40 to 85 years, having a first positive SARS-CoV-2 test between April 1, 2021, and May 31, 2022 (baseline, T\u003csub\u003e0\u003c/sub\u003e), and having no COVID-19 vaccination prior to index date. Individuals with diagnoses of acute cardiovascular events (see Supplementary, Table\u0026nbsp;2 for clinical diagnoses included in MACE codes, as classified by International Classification of Disease, 10th Revision (ICD-10)) during the five years preceding the index date, or a positive SARS-CoV-2 test\u0026thinsp;\u0026lt;\u0026thinsp;14 days or \u0026gt;\u0026thinsp;180 days after COVID-19 vaccination, were also excluded. Only the first positive SARS-CoV-2 test was considered, regardless of possible reinfections (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExposure\u003c/h2\u003e \u003cp\u003eWe considered COVID-19 vaccination status as treatment (exposure) in our study. The vaccinated cohort consisted of individuals who completed primary COVID-19 vaccine series (two doses of BNT162b2 (Pfizer/BioNTech), mRNA-1273 (Moderna), AZD1222 (Oxford/AstraZeneca) or one dose of Ad26.COV2 (Janssen/Johnson \u0026amp; Johnson) 14\u0026ndash;180 days before SARS-CoV-2 infection). Supplementary vaccine doses administered after the primary series were not considered in this analysis.\u003c/p\u003e \u003cp\u003eThe unvaccinated cohort included individuals who were not unvaccinated prior to their first SARS-CoV-2 infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003ePrimary outcome: incident MACE\u003c/h2\u003e \u003cp\u003eMACE (major acute cardiovascular events) is a composite of clinical events previously described in various clinical trials aimed at evaluating the effectiveness and safety of cardiovascular interventions.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe used a two-point MACE outcome, defined as a composite of acute myocardial infarction (AMI) and stroke occurring 0-365 days after the 1st positive SARS-CoV-2 test, as the primary outcome. Our focus on two-point MACE in COVID-19 sequelae research is driven by their significant impact on individuals and health systems, potentially causing long-term disability, reduced quality of life, and premature death. We defined MACE components based on the ICD-10 codes in EHIF data (MACE diagnosis codes are presented in Supplementary, Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSecondary outcome: All-cause mortality\u003c/h2\u003e \u003cp\u003eWe defined all-cause mortality as any death occurring 0-365 days after the 1st positive SARS-CoV-2 test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up period\u003c/h2\u003e \u003cp\u003eFollow-up began at baseline (T\u003csub\u003e0\u003c/sub\u003e or index date; the date of the 1st positive SARS-CoV-2 test) and continued until the occurrence of outcome (MACE, all-cause death), a competing event (all-cause death for analysis of MACE only), or 365 days post-baseline, whichever occurred first. Thus, we incorporate all symptomatic and asymptomatic COVID-19 cases, as well as outcomes resulting in the acute and post-acute phases of COVID-19.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eThe analysis included the age (in years), sex (male, female), and education level of the study participants. Study participants' education levels were categorized into three groups: primary (basic education or below), secondary (general secondary or vocational education), and higher education (higher or tertiary education). \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePre-COVID-19 comorbidities\u003c/h2\u003e \u003cp\u003eWe obtained data about diabetes (type 1 and 2), chronic pulmonary diseases, and cardiovascular diseases, recognizing their potential to increase the risk of contracting COVID-19 and experiencing severe disease course, as well as the potential of these diseases to increase the risk of MACE and mortality both during and after the acute phase of infection.\u003c/p\u003e \u003cp\u003e Comorbidities were identified using ICD-10 codes (see detailed description in Supplementary, Table\u0026nbsp;3), as any primary or secondary diagnosis code in the claim or diagnosis of any type (hospital or outpatient) health care claims during the five years preceding the index date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSeverity of COVID-19\u003c/h2\u003e \u003cp\u003eSevere COVID-19 was defined as hospitalization occurring within 3 days before to 14 days after a positive SARS-CoV-2 test. We set additional criteria for COVID-19-related hospitalization to avoid misclassification of COVID-19 cases identified through routine screening during hospitalization for other reasons (e.g. childbirth). The diagnoses associated with hospitalization had to include an ICD-10-based COVID-19 diagnosis (U07.1, U07.2) and at least one additional diagnosis indicating a COVID-19-related condition, such as acute upper or lower respiratory tract infection or acute respiratory failure (Supplementary, Table\u0026nbsp;4). \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCausal inference strategies\u003c/h2\u003e \u003cp\u003eThis study was designed in the target trial emulation framework, as described in the supplementary material (Supplementary, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and Supplementary discussion). \u003csup\u003e41 52\u003c/sup\u003e Briefly, we developed a protocol for a hypothetical target trial where the treatment is vaccination against COVID-19 or no vaccination; the outcomes are MACE and all-cause death. Our target trial investigates the average causal effect of COVID-19 vaccination on one-year risk of MACE and all-cause mortality in people who have had a SARS-CoV-2 infection.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eParticipant characteristics are presented as means with standard deviation (SD) for continuous variables and frequencies and percentages for categorical variables. We estimated the crude cumulative incidence (cIR) of death to assess the influence of age group and sex on outcomes following COVID-19 diagnosis, stratified by vaccination status. A competing risks model was employed to evaluate the cumulative incidence of MACE over 365 days \u003csup\u003e53 54\u003c/sup\u003e (Supplementary, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs individuals were not randomly allocated to the different exposure groups, causal inferences from exposure to outcomes can be biased. Inverse probability of treatment weighting (IPTW) was used to adjust for confounding. \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e IPTW is a propensity score-based method, with the propensity scores reflecting their probability of belonging to the fully vaccinated group, which were calculated from logistic regression models that included the minimally sufficient set of covariates: calendar time, sex, age, education (primary, secondary, higher), native language (Estonian, Russian/Ukrainian, other), previous diabetes, cardiovascular disease and pulmonary disease (Supplementary, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the DAG and Table\u0026nbsp;3 for the definitions of comorbidities). These covariates were derived from a directed acyclic graph (DAG) constructed in daggity.net.\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIPTW creates a pseudo-population in which the exposure is independent of measured confounders.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e We used the IP weights in a marginal structural Poisson survival model where the time from T\u003csub\u003e0\u003c/sub\u003e and the vaccination status were the sole predictors, which were given interaction in the spline model (see code in Supplement 2 for details).\u003c/p\u003e \u003cp\u003eWeighted standardized mean differences (SMD) were calculated with the tidysmd package v. 0.2.0 and used to assess the balance of covariates between groups, with SMD\u0026thinsp;\u0026le;\u0026thinsp;0.1 regarded as a sufficient balance.\u003csup\u003e58 59\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe occurrence of main outcomes was assessed using incidence rates for 100 person-years (IR) and the treatment effects were evaluated using incidence rate ratios averaged over the entire one-year follow-up period (IRR). We employed a marginal structural model with IPT weights to estimate adjusted incidence rates (wIRs) per 100 person-years and incidence rate ratios (wIRRs, averaged over the one-year follow-up period) (Table S5). The model utilized Poisson regression with an unequally split timescale (0, 10, 30, 50, 75, 100, 150, 200, 300, 365 days) and modelled the follow-up time with penalized splines from the R::mgcv package, incorporating an interaction with vaccination status. The adjusted models were used to estimate IRs and IRRs with their corresponding 95% confidence intervals, which were calculated using the delta method by the marginal effects package. \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e The results were stratified by sex and age group (\u0026lt;\u0026thinsp;70 and \u0026ge;\u0026thinsp;70 years). We then analyzed the results for individuals with severe and non-severe COVID-19 separately to control for the effect of disease severity on the main outcomes. Missing data (education for 1003 individuals) were addressed through single imputation using the R::mice package.\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAll statistical analyses were done in R v.4.3.2.\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e The R code for reproducing the figures, including the modelling code, as well as datasets (slightly modified for privacy reasons) that can be used to run the code, can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datadoi.ee/handle/33/667\u003c/span\u003e\u003cspan address=\"https://datadoi.ee/handle/33/667\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23673/re-505\u003c/span\u003e\u003cspan address=\"10.23673/re-505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e All research was carried out in accordance with relevant guidelines and regulations. The Research Ethics Committee of the University of Tartu approved the study by the 15th of March 2021 (No. 337/M-27). The need for informed consent was waived by the Ethics committee of the University of Tartu due to the retrospective nature of the study.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eData sharing\u003c/h2\u003e \u003cp\u003eAccording to legislative regulation and data protection law in Estonia, the authors cannot publicly release the raw data received from the health data registers in Estonia. However, data are available from the corresponding author ([email protected]) upon reasonable request and with permission of EHIF, ENHIS, The Causes of Death Registry, and Population Registry.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor`s contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAU and ÜM had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. Study concept and design - AU, ÜM, and TM. Acquisition, analysis, or interpretation of the data - AU, ÜM, TM, KS, AT, and KT. Drafting the manuscript - TM and AU. Critical revision of the manuscript - AU, ÜM, TM, KS, RK, AT, RK, and KT. Statistical analysis - ÜM, KT, TM. Model development and visualization – ÜM. Obtaining funding – AU. Reading and agreeing to the final version of the manuscript AU, ÜM, TM, KS, RK, AT, and KT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors meet the International Committee of Medical Journal Editors criteria for authorship and have confirmed their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was carried out with the support of the Estonian Research Council grant PRG2218.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics committee approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Research Ethics Committee of the University of Tartu (protocol number 323/T-28, 21.09.2020).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWoodrow, M. et al. Systematic Review of the Prevalence of Long COVID. \u003cem\u003eOpen. Forum Infect. Dis.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, ofad233 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReyes, L. F. et al. Major adverse cardiovascular events (MACE) in patients with severe COVID-19 registered in the ISARIC WHO clinical characterization protocol: A prospective, multinational, observational study. \u003cem\u003eJ. Crit. Care\u003c/em\u003e. \u003cb\u003e77\u003c/b\u003e, 154318 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolchin Vafa, R. et al. The long-term effects of the Covid-19 infection on cardiac symptoms. \u003cem\u003eBMC Cardiovasc. Disord\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e, 286 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, Y., Xu, E. \u0026amp; Bowe, B. Al-Aly, Z. Long-term cardiovascular outcomes of COVID-19. \u003cem\u003eNat. 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Softw.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 1\u0026ndash;67 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR: The R Project for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, vaccination, death, mortality, long COVID, PACS, post-acute covid syndrome, MACE, cardiac events, target trial emulation, DAG.","lastPublishedDoi":"10.21203/rs.3.rs-6319577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6319577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic presents significant health challenges, including increased risk of mortality and long-term complications. While vaccination has proven remarkably effective in mitigating severe disease and mortality associated with acute COVID-19 infection, the long-term implications of vaccination, particularly its influence on post-COVID cardiovascular events and the temporal dynamics of such effects, remain poorly understood. This target trial emulation study utilizes real-world electronic medical record data from April 2021 to March 2023 to address this gap. We evaluate the effect of pre-infection COVID-19 vaccination on the risk of major acute cardiovascular events (MACE) and all-cause mortality in individuals aged 40\u0026ndash;85 years during one year after SARS-CoV-2 infection. Among individuals with COVID-19 (n\u0026thinsp;=\u0026thinsp;18,223 vaccinated, n\u0026thinsp;=\u0026thinsp;15,331 not vaccinated), vaccination provided a significant protective effect against MACE (weighted incidence rate ratio [wIRR] 0.71, 95% CI 0.58\u0026ndash;0.84) and all-cause mortality (wIRR 0.32, 95% CI 0.28\u0026ndash;0.36). This effect persisted for approximately three months post-acute infection. These findings underscore the importance of COVID-19 vaccination in reducing both short-term and long-term health risks associated with the infection.\u003c/p\u003e","manuscriptTitle":"The effect of vaccination on post-COVID-19 major acute cardiac events and mortality: a target trial emulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 18:07:36","doi":"10.21203/rs.3.rs-6319577/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T08:40:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-30T07:13:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-22T14:45:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292954028325852098883335668332332995198","date":"2025-05-21T13:36:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14582752112143563300971930622477509936","date":"2025-05-20T04:20:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T22:09:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T22:03:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-01T13:02:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T04:05:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-27T10:21:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebaebebb-7225-4f10-8934-278e1943fa15","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48652130,"name":"Health sciences/Medical research/Epidemiology"},{"id":48652131,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"}],"tags":[],"updatedAt":"2025-08-04T16:48:23+00:00","versionOfRecord":{"articleIdentity":"rs-6319577","link":"https://doi.org/10.1038/s41598-025-13043-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-29 16:39:17","publishedOnDateReadable":"July 29th, 2025"},"versionCreatedAt":"2025-05-16 18:07:36","video":"","vorDoi":"10.1038/s41598-025-13043-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-13043-x","workflowStages":[]},"version":"v1","identity":"rs-6319577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6319577","identity":"rs-6319577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}