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• Waning of vaccine...

Waning of vaccine effectiveness against moderate and severe covid-19 among adults in the US from the VISION network: test negative, case-control study

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• Peer review
• Suchitra Rao , associate professor of pediatrics 2 ,
• Brian E Dixon , director of public health informatics 3 4 ,
• Patrick K Mitchell , senior epidemiologist 5 ,
• Malini B DeSilva , internal medicine specialist 6 ,
• Stephanie A Irving , project director 7 ,
• Ned Lewis , data manager 8 ,
• Karthik Natarajan , assistant professor of biomedical informatics 9 10 ,
• Edward Stenehjem , infectious disease specialist 11 ,
• Shaun J Grannis , vice president of data analytics 3 12 ,
• Jungmi Han , research analyst 9 ,
• Charlene McEvoy , internal medicine specialist 6 ,
• Toan C Ong , research instructor 2 ,
• Allison L Naleway , senior epidemiologist 7 ,
• Sarah E Reese , senior biostatistician 5 ,
• Peter J Embi , professor of medicine ,
• Kristin Dascomb , medical director infection prevention 11 ,
• Nicola P Klein , senior research scientist 8 ,
• Eric P Griggs , epidemiologist 1 ,
• I-Chia Liao , analytics developer 13 ,
• Duck-Hye Yang , senior epidemiologist 5 ,
• William F Fadel , clinical assistant professor 3 4 ,
• Nancy Grisel , analyst 11 ,
• Kristin Goddard , senior research manager 8 ,
• Palak Patel , epidemiologist 1 ,
• Kempapura Murthy , SAS programmer 13 ,
• Rebecca Birch , senior epidemiologist 5 ,
• Nimish R Valvi , postdoctoral fellow 3 ,
• Julie Arndorfer , analyst 11 ,
• Ousseny Zerbo , research scientist 8 ,
• Monica Dickerson , epidemiologist 1 ,
• Chandni Raiyani , biostatistician 13 ,
• Jeremiah Williams , surveillance coordinator 1 ,
• Catherine H Bozio , epidemiologist 1 ,
• Lenee Blanton , research epidemiologist 1 ,
• Ruth Link-Gelles , epidemiologist 1 ,
• Michelle A Barron , senior medical director 2 ,
• Manjusha Gaglani , chief of pediatric infectious diseases 13 ,
• Mark G Thompson , epidemiologist 1 ,
• Bruce Fireman , biostatistician 8
• 1 Centers for Disease Control and Prevention COVID-19 Response Team, Atlanta, GA, USA
• 2 Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
• 3 Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA
• 4 Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
• 5 Westat, Rockville, MD, USA
• 6 HealthPartners Institute, Minneapolis, MN, USA
• 7 Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA
• 8 Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
• 9 Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
• 10 New York Presbyterian Hospital, New York, NY, USA
• 11 Division of Infectious Diseases and Clinical Epidemiology, Intermountain Healthcare, Salt Lake City, UT, USA
• 12 Indiana University School of Medicine, Indianapolis, IN, USA
• 13 Baylor Scott &White Health, Temple, TX, USA
• Correspondence to: J M Ferdinands zdn5{at}cdc.gov

† Patients aged <50 years were excluded from estimates of fourth dose effectiveness; thus, column sum might not equal 100% of encounters.

• Accepted 9 September 2022

Objective To estimate the effectiveness of mRNA vaccines against moderate and severe covid-19 in adults by time since second, third, or fourth doses, and by age and immunocompromised status.

Design Test negative case-control study.

Setting Hospitals, emergency departments, and urgent care clinics in 10 US states, 17 January 2021 to 12 July 2022.

Participants 893 461 adults (≥18 years) admitted to one of 261 hospitals or to one of 272 emergency department or 119 urgent care centers for covid-like illness tested for SARS-CoV-2.

Main outcome measures The main outcome was waning of vaccine effectiveness with BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine during the omicron and delta periods, and the period before delta was dominant using logistic regression conditioned on calendar week and geographic area while adjusting for age, race, ethnicity, local virus circulation, immunocompromised status, and likelihood of being vaccinated.

Results 45 903 people admitted to hospital with covid-19 (cases) were compared with 213 103 people with covid-like illness who tested negative for SARS-CoV-2 (controls), and 103 287 people admitted to emergency department or urgent care with covid-19 (cases) were compared with 531 168 people with covid-like illness who tested negative for SARS-CoV-2. In the omicron period, vaccine effectiveness against covid-19 requiring admission to hospital was 89% (95% confidence interval 88% to 90%) within two months after dose 3 but waned to 66% (63% to 68%) by four to five months. Vaccine effectiveness of three doses against emergency department or urgent care visits was 83% (82% to 84%) initially but waned to 46% (44% to 49%) by four to five months. Waning was evident in all subgroups, including young adults and individuals who were not immunocompromised; although waning was morein people who were immunocompromised. Vaccine effectiveness increased among most groups after a fourth dose in whom this booster was recommended.

Conclusions Effectiveness of mRNA vaccines against moderate and severe covid-19 waned with time after vaccination. The findings support recommendations for a booster dose after a primary series and consideration of additional booster doses.

Introduction

Randomized trials of BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) vaccines showed 94-95% protection against covid-19 among adults and suggested efficacy against covid-19 requiring hospital admission. 1 2 Since the introduction of these vaccines in December 2020, evidence has accumulated that their effectiveness wanes over time since vaccination, especially against milder disease, 3 4 5 6 7 8 9 they are less effective against omicron than earlier SARS-CoV-2 variants, 10 and a third (booster) dose restores high effectiveness against severe disease. 10 11 12 13 Although protection against severe omicron related disease is believed to be high for several months after a third dose, the durability of protection and how this effect can vary by age group, immunocompromised status, and vaccine product is uncertain. In March 2022, the US Centers for Disease Control and Prevention recommended a second booster dose only for specific subgroups at high risk (such as adults aged 50 and older). 14 A more complete understanding of the effectiveness and durability of third and fourth doses of the mRNA vaccines is important to inform policy about booster doses.

The CDC’s VISION network previously examined the effectiveness of mRNA vaccines against admissions to hospital or emergency visits and urgent care visits associated with covid-19, with data from eight healthcare systems. 15 In this article, we update VISION’s analyses of mRNA vaccine effectiveness, focusing on the durability of three and four dose protection against severe disease (ie, admission to hospital) during the omicron period. We assess the trajectory of vaccine effectiveness overall and in subgroups defined by age, immunocompromised status, and vaccine product.

Study design

The VISION network has been described previously. 15 We applied a test negative design to estimate vaccine effectiveness of mRNA vaccines using retrospectively collected data. We focused on mRNA vaccines because they comprise more than 95% of covid vaccines administered in the US. 16 Separate analyses were done of patients who were admitted to hospital (hospital sample) and patients who received care in an emergency department or urgent care clinic (emergency department or urgent care sample).

Study population and setting

The study population included adults (≥18 years) who received care for covid-like illness at a VISION network hospital or emergency department or urgent care center and had molecular testing for SARS-CoV-2 at least 14 days after vaccines became locally available for their age group (17 January to 3 May 2021). The last contact included in this study period occurred on 12 July 2022. We excluded individuals who received any vaccine other than the BNT162b2 or mRNA-1273 vaccine, individuals who received more than four doses of an mRNA vaccine before the index medical contact, individuals who received only one dose of an mRNA vaccine less than 14 days before the index contact or who had a third or fourth dose less than seven days before the index contact, individuals known to have a positive laboratory test result for a SARS-CoV-2 infection more than 14 days before the index contact, and individuals with a positive SARS-CoV-2 test result but no diagnoses or symptoms suggesting covid-19 illness.

Vaccination status

Vaccination status was categorized by the number of doses received and the number of months between the most recent vaccine dose and the index contact date (referred to as time since vaccination). Patients were considered partially vaccinated if they received only one dose at least 14 days prior to the index contact date or had received a second dose less than 14 days previously. Patients with no record of vaccination before the index contact date were considered unvaccinated. Patients with three doses were those who received a third dose in a primary vaccination series (eg, among immunocompromised individuals) or a booster dose after a primary series of two doses. Aligning with recommendations for receipt of a fourth dose, we examined the effectiveness of four doses among adults aged 50 years or older and among immunocompromised adults of any age. Vaccination status was ascertained from immunization registries, electronic health records, and insurance claims.

The primary outcome was a positive or negative molecular SARS-CoV-2 result for a test done within 14 days before a medical contact to less than 72 h after among patients presenting with covid-like illness, as identified from ICD-9 and ICD-10 (international classification of diseases, ninth and 10th revision, respectively) diagnostic codes (supplemental methods; supplemental table S1). The index date for each contact was the earlier of either the contact date or the date of the closest SARS-CoV-2 molecular assay. An individual could be included as a case once in the emergency department or urgent care sample and once in the hospital sample. Individuals could be included as a control multiple times.

Statistical analysis

We used a test negative case-control design in which cases were patients with covid-like illness with laboratory confirmed covid-19 and controls were patients with covid-like illness and negative SARS-CoV-2 test results (controls could have had positive test results for other respiratory viruses such as influenza). We compared cases with controls in the hospital sample, and separately compared cases with controls in the emergency or urgent care sample. Cases were not individually matched to controls.

Conditional logistic regression was used to examine case-control status in relation to vaccination status categorized as vaccinated with four, three, or two doses, or partially vaccinated; unvaccinated individuals were used as the reference group. To examine waning of vaccine effectiveness, we categorized people who were vaccinated using time specific indicators defined by two month intervals of time since vaccination; unvaccinated individuals were used as the reference group. We exponentiated the regression coefficient of each vaccination status indicator to yield an odds ratio, subtracted the odds ratio from 1 to estimate vaccine effectiveness, and multiplied by 100 to scale vaccine effectiveness as a percentage. In several analyses, a sparse bimonthly interval for which the vaccine effectiveness estimate had a confidence interval wider than 50 percentage points was combined with the previous bimonthly interval to provide a more precise estimate of vaccine effectiveness (see supplemental methods). Vaccine effectiveness estimates (and confidence limits) were scaled to a range of –100% to 100%. 17

Logistic regression models were conditioned by calendar week and geographical area such that we compared cases with controls tested during the same week in the same region (supplemental table S2). Covariates included in the models were those determined through bivariate analyses to be statistically significantly associated with both the outcome and vaccination status, as well as those specified a priori as established confounders, including age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromised status, and local viral circulation. Cubic splines were used for age, seven day average positivity of SARS-CoV-2 test in the area of the contact, and the propensity to be vaccinated; others were indicator variables. Propensity scores (supplemental methods) predicted vaccination (any versus none) based on demographics, comorbidities (supplemental table S3), and characteristics of the facility (supplemental table S4), and were derived independently for each period of variant dominance (supplemental table S5). Patients who were immunocompromised were identified by ICD-9 and ICD-10 diagnostic codes (supplemental methods). 18 We conducted separate analyses for three periods based on when a variant accounted for 50% or more of sequenced isolates in each site: before delta was predominant, when delta was predominant, and when omicron was predominant (supplemental table S6). We assessed the magnitude of waning as the difference in vaccine effectiveness between patients who had recently been vaccinated (defined as less than two months) and patients at a specified level of time since vaccination (eg, four to five months from dose 3), and we examined waning by age (18-44 years, 45-64 years, ≥65 years), vaccine product, and immunocompromised status. Bootstrapping was used to estimate a 95% confidence interval around the difference between vaccine effectiveness at less than two months and vaccine effectiveness at four to five months.

We conducted several sensitivity analyses. First, we added to the study population patients with a known prior infection to assess the sensitivity of results to whether previously infected patients are included or excluded.. Second, we wanted to distinguish results between patients who had been admitted to hospital and patients who had been admitted to an emergency department or to urgent care. Therefore, we examined vaccine effectiveness in the emergency department or urgent care sample and omitted patients admitted to hospital within 30 days. Third, we investigated a negative control exposure 19 by examining vaccine effectiveness in patients who received their first dose less than 14 days before the index date of contact. These patients were not expected to have substantial vaccine induced protection, and a vaccine effectiveness estimate substantially more than zero would be evidence of residual confounding. 20

Analyses were conducted with SAS version 9.4 and R version 4.1.2. All confidence limits are 95% intervals. Confidence intervals excluding the null value were considered statistically significant.

Patient and public involvement

Study participants contributed in important ways to this research by supplying the underlying data on which the study is based. It was not, however, feasible to involve them in the design, conduct, reporting, or dissemination of this study because the study was conducted under the CDC’s covid-19 incident response structure and limited to analysis of retrospectively collected electronic data only, with no patient interaction.

Study population

From 17 January 2021 to 12 July 2022, 259 006 patients were admitted to 261 hospitals and 634 455 were admitted to 272 emergency departments or to 119 urgent care centers. The hospital sample included 17 446 people with covid-19 during the omicron period, 23 379 during the delta period, and 5078 before delta was dominant. The emergency department or urgent care sample included 57 174 people with covid-19 during the omicron period, 39 909 during the delta period, and 6204 before delta was dominant ( table 1 ; supplementary figs S1-S18).

Characteristics of adults with covid-19-like illness who were admitted to hospital or to an emergency department or urgent care, and percentage with laboratory confirmed SARS-CoV-2 infection. Data are number of patients (percentage of column or row) unless stated otherwise

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In the hospital sample, the median age was 69 years (interquartile range 56-79, 11.2% were black participants, 9.8% were Hispanic, and 23.3% had an immunocompromising condition. In the emergency department or urgent care sample, the median age was 51 years (interquartile range 33-69), 11.0% were black participants, 13.3% were Hispanic, and 4.5% had an immunocompromising condition ( table 1 ). Characteristics by vaccination status are given in supplemental tables S7 and S8. Median times between the last vaccination date and index contact date in the hospital sample were 173 (interquartile range 97-248) days for two doses, 105 (56-156) days for three doses, and 33 (19-50) days for four doses, and in the emergency department or urgent care sample were 179 (110-247) days for two doses, 100 (52-155) days for three doses, and 34 (20-52) days for four doses.

Vaccine effectiveness

Vaccine effectiveness estimates from the hospital and emergency department or urgent care samples are shown in figures 1 and figure 2 and detailed in supplemental tables S9-S14.

Vaccine effectiveness (%) against covid-19-associated hospital admissions by time since vaccination and period of variant predominance. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromise status, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column. *Figure 3 presents 4 findings for 4-dose recipients in the subgroups recommended for a fourth dose

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Vaccine effectiveness (%) against covid-19-associated emergency department and urgent care visits by time since vaccination and period of variant predominance. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromise status, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column. *Supplemental Table 14 presents findings for 4-dose recipients in the subgroups recommended for a fourth dose

Vaccine effectiveness against covid-19 requiring hospital admission was 94% (95% confidence interval 93% to 95%) in the pre-delta period and 96% (95% to 97%) in the delta period, during the initial two months after the second dose. By months four to five after the second dose, vaccine effectiveness against hospital admission decreased to 87% (77% to 93%) in the pre-delta period and 89% (88% to 90%) in the delta period. In the omicron period, two dose vaccine effectiveness against hospital admission was lower than in the earlier periods, both before and when delta was dominant, and waned more, decreasing from 73% (63% to 80%) initially to 57% (51% to 62%) by four to five, and to 40% (32% to 47%) by 12 months after the second dose.

The patterns of vaccine effectiveness estimates from the emergency department or urgent care sample were similar. Vaccine effectiveness of two doses against emergency department or urgent care visits was initially high in the pre-delta period (95%; 94% to 96%) and delta period (93%; 92% to 94%) and then waned. During the omicron period, vaccine effectiveness of two doses against emergency department or urgent care visits was lower initially (63%; 57% to 68%) than in the earlier pre-delta and delta periods and then waned more. From up to one month after the second dose to months four to five, the vaccine effectiveness of a second dose decreased by 9 percentage points (95% confidence interval 4 to 16) during the pre-delta period, by 7 percentage points (7 to 9) during the delta period, and by 26 percentage points (19 to 32) during the omicron period.

A third dose initially restored high levels of protection against both hospital admissions and emergency department or urgent care visits, then began to wane. In the hospital sample, vaccine effectiveness of three doses was initially 96% (95% to 96%) during the delta period and 89% (88% to 90%) during the omicron period. Similarly, in the emergency department or urgent care sample, the vaccine effectiveness of a third dose was initially 96% (95% to 96%) during the delta period and 83% (82% to 84%) during the omicron period. Waning was evident in both samples by four to five months after the third dose during the omicron period, when vaccine effectiveness decreased to 66% (63% to 68%) against hospital admission and to 46% (44% to 49%) against emergency department or urgent care visits.

Vaccine effectiveness against hospital admission after a fourth dose increased to 72% (51% to 83%) in the 50-64 year group and to 76% (71% to 80%) in the 65 years and older age group ( fig 3 ). Similarly, vaccine effectiveness against emergency department or urgent care visits after a fourth dose increased to 57% (47% to 65%) and 73% (69% to 76%) among the 50-64 year and 65 years and older age groups, respectively (supplemental table S14). Vaccine effectiveness of a fourth dose among immunocompromised individuals in the hospital sample was 48% (29% to 62%; fig 4 ), but we were unable to measure this precisely enough in the emergency department or urgent care sample.

Vaccine effectiveness (%) against covid-19-associated hospital admissions by time since vaccination and age group, restricted to omicron period. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromise status, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column. *Patients aged <50 years were excluded from the estimate of fourth dose effectiveness for the subgroup aged 45-64 years.

Vaccine effectiveness (%) against covid-19-associated hospital admissions by time since vaccination and immunocompromise status, restricted to omicron period. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column

Vaccine effectiveness in subgroups

In all subgroups examined, vaccine effectiveness waned as time elapsed after the second dose, increased markedly with a third dose, and waned as time elapsed (supplemental tables S9-14). Vaccine effectiveness also substantially improved after a fourth dose among most subgroups for whom this booster dose was recommended. Comparing the initial two months after the third dose with months four to five, vaccine effectiveness against hospital admission during the omicron period decreased by 33 percentage points (95% confidence interval 16 to 56) in the 18-44 years group, 31 (21 to 40) in the 45-64 years group, and 19 (16 to 22) in the 65 years or older group ( fig 3, table 2 ). Results were similar in post hoc analyses that were restricted to individuals without immunocompromising conditions (supplemental table S15).

Estimates of mRNA vaccine effectiveness against covid-19 related hospital admissions during omicron period by age group. Data are number of patients (percentage of column or row) unless stated otherwise.

Vaccine effectiveness was higher in recipients of the mRNA-1273 than BNT162b2 vaccine in all three variant periods in both the hospital sample and the emergency department or urgent care sample. Vaccine effectiveness waned in recipients of both vaccine products. In the hospital sample during the omicron period, vaccine effectiveness of mRNA-1273 waned from 91% (89% to 92%) to 65% (60% to 70%) by four to five months after three doses whereas vaccine effectiveness of BNT162b2 waned from 88% (86% to 90%) to 66% (63% to 70%) after three doses ( table 3 ).

Estimates of vaccine effectiveness against covid-19 related hospital admissions during omicron period by mRNA vaccine product. Data are number of patients (percentage of column or row) unless stated otherwise

Vaccine effectiveness after two and three doses was generally lower among individuals who were immunocompromised, in both the hospital and the emergency department or urgent care samples, in each period and at all times since vaccination ( fig 4 , table 4 , supplemental tables S9-S14). In the omicron period, vaccine effectiveness of three doses against hospital admission waned from 78% (73% to 82%) to 48% (40% to 55%) by months four to five in the immunocompromised subgroup compared with 91% (90% to 92%) to 71% (68% to 74%) in the subgroup without immunocompromise ( table 4 ).

Estimates of mRNA vaccine effectiveness against covid-19 related hospital admissions during omicron period by immunocompromised status. Data are number of patients (percentage of column or row) unless stated otherwise.

Sensitivity analyses

In the first sensitivity analysis, vaccine effectiveness estimates in both samples were similar but slightly lower if patients with previous SARS-CoV-2 infection were included (supplemental tables S16 and S17). In the second sensitivity analysis, vaccine effectiveness estimates were similar but lower if the emergency department or urgent care sample excluded patients who were later admitted to hospital. In the third sensitivity analysis, vaccine effectiveness ranged from –5% to 24% among patients whose index date for medical contact was less than 14 days after the first dose, consistent with the little protection induced by the vaccine during this two week period.

Principal findings

Protection against severe omicron related covid-19 was high after three doses of an mRNA vaccine but began to wane less than six months after the third dose. In the hospital sample, vaccine effectiveness after a third doses was 89% among individuals within two months but decreased to 66% among individuals at four to five months. In the emergency department or urgent care sample, vaccine effectiveness of a third dose was 83% within two months but decreased to 46% at four to five months. In all subgroups defined by age, immunocompromised status, and vaccine product, the third dose was initially associated with markedly increased protection, but vaccine effectiveness was lower by four to five months. Vaccine effectiveness increased after a fourth dose for most subgroups for whom this booster dose is recommended in the US. Although we have not yet observed events more than four months from a fourth dose, our results suggest that protection after the fourth dose begins to wane after a few months.

Comparison with other studies

Our vaccine effectiveness estimates for mRNA vaccines are broadly consistent with those in other reports: vaccine effectiveness was lower against the omicron variant than earlier variants, 10 21 22 vaccine effectiveness waned after a second dose, 3 4 5 6 7 8 9 and a third dose restored high levels of protection against severe covid-19 during the omicron and delta periods. 10 11 12 13 Our results are also consistent with other reports of waning protection after three mRNA doses. 23 24 25 As with others, we noted less waning against more severe outcomes, 3 26 lower vaccine effectiveness among individuals who were immunocompromised, 17 27 and higher vaccine effectiveness among recipients of mRNA-1273 compared with recipients of BNT162b2. 10 23 24 We also observed improvement in vaccine effectiveness after a fourth dose. 28

Strengths and limitations of this study

One strength of our study is the number and diversity of sites and inclusion of outcomes of varying severity. Additionally, our sample size was large enough to detect modest waning of vaccine protection and to allow stratification of vaccine effectiveness estimates by immunocompromise status. We rigorously controlled for calendar time and geography such that cases were compared with controls tested during the same week in the same geographical area. This comparison allowed us to distinguish differences in vaccine effectiveness attributable to the waning of vaccine induced immunity from those attributable to the change in dominance of SARS-CoV-2 variants.

Our study has limitations. First there is residual confounding if the timing of primary vaccination or booster doses was related to covid-19 risk in unmeasured ways (eg, mask use or occupation). However, we did not observe substantial vaccine protection in the two weeks after a first dose, which provides reassurance that residual confounding is limited. Second, although our test negative design is intended to avoid selection bias from healthcare seeking behavior, the design could induce selection bias arising from factors associated with a covid-like illness but not with covid-19. For example, inclusion of individuals who had influenza as controls could underestimate vaccine effectiveness due to the correlation between covid-19 vaccination and influenza vaccination. Because fewer than 5% of people in the control group in our study were positive for influenza, we expect this bias to be minimal. Also, we cannot rule out selection bias arising from reliance on clinician directed testing, although we note that almost all the patients admitted to hospital with covid-like illness were tested for SARS-CoV-2. Third, immunocompromised status was ascertained only from diagnostic codes at the time of medical contact (without data on prescriptions or laboratory tests), and we could not distinguish whether a third dose was in a primary series for people who were immunocompromised or was a booster dose. Insufficient adjustment for immunocompromised status might have biased vaccine effectiveness estimates downward, especially for those who were vaccinated and received a booster dose relatively early. However, we found waning protection in stratified analyses among both individuals who were immunocompromised and individuals who were not immunocompromised. Fourth, we did not have viral genomic sequence data. Fifth, although we excluded individuals with documented previous SARS-CoV-2 infection, our data might have missed many past infections. Sensitivity analyses that included people with known previous infections suggest that our vaccine effectiveness estimates would be higher if we could have ascertained and excluded everyone with protection induced by infection. Sixth. although we interpret our analyses of the hospital sample as pertaining to severe covid-19, some patients admitted to hospital could have tested positive for other reasons while being in hospital, especially during the omicron period. 29 To address this, patients were not eligible for inclusion if they had a positive SARS-CoV-2 test result but no diagnoses suggesting a covid-19 infection. Seventh, although our sample includes enough outcome events to yield precise estimates of vaccine effectiveness for the overall adult population, estimates of vaccine effectiveness against admissions to hospital for covid-19 were less precise for younger adults and individuals who were immunocompromised owing to smaller sample sizes. Finally, we pooled data from heterogeneous populations in 10 US states; however, our findings might not be generalizable to other populations.

Policy implications

To evaluate the clinical significance of waning vaccine effectiveness, consideration of the absolute number of people admitted to hospital that would have been prevented had no waning occurred is helpful. However, this number depends on the background rate of severe covid-19, which sometimes varied 10-fold or more over several weeks. In this context, hospital admissions that would be prevented during an anticipated surge are an appropriate alternative. For example, the rate of hospital admissions related to covid-19 reached about 1500 per million unvaccinated adults each week in January 2022 in the US 30 ; if incidence surges that high again, then for every million adults who lose 20 percentage points of vaccine protection, about 300 additional people each week (1500×0.20) will be admitted to hospital owing to covid-19 compared with no waning effect. During the omicron period, vaccine effectiveness waned within six months of the third dose by about 20 percentage points among those without immunocompromising conditions and by more than 40 percentage points among those with immunocompromising conditions. This amount of waning is enough to be relevant for clinical and policy considerations about the need for boosters or other protective measures. Combined with evidence of the safety and immunogenicity of an additional vaccine dose, 31 32 33 our findings lend support for consideration of additional doses beyond the primary series.

Conclusions

Protection conferred by mRNA vaccines against moderate (emergency department or urgent care) and severe (hospital admission) covid-19 waned during the months after primary vaccination, increased substantially after the third dose, and waned again by four to five months. A fourth dose improved vaccine effectiveness among those for whom this booster dose was recommended. Vaccine effectiveness waned less against severe disease than against moderate disease. Vaccine effectiveness of either mRNA vaccine waned among adults of all ages. Among immunocompromised individuals, vaccine effectiveness was lower and waning was more noticeable. These findings support recommendations for a third vaccine dose and consideration of additional booster doses.

What is already known on this topic

Studies of the BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) covid-19 vaccines suggest that their effectiveness decreases over time and increases with an additional dose

How this pattern has varied with the dominant variant and number of vaccine doses, or by age group, immunocompromise status, and vaccine product is, however, not known

What this study adds

Among US adults of all ages, protection provided by either mRNA vaccine against moderate and severe covid-19 waned after primary vaccination, increased markedly after a third dose, and then waned again by four to five months after a third dose

Vaccine effectiveness diminished less against severe disease than against moderate disease

A fourth dose improved vaccine effectiveness among most subgroups for whom it was recommended; overall, our findings support recommendations for broad use of booster doses

Ethics statements

Ethical approval.

This study was approved by the institutional review board of Westat.

Data availability statement

No additional data available.

Contributors: All authors contributed to the design of the study. PKM, SER, RB, and DY performed the statistical analysis. SR, BD, MBD, SAI, NL, KN, ED, SJG, JH, CM, TCO, ALN, PJE, KD, NPK, IL, WFF, NG, KG, KP, NRV, JA, OZ, CR, MB, MG, and BF were involved in data collection and study coordination at partner sites. EPG, PP, MD, JW, CHB, LB, and RL provided data collection and central study coordination at US Centers for Disease Control and Prevention, supervised by MT. JMF and BF produced the first draft of this manuscript and all authors reviewed, edited, and approved the final version. JMF is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This study was funded by the Centers for Disease Control and Prevention through contract 75D30120C07986 to Westat and contract 75D30120C07765 to Kaiser Foundation Hospitals.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: NPK reports institutional support from Pfizer, Merck, GlaxoSmithKline, Sanofi Pasteur, and Protein Sciences (now Sanofi Pasteur) for unrelated studies and institutional support from Pfizer for a covid-19 vaccine trial. CM received institutional support from AstraZeneca for a covid-19 vaccine trial. ALN received institutional support from Pfizer for an unrelated study of meningococcal B vaccine safety during pregnancy. SR received grant funding from GlaxoSmithKline and Biofire Diagnostics. Authors declare no financial relationships with any organizations that might have an interest in the submitted work in the previous three years, and no other relationships or activities that could appear to have influenced the submitted work.

The lead author (JMF) affirms that this manuscript is an accurate and transparent account of the study being reported and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: The individual level dataset from this study is held securely in limited deidentified form at the US Centers for Disease Control and Prevention. Data sharing agreements between CDC and data providers prohibit CDC from making this dataset publicly available. CDC will share aggregate study data once study objectives are complete, consistent with data use agreements with partner institutions.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• Polack FP ,
• Thomas SJ ,
• Kitchin N ,
• C4591001 Clinical Trial Group
• El Sahly HM ,
• COVE Study Group
• Feikin DR ,
• Higdon MM ,
• Abu-Raddad LJ ,
• Goldberg Y ,
• Bar-On YM ,
• Chemaitelly H ,
• Andrews N ,
• Tessier E ,
• Tartof SY ,
• Slezak JM ,
• Fischer H ,
• Nordström P ,
• Nordström A
• Bruxvoort KJ ,
• Thompson MG ,
• Natarajan K ,
• Irving SA ,
• Ferdinands JM ,
• ↵ Centers for Disease Control and Prevention. ACIP Update to the evidence to recommendations for a 2nd COVID-19 booster dose in adults ages 50 years and older and immunocompromised individuals. https://www.cdc.gov/vaccines/acip/recs/grade/covid-19-second-booster-dose-etr.html. Accessed 12 Aug 2022.
• Stenehjem E ,
• Grannis S ,
• ↵ CDC. COVID-19 vaccination in the United States; https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total . Accessed 12 Aug 2022.
• Greenland S ,
• Naleway AL ,
• Lipsitch M ,
• Tchetgen Tchetgen E ,
• Hitchings MDT ,
• Lewnard JA ,
• Feldstein LR ,
• Barkley E ,
• Schelde AB ,
• Schelde A ,
• Moustsen-Helm I ,
• Starrfelt J ,
• Danielsen AS ,
• Buanes EA ,
• Přibylová L ,
• Puzniak L ,
• Tenforde MW ,
• Gaglani M ,
• Kitchen SA ,
• ↵ Centers for Disease Control and Prevention. COVID Data Tracker. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; CDC COVID data tracker: COVID-NET hospitalizations by vaccination status. https://covid.cdc.gov/covid-data-tracker/#covidnet-hospitalizations-vaccination . Accessed 4 May 2022.
• Deming ME ,
• DMID 21-0012 Study Group

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Effectiveness of Pfizer-BioNTech mRNA Vaccination Against COVID-19 Hospitalization Among Persons Aged 12–18 Years — United States, June–September 2021

Weekly / October 22, 2021 / 70(42);1483–1488

On October 19, 2021, this report was posted online as an MMWR Early Release.

Samantha M. Olson, MPH 1, *; Margaret M. Newhams, MPH 2, *; Natasha B. Halasa, MD 3 ; Ashley M. Price, MPH 1 ; Julie A. Boom, MD 4 ; Leila C. Sahni, PhD 4 ; Katherine Irby, MD 5 ; Tracie C. Walker, MD 6 ; Stephanie P. Schwartz, MD 6 ; Pia S. Pannaraj, MD 7 ; Aline B. Maddux, MD 8 ; Tamara T. Bradford, MD 9 ; Ryan A. Nofziger, MD 10 ; Benjamin J. Boutselis 2 ; Melissa L. Cullimore, MD 11 ; Elizabeth H. Mack, MD 12 ; Jennifer E. Schuster, MD 13 ; Shira J. Gertz, MD 14 ; Natalie Z. Cvijanovich, MD 15 ; Michele Kong, MD 16 ; Melissa A. Cameron, MD 17 ; Mary A. Staat, MD 18 ; Emily R. Levy, MD 19 ; Brandon M. Chatani, MD 20 ; Kathleen Chiotos, MD 21 ; Laura D. Zambrano, PhD 1 ; Angela P. Campbell, MD 1 ; Manish M. Patel, MD 1, *; Adrienne G. Randolph, MD 2 ,22, *; Overcoming COVID-19 Investigators ( View author affiliations )

What is already known about this topic?

Persons aged 12–18 years are eligible to receive COVID-19 vaccine. Currently, data are lacking on real-world vaccine effectiveness against COVID-19 hospitalization in adolescents.

What is added by this report?

Among hospitalized U.S. patients aged 12–18 years, vaccine effectiveness of 2 doses of Pfizer-BioNTech vaccine against COVID-19 hospitalization during June–September 2021, was 93% (95% confidence interval = 83%–97%).

What are the implications for public health practice?

This evaluation demonstrated that 2 doses of Pfizer-BioNTech vaccine were highly effective in preventing COVID-19 hospitalization among persons aged 12–18 years. Findings reinforce the importance of vaccination to protect U.S. youths against severe COVID-19.

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Pfizer-BioNTech COVID-19 vaccine is authorized for use in children and adolescents aged 12–15 years and is licensed by the Food and Drug Administration (FDA) for persons aged ≥16 ( 1 ). A randomized placebo-controlled trial demonstrated an efficacy of 100% (95% confidence interval [CI] = 75.3%–100%) in preventing outpatient COVID-19 in persons aged 12–15 years ( 2 ); however, data among adolescents on vaccine effectiveness (VE) against COVID-19 in real-world settings are limited, especially among hospitalized patients. In early September 2021, U.S. pediatric COVID-19 hospitalizations reached the highest level during the pandemic ( 3 , 4 ). In a test-negative, case-control study at 19 pediatric hospitals in 16 states during June 1–September 30, 2021, the effectiveness of 2 doses of Pfizer-BioNTech vaccine against COVID-19 hospitalization was assessed among children and adolescents aged 12–18 years. Among 464 hospitalized persons aged 12–18 years (179 case-patients and 285 controls), the median age was 15 years, 72% had at least one underlying condition, including obesity, and 68% attended in-person school. Effectiveness of 2 doses of Pfizer-BioNTech vaccine against COVID-19 hospitalization was 93% (95% CI = 83%–97%), during the period when B.1.617.2 (Delta) was the predominant variant. This evaluation demonstrated that 2 doses of Pfizer-BioNTech vaccine are highly effective at preventing COVID-19 hospitalization among persons aged 12–18 years and reinforces the importance of vaccination to protect U.S. youths against severe COVID-19.

This study used a test-negative design, similar to other postauthorization VE evaluations, in which vaccine performance is assessed by comparing the odds of antecedent vaccination among laboratory-confirmed case-patients hospitalized with COVID-19 and hospitalized controls without COVID-19 ( 5 ). Participants were aged 12–18 years and were admitted to 19 pediatric hospitals in the CDC-funded Overcoming COVID-19 Network during June 1–September 30, 2021 ( 6 ). Case-patients † were hospitalized with symptomatic COVID-19–like illness and a positive SARS-CoV-2 reverse transcription–polymerase chain reaction (RT-PCR) or antigen test result; no case-patients received a diagnosis of multisystem inflammatory syndrome in children (MIS-C) during their enrolling hospitalization. Two hospitalized control groups were enrolled: 1) patients with symptoms compatible with COVID-19 with negative SARS-CoV-2 RT-PCR or antigen test results (test-negative) and 2) patients without COVID-19–associated symptoms who might or might not have received SARS-CoV-2 testing (syndrome-negative). § Baseline demographic characteristics, clinical information about the current illness, and SARS-CoV-2 testing history were obtained through parent or guardian interviews performed by trained study personnel and review of electronic medical records. Parents or guardians were asked about COVID-19 vaccination history, including number of doses and whether the most recent dose occurred in the last 14 days, location where vaccination occurred, vaccine manufacturer, and availability of a COVID-19 vaccination card. Study personnel searched sources, including state vaccination registries, electronic medical records, or other sources (including documentation from pediatricians) to verify reported or unknown vaccination status.

Patients were considered to have received COVID-19 vaccination based on source documentation or by plausible self-report (vaccination dates and location were provided). Because vaccination with Moderna or Janssen vaccine were not authorized for persons aged <18 years at the time of this evaluation, only receipt of Pfizer-BioNTech vaccine was assessed in this analysis. The study included fully vaccinated persons aged 12–18 years with COVID-19 vaccination status categorized as 1) unvaccinated (no receipt of any COVID-19 vaccine before illness onset ¶ ) or 2) fully vaccinated (receipt of 2 doses of Pfizer-BioNTech vaccine, with the second dose administered ≥14 days before illness onset). Patients who were partially vaccinated (i.e., received only 1 dose or received a second dose <14 days before illness onset) were excluded from the analysis. Descriptive statistics were used to compare characteristics of case-patients and controls. Pearson chi-square tests (categorical outcomes) or Wilcoxon rank-sum test for medians (continuous outcomes) were used to make comparisons between groups; statistical significance was defined as p<0.05. VE against COVID-19 hospitalization was calculated by comparing the odds of full COVID-19 vaccination among case-patients and controls using the equation VE = 100 × (1 – adjusted odds ratio), determined from logistic regression models. Firth penalized regression was used for models with six or fewer vaccinated case-patients. Models were adjusted for U.S. Census region, calendar month of admission, age, sex, and race/ethnicity ( 5 ). Other factors were assessed (underlying health conditions and social vulnerability index) but were not included in the final model because they did not change the odds ratio of vaccination by >5% ( 5 ). Sensitivity analyses were performed to evaluate whether VE differed by control group. VE was also stratified by age groups (12–15 and 16–18 years). Statistical analyses were conducted using SAS (version 9.4; SAS Institute). This activity was reviewed by CDC and the other participating institutions and was conducted consistent with applicable federal law and CDC policy.**

During June 1–September 30, 2021, among 572 eligible patients, 108 were excluded, including 56 who were partially vaccinated or who completed vaccination 0–13 days before illness onset, 20 who were hospitalized >14 days after illness onset, 14 case-patients who received a positive SARS-CoV-2 test result but were admitted for non–COVID-19 reasons, and 18 who were excluded for other reasons. †† The 464 patients in the final analysis comprised 179 case-patients and 285 controls (122 [43%] test-negative and 163 [57%] syndrome-negative). Among case-patients and all controls, the median age was 15 years, 72% had at least one underlying condition, including obesity, and 68% attended in-person school ( Table 1 ). Vaccination coverage was 3% among case-patients and 33% among controls. Case-patients more frequently resided in areas with higher social vulnerability index scores §§ (median = 0.67) than did controls (median = 0.58) (p = 0.02). The distribution of most underlying conditions was not significantly different between case-patients and controls; however, diabetes was more prevalent among case-patients (12%) than among controls (5%) (p = 0.01), and neurologic or neuromuscular disorders were more prevalent among controls (28%) than among case-patients (12%) (p<0.01).

Among 179 COVID-19 case-patients, six (3%) were vaccinated and 173 (97%) were unvaccinated ( Table 2 ). Overall, 77 (43%) case-patients were admitted to an intensive care unit, and 29 (16%) critically ill case-patients received life support during hospitalization, including invasive mechanical ventilation, vasoactive infusions, or extracorporeal membrane oxygenation; two of these 29 critically ill patients (7%) died. All 77 case-patients admitted to the intensive care unit, all 29 critically ill case-patients, and both deaths occurred among unvaccinated case-patients. Among 169 case-patients with available hospital discharge data, the median length of hospital stay was 5 days (interquartile range [IQR] = 2–9 days) for unvaccinated case-patients and 3 days (IQR = 2–4 days) for vaccinated case-patients.

VE against COVID-19 hospitalization was 93% (95% CI = 83%–97%) ( Table 3 ), during the period when B.1.617.2 (Delta) was the predominant variant. Among all 99 patients classified as fully vaccinated, 96 (97%) had documentation of vaccination status. In a sensitivity analysis, VE was similar for each control group assessed independently (test-negative VE = 94%, 95% CI = 85%–98%; syndrome-negative VE = 92%, 95% CI = 80%–97%). In addition, VE was similar among 106 case-patients aged 12–15 years (VE = 91%) and 73 case-patients aged 16–18 years (VE = 94%).

During June–September 2021, receipt of 2 doses of Pfizer-BioNTech vaccine provided a high level of protection against COVID-19 hospitalization among children and adolescents aged 12–18 years in a real-world evaluation at 19 U.S. pediatric hospitals. This evaluation demonstrated that nearly all (97%) persons aged 12–18 years hospitalized with COVID-19 were unvaccinated (versus fully vaccinated) and reinforces the importance of vaccination to protect U.S. youths against severe COVID-19.

These findings are consistent with efficacy data from the Pfizer-BioNTech clinical trial among persons aged 12–15 years, which found an observed vaccine efficacy of 100% (95% CI = 75.3%–100%) ( 2 ). However, that trial was not powered to assess efficacy against hospitalized COVID-19. Another study reported VE against COVID-19 hospitalization of 81% for fully vaccinated patients aged 12–15 years; however, that study assessed only 45 cases and thus had wide CIs (–55% to 98%) ( 7 ). One other evaluation from Israel evaluated Pfizer-BioNTech VE against SARS-CoV-2 infection in patients aged 12–15 years and found similarly high VE (91.5%; 95% CI = 88.2%–93.9%), but the study did not include enough cases to examine VE against hospitalized COVID-19 ( 8 ). In this real-world analysis, in which all case-patients were hospitalized, vaccination reduced the risk for COVID-19 hospitalization in persons aged 12–18 years by 93%. Moreover, 16% of patients hospitalized with COVID-19 had critical illness requiring life support; all were unvaccinated. Taken together, these findings contribute to the growing knowledge regarding VE against pediatric COVID-19, as updated FDA Emergency Use Authorizations to expand COVID-19 vaccine eligibility to younger ages are considered.

The findings in this report are subject to at least six limitations. First, VE could not be assessed directly against specific variants; the predominant variant during the evaluation period was B.1.617.2 (Delta) ( 9 ). Second, the sample was too small to assess VE by underlying conditions or by other subgroups of interest, including against critical illness. Third, because this analysis included self-reported data from some participants, vaccination status might have been misclassified in a few case-patients or controls, or there might have been imperfect recollection of illness onset dates. Fourth, because of high levels of COVID-19 transmission in southern states during this period, the majority of patients in this analysis (61%) were from the South; this might limit the representativeness of the sample. Fifth, this report only assessed VE for the Pfizer-BioNTech vaccine. Finally, because vaccination of persons aged 12–15 years commenced only recently, evaluation of duration of protection was not possible.

As of October 18, 2021, 46% of U.S. children and adolescents aged 12–15 years and 54% of those aged 16–17 years were fully vaccinated against COVID-19 ( 10 ). In a multistate network of U.S. pediatric hospitals, this study found that receipt of 2 doses of Pfizer-BioNTech vaccine was highly effective in preventing COVID-19 hospitalization among persons aged 12–18 years. These data suggest that increasing vaccination coverage among this group could reduce the incidence of severe COVID-19 in the United States. Further, as in-person school attendance increases, multicomponent preventive measures to reduce the incidence of severe COVID-19 among adolescents, including vaccination, are imperative. ¶¶

Overcoming COVID-19 Investigators

Meghan Murdock, Children’s of Alabama, Birmingham, Alabama; Mary Glas Gaspers, University of Arizona, Tucson, Arizona; Katri V. Typpo, University of Arizona, Tucson, Arizona; Connor P. Kelley, University of Arizona, Tucson, Arizona; Ronald C. Sanders, Arkansas Children’s Hospital, Little Rock, Arkansas; Masson Yates, Arkansas Children’s Hospital, Little Rock, Arkansas; Chelsea Smith, Arkansas Children’s Hospital, Little Rock, Arkansas; Katheryn Crane, Rady Children’s Hospital, San Diego, California; Geraldina Lionetti, University of California, San Francisco Benioff Children’s Hospital Oakland, Oakland, California; Juliana Murcia-Montoya, University of California, San Francisco Benioff Children’s Hospital Oakland, Oakland, California; Matt S. Zinter, University of California, San Francisco Benioff Children’s Hospital, San Francisco, California; Denise Villarreal-Chico, University of California, San Francisco Benioff Children’s Hospital, San Francisco, California; Adam L. Skura, Children’s Hospital Los Angeles, Los Angeles, California; Daniel Hakimi, Children’s Hospital Los Angeles, Los Angeles, California; Harvey Peralta, Children’s Hospital Los Angeles, Los Angeles, California; Emily Port, Children’s Hospital Colorado, Aurora, Colorado; Imogene A. Carson, Children’s Hospital Colorado, Aurora, Colorado; Justin M. Lockwood, Children’s Hospital Colorado, Aurora, Colorado; Satoshi Kamidani, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia; Keiko M. Tarquinio, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia; Caitlen E. Taylor, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia; Kelly N. Michelson, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois; Bria M. Coates, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois; Marla S. Johnston, Children’s Hospital of New Orleans, New Orleans, Louisiana; Suden Kucukak, Boston Children’s Hospital, Boston, Massachusetts; Sabrina R. Chen, Boston Children’s Hospital, Boston, Massachusetts; Amber O. Orzel, Boston Children’s Hospital, Boston, Massachusetts; Edie Weller, Boston Children’s Hospital, Boston, Massachusetts; Laura Berbert, Boston Children’s Hospital, Boston, Massachusetts; Jie He, Boston Children’s Hospital, Boston, Massachusetts; Sabrina M. Heidemann, Children’s Hospital of Michigan, Detroit, Michigan; Janet R. Hume, University of Minnesota Masonic Children’s Hospital, Minneapolis, Minnesota; Ellen R. Bruno, University of Minnesota Masonic Children’s Hospital, Minneapolis, Minnesota; Lexie A. Goertzen, University of Minnesota Masonic Children’s Hospital, Minneapolis, Minnesota; Supriya Behl, Mayo Clinic, Rochester, Minnesota; Noelle M. Drapeau, Mayo Clinic, Rochester, Minnesota; Shannon M. Hill, Children’s Mercy Hospital, Kansas City, Missouri; Abigail Kietzman, Children’s Mercy Hospital, Kansas City, Missouri; Valerie Rinehart, Children’s Hospital & Medical Center, Omaha, Nebraska; Lauren A. Hoody, Children’s Hospital & Medical Center, Omaha, Nebraska; Angelo G. Navas, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Paris C. Bennett, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Nicole A. Twinem, Rebecca D. Considine Research Institute, Akron Children’s Hospital Akron, Ohio; Merry L. Tomcany, Rebecca D. Considine Research Institute, Akron Children’s Hospital Akron, Ohio; Chelsea C. Rohlfs, Cincinnati Children’s Hospital, Cincinnati, Ohio; Rebecca L. Douglas, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania; Megan M. Bickford, Medical University of South Carolina Children’s Health, Charleston, South Carolina; Lauren E. Wakefield, Medical University of South Carolina Children’s Health, Charleston, South Carolina; Janet B. Nicotera, Monroe Carell Jr. Children’s Hospital at Vanderbilt, Nashville, Tennessee; Meenakshi Golchha, Monroe Carell Jr. Children’s Hospital at Vanderbilt, Nashville, Tennessee; Jennifer N. Oates, Texas Children’s Hospital, Houston, Texas

Corresponding author: Samantha M. Olson, [email protected] .

1 CDC COVID-19 Response Team; 2 Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts; 3 Division of Pediatric Infectious Diseases, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee; 4 Department of Pediatrics, Baylor College of Medicine, Immunization Project, Texas Children’s Hospital, Houston, Texas; 5 Section of Pediatric Critical Care, Department of Pediatrics, Arkansas Children’s Hospital, Little Rock, Arkansas; 6 Department of Pediatrics, University of North Carolina at Chapel Hill Children’s Hospital, Chapel Hill, North Carolina; 7 Division of Infectious Diseases, Children’s Hospital Los Angeles and Departments of Pediatrics and Molecular Microbiology and Immunology, University of Southern California, Los Angeles, California; 8 Department of Pediatrics, Section of Critical Care Medicine, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, Colorado; 9 Department of Pediatrics, Division of Cardiology, Louisiana State University Health Sciences Center and Children’s Hospital of New Orleans, New Orleans, Louisiana; 10 Division of Critical Care Medicine, Department of Pediatrics, Akron Children’s Hospital, Akron, Ohio; 11 Division of Pediatric Critical Care, Department of Pediatrics, Children’s Hospital and Medical Center, Omaha, Nebraska; 12 Division of Pediatric Critical Care Medicine, Medical University of South Carolina, Charleston, South Carolina; 13 Division of Pediatric Infectious Diseases, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri; 14 Division of Pediatric Critical Care, Department of Pediatrics, Saint Barnabas Medical Center, Livingston, New Jersey; 15 Division of Critical Care Medicine, University of California, San Francisco Benioff Children’s Hospital Oakland, Oakland, California; 16 Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama; 17 Division of Pediatric Hospital Medicine, University of California San Diego-Rady Children’s Hospital, San Diego, California; 18 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; 19 Divisions of Pediatric Infectious Diseases and Pediatric Critical Care Medicine, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota; 20 Division of Pediatric Infectious Diseases, Department of Pediatrics, UHealth/Holtz Children’s Hospital, Miami, Florida; 21 Division of Critical Care Medicine, Department of Anesthesiology and Critical Care, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania; 22 Departments of Anesthesia and Pediatrics, Harvard Medical School, Boston, Massachusetts.

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Natasha B. Halasa reports grants from Sanofi and Quidel outside of the submitted work. Jennifer E. Schuster reports grants from Merck outside of the submitted work. Pia S. Pannaraj reports grants from AstraZeneca and Pfizer outside of the submitted work, and personal fees from Seqirus and Nestle outside of the submitted work. No other potential conflicts of interest were disclosed.

* These authors contributed equally to this report.

† Symptomatic COVID-19–like illness was defined as one or more of the following: fever, cough, shortness of breath, loss of taste, loss of smell, gastrointestinal symptoms (e.g., diarrhea, vomiting, or stomachache), use of respiratory support (e.g., high flow oxygen by nasal cannula, new invasive or noninvasive ventilation) for the acute illness, or new pulmonary findings on chest imaging consistent with pneumonia. Patients with COVID-19 as the primary reason for admission were categorized as symptomatic COVID-19 patients. Seventeen case-patients had some missing data on positive testing and were not retested at the hospital: 15 patients had positive test results with a date and unconfirmed test type, and two patients had positive test results but were missing the date of testing.

§ Syndrome-negative controls had no signs or symptoms of COVID-19 (including fever, cough, shortness of breath, loss of taste, loss of smell, gastrointestinal symptoms, use of respiratory support for the acute illness, or new pulmonary findings on chest imaging consistent with pneumonia) and were not clinically suspected to have COVID-19. Among 163 syndrome-negative controls, 10 (6%) did not receive SARS-CoV-2 testing.

¶ The date of illness onset was used for case-patients and controls with COVID-19–like illness with median value imputed if missing. For controls without COVID-19–like illness, the date of admission was used for a date of illness onset, also referred to as illness onset for this report.

** 45 C.F.R. part 46.102(l)(2), 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

†† Other reasons for excluding patients from the analysis included SARS-CoV-2 testing >10 days after illness onset or >3 days from hospitalization (three), onset of COVID-19–like illness after admission (14), and documentation of full vaccination with Moderna COVID-19 vaccine (one).

§§ Documentation for CDC/ATSDR social vulnerability index (SVI) is available at https://www.atsdr.cdc.gov/placeandhealth/svi/index.html . Median SVI for case-patients and controls are based on US 2018 SVI data.

¶¶ Guidance for COVID-19 prevention in kindergarten through grade 12 schools is available at https://www.cdc.gov/coronavirus/2019-ncov/community/schools-childcare/k-12-guidance.html .

• Food and Drug Administration. FDA approves first COVID-19 vaccine. Silver Spring, MD: US Department of Health and Human Services, Food and Drug Administration; 2021. https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine
• Frenck RW Jr, Klein NP, Kitchin N, et al.; C4591001 Clinical Trial Group. Safety, immunogenicity, and efficacy of the BNT162b2 Covid-19 vaccine in adolescents. N Engl J Med 2021;385:239–50. https://doi.org/10.1056/NEJMoa2107456 PMID:34043894
• CDC. COVID-NET: laboratory-confirmed COVID-19–associated hospitalizations. Atlanta, GA: US Department of Health and Human Services, CDC; 2021. Accessed October 15, 2021. https://gis.cdc.gov/grasp/covidnet/COVID19_5.html
• Delahoy MJ, Ujamaa D, Whitaker M, et al.; COVID-NET Surveillance Team. Hospitalizations associated with COVID-19 among children and adolescents—COVID-NET, 14 states, March 1, 2020–August 14, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1255–60. https://doi.org/10.15585/mmwr.mm7036e2 PMID:34499627
• Tenforde MW, Patel MM, Ginde AA, et al.; Influenza and Other Viruses in the Acutely Ill (IVY) Network. Effectiveness of SARS-CoV-2 mRNA vaccines for preventing Covid-19 hospitalizations in the United States. Clin Infect Dis 2021. Epub August 6, 2021. https://doi.org/10.1093/cid/ciab687 PMID:34358310
• Son MBF, Murray N, Friedman K, et al.; Overcoming COVID-19 Investigators. Multisystem inflammatory syndrome in children—initial therapy and outcomes. N Engl J Med 2021;385:23–34. https://doi.org/10.1056/NEJMoa2102605 PMID:34133855
• Tartof SY, Slezak JM, Fischer H, et al. Effectiveness of mRNA BNT162b2 COVID-19 vaccine up to 6 months in a large integrated health system in the USA: a retrospective cohort study. Lancet 2021. Epub October 4, 2021. PMID:34619098
• Glatman-Freedman A, Hershkovitz Y, Kaufman Z, Dichtiar R, Keinan-Boker L, Bromberg M. Effectiveness of BNT162b2 vaccine in adolescents during outbreak of SARS-CoV-2 Delta variant infection, Israel, 2021. Emerg Infect Dis 2021. Epub September 27, 2021. PMID:34570694
• CDC. COVID data tracker: variant proportions. Atlanta, GA: US Department of Health and Human Services, CDC; 2021. Accessed October 15, 2021. https://covid.cdc.gov/covid-data-tracker/#variant-proportions
• CDC. COVID data tracker: percent of people receiving COVID-19 vaccine by age and date reported to CDC, United States. Atlanta, GA: US Department of Health and Human Services, CDC; 2021. Accessed October 18, 2021. https://covid.cdc.gov/covid-data-tracker/#vaccination-demographics-trends

Abbreviations: IQR = interquartile range; SVI = social vulnerability index. * Patients were enrolled from 19 pediatric hospitals in 16 states. Northeast : Boston Children’s Hospital (Massachusetts), Saint Barnabas Medical Center (New Jersey), Midwest : Akron Children’s Hospital (Ohio), Children’s Mercy Kansas City (Missouri), Children’s Hospital and Medical Center: Nebraska (Nebraska), Cincinnati Children’s Hospital Medical Center (Ohio), Mayo Clinic (Minnesota), South : Arkansas Children’s Hospital (Arkansas), University of North Carolina at Chapel Hill Children’s Hospital (North Carolina), Children’s of Alabama (Alabama), Monroe Carell Jr. Children’s Hospital at Vanderbilt (Tennessee), Medical University of South Carolina Children’s Health (South Carolina), Texas Children’s Hospital (Texas), Holtz Children’s Hospital (Florida), Children’s Hospital of New Orleans (Louisiana), West : University of California San Francisco Benioff Children’s Hospital Oakland (California), Children’s Hospital Colorado (Colorado), Children’s Hospital Los Angeles (California), University of California San Diego-Rady Children’s Hospital (California). † Testing for statistical significance was conducted using the Pearson chi-square test to compare categorical variables or Wilcoxon rank-sum test for medians to compare continuous data. § CDC/ATSDR SVI documentation is available at https://www.atsdr.cdc.gov/placeandhealth/svi/index.html . Median SVI for case-patients and controls are based on US 2018 SVI data. ¶ Other chronic conditions included rheumatologic/autoimmune disorder, hematologic disorder, renal or urologic dysfunction, gastrointestinal/hepatic disorder, metabolic or confirmed or suspected genetic disorder (including obesity), or atopic or allergic condition. ** COVID-19 vaccination status included the following two categories: 1) unvaccinated, defined as no receipt of any SARS-CoV-2 vaccine before illness onset and 2) fully vaccinated, defined as receipt of both doses of a 2-dose Pfizer-BioNTech vaccination ≥14 days before illness onset. †† Dates are based on those with documented vaccination, not plausible self-report. The date of illness onset was used for case-patients and controls with COVID-19–like illness with median value imputed if missing. For controls without COVID-19–like illness, the date of admission was used for a date of illness onset, also referred to as illness onset for this report.

Abbreviations: ICU = intensive care unit; IQR = interquartile range. * COVID-19 vaccination status included the following two categories: 1) unvaccinated, defined as no receipt of any SARS-CoV-2 vaccine before illness onset and 2) fully vaccinated, defined as receipt of both doses of a 2-dose Pfizer-BioNTech vaccination ≥14 days before illness onset. † Patients were vaccinated and unvaccinated persons aged 12–18 years enrolled from 19 pediatric hospitals in 16 states. Northeast : Boston Children’s Hospital (Massachusetts), Saint Barnabas Medical Center (New Jersey), Midwest : Akron Children’s Hospital (Ohio), Children’s Mercy Kansas City (Missouri), Children’s Hospital and Medical Center: Nebraska (Nebraska), Cincinnati Children’s Hospital Medical Center (Ohio), Mayo Clinic (Minnesota), South : Arkansas Children’s Hospital (Arkansas), University of North Carolina at Chapel Hill Children’s Hospital (North Carolina), Children’s of Alabama (Alabama), Monroe Carell Jr. Children’s Hospital at Vanderbilt (Tennessee), Medical University of South Carolina Children’s Health (South Carolina), Texas Children’s Hospital (Texas), Holtz Children’s Hospital (Florida), Children’s Hospital of New Orleans (Louisiana), West : University of California San Francisco Benioff Children’s Hospital Oakland (California), Children’s Hospital Colorado (Colorado), Children’s Hospital Los Angeles (California), University of California San Diego-Rady Children’s Hospital (California).

Abbreviation: CI = confidence interval. * Vaccine effectiveness estimates were based on odds of antecedent vaccination in case-patients vs controls adjusted for U.S. Census region, calendar month of admission, continuous age in years, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic other, Hispanic of any race, or unknown). Firth penalized regression was used for models with six or fewer vaccinated cases. † COVID-19 vaccination status included the following two categories: 1) unvaccinated, defined as no receipt of any SARS-CoV-2 vaccine before illness onset and 2) fully vaccinated, defined as receipt of both doses of a 2-dose Pfizer-BioNTech vaccination ≥14 days before illness onset. § Patients were enrolled from 19 pediatric hospitals in 16 states. Northeast : Boston Children’s Hospital (Massachusetts), Saint Barnabas Medical Center (New Jersey), Midwest : Akron Children’s Hospital (Ohio), Children’s Mercy Kansas City (Missouri), Children’s Hospital and Medical Center: Nebraska (Nebraska), Cincinnati Children’s Hospital Medical Center (Ohio), Mayo Clinic (Minnesota), South : Arkansas Children’s Hospital (Arkansas), University of North Carolina at Chapel Hill Children’s Hospital (North Carolina), Children’s of Alabama (Alabama), Monroe Carell Jr. Children’s Hospital at Vanderbilt (Tennessee), Medical University of South Carolina Children’s Health (South Carolina), Texas Children’s Hospital (Texas), Holtz Children’s Hospital (Florida), Children’s Hospital of New Orleans (Louisiana), West : University of California San Francisco Benioff Children’s Hospital Oakland (California), Children’s Hospital Colorado (Colorado), Children’s Hospital Los Angeles (California), University of California San Diego-Rady Children’s Hospital (California).

Suggested citation for this article: Olson SM, Newhams MM, Halasa NB, et al. Effectiveness of Pfizer-BioNTech mRNA Vaccination Against COVID-19 Hospitalization Among Persons Aged 12–18 Years — United States, June–September 2021. MMWR Morb Mortal Wkly Rep 2021;70:1483–1488. DOI: http://dx.doi.org/10.15585/mmwr.mm7042e1 .

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Pfizer and BioNTech Conclude Phase 3 Study of COVID-19 Vaccine Candidate, Meeting All Primary Efficacy Endpoints

• Primary efficacy analysis demonstrates BNT162b2 to be 95% effective against COVID-19 beginning 28 days after the first dose; 170 confirmed cases of COVID-19 were evaluated, with 162 observed in the placebo group versus 8 in the vaccine group
• Efficacy was consistent across age, gender, race and ethnicity demographics; observed efficacy in adults over 65 years of age was over 94%
• Safety data milestone required by U.S. Food and Drug Administration (FDA) for Emergency Use Authorization (EUA) has been achieved
• Data demonstrate vaccine was well tolerated across all populations with over 43,000 participants enrolled; no serious safety concerns observed; the only Grade 3 adverse event greater than 2% in frequency was fatigue at 3.8% and headache at 2.0%
• Companies plan to submit within days to the FDA for EUA and share data with other regulatory agencies around the globe
• The companies expect to produce globally up to 50 million vaccine doses in 2020 and up to 1.3 billion doses by the end of 2021
• Pfizer is confident in its vast experience, expertise and existing cold-chain infrastructure to distribute the vaccine around the world

NEW YORK & MAINZ, Germany--(BUSINESS WIRE)-- Pfizer Inc. (NYSE: PFE) and BioNTech SE (Nasdaq: BNTX) today announced that, after conducting the final efficacy analysis in their ongoing Phase 3 study, their mRNA-based COVID-19 vaccine candidate, BNT162b2, met all of the study’s primary efficacy endpoints. Analysis of the data indicates a vaccine efficacy rate of 95% (p<0.0001) in participants without prior SARS-CoV-2 infection (first primary objective) and also in participants with and without prior SARS-CoV-2 infection (second primary objective), in each case measured from 7 days after the second dose. The first primary objective analysis is based on 170 cases of COVID-19, as specified in the study protocol, of which 162 cases of COVID-19 were observed in the placebo group versus 8 cases in the BNT162b2 group. Efficacy was consistent across age, gender, race and ethnicity demographics. The observed efficacy in adults over 65 years of age was over 94%.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20201118005595/en/

There were 10 severe cases of COVID-19 observed in the trial, with nine of the cases occurring in the placebo group and one in the BNT162b2 vaccinated group.

To date, the Data Monitoring Committee for the study has not reported any serious safety concerns related to the vaccine. A review of unblinded reactogenicity data from the final analysis which consisted of a randomized subset of at least 8,000 participants 18 years and older in the phase 2/3 study demonstrates that the vaccine was well tolerated, with most solicited adverse events resolving shortly after vaccination. The only Grade 3 (severe) solicited adverse events greater than or equal to 2% in frequency after the first or second dose was fatigue at 3.8% and headache at 2.0% following dose 2. Consistent with earlier shared results, older adults tended to report fewer and milder solicited adverse events following vaccination.

In addition, the companies announced that the safety milestone required by the U.S. Food and Drug Administration (FDA) for Emergency Use Authorization (EUA) has been achieved. Pfizer and BioNTech plan to submit a request within days to the FDA for an EUA based on the totality of safety and efficacy data collected to date, as well as manufacturing data relating to the quality and consistency of the vaccine. These data also will be submitted to other regulatory agencies around the world.

“The study results mark an important step in this historic eight-month journey to bring forward a vaccine capable of helping to end this devastating pandemic. We continue to move at the speed of science to compile all the data collected thus far and share with regulators around the world,” said Dr. Albert Bourla, Pfizer Chairman and CEO. “With hundreds of thousands of people around the globe infected every day, we urgently need to get a safe and effective vaccine to the world.”

“We are grateful that the first global trial to reach the final efficacy analysis mark indicates that a high rate of protection against COVID-19 can be achieved very fast after the first 30 µg dose, underscoring the power of BNT162 in providing early protection,” said Ugur Sahin, M.D., CEO and Co-founder of BioNTech. “These achievements highlight the potential of mRNA as a new drug class. Our objective from the very beginning was to design and develop a vaccine that would generate rapid and potent protection against COVID-19 with a benign tolerability profile across all ages. We believe we have achieved this with our vaccine candidate BNT162b2 in all age groups studied so far and look forward to sharing further details with the regulatory authorities. I want to thank all the devoted women and men who contributed to this historically unprecedented achievement. We will continue to work with our partners and governments around the world to prepare for global distribution in 2020 and beyond.”

The Phase 3 clinical trial of BNT162b2 began on July 27 and has enrolled 43,661 participants to date, 41,135 of whom have received a second dose of the vaccine candidate as of November 13, 2020. Approximately 42% of global participants and 30% of U.S. participants have racially and ethnically diverse backgrounds, and 41% of global and 45% of U.S. participants are 56-85 years of age. A breakdown of the diversity of clinical trial participants can be found here from approximately 150 clinical trials sites in United States, Germany, Turkey, South Africa, Brazil and Argentina. The trial will continue to collect efficacy and safety data in participants for an additional two years.

Based on current projections, the companies expect to produce globally up to 50 million vaccine doses in 2020 and up to 1.3 billion doses by the end of 2021. Four of Pfizer’s facilities are part of the manufacturing and supply chain; St. Louis, MO; Andover, MA; and Kalamazoo, MI in the U.S.; and Puurs in Belgium. BioNTech’s German sites will also be leveraged for global supply.

Pfizer is confident in its vast experience, expertise and existing cold-chain infrastructure to distribute the vaccine around the world. The companies have developed specially designed, temperature-controlled thermal shippers utilizing dry ice to maintain temperature conditions of -70°C±10°C. They can be used be as temporary storage units for 15 days by refilling with dry ice. Each shipper contains a GPS-enabled thermal sensor to track the location and temperature of each vaccine shipment across their pre-set routes leveraging Pfizer’s broad distribution network.

Pfizer and BioNTech plan to submit the efficacy and safety data from the study for peer-review in a scientific journal once analysis of the data is completed.

About Pfizer: Breakthroughs That Change Patients’ Lives

At Pfizer, we apply science and our global resources to bring therapies to people that extend and significantly improve their lives. We strive to set the standard for quality, safety and value in the discovery, development and manufacture of health care products, including innovative medicines and vaccines. Every day, Pfizer colleagues work across developed and emerging markets to advance wellness, prevention, treatments and cures that challenge the most feared diseases of our time. Consistent with our responsibility as one of the world's premier innovative biopharmaceutical companies, we collaborate with health care providers, governments and local communities to support and expand access to reliable, affordable health care around the world. For more than 150 years, we have worked to make a difference for all who rely on us. We routinely post information that may be important to investors on our website at www.Pfizer.com . In addition, to learn more, please visit us on www.Pfizer.com and follow us on Twitter at @Pfizer and @Pfizer News , LinkedIn , YouTube and like us on Facebook at Facebook.com/Pfizer .

Pfizer Disclosure Notice

The information contained in this release is as of November 18, 2020. Pfizer assumes no obligation to update forward-looking statements contained in this release as the result of new information or future events or developments.

This release contains forward-looking information about Pfizer’s efforts to combat COVID-19, the collaboration between BioNTech and Pfizer to develop a potential COVID-19 vaccine, the BNT162 mRNA vaccine program, and modRNA candidate BNT162b2 (including qualitative assessments of available data, potential benefits, expectations for clinical trials, anticipated timing of regulatory submissions and anticipated manufacturing, distribution and supply), that involves substantial risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Risks and uncertainties include, among other things, the uncertainties inherent in research and development, including the ability to meet anticipated clinical endpoints, commencement and/or completion dates for clinical trials, regulatory submission dates, regulatory approval dates and/or launch dates, as well as risks associated with clinical data (including the Phase 3 data that is the subject of this release), including the possibility of unfavorable new preclinical or clinical trial data and further analyses of existing preclinical or clinical trial data; the ability to produce comparable clinical or other results, including the rate of vaccine effectiveness and safety and tolerability profile observed to date, in additional analyses of the Phase 3 trial or in larger, more diverse populations upon commercialization; the risk that clinical trial data are subject to differing interpretations and assessments, including during the peer review/publication process, in the scientific community generally, and by regulatory authorities; whether and when data from the BNT162 mRNA vaccine program will be published in scientific journal publications and, if so, when and with what modifications; whether regulatory authorities will be satisfied with the design of and results from these and any future preclinical and clinical studies; whether and when any biologics license and/or emergency use authorization applications may be filed in any jurisdictions for BNT162b2 or any other potential vaccine candidates; whether and when any such applications may be approved by regulatory authorities, which will depend on myriad factors, including making a determination as to whether the vaccine candidate’s benefits outweigh its known risks and determination of the vaccine candidate’s efficacy and, if approved, whether it will be commercially successful; decisions by regulatory authorities impacting labeling, manufacturing processes, safety and/or other matters that could affect the availability or commercial potential of a vaccine, including development of products or therapies by other companies; disruptions in the relationships between us and our collaboration partners or third-party suppliers; risks related to the availability of raw materials to manufacture a vaccine; challenges related to our vaccine candidate’s ultra-low temperature formulation and attendant storage, distribution and administration requirements, including risks related to handling after delivery by Pfizer; the risk that we may not be able to successfully develop non-frozen formulations; the risk that we may not be able to create or scale up manufacturing capacity on a timely basis or have access to logistics or supply channels commensurate with global demand for any potential approved vaccine, which would negatively impact our ability to supply the estimated numbers of doses of our vaccine candidate within the projected time periods indicated; whether and when additional supply agreements will be reached; uncertainties regarding the ability to obtain recommendations from vaccine technical committees and other public health authorities and uncertainties regarding the commercial impact of any such recommendations; uncertainties regarding the impact of COVID-19 on Pfizer’s business, operations and financial results; and competitive developments.

A further description of risks and uncertainties can be found in Pfizer’s Annual Report on Form 10-K for the fiscal year ended December 31, 2019 and in its subsequent reports on Form 10-Q, including in the sections thereof captioned “Risk Factors” and “Forward-Looking Information and Factors That May Affect Future Results”, as well as in its subsequent reports on Form 8-K, all of which are filed with the U.S. Securities and Exchange Commission and available at www.sec.gov and www.pfizer.com .

About BioNTech

Biopharmaceutical New Technologies is a next generation immunotherapy company pioneering novel therapies for cancer and other serious diseases. The Company exploits a wide array of computational discovery and therapeutic drug platforms for the rapid development of novel biopharmaceuticals. Its broad portfolio of oncology product candidates includes individualized and off-the-shelf mRNA-based therapies, innovative chimeric antigen receptor T cells, bi-specific checkpoint immuno-modulators, targeted cancer antibodies and small molecules. Based on its deep expertise in mRNA vaccine development and in-house manufacturing capabilities, BioNTech and its collaborators are developing multiple mRNA vaccine candidates for a range of infectious diseases alongside its diverse oncology pipeline. BioNTech has established a broad set of relationships with multiple global pharmaceutical collaborators, including Genmab, Sanofi, Bayer Animal Health, Genentech, a member of the Roche Group, Regeneron, Genevant, Fosun Pharma, and Pfizer. For more information, please visit www.BioNTech.de .

BioNTech Forward-looking statements

This press release contains “forward-looking statements” of BioNTech within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements may include, but may not be limited to, statements concerning: BioNTech’s efforts to combat COVID-19; the collaboration between BioNTech and Pfizer to develop a potential COVID-19 vaccine; our expectations regarding the potential characteristics of BNT162b2 in our Phase 2/3 trial and/or in commercial use based on data observations to date; the expected timepoint for additional readouts on efficacy data of BNT162b2 in our Phase 2/3 trial; the nature of the clinical data, which is subject to ongoing peer review, regulatory review and market interpretation; the timing for submission of data for, or receipt of, any potential Emergency Use Authorization; the timing for submission of manufacturing data to the FDA; and the ability of BioNTech to supply the quantities of BNT162 to support clinical development and, if approved, market demand, including our production estimates for 2020 and 2021. Any forward-looking statements in this press release are based on BioNTech current expectations and beliefs of future events, and are subject to a number of risks and uncertainties that could cause actual results to differ materially and adversely from those set forth in or implied by such forward-looking statements. These risks and uncertainties include, but are not limited to: the ability to meet the pre-defined endpoints in clinical trials; competition to create a vaccine for COVID-19; the ability to produce comparable clinical or other results, including our stated rate of vaccine effectiveness and safety and tolerability profile observed to date, in the remainder of the trial or in larger, more diverse populations upon commercialization; the ability to effectively scale our productions capabilities; and other potential difficulties. For a discussion of these and other risks and uncertainties, see BioNTech’s Annual Report on Form 20-F filed with the SEC on March 31, 2020, which is available on the SEC’s website at www.sec.gov . All information in this press release is as of the date of the release, and BioNTech undertakes no duty to update this information unless required by law.

View source version on businesswire.com : https://www.businesswire.com/news/home/20201118005595/en/

Pfizer: Media Relations Amy Rose +1 (212) 733-7410 [email protected]

Investor Relations Chuck Triano +1 (212) 733-3901 [email protected]

BioNTech: Media Relations Jasmina Alatovic +49 (0)6131 9084 1513 or +49 (0)151 1978 1385 [email protected]

Investor Relations Sylke Maas, Ph.D. +49 (0)6131 9084 1074 [email protected]

Source: Pfizer Inc.

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Genomic insights into mRNA COVID-19 vaccines efficacy: Linking genetic polymorphisms to waning immunity

Affiliations.

• 1 Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
• 2 Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
• 3 Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan.
• 4 Department of Information Management, Taipei Veterans General Hospital, Taipei, Taiwan.
• 5 Department of Statistics, Tamkang University, New Taipei, Taiwan.
• 6 Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
• 7 Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
• 8 School of medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
• 9 Biosafety level 3 laboratory, Taipei Veterans General Hospital, Taipei, Taiwan.
• 10 Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
• 11 Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan.
• 12 Department of Family Medicine, Taipei Veterans General Hospital Yuli Branch, Hualien, Taiwan.
• PMID: 39254005
• DOI: 10.1080/21645515.2024.2399382

Genetic polymorphisms have been linked to the differential waning of vaccine-induced immunity against COVID-19 following vaccination. Despite this, evidence on the mechanisms behind this waning and its implications for vaccination policy remains limited. We hypothesize that specific gene variants may modulate the development of vaccine-initiated immunity, leading to impaired immune function. This study investigates genetic determinants influencing the sustainability of immunity post-mRNA vaccination through a genome-wide association study (GWAS). Utilizing a hospital-based, test negative case-control design, we enrolled 1,119 participants from the Taiwan Precision Medicine Initiative (TPMI) cohort, all of whom completed a full mRNA COVID-19 vaccination regimen and underwent PCR testing during the Omicron outbreak. Participants were classified into breakthrough and protected groups based on PCR results. Genetic samples were analyzed using SNP arrays with rigorous quality control. Cox regression identified significant single nucleotide polymorphisms (SNPs) associated with breakthrough infections, affecting 743 genes involved in processes such as antigenic protein translation, B cell activation, and T cell function. Key genes identified include CD247, TRPV1, MYH9, CCL16, and RPTOR, which are vital for immune responses. Polygenic risk score (PRS) analysis revealed that individuals with higher PRS are at greater risk of breakthrough infections post-vaccination, demonstrating a high predictability (AUC = 0.787) in validating population. This finding confirms the significant influence of genetic variations on the durability of immune responses and vaccine effectiveness. This study highlights the importance of considering genetic polymorphisms in evaluating vaccine-induced immunity and proposes potential personalized vaccination strategies by tailoring regimens to individual genetic profiles.

Keywords: COVID-19; genetic polymorphisms; long-term memory CD8+ T cells; mRNA-based vaccines; waning immunity.

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Open Access

Peer-reviewed

Research Article

Immunogenicity and real-world effectiveness of COVID-19 vaccines in Lebanon: Insights from primary and booster schemes, variants, infections, and hospitalization

Roles Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] , [email protected]

Affiliation Division of Infectious Diseases, Department of Internal Medicine, Lebanese American University Medical Center-Rizk Hospital, Beirut, Lebanon

Contributed equally to this work with: Wajdi Haddad, Nayla Jbeily

Roles Data curation, Investigation, Methodology, Resources, Software

Affiliation Department of Internal Medicine, Central Military Hospital, Military Healthcare, Lebanese Army, Beirut, Lebanon

Roles Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation

Affiliation Head of Laboratory Department, FMPS Holding S.A.L., Beirut, Lebanon

Roles Data curation, Methodology, Project administration

Affiliation Nursing Office, Makassed General Hospital, Beirut, Lebanon

Roles Data curation, Formal analysis, Investigation, Methodology

¶ ‡ SE, HB and MS also contributed equally to this work.

Affiliation Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Byblos, Lebanon

Roles Data curation, Formal analysis, Investigation, Methodology, Software

Affiliation Laboratory Department, FMPS Holding S.A.L., Beirut, Lebanon

Roles Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

Affiliation Pharmacy Department, Makassed General Hospital, Beirut, Lebanon

Roles Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing

Affiliation American University of Beirut, Beirut, Lebanon

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization

Affiliations American University of Beirut, Beirut, Lebanon, Department of Health and Human Services, GAP Solutions (Contract No. 75N93019D00026), National Institute of Allergy and Infectious Diseases, National Institutes of Health, United States of America

• Rima Moghnieh, 
• Wajdi Haddad, 
• Nayla Jbeily, 
• Salam El-Hassan, 
• Shadi Eid, 
• Hicham Baba, 
• Marilyne Sily, 
• Yara Saber, 
• Dania Abdallah, 

• Published: September 13, 2024
• https://doi.org/10.1371/journal.pone.0306457
• Reader Comments

In this study, we conducted a case-control investigation to assess the immunogenicity and effectiveness of primary and first booster homologous and heterologous COVID-19 vaccination regimens against infection and hospitalization, targeting variants circulating in Lebanon during 2021–2022. The study population comprised active Lebanese military personnel between February 2021 and September 2022. Vaccine effectiveness (VE) against laboratory-confirmed SARS-CoV-2 infection and associated hospitalization was retrospectively determined during different variant-predominant periods using a case-control study design. Vaccines developed by Sinopharm, Pfizer, and AstraZeneca as well as Sputnik V were analyzed. Prospective assessment of humoral immune response, which was measured based on the SARS-CoV-2 antispike receptor binding domain IgG titer, was performed post vaccination at various time points, focusing on Sinopharm and Pfizer vaccines. Statistical analyses were performed using IBM SPSS and GraphPad Prism. COVID-19 VE remained consistently high before the emergence of the Omicron variant, with lower estimates during the Delta wave than those during the Alpha wave for primary vaccination schemes. However, vaccines continued to offer significant protection against infection. VE estimates consistently decreased for the Omicron variant across post-vaccination timeframes and schemes. VE against hospitalization declined over time and was influenced by the variant. No breakthrough infections progressed to critical or fatal COVID-19. Immunogenicity analysis revealed that the homologous Pfizer regimen elicited a stronger humoral response than Sinopharm, while a heterologous Sinopharm/Pfizer regimen yielded comparable results to the Pfizer regimen. Over time, both Sinopharm’s and Pfizer’s primary vaccination schemes exhibited decreased humoral immunity titers, with Pfizer being a more effective booster than Sinopharm. This study, focusing on healthy young adults, provides insights into VE during different pandemic waves. Continuous research and monitoring are essential for understanding vaccine-mediated immune responses under evolving circumstances.

Citation: Moghnieh R, Haddad W, Jbeily N, El-Hassan S, Eid S, Baba H, et al. (2024) Immunogenicity and real-world effectiveness of COVID-19 vaccines in Lebanon: Insights from primary and booster schemes, variants, infections, and hospitalization. PLoS ONE 19(9): e0306457. https://doi.org/10.1371/journal.pone.0306457

Editor: Fadi Aljamaan, King Saud University College of Medicine, SAUDI ARABIA

Received: December 26, 2023; Accepted: June 18, 2024; Published: September 13, 2024

Copyright: © 2024 Moghnieh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The vaccine immunogenicity laboratory work was supported and funded by bioMérieux-PERI- & POST-LAUNCH STUDY (PPLS) BMX PROGRAM and FMPS Holding – BIOTECK Laboratory. There was no additional external funding received for this study.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The COVID-19 pandemic, although expected by some scientists, took the world by surprise and had a catalytic impact on the lives of individuals worldwide [ 1 ]. It fragmented human memory into eras before, during, and after the pandemic. For the past few years, it has been an unprecedented global health crisis, profoundly impacting the lives of individuals and communities worldwide, exacerbating existing crises and humanitarian needs, and drawing all nations’ frailties and inequities into sharp focus [ 2 ]. It has also triggered a socioeconomic crisis of unprecedented proportion [ 3 ]. This crisis parallels past events in history, such as the Spanish flu pandemic, which claimed millions of lives, and the subsequent economic crisis that paved the way for World War II [ 4 , 5 ].

The COVID-19 pandemic has brought to the forefront the risk of major disease outbreaks and underscored countries’ lack of preparedness to fight them, even the highly resourced ones [ 6 – 8 ]. Moreover, the pandemic highlighted previously existing weaknesses, including disparities in healthcare access and ineffective communication between public health and healthcare delivery systems [ 6 – 8 ].

Pandemic preparedness and disease surveillance anchored in strong healthcare delivery systems that reach all people, especially the most vulnerable, are crucial to ensure better protection from major disease outbreaks [ 6 – 8 ].

Vaccine science and the pharmaceutical industry are at the forefront of pandemic preparedness. Nevertheless, the use of vaccines against COVID-19 has shown that gaps regarding our comprehension of vaccines and their interaction with the dynamic nature of microorganisms, namely viruses, still exist [ 9 , 10 ]. During the pandemic, different parts of the world adopted various vaccine platforms, leading to a significant amount of confusion and speculation about which vaccine or vaccine platform is superior. To shed light on this matter, laboratory-based immunogenicity studies were conducted, followed by randomized controlled trials to assess the efficacy of these vaccines [ 9 , 11 , 12 ]. The results of these studies appeared promising, reporting high levels of efficacy [ 9 – 11 ]. However, when it came to real-world effectiveness, conflicting information emerged in comparison with the results of immunogenicity and efficacy trials [ 13 ]. This discrepancy may be attributed to several factors, such as these trials were conducted at different times and countries during the pandemic and with different newly emerging and circulating SARS-CoV-2 variants, which may not align well with the vaccines available at a particular time and place [ 13 ].

In Lebanon, the national COVID-19 vaccination campaign started in mid-February, 2021. Several vaccines using various platforms received emergency use authorization from the Lebanese Ministry of Health (MoH). These vaccines included those from Pfizer-BioNTech (BNT162b2), Sinopharm (BBIBP-CorV), Gamaleya Sputnik V (Gam-COVID-Vac), and AstraZeneca (ChAdOx1 nCoV-19). These vaccines were distributed through the Lebanese MoH, private sector procurement, and donations. This unique situation provided an opportunity to assess the immunogenicity and effectiveness of these vaccines, both as primary vaccinations and first booster regimens, against COVID-19 variants prevalent in Lebanon during 2021 and 2022.

In this study, we conducted a case-control investigation to determine the effectiveness of available primary and initial booster COVID-19 vaccination regimens against infection using notable variants of concern that predominantly circulated in Lebanon during different periods. These variants included the Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529) variants along with its sublineages BA.1, BA.1.1, BA.2, BA.4, and BA.5. We also assessed vaccine effectiveness (VE) in preventing hospitalization during these timeframes. In addition, we measured the SARS-CoV-2 antispike receptor binding domain (RBD) immunoglobulin G (anti-S-IgG) titer as a marker of the immunogenicity of certain vaccine regimens to compare the potential level of protection against infection among vaccinees.

2. Materials and methods

2.1 study population, data sources, and study design.

This study is divided into two parts:

• Sinopharm (BBIBP-CorV)
• Pfizer (BNT162b2)
• Sputnik V (Gam-COVID-Vac)
• AstraZeneca (ChAdOx1 nCoV-19)
• A prospective analysis dealing with the immunogenicity of different homologous and heterologous vaccination schemes at different time points.

2.2 VE against COVID-19 infection and hospitalization

This retrospective cohort case-control study examined the effectiveness of two-dose primary vaccination schemes (2× Sinopharm, 2× Pfizer, 2× Sputnik V, 2× AstraZeneca, or 1× Sinopharm/1× Pfizer) and homologous (2× Sinopharm/1× Sinopharm or 2× Pfizer/1× Pfizer) or heterologous (2× Sinopharm/1× Pfizer, 2× Sputnik V/1× Pfizer, or 2× Pfizer/1× Sinopharm) first booster vaccination schemes against laboratory-confirmed SARS-CoV-2 infections and subsequent hospitalization during the Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529, with its sublineages BA.1, BA.2, BA.4, and BA.5) variants that circulated in Lebanon between 2021 and 2022, compared with unvaccinated individuals without previous exposure to COVID-19.

The study population comprised active Lebanese military personnel between February 2021 and September 2022, who were followed longitudinally throughout the study period. Data were retrieved using the Lebanese Ministry of Defense (MoD) COVID-19 electronic healthcare database. The study included personnel who had received the mentioned vaccination schemes as well as unvaccinated personnel, both with and without previous exposure to COVID-19. Personnel who were partially vaccinated or who received vaccination schemes other than those previously mentioned were excluded from the analysis. Data, including SARS-CoV-2 polymerase chain reaction (PCR) testing results; COVID-19 vaccination history; clinical infection and hospitalization; and other demographic and comorbidity information, including cardiovascular disease, pulmonary disease, kidney disease, diabetes mellitus, malignancy, and obesity, were extracted by the principal investigators from the digital health information database.

Since the onset of the pandemic, the MoD followed the Lebanese MoH recommendations and guidelines regarding SARS-CoV-2 PCR testing, contact tracing, isolation and quarantine, absenteeism, and management and treatment of infection, which were in line with the recommendations of the United States (US) Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) [ 14 – 17 ]. In alignment with the national vaccination campaign, the MoD initiated COVID-19 vaccinations for military personnel in mid-February 2021 and began administering booster doses in November 2021. The MoD followed the Lebanese MoH’s guidance on COVID-19 vaccination schedules, which were based on the recommendations of the CDC and WHO [ 15 , 17 ]. Personnel received vaccines at military treatment facilities and non-military vaccination centers. Regarding the type of vaccine administered, there was no preference, as all were donated to the Lebanese military institution.

A PCR-positive swab, irrespective of the reason for PCR testing or the presence of symptoms, was used to define laboratory-confirmed SARS-CoV-2 infections. Breakthrough SARS-CoV-2 infections were considered to occur at least 14 days after the second dose of the primary vaccination regimen listed above and at least 7 days after receiving the booster dose [ 18 – 20 ]. A positive SARS-CoV-2 PCR test within 3 weeks of a previous positive SARS-CoV-2 PCR test was excluded and not counted as a new infection. Infection severity classification followed the WHO guidelines for COVID-19 case severity (acute-care hospitalizations) [ 14 ].

In Lebanon, the COVID-19 pandemic was characterized by the dominance of three SARS-CoV-2 variants during 2021 and 2022, as determined by analyzing the Lebanese dataset in GISAID [ 21 – 24 ]:

• The Alpha (B.1.1.7) variant predominantly circulated until the end of May 2021.
• By the end of July 2021, the Delta (B.1.617.2) variant had completely replaced the Alpha (B.1.1.7) variant.
• The Delta (B.1.617.2) variant, in turn, was gradually replaced by the Omicron (B.1.1.529, sublineages BA.1 and BA.1.1) variant in the second week of November 2021 until the end of December 2021.
• The Omicron (B.1.1.529, sublineages BA.1, BA.1.1, or BA.2) variant predominantly circulated between January 2022 and May 2022.
• By mid-June 2022, the Omicron variant of concern (B.1.1.529, sublineages BA.4 or BA.5) had completely replaced the previously mentioned sublineages.

Coincidentally, the appearance of each variant and/or sub-lineage was followed by new surges in the number of cases in Lebanon [ 21 – 24 ]. We chose to study VE during five periods of high or peak circulation of SARS-CoV-2 variants, excluding data from the early and late stages of the wave. In the initial stages, transmission is slower, and toward the end of the wave, herd immunity begins to develop ( Fig 1 ). Personnel who tested positive were only included up to September 8, 2022, as it marked the end of the study period. Both symptomatic and asymptomatic laboratory-confirmed infections were included. This approach ensured that the same individuals were followed over time, allowing for a comprehensive assessment of VE across multiple periods.

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Reference: Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina and Max Roser (2020)—"Coronavirus Pandemic (COVID-19)". Published online at OurWorldInData.org. Retrieved from: ’ https://ourworldindata.org/coronavirus ’ [Online Resource]. N.B.: X-axis: month-year; Y-axis left: number of confirmed daily cases. In this study, we examined vaccine effectiveness during five periods of high or peak circulation of SARS-CoV-2 variants [ 21 – 24 ]: 1. SARS-CoV-2 Alpha cases: Personnel were categorized as such if they initially tested positive between February 21st, 2021, and May 31st, 2021, as the majority of cases in Lebanon were primarily attributable to the circulating Alpha variant during that period. 2. SARS-CoV-2 Delta cases: Personnel were classified as such if they first tested positive between July 21st, 2021, and November 7th, 2021, as the majority of cases in Lebanon were predominantly attributable to the circulating Delta variant during that time. 3. SARS-CoV-2 Mixed (Delta and Omicron (B.1.1.529)) cases: Personnel fell into this category if they first tested positive between November 8th, 2021, and December 31st, 2021. This period represented an overlap between two variants, with residual Delta infection incidence in the community alongside the gradual increase in Omicron infections. 4. SARS-CoV-2 Omicron (B.1.1.529, sublineages BA.1, BA.1.1, or BA.2) cases: Personnel were designated as such if they first tested positive between January 1st, 2022, and March 7th, 2022, as these sub-lineages predominantly circulated during that time frame. 5. SARS-CoV-2 Omicron (B.1.1.529, sublineages BA.4 or BA.5) cases: Personnel were identified as such if they first tested positive between June 21st, 2022, and September 7th, 2022, as these sub-lineages predominantly circulated during that period.

https://doi.org/10.1371/journal.pone.0306457.g001

In each of the five periods, VE analyses were stratified according to primary immunization schemes (2× Sinopharm, 2× Pfizer, 2× Sputnik V, 2× AstraZeneca, or 1× Sinopharm/1× Pfizer). Cases and controls were categorized based on the elapsed time since the last vaccine dose. VE was assessed for each primary course in intervals of < 3 months, ≥ 3 –<6 months, and ≥6 months after the second dose. VE was assessed for the first booster (Sinopharm or Pfizer vaccine) in intervals of < 3 months, ≥ 3 –< 6 months, and ≥ 6 months after receiving the booster dose. VE was calculated for each vaccination scheme, including all vaccinees with and without previous exposure to COVID-19.

2.3 Humoral immunity

Between December 2022 and January 2023, we conducted a random prospective assessment of immunogenicity at various time points in vaccination subgroups that had previously received Sinopharm and Pfizer vaccines, and had not been exposed to COVID-19. Participants were categorized into different groups based on their primary vaccination type and whether they had received a booster dose ( Table 1 ). The number of participants in each group during the immunogenicity assessment period was determined by the temporal definitions of the groups. Participants were further stratified by age and gender.

https://doi.org/10.1371/journal.pone.0306457.t001

2.3.1 Blood sample collection and humoral immunity measurement.

Blood samples were collected from participants in all groups at the Lebanese Army Military Hospital COVID-19 Vaccination Center and Makassed General Hospital COVID-19 Vaccination Center in Beirut. These samples were then sent to FMPS Holding–BIOTECK Laboratory for the determination of SARS-CoV-2 anti-S-IgG titers using immunoassay techniques. VIDAS®3 SARS-CoV-2 IgG (BioMérieux, France) assay was used to measure IgG against the RBD of the spike protein [ 25 – 27 ]. The assay principle is based on a 2-step enzyme immunoassay combined with an enzyme-linked fluorescent assay [ 25 – 27 ]. The test was conducted following the manufacturer’s instructions. Results were calculated as an index, representing the ratio between the relative fluorescence value (RFV) measured in the sample and the RFV obtained for the calibrator [ 25 – 27 ]. A result was considered negative when the index was <1.00 and positive when the index was ≥1.00 [ 25 – 27 ]. All readings were standardized to BAU/mL using the WHO international standard for the VIDAS®3 SARS-CoV-2 IgG (VIDAS®3 SARS-CoV-2 IgG index = 1 (cutoff) = 20.33 BAU/mL) [ 25 – 27 ].

2.4 Ethics approval

This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines ( S1 Table ). It was conducted in accordance with the guidelines of the Declaration of Helsinki, and ethics approval was obtained from Makassed General Hospital’s Institutional Clinical Research Ethics Committee (approval numbers: 1112021 and 1102022). The section of the study related to VE was deemed not to involve human participants due to the retrospective nature of the study; thus, the requirement for informed consent was waived. During the data collection phase, only subject case numbers were included. At a later stage, a different number was assigned to each case to safeguard subject privacy. All methods were performed in accordance with the hospital’s institutional review board committee guidelines and regulations. However, participants in the immunogenicity subgroup analyses provided their written informed consent for the collection of their health information and blood samples, with a focus on ensuring anonymity.

2.5 Statistical analysis

Sociodemographic cohort characteristics, including age categories (<50 and ≥50–<65 years), gender (male and female), COVID-19 infection/recovery history before each variant surge (yes and no), personnel function (healthcare worker and non-healthcare worker), and the presence of comorbidities (none, one comorbidity or more), were compared between vaccination groups and presented as numbers and percentages. Categorical analyses were performed using the chi-square test.

For VE against infection during each of the five periods of high or peak circulation of SARS-CoV-2 variants, multivariable logistic regression was used, with laboratory-confirmed SARS-CoV-2 infections as the dependent variable and case participants being those who tested positive. Controls included personnel with no record of a positive SARS-CoV-2 PCR test during each period, either due to a lack of clinical suspicion or any possibility of contracting COVID-19 based on the contact tracing system implemented by the MoD since the start of the pandemic, irrespective of their vaccination status.

For VE against hospitalization during each of the five previously mentioned periods, multivariable logistic regression was used, with acute-care hospitalization due to laboratory-confirmed SARS-CoV-2 infections as the dependent variable, and case participants being those hospitalized. Controls included personnel with no record of acute-care hospitalization due to laboratory-confirmed SARS-CoV-2 infections during each period, as documented in the MoD healthcare database, irrespective of their vaccination status.

For VE against infection and hospitalization calculation, vaccination status was included as the independent variable, and the effectiveness of different vaccination schemes over the five time periods and the associated 95% confidence interval (CI) were calculated using the following equation: VE = [1 − adjusted odds ratio (aOR) of vaccination among cases compared with controls] × 100. VE was adjusted in logistic regression models for age categories, gender, COVID-19 infection/recovery history before each variant surge, personnel function, and the presence of comorbidities. These factors were all considered potential confounders and were included in all models. VE was calculated in the entire cohort, with the reference group for all estimates being the unvaccinated COVID-19-naïve personnel (i.e., individuals with no previous infection) in each of the five periods. Only those vaccinated in this specific time-since-vaccination stratum and those unvaccinated in each VE (infection) analysis for a specific time-since-vaccination stratum were included. Consequently, the number of cases and controls varied across the time-since-vaccination analyses.

Regarding immunogenicity subgroup analysis, categorical analyses on age and gender, presented as numbers and percentages, were performed using the chi-square test for different vaccination schemes at different time points among the selected participants. Antibody titers were presented as geometric mean titers (GMT) and 95% CIs. One-sample Kolmogorov–Smirnov test was used to check for data distribution normality. Kruskal–Wallis test, followed by Dunn’s multiple comparison post-hoc test, was performed to compare unpaired nonparametric data between the groups (antibody levels).

Statistical significance was defined as p < 0.05. The IBM Statistical Package for the Social Sciences program for Windows (version 23.0) (Armonk, NY, USA: IBM Corp.) and GraphPad Prism 9.0 software (GraphPad Software, Inc., San Diego, CA, USA) were used to conduct statistical analyses using two-tailed tests.

3.1 Characteristics of the study population for VE analyses

The total number of active military personnel identified in the COVID-19 healthcare database of the Lebanese MoD and longitudinally evaluated for VE across different time periods was 83,760. The distribution of vaccinees based on different vaccination schemes and non-vaccinated personnel during the study period is illustrated in Fig 2 . Baseline demographics and characteristics of the study cohort according to the different vaccination combination groups are provided in S2 Table .

Abbreviations: VE = vaccine effectiveness, SPh = Sinopharm (BBIBP-CorV), AZ = AstraZeneca (ChAdOx1 nCoV-19), PFZ = Pfizer-BioNTech (BNT162b2), SPTV = Sputnik V (Gam-COVID-Vac).

https://doi.org/10.1371/journal.pone.0306457.g002

3.1.1 Alpha variant surge period.

During the period from February 21, 2021, to May 31, 2021, a total of 77,600 military personnel meeting the study inclusion criteria were included in the main analysis. The majority of participants were under 50 years of age (97.4%), with a median age of 32 years and an interquartile range of 27–38 years. In addition, 93.3% were males, 95.8% were non-healthcare workers, 4.2% were military healthcare workers, and 95.9% were healthy with no comorbidities.

3.1.2 Delta variant surge period.

Between July 21, 2021, and November 7, 2021, a total of 78,684 military personnel were included in the main analysis. The demographic distribution among different subgroups, including age, gender, function, and comorbidities, was comparable to that of the Alpha variant wave ( S3 Table ).

3.1.3 “Mixed” (Delta and Omicron) variant surge period.

Between November 8, 2021, and December 31, 2021, a total of 81,459 military personnel were included in the main analysis. Demographic characteristics among different subgroups remained consistent with those observed during previous waves ( S4 Table ).

3.1.4 Omicron (B.1.1.529, sublineages BA.1, BA.1.1, or BA.2) variant surge period.

Between January 1st, 2022, and March 7th, 2022, a total of 81,799 military personnel met the study inclusion criteria. Demographic characteristics among different subgroups remained consistent with those observed during previous waves ( S5 Table ).

3.1.5 Omicron (B.1.1.529, sublineages BA.4 or BA.5) variant surge period.

Between June 21st, 2022, and September 7th, 2022, a total of 81,657 military personnel met the study inclusion criteria. Demographic characteristics among different subgroups remained consistent with those observed during previous waves ( S6 Table ).

3.2 VE against COVID-19 infection across various periods and time points

3.2.1 alpha variant infection..

3 . 2 . 1 . 1 At less than 3 months after the second dose . The effectiveness of Sinopharm primary vaccination scheme against infection was highest at 99.8% (95% CI, 99.6–99.9), similar to Pfizer primary vaccination scheme which reached 98.8% (95% CI, 97.84–99.36) (see Fig 3A and Table 2 ). No assessment of VE change with time against the Alpha variant infection was possible for any primary scheme during this wave.

Effectiveness of the available COVID-19 vaccination schemes against laboratory-confirmed infections during the (A) Alpha variant wave, (B) Delta variant wave, (C) Mixed Delta and Omicron BA.1 variant wave,(D) BA.1/BA.2 Omicron variant wave, and (E) BA.4/BA.5 Omicron variant wave. Abbreviations: AZ = AstraZeneca (ChAdOx1 nCoV-19), mo = months, PFZ = Pfizer-BioNTech (BNT162b2), SPh = Sinopharm (BBIBP-CorV), SPTV = Gamaleya’s Sputnik V (Gam-COVID-Vac), VE = vaccine effectiveness. N.B. Data are presented as effectiveness point estimates, with error bars indicating the corresponding 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0306457.g003

https://doi.org/10.1371/journal.pone.0306457.t002

3.2.2 Delta variant infection.

Overall, VE estimates for primary schemes during the Delta wave were lower than those reported during the Alpha wave (see Fig 3B and Table 2 ).

3 . 2 . 2 . 1 At less than the first 3 months after the second dose . Sinopharm’s VE against the Delta variant was 76.1% (95% CI, 72.8–79.0) at less than 3 months following the second dose, while Pfizer and Sputnik V primary regimens exhibited higher estimates, reaching 91.5% (95% CI, 88.2–94.1) and 97.1% (92.5–99.3), respectively (see Fig 3B and Table 2 ).

3 . 2 . 2 . 2 Between 3 and less than 6 months after the second dose . Effectiveness declined in primary schemes between 3 and less than 6 months after the second dose, with Sinopharm measuring 63% (57.6–67.7) and Pfizer measuring 77.2% (64.2–86.0) (see Fig 3B and Table 2 ). Changes in estimates of effectiveness against infection with the Delta variant could not be assessed for Sputnik V primary schemes during this timeframe.

There was no available data for VE at 6 months and beyond for any of the mentioned vaccination schemes against infection with the Delta variant.

3.2.3 “Mixed” (Delta and Omicron) variant infection.

Similar to the Delta variant phase, the effectiveness of primary schemes began to decline during this period compared with the Alpha variant wave and the period when the Delta variant was the dominant strain (see Fig 3C and Table 2 ).

3 . 2 . 3 . 1 At less than 3 months after the second dose . Sinopharm’s VE against “Mixed” variant infections was 75.3% (95% CI, 67.9–81.0) at less than 3 months after the second dose. The corresponding VE estimates for the Pfizer and AstraZeneca primary schemes were almost identical, measuring 76.9% (95% CI, 68.1–83.5) and 79.2% (95% CI, 44.7–94.9), respectively. Sputnik V exhibited the lowest effectiveness against infection, reaching 58.4% (95% CI, 46.2–67.8) (see Fig 3C and Table 2 ).

3 . 2 . 3 . 2 Between 3 and less than 6 months after the second dose . Between 3 and less than 6 months after the second dose, effectiveness similarly declined for Sinopharm and Pfizer primary schemes, measuring 55.0% (95% CI, 45.0–63.0) and 58.3% (95% CI, 42.3–70.2), respectively. (see Fig 3C and Table 2 ) Changes in VE against infection could not be assessed for Sputnik V and AstraZeneca primary schemes during this timeframe.

3 . 2 . 3 . 3 At 6 months and beyond after the second dose . At 6 months and beyond after the second dose, a sharp decline was observed in the effectiveness of Sinopharm, which reached 5.0% (95% CI, −15.0–22.5) (see Fig 3C and Table 2 ). The corresponding estimate for Pfizer was higher, measuring 27.9% (95% CI, −24.3–59.0). Changes in VE against infection could not be assessed for Sputnik V and AstraZeneca primary schemes during this timeframe.

3 . 2 . 3 . 4 At less than 3 months after the booster dose . Following the first homologous booster dose, VE rebounded to 86.0% (95% CI, 37.0–99.2) for 2× Sinopharm/1× Sinopharm regimen and to 74.1% (95% CI, 18.7–94.2) for 2× Pfizer/1× Pfizer regimen (see Fig 3C and Table 2 ). In contrast, following the first heterologous booster dose, VE rebounded significantly to 93.0% (95% CI, 89.2–95.6) for 2× Sinopharm/1× Pfizer regimen but did not result in an increase in VE estimates for 2× Pfizer/1× Sinopharm regimen, with a value of −12.6% (95% CI, −498.2–93.94) (see Fig 3C and Table 2 ). No data regarding boosting Sputnik V and AstraZeneca primary schemes were available during this timeframe. Changes in VE estimates against infection at different time points could not be assessed for booster schemes during this period.

3.2.4 Omicron (B.1.1.529, sublineages BA.1, BA.1.1, or BA.2) variant infection.

VE estimates showed a consistent decrease for the Omicron variant compared with the earlier variant waves across all post-vaccination timeframes and for all combinations of primary and first booster schemes investigated (see Fig 3D and Table 2 ).

3 . 2 . 4 . 1 At less than 3 months after the second dose . Sinopharm’s VE against Omicron variant infections was at its lowest, measuring 18.9% (95% CI, −3.8–37.0), at less than 3 months after the second dose (see Fig 3D and Table 2 ). Similarly, the VE estimate for Pfizer was also low, measuring 11.9% (95% CI, −7.2–27.5). Sputnik V and AstraZeneca primary schemes did not protect against infection, with VE estimates of −28.1% (95% CI, −52.1–−8.5) and −77.8% (95% CI, −154.9–−20.7), respectively. Interestingly, the VE estimate for the heterologous primary scheme, 1× Sinopharm/1× Pfizer, was surprisingly high, reaching 80.5% (95% CI, 11.8–98.9).

3 . 2 . 4 . 2 Between 3 and less than 6 months after the second dose . Between 3 and less than 6 months after the second dose, effectiveness remained consistently low for Sinopharm primary scheme, measuring 21.8% (95% CI, 7.2–33.9) (see Fig 3D and Table 2 ). The VE estimate of Pfizer primary scheme was consistently low for the previous time point, reaching negative values at −37.6% (95% CI, −63.20–−16.4). Similarly, the VE estimate of AstraZeneca primary scheme was negative, reaching −133.7% (95% CI, −228.4–−62.9). Interestingly, the VE estimate for Sputnik V primary scheme increased to 56.6% (95% CI, 45.8–65.3) (see Fig 3D and Table 2 ). There was no available data for the effectiveness of the heterologous primary scheme, 1× Sinopharm/1× Pfizer, at this time point.

3 . 2 . 4 . 3 At 6 months and beyond after the second dose . At 6 months and beyond after the second dose, effectiveness remained stable for Sinopharm for the previous time point, measuring 17.9% (95% CI, 2.8–30.4). (see Fig 3D and Table 2 ). The corresponding estimate for the Pfizer increased compared with that of the previous time point, reaching 16.2% (95% CI, −9.8–36.3) (see Fig 3D and Table 2 ). Changes in effectiveness could not be assessed for Sputnik V, AstraZeneca, and 1× Sinopharm/1× Pfizer primary schemes at this time point.

3 . 2 . 4 . 4 At less than 3 months after the booster dose . After receiving the first homologous booster dose, no protection against infection was provided in 2× Sinopharm/1× Sinopharm scheme, with values measuring −39.2% (95% CI, −117.2–15.2) (see Fig 3D and Table 2 ). However, the corresponding VE estimate for 2× Pfizer/1× Pfizer rebounded to 42.3% (95% CI, 28.1–53.7). Following the first heterologous booster dose, VE was low at 17.4% (95% CI, 3.17–29.18) for 2× Sinopharm/1× Pfizer regimen (see Fig 3D and Table 2 ). The VE estimates were negative in the case of 2× Pfizer/1× Sinopharm scheme, with a value of −240.0% (95% CI, −666.0–−36.5). Interestingly, the VE estimates for 2× Sputnik V/1× Pfizer were the highest, measuring 94.2% (95% CI, 89.7–97.0) (see Fig 3D and Table 2 ). No data were available regarding the boosting of AstraZeneca and the 1× Sinopharm/1× Pfizer primary schemes during this timeframe. Changes in VE estimates against infection with the Omicron variant at different time points could not be assessed for all booster schemes during this period.

3.2.5 Omicron (B.1.1.529, sublineages BA.4 or BA.5) variant infection.

VE estimates consistently decreased for the Omicron (B.1.1.529, sub-lineages BA.4 or BA.5) variant when compared with the earlier variant waves for the primary schemes investigated (see Fig 3E and Table 2 ). However, booster dose VE estimates showed variable results for the Omicron variant compared to earlier waves across all post-vaccination timeframes and booster schemes examined.

3 . 2 . 5 . 1 At different time points after the second dose . Data for VE of primary vaccination schemes were unavailable for timeframes less than 3 months after receiving the second dose and from 3 to less than 6 months after the second dose. At 6 months and beyond after the second dose, none of the primary schemes provided protection against infection (see Fig 3E and Table 2 ).

3 . 2 . 5 . 2 At less than 3 months after the booster dose . After receiving the first booster dose, the effectiveness of 2× Pfizer/1× Pfizer regimen rapidly rebounded to 68.8% (95% CI, 36.7–86.6) at less than 3 months after the booster dose. In contrast, the corresponding VE estimate for 2× Sinopharm/1× Pfizer regimen rebounded to 84.8% (95% CI, 58.9–96.3). Similarly, the VE estimates for 2× Sputnik V/1× Pfizer regimen rebounded to 58.0% (95% CI, 29.0–76.6) at less than 3 months after the booster dose (see Fig 3E and Table 2 ).

3 . 2 . 5 . 3 Between 3 and less than 6 months after the booster dose . Between 3 and less than 6 months after the booster dose, the effectiveness of 2× Pfizer/1× Pfizer regimen declined to −9.1% (95% CI, -48.4–19.2). The decline in VE estimates for 2× Sinopharm/1× Pfizer regimen was less steep, reaching 69.1% (95% CI, 58.7–76.6). Similarly, there was a sharp decline in the effectiveness of 2× Sputnik V/1× Pfizer regimen, measuring −19.0% (95% CI, −60.2–10.6) (see Fig 3E and Table 2 ).

3 . 2 . 5 . 4 At 6 months and beyond after the booster dose . At 6 months and beyond after the booster dose, the VE estimates of 2× Pfizer/1× Pfizer regimen remained low, measuring 2.81% (95% CI, -34.6–29.52). The corresponding VE estimates of 2× Sinopharm/1× Pfizer sharply declined to negative values, reaching −74.1% (95% CI, −125.1– −37.0). Interestingly, the VE estimates for the 2× Sputnik V/1× Pfizer regimen rebounded, reaching 90.8% (95% CI, 81.9–96.0) (see Fig 3E and Table 2 ).

3.3 VE against acute-care hospitalization due to COVID-19 infection across various periods and time points

3.3.1 alpha variant infection-related acute-care hospitalization..

3 . 3 . 1 . 1 At less than 3 months after the second dose . Both the Sinopharm and Pfizer primary vaccination schemes demonstrated high effectiveness against hospitalization at less than 3 months following the second dose, with estimates of 98.6% (95% CI, 95.6–99.8) and 96.4% (95% CI, 83.0–99.8), respectively.

No significant change in effectiveness was observed for any primary scheme during this period.

Additionally, no critical or fatal COVID-19 cases were reported among breakthrough infections. (see Fig 4A and Table 3 ).

Effectiveness of the available COVID-19 vaccination schemes against acute-care hospitalization attributed to laboratory-confirmed infections during the (A) Alpha variant wave, (B) Delta variant wave, (C) Mixed Delta and Omicron BA.1 variant wave, and (D) BA.1/BA.2 Omicron variant wave. Abbreviations: AZ = AstraZeneca (ChAdOx1 nCoV-19), mo = months, PFZ = Pfizer-BioNTech (BNT162b2), SPh = Sinopharm (BBIBP-CorV), SPTV = Gamaleya’s Sputnik V (Gam-COVID-Vac), VE = vaccine effectiveness. N.B. Data are presented as effectiveness point estimates, with error bars indicating the corresponding 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0306457.g004

https://doi.org/10.1371/journal.pone.0306457.t003

3.3.2 Delta variant infection-related acute-care hospitalization.

Overall, VE estimates against hospitalization for primary schemes during the Delta wave were lower than those reported during the Alpha wave (see Fig 4B and Table 3 ).

3 . 3 . 2 . 1 At less than 3 months after the second dose . Sinopharm’s effectiveness was highest at 84.5% (95% CI, 68.7–92.8) at less than 3 months following the second dose, while Pfizer’s estimate was higher at 92.4% (95% CI, 64.1–99.6). No acute-care hospitalizations were reported for Sputnik V primary regimen during this timeframe. (see Fig 4B and Table 3 ).

3 . 3 . 2 . 2 Between 3 and less than 6 months after the second dose . Sinopharm’s effectiveness slightly declined to 80.2% (95% CI, 55.4–92.3) between 3 and less than 6 months after the second dose. No acute-care hospitalizations were reported for Pfizer primary regimen during this timeframe. Changes in the VE estimates could not be assessed for Sputnik V primary scheme during this timeframe.

There was no available data for VE against hospitalization at 6 months and beyond for any of the mentioned vaccination schemes.

None of the breakthrough infections in any of the vaccination groups progressed to critical or fatal COVID-19 during this period. (see Fig 4B and Table 3 ).

3.3.3 “Mixed” (Delta and Omicron) variant infection-related acute-care hospitalization.

3 . 3 . 3 . 1 At less than 3 months after the second dose . Sinopharm’s effectiveness against “Mixed” variant infections-related acute-care hospitalization was 90.5% (95% CI, 26.1–99.5) at less than 3 months after the second dose (see Fig 4C and Table 3 ). No acute-care hospitalizations were reported for Pfizer and AstraZeneca primary regimens during this timeframe. Sputnik V’s effectiveness against acute-care hospitalization was the lowest among all mentioned primary schemes, reaching 57.6% (95% CI, −129.0–92.2) (see Fig 4C and Table 3 ).

3 . 3 . 3 . 2 Between 3 and less than 6 months after the second dose . Between 3 and less than 6 months after the second dose, the VE estimate against hospitalization declined for Sinopharm and Pfizer primary schemes, measuring 63.6% (95% CI, −64.2–89.1) and 38.9% (95% CI, −269.1–92.0), respectively. (see Fig 4C and Table 3 ). Changes in VE against hospitalization could not be assessed for Sputnik V and AstraZeneca primary schemes during this period.

3 . 3 . 3 . 3 At 6 months and beyond after the second dose . At 6 months and beyond after the second dose, effectiveness further declined for Sinopharm primary schemes, measuring 37.3% (95% CI, −186.2–81.9). (see Fig 4C and Table 3 ). A steep decline was reported for Pfizer primary schemes against hospitalization, measuring −3.9% (95% CI, −711.9–94.9) (see Fig 4C and Table 3 ). Changes in VE against hospitalization could not be assessed for Sputnik V and AstraZeneca primary schemes during this period.

3 . 3 . 3 . 4 At less than 3 months after the booster dose . Following the first homologous or heterologous booster dose in Sinopharm and Pfizer primary schemes, no breakthrough infections were reported in any group less than 3 months after the third dose (see Fig 4C and Table 3 ). No data was available regarding the boosting of Sputnik V and AstraZeneca primary schemes during this period.

Changes in the effectiveness estimates at different time points could not be assessed for booster schemes during this period.

None of the breakthrough infections in any of the primary or booster vaccination groups progressed to critical or fatal COVID-19 during this period.

3.3.4 Omicron (B.1.1.529, sublineages BA.1, BA.1.1, or BA.2) variant infection-related acute-care hospitalization.

3 . 3 . 4 . 1 At less than 3 months after the second dose . No acute-care hospitalizations were reported for the Sinopharm, AstraZeneca, or 1× Sinopharm/1× Pfizer primary regimens at less than 3 months after the second dose. Pfizer’s effectiveness was 28.9% (95% CI, −492.4–91.5) during the same time point. However, Sputnik V’s effectiveness against hospitalization was the lowest among all mentioned primary schemes, measuring −129.7% (95% CI, −1363–35.86) (see Fig 4D and Table 3 ).

3 . 3 . 4 . 2 Between 3 and less than 6 months after the second dose . Between 3 and less than 6 months after the second dose, effectiveness declined for the Sinopharm primary scheme, measuring 64.8% (95% CI, −167.3–94.2) (see Fig 4D and Table 3 ). However, it was not the case for Pfizer and Sputnik V where VE estimates were unexpectedly higher than that of the previous time point, reaching 57.01% (95% CI, −258.2–94.84) and 47.11% (95% CI, −340.7–93.65), respectively (see Fig 4D and Table 3 ).

3 . 3 . 4 . 3 At 6 months and beyond after the second dose . At 6 months and beyond after the second dose, effectiveness slightly declined for the Sinopharm primary schemes for the previous time point, measuring 53.1% (95% CI, −238–90.86) (see Fig 4D and Table 3 ). A decline was reported for the Pfizer primary schemes, measuring 16.81% (95% CI, −768.6–96.13) (see Fig 4D and Table 3 ).

3 . 3 . 4 . 4 At less than 3 months after the booster dose . After receiving the first booster dose, effectiveness against hospitalization rebounded and was reported to be similar between 2× Sinopharm/1× Pfizer and 2× Pfizer/1× Pfizer, measuring 61.0% (95% CI, −161.8–90.6) and 64.3% (95% CI, −272.6–98.3), respectively, at less than 3 months after the booster dose (see Fig 4D and Table 3 ).

No acute-care hospitalizations were reported for 2× Sinopharm/1× Sinopharm, 2× Pfizer/1× Sinopharm, and 2× Sputnik V/1× Pfizer regimens at less than 3 months after the third dose. No data were available regarding boosting the AstraZeneca primary scheme during this period.

Changes in the VE estimates at different time points could not be assessed for booster schemes during this period.

3.3.5 VE against omicron (B.1.1.529, Sublineages BA.4 or BA.5) variant infection-related acute-care hospitalization.

3 . 3 . 5 . 1 Across different time points after the second dose . No acute-care hospitalizations were reported across all post-vaccination timeframes for the investigated primary schemes ( Table 3 ).

3 . 3 . 5 . 2 At less than 3 months after the booster dose . No acute-care hospitalizations were reported across all combinations for the investigated booster schemes ( Table 3 ).

3 . 3 . 5 . 3 Between 3 and less than 6 months after the booster dose . No acute-care hospitalizations were reported for 2×Sinopharm/1× Pfizer and 2× Pfizer/1× Pfizer regimens between 3 and less than 6 months after the third dose ( Table 3 ). However, the VE estimate of the 2× Sputnik V/1× Pfizer regimen measured 51.2% (95% CI, −268.3–98.1).

3 . 3 . 5 . 4 At 6 months and beyond after the booster dose . No acute-care hospitalizations were reported for 2× Sinopharm/1× Pfizer and 2× Sputnik V/1× Pfizer regimens at 6 months and beyond after the booster dose ( Table 3 ). However, the VE estimate of the 2× Pfizer/1× Pfizer regimen measured 37.2% (95% CI, −270.5–97.5).

3.4 Assessment of humoral immunity of different vaccination schemes in the subgroup analysis

The majority of participants included in this subgroup analysis, who received various vaccination schemes, were predominantly males under 50 years of age and had no comorbidities. Demographic characteristics of the participants recruited for the immunogenicity analysis are presented in S7 Table . Results of the anti-S-IgG GMT (BAU/ml) in the groups are presented in Fig 5A–5C and Table 4 .

Abbreviations: IgG = immunoglobulin G, BAU = Binding Antibody Unit, mo = months, PFZ = Pfizer-BioNTech (BNT162b2), SPh = Sinopharm (BBIBP-CorV). N.B: The initial measurement for anti-S-IgG was deemed negative if the index was < 1.00 (seronegative) and positive if the index was ≥ 1.00 (seropositive). All readings were standardized to BAU/mL using the WHO international standard for the VIDAS®3 SARS-CoV-2 IgG (VIDAS®3 SARS-CoV-2 IgG index = 1 (cutoff) = 20.33 BAU/mL). Antibody titers were reported as geometric mean titers (GMT) with corresponding 95% confidence intervals (CIs). One-sample Kolmogorov–Smirnov test was used to assess data distribution normality. Kruskal–Wallis test, followed by Dunn’s multiple comparison post-hoc test, was used to compare unpaired nonparametric data among the groups (antibody levels). ns (not significant), P > 0.05, *: P ≤ 0.05, ***: P ≤ 0.001, ****: P ≤ 0.0001.

https://doi.org/10.1371/journal.pone.0306457.g005

https://doi.org/10.1371/journal.pone.0306457.t004

• The initial measurement for anti-S-IgG was deemed negative if the index was < 1.00 (seronegative) and positive if the index was ≥ 1.00 (seropositive).
• All readings were standardized to BAU/mL using the WHO international standard for the VIDAS®3 SARS-CoV-2 IgG (VIDAS®3 SARS-CoV-2 IgG index = 1 (cutoff) = 20.33 BAU/mL).

3.4.1 Sinopharm’s primary vaccination scheme.

Anti-S-IgG GMT was 207.3 BAU/ml (95% CI, 120.8–355.8) at less than 3 months after the second Sinopharm dose among COVID-19-naïve participants (see Fig 5A and Table 4 ). This value significantly decreased to reach 16.5 BAU/ml (95% CI, 12.1–22.4) 6 months after the second dose or thereafter ( p = 0.04).

3.4.2 Pfizer’s primary vaccination scheme.

Anti-S-IgG GMT was 716.8 BAU/ml (95% CI, 581.5–883.5) at less than 3 months after the second Pfizer dose among COVID-19-naïve participants (see Fig 5B and Table 4 ). This value decreased to reach 132.5 BAU/ml (95% CI, 96.2–182.4) 6 months after the second dose or thereafter ( p < 0.0001).

Notably, anti-S-IgG GMT achieved by 2× Pfizer was significantly higher than that achieved by 2× Sinopharm ( p < 0.0001) at less than 3 months after the second dose.

3.4.3 1× Sinopharm/1× Pfizer primary vaccination scheme.

Among participants who received 1× Sinopharm/1× Pfizer as a heterologous primary vaccination scheme, anti-S-IgG GMT measured highest at 677.7 BAU/ml (95% CI, 491.9–933.7) at less than 3 months of the second dose (see Fig 5A and Table 4 ). For comparison with Sinopharm primary scheme, this titer was significantly higher than that achieved after 2× Sinopharm ( p = 0.03) at the same timeframe from the last dose. In addition, anti-S-IgG GMT achieved by 1× Sinopharm/1× Pfizer was almost comparable to that achieved by 2× Pfizer ( p > 0.99) at less than 3 months after the second dose (see Fig 5C and Table 4 ).

3.4.4 Pfizer’s booster vaccination scheme.

After receiving the Pfizer booster dose, the anti-S-IgG GMT significantly rebounded to 1005 BAU/ml (95% CI, 971.6–1040) for 2× Sinopharm/1× Pfizer (p < 0.0001) and to 1007 BAU/ml (95% CI, 960.9–1055) for 2× Pfizer/1× Pfizer (p = 0.01) at less than 3 months after the booster dose (see Fig 5A and 5B and Table 4 ). Anti-S-IgG GMT achieved by 2× Pfizer/1× Pfizer and 2× Sinopharm/1× Pfizer were identical (p > 0.99) (see Fig 5C and Table 4 ). Notably, the anti-S-IgG GMT among participants who received 1× Sinopharm/1× Pfizer as a heterologous primary vaccination scheme was significantly lower than that achieved after 2× Sinopharm/1× Pfizer (p = 0.01) and 2× Pfizer/1× Pfizer (p < 0.0001).

3.4.5 Sinopharm’s booster vaccination scheme.

Following Sinopharm booster dose at less than 3 months, the anti-S-IgG GMT measured 154.4 BAU/ml (95% CI, 134.9–176.8) for individuals who received 2× Sinopharm/1× Sinopharm, showing no significant increase compared to Sinopharm primary scheme titers ( p > 0.99) (see Fig 5A and Table 4 ). After receiving a Sinopharm booster dose at less than 3 months, anti-S-IgG GMT measured 199.2 BAU/ml (95% CI, 150.6–263.3) for individuals who received 2× Pfizer/1× Sinopharm, demonstrating a significant decrease compared to the titers achieved with the 2× Pfizer scheme at the same timeframe ( p < 0.0001) (see Fig 5B and Table 4 ).

4. Discussion

4.1 study scope and cohort characteristics.

After 4 years of the COVID-19 pandemic, we have gained a comprehensive understanding of vaccine dynamics [ 17 , 28 – 30 ]. Our study provided a unique opportunity to compare the effectiveness of different vaccines administered to the same population simultaneously, a scenario that is uncommon in many settings. Typically, countries rely on a single vaccine type or platform during specific periods as part of their national vaccination programs. For instance, the US and most European countries predominantly used mRNA vaccines, while China relied on inactivated vaccines [ 17 , 28 – 30 ].

In contrast to controlled trials and laboratory studies that assess immunogenicity or efficacy, which are conducted in controlled environments with specific timeframes, subjects, and locations, VE measurement relies on real-world data [ 13 , 31 ]. Real-world effectiveness of vaccines is influenced by numerous factors, including individual immune responses, adherence to vaccination schedules, and community-level influences [ 13 , 31 ]. In addition, VE can vary even for the same vaccine within the same population, depending on when the effectiveness was assessed during a variant wave [ 19 ]. As previously mentioned, at the time of the outbreak, transmission is slower, and toward the end of the wave, herd immunity builds up, potentially leading to a false negative impact on calculated VE. This effect could mask the true effectiveness of the vaccine when calculated. Given these considerations, we chose to focus solely on the peak of the variant wave, excluding data from the early and late stages of the wave.

Our study cohort exhibited relatively homogenous demographic and other characteristics. It primarily comprised young, healthy, nonhealthcare worker males, with a median age of 32 years (interquartile range, 27–38 years). Vaccine responsiveness and severity of breakthrough infections are influenced by factors such as age, immune conditions, and comorbidities [ 32 ]. To minimize the impact of these confounding variables in more diverse populations, we selected a relatively homogeneous cohort of generally healthy individuals for immunogenicity and effectiveness studies. However, the effects of the vaccine on patients with different clinical characteristics should be explored in separate, well-defined cohorts. This approach offers a more comprehensive perspective by examining various facets of VE and mitigating the influence of confounding factors.

4.2 VE against documented infection across the different vaccination schemes and variants

In this case-control study, the effectiveness of primary COVID-19 vaccine schemes, including Sinopharm, Pfizer, and Sputnik V, remained consistently high during the Alpha and Delta waves, despite a decline in the latter. However, during the Omicron wave, significant declines in VE against infection were observed across all vaccine platforms. This decline began during the phase when “Mixed” Delta and Omicron variants were present between November and December 2021 and persisted until the Omicron variants predominated in 2022. Primary vaccination schemes involving Sinopharm, Pfizer, Sputnik V, and AstraZeneca lost their protective effect against infection. Nonetheless, a heterologous primary vaccination scheme (1× Sinopharm/1× Pfizer) demonstrated a noteworthy protective effect. It is important to interpret this result with caution, as the number of individuals who received this scheme was relatively small (n = 197) compared with the other groups, although the difference reached statistical significance.

These findings align with several real-world effectiveness studies conducted globally that have consistently documented substantially lower protection by the different vaccines against infection with the Delta and Omicron variants compared with previous variants such as Alpha or the wild-type SARS-CoV-2 strain [ 28 , 29 , 33 , 34 ]. COVID-19 vaccine efficacy was outpaced by viral mutations, explaining why various vaccines were highly effective against earlier variants but less so against Delta and Omicron variants. The emergence of these variants led to multiple waves, both locally and globally [ 35 ].

For instance, a nationwide Hungarian study evaluating VE during the Alpha wave found adjusted effectiveness against infection as follows: Pfizer: 83.3%; Sputnik V: 85.7%; AstraZeneca: 71.5%; and Sinopharm: 68.7% [ 33 ]. Similarly, a study from the UK observed high VE during the Alpha wave for Pfizer’s and AstraZeneca’s primary vaccination schemes but noted declines during the Delta wave [ 34 ].

In a nationwide Danish cohort study, mRNA vaccines were found to be highly effective against Delta variant infection but less so against Omicron, with VE estimates of 92% and 40%, respectively, during the first month after vaccination, in individuals aged < 60 years [ 29 ]. Similar results were reported in Belgium, where Pfizer’s VE was 96% against Alpha, 87% against Delta, and 31% against Omicron [ 28 ].

A recent systematic review and meta-analysis of studies on inactivated SARS-CoV-2 vaccines’ real-world effectiveness against infection revealed a pooled VE estimate of 53% against the Delta variant and a lower estimate of 16% against the Omicron variant, which circulated during 2021 and 2022 [ 36 ], consistent with our findings.

Early concerns about SARS-CoV-2 evolution focused on the potential emergence of variants with significant antigenic differences that could evade immunity acquired through vaccination or previous infection [ 37 , 38 ]. Most widely utilized COVID-19 vaccines, which employ various platforms, as listed above, were initially developed based on the spike protein from early virus variants or the original wild-type virus [ 39 ].

While the Alpha variant exhibited limited antigenic changes, the Delta variant had a moderate ability to evade vaccine-induced antibodies [ 37 , 38 , 40 , 41 ]. However, VE estimates remained acceptable against Delta [ 37 , 38 , 40 , 41 ]. The Omicron variant, with its numerous antigenic sub-lineages, possesses a higher degree of mutation and significant antigenic drift, rendering it less neutralized by first-generation vaccines [ 37 , 38 , 40 , 41 ]. Real-world data on VE against infections reflect these antigenic changes [ 37 , 38 , 40 , 41 ].

In our cohort, booster doses were administered mostly 6 months after the second dose, coinciding with the transition from “Mixed” variants to the predominance of Omicron. VE generally increased, providing higher protection within three months after receiving a Pfizer booster. This trend was observed across primary vaccination schemes, but boosting with Sinopharm did not protect against infection in those initially vaccinated with Pfizer’s and Sinopharm’s primary schemes during Omicron phases. These results suggest that boosting is more effective with mRNA vaccines.

Consistent with our findings, a national study conducted in Malaysia compared the effectiveness of homologous and heterologous boosters. The results revealed that homologous inactivated vaccine boosting (VE = 33%) and adenovirus vaccine boosting (VE = 30%) were less effective than heterologous boosting (VE = 48%) and homologous boosting (VE = 51%) using Pfizer during the predominant Omicron period [ 42 ]. In addition, a previous study in Lebanon during the first Omicron wave showed similar effectiveness of mRNA boosters against infection, with VE measured at 64% and 57% for individuals who had previously received Pfizer’s and Sputnik V’s primary vaccination schemes, respectively [ 19 ].

4.3 Effect of time on VE against documented infection across the different vaccination schemes and variants

As previously discussed, vaccine-mediated protection against COVID-19 infection diminishes over time, particularly with the emergence of diverse SARS-CoV-2 variants such as Delta and Omicron. Consequently, booster doses have been introduced to strengthen and prolong immunity, with updated versions of COVID-19 vaccines targeting Omicron subvariants. In our cohort, changes in VE were evaluated from the Delta wave onwards. During the Delta wave and the phase characterized by “Mixed” variants, we observed fluctuations in VE estimates for Sinopharm’s and Pfizer’s primary vaccination schemes. VE declined between 3 to less than 6 months after the second dose but remained above 50%. However, VE of all primary schemes declined more rapidly during the Omicron phases, dropping below 20% at 3 to less than 6 months after the second dose and reaching null values at 6 months and beyond. Assessment of booster doses’ protection against infection was only possible during the second Omicron wave (BA.4/BA.5), which began in June 2022. The decline of booster VE with time was similarly steep for all homologous and heterologous booster schemes, with a slightly steeper decline observed for the homologous boosters from 3 to more than 6 months after the booster and beyond.

These findings align with real-world effectiveness reports consistently showing a significant decline in immunity against infection over time, with the most notable decrease observed with the Omicron variant compared to earlier variants such as Alpha or Delta [ 28 , 29 , 36 ]. In the previously mentioned pooled analysis of VE estimates of the primary schemes of inactivated vaccines, protection against SARS-CoV-2 infection decreased significantly after 6 months following primary vaccination, reaching 21% during the Delta wave and 7% during the Omicron wave [ 36 ]. For primary schemes of mRNA vaccines, the aforementioned Danish study reported waning immunity of the primary schemes across several variants, with effectiveness at 73.2% for Alpha, 50% for Delta, and 4% for Omicron, at more than 4 months since vaccination [ 29 ].

Regarding booster shots with inactivated vaccines for individuals who had received inactivated vaccines as their primary series, Kyaw and colleagues reported a complete loss of protection against infection just 2 months after the booster dose in a pooled analysis of 6 real-world effectiveness studies against the Omicron variant [ 43 ]. However, they also noted a less steep decline in VE against Omicron for BNT162b2 heterologous boosters in individuals who had initially received inactivated vaccines as their primary series. VE decreased from 57% in less than 3 months after the booster receipt to 35% thereafter [ 43 ].

Notably, the effectiveness estimate for the Sputnik V primary scheme increased from negative values within less than 3 months from the second dose to 57% within the 3 to less than 6 months following the second dose, particularly during the Omicron BA.1/BA.2 wave. Similarly, during the Omicron BA.4/BA.5 wave, VE estimates for the 2× Sputnik V/1× Pfizer regimen intriguingly rebounded from negative values within 3–<6 months after the booster to a remarkable 91% at 6 months and beyond. Dolzhikova and colleagues analyzed neutralizing antibody responses against variants of concern in sera samples of individuals vaccinated with Sputnik V, including those who were revaccinated with Sputnik Light [ 44 ]. Their study revealed that antibodies triggered by the Sputnik V vaccination matured and developed a broader capacity to neutralize emerging variants of concern within 6–9 months after the initial vaccination and 2–3 months after receiving a booster [ 44 ]. Similarly, a longitudinal study from Argentina demonstrated robust SARS-CoV-2 neutralizing antibodies and reduced viral variant escape to neutralization over time, particularly 6 months after primary vaccination with Sputnik V [ 45 ]. Investigators reported that antibodies produced in individuals vaccinated with Sputnik V exhibited increased cross-neutralization capacity against variants of concern as time passed [ 45 ]. Martynova and colleagues demonstrated that Sputnik V vaccination elicits a robust antibody response, recognizing diverse epitopes on the S-protein and a strong cellular response. These responses remain detectable for more than 3 months postvaccination, indicating the vaccine’s long-term efficacy [ 46 ].

4.4 VE against acute-care hospitalization

The significance of VE against hospitalization becomes apparent when VE estimates against infection for specific variants with particular vaccine protocols decline. Overall, our results revealed that VE estimates against hospitalization for primary vaccination schemes were generally lower during the Delta wave compared with the Alpha wave. However, all primary schemes remained highly protective, with effectiveness above 80% against hospitalization across all postvaccination timeframes. During the phase characterized by the presence of “Mixed” Delta and Omicron variants and continuing thereafter during the Omicron-predominant waves, sharp declines in effectiveness against acute-care hospitalization were observed for primary vaccination schemes. Booster schemes provided full protection against hospitalization during the “Mixed” phase of the Delta and Omicron variants, but a decline was noted in booster protection when the Omicron BA.1/BA.2 variant predominated. Notably, all primary and booster vaccination schemes provided 100% protection against critical illness and mortality during all waves in our cohort. Contrary to the common belief that effectiveness against infection wanes over time while effectiveness against hospitalization lasts, our data has shown that effectiveness against hospitalization also declines with time across all primary and booster schemes, especially during Omicron predominance.

The time-sensitive waning of VE against hospitalization was observed against Delta infection-induced hospitalization, but it is dramatically accelerated against Omicron infection-induced hospitalization [ 47 ]. In an observational prospective case-control study involving 21 hospitals in the US, Pfizer’s primary series was effective against both Alpha and Delta variant-associated hospitalization, with VE measuring 85% [ 48 ]. However, lower protection was observed against Omicron-associated hospitalization, reaching 65% [ 48 ]. mRNA boosters increased effectiveness against Delta and Omicron-induced hospitalizations to 94% and 86%, respectively [ 48 ]. In a Brazilian nationwide case-control study, the effectiveness of the CoronaVac primary series against COVID-19 associated hospitalization was substantially lower during an Omicron-dominated period (56%) compared with a Delta-dominated period (87%) [ 49 ]. During the Omicron-dominated period, a homologous CoronaVac booster dose conferred a smaller increase in protection against severe disease (74%) compared with a heterologous Pfizer booster (86%) [ 49 ]. Notably, the increased protection afforded by a homologous booster against severe disease waned during the 4 months after its administration, in contrast to the durable effectiveness of the heterologous Pfizer booster [ 49 ].

VE against infection exhibited statistically significant negative values within both primary and booster vaccination schemes during the periods when the Omicron variant predominated. However, it is crucial to note that these negative estimated values of effectiveness likely reflect the influence of certain factors rather than indicating a true negative biological effectiveness of the vaccines [ 50 , 51 ]. These factors include vaccinated individuals having a higher risk of network-level exposure, increased social contact rates, greater susceptibility, lower rates of prior infection, or less adherence to safety measures and barriers such as mask usage, hand hygiene, and social distancing compared with unvaccinated individuals [ 50 , 51 ]. In addition, over time, the number of vaccinated individuals increased, along with those previously exposed to the virus, leaving unvaccinated, COVID-naïve adults as a minority, especially during later waves of the pandemic [ 52 – 54 ]. These factors significantly impact vaccine’s effectiveness against recent variants like Omicron compared with earlier variants.

Based on our data, which demonstrates that vaccine protection against critical illness and death in a generally young and healthy population remains preserved over time, frequent booster shots may not be necessary for this group, particularly those who can tolerate infection without severe complications. This is especially pertinent as VE against infection tends to diminish with time and with the emergence of new variants. We propose that young and healthy individuals, such as those included in this cohort, may not require regular vaccine boosters once they have established protection against critical illness and death through primary vaccination. Their immunity can potentially be naturally reinforced, akin to the immune response historically seen with common colds before the COVID-19 pandemic. However, this conclusion may not apply to more vulnerable individuals, such as those with comorbidities or immune deficiencies that impede the initial vaccine response. Separate studies should focus on these individuals to assess VE against infection, hospitalization, critical illness, or COVID-19-associated mortality. This will enable evidence-based decisions regarding the optimal vaccine regimens and booster schedules.

4.5 Immunogenicity of vaccination schemes involving Sinopharm and Pfizer

In primary vaccination schemes, the homologous Pfizer regimen elicited a higher humoral response compared to Sinopharm. However, the humoral response generated by the homologous Pfizer regimen was comparable to that of the heterologous primary regimen 1× Sinopharm/1× Pfizer. Over time, we observed a decline in antispike IgG GMT for both Sinopharm’s and Pfizer’s primary vaccination schemes. Pfizer’s booster dose elicited a similarly robust humoral response in individuals who initially received either Pfizer’s or Sinopharm’s primary vaccination schemes. However, a Sinopharm booster dose induced a significantly lower humoral response compared to the latter booster schemes in individuals who had initially received either Pfizer or Sinopharm as their primary vaccination.

These findings are consistent with results from other immunogenicity studies in the literature, where antispike IgG GMT levels were consistently higher among recipients of the primary Pfizer vaccine scheme compared to those who received Sinopharm [ 55 , 56 ]. Humoral immunity was observed to be short-lived and tended to decline over time, as documented in other studies [ 57 – 59 ]. Heterologous boosting has been explored in individuals who previously received primary inactivated vaccine schemes. In a prospective cohort study evaluating the immunogenicity of Pfizer’s booster among Peruvian healthcare workers who had previously received the primary Pfizer or Sinopharm vaccination schemes, investigators reported that the antispike IgG GMT levels produced by the heterologous vaccine regimen was significantly higher than that elicited by the homologous booster regimen [ 56 ].

The concept of an immune correlate of protection (or immuno-bridging) aims to utilize immunological measurements as predictive markers for protection against infectious diseases [ 37 ]. Before the emergence of variants that could evade vaccine efficacy, serology testing and the stratification of seroconversion levels proved useful in promptly identifying high-risk groups of vaccine non-responders. These groups may not develop a viral neutralizing response, even if they show seroconversion, and therefore could remain at a higher risk of infection despite vaccination [ 60 ].

Throughout the pandemic, studies have consistently indicated a strong relationship between high titers of antispike RBD IgG and a less severe disease state, as well as a lower incidence of breakthrough infections. Higher IgG binding has been associated with increased protection against infection with the wild-type, Alpha, and Delta variant strains of the SARS-CoV-2 virus [ 61 – 64 ]. Furthermore, a robust correlation has been observed between antispike RBD IgG titers and neutralizing antibody titers, suggesting that IgG concentrations provide insights into protection against infection with the ancestral strain or relatively homologous variants of concern [ 64 – 66 ].

In our study, Pfizer vaccine elicited a significantly higher immune response compared to Sinopharm after initial vaccination. Nevertheless, both vaccines demonstrated high levels of protection against infection caused by the Alpha and Delta variants, but their effectiveness gradually decreased and diverged from each other when it came to the Omicron variants.

Consistent with our findings, a study from Thailand by Takheaw and colleagues determined the correlations between the levels of anti-RBD IgG and neutralizing antibodies against SARS-CoV-2 variants in vaccinated individuals with two doses of CoronaVac or one dose each of CoronaVac and AstraZeneca [ 67 ]. The investigators reported a high correlation between anti-RBD IgG and neutralizing antibodies against the wild-type, Alpha, and Delta variants but not with the Omicron variant [ 67 ]. Among individuals with high levels of anti-RBD IgG, 93% had neutralizing antibodies against the wild-type, Alpha, and Delta variants, but none had neutralizing antibodies against Omicron [ 67 ]. The study concluded that anti-RBD IgG levels cannot predict the presence of neutralizing antibodies against the Omicron variant [ 67 ].

In a study from Sweden, Vikström and colleagues found no indication that levels of vaccine-induced antibodies after an mRNA booster dose protected against infection with the Omicron variant [ 68 ]. They found no pattern supporting that higher levels of circulating S-binding IgG protected against SARS-CoV-2 infection [ 68 ]. They attributed their findings to the Omicron variant’s significant escape from neutralizing antibodies [ 68 ].

4.6 Limitations

This study, relying on real-time data, encountered several limitations. First, there were instances of missing data, particularly those regarding the progression of VE against specific variants over time within defined time points and for particular vaccination schemes. This limitation arose from the retrospective collection of data during different waves of the pandemic, with the follow-up period coinciding with subsequent surge in cases related to other emerging variants.

Second, a relatively young and healthy cohort was studied, which may restrict the generalizability of our findings to the entire Lebanese population. However, this demographic characteristic can also be viewed as a strength, as it allows for the study of effectiveness while controlling for confounding factors such as age and comorbidities.

The detection of laboratory-confirmed infections relied on SARS-CoV-2 PCR testing of symptomatic and asymptomatic individuals during contact tracing. Ideally, systematic screening at multiple time intervals would have allowed us to detect more asymptomatic infections. Nevertheless, our study was based on real-world data in a low-income country with limited resources, facing a socioeconomic crisis. The bias toward including only symptomatic infections rather than all infections was minimized by the vigilant contact tracing system established by the MoD since the start of the pandemic. In addition, the definition of variant-specific cases was based on epidemiologic evidence rather than genomic testing of the variants in the studied population.

The negative estimated effectiveness values during the Omicron waves were more likely a result of bias rather than a genuine indication of the vaccines having negative biological effectiveness. This bias may have arose from factors such as the gradual reduction of the unvaccinated population without prior COVID-19 exposure over time and the increased exposure of vaccinated individuals to the virus. In addition, there may be unequal compliance with safety measures when compared with the unvaccinated population. These factors can lead to an underestimation of VE [ 69 ].

It is important to note that the sample size for VE against COVID-19 acute-care hospitalization during Omicron periods was limited in this study. While every effort was made to include all eligible cases, the small sample size during these periods may affect the generalizability of our findings. Thus, caution should be exercised when interpreting the results from these time periods.

The lack of cellular immunity and serum-neutralizing antibody testing in the immunogenicity evaluation was a limitation. Notably, humoral immunity was not assessed for vaccination schemes including Sputnik V or AstraZeneca vaccines. Furthermore, assessing changes in humoral response beyond 3 months post-first booster in any scheme was not possible.

Despite these limitations, our study offers a comprehensive view of real-world effectiveness for different homologous and heterologous primary and booster vaccination schemes against various vaccine-tolerant variants, specifically during periods of high viral circulation rather than throughout the entire wave.

5. Conclusion

In this case-control study, we observed consistent high effectiveness of COVID-19 vaccines during the pre-Omicron waves, with the duration since vaccination and the emergence of new variants of concern playing pivotal roles in real-world effectiveness. VE estimates for primary vaccination schemes during the Delta wave were generally lower than those reported during the Alpha wave, yet they provided significant protection against infection. However, VE estimates showed a consistent decline for the Omicron variant across all post-vaccination timeframes and for all combinations of primary and initial booster schemes examined. Despite this decline, none of the breakthrough infections in any vaccinated group progressed to critical or fatal COVID-19 during the study period.

In terms of immunogenicity analysis, the homologous Pfizer regimen elicited a stronger humoral response than Sinopharm, while the heterologous 1× Sinopharm/1× Pfizer primary regimen produced comparable results to the homologous Pfizer regimen. Both Sinopharm and Pfizer primary vaccination schemes showed a decrease in humoral immunity titers over time. Boosting with Pfizer after a primary Pfizer or Sinopharm vaccination induced a significant increase in humoral immunity, whereas Sinopharm acted as a weaker booster for immunity.

While our results offer valuable insights into VE in healthy young adults, it is crucial to recognize that conclusions about vaccination and booster strategies may differ in older and immunocompromised individuals. Future studies involving these groups will provide additional insights. Continuous research and monitoring are essential to comprehensively understand vaccine-mediated immune responses, particularly during pandemics, and will be crucial for our preparedness in facing future health crises.

Supporting information

S1 table. strobe statement..

https://doi.org/10.1371/journal.pone.0306457.s001

S2 Table. Demographic and clinical characteristics of the participants used to estimate effectiveness of the different vaccination schemes during the Alpha variant wave.

Abbreviations: PFZ = Pfizer-BioNTech (BNT162b2), SPh = Sinopharm (BBIBP-CorV).

https://doi.org/10.1371/journal.pone.0306457.s002

S3 Table. Demographic and clinical characteristics of the participants used to estimate effectiveness of the different vaccination schemes during the Delta variant wave.

Abbreviations: AZ = AstraZeneca (ChAdOx1 nCoV-19), PFZ = Pfizer-BioNTech (BNT162b2), SPh = Sinopharm (BBIBP-CorV), SPTV = Gamaleya Sputnik V (Gam-COVID-Vac).

https://doi.org/10.1371/journal.pone.0306457.s003

S4 Table. Demographic and clinical characteristics of the participants used to estimate effectiveness of the different vaccination schemes during the mixed Delta and Omicron variant wave.

https://doi.org/10.1371/journal.pone.0306457.s004

S5 Table. Demographic and clinical characteristics of the participants used to estimate effectiveness of the different vaccination schemes during the Omicron BA.1/BA.2 variant wave.

https://doi.org/10.1371/journal.pone.0306457.s005

S6 Table. Demographic and clinical characteristics of the participants used to estimate effectiveness of the different vaccination schemes during the Omicron BA.4/BA.5 variant wave.

https://doi.org/10.1371/journal.pone.0306457.s006

S7 Table. Demographic data of participants in immunogenicity subgroup analyses.

Abbreviations: mo = months, PFZ = Pfizer-BioNTech (BNT162b2), SPh = Sinopharm (BBIBP-CorV).

https://doi.org/10.1371/journal.pone.0306457.s007

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• 2. World Health Organization (WHO). Current context: the COVID-19 pandemic and continuing challenges to global health Geneva: World Health Organization (WHO); 2023. updated [accessed: 10 September 2023]. Available at: https://www.who.int/about/funding/invest-in-who/investment-case-2.0/challenges .
• PubMed/NCBI
• 7. COVID-19 Strategic Preparedness and Response Plan 2022: Global Monitoring and Evaluation Framework. Geneva: World Health Organization; 2022.
• 8. World Health Organization & the World Bank. Analysis of Pandemic Preparedness and Response (PPR) architecture, financing needs, gaps and mechanisms. Prepared for the G20 Joint Finance & Health Task Force Paper, 22 March 2022. [accessed: 10 September 2023]. Available at: https://thedocs.worldbank.org/en/doc/5760109c4db174ff90a8dfa7d025644a-0290032022/original/G20-Gaps-in-PPR-Financing-Mechanisms-WHO-and-WB-pdf.pdf
• 14. World Health Organization. COVID-19 clinical management: living guidance (last updated: 23 November 2021). Available from: https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-2 . Accessed on: August 3, 2023.
• 15. World Health Organization. Covid-19 vaccine platform specific recommendations, Strategic Advisory Group of Experts on Immunization (SAGE). Available at: https://www.who.int/groups/strategic-advisory-group-of-experts-on-immunization/covid-19-materials (accessed on 01 December 2023).
• 16. Centers for Diseases Control and Prevention. Clinical Care Considerations, Clinical considerations for care of children and adults with confirmed COVID-19, Updated Aug. 4, 2023. Available at: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/clinical-considerations-index.html (accessed on 01 December 2023).
• 17. Centers for Diseases Control and Prevention. Use of COVID-19 Vaccines in the United States, Interim Clinical Considerations, (last updated November 3, 2023). Available at: https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html (accessed on 01 December 2023).

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Case Study: Accelerating Vaccine Access in a Post COVID-19 Environment

Case Study: Accelerating Vaccine Access in a Post COVID-19 Environment – Concurrent Pneumococcal Conjugate Vaccine (PCV) and Rotavirus Vaccine (RVV) Introductions in The Republic of Indonesia

The Republic of Indonesia's proactive approach to child health, marked by the introduction of the Pneumococcal Conjugate Vaccine (PCV) in September 2022 and the Rotavirus Vaccine (RVV) in December 2022 across 17 districts in 14 provinces, showcases a strategic defense against the prevalent threats of pneumonia and diarrheal diseases, and a strong commitment to safeguarding child health. The concurrent introduction of these two vaccines within a year illustrates Indonesia's capacity to adapt and strategize effectively against logistical and healthcare challenges, laying a foundation for future public health endeavors.

In pursuit of documenting and analyzing this landmark effort, the Global Advocacy for PCV (GAP) project conducted six semi-structured interviews with national and sub-national staff from the Clinton Health Access Initiative (CHAI) in Indonesia, highlighting critical enablers and opportunities that have underpinned Indonesia’s successful vaccination initiatives, offering valuable lessons for global health stakeholders considering similar concurrent vaccine introductions. Key enablers included strong political commitment, improved electronic data systems, and enhanced healthcare services. The initiative also leveraged private sector engagement and innovative approaches, such as integrated vaccine delivery and health worker training.

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• Published: 13 September 2024

Risk benefit analysis to evaluate risk of thromboembolic events after mRNA COVID-19 vaccination and COVID-19

• Huong N. Q. Tran   ORCID: orcid.org/0000-0003-3828-3862 1 ,
• Malcolm Risk 2 ,
• Girish B. Nair 3 &
• Lili Zhao 4  

npj Vaccines volume  9 , Article number:  166 ( 2024 ) Cite this article

Metrics details

• Epidemiology
• Thromboembolism
• Viral infection

We compared the risks and benefits of COVID-19 vaccines using a causal pathway analysis to weigh up possible risk factors of thromboembolic events post-vaccination. The self-controlled case series (SCCS) method examined the association between thromboembolic events and vaccination while a case-control study assessed the association between thromboembolic events and COVID-19, addressing under-reported infection data issues. The net vaccine effect was estimated using results from SCCS and case-control studies. We used electronic health record data from Corewell Health (16,640 subjects in SCCS and 106,143 in case-control). We found increased risks of thromboembolic events post-vaccination (incidence rate ratio: 1.19, 95% CI: [1.08, 1.31] after the first dose; 1.22, 95% CI: [1.11, 1.34] after the second dose). Vaccination attenuated infection-associated thromboembolic risks (odds ratio: 4.65, 95% CI: [4.18, 5.17] in unvaccinated vs 2.77, 95% CI: [2.40, 3.24] in vaccinated). After accounting for vaccine efficacy and protection against infection-associated thromboembolic events, vaccination decreases thromboembolic event risk, especially during high infection rate periods.

Introduction

The Coronavirus Disease 2019 (COVID-19) pandemic prompted a race to develop and distribute effective vaccines. Approximately 81.4% of the US population have been vaccinated with at least one dose, and 69.5% have completed the primary series of COVID-19 vaccination 1 . While the benefits of vaccination are widely acknowledged, concerns have emerged regarding the development of thromboembolic events after vaccination 2 . Phase 3 clinical trials were not statistically powered to identify rare adverse events 3 . The risks of new vaccines were not fully known during regulatory approval, particularly for mRNA-based vaccines (mRNA-1273 or BNT162b2), which were under authorized emergency use. Therefore, it is important to conduct post-marketing safety surveillance of the vaccines. More specifically, cases of venous thromboembolism following a mRNA-based vaccination were reported in 2022 after COVID-19 vaccines were administered in the US and some other countries 4 , 5 , 6 , 7 , drawing attention to the potential risk of thromboembolic events after the first vaccination dose. One study confirmed an increased risk of thromboembolism, ischemic stroke, and cerebral venous sinus thrombosis after the first dose of BNT162b2 8 , and another retrospective cohort study found an increased risk of cerebral venous thrombosis and portal vein thrombosis after any mRNA-based vaccination 9 . Moreover, a recent systematic review 10 has shown that thromboembolism is the most frequent cardiovascular complication following a mRNA-based vaccination. Despite those findings, vaccination is still recommended to reduce the likelihood of COVID-19, hospitalization, and mortality 8 , 11 . Furthermore, COVID-19 itself substantially increases the risk of thromboembolic events 12 , 13 , 14 , 15 , 16 , 17 , 18 , with a more prolonged and significant threat compared to vaccine-associated risks 8 . Therefore, studying the risk of thromboembolic events after COVID-19 vaccination should incorporate the protective effect of vaccines against COVID-19 severity and hence COVID-19-associated thromboembolic events.

Several studies have reported a positive correlation between thromboembolic events and mRNA-based vaccines, with reported incidence rate ratios (IRRs) between 1.04 and 1.22 8 , 19 , 20 , 21 , 22 . These studies used the self-controlled case series 23 (SCCS) design, which is a standard approach to studying adverse events of vaccines. The same design was used to evaluate the risk of thromboembolic events after COVID-19, with reported IRRs between 6.18 and 63.52 8 , 11 , 14 . However, since a thromboembolic event typically requires a hospital visit (emergency visit or hospital admission), subjects with a thromboembolic event are subject to a higher rate of COVID-19 testing, and so at a lower likelihood of misclassification as uninfected compared to subjects without an event. Hence, the SCCS design is subject to some risks of bias 24 , which we would expect to inflate the SCCS estimated relative risk (RR) of thromboembolic events after COVID-19.

The objective of this study is to evaluate whether the overall effect of the COVID-19 vaccination is to increase or decrease the risk of thromboembolic events. To do so, we first quantified the risk of thromboembolic events after mRNA-based vaccination using the SCCS method. Secondly, we evaluated the association between thromboembolic events and COVID-19 using a case-control study, avoiding the misclassification bias associated with the SCCS method. Finally, we conducted a risk-benefit analysis by comparing the magnitude of the increased risk through the direct effect of the COVID-19 vaccination with the reduced risk through the indirect pathway via protection against infection-associated thromboembolic events.

Our studies used electronic health record (EHR) data from the Corewell Health East (CHE, formerly known as Beaumont Health) and Corewell Health West (CHW, formerly known as Spectrum Health) healthcare systems, which includes demographics, mortality, hospital admissions, and COVID-19 testing. We obtained accurate COVID-19 vaccination records (vaccine types, dates, and doses) by linking EHR data at Corewell Health with the Michigan Care Improvement Registry (MCIR), giving more complete data for individuals who received the COVID-19 vaccines outside the healthcare system. We included all patients aged ≥ 18-years-old and were registered with a primary care physician within 18 months before Jan 1st, 2021.

We identified thromboembolic events based on ICD-10 (International Classification of Diseases version 10) codes from a hospital visit (emergency visit or hospital admission). These ICD-10 codes represent diagnoses for venous thromboembolism, arterial thrombosis, cerebral venous sinus thrombosis, ischemic stroke, and myocardial infarction (Supplementary Table 1 ). We also used patients with physical injury at a hospital visit (list of ICD-10 codes in Supplementary Table 2 ) to identify potential bias related to the misclassification and further leveraged them as a control group to estimate the effect of COVID-19 on thromboembolic events.

Estimate effect of mRNA-vaccination on thromboembolic events

We used the SCCS design to examine the association of thromboembolic events and the first two doses of mRNA-based COVID-19 vaccines (mRNA-1273 or BNT162b2) from December 1st, 2020, to August 31st, 2022. The SCCS method compares the incidence rate of thromboembolic events before and after vaccination. In this method, subjects are under their own control, and comparisons are made within subjects, thus avoiding any time-invariant confounding. We included subjects who had a thromboembolic event and received at least one dose of the primary series of mRNA-based vaccines in the study period. The control period was defined from December 1st, 2020, to 28 days before the first dose of vaccination, excluding the period of 28 days prior to vaccination to avoid bias due to contra-indications 25 . Two separate risk periods for the first and second doses were defined until 28 days after vaccination, death, or August 31st, 2022, whichever occurred first (Supplementary Fig. 1 ). We also excluded subjects who had COVID-19 within 90 days before a thromboembolic event to remove the confounding effect of infection on that event. We used a conditional Poisson regression 22 with an offset for the length of each period to estimate the IRRs of dose one and dose two simultaneously. Specifically, the model has an independent variable of the period with three categories (control periods, and two risk periods after the first and second dose). Using the control period as the reference, we derived the IRRs for the two doses. As Poisson regression assumes the independence between recurrent events, therefore, we considered only events that occurred at least one year after the previous events.

Estimate effect of COVID-19 on thromboembolic events

In an initial analysis of the association between thromboembolic events and COVID-19, we used the SCCS design and included patients who had at least one positive COVID-19 test (PCR or antigen) and a thromboembolic event at a hospital visit during the same period as in the previous study of vaccination. However, due to the missing infection data in patients who did not have any hospital visits for thromboembolic events or other reasons, the SCCS design resulted in a biased estimate of the association between thromboembolic events and COVID-19. Patients visiting the hospital, almost always received a COVID-19 (PCR or antigen) test, especially early in the pandemic, while patients who did not visit the hospital were subject to underreporting infection data. This underreporting (or misclassification of infected as uninfected) led to an inflated IRR of thromboembolic events after COVID-19.

We proposed a simple and efficient method to quantify the association between thromboembolic events and COVID-19 while dealing with the misclassification issue. The main idea is to select a subset of control (i.e., subjects without thromboembolic events) who had a hospital visit for reasons independent of COVID-19 and therefore had complete infection data. To this end, we used patients who had a diagnosis code for physical injury (see Supplementary Table 2 ) at a hospital visit as the control group, since we would not expect any causal association between physical injury and COVID-19. We used a case-control design, in which patients with a thromboembolic event are considered as cases, and patients with a physical injury are considered as controls. If an individual had multiple hospital visits for thromboembolic events or physical injuries, we considered only the first visit. As physical injuries can be risk factors for thromboembolic events 26 , 27 , we therefore excluded patients who experienced both events at the same visit. We determined the COVID-19 status based on the COVID-19 test results during the 28 days prior to the date of the event (Supplementary Fig. 2 ). If an individual had a positive test result, this subject was classified as exposed to COVID-19, otherwise, unexposed. We compared the odds of infection (exposed) vs no infection (unexposed) in the cases (with thromboembolic events) vs controls (with physical injury) using a logistic regression model adjusted for age, race, gender, Charlson comorbidity index (CCI), number of visits, and prior vaccination status (yes/no). Patients who had any COVID-19 vaccine between the date of the positive COVID-19 test and the date of the event were removed. The number of visits was fit with a natural spline with three degrees of freedom. The CCI was obtained using the R package comorbidity and categorized into four categories, ‘0’, ‘1–2’, ‘3–4’, and ‘ ≥ 5’ 28 , 29 . Analyses were done after excluding patients with incomplete covariate data.

Estimate the net effect of mRNA-vaccination on thromboembolic events: a risk-benefit analysis

COVID-19 vaccines are protective against COVID-19 and COVID-19 severity 30 , 31 , 32 , and so can indirectly decrease the likelihood of experiencing a thromboembolic event. Hence, we conducted a risk-benefit analysis to estimate the net RR of thromboembolic events after vaccination by considering the role of vaccination in preventing infection-associated thromboembolic events. Figure 1 illustrates the direct and indirect effect of the COVID-19 vaccination on the occurrence of thromboembolic events while considering vaccine efficacy (VE). As presented in the diagram, the association between thromboembolic events and COVID-19 vaccination is described by two paths, the direct association between thromboembolic events and vaccination, and the indirect association between thromboembolic events and vaccination via potential reduction in the risk of thromboembolic events through decreasing the risk of COVID-19. We estimated the overall influence of vaccination on the occurrence of thromboembolic events by considering both direct and indirect paths.

COVID-19 (I), individuals with COVID-19. COVID-19 vaccination (V), individuals with COVID-19 vaccines. Thromboembolic events (Y), individuals with thromboembolic events. V → I indicates vaccine effect (VE) in preventing COVID-19, V → Y indicates the risk of thromboembolic events after COVID-19 vaccination, I → Y indicates the risk of thromboembolic events after COVID-19, V → Y (via I) indicates the risk of thromboembolic events after vaccination accounting for vaccine effect in reducing infection-associated thromboembolic events.

Let \({\rm{P}}\left({\rm{I}}|{\rm{V}}\right)\) and \({\rm{P}}\left({\rm{I}}|\bar{{\rm{V}}}\right)\) be the probability of COVID-19 ( \({\rm{I}})\) in vaccinated ( \({\rm{V}}\) ) and unvaccinated ( \(\bar{{\rm{V}}}\) ) subjects, respectively. Let \({\rm{P}}\left({\rm{Y}}|\bar{{\rm{V}}},\bar{{\rm{I}}}\right),{\rm{P}}\left({\rm{Y}}|{\rm{V}},\bar{{\rm{I}}}\right),{\rm{P}}\left({\rm{Y}}|{\rm{I}},\bar{{\rm{V}}}\right),\) and \({\rm{P}}\left({\rm{Y}}|{\rm{I}},{\rm{V}}\right)\) be the probability (or risk) of thromboembolic events ( \({\rm{Y}})\) in unvaccinated and uninfected, vaccinated and uninfected, unvaccinated and infected, and vaccinated and infected subjects, respectively.

With the above notations, for a vaccinated subject, the total risk of thromboembolic events is \({\rm{P}}\left({\rm{Y}}|{\rm{V}},\bar{{\rm{I}}}\right)+{\rm{P}}\left({\rm{I}}|{\rm{V}}\right)\times {\rm{P}}\left({\rm{Y}}|{\rm{I}},{\rm{V}}\right)\) , where the product \({\rm{P}}\left({\rm{I}}|{\rm{V}}\right)\times {\rm{P}}\left({\rm{Y}}|{\rm{I}},{\rm{V}}\right)\) is the indirect risk calculated by multiplying the risk of COVID-19 of a vaccinated subject and the risk of thromboembolic events given a COVID-19 in the vaccinated group. Similarly, the overall risk of thromboembolic events for an unvaccinated subject is given by \({\rm{P}}\left({\rm{Y}}|\bar{{\rm{V}}},\bar{{\rm{I}}}\right)+{\rm{P}}\left({\rm{I}}|\bar{{\rm{V}}}\right)\times {\rm{P}}\left({\rm{Y}}|{\rm{I}},\bar{{\rm{V}}}\right)\) . Hence the net RR ( \({{\rm{RR}}}_{{\rm{Net}}}\) ) of thromboembolic events for a vaccinated subject compared to an unvaccinated subject is

The terms \({{\rm{RR}}}_{{\rm{V}}}\) is the RR of thromboembolic events comparing vaccinated versus unvaccinated in subjects without COVID-19, and \({{\rm{RR}}}_{{\rm{I|}}\bar{{\rm{V}}}}\) is the RR of thromboembolic events comparing subjects with and without COVID-19 in the unvaccinated group. The term \({{\rm{RR}}}_{{\rm{IV}}}\) is the RR of thromboembolic events in subjects who have both vaccination and infection, compared to the group of subjects who do not have any exposures.

We further defined VE as \({\rm{VE}}=1-{\rm{P}}({\rm{I|V}})/{\rm{P}}({\rm{I|}}\bar{{\rm{V}}})\) , then plugged VE into Eq. (1) to obtain

If \({{\rm{RR}}}_{{\rm{Net}}}\) is smaller than one, COVID-19 vaccination offers protection against thromboembolic events, with a lower \({{\rm{RR}}}_{{\rm{Net}}}\) implying a stronger protection.

Statistical analyses were performed in R 4.3.0. We reported odds ratio (OR) and IRR with 95% CIs and p -values from the two-sided test. We generated a figure for \({{\rm{RR}}}_{{\rm{Net}}}\) over a range of VE values based on the estimates of ORs and IRRs.

We used de-identified EHR data, the use of which was approved by the Institutional Review Board of Corewell Health.

Study population

During the study period from December 1st, 2020, to August 31st, 2022, there were 747,070 subjects at Corewell Health who received mRNA-based vaccines, among which 279,229 (37.38%) had the primary series of mRNA-1273 and 467,841 (62.62%) took BNT162b2. Overall, the number of fully vaccinated patients was 711,460 (95.23%), and 35,610 (4.77%) patients received only one dose. The median age was 57 (with interquartile range [IQR]: 40–69), and 59.81% of patients were female. There were 367,105 patients taking at least one COVID-19 test (antigen or PCR), among which 78,568 (21.4%) patients received positive results. The median age was 52 (with interquartile range [IQR]: 34–67), and 61.44% of patients were female.

In the study cohort of vaccination exposure, there were 16,640 patients who had at least one thromboembolic event and had the first dose of either mRNA-1273 or BNT162b2 vaccine. Patient demographics are presented in Table 1 . We identified 2724 events in the control period, 722 events within 28 days after the first dose, and 786 events within 28 days after the second dose.

In the study cohort of COVID-19 exposure, there were 18,004 patients who had a thromboembolic event (cases) and 88,139 patients who had a physical injury (controls) at a hospital visit. 16.96% of cases and 1.48% of controls had COVID-19 within 28 days before the event. Demographics of patients are presented in Table 2 .

Based on the SCCS analysis, we found an increased risk of thromboembolic events 28 days after the first dose (IRR = 1.19, 95% confidence interval (CI): [1.08, 1.31], p -value < 0.001), and after the second dose (IRR = 1.22, 95% CI: [1.11, 1.34], p -value < 0.001) of the mRNA-based vaccines.

We studied the risk of thromboembolic events in a 28-day window after vaccination based on prior research 8 . An event that occurs in a short period (such as 28 days) is more likely to be attributable to the vaccines. We also conducted a sensitivity analysis using a 60-day window after vaccination. The conclusions remained the same with slightly lower IRRs (IRR = 1.13, 95% CI: [1.03, 1.24] after the first dose, and IRR = 1.14, 95% CI: [1.05, 1.3] after the second dose).

Supplementary Figs. 3 and 4 show the IRRs for subgroup analyses by age (“18–31”, “31–50”, and “≥51”) and gender (female/male). We found that the effects of vaccination on thromboembolic events were similar between age groups and gender groups.

Naïve SCCS analysis showed a very large increased risk of thromboembolic events associated with COVID-19 (IRR = 19.36, 95% CI: [17.64, 21.26], p -value < 0.001). However, a similar analysis using the physical injury as an event also derived a large increased risk (IRR = 3.31, 95% CI: [3.10, 3.54], p -value < 0.001), indicating misclassification bias as COVID-19 should not substantially increase the risk of physical injury. In the case-control analysis with controls having a physical injury, we found that COVID-19 increased the risk of thromboembolic events but with a much smaller magnitude than the risk in the SCCS analysis (although it is still larger than the vaccination exposure). Moreover, the degree of the increased risks was modified by vaccination status (Fig. 2 ). The reported OR for the unvaccinated group was 4.65 (95% CI: [4.18, 5.17], p -value < 0.001) compared to 2.77 (95% CI: [2.40, 3.24], p -value < 0.001) for the vaccinated group. We observed the increased risks of thromboembolic events after COVID-19 in both groups, but vaccination appears to confer some protection against infection-associated thromboembolic events, given the lower OR. Alternatively, we divided the vaccinated group into four categories based on the time to the last vaccination (“≥365 days”, “180–365 days”, “90–180 days”, and “<90 days”). The effects of COVID-19 on thromboembolic events were similar across the four vaccinated groups. The results are in Supplementary Fig. 5 .

OR is denoted by a solid circle and a 95% CI is represented by a line. The x -axis is plotted on the natural log scale. CCI Charlson comorbidity index. Infection or non-infection refers to COVID-19.

We also conducted two sensitivity analyses. In the first analysis, rather than adjusting for the CCI, we adjusted individual risk factors that might be related to a thromboembolic event. These are congestive heart failure, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, diabetes with complications, cancer, moderate or severe liver disease, and metastatic solid tumors. We included the above eight risk factors (present or absent) in the logistic regression model. The effect of COVID-19 on the outcome of thromboembolic events was similar to the analysis with CCI. Results can be found in Supplementary Fig. 6 .

We assumed that patients who visited hospitals were routinely tested for COVID-19, especially during the early pandemic. Based on Corewell Health’s policy, patients who visited the healthcare system before March 1st, 2022, were tested for COVID-19. In our study cohort, 74.05% of participants had a hospital visit before March 1st, 2022. We conducted a sensitivity analysis using only these patients and the conclusions remained the same. See results in Supplementary Fig. 7 .

Our analysis in the previous sections gave an IRR of 1.22 as the measure of the association between thromboembolic events and the second dose of COVID-19 vaccination, therefore, we set \({{\rm{RR}}}_{{\rm{V}}}\)  = 1.22. We also obtained odd ratios \({{\rm{OR}}}_{{\rm{I|}}\bar{{\rm{V}}}}\)  = 4.65 and \({{\rm{OR}}}_{{\rm{IV}}}\)  = 2.82 from the analysis using the case-control design. Since the RR is very close to the OR when the event is rare, we therefore set \({{\rm{RR}}}_{{\rm{I|}}\bar{{\rm{V}}}}\)  = 4.65 and \({{\rm{RR}}}_{{\rm{IV}}}\)  = 2.82, as the thromboembolic events are rare 33 . Hence, plugging these estimators into Eq. (2), the \({{\rm{RR}}}_{{\rm{Net}}}\) becomes

Figure 3 illustrates the \({{\rm{RR}}}_{{\rm{Net}}}\) of thromboembolic events after COVID-19 vaccination as a function of VE. As VE increases from 0 to 1, \({{\rm{RR}}}_{{\rm{Net}}}\) decreases and reaches a point where vaccine benefits outweigh the harms. Specifically, vaccines with higher VE offer higher protection against thromboembolic events. For example, the effectiveness of mRNA-based COVID-19 vaccines against infection was 61% during the Delta period and 46% during the Omicron period 34 , 35 , 36 . Given an infection rate of 0.08 among unvaccinated subjects, the risk of thromboembolic events was decreased by 4.62% in the Delta period, which is higher than 2.07% in the Omicron period. Moreover, vaccines offer stronger protection during periods with higher infection rates. For example, with the infection rate of 0.1 in unvaccinated subjects, the reduction of the risk of thromboembolic events was higher (by 9.19% in Delta and 6.23% in the Omicron period), compared to the scenario when the infection rate was 0.08.

The x -axis is VE, and the y -axis is the net RR of thromboembolic events.

The list of ICD-10 codes for thromboembolic events is based on a previous publication 8 , including old myocardial infarction (I252). Old myocardial infarction (I252) reports for any myocardial infarction described as older than four weeks. However, our study cohort removed subjects with an I252 code who had any thromboembolic event with ICD-10 codes listed in Table S1 in the prior year. Therefore, we can consider observing I252 in the study period as a new incidence. There were 20,002 (18.84%) patients with a hospital visit associated with the I252 code. We conducted a sensitivity analysis by excluding these patients and the conclusions did not change. The estimated IRRs of thromboembolic events are 1.16 and 1.17 after vaccine dose 1 and dose 2, respectively, which are slightly smaller than the original results including the I252 code (IRRs were 1.19 and 1.22 after the first and second dose). The association between COVID-19 and thromboembolic events is higher in the unvaccinated group (OR = 5.77 without I252 and OR = 4.65 with I252) and similar in the vaccinated group (OR = 2.80 without I252 and OR = 2.77 with I252). Hence, given the same infection rate and VE, vaccination offered a stronger protection, compared to the analysis with the I252 codes. For example, given an infection rate in the unvaccinated population of 0.08 and a VE of 0.8, vaccination lowers the risk of thromboembolic events by 17.14% without I252, compared to 6.67% in the analysis with I252. Detailed results are in Supplementary Figs. 8 and 9 . We considered the analysis that includes the I252 code as the main analysis to represent more conservative results.

We found that both COVID-19 vaccination and COVID-19 increase the risk of thromboembolic events. However, evidence implies that the likelihood of experiencing a thromboembolic event after COVID-19 is much higher than after vaccination. Our analysis agrees with previous research, indicating that COVID-19 is a more dangerous risk factor for thromboembolic events than vaccination 8 , 11 , 12 , 13 , 14 .

Different from existing work, we evaluated the association between thromboembolic events and COVID-19 using a case-control study, avoiding the misclassification issue associated with the SCCS design. We also studied the effect of prior vaccination on reducing infection-associated thromboembolic events. Moreover, we included both COVID-19 vaccination and COVID-19 in the analysis of the risk of thromboembolic events and conducted a risk-benefit analysis by comparing the magnitude of the increased risk through the direct effect of COVID-19 vaccination with the reduced risk through the indirect pathway via protection against severe diseases. Our analysis provides evidence that COVID-19 vaccination directly increases the risk of thromboembolic events, but indirectly reduces the risk of infection-associated events. Results show that the indirect benefit of preventing infection-associated thromboembolic events outweighs the direct harm if the VE and infection rate reaches certain levels. Moreover, COVID-19 vaccination may have additional benefits in preventing thromboembolic events associated with COVID-19, as a higher rate of vaccination increases the overall level of immunity in the population, reducing the spread of the virus and conferring collective protection against infection-associated thromboembolic events and other health risks associated with COVID-19.

There are several limitations to this study. First, using ICD-10 codes to identify thromboembolic events may be subject to phenotype errors. Second, Corewell Health has 22 hospitals, and the catchment area for these hospitals is across many counties, hence patients may seek care at other facilities outside the Corewell Health system, leading to missing data such as infection data. To deal with the missing infection data, we used the case-control study. Moreover, the use of a prior number of hospital visits as covariates in the regression model mitigates the bias due to differing degrees of interaction with the Corewell Health system between infected and control subjects. However, patients with a hospital visit due to injuries may not be the perfect control group, but it is clearly better than a control group of patients without thromboembolic events. Therefore, we may not totally correct the bias, but we reduce it. Finally, the study population for vaccine doses 1 and 2 are different. If a subject had a thromboembolic event after the first vaccine dose, this subject is unlikely to receive the second dose, therefore, the population who received the second dose only includes subjects who did not have a thromboembolic event after the first dose.

Despite these limitations, our study makes a critical contribution to quantifying the net risk of thromboembolic events associated with COVID-19 vaccination. It accounts for both the direct effects of vaccination and the indirect effects of protection against COVID-19 and severe diseases. The dual consideration is vital for a comprehensive understanding of the risk-benefit profile. The mechanism of vaccination is to simulate the immune response the body has against infection using a dead/attenuated virus or mRNA, which can lead to side effects similar to those of the virus, albeit in a less severe form (e.g., thromboembolic events, myocarditis 37 , acute kidney injury 38 , 39 ). Our finding highlights the necessity of evaluating both the indirect benefits and direct harms of vaccination to provide a complete and accurate assessment of vaccine safety. This comprehensive approach ensures a balanced understanding of the risks and the benefits, reinforcing the overall safety and efficacy of vaccination programs.

Our risk-benefit analysis was conducted on the population level. This analysis can also be stratified by patient groups of interest. For example, the risk-benefit of vaccination might be different between older and younger populations. Moreover, our findings are for a broad range of thromboembolic conditions, so more research is needed on the specific biological mechanisms connecting COVID-19 and mRNA vaccination to these events, both to establish causality and help identify a more specific set of conditions or risk factors.

Data availability

The datasets analyzed during the current study are not publicly available due to privacy or ethical restrictions.

Code availability

Code for this study is available from the corresponding author on request.

Centers for Disease Control and Prevention. COVID Data Tracker. Atlanta, GA: U.S. Department of Health and Human Services, CDC ; https://covid.cdc.gov/covid-data-tracker (2023).

Avinash, M. & Vineeta, O. Thromboembolism after COVID-19 vaccination: a systematic review of such events in 286 patients. Ann. Vasc. Surg. 84 , 12–20 (2022).

Article   Google Scholar  

Fan, B. E. et al. COVID-19 mRNA vaccine-associated cerebral venous thrombosis: rare adverse event or coincidence? Am. J. Hematol . 98 , E4–E7 (2023).

Leonor, D. et al. Cerebral venous thrombosis after BNT162b2 mRNA SARS-CoV-2 vaccine. J. Stroke Cerebrovasc. Dis . 30 , 105906 (2022).

Elizabeth, A. A., Rohan, K., Mirnal, C. & Ulka, S. Three cases of acute venous thromboembolism in females after vaccination for coronavirus disease 2019. J. Vasc. Surg. Venous Lymphat. Disord. 10 , 14–17 (2021).

Google Scholar  

Giuseppe, C., Ilaria, N., Marco, R., Salvatore, B., & Alberto, T. Deep vein thrombosis (DVT) occurring shortly after the second dose of mRNA SARS-CoV-2 vaccine. Intern. Emerg. Med . 16 , 803–804 (2021).

Zaitun, Z., Nur, A. S. & Abdul, R. I. G. Cerebral venous sinus thrombosis 2 weeks after the first dose of mRNA SARS-CoV-2 vaccine. Acta Neurochir. 163 , 2359–2362 (2021).

Julia, H. et al. Risk of thrombocytopenia and thromboembolism after COVID-19 vaccination and SARS-CoV-2 positive testing: self-controlled case series study. BMJ . 374 , n1931 (2021).

Maxime, T., Masud, H., John, R. G., Sierra, L., & Paul, J. H. Cerebral venous thrombosis and portal vein thrombosis: a retrospective cohort study of 537,913 COVID-19 cases. EClinicalMedicine . 39 , 101061 (2021).

Farah, Y. et al. Adverse events following COVID‐19 mRNA vaccines: a systematic review of cardiovascular complication, thrombosis, and thrombocytopenia. Immun. Inflamm. Dis . 11 , e807 (2023).

Frederick, K. H. et al. Thromboembolic risk in hospitalized and nonhospitalized COVID-19 patients: a self-controlled case series analysis of a nationwide cohort. Mayo Clin. Proc . 96 , 2587–2597 (2021).

Xiaoming, X., Jianhua, C., & Qinglei, G. Prevalence and risk factors of thrombotic events on patients with COVID-19: a systematic review and meta‐analysis. Thromb. J . 19 , 32 (2021).

Sam, S., Yu, H., & Stavros, K. Venous thromboembolism in COVID-19. Thromb. Haemost . 120 , 1642–1653 (2020).

Ioannis, K., Osvaldo, F., Paddy, F., Krister, L., & Anne-Marie, F. C. Risk of acute myocardial infarction and ischaemic stroke following COVID-19 in Sweden: a self-controlled case series and matched cohort study. Lancet . 398 , 599–607 (2021).

Mahmoud, B. M. et al. Thromboembolism risk of COVID-19 is high and associated with a higher risk of mortality: a systematic review and meta-analysis. EClinicalMedicine . 29 , 100639 (2020).

Ahuja, N. et al. Venous thromboembolism in patients with COVID-19 infection: risk factors, prevention, and management. Semin. Vasc. Surg . 34 , 101–116 (2021).

Yu, Y. et al. Incidence and risk factors of deep vein thrombosis in hospitalized COVID-19 patients. Clin. Appl. Thromb. Hemost . 26 , 1076029620953217 (2020).

Klok, F. A. et al. Confirmation of the high cumulative incidence of thrombotic complications in critically ill ICU patients with COVID-19: an updated analysis. Thromb. Res. 191 , 148–150 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Simpson, C. R. et al. First-dose ChAdOx1 and BNT162b2 COVID-19 vaccines and thrombocytopenic, thromboembolic and hemorrhagic events in Scotland. Nat. Med. 29 , 1290–1297 (2021).

Marie, J. J. et al. Myocardial infarction, stroke, and pulmonary embolism after BNT162b2 mRNA COVID-19 vaccine in people aged 75 years or older. JAMA 327 , 80–82 (2022).

Jacob, D. B. et al. Analysis of thromboembolic and thrombocytopenic events after the AZD1222, BNT162b2, and MRNA-1273 COVID-19 vaccines in 3 Nordic Countries. JAMA Netw. Open . 5 , e2217375 (2022).

Chui, C. S. L. et al. Thromboembolic events and hemorrhagic stroke after mRNA (BNT162b2) and inactivated (CoronaVac) covid-19 vaccination: a self-controlled case series study. EClinicalMedicine 50 , 101504 (2020).

Whitaker, H. J., Farrington, C. P., Spiessens, B., & Musonda P. Tutorial in biostatistics: the self-controlled case series method. Stat. Med . 25 , 1768–1797 (2006).

Gareth, J. G. et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat. Commun . 11 , 5749 (2020).

Bu, F. et al. Bayesian safety surveillance with adaptive bias correction. Stat. Med. 43 (2), 395–418 (2023).

Article   PubMed   Google Scholar  

Knudson, M. M., Ikossi, D. G., Khaw, L., Morabito, D. & Speetzen, L. S. Thromboembolism after trauma: an analysis of 1602 episodes from the American College of Surgeons National Trauma Data Bank. Ann. Surg. 240 , 490–498 (2004).

Article   PubMed   PubMed Central   Google Scholar  

Van, S. K. J., Rosendaal, F. R., & Doggen, C. J. Minor injuries as a risk factor for venous thrombosis. Arch. Intern. Med . 168 , 21–26 (2008).

Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40 , 373–383 (1987).

Article   CAS   PubMed   Google Scholar  

Gasparini, A. An R package for computing comorbidity scores. J. Open Source Softw. 3 , 648 (2018).

Fleming-Dutra, K. E. et al. Association of prior BNT162b2 COVID-19 vaccination with symptomatic SARS-CoV-2 infection in children and adolescents during omicron predominance. JAMA 327 , 2210–2219 (2022).

Katherine, E. F. et al. Preliminary estimates of effectiveness of monovalent mRNA vaccines in preventing symptomatic SARS-CoV-2 infection among children aged 3–5 years—increasing community access to testing program, United States, July 2022–February 2023. MMWR Morb. Mortal. Wkly. Rep. 72 , 177–182 (2023).

Kelly, M. H. et al. Effectiveness of coronavirus disease 2019 (COVID-19) vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among residents of US nursing homes before and during the delta variant predominance. Clin. Infect. Dis . 75 , S147–S154 (2022).

Silverstein, M. D., Heit, J. A., Mohr, D. N., Petterson, T. M., O’Fallon, W. M., & Melton. L. J. Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study. Arch. Intern. Med . 158 , 585–593 (1999).

Chen, S. et al. Efficacy of COVID-19 vaccines in patients taking immunosuppressants. Ann. Rheum. Dis. 81 , 875–880 (2022).

Malcolm, R. et al. Comparative effectiveness of coronavirus disease 2019 (COVID-19) vaccines against the delta variant. Clin. Infect. Dis . 75 , e623–e629 (2022).

Malcolm, R. et al. COVID-19 vaccine effectiveness against omicron (B.1.1.529) variant infection and hospitalisation in patients taking immunosuppressive medications: a retrospective cohort study. Lancet Rheumatol. 4 , e775–e784 (2022).

Martian, P. et al. Risk of myocarditis after sequential doses of COVID-19 vaccine and SARS-CoV-2 infection by age and sex. Circulation . 146 , 743–754 (2022).

Huiting, L. et al. Acute kidney injury after COVID-19 vaccines: a real-world study. Ren. Fail . 44 , 958–965 (2022).

Fabrizi, F. et al. COVID-19 and acute kidney injury: a systematic review and meta-analysis. Pathogens 9 , 1052 (2020).

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Acknowledgements

We thank Kevin Heinrich at Quire and Martin Witteveen-Lane for querying the data from the Corewell Health Epic system. This study was funded by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number R01AI158543. The funder played no role in the study design, data collection, analysis, and interpretation of data, or the writing of this manuscript.

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Tran, H.N.Q., Risk, M., Nair, G.B. et al. Risk benefit analysis to evaluate risk of thromboembolic events after mRNA COVID-19 vaccination and COVID-19. npj Vaccines 9 , 166 (2024). https://doi.org/10.1038/s41541-024-00960-7

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A, Edematous pink plaques. B, Annular pink plaques.

A and B, Punch biopsy specimen demonstrating mild predominantly perivascular and focal interstitial mixed infiltrate with lymphocytes and eosinophils (hematoxylin-eosin). C and D, Perivascular eosinophils (hematoxylin-eosin).

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Johnston MS , Galan A , Watsky KL , Little AJ. Delayed Localized Hypersensitivity Reactions to the Moderna COVID-19 Vaccine : A Case Series . JAMA Dermatol. 2021;157(6):716–720. doi:10.1001/jamadermatol.2021.1214

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Delayed Localized Hypersensitivity Reactions to the Moderna COVID-19 Vaccine : A Case Series

• 1 Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut
• 2 Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
• Medical News & Perspectives JAMA Network Journals’ Articles of the Year 2021 Jennifer Abbasi JAMA

Question   What are the clinical course and histopathologic examination findings for delayed injection-site reactions to the Moderna coronavirus disease 2019 (COVID-19) vaccine?

Findings   The Moderna COVID-19 vaccine may cause a delayed localized hypersensitivity reaction with a median latency to onset of 7 days after vaccine administration. This pruritic and variably tender reaction has a median duration of 5 days, but may persist for up to 21 days, and may occur again and sooner after the second vaccine dose; no serious adverse events were observed in association with this cutaneous reaction to the Moderna COVID-19 vaccine.

Meaning   Self-limited localized delayed hypersensitivity reactions to the Moderna COVID-19 vaccine may occur, and in contrast with immediate hypersensitivity reactions, these delayed hypersensitivity reactions are not a contraindication to subsequent vaccination.

Importance   In response to the coronavirus disease 2019 (COVID-19) pandemic, 2 mRNA vaccines (Pfizer-BioNTech and Moderna) received emergency use authorization from the US Food and Drug Administration in December 2020. Some patients in the US have developed delayed localized cutaneous vaccine reactions that have been dubbed “COVID arm.”

Objective   To describe the course of localized cutaneous injection-site reactions to the Moderna COVID-19 vaccine, subsequent reactions to the second vaccine dose, and to characterize the findings of histopathologic examination of the reaction.

Design, Setting, and Participants   This retrospective case series study was performed at Yale New Haven Hospital, a tertiary medical center in New Haven, Connecticut, with 16 patients referred with localized cutaneous injection-site reactions from January 20 through February 12, 2021.

Main Outcomes and Measures   We collected each patient’s demographic information, a brief relevant medical history, clinical course, and treatment (if any); and considered the findings of a histopathologic examination of 1 skin biopsy specimen.

Results   Of 16 patients (median [range] age, 38 [25-89] years; 13 [81%] women), 14 patients self-identified as White and 2 as Asian. The delayed localized cutaneous reactions developed in a median (range) of 7 (2-12) days after receiving the Moderna COVID-19 vaccine. These reactions occurred at or near the injection site and were described as pruritic, painful, and edematous pink plaques. None of the participants had received the Pfizer-BioNTech vaccine. Results of a skin biopsy specimen demonstrated a mild predominantly perivascular mixed infiltrate with lymphocytes and eosinophils, consistent with a dermal hypersensitivity reaction. Of participants who had a reaction to first vaccine dose (15 of 16 patients), most (11 patients) developed a similar localized injection-site reaction to the second vaccine dose; most (10 patients) also developed the second reaction sooner as compared with the first-dose reaction.

Conclusions and Relevance   Clinical and histopathologic findings of this case series study indicate that the localized injection-site reactions to the Moderna COVID-19 vaccine are a delayed hypersensitivity reaction. These reactions may occur sooner after the second dose, but they are self-limited and not associated with serious vaccine adverse effects. In contrast to immediate hypersensitivity reactions (eg, anaphylaxis, urticaria), these delayed reactions (dubbed “COVID arm”) are not a contraindication to subsequent vaccination.

The coronavirus disease-2019 (COVID-19) outbreak was declared a pandemic by the World Health Organization on March 11, 2020, and development of a safe, effective COVID-19 vaccine rapidly became a global priority. 1 In the US, the Pfizer-BioNTech and Moderna mRNA COVID-19 vaccines were granted emergency use authorization in December 2020, with more than 48 million doses administered nationwide to date. 2 As vaccine administration increases, recognition and understanding of these novel vaccines’ adverse effects are essential. In this Brief Report, we present a series of localized injection-site reactions to the Moderna COVID-19 vaccine that are consistent with clinical and histopathologic examination findings for delayed-type hypersensitivity reactions.

A retrospective case-series study was performed at Yale New Haven Hospital in New Haven, Connecticut, to assess clinical and histopathologic features of injection-site reactions to COVID-19 vaccines. The Yale University Institutional Review Board approved the study, and informed consent was waived because data were retrospective and deidentified.

All 16 patients referred to Yale New Haven Hospital’s Dermatology Services from January 20 through February 12, 2021, were included. We collected each patient’s demographic information, vaccine indication and manufacturer, medical history, medications, allergies, prior vaccine reactions, latency and duration of injection-site reactions, other symptoms, and treatment. We also reviewed clinical photographs of 13 of the 16 patients and histopathologic examination findings for 1 skin biopsy specimen from 1 of the 16 patients.

Of 16 patients, (median [range] age, 38 [25-89] years; 13 [81%] women) 14 patients self-identified as White and 2 as Asian. The main characteristics of these patients with delayed cutaneous hypersensitivity reactions are shown in the Table . All 16 participants received the Moderna COVID-19 vaccine, and only 1 reported a prior localized vaccine reaction (mild reaction to an influenza vaccine). Most (13 of 16) of the patients were health care workers; for the others (3 of 16), vaccine indication was for older age. Of the 16 patients, 15 developed localized cutaneous reactions after the first dose; 1 patient developed a reaction only after the second dose. After the first dose, pruritic and variably painful erythematous reactions near the injection site developed in a median (range) of 7 (2-12) days after vaccine administration. The pink plaques were variably edematous or indurated and were typically homogenous ( Figure 1 , A and B) or less commonly annular ( Figure 1 , C and D). Treatments included topical steroids, oral antihistamines, and cool compresses; 1 patient had received cephalexin for presumed cellulitis. Reactions to the first vaccine dose had a median (range) duration of 5 (1-21) days.

Of the 16 patients, 12 developed injection-site reactions to the second dose, with a median (range) onset of 2 (0-5) days after vaccine administration; 1 of these 12 patients had no reaction after the first dose. Among the 15 patients who had experienced a reaction after the first dose, most (11 patients) developed a reaction to the second dose. Of these, most (10 patients) developed the second-dose reaction sooner as compared with their first-dose reaction. Second-dose reactions had a median (range) duration of 3 (1-5) days and were clinically similar to the first-dose reactions. Findings of an histologic examination of a skin punch-biopsy specimen of a second-dose reaction demonstrated mild predominantly perivascular and focal interstitial mixed infiltrate with lymphocytes and eosinophils consistent with a dermal hypersensitivity reaction ( Figure 2 ).

We report on 16 cases of delayed localized cutaneous reactions to the Moderna mRNA COVID-19 vaccine, dubbed “COVID arm,” which were consistent with clinical and histopathologic examinations findings for delayed-type hypersensitivity reactions. This delayed reaction is distinctly different from the local pain, redness, and swelling observed on average 1 day after either dose with a median duration of 2 to 3 days, as reported in the Moderna COVID-19 vaccine trial. 3 Instead, COVID arm represents a delayed injection-site reaction characterized by erythema, pruritus, induration, and tenderness. In this case series, the median (range) onset of the reactions was 7 (2-12) days after the first dose, and the reaction had a median duration of 5 days.

Similarly, COVID arm symptoms reported in the Moderna trial 4 appeared on or after day 8 following the first dose (244 of 30 351 total trial participants; 0.8%) or the second dose (68 of 30 351 total trial participants; 0.2%) and resolved after 4 to 5 days. Delayed localized reactions may have been underreported by the Moderna trial because local reactions were actively solicited only until day 7, with unsolicited adverse events collected thereafter. In this case series, the second vaccine reactions developed more quickly in 10 of 11 patients who experienced reactions to both vaccine doses, with a median onset of 2 days after the second dose. This may have been captured by the increased frequency of erythema within 7 days of the second dose of the mRNA vaccine (1257 of 14 677 participants; 8.5%) vs the first dose (430 of 15 168 participants; 2.8%) in the Moderna trial.

In the present case-series study, the timing and histopathologic examination findings of the reaction suggest cell-mediated immunity associated with delayed-type hypersensitivity reactions. These observations are consistent with a recent case-series study by Blumenthal and colleagues 5 of 12 delayed localized cutaneous reactions to the Moderna vaccine. The present case-series supports and expands on the findings of those authors by reporting on cases among patients with a broader age range (25-89 years) and additional histopathologic examination findings. Similar to the findings of the present study, results of a skin biopsy specimen in the prior study 5 demonstrated perivascular and interstitial inflammatory infiltrate with lymphocytes, eosinophils, and minimal epidermal change. At our institution, we too have observed these findings in the results of 3 additional biopsy specimens of delayed localized Moderna vaccine reactions from patients not among the 16 described in this Brief Report.

The histopathologic findings observed in the present case series are characteristic of dermal hypersensitivity reactions, which may be seen in response to medications. 6 Such medication-associated delayed hypersensitivity reactions are T-cell mediated, 7 and similarly, we hypothesize that delayed localized cutaneous reactions to Moderna COVID-19 vaccine may be associated with T-cell responses to a vaccine excipient, lipid nanoparticle, or mRNA component. Polyethylene glycol, present in both the Moderna and the Pfizer vaccine, has been implicated in some immediate hypersensitivity reactions, 8 but its role in delayed-type hypersensitivity reactions remains unknown. Patch testing of patients with COVID arm using vaccine components may be informative.

All of the patients in this case-series study had received the Moderna vaccine. Although similar amounts of the Moderna and Pfizer vaccines have been allocated to Connecticut, 9 , 10 it is possible that the New Haven area may have received more of the Moderna vaccine. To determine the incidence of COVID arm for each vaccine, we suggest that the Centers for Disease Control and Prevention add questions about this delayed reaction to the v-safe health checker ( https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/vsafe.html ), and we encourage health care professionals to submit cases of cutaneous COVID-19 vaccine reactions to the American Academy of Dermatology registry ( https://www.aad.org/member/practice/coronavirus/registry ).

Most (14 of 16) of the patients in this series were White, possibly because of Connecticut’s demographic composition and the national distribution trends of COVID-19 vaccine. 11 Another reason for the predominantly White cohort is that erythema may be overlooked or not as obvious on darker phototype skin. Most (13 of 16) of the patients were also women. Women have received more (59.2%) of the COVID-19 vaccines in the US to date. 11 Women may also be more prone to developing COVID arm, just as women are more likely to develop anaphylaxis, immediate hypersensitivity, and injection-site reactions to other vaccines. 12 , 13 Finally, it is possible that women may be more likely to report symptoms or to seek medical care for such reactions.

As COVID-19 vaccine administration increases, clinicians and the public should be aware of COVID arm. In this case-series cohort, the clinical characteristics and the findings of histopathologic examinations were consistent with delayed-type hypersensitivity reactions, and no serious vaccine adverse events occurred in association with these cutaneous reactions. It is critical that health care professionals distinguish these delayed-type reactions from immediate-type hypersensitivity reactions and from cellulitis. The Centers for Disease Control and Prevention currently recommends 14 that patients who experience immediate hypersensitivity reactions, including urticaria, within 4 hours of receiving a COVID-19 vaccine postpone the second dose until after consulting an allergist-immunologist. In contrast, the delayed localized hypersensitivity reaction we describe in this case-series study is not a contraindication to subsequent vaccination and patients and health care professionals should be aware that this type of reaction may develop more rapidly after the second vaccine dose.

This case series was from a single center during a short period of time. Most of the patients were health care workers, which may limit generalizability.

In this case series, we characterize delayed localized injection-site reactions to the Moderna COVID-19 vaccine, dubbed COVID arm, which we suggest renaming “COVID vaccine arm.” These cutaneous reactions occur near the injection site and are benign and self-limited. These reactions appear a median of 7 days after the first vaccine dose and 2 days after the second dose. In contrast to immediate hypersensitivity reactions (eg, anaphylaxis and urticaria) that present within 4 hours of vaccine administration, these delayed localized hypersensitivity reactions are not a contraindication to subsequent vaccination.

Accepted for Publication: March 18, 2021.

Published Online: May 12, 2021. doi:10.1001/jamadermatol.2021.1214

Corresponding Author: Alicia J. Little, MD, PhD, Department of Dermatology, Yale School of Medicine, 333 Cedar St, PO Box 208059, New Haven, CT 06520 ( [email protected] ).

Author Contributions: Drs Little and Johnston had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data : All authors.

Drafting of the manuscript : Johnston, Galan, Little.

Critical revision of the manuscript for important intellectual content : All authors.

Statistical analysis : Johnston, Little.

Administrative, technical, or material support : Johnston, Galan, Watsky.

Supervision : Little.

Conflict of Interest Disclosures: Dr Little reported a grant from the National Center for Advancing Translational Science (CTSA No. KL2 TR001862), which is a component of the National Institutes of Health, and a Women’s Health Career Development Award from the Dermatology Foundation during the conduct of the study. Dr Watsky reported equity in Johnson & Johnson held by his spouse’s retirement fund outside the submitted work. No other disclosures were reported.

Disclaimer: The publication's contents are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health.

Additional Contributions: We thank the 4 patients whose images were published for granting permission. We thank Christine Ko, MD, Department of Dermatology, Yale University School of Medicine, for mentorship and assistance with editing the manuscript. For assistance with referred patients, we thank Jacob Siegel, MD, Kathleen Suozzi, MD, Mark Goldstein, MD, Mark Grossman, MD, and Barry Richter, MD, Department of Dermatology, Yale University School of Medicine, and Mark Abdelmalek, MD, Dermatology of Philadelphia–Mohs Surgery Center and Department of Dermatology, University of Pennsylvania Perelman School of Medicine. We thank Gauri Panse, MD, Departments of Dermatology and Pathology, Yale University School of Medicine, for assistance with reviewing the histopathology findings. We thank Mary Tomayko, MD, PhD, Department of Dermatology, Yale University School of Medicine, for insights into potential immunopathogenesis of “COVID arm.” We thank David Banach, MD, Department of Medicine (Infectious Disease), University of Connecticut School of Medicine, and Richard Martinello, MD, Departments of Medicine (Infectious Disease) and Pediatrics, Yale University School of Medicine and Department of Infection Prevention, Yale New Haven Health, for insights into SARS-CoV-2 vaccine administration and reported adverse effects. None of these individuals were compensated.

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COVID-19 after vaccination doesn't raise risk of autoimmune disease, data suggest

A  study of 1.8 million adults published in JAMA Network Open suggests that—except for a slightly higher risk of inflammatory bowel disease and blistering skin disorders in a subgroup hospitalized for SARS-CoV-2 Omicron variant infection—Delta or Omicron BA.1 or BA.2 infection in highly vaccinated adults doesn't significantly raise the long-term risk of autoimmune diseases.

Led by investigators from the National Centre for Infectious Diseases in Singapore, the study team used the SARS-CoV-2 registry and a healthcare claims database to compare the long-term risk of new autoimmune diseases after Delta or Omicron BA.1 or BA.2 infection in recipients of COVID-19 vaccines and boosters with that in uninfected controls. The study period was September 2021 to March 2022, with a 300-day follow-up.

Of all participants, 27.2% had COVID-19, 72.8% were controls, 51.9% were women, and the average age was 49 years.

"Studies have reported increased risk of autoimmune sequelae after SARS-CoV-2 infection," the researchers wrote. "However, risk may potentially be attenuated by milder Omicron (B.1.1.529) variant infection and availability of booster vaccination."

Boosters may lower risk of new autoimmune disease

During Delta predominance, 104,179 participants had COVID-19 infections and 666,575 were controls, while 375,903 and 619,379 controls, respectively were infected during Omicron predominance. A total of 81.1% of infected participants had completed the primary two-dose COVID-19 mRNA vaccine series amid the Delta era, and 74.6% received boosters during the Omicron period.

Continued surveillance for autoimmune conditions arising after COVID-19 is still necessary during the Omicron variant era.

A significantly higher risk of 12 studied autoimmune diseases wasn't observed during the Delta or Omicron periods, except for inflammatory bowel disease (adjusted hazard ratio [aHR], 2.23) and bullous (blistering) skin disorders (aHR, 4.88) in hospitalized COVID-19 patients amid Omicron. An elevated risk of vasculitis was documented in vaccinated Omicron patients (aHR, 5.74) but not those who received boosters.

The study authors concluded, "Continued surveillance for autoimmune conditions arising after COVID-19 is still necessary during the Omicron variant era."

Narrow-spectrum drug shows promise against C diff infection in phase 2 trial

Biopharmaceutical company Crestone Pharmaceuticals last week announced positive topline results from a phase 2 trial of its investigational drug treatment for Clostridioides difficile infection (CDI).

The trial evaluated the safety and efficacy of two different dosages of CRS3123, a small-molecule protein synthesis inhibitor, administered twice daily in adults diagnosed with a primary episode or first recurrence of CDI. Vancomycin was the comparator drug.

Among the 43 patients in the primary intention-to-treat analysis, 28 of 29 (97%) who received one of the two dosages of CRS3123 achieved clinical cure at the day 12 test-of-cure visit, compared with 13 of 14 (93%) who were treated with vancomycin. In addition, only 4% of CRS3123 patients experienced CDI recurrence at day 40, compared with 23% in the vancomycin group. CRS3121 was also well-tolerated, with no serious treatment-emergent adverse events reported. 

Minimal microbiome disruption

One of the advantages of CRS3123 over current therapies is its narrow spectrum, which enables it to target C difficile bacteria and inhibit toxin production with minimal disruption to other microbes in the gut. Vancomycin is a broad-spectrum antibiotic known to disrupt the gut microbiome.

CDI is the most common healthcare-associated infection in the United States, with an estimated 500,000 cases occurring each year. Roughly 1 in 6 CDI patients experience a recurrence within 2 to 8 weeks.

"Treatment of  C. difficile  infection remains in urgent need of agents that spare normal gut microbes, so they can reconstitute the microbiome and prevent further recurrences of CDI," lead trial investigator Thomas Louie, MD, of the University of Calgary said in a company  press release . "The findings of this study support CRS3123 as such a candidate for further development."

Crestone also announced that, based on the results of the trial, the National Institute of Allergy and Infectious Diseases will provide $4.5 million in new funding for microbiome analyses, manufacturing process optimization, and other phase 2 supporting studies.

Treatment of  C. difficile  infection remains in urgent need of agents that spare normal gut microbes, so they can reconstitute the microbiome and prevent further recurrences of CDI.

Modeling study touts cost savings of RSV vaccination in older adults

Targeting older adults with underlying health conditions—as opposed to the general population—for respiratory syncytial virus (RSV) vaccines would reduce spending and prevent illness, according to a modeling study yesterday in the Canadian Medical Association Journal ( CMAJ ).

The study compared the cost-effectiveness of different vaccine programs in different age groups with different medical risks.  

The model considered a population of 100,000 people aged 50 years and older. Vaccine characteristics were based on RSV vaccines authorized in Canada as of May 2024, with vaccine protection assumed to last 2 years.  

The cost-effectiveness threshold was $50,000 per quality-adjusted life year (QALY).

Optimal in oldest adults with underlying conditions

According to the study authors, without vaccination, they projected 131,389 (95% credible interval [CrI], 120,070 to 143,581) medically attended RSV cases, 12,068 (95% CrI, 10,324 to 13,883) hospital admissions, and 1,015 (95% CrI, 617 to 1,450) deaths annually among Canadians aged 60 years and older.

Vaccinating strategies based on age plus risk for RSV-related complications were projected to avert a median of 20% to 31% of outpatient cases, 38% to 42% of hospital cases, and 39% to 42% of deaths, the authors said.   Vaccines were most cost-effective, according to the model, when given to adults ages 70 and older, with one or more chronic medical condition.

We found that vaccination of older adults may be less costly and more effective than no vaccination.

"We found that vaccination of older adults may be less costly and more effective than no vaccination and that vaccinating people aged 70 years and older with chronic medical conditions is likely to be cost-effective based on commonly used cost-effectiveness thresholds," said Ashleigh Tuite, PhD from the Public Health Agency of Canada in a CMAJ press release .

"Strategies focused on adults with underlying medical conditions that place them at increased risk of RSV disease are more likely to be cost-effective than general age-based strategies," Tuite added.  

Quick takes: NC measles case, flu vaccine supply estimate, polio vax campaign shifts to northern Gaza

• The North Carolina Department of Health and Human Services yesterday reported the state’s first measles case since 2018. In a  statement , health officials said the patient is a child in Mecklenburg County who was probably exposed during international travel. The parents kept the child home after returning to the state, except for one medical visit during which health providers took appropriate precautions. Cases in the United States are up sharply this year, part of a global rise in cases.
• The US Centers for Disease Control and Prevention (CDC) last week in an  update that it projects that vaccine manufacturers will supply the US market with 148 million flu vaccine doses for the 2024-2025 season, with trivalent (three-strain) vaccine making up all formulations. Vaccine makers aren’t reporting any manufacturing delays, the CDC said. Nearly all (94%) of the supply will be thimerosal-free, and about 80% will be made using egg-based manufacturing technology. For comparison, the CDC had projected as many as 156.2 million to 170 million for the 2023-2024 season. It emphasized that projections may change as the season progresses.
• A polio vaccination campaign under way in Gaza has now moved to the northern part of the country, with activities slated to last until September 12, World Health Organization (WHO) Director-General Tedros Adhanom Ghebreyesus, PhD,  said today on X . He called on groups in the region to maintain a humanitarian pause and respect the safety of healthcare workers. Following the recent detection of circulating vaccine-derived poliovirus type 2 in a child from Gaza, along with environmental positives, health groups  planned and launched a two-round vaccine campaign earlier this month targeting 640,000 children. 

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• Published: 12 September 2024

Evaluating the impact of extended dosing intervals on mRNA COVID-19 vaccine effectiveness in adolescents

• Tim K. Tsang 1 , 2 ,
• Sheena G. Sullivan 3 , 4 ,
• Yu Meng 1 ,
• Francisco Tsz Tsun Lai 2 , 5 ,
• Min Fan 5 ,
• Xiaotong Huang 1 ,
• Yun Lin 1 ,
• Liping Peng 1 ,
• Chengyao Zhang 1 ,
• Bingyi Yang 1 ,
• Kylie E. C. Ainslie 1 , 6 &
• Benjamin J. Cowling 1 , 2  

BMC Medicine volume  22 , Article number:  384 ( 2024 ) Cite this article

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Metrics details

Extending the dosing interval of a primary series of mRNA COVID-19 vaccination has been employed to reduce myocarditis risk in adolescents, but previous evaluation of impact on vaccine effectiveness (VE) is limited to risk after second dose.

We quantified the impact of the dosing interval based on case notifications and vaccination uptake in Hong Kong from January to April 2022, based on calendar-time proportional hazards models and matching approaches.

We estimated that the hazard ratio (HR) and odds ratio (OR) of infections after the second dose for extended (28 days or more) versus regular (21–27 days) dosing intervals ranged from 0.86 to 0.99 from calendar-time proportional hazards models, and from 0.85 to 0.87 from matching approaches, respectively. Adolescents in the extended dosing groups (including those who did not receive a second dose in the study period) had a higher hazard of infection than those with a regular dosing interval during the intra-dose period (HR 1.66; 95% CI 1.07, 2.59; p  = 0.02) after the first dose.

Conclusions

Implementing an extended dosing interval should consider multiple factors including the degree of myocarditis risk, the degree of protection afforded by each dose, and the extra protection achievable using an extended dosing interval.

Peer Review reports

For most COVID-19 vaccines, the primary vaccination series consists of two doses, separately administered over weeks or months. The second dose is essential for increasing the immunogenicity and effectiveness of these vaccines [ 1 , 2 ]. Some countries have extended the recommended interval between the first and second doses of COVID-19 vaccines to more rapidly increase the proportion of the population who have had at least one dose (i.e., dose-sparing) [ 3 , 4 ] and minimize the risk of myocarditis after vaccination, especially in children and adolescents [ 5 , 6 , 7 , 8 ].

Extending the dosing interval has been associated with a stronger neutralizing antibody response [ 9 , 10 ] and higher vaccine effectiveness (VE) [ 11 , 12 , 13 ]. Test-negative design studies in Canada have reported a 5–10% absolute increase in VE for adults (aged 18 +) and adolescents (aged 12–17) who received their primary series vaccinations 49 days or more apart (extended dosing interval) compared to those who received their primary series 21–48 days apart (regular dosing interval) [ 12 , 13 ]. Similarly, a study in the UK reported minor increments in VE (5–10% absolute increase) against hospitalization among individuals who were vaccinated according to an extended versus regular dosing interval (Table S1). In contrast, a nested case–control study in Hong Kong [ 11 ] reported an odds ratio (OR) for infection of 0.7 for children and adolescents with a 28 days or more gap between doses (extended dosing interval) compared to 27 days (regular dosing interval), corresponding to increase in VE of 30%.

Clarifying the degree of extra protection gained from extending dosing interval is important for vaccine policy. The discrepancies between the Hong Kong study and other studies may be due to differences in geography or differences in public health policy context. In Hong Kong, the first large community outbreak occurred in January–April 2022, which was dominated by the Omicron variant. In this outbreak, the Hong Kong government adopted a “work from home” policy for government workers which was followed by most of the private sector. People were also self-isolated for self-protection [ 14 , 15 ]. Despite this, there were more than 1 million reported cases in this outbreak. The decision to extend the dosing interval in Hong Kong occurred in November 2021 prior to this outbreak. Consequently, by the time the epidemics occurred, a majority of adolescents vaccinated under a regular dosing interval would also have experienced a longer time since vaccination. Therefore, it may also be biased due to inappropriate handling of VE waning. It is well established that the effectiveness of these vaccines wanes with time [ 16 , 17 ]. Failure to handle this waning effect could have artificially inflated the apparent gains from an extended dosing interval because those in the regular interval group would have experienced more time since vaccination and therefore more opportunity for waning, and hence lower VE compared with extended interval group. This problem could be avoided by restricting the comparison group for an extended dosing interval to a time-matched regular-interval comparison group with the same duration of follow-up since second dose. Instead, the Hong Kong study used an adjustment approach that put time since vaccination of second dose as a covariate in the model [ 11 ].

A further consideration for studies wishing to examine the protective benefits of extending the primary vaccination series is whether the extended interval between doses may increase the opportunity for infection during the inter-dose period. Most of the studies described in Table S1 only counted the time since dose 2; they did not consider infections that occurred during the inter-dose period and therefore did not assess whether the extended dose group was also at greater risk of infection while waiting for their second dose. Again, a calendar-time-matched comparison group, at equal risk of infection because they experience vaccination at the same stage of the epidemic, could permit estimation of the increased risk of infection among those in the extended dosing interval group, thereby providing a more reliable estimate of the true gains of an extended dose interval.

Here, we conducted a comprehensive analysis on adolescents (aged 12–17 years) who received their primary of mRNA vaccination series in Hong Kong to evaluate the impact of extending dosing intervals. We first estimated protection from an extended versus regular dosing intervals since receipt of the second dose (the relative VE of an extended versus regular dosing intervals). We used various methods, including a calendar-time proportional hazards model and case–control approaches with case-density sampling, to handle waning VE and estimate this protection. We also conducted simulation studies to compare these methods and evaluate their validity. Then, we used calendar-time proportional hazard model to evaluate the increased infection risk during the inter-dose period.

We aimed to determine whether primary vaccination with extended dosing intervals provides higher protection than regular intervals for adolescents aged 12–17 receiving mRNA vaccines. Specifically, we examined [ 1 ] whether primary vaccination with an extended dosing interval provides higher protection against infections after receiving the second dose compared to primary vaccination with a regular dosing interval and [ 2 ] whether the increased risk during the inter-dose period due to an extended dosing interval may counterbalance the additional protection gained from extending the dosing interval. Analysis was restricted to the age group because > 99% of children aged 0–11 receiving mRNA vaccines with the interval between first dose and second dose ≥ 56 days.

Study population and vaccination eligibility

Hong Kong had a population of 7.4 million people, including 389,400 adolescents aged 12–17 years at the end of 2021 [ 18 ]. Adolescents became eligible for COVID-19 vaccination on 4 April 2021 and were able to receive CoronaVac (Sinovac Biotech) and Comirnaty® (BNT162b2, Pfizer-BioNTech) vaccines. Initially, the recommended dosing interval was 21 days. To reduce the myocarditis risk associated with mRNA COVID-19 vaccine, the recommended dosing interval was extended to 84 days from 23 December 2021. The recommended dosing interval was reduced to 56 days from 17 June 2022 [ 11 ].

Data sources

We obtained a COVID-19 database from the Center of Health Protection (CHP) in Hong Kong, which included vaccination records and case details linked by unique identifiers. In Hong Kong, COVID-19 vaccination records and positive test results for SARS-CoV-2 were required to be reported to the CHP from January 2020 to January 2023 [ 14 ]. Positive rapid test results were required to be reported to CHP from 26 February 2022 to 29 January 2023. Rapid tests were available to purchase and free kits were distributed by the government. Both datasets contained demographic and relevant medical information, including date of birth, sex, and underlying conditions. Vaccinees were required to reporting underlying conditions before they received vaccination. We excluded adolescents with underlying conditions to avoid potential confounding associated with preferential vaccination for people with underlying conditions.

The vaccination dataset included individuals with any recorded vaccination, detailing the date and type of vaccine for each dose. We assumed that each vaccine dose required 14 days to become effective. Those vaccinated within 14 days of SARS-CoV-2 notification were considered unvaccinated; i.e., observation time commenced 14 days after the second dose. Individuals infected within 14 days after dose 2 vaccination were excluded in the analysis. Adolescents were considered to have received a regular dosing schedule if the interval between their first and second dose was 21–27 days, while an extended interval was assigned if doses were received more than or equal to 28 days apart.

The case dataset included all confirmed COVID-19 cases (PCR or RAT) in Hong Kong, with their notification date, hospitalization outcome, and mortality outcome (all-cause or COVID-related). Cases detected prior to 1 January 2022 were excluded from the analysis because all were infected by ancestral strains in 2020–2021 and the incidence rate was very low (12,631 cases among 7 million residents) [ 14 , 15 ].

Observed relative vaccine effectiveness of extended versus regular dosing interval since second dose

Statistical analyses were performed using R version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria). Individuals infected prior to receipt of their second dose during the Omicron outbreaks were excluded due to potential hybrid immunity [ 19 ]. Data were analyzed to estimate the relative hazard of infection among adolescents receiving vaccination according to an extended versus regular dosing schedule. Analyses were restricted to the period 1 January to April 30, 2022, marking the first Omicron wave and prior to the introduction of new circulating Omicron variants in May 2022. During this period, all RAT-confirmed cases required a mandatory confirmatory PCR test. We assumed that each individual could only be infected once during the fifth wave between January and April 2022 [ 20 , 21 ]. In all analysis, we assumed each vaccine dose required 14 days to be effective.

We employed a Cox proportional hazard model to estimate the hazard ratio (HR) of infection for extended versus regular dosing intervals. To account for the varying infection risk during an epidemic the time-to-event was based on calendar time, with observation time commencing 1 January 2022 and ending on the date of case notification or 30 April 2022. Individuals started to contribute person-time to the analysis 14 days after receiving their second dose and after 1 January 2022 [ 22 ]. The exposure was the vaccination dosing interval, dichotomized to regular (21–27 days) and extended (28 + days). In sensitivity analyses, the threshold was varied to 56 days (see below). To account for waning VE, time since vaccination was included as a time-varying term, calculated as the number of days since 14 days post-second-dose. Other variables included were age and sex, which were treated as time-independent variables. Therefore, the equation of the cox model was:

where \(Z\) was the indicator of extended dosing group, \(X\) was the days since second dose of vaccination, and \(f\) is the function of waning, \({\upbeta }_{1}\) , \({\upbeta }_{2}\) , \({\upbeta }_{3}\) , and \({\upbeta }_{4}\) were the effects associated with extending dosing group, sex, age, and VE waning respectively.

Unvaccinated adolescents were not considered; therefore, VE estimates of primary series were therefore projected backward to day 0 based on the estimated waning rate. Primary series VE was derived from the relative risk of infection at 14 days after vaccination (r 1 ), versus the risk of infection at the end of VE waning (r 2 ); i.e., VE = 1 − r 1 /r 2 (Fig.  1 ). The risk of infection was assumed to increase over time consistent with waning VE, such that r 1  < r 2 . We tested the impact on different assumptions on primary series VE and VE waning, or allowed them to be estimated from data. In our main analysis, when the end day of waning was set to Y days, the assumed function of waning was f(X)  =  minimum(X,Y) , and put into the regression, so that the infection risk increased log-linearly from r 1 to r 2 in Y days, and protection could reach 0% by 90 or 180 days and then stay at zero thereafter. This regression coefficient was estimated, and therefore when it was estimated to be positive, the infection risk was increasing since second dose and hence the primary series VE was positive, and vice versa. When waning was incorporated into the model as a time-varying linear term defined by the number of days since second dose with an assumed function of f(X)  =  X . There was no upper bound to the number of days since second dose; i.e., waning was assumed to never end and decline beyond zero to negative values (replicating the assumptions of Lai et al. [ 11 ]).

Study flowchart

VE was estimated from the model at different assumptions about waning, where protection was assumed to wane to 0% perpetually or within 90 or 180 days, since previous studies suggest that VE wanes to negligible around 90–180 days [ 23 , 24 , 25 , 26 , 27 ]. Waning after 90 days in the regular dosing interval group and 180 days in the extended dosing interval group was also modeled, to test the potential of reducing waning rate from extended dosing intervals. In addition, we explored the effect of waning when VE was set to 40% or 25% [ 28 , 29 , 30 , 31 ]. We conducted sex-specific analyses and sensitivity analyses. We varied the threshold for the dosing interval to 56 days instead of 28 days and excluded participants with extreme dosing intervals (> 100 days). We conducted sensitivity analyses that fitting the models restricted to 90 or 180 days after vaccination.

We tested a case-density sampling approach, which allows cases to be selected as controls during their period at risk (i.e., prior to infection) in a matching analysis. This approach can accommodate the time-varying infection risk observed during epidemics. Also, this approach can address potential “positivity” [ 32 ] may arise because of the non-overlapping periods during which both groups could potentially become infected. The case-to-control ratio was 1:4, matched by age and sex. Specifically, for each case with a known infection date, we randomly chose 4 control individuals who had not been infected by that date and shared the same age and sex as the case. In these simulations, we examined whether the HR could be reliably approximated by the odds ratio (OR). Two conditional logistic regression models were explored. In the first, the data were additionally matched by the date of the second vaccine dose, which more closely resembles density sampling. In the second, days since the second dose was included as an unmatched covariate and incorporated into the model as a time-varying linear term, as used by Lai et al. [ 11 ].

Simulation study for validation of approaches

To assess the impact of approaches or assumptions on primary series VE or duration of protection in estimating the protection of extended versus regular dosing intervals, we developed a simulation model to test different estimation approaches to determine if they could provide unbiased estimates (Supplementary information). This model described infection risk since the first dose, assuming risk was proportional to community case numbers. We then excluded individuals with infections before their second dose, mimicking the construction of the real dataset. We tested the true value of HR = 1 and 0.85, corresponding to no effect and moderate effect of extended versus regular dosing interval.

Observed relative vaccine effectiveness of extended versus regular dosing interval since first dose

Restricting the comparison of extended versus regular dosing intervals to the infection risk since the second dose ignores the potential increased risk of infection during the inter-dose interval. Therefore, we used the same calendar-time proportional hazard model to evaluate the impact of increased risk of infection during the inter-dose period. In this analysis, individuals infected prior to receipt of their first dose during the Omicron outbreaks were excluded due to potential hybrid immunity [ 19 ]. We compared the infection risk for adolescents who received vaccination 21–27 days since first dose (regular dosing interval) versus those were not, including those who receive a second dose 28 days or more after first dose (extended dosing interval), or who did not. Two ranges of intervals were examined: [ 1 ] 42–98 days, consistent with the recommendation to separate doses by 84 days from 23 December 2021 and adopted during the study period, and [ 2 ] 42–70 days after first dose, corresponding to a 28-day inter-dose interval + 14 days to allow for seroconversion, and a 56 days interval, which was the interval recommended by the Hong Kong government from 17 June 2022.

Comparison of infection risk since first dose instead of second dose based on simulation studies

Given that almost all adolescents (98%) who received vaccination in 2022 had an extended dosing interval and the Omicron outbreak in Hong Kong also started in January 2022, comparing the risk of infection since first dose among adolescents with regular and extended dosing intervals in the real dataset may not be robust. Therefore, we further used simulations to compare the risk of infection during the inter-dose period for the extended and regular dosing groups. In the simulation, the VEs of primary series were ranged from 0 to 45%, the HR of infection of extended versus regular dosing intervals ranged from 0.6 to 1, and duration of protection were set to 90 days in both vaccination groups. In a sensitivity analysis the duration of protection was extended to 180 days for the extended dosing group.

Study participants

Between 4 April 2021 and 30 April 2022, 385,086 adolescents aged 12–17 years had completed their primary vaccination series (Fig.  2 ), of whom 200,070 received 2 doses of an mRNA vaccines. Excluded from further analysis were 3457 with at least one underlying condition, 665 participants infected between dose 1 and 2, seven infected prior to dose 1, and 106 who received their booster dose before 1 January 2022 (the start of the Omicron outbreak). Of the remaining 195,835, 137,701 (70%) completed a regular dosing series, while 58,134 (30%) completed an extended series (Fig.  2 A). The recommended inter-dose interval was changed on 23 Dec 2021; therefore, the distribution of those completing a regular versus extended dosing series was not consistent over time, and 95% (136,944/144,831) of adolescents vaccinated in 2021 were vaccinated according to a regular dosing interval (21–27 days), while only 1.5% (757/51,004) of adolescents vaccinated in 2022 received a regular dosing interval (28 days or more).

Time series of mRNA-vaccinations, COVID-19 cases, hospitalizations, and deaths among adolescents aged 12–17 years in Hong Kong, March 10, 2021, to April 30, 2022. A The time series of first and second doses of mRNA vaccine, categorized by regular dosing interval (< 28 days) or extended dosing intervals (28 days or more). B The time series of COVID-19 cases by vaccination status. C The number of COVID-19 hospitalizations and deaths

Hong Kong maintained low COVID-19 incidence for most of 2020–2021 [ 33 ] but experienced a large Omicron BA.2 wave (Hong Kong’s “fifth wave”) in early 2022. Between 1 January and 30 April, there were 35,759 COVID-19 cases among adolescents aged 12–17 years (Fig.  3 ), among whom there were 600 hospitalizations, 5 all-cause deaths, and 1 COVID-related death. The 35,759 COVID-19 cases included 13,144 who had received their primary mRNA (2-dose) series, 3495 unvaccinated adolescents, and 19,120 who had received other vaccines or combinations of mRNA and other vaccines. We restricted this analysis to those who received mRNA, only, giving a final sample of 195,835 adolescents who had completed their primary vaccination series, including 13,144 COVID-19 cases and 182,691 non-cases. The age, sex, and dosing intervals by case status are summarized in Table S2-3.

Graphical description of the modelling approach used to calculate vaccine effectiveness (VE). The risk of infection is assumed to increase with increasing time since second dose vaccination because the duration of protection is limited (i.e., VE wanes). VE is estimated from the relative risk of infection at the beginning of the period of protection versus the day at which protection wanes to VE = 0% (end day of VE waning)

Comparison of infection risk for extended versus regular dosing intervals

Figure  4 summarizes the results of the analysis based on Cox proportional hazards model using a calendar time scale. When waning was assumed to be perpetual (i.e., no upper bound to the duration of waning VE), the hazard ratio (HR) comparing the risk of infection for an extended versus regular dosing interval was 0.57 (95% confidence interval (CI) 0.53, 0.62), corresponding to a VE estimate for primary series vaccination of − 50% (95% CI − 63%, − 38%). However, when we fixed the duration of protection to 90 or 180 days with zero protection thereafter, there were more modest differences in infection risk between the two dosing intervals, with HR = 0.88 (95% CI 0.81, 0.95) at 90 days and 0.86 (95% CI 0.79, 0.94) at 180 days, corresponding to VE estimates of 20% (95% CI 10%, 30%) and 32% (− 15%, 60%), respectively. When waning was shorter for the regular dose group (90 days versus 180 days for the extended group), there was no difference in infection risk between dosing intervals (HR = 0.99, 95% CI 0.58, 1.69).

The hazard ratio (HR) of infection for extended versus regular dosing intervals, estimated from the Hong Kong Center of Health Protection data by a proportional hazard model using a calendar time scale. HRs were estimated under different assumptions about the vaccine effectiveness (VE) of primary series at the vaccination date (VE estimated from the data, VE = 40% and VE = 25%) and the duration of protection (days from second dose until protection wanes to VE = 0%) for regular and extended dosing intervals

Next, we fixed primary series VE to 40 or 25% (Fig.  4 ) and re-estimated the HRs. When primary series VE was assumed to be 25%, the risk of infection in the extended dosing interval group remained lower than the regular dosing group (HR = 0.86, 95% CI 0.81, 0.91 for 90 days; HR = 0.89, 95% CI 0.85, 0.94 for 180 days). When VE was increased to 40% there was a lower infection risk for the extended dosing interval group when protection was assumed to wane by 90 days (HR = 0.92, 95% CI 0.88, 0.98), but not by 180 days (HR = 0.99, 95%CI 0.93, 1.04).

In subgroup analyses, the infection risk among extended versus regular dosing interval groups were similar for females and males and were generally similar to the results in the primary analysis (Additional file 1: Fig. S1). In a sensitivity analysis excluding participants with extreme dosing intervals (> 100 days), the estimated HRs were similar to results in the primary analysis. When 56 days was used to define the extended dosing interval (Additional file 1: Fig. S1), HRs were similar to those obtained when the interval was 28 days when VE was fixed to 25 or 40%, but not when waning was assumed to be perpetual, and the VE was estimated from the data.

In the sensitivity analysis that restricted the analyses to the time period within 90 or 180 days after vaccination (Additional file 1: Fig. S2-3), under different assumptions on VE of primary vaccination (estimated from data, or fixed to 25 or 40%), the estimates of HR for an extended versus regular dosing interval ranged from 0.76 to 1.07. The VE estimates for primary vaccination ranged from 31 to 53%. These estimates were similar to the primary analysis except there were no negative VE estimates.

When cases and controls were matched by age and sex, and the days since vaccination of second dose was included as a covariate (waning was assumed to be perpetual) in a matching approach using a conditional logistic regression, the estimated OR for extended versus regular dosing intervals was 0.56 (95% CI 0.52, 0.62), but this OR was 0.87 (95% CI 0.80, 0.95) and 0.85 (95% CI 0.78, 0.94) when the duration of protection was assumed to be 90 and 180 days respectively. When cases and controls were matched by age and sex and days since second dose, the estimated OR for extended versus regular dosing intervals was 0.86 (95% CI 0.78, 0.94).

Simulation study for validity of estimates

We examined the impact of our assumptions about waning and primary series VE in the estimation of the HRs by constructing synthetic datasets that simulated infection outcomes since the first dose for each individual and removed those infected before the second dose (Fig.  5 ). The simulations showed that our proportional hazards model could recover the true HR under realistic assumptions about the duration of protection (Fig.  6 ); i.e., that waning has some finite values. When waning is assumed to continue perpetually, which allows protection to reduce beyond 0%, HRs were under-estimated.

Proof-of-concept figure illustrating the impact of duration of protection (days) assumptions on estimating the hazard ratio (HR) of infection for extended versus regular dosing intervals. Panel A shows the scenarios with realistic assumptions about the finite duration of waning. Panel B show the unrealistic assumption that VE continues to wane perpetually and can allow VE < 0%. As indicated, assuming VE continued to wane without an end day would lead to overestimating the risk reduction

Simulation studies of the proposed proportional hazard model using a calendar time scale to explore assumption about waning VE. Two simulations were conducted in which the true value of the hazard ratio of infection comparing an extended versus regular dosing interval were set to 0.85 or 1. For each set of model parameters (vaccine effectiveness (VE) of primary series, duration of protection of regular and extended dosing intervals), 50 replications were conducted. Points and bars represent the mean, 2.5, and 97.5 percentiles of the 50 replications

We also tested the matching approach in simulated case-density datasets (Fig.  7 ). In the first model, the date of vaccination of second dose was included as a matching variable and the days since second dose was not adjusted for in the model. In nearly all cases and for all durations of protection assessed the OR could approximate the true values of the HRs. In the second model, the number of days since second dose was included as a covariate. When waning was assumed to continue perpetually, the recovered ORs underestimated the true HR for when VE = 40% or VE = 25%.

Simulation studies using a case-density matching approach. Hazard ratios (HR) were estimated using conditional logistic regression. Two simulations with setting the true value of hazard ratio of infection of extended versus regular dosing intervals to be 0.85 or 1 were conducted. For each set of model parameters (vaccine effectiveness (VE) of primary series, waning end days of regular and extended dosing intervals), 50 replications were conducted. Points and bars represent the mean, 2.5, and 97.5 percentiles of the 50 replications

Comparison of infection risk of extended versus regular dosing intervals since first dose instead of second dose based on real-world data

Based on the calendar-time proportional hazards model (Table  1 ), we estimated that adolescents in the extended dosing groups (including those did not receive second dose in the study period) had a higher hazard of infection than regular dosing groups in both 42–98 days (HR 1.66; 95% CI 1.07, 2.59; p  = 0.02) and 42–70 days (HR 1.71; 95% CI 1.06, 2.77; p  = 0.03) after first dose. In sensitivity analyses that allowing 7 days instead of 14 days for vaccine to take effects, there were still higher hazard of infection for extended dosing groups compared with regular dosing group (Table  1 ).

Simulation study to compare the risk of infection since first dose instead of second dose

In a simulation study that the baseline risk of infection was varying and set to be proportional to the observed infection rate in January 1 to April 30, 2022, in Hong Kong (Fig.  8 ), the RR of infection since second and first dose ranged from 0.16 to 0.28, and from 0.91 to 1.16, respectively when the duration of protection for regular and extended dosing intervals were 90 days. The simulation results were similar if the duration of protection for regular and extended dosing intervals were 90 days and 180 days respectively (Additional file 1: Fig. S4). If the baseline risk of infection was set to be constant, the RR of infection since second and first dose ranged from 0.34 to 0.56, and from 0.85 to 1.02, respectively when the duration of protection for regular and extended dosing intervals were 90 days (Additional file 1: Fig. S5). The simulation results were similar if the duration of protection for regular and extended dosing intervals were 90 days and 180 days respectively (Additional file 1: Fig. S6).

Simulation studies comparing the relative risk of infection for extended versus regular dosing intervals since the first dose rather than the second dose. Panels A and B show the relative risk of infection since first dose and second dose, respectively. In this simulation, regular and extended dosing intervals were defined as 21 and 56 days, respectively. For each set of parameters, 100 replications on 20,000 participants with equal proportions of individuals receiving extended and regular dosing intervals were simulated. The mean relative risk of infection of 100 replications was recorded. The waning end day was set to 90 days for both regular and extended dosing intervals. The daily risk of infection was set to be proportional to the epidemic curve in the fifth wave in Hong Kong

In this study, we conducted a comprehensive analysis to estimate the relative risk of infection among adolescents receiving a primary series of mRNA vaccine with regular (21–27 days) or extended (≥ 28 days) dosing intervals in Hong Kong. Overall, we estimated that the risk of infection among adolescents receiving their primary vaccination series was lower for those receiving an extended versus regular dosing interval (HR ranged from 0.86 to 0.99), based on reasonable assumptions, and corresponding to an absolute increase in VE of 1 to 14%. Furthermore, we estimated that there was an increased risk during the inter-dose period for adolescents in extended dosing groups.

Although the definition of an extended dosing interval varied (ranging from ≥ 28 to ≥ 84 days), our estimates were consistent with previous studies that suggest moderate benefit of extended dosing intervals. Studies among adolescents [ 13 ] and healthcare workers in Canada [ 34 ], and a study among individuals aged 50 + in the UK [ 35 ] all observed only a 5–10% absolute increase in VE when the dosing interval was extended. Other studies, including a UK household [ 36 ] and healthcare workers study [ 37 ] found no difference. Only one study has observed a much higher VE (28% absolute increase) after an extended dosing interval, which was the paper by Lai et al. from Hong Kong [ 11 ].

The higher VEs reported in the literature refer to infection risk after the second vaccination dose. Here, we estimated that the infection risk during the inter-dose period was significantly increased, among adolescents in the extended dosing group.

The VE is not appreciably improved by an extended dosing interval, and there was potential increased risk of infection during the inter-dose period. Taken together, the decision to recommend delaying second doses or not could also consider other factors such as [ 1 ] whether the population immunity achievable from administering a single dose to more people more quickly exceeds what could be achieved by administering two doses to a smaller group [ 38 , 39 ]; [ 2 ] the duration of the extra protection; [ 3 ] the potential for near-future outbreaks requiring rapid distribution of vaccine to provide protection; [ 4 ] the reduction in risk of severe adverse events following vaccination, such as myocarditis, balanced with the risk of these events from infections [ 40 ].

We also explored alternative approaches for estimating the HR of infections for extended versus regular dosing intervals. We observed that adjusting for waning by simply including the days since second dose as a linear covariate could underestimate the HR (overestimate VE), as this approach implicitly assumes that the waning never ends. More robust estimates can be recovered when the duration of waning has an upper limit. This problem persists when a matched approach is used. Thus, we suggest that the previously reported 28% higher VE for extended versus regular dosing intervals [ 11 ] was likely an overestimate. Although we focused on estimating relative VE for extended versus regular dosing intervals, incorrect assumptions about waning could also affect the estimation of absolute VE and booster dose VE.

It should be noted that VE estimates for the primary series in this study should be interpreted with caution, as they are projected from waning VE, and no unvaccinated individuals were included. The validity for VE estimated from this method and the potential of depletion of susceptible bias has been discussed in previous studies [ 41 , 42 ]. In brief, the estimate was unbiased when there was no VE waning, and underestimated the degree of waning when there was VE waning. Hence, we also adopted the approach that estimating the relative VE for primary series vaccination (compared with the first day of vaccination) and the hazard ratio of infection for extended versus regular dosing intervals.

There were some limitations in our study. First, as an observational study, we cannot rule out the potential for unidentified confounders, such as unmeasured differences that may exist among individuals who chose regular or extended dosing intervals or were eligible for vaccination early and thus received a regular dosing interval. Second, information on case variants was unavailable, but the predominant variant during our study period was Omicron BA.2 [ 43 ]. Finally, in the simulations, we used the infection rate observed from January 1 to April 30, 2022, as an input of the model and simulated the outcomes to the observed data (using same covariates) to generate synthetic datasets. However, some milder or asymptomatic cases could be missed despite the compulsory reporting of cases implemented in Hong Kong [ 44 ]. Also, it is possible that the decision of vaccination, and choice of an extended or regular dosing interval may change depending on the epidemic trajectory [ 45 , 46 ].

Our analysis of population-based case and vaccination data found that the VE for extended BNT162b2 dosing intervals was 1–14% higher than for regular intervals, under reasonable assumptions regarding duration of VE waning and the VE of primary vaccination. Our simulation study suggested that unreasonable assumptions may overestimate the extra protection afforded by extended dosing intervals and we recommend that any VE analysis carefully consider how waning is parameterized. Although the additional protection afforded by an extended dosing interval may be limited, other public health considerations may drive recommendations to extend dosing intervals.

Availability of data and materials

Access to the case, hospitalization and vaccination data from the electronic medical record system managed by the Hospital Authority and other databases by the Centre for Health Protection in Hong Kong is subject to the approval from the two agencies.

Abbreviations

Hazard ratio

• Vaccine effectiveness

Link-Gelles R, Ciesla AA, Rowley EAK, Klein NP, Naleway AL, Payne AB, et al. Effectiveness of Monovalent and Bivalent mRNA Vaccines in Preventing COVID-19-Associated Emergency Department and Urgent Care Encounters Among Children Aged 6 Months-5 Years - VISION Network, United States, July 2022-June 2023. MMWR Morb Mortal Wkly Rep. 2023;72(33):886–92.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Florentino PTV, Millington T, Cerqueira-Silva T, Robertson C, de Araujo OV, Junior JBS, et al. Vaccine effectiveness of two-dose BNT162b2 against symptomatic and severe COVID-19 among adolescents in Brazil and Scotland over time: a test-negative case-control study. Lancet Infect Dis. 2022;22(11):1577–86.

Imai N, Rawson T, Knock ES, Sonabend R, Elmaci Y, Perez-Guzman PN, et al. Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study. Lancet Public Health. 2023;8(3):e174–83.

Article   PubMed   PubMed Central   Google Scholar  

Liu Y, Pearson CAB, Sandmann FG, Barnard RC, Kim JH, et al. Dosing interval strategies for two-dose COVID-19 vaccination in 13 middle-income countries of Europe: Health impact modelling and benefit-risk analysis. Lancet Reg Health Eur. 2022;17:100381.

Diaz GA, Parsons GT, Gering SK, Meier AR, Hutchinson IV, Robicsek A. Myocarditis and Pericarditis After Vaccination for COVID-19. JAMA. 2021;326(12):1210–2.

Chua GT, Kwan MYW, Chui CSL, Smith RD, Cheung ECL, Ma T, et al. Epidemiology of Acute Myocarditis/Pericarditis in Hong Kong Adolescents Following Comirnaty Vaccination. Clin Infect Dis. 2022;75(4):673–81.

Article   PubMed   Google Scholar  

Lai FTT, Li X, Peng K, Huang L, Ip P, Tong X, et al. Carditis After COVID-19 Vaccination With a Messenger RNA Vaccine and an Inactivated Virus Vaccine : A Case-Control Study. Ann Intern Med. 2022;175(3):362–70.

Le Vu S, Bertrand M, Jabagi MJ, Botton J, Drouin J, Baricault B, et al. Age and sex-specific risks of myocarditis and pericarditis following Covid-19 messenger RNA vaccines. Nat Commun. 2022;13(1):3633.

Chantasrisawad N, Techasaensiri C, Kosalaraksa P, Phongsamart W, Tangsathapornpong A, Jaru-Ampornpan P, et al. The immunogenicity of an extended dosing interval of BNT162b2 against SARS-CoV-2 Omicron variant among healthy school-aged children, a randomized controlled trial. Int J Infect Dis. 2023;130:52–9.

Hall VG, Ferreira VH, Wood H, Ierullo M, Majchrzak-Kita B, Manguiat K, et al. Delayed-interval BNT162b2 mRNA COVID-19 vaccination enhances humoral immunity and induces robust T cell responses. Nat Immunol. 2022;23(3):380–5.

Article   CAS   PubMed   Google Scholar  

Lai FTT, Fan M, Huang C, Chui CSL, Wan EYF, Li X, et al. Effectiveness of BNT162b2 after extending the primary series dosing interval in children and adolescents aged 5–17. Nat Commun. 2023;14(1):1845.

Skowronski DM, Febriani Y, Ouakki M, Setayeshgar S, El Adam S, Zou M, et al. Two-Dose Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine Effectiveness With Mixed Schedules and Extended Dosing Intervals: Test-Negative Design Studies From British Columbia and Quebec. Canada Clin Infect Dis. 2022;75(11):1980–92.

Ionescu IG, Skowronski DM, Sauvageau C, Chuang E, Ouakki M, Kim S, et al. BNT162b2 Effectiveness Against Delta and Omicron Variants of Severe Acute Respiratory Syndrome Coronavirus 2 in Adolescents Aged 12–17 Years, by Dosing Interval and Duration. J Infect Dis. 2023;227(9):1073–10783.

Yang B, Lin Y, Xiong W, Liu C, Gao H, Ho F, et al. Comparison of control and transmission of COVID-19 across epidemic waves in Hong Kong: an observational study. Lancet Reg Health West Pac. 2024;43: 100969.

PubMed   Google Scholar  

Yang B, Tsang TK, Gao H, Lau EHY, Lin Y, Ho F, et al. Universal Community Nucleic Acid Testing for Coronavirus Disease 2019 (COVID-19) in Hong Kong Reveals Insights Into Transmission Dynamics: A Cross-Sectional and Modeling Study. Clin Infect Dis. 2022;75(1):e216–23.

Ferdinands JM, Rao S, Dixon BE, Mitchell PK, DeSilva MB, Irving SA, et al. Waning of vaccine effectiveness against moderate and severe covid-19 among adults in the US from the VISION network: test negative, case-control study. BMJ. 2022;379:e072141.

Menegale F, Manica M, Zardini A, Guzzetta G, Marziano V, d’Andrea V, et al. Evaluation of Waning of SARS-CoV-2 Vaccine-Induced Immunity: A Systematic Review and Meta-analysis. JAMA Netw Open. 2023;6(5):e2310650.

Census and Statistics Department (The government of the Hong Kong Special Administractive Region). Table 110–01002 : Population by Sex and Age [updated 20 Feb 2024. Available from: https://www.censtatd.gov.hk/en/web_table.html?id=110-01002 .

Covid- Forecasting Team. Past SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysis. Lancet. 2023;401(10379):833–42.

Article   Google Scholar  

Kahn F, Bonander C, Moghaddassi M, Christiansen CB, Bennet L, Malmqvist U, et al. Previous SARS-CoV-2 infections and their impact on the protection from reinfection during the Omicron BA.5 wave - a nested case-control study among vaccinated adults in Sweden. IJID Reg. 2024;10:235–9.

Perez-Guzman PN, Knock E, Imai N, Rawson T, Elmaci Y, Alcada J, et al. Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England. Nat Commun. 2023;14(1):4279.

Griffin BA, Anderson GL, Shih RA, Whitsel EA. Use of alternative time scales in Cox proportional hazard models: implications for time-varying environmental exposures. Stat Med. 2012;31(27):3320–7.

Andrews N, Stowe J, Kirsebom F, Toffa S, Rickeard T, Gallagher E, et al. Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant. N Engl J Med. 2022;386(16):1532–46.

Gram MA, Emborg HD, Schelde AB, Friis NU, Nielsen KF, Moustsen-Helms IR, et al. Vaccine effectiveness against SARS-CoV-2 infection or COVID-19 hospitalization with the Alpha, Delta, or Omicron SARS-CoV-2 variant: A nationwide Danish cohort study. PLoS Med. 2022;19(9):e1003992.

Chemaitelly H, AlMukdad S, Ayoub HH, Altarawneh HN, Coyle P, Tang P, et al. Covid-19 Vaccine Protection among Children and Adolescents in Qatar. N Engl J Med. 2022;387(20):1865–76.

Fleming-Dutra KE, Britton A, Shang N, Derado G, Link-Gelles R, Accorsi EK, et al. Association of Prior BNT162b2 COVID-19 Vaccination With Symptomatic SARS-CoV-2 Infection in Children and Adolescents During Omicron Predominance. JAMA. 2022;327(22):2210–9.

Glatman-Freedman A, Hershkovitz Y, Dichtiar R, Rosenberg A, Keinan-Boker L, Bromberg M. Effectiveness of BNT162b2 Vaccine against Omicron Variant Infection among Children 5–11 Years of Age. Israel Emerg Infect Dis. 2023;29(4):771–7.

Khan FL, Nguyen JL, Singh TG, Puzniak LA, Wiemken TL, Schrecker JP, et al. Estimated BNT162b2 Vaccine Effectiveness Against Infection With Delta and Omicron Variants Among US Children 5 to 11 Years of Age. JAMA Netw Open. 2022;5(12):e2246915.

Ionescu IG, Skowronski DM, Sauvageau C, Chuang E, Ouakki M, Kim S, et al. BNT162b2 effectiveness against Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2 in adolescents aged 12–17 years, by dosing interval and duration. J Infect Dis. 2023;227(9):1073–83.

Castelli JM, Rearte A, Olszevicki S, Voto C, Del Valle JM, Pesce M, et al. Effectiveness of mRNA-1273, BNT162b2, and BBIBP-CorV vaccines against infection and mortality in children in Argentina, during predominance of delta and omicron covid-19 variants: test negative, case-control study. BMJ. 2022;379:e073070.

McMenamin ME, Nealon J, Lin Y, Wong JY, Cheung JK, Lau EHY, et al. Vaccine effectiveness of one, two, and three doses of BNT162b2 and CoronaVac against COVID-19 in Hong Kong: a population-based observational study. Lancet Infect Dis. 2022;22(10):1435–43.

Westreich D, Cole SR. Invited commentary: positivity in practice. Am J Epidemiol. 2010;171(6):674–7; discussion 8–81.

Mefsin YM, Chen D, Bond HS, Lin Y, Cheung JK, Wong JY, et al. Epidemiology of Infections with SARS-CoV-2 Omicron BA.2 Variant, Hong Kong, January-March 2022. Emerg Infect Dis. 2022;28(9):1856–8.

El Adam S, Zou M, Kim S, Henry B, Krajden M, Skowronski DM. SARS-CoV-2 mRNA Vaccine Effectiveness in Health Care Workers by Dosing Interval and Time Since Vaccination: Test-Negative Design, British Columbia, Canada. Open Forum Infect Dis. 2022;9(5):ofac178.

Amirthalingam G, Bernal JL, Andrews NJ, Whitaker H, Gower C, Stowe J, et al. Serological responses and vaccine effectiveness for extended COVID-19 vaccine schedules in England. Nat Commun. 2021;12(1):7217.

Pouwels KB, Pritchard E, Matthews PC, Stoesser N, Eyre DW, Vihta KD, et al. Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK. Nat Med. 2021;27(12):2127–35.

Hall V, Foulkes S, Insalata F, Kirwan P, Saei A, Atti A, et al. Protection against SARS-CoV-2 after Covid-19 Vaccination and Previous Infection. N Engl J Med. 2022;386(13):1207–20.

Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331–7.

Yang B, Huang X, Gao H, Leung NH, Tsang TK, Cowling BJ. Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis. BMC Med. 2022;20(1):409.

Boehmer TK, Kompaniyets L, Lavery AM, Hsu J, Ko JY, Yusuf H, et al. Association Between COVID-19 and Myocarditis Using Hospital-Based Administrative Data - United States, March 2020-January 2021. MMWR Morb Mortal Wkly Rep. 2021;70(35):1228–32.

Lipsitch M, Goldstein E, Ray GT, Fireman B. Depletion-of-susceptibles bias in influenza vaccine waning studies: how to ensure robust results. Epidemiol Infect. 2019;147: e306.

Ray GT, Lewis N, Klein NP, Daley MF, Lipsitch M, Fireman B. Depletion-of-susceptibles Bias in Analyses of Intra-season Waning of Influenza Vaccine Effectiveness. Clin Infect Dis. 2020;70(7):1484–6.

Xie R, Edwards KM, Adam DC, Leung KSM, Tsang TK, Gurung S, et al. Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong. Nat Commun. 2023;14(1):2422.

Tsang TK, Wang C, Yang B, Cauchemez S, Cowling BJ. Using secondary cases to characterize the severity of an emerging or re-emerging infection. Nat Commun. 2021;12(1):6372.

Yang B, Wu P, Lau EHY, Wong JY, Ho F, Gao H, et al. Changing disparities in coronavirus disease 2019 (COVID-19) burden in the ethnically homogeneous population of Hong Kong through pandemic waves: an observational study. Clin Infect Dis. 2021;73(12):2298–305.

Yuan J, Xu Y, Wong IOL, Lam WWT, Ni MY, Cowling BJ, et al. Dynamic predictors of COVID-19 vaccination uptake and their interconnections over two years in Hong Kong. Nat Commun. 2024;15(1):290.

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Acknowledgements

The authors thank Julie Au for the administrative support.

This project was supported by the National Institute of General Medical Sciences (grant no. R01 GM139926), and the Theme-based Research Scheme (Project No. T11-705/21-N) of the Research Grants Council of the Hong Kong SAR Government. BJC is supported by an RGC Senior Research Fellowship (grant number: HKU SRFS2021-7S03) and the AIR@innoHK program of the Innovation and Technology Commission of the Hong Kong SAR Government. The WHO Collaborating Centre for Reference and Research on Influenza is supported by the Australian Government Department of Health and Aged Care.

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Study design: TKT, SGS, and BJC. Data collection: TKT, YM, XH, YL, LP, and CZ. Data analysis: TKT. Data interpretation: TKT, SGS, FTTL, MF, KECA, BY, and BJC. Wrote the first draft: TKT. All authors contributed to the final draft and read and approved the final manuscript.

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BJC reports honoraria from AstraZeneca, Fosun Pharma, GSK, Haleon, Moderna, Novavax, Pfizer, Roche, and Sanofi Pasteur. SGS reports honoraria from CSL Seqirus, Evo Health, Moderna, Novavax, and Pfizer. All other authors declare that they have no competing interests.

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Additional file 1: Fig. S1. Sensitivity analysis of the hazard ratio (HR) of infection for extended versus regular dosing interval. Fig. S2. Sensitivity analysis of the hazard ratio (HR) of infection for extended versus regular dosing intervals restricted to the time period within 90 days after vaccination. Fig. S3. Sensitivity analysis of the hazard ratio (HR) of infection for extended versus regular dosing intervals restricted to the time period within 180 days after vaccination. Fig. S4. Simulation studies to compare the relative risk of infection of extended verses regular dosing interval, since the first dose instead of second dose, setting the end day of waning to be 90 and 180 days for both regular and extended dosing intervals respectively. Fig. S5. Simulation studies to compare the relative risk of infection of extended verses regular dosing interval, since the first dose instead of second dose, setting the risk of infection to be constant. Fig. S6. Simulation studies to compare the relative risk of infection of extended verses regular dosing interval, since the first dose instead of second dose, setting the risk of infection to be constant, and the end day of waning to be 90 and 180 days for both regular and extended dosing intervals respectively. Table S1. Summary of previous studies that explored the impact on extended versus regular dosing intervals. Table S2: Characteristics of case and non-cases in our study. Table S3: Intervals of vaccination to infection between extended and regular dosing group.

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Tsang, T.K., Sullivan, S.G., Meng, Y. et al. Evaluating the impact of extended dosing intervals on mRNA COVID-19 vaccine effectiveness in adolescents. BMC Med 22 , 384 (2024). https://doi.org/10.1186/s12916-024-03597-4

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Anti-science case study: COVID-19 vaccines’ effectiveness and safety exaggerated

Plaudits are due to the Public Health in Practice editorial team and Paul et al. for their recent article on unwarranted accusations of anti-science, which is used “to discredit scientists who hold opposing views”, and their call for “a debate amongst scientists and decision-makers” in light of emerging evidence [ 1 ]. The authors boldly focus on the COVID-19 vaccines, noting: “an abundant literature has since depicted a far more nuanced picture of the effectiveness and safety of those vaccines over the medium-term”. Here I argue in support of Paul et al. pointing to yet more evidence that the effectiveness and safety of the COVID-19 vaccines have been exaggerated, in the clinical trials and observational studies, largely due to inadequate counting windows - pertaining to infections and adverse effects.

This has been decisively argued in an unofficial series in the Journal of Evaluation in Clinical Practice , involving BMJ editor Peter Doshi, with the authors collectively finding: infections being overlooked in the ‘partially vaccinated’; such infections being ascribed to unvaccinated groups; numerous suspected infections overlooked as ‘unconfirmed’ (divided roughly equally between vaccinated and unvaccinated); adverse effects being overlooked in the ‘partially vaccinated’; adverse effect reporting reliant on solicited reports; longer-term adverse effects overlooked; numerous trial participants lost to follow-up; long-term impacts impossible to discern due to unblinding; and financial conflicts of interest [ [2] , [3] , [4] , [5] ]. Also discussed were vaccine-related myocarditis, with recent research on this one adverse effect alone showing incident rates far exceeding UK government estimates on the numbers needed to vaccinate in various groups to prevent a severe COVID-19 hospitalisation; and some of the evidence for perceived negative effectiveness, where the vaccines are associated with increased COVID-19 infections, hospitalisations, and even deaths.

Paul et al. are aware of the “suspicion of data falsification, unblinding of patients, and lack of controls” concerning the Pfizer trial, reported in Thacker [ 6 ]; the revelation that “the mRNA vaccines were associated with an excess risk of “serious adverse events of special interest”” in Fraiman et al. [ 7 ]; and Benn et al. who noted that there was no statistically significant decrease in COVID-19 deaths in the mRNA vaccine clinical trials, while there was an increase (though also not statistically significant) in total deaths [ 8 ]. These 7 articles alone should have us wondering if the benefits of the vaccines outweighed the risks for all groups even then, when the earlier and deadlier variants were rampant, to say nothing of Pfizer admitting now in 2024 that they are still trying to “determine if COMIRNATY is safe and effective, and if there is a myocarditis/pericarditis association that should be noted” [ 9 ].

Further research about the potential effects of the COVID-19 vaccines beyond the initial trials are also concerning. Raethke et al. discovered a rate of serious adverse drug reactions of 0.24% for the primary series vaccinations and 0.26% for boosters, approximating to 1 serious adverse drug reaction per 400 people [ 10 ]. Compare this again to the UK government data cited above, indicating that hundreds of thousands need to be vaccinated for a single positive outcome. Paul et al. appear to be justified in stating that “adolescents do not benefit from the Pfizer vaccine, except for non-immune girls with comorbidities”. And Faksova et al. demonstrated that the vaccines are associated with “myocarditis, pericarditis, Guillain-Barré syndrome, and cerebral venous sinus thrombosis”, also pointing to additional safety signals [ 11 ]. Even more adverse events could have been found with more robust counting windows extending beyond “42 days following vaccination”.

It seems obvious that the COVID-19 vaccines are not as effective or safe as advertised, and yet those asking legitimate questions about the scientific data and methods have been heavily censured and even persecuted. None of this is to say that the vaccines are bioweapons cooked up in Bill Gates' basement that will magnetise and kill over half the world's population. The truth is somewhere between these extremes, and it is our job as doctors, scientists, and researchers to get as close to the truth as possible, utilising different approaches, considering alternative perspectives, and all while still remembering that we must always be intellectually humble, recognising that absolute certainty will almost certainly remain out of reach.

Paul et al. are right to call for science to be freed from “the pervasive influence of political expediency, industrial interests and corruption in healthcare and medicine”. There is much more that can - and must - be said about misinformation and reverse misinformation regarding COVID-19 (such as the inexplicable denigration of natural immunity), but that will have to wait for another time.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

• Open access
• Published: 13 September 2024

Group B streptococcus colonization in pregnancy and neonatal outcomes: a three-year monocentric retrospective study during and after the COVID-19 pandemic

• Gregorio Serra   ORCID: orcid.org/0000-0002-2918-9826 1 ,
• Lucia Lo Scalzo 1 ,
• Maria Giordano 1 ,
• Mario Giuffrè 1 ,
• Pietro Trupiano 1 ,
• Renato Venezia 1 &
• Giovanni Corsello 1  

Italian Journal of Pediatrics volume  50 , Article number:  175 ( 2024 ) Cite this article

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Group B Streptococcus (GBS) is a major cause of sepsis and meningitis in newborns. The Centers for Disease Control and Prevention (CDC) recommends to pregnant women, between 35 and 37 weeks of gestation, universal vaginal-rectal screening for GBS colonization, aimed at intrapartum antibiotic prophylaxis (IAP). The latter is the only currently available and highly effective method against early onset GBS neonatal infections. Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, the preventive measures implemented to mitigate the effects of SARS-CoV-2 infection led to the reduction in the access to many health facilities and services, including the obstetric and perinatal ones. The purpose of the present study was to evaluate the prevalence of maternal GBS colonization, as well as use of IAP and incidence of episodes of neonatal GBS infection when antibiotic prophylaxis has not been carried out in colonized and/or at risk subjects, in a population of pregnant women during (years 2020–2021) and after (year 2022) the COVID-19 pandemic, also with the aim to establish possible epidemiological and clinical differences in the two subjects’ groups.

We retrospectively analyzed the clinical data of pregnant women admitted to, and delivering, at the Gynaecology and Obstetrics Unit, Department of Sciences for Health Promotion and Mother and Child Care, of the University Hospital of Palermo, Italy, from 01.01.2020 to 31.12.2022. For each of them, we recorded pertinent socio-demographic information, clinical data related to pregnancy, delivery and peripartum , and specifically execution and status of vaginal and rectal swab test for GBS detection, along with eventual administration and modality of IAP. The neonatal outcome was investigated in all cases at risk (positive maternal swabs status for GBS, either vaginal or rectal, with or without/incomplete IAP, preterm labor and/or delivery, premature rupture of membranes ≥ 18 h, previous pregnancy ended with neonatal early onset GBS disease [EOD], urine culture positive for GBS in any trimester of current gestation, intrapartum temperature ≥ 38 °C and/or any clinical/laboratory signs of suspected chorioamnionitis). The data concerning mothers and neonates at risk, observed during the pandemic (years 2020–2021), were compared with those of both subjects’ groups with overlapping risk factors recorded in the following period (year 2022). The chi squared test has been applied in order to find out the relationship between pregnant women with GBS colonization receiving IAP and outcome of their neonates.

The total source population of the study consisted of 2109 pregnant women, in addition to their 2144 newborns. Our analysis, however, focused on women and neonates with risk factors. The vaginal-rectal swab for GBS was performed in 1559 (73.92%) individuals. The test resulted positive in 178 cases overall (11.42% of those undergoing the screening). Amongst our whole sample of 2109 subjects, 298 women had an indication for IAP (vaginal and/or rectal GBS colonization, previous pregnancy ended with neonatal GBS EOD, urine culture positive for GBS in any trimester of current gestation, and unknown GBS status at labor onset with at least any among delivery at < 37 weeks’ gestation, amniotic membranes rupture ≥ 18 h and/or intrapartum temperature ≥ 38.0 °C), and 64 (21.48%) received adequate treatment; for 23 (7.72%) it was inadequate/incomplete, while 211 (70.8%) did not receive IAP despite maternal GBS colonization and/or the presence of any of the above mentioned risk factors. Comparing the frequency of performing vaginal-rectal swabs in the women admitted in the two time periods, the quote of those screened out of the total in the pandemic period (years 2020–2021) was higher than that of those undergoing GBS screening out of the total admitted in the year 2022 (75.65% vs. 70.38%, p  = 0.009), while a greater number (not statistically significant, p  = 0.12) of adequate and complete IAP was conducted in 2022, than in the previous biennium (26.36 vs. 18.62%). During the whole 3 years study period, as expected, none of the newborns of mothers with GBS colonization and/or risk factors receiving IAP developed EOD. Conversely, 13 neonates with EOD, out of 179 (7.3%) born to mothers with risk factors, were observed: 3 among these patients’ mothers performed incomplete IAP, while the other 10 did not receive IAP. Neither cases of neonatal meningitis, nor deaths were observed. The incidence rate in the full triennium under investigation, estimated as the ratio between the number of babies developing the disease out of the total of 2144 newborns, was 6.06‰; among those born to mothers with risk factors, if comparing the two time periods, the incidence was 8.06% in the pandemic biennium, while 5.45% in the following year, evidencing thus no statistical significance ( p  = 0.53).

Conclusions

The present study revealed in our Department an increased prevalence of pregnant women screened for, and colonized by GBS, in the last decade. However, an overall still low frequency of vaginal-rectal swabs performed for GBS, and low number of adequate and complete IAP despite the presence of risk factors have been found, which did not notably change during the two time periods. Moreover, significant EOD incidence rates have been reported among children of mothers carrying risk factors, although also in this case no statistically significant differences have been observed during and after the pandemic. Such data seem to be in contrast to those reported during the COVID-19, showing a decrease in the access to health facilities and increased mortality/morbidity rates also due to the restrictive measures adopted to mitigate the effects of the pandemic. These findings might be explained by the presence within the same metropolitan area of our Department of a COVID hospital and birthing center, which all the patients with SARS-CoV-2 infection referred to, and likely leading to a weaker concern of getting sick perceived by our patients. Although IAP is an easy procedure to implement, however adherence and uniformity in the management protocols are still not optimal. Therefore, the prophylactic measures adopted to date cannot be considered fully satisfactory, and should be improved. Better skills integration and obstetrical-neonatological collaboration, in addition to new effective preventive tools, like vaccines able to prevent invasive disease, may allow further reduction in morbidity and mortality rates related to GBS perinatal infection.

Group B Streptococcus (GBS), also known as Streptococcus agalactiae for its causative role in bovine mastitis, is present in the genitourinary and gastrointestinal tracts of pregnant women. Maternal GBS colonization rates vary worldwide from 10 to 40%, with mean prevalence of 18% [ 1 , 2 , 3 , 4 ]. In the newborn, GBS infection may give rise to both early (EOD) and late onset diseases (LOD). EOD is generally acquired by vertical transmission, and its most common clinical pictures include sepsis, pneumonia and meningitis. LOD, conversely, may be observed between 7 and 90 days of life, and the association with maternal colonization is not as strong as for EOD [ 5 , 6 , 7 ]. Intrapartum antibiotic prophylaxis (IAP), administrated ≥ 4 h before delivery, is the only currently available and highly effective method against early onset GBS neonatal infections. It allowed a reduction in the incidence of EOD of more than 80%, from 1.8 newborns per 1,000 live births in the 1990s to 0.23 newborns per 1,000 live births in 2015, although not being able to limit the impact of LOD [ 8 , 9 ]. Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, more than 6.9 million people died worldwide due to SARS-CoV-2 infection [ 10 ]. In Italy, it has been associated with significant clinical and psychological effects, also in pregnant women. Indeed, the infection control and the preventive measures (social distancing, mask wearing, hand hygiene and quarantine) implemented to mitigate the effects of the pandemic, led to the reduction in the access to many health facilities and services, including the obstetric and perinatal ones (only 28% of maternal and perinatal healthcare facilities continued to provide outpatient routine visits and examinations as usual, while 59% of them provided visits to a limited extent) [ 11 ]. Such decrease was linked both to the difficulties encountered by people in keeping the support from other family members within the hospital, and to the fear of contracting the infection [ 12 , 13 ]. This additional negative impact of COVID-19 (besides the direct one caused by the infection) might have caused also worse health outcomes in pregnant women, especially in groups at major risk for social or economic reasons. The purpose of the present study was to retrospectively evaluate the prevalence rates of maternal vaginal-rectal GBS colonization, as well as use of IAP and incidence of episodes of neonatal GBS infection when antibiotic prophylaxis has not been carried out in colonized and/or at risk subjects, in a population of pregnant women during (years 2020–2021) and after (year 2022) the COVID-19 pandemic, also with the aim to establish possible epidemiological and clinical differences in the two subjects’ groups.

We retrospectively analyzed the clinical data of pregnant women admitted to, and delivering, at the Gynaecology and Obstetrics Unit, Department of Sciences for Health Promotion and Mother and Child Care, of the University Hospital of Palermo, Italy, from 01.01.2020 to 31.12.2022. For each of them, we recorded the pertinent socio-demographic information, and the clinical data related to pregnancy, delivery and peripartum . Such items are detailed and presented in Table  1 .

We considered the results of vaginal and rectal swabs for GBS, performed between weeks 35 + 0 and 37 + 0 of gestation. This procedure is usually performed routinely in our Hospital in such time window, according to the indications of the Centers for Disease Control and Prevention (CDC) [ 8 , 14 ]. Specifically, IAP was recommended in women with a positive GBS screening culture (excluding those undergoing cesarean delivery with intact amniotic membranes before labor onset), a previous child with early onset GBS disease, bacteriuria documenting GBS in the current pregnancy, and in those with unknown GBS status at labor onset and at least one of the following risk factors: delivery at < 37 weeks’ gestation, amniotic membranes rupture ≥ 18 h and/or intrapartum temperature ≥ 38.0 °C [ 15 , 16 , 17 ]. However, many variations of practice, based on the individual gynecologist and/or on mother’s compliance, have been observed during the study period in our sample population. Ampicillin was the first-line drug used, and it was administered intravenously (IV) at the dose of 2 g, followed by 1 g IV every 4 h until delivery. Cefazolin was the option chosen for women allergic to penicillin but not at high risk for anaphylaxis, while clindamycin or vancomycin have been used for high risk of anaphylaxis to penicillin, according to guidelines [ 18 , 19 ]. IAP was considered adequate and complete when administered ≥ 4 h before delivery [ 8 ]. We focused on the rate of patients undergoing GBS screening, and on those with positive GBS screening tests. We also evaluated the indications for IAP, as well as the modality of IAP execution (if either adequate/complete or not).

The neonatal outcome was investigated in all cases at risk (positive maternal swabs status for GBS, either vaginal or rectal, with or without/incomplete IAP, preterm labor and/or delivery, premature rupture of membranes ≥ 18 h, previous pregnancy ended with neonatal EOD, urine culture positive for GBS in any trimester of current gestation, intrapartum temperature ≥ 38 °C, and/or any clinical/laboratory signs of suspected chorioamnionitis). In-depth data analyzed for each newborn are reported in Table  2 .

The data observed during the pandemic (years 2020–2021) were compared with those recorded in the following period (year 2022).

Statistical analysis

We used R version 4.0.4 (R Foundation for Statistical Computing, Vienna, Austria) for data analysis. Simple descriptive statistics were expressed as frequency and percentage for categorical variables, mean and standard deviation for continuous variables. Paired-samples t-test was used to compare data on the vaginal-rectal GBS colonization rate, as well as use of IAP and its effects on neonatal outcomes during (years 2020–2021) and after (year 2022) the COVID-19 pandemic. The Chi-squared test has been applied for comparison between two groups, and precisely in order to find out the relationship between pregnant women with GBS colonization receiving IAP and outcome of their neonates.

A p value lower than 0.05 was considered statistically significant.

Socio-demographic information, and clinical data related to pregnancy, delivery and peripartum

The total number of deliveries observed during the study period was 2315, including 35 twin births (all bigeminal). The medical records were not available for 206 mothers, and therefore the source population of the study involved 2109 pregnant women, in addition to their 2144 newborns. Evaluated by year, the total number of delivering women was as follows: 660 in 2020, 757 in 2021, and 692 in 2022. There were 141 preterm (< 37 weeks of gestational age) deliveries, while the other 1968 were full-term ones. In the population under investigation, the average age was 30.42 ± 6 years, ranging between 15 and 52. Foreign mothers, defined as those who were not born in Italy, were the 12.94%. The most frequent countries of birth were Bangladesh (40.2%), Nigeria (15.01%), Morocco (7.69%), Romania (7.47%) and Tunisia (5.1%). The 69.41% of participants were resident in urban areas, while 30.59% came from rural ones. In regard with mothers’ occupation, 73.82% were housewives, 14.22% employees, 10.38% freelance professionals and 1.57% craftswomen/tradeswomen. In our sample, 1004 women (47.61%) had vaginal deliveries, while 650 (30.82%) and 455 (21.57%) underwent elective and emergency cesarean sections, respectively. 779 participants (36.94%) were primiparous. Sociodemographic and clinical data related to pregnancy, delivery and peripartum of the source population of women are summarized in Table  3 .

Prevalence rates of maternal GBS colonization and use of IAP

The vaginal-rectal swab for GBS was performed in 1559 (73.92%) individuals. More precisely, 512 were carried out in 2020, 560 in 2021 and 487 in 2022. The test resulted positive in 178 cases overall (11.42% of those undergoing the screening): 56 were those in 2020, 66 in 2021, and 56 in 2022.

Among GBS-positive patients, 41 (23.03%) received complete IAP, while to 20 (11.24%) an incomplete IAP was administered. 48 women (26.97%) did not receive IAP, due to cesarean sections performed before the onset of labor and with intact amniotic membranes; 69 subjects (38.76%), conversely, did not undergo IAP despite the presence of one or more clinical indications (Table  4 ).

Of the 550 (26.08%) pregnant women with unknown GBS colonization status, 120 (21.82%) had intrapartum risk factors. In this group, preterm delivery (< 37 weeks of gestation) was the only risk condition in 65 patients (11.82%), PROM ≥ 18 h in 43 (7.82%), while 12 (2.18%) of them had both risk factors (preterm delivery and PROM ≥ 18 h). No women presented with fever and/or other signs of chorioamnionitis. Considering only the individuals with intrapartum risk factors, 23 (19.17%) received complete IAP, the prophylaxis was incomplete in 3 (2.5%) cases, and for 94 (78.33%) it was not administered (Table  5 ).

Amongst our overall sample of 2109 subjects, 298 women had an indication for IAP (vaginal and/or rectal GBS colonization, previous child with EOD, bacteriuria documenting GBS in the current pregnancy, and unknown GBS status at labor onset and at least any among delivery at < 37 weeks’ gestation, amniotic membranes rupture ≥ 18 h and/or intrapartum temperature ≥ 38.0 °C), and 64 (21.48%) received adequate treatment; for 23 (7.72%) it was inadequate/incomplete, while 211 (70.8%) did not receive IAP despite maternal GBS colonization and/or the presence of any of the above mentioned risk factors. Most cases where the prophylaxis was indicated, but in which it was not performed or was inadequate/incomplete, were represented by pregnant women admitted to hospital in advanced labor or presenting with precipitous delivery. In a few subjects IAP was simply omitted, probably for misinterpreted/incorrect data on GBS swabs at the time of birth.

Comparing the Italian mothers with the foreign ones, a higher ( p  < 0.0001 ) frequency of vaginal-rectal swabs for GBS was found in the whole period under investigation among the former (75.49% vs. 63.37%), as well as a greater number (although not statistically significant, p  = 0.72) of adequate and complete IAP (21.86% vs. 19.61%). Conversely, the rate of positive GBS swabs was significantly higher among the foreign mothers (10.46% in the group of Italian women vs. 19.08% in the latter, p  = 0.0008). Comparing the frequency of performing vaginal-rectal swabs in the women admitted in the two time periods, the quote of those screened out of the total in the pandemic period (years 2020–2021) was higher than that of those undergoing GBS screening out of the total admitted in the year 2022 (75.65% vs. 70.38%, p  = 0.009), while a greater number (however not statistically significant, p  = 0.12) of adequate and complete IAP was conducted in 2022, than in the previous biennium (26.36 vs. 18.62%). Finally, the comparison between the periods during and after COVID-19 revealed a mildly lower (without statistical significance, p  = 0.94) GBS colonization rate during the pandemic than the following year (11.38% vs. 11.5%; Table  6 ).

Effects of maternal GBS colonization in the newborn

During the study period, as expected, none of the newborns of mothers with GBS colonization and/or risk factors receiving IAP developed EOD. Conversely, 13 neonates with EOD, out of 179 (7.3%) born to mothers with risk factors (including overall those showing positive, negative, and unknown GBS status, i.e. 60, 11 and 108 respectively), were observed: the incidence rate, estimated as the ratio between the number of babies developing the disease out of the total of 2144 newborns delivered in the 3 years studied, was 6.06‰. Neonatal sepsis was noted in 10 babies born to 121 mothers who did not perform IAP (8.2%), and in 3 neonates born to 19 women whose prophylaxis was incomplete (15.7%). Furthermore, EOD incidence in the COVID-19 period was 8.06% (10 cases out of 124 women with risk factors), while that of the post-pandemic year analyzed was 5.45% (3/55 born to mothers at risk), without a statistically significant difference between the two time periods ( p  = 0.53). Among the infected neonates, 9 were male and 4 female. Mean gestational age was 39 + 4 weeks. All newborns had normal Apgar scores (> 7) at 1 and 5 min. The average birth weight was 3249 ± 482 g, length 49.6 ± 2.7 cm, and occipitofrontal circumference 34.0 ± 1.5 cm. 2 of them were small for gestational age (SGA), while 11 were appropriate for gestational age (AGA).

Clinical manifestations included septic shock (1), jaundice (1), respiratory distress (4), feeding difficulties/regurgitation associated with hypotonia (6), while hyperpyrexia was present in 1 case (Fig.  1 ).

Clinical manifestations of EOD neonates

Increased inflammation indices (CRP and/or PCT) were detected in all newborns. Blood cultures were carried out in all subjects before the start of antibiotic therapy, and resulted negative in all cases. 4 subjects required hospitalization in the NICU, while in 9 cases the admission to the Neonatal Pathology Unit (sub-intensive care setting) was necessary. The patients were hospitalized for an average of 11 ± 3 days. The mean duration of antibiotic therapy was 7 ± 3 days. Empiric therapy with ampicillin (100 mg/kg/dose every 12 h) and gentamicin (4 mg/kg/dose every 24 h) was promptly started in all neonates. The antimicrobial treatment was continued until clinical symptoms disappeared, as well as complete blood counts, inflammation indices, and blood culture tests gave normal/negative results. There was no evidence of meningitis in any case, and no deaths were observed (Table  7 ).

Group B Streptococcus is a major cause of invasive infections in neonates, with the colonization of the vaginal-rectal tract of pregnant women being the main transmission source. Our data provide updated insights about the prevalence of vaginal-rectal GBS colonization in pregnancy. In addition, the present study shows the rates of adhesion to GBS screening and to IAP in a cohort of pregnant women referring to a II level University Hospital in the city of Palermo, Italy. In our sample, the quote of subjects screened for GBS (in all of them a complete vaginal–rectal swab was performed) out of the total addressed to our Mother and Child Department was 73.92%. Such data were higher than those of a previous retrospective study carried out in our Hospital in 2012, and also than the rates recorded by Berardi A. et al. in 2011 in Central Italy, which were 66.03% and 67.9% respectively (Fig.  2 a) [ 20 , 21 ]. According with CDC and the Italian Obstetrics Society guidelines, the execution of vaginal–rectal cultures for GBS is recommended between 35 and 37 weeks of gestation, and such indications were those followed also in the present study [ 8 , 14 ]. In our population vaginal and rectal swabs were positive for GBS in the 10.42% of cases; this value is at the lower range of the national average, which is between 10 and 20% [ 22 ]. Comparing the current analysis with that carried out in 2012 in our Hospital [ 20 ], an increased prevalence of GBS colonization in our population has been observed in the last few years (from 7.98 to 11.42%) (Fig.  2 b) [ 19 ].

Comparison of GBS screening among the current study and those previously reported in our Hospital and in Central Italy (a) , and of maternal GBS colonization between the present analysis and that conducted by Puccio et al. in 2012 in our Department (b)

Worldwide, frequencies of maternal GBS carriers have been reported to range from 14 to 30% in high-income countries (mildly higher than the present survey), to be around 19% in the Sub-Saharan region, and 12–15% in India and Pakistan [ 23 , 24 , 25 ]. Differences in the detected rate of vaginal-rectal GBS colonization may reflect the different demographic characteristics of the populations under investigation. Actually, GBS incidence rates can vary, either according to geographical region or time period [ 26 ]. Indeed, when comparing COVID-19 with the post-pandemic scenario , we detected a mild decrease in GBS maternal colonization during the years 2020–2021 (11.38% vs. 11.5%).

Amongst our overall sample, only 21.48% women received adequate IAP in presence of clinical indications (positive GBS screening culture or intrapartum risk factors). The consequent higher rate of subjects who did not receive or performed incomplete/inadequate IAP can be due to those women admitted in advanced labor or presenting with a precipitous one, in addition to the few cases in which it was omitted for misinterpreted/incorrect data on GBS status at delivery. Such gap is a critical issue which clinicians must be focused on, aiming at reducing the preventable maternal and neonatal adverse outcomes, implementing awareness, antenatal care programs and dedicated operative Department protocols. Indeed, in Central Italy a major proportion (> 90%) of individuals showing GBS-positive cultures received adequate treatment [ 21 ]. In the USA, the prevalence of mothers with an indication for IAP who received adequate treatment increased, from 73.8% between 1998 and 1999 to 85.1% between 2003 and 2004 [ 27 , 28 ]. Comparing the pandemic period (years 2020–2021) with the following one (2022), a higher frequency in the execution of vaginal-rectal swabs for GBS and a lower (although not statistically significant) of adequate and complete IAP, were found in the first two years than in 2022. Actually, despite the infection control and preventive measures adopted to lessen the pandemic’s effects resulted in a decrease in the access to various health facilities, including obstetric and perinatal care services, however in our care setting such reduction was not observed, probably due to the presence within the same metropolitan area of our Department, of a dedicated COVID hospital and birthing center (as documented also by the decrease of the total number of deliveries evidenced during the year 2022, corresponding to the suspension of the COVID hospital activity and its reconversion to regular health care), which all patients with SARS-CoV-2 infection referred to. Therefore, it may be likely that the reduction in the health care accesses reported during the pandemic did not occur in the present experience due to a weaker concern of getting sick perceived by our patients, as well as to the availability offered in our facility to maintain the support of other family members during hospital stay and antenatal care visits [ 11 , 12 , 13 ]. Finally, we detected inequalities between the Italian women and the foreign ones due to the major number of swabs performed among the former and, although not statistically significant, higher colonization rates in the latter.

We reported an EOD incidence of 7.69% among children of mothers carrying risk factors, and of 6.06‰ out of the total number of newborns delivered during the 3-year investigation (i.e., n  = 2144). In our study the clinical picture of the early form of disease was represented by sepsis. According to literature, respiratory signs were the initial most common typical symptoms, only preceded by poor feeding/regurgitation associated with hypotonia, frequently described in literature reports as well [ 29 ]. The other less common clinical manifestations identified were fever, jaundice, and septic shock, which are not typical of GBS, and which can occur in other bacterial infections. Mortality is estimated to be 2–5% in full-term children, and increases by 25% in preterm infants; nonetheless, in our sample (in which, however, no preterm babies were present) neither deaths nor meningitis were documented [ 30 , 31 ]. It is noteworthy that, as expected, none of the mothers’ patients received adequate/complete IAP, with evidence of EOD both in the group of those born to women with non-performed (8.2%) or incomplete (15.7%) prophylaxis. These data further highlight how relevant could be to begin IAP as soon as possible, when a clinical indication is identified, due to the beneficial effects of a prompt IAP (at least four hours before birth).

Our results demonstrate that there is still a relevant number of women who do not perform appropriate IAP despite being properly identified as colonized with GBS at delivery, in addition to those who are not even recognized as GBS-positive by antenatal screening cultures. The identification and treatment of candidates for IAP are necessary, as moreover evidenced by the present study, also owing to the higher risk of developing EOD for neonates born to mothers without GBS screening and not receiving adequate and/or complete IAP. In order to stop and/or limit GBS infections, local public health organizations should support both microbiological surveillance and educational initiatives [ 32 , 33 ]. These interventions, actually, are able to reduce by 80% the risk of neonatal sepsis or meningitis, specifically early onset ones, i.e. those between birth and the completion of the 6th day of life [ 34 , 35 , 36 ]. Indeed, such strategies cannot be effective in the remaining 20% of early infections, as they are not linked to fetal contamination with the bacteria encountered during the passage through the vaginal canal at birth. They are, rather, dependent on infections contracted prior to the delivery, due to the ascending passage of germs to the fetus, especially in case of premature rupture of membranes. Although the total number of cases of neonatal GBS infection is not reported to be overly high, as highlighted also in the present analysis, however it is clear that the prophylaxis measures adopted to date cannot be considered fully satisfactory. Pregnant woman screening, indeed, is not always easy to implement, as well as the administration of intrapartum antibiotics, which often does not follow in the clinical daily practice (as evidenced in our experience), the effective modalities established by CDC guidelines for the eradication of the bacterium. Clinicians, then, need to be careful and accurate in the correct adhesion to care protocols, also in consideration of the high number of inadequate and/or missing IAP administrations, as documented by the present analysis. In addition to the implementation and improvement of antibiotic prophylaxis, however, the search for alternative preventive tools, such as the production of an effective and safe vaccine administered to the mother, appears urgent and not postponable [ 37 , 38 , 39 , 40 ].

The present study revealed in our Department an increased prevalence of pregnant women screened for, and colonized by GBS, in the last decade. However, an overall still low frequency of vaginal-rectal swabs performed for GBS, and low number of adequate and complete IAP despite the presence of risk factors have been found, which did not notably change during the two time periods. Moreover, relevant EOD incidence rates have been reported among children of mothers carrying risk factors, although no statistically significant differences have been observed during and after the COVID-19. Such data seems to be in contrast with those observed during the pandemic for other care settings (especially emergency care areas, as well as surgery and diagnostic services), where notable delays in diagnosis and treatment, and increase in mortality/morbidity rates due to the indirect effects of COVID-19 (reduction in the number of clinical checks, fear in the access to health facilities) have been described. However, in our care setting such findings were not observed, probably due to the presence, within the same metropolitan area of our Department, of a dedicated COVID hospital and birthing center, which all subjects with SARS-CoV-2 infection referred to. This likely led to a weaker concern of getting sick perceived by our patients, as well as to the availability offered in our facility to maintain the support of other family members during the hospital stay and the antenatal care visits. Furthermore, inequalities in the number of swabs performed persist, compared to the past, between Italian and foreign women, highlighting an insufficient health support provided to migrant and at risk populations [ 32 ].

Although IAP is an easy procedure to implement, and our population of women subjected to screening increased in the last years, nonetheless adherence and uniformity of its management protocols are still not optimal. Despite the total number of neonatal GBS infections is not reported to be overly high, as documented also in the present analysis, however the prophylactic measures adopted to date cannot be considered fully satisfactory, and therefore should be improved. Better skills integration and obstetric-neonatological collaboration, in addition to new effective preventive tools, like vaccines [ 41 , 42 ] able to prevent invasive disease, may allow further reduction in morbidity and mortality rates related to GBS perinatal infection.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Centers for Disease Control and Prevention

Coronavirus disease 2019

C-reactive protein

Early onset disease

Group B Streptococcus

• Intrapartum antibiotic prophylaxis

Intravenous

Late onset disease

Neonatal intensive care unit

Procalcitonin

Premature rupture of membranes

World Health Organization. Group B Streptococcus Vaccine Development Technology Roadmap: Priority activities for Development, Testing, Licensure and global availability of Group B Streptococcus vaccines. World Health Organization: Geneva, Switzerland,; 2017.

Seale AC, Bianchi-Jassir F, Russell NJ, Kohli-Lynch M, Tann CJ, Hall J, Madrid L, Blencowe H, Cousens S, Baker CJ, Bartlett L, Cutland C, Gravett MG, Heath PT, Ip M, Le Doare K, Madhi SA, Rubens CE, Saha SK, Schrag SJ, Sobanjo-Ter Meulen A, Vekemans J, Lawn JE. Estimates of the Burden of Group B Streptococcal Disease Worldwide for pregnant women, Stillbirths, and children. Clin Infect Dis. 2017;65(suppl2):S200–19.

Article   PubMed   PubMed Central   Google Scholar  

Zanini da Rocha J, Feltraco J, Radin V, Vitola Gonçalves C, Almeida da Silva PE, Von Groll A. Streptococcus agalactiae colonization and screening approach in high-risk pregnant women in southern Brazil. J Infect Dev Ctries. 2020;14(4):332–40.

Article   PubMed   Google Scholar  

Regan JA, Klebanoff MA, Nugent RP, Eschenbach DA, Blackwelder WC, Lou Y, Gibbs RS, Rettig PJ, Martin DH, Edelman R. Colonization with group B Streptococci in pregnancy and adverse outcome. VIP Study Group. Am J Obstet Gynecol. 1996;174(4):1354–60.

Article   CAS   PubMed   Google Scholar  

Krohn MA, Hillier SL, Baker CJ. Maternal peripartum complications associated with vaginal group B streptococci colonization. J Infect Dis. 1999;179(6):1410–5.

Heath PT, Balfour GF, Tighe H, Verlander NQ, Lamagni TL, Efstratiou A, HPA GBS Working Group. Group B streptococcal disease in infants: a case control study. Arch Dis Child. 2009;94(9):674–80.

Pettersson K. Perinatal infection with Group B Streptococci. Semin Fetal Neonatal Med. 2007;12(3):193–7.

Verani JR, McGee L, Schrag SJ, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC). Prevention of perinatal group B streptococcal disease–revised guidelines from CDC, 2010. MMWR Recomm Rep. 2010;59(RR–10):1–36.

PubMed   Google Scholar  

Nanduri SA, Petit S, Smelser C, Apostol M, Alden NB, Harrison LH, Lynfield R, Vagnone PS, Burzlaff K, Spina NL, Dufort EM, Schaffner W, Thomas AR, Farley MM, Jain JH, Pondo T, McGee L, Beall BW, Schrag SJ. Epidemiology of Invasive Early-Onset and late-Onset Group B Streptococcal Disease in the United States, 2006 to 2015: Multistate Laboratory and Population-based surveillance. JAMA Pediatr. 2019;173(3):224–33.

Cascella M, Rajnik M, Aleem A, Dulebohn SC, Di Napoli R, Features. Evaluation, and treatment of coronavirus (COVID-19). 2023 Aug 18. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024. Jan–.

Google Scholar  

Vilca LM, Sarno L, Passoni D, Antonazzo P, Pellegrini E, Guida M, Cesari E, Cetin I. Impact of the COVID-19 pandemic on prenatal care utilization among Italian and immigrant pregnant women: a Multicenter Survey. Int J Public Health. 2024;69:1606289.

Kotlar B, Gerson EM, Petrillo S, Langer A, Tiemeier H. The impact of the COVID-19 pandemic on maternal and perinatal health: a scoping review. Reprod Health. 2021;18(1):10.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cena L, Rota M, Calza S, Massardi B, Trainini A, Stefana A. Estimating the impact of the COVID-19 pandemic on maternal and Perinatal Health Care Services in Italy: results of a self-administered survey. Front Public Health. 2021;9:701638.

Linee Guida Nazionali Gravidanza fisiologica. (Aggiornamenti del 2011). Sistema nazionale per le linee guida (SNLG), Istituto superiore di sanità (ISS), Centro nazionale epidemiologia, sorveglianza e promozione della salute (CNESPS). https://www.salute.gov.it/imgs/C_17_pubblicazioni_1436_allegato.pdf

Puopolo KM, Lynfield R, Cummings JJ. Management of infants at risk for group B streptococcal disease. Pediatrics. 2019;144(2):e20191881.

Prevention of Early-onset Neonatal Group B Streptococcal Disease. Green-top Guideline 36 BJOG. 2017;124(12):e280–305.

Prevention of Group B Streptococcal Early-Onset Disease in Newborns. ACOG Committee Opinion, Number 797. Obstet Gynecol. 2020;135(2):e51–72.

Article   Google Scholar  

Schrag SJ, Verani JR. Intrapartum antibiotic prophylaxis for the prevention of perinatal group B streptococcal disease: experience in the United States and implications for a potential group B Streptococcal vaccine. Vaccine. 2013;31(Suppl 4):D20–6.

Zhu Y, Lin XZ. Updates in prevention policies of early-onset group B streptococcal infection in newborns. Pediatr Neonatol. 2021;62(5):465–75.

Puccio G, Cajozzo C, Canduscio LA, Cino L, Romano A, Schimmenti MG, Giuffrè M, Corsello G. Epidemiology of Toxoplasma and CMV serology and of GBS colonization in pregnancy and neonatal outcome in a sicilian population. Ital J Pediatr. 2014;40:23.

Berardi A, Di Fazzio G, Gavioli S, Di Grande E, Groppi A, Papa I, Piccinini G, Simoni A, Tridapalli E, Volta A, Facchinetti F, Ferrari F, GBS Prevention Working Group. Emilia-Romagna. Universal antenatal screening for group B streptococcus in Emilia-Romagna. J Med Screen. 2011;18(2):60–4.

Infezioni neonatali precoci e tardive da streptococco di gruppo B in Italia. Edited by Creti R. Roma: Istituto Superiore di Sanità. 2011. Rapporti ISTISAN 11/7.

Prevalence of maternal. Colonisation with group B streptococcus: a systematic review and meta-analysis. Lancet Infect Dis. 2016;16(9):1076–84.

Kwatra G, Cunnington MC, Merrall E, Adrian PV, Ip M, Klugman KP, Tam WH, Madhi SA, Berkley JA, Lowe BS, Mwangi I, Williams T, Bauni E, Mwarumba S, et al. Bacteremia among children admitted to a rural hospital in Kenya. N Engl J Med. 2005;352(1):39–47.

Chaudhary M, Rench MA, Baker CJ, Singh P, Hans C, Edwards MS. Group B Streptococcal colonization among pregnant women in Delhi, India. Pediatr Infect Dis J. 2017;36(7):665–9.

Costa NS, Rio-Tinto A, Pinto IBF, Dos Santos Silva Alvim DC, de Assis Rocha A, Oliveira LMA, Botelho ACN, Fracalanzza SEL, Teixeira LM, Rezende-Filho J, Marinho PS, Amim Júnior J, Taylor S, Thomas S, Pinto TCA. Changes in Group B Streptococcus colonization among pregnant women before and after the onset of the COVID-19 pandemic in Brazil. Pathogens. 2022;11(10):1104.

Van Dyke MK, Phares CR, Lynfield R, Thomas AR, Arnold KE, Craig AS, Mohle-Boetani J, Gershman K, Schaffner W, Petit S, Zansky SM, Morin CA, Spina NL, Wymore K, Harrison LH, Shutt KA, Bareta J, Bulens SN, Zell ER, Schuchat A, Schrag SJ. Evaluation of universal antenatal screening for group B streptococcus. N Engl J Med. 2009;360(25):2626–36.

Schrag SJ, Zell ER, Lynfield R, Roome A, Arnold KE, Craig AS, Harrison LH, Reingold A, Stefonek K, Smith G, Gamble M, Schuchat A. Active Bacterial Core Surveillance Team. A population-based comparison of strategies to prevent early-onset group B streptococcal disease in neonates. N Engl J Med. 2002;347(4):233–9.

Edmond KM, Kortsalioudaki C, Scott S, Schrag SJ, Zaidi AK, Cousens S, Heath PT. Group B streptococcal disease in infants aged younger than 3 months: systematic review and meta-analysis. Lancet. 2012;379:547–56.

Baker CJ. Early onset group B streptococcal disease. J Pediatr. 1978;93(1):124–5.

Piro E, Serra G, Schierz IAM, Giuffrè M, Corsello G. Fetal growth restriction: a growth pattern with fetal, neonatal and long-term consequences. Euromediterranean Biomedical J. 2019;14(09):038–44.

Serra G, Giuffrè M, Piro E, Corsello G. The social role of pediatrics in the past and present times. Ital J Pediatr. 2021;47(1):239.

Serra G, Miceli V, Albano S, Corsello G. Perinatal and newborn care in a two years retrospective study in a first level peripheral hospital in Sicily (Italy). Ital J Pediatr. 2019;45(1):152.

Boyer KM, Gadzala CA, Kelly PD, Gotoff SP. Selective intrapartum chemoprophylaxis of neonatal group B streptococcal early-onset disease. III. Interruption of mother-to-infant transmission. J Infect Dis. 1983;148:810–6.

Anthony BF, Okada DM, Hobel CJ. Epidemiology of the group B streptococcus: maternal and nosocomial sources for infant acquisitions. J Pediatr. 1979;95(3):431–6.

Godambe S, Shah PS, Shah V. Breast milk as a source of late onset neonatal sepsis. Pediatr Infect Dis J. 2005;24(4):381–2.

Trijbels-Smeulders MA, Kollée LA, Adriaanse AH, Kimpen JL, Gerards LJ. Neonatal group B streptococcal infection: incidence and strategies for prevention in Europe. Pediatr Infect Dis J. 2004;23(2):172–3.

Johri AK, Paoletti LC, Glaser P, Dua M, Sharma PK, Grandi G, Rappuoli R. Group B Streptococcus: global incidence and vaccine development. Nat Rev Microbiol. 2006;4(12):932–42.

Jordan HT, Farley MM, Craig A, Mohle-Boetani J, Harrison LH, Petit S, Lynfield R, Thomas A, Zansky S, Gershman K, Albanese BA, Schaffner W, Schrag SJ. Active bacterial core surveillance (ABCs)/Emerging Infections Program Network, CDC. Revisiting the need for vaccine prevention of late-onset neonatal group B streptococcal disease: a multistate, population-based analysis. Pediatr Infect Dis J. 2008;27(12):1057–64.

Madhi SA, Anderson AS, Absalon J, Radley D, Simon R, Jongihlati B, Strehlau R, van Niekerk AM, Izu A, Naidoo N, Kwatra G, Ramsamy Y, Said M, Jones S, Jose L, Fairlie L, Barnabas SL, Newton R, Munson S, Jefferies Z, Pavliakova D, de Silmon NC, Gomme E, Perez JL, Scott DA, Gruber WC, Jansen KU. Potential for maternally administered:Vaccine for Infant Group B Streptococcus. N Engl J Med. 2023;389(3):215–27.

Matranga D, Lumia C, Guarneri R, Arculeo VM, Noto M, Pivetti A, Serra G, Guarneri MF, Spera A. The vaccinaTion & hpv knowledge (THinK) questionnaire: a reliability and validity study on a sample of women living in Sicily (southern-Italy). PeerJ. 2019;7:e6254.

Costantino C, Conforto A, Bonaccorso N, Cimino L, Sciortino M, Palermo M, Maiolo K, Tina LG, Betta PM, Caracciolo M, Loretta CM, Arco A, Gitto E, Vitaliti SM, Mancuso D, Vitaliti G, Rosella V, Pinello G, Corsello G, Serra G, Gabriele B, Tramuto F, Restivo V, Amodio E, Vitale F. Safety of Rotavirus Vaccination in Preterm infants admitted in neonatal intensive care units in Sicily, Italy: a Multicenter Observational Study. Vaccines. 2023;11:718.

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Gregorio Serra, Lucia Lo Scalzo, Maria Giordano, Mario Giuffrè, Pietro Trupiano, Renato Venezia & Giovanni Corsello

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GS drafted the manuscript and took care of the patients. LLS performed the statistical analysis and drafted the first version of the paper. MGio gathered the data related to pregnant women. MGiu revised the manuscript. PT reviewed the literature, made the database and analyzed the data. RV supervised the study and revised the paper. GC conceived the study, revised the manuscript and gave final approval of the version to be submitted. All authors red and approved the manuscript as submitted.

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Correspondence to Gregorio Serra .

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The study was approved by the Mother and Child Department of the University of Palermo, ethics committee Palermo 1 (Palermo, Italy). All procedures performed in this report were in accordance with the ethical standards of the institutional and national research committee, and with the 1964 Helsinki declaration and its later amendments, or comparable ethical standards.

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We declare that the first and last author of the manuscript, Gregorio Serra and Giovanni Corsello, have the role, within Italian Journal of Pediatrics, as Associate Editor and Editor-in-chief respectively.

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Serra, G., Scalzo, L.L., Giordano, M. et al. Group B streptococcus colonization in pregnancy and neonatal outcomes: a three-year monocentric retrospective study during and after the COVID-19 pandemic. Ital J Pediatr 50 , 175 (2024). https://doi.org/10.1186/s13052-024-01738-2

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Received : 25 April 2024

Accepted : 31 August 2024

Published : 13 September 2024

DOI : https://doi.org/10.1186/s13052-024-01738-2

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