1
|
Magen O, Waxman JG, Makov-Assif M, Vered R, Dicker D, Hernán MA, Lipsitch M, Reis BY, Balicer RD, Dagan N. Fourth Dose of BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Setting. N Engl J Med 2022; 386:1603-1614. [PMID: 35417631 PMCID: PMC9020581 DOI: 10.1056/nejmoa2201688] [Citation(s) in RCA: 169] [Impact Index Per Article: 84.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND With large waves of infection driven by the B.1.1.529 (omicron) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), alongside evidence of waning immunity after the booster dose of coronavirus disease 2019 (Covid-19) vaccine, several countries have begun giving at-risk persons a fourth vaccine dose. METHODS To evaluate the early effectiveness of a fourth dose of the BNT162b2 vaccine for the prevention of Covid-19-related outcomes, we analyzed data recorded by the largest health care organization in Israel from January 3 to February 18, 2022. We evaluated the relative effectiveness of a fourth vaccine dose as compared with that of a third dose given at least 4 months earlier among persons 60 years of age or older. We compared outcomes in persons who had received a fourth dose with those in persons who had not, individually matching persons from these two groups with respect to multiple sociodemographic and clinical variables. A sensitivity analysis was performed with the use of parametric Poisson regression. RESULTS The primary analysis included 182,122 matched pairs. Relative vaccine effectiveness in days 7 to 30 after the fourth dose was estimated to be 45% (95% confidence interval [CI], 44 to 47) against polymerase-chain-reaction-confirmed SARS-CoV-2 infection, 55% (95% CI, 53 to 58) against symptomatic Covid-19, 68% (95% CI, 59 to 74) against Covid-19-related hospitalization, 62% (95% CI, 50 to 74) against severe Covid-19, and 74% (95% CI, 50 to 90) against Covid-19-related death. The corresponding estimates in days 14 to 30 after the fourth dose were 52% (95% CI, 49 to 54), 61% (95% CI, 58 to 64), 72% (95% CI, 63 to 79), 64% (95% CI, 48 to 77), and 76% (95% CI, 48 to 91). In days 7 to 30 after a fourth vaccine dose, the difference in the absolute risk (three doses vs. four doses) was 180.1 cases per 100,000 persons (95% CI, 142.8 to 211.9) for Covid-19-related hospitalization and 68.8 cases per 100,000 persons (95% CI, 48.5 to 91.9) for severe Covid-19. In sensitivity analyses, estimates of relative effectiveness against documented infection were similar to those in the primary analysis. CONCLUSIONS A fourth dose of the BNT162b2 vaccine was effective in reducing the short-term risk of Covid-19-related outcomes among persons who had received a third dose at least 4 months earlier. (Funded by the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.).
Collapse
Affiliation(s)
- Ori Magen
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Jacob G Waxman
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Maya Makov-Assif
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Roni Vered
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Dror Dicker
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Miguel A Hernán
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Marc Lipsitch
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Ben Y Reis
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Ran D Balicer
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| | - Noa Dagan
- From the Clalit Research Institute, Innovation Division (O.M., J.G.W., M.M.-A., R.D.B., N.D.), and the Tel-Aviv District, Community Division (R.V.), Clalit Health Services, and the Sackler Faculty of Medicine, Tel-Aviv University (D.D.), Tel Aviv, the Department of Internal Medicine D, Hasharon Hospital Rabin Medical Center, Petah Tikva (D.D.), and the School of Public Health, Faculty of Health Sciences (R.D.B.), and the Department of Software and Information Systems Engineering (N.D.), Ben Gurion University of the Negev, Be'er Sheva - all in Israel; and the Departments of Epidemiology and Biostatistics and CAUSALab (M.A.H.) and the Center for Communicable Disease Dynamics, Departments of Epidemiology and of Immunology and Infectious Diseases (M.L.), Harvard T.H. Chan School of Public Health, the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute (B.Y.R., R.D.B., N.D.), the Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital (B.Y.R.), and the Departments of Pediatrics (B.Y.R.) and Biomedical Informatics (B.Y.R., N.D.), Harvard Medical School - all in Boston
| |
Collapse
|
2
|
Waxman JG, Makov-Assif M, Reis BY, Netzer D, Balicer RD, Dagan N, Barda N. Comparing COVID-19-related hospitalization rates among individuals with infection-induced and vaccine-induced immunity in Israel. Nat Commun 2022; 13:2202. [PMID: 35459237 PMCID: PMC9033865 DOI: 10.1038/s41467-022-29858-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 03/30/2022] [Indexed: 11/09/2022] Open
Abstract
With the COVID-19 pandemic ongoing, accurate assessment of population immunity and the effectiveness of booster and enhancer vaccine doses is critical. We compare COVID-19-related hospitalization incidence rates in 2,412,755 individuals across four exposure levels: non-recent vaccine immunity (two BNT162b2 COVID-19 vaccine doses five or more months prior), boosted vaccine immunity (three BNT162b2 doses), infection-induced immunity (previous COVID-19 without a subsequent BNT162b2 dose), and enhanced infection-induced immunity (previous COVID-19 with a subsequent BNT162b2 dose). Rates, adjusted for potential demographic, clinical and health-seeking-behavior confounders, were assessed from July-November 2021 when the Delta variant was predominant. Compared with non-recent vaccine immunity, COVID-19-related hospitalization incidence rates were reduced by 89% (87–91%) for boosted vaccine immunity, 66% (50–77%) for infection-induced immunity and 75% (61–83%) for enhanced infection-induced immunity. We demonstrate that infection-induced immunity (enhanced or not) provides more protection against COVID-19-related hospitalization than non-recent vaccine immunity, but less protection than booster vaccination. Additionally, our results suggest that vaccinating individuals with infection-induced immunity further enhances their protection. The relative degree of immunity to SARS-CoV-2 provided by combinations of natural infection, vaccination, and booster doses is unknown. Here, the authors show that infection-induced immunity provides more protection against COVID-19-related hospitalization than non-recent vaccine immunity, but less than booster vaccination.
Collapse
Affiliation(s)
- Jacob G Waxman
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel.
| | - Maya Makov-Assif
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel
| | - Ben Y Reis
- Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA
| | - Doron Netzer
- Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel
| | - Ran D Balicer
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel.,The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.,School of Public Health, Faculty of Health Sciences, Ben Gurion University of the Negev, Be'er Sheva, Israel
| | - Noa Dagan
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel.,The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.,Software and Information Systems Engineering, Ben Gurion University, Be'er Sheva, Israel.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Noam Barda
- Software and Information Systems Engineering, Ben Gurion University, Be'er Sheva, Israel.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Arc Innovation Center, Sheba Medical Center, Ramat-Gan, Israel
| |
Collapse
|
3
|
Makov-Assif M, Krispin S, Ben-Shlomo Y, Holander T, Dagan N, Balicer R, Barda N. The association between real-time reverse transcriptase polymerase chain reaction cycle threshold values, symptoms and disease severity among COVID-19 patients in the community: a retrospective cohort study. Infect Dis (Lond) 2021; 54:205-212. [PMID: 34743662 DOI: 10.1080/23744235.2021.1998606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
BACKGROUND COVID-19 continues to spread throughout the world. Real-time reverse transcriptase polymerase chain reaction (RT-PCR) is used to diagnose COVID-19, with its cycle threshold (Ct) value inversely related to the viral load. The association between Ct values and COVID-19 related outcomes has been studied in the hospital setting but less so in the community. We aimed to estimate the association between Ct values and the severity of community-diagnosed COVID-19 to provide evidence on the utility of Ct testing in this setting. METHODS This was a retrospective cohort study based on data from Israel's largest health organization. The study population included 34,658 individuals who tested positive for COVID-19 by RT-PCR and had available Ct values between June 1st and December 21st, 2020. Outcomes included COVID-19 related symptoms, hospitalization, severe disease, and death. Ct values were modelled both as discrete and continuous exposures. RESULTS After adjusting for known risk factors for severe COVID-19, low Ct values were associated with symptomatic disease (odds ratio [OR]: 1.51; 95% confidence interval [CI]:1.21-1.84), hospitalization (OR: 1.27; 95%CI: 1.12-1.49), severe disease (OR: 1.80; 95%CI: 1.43-2.27), and death (OR: 1.64; 95%CI: 1.06-2.59). By modelling the exposure as continuous, we noticed a dose-response relationship, with the risk gradually rising with lower Ct values. CONCLUSIONS This study found a significant association between low Ct values and severe COVID-19 related outcomes, with a dose-response relationship. This suggests that Ct values could be helpful in identifying high-risk patients diagnosed in the community.
Collapse
Affiliation(s)
- Maya Makov-Assif
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel
| | - Sydney Krispin
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel
| | - Yatir Ben-Shlomo
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel
| | - Tal Holander
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel
| | - Noa Dagan
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Ran Balicer
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.,Faculty of Health Sciences, School of Public Health, Ben Gurion University of the Negev, Sheva, Be'er, Israel
| | - Noam Barda
- Innovation Division, Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| |
Collapse
|