The study period started on August 6, 2021, which was 7 days after the approval of the booster for use in persons 60 years of age or older in Israel. The study period ended on September 29, 2021, which was the last date for which data regarding confirmed deaths due to Covid-19 were available on the day the data were extracted (October 3, 2021). The study timeline is depicted in Figure S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org.
The Clalit Health Services (CHS) Community Helsinki Committee and the CHS Data Utilization Committee approved the study. The study was exempt from the requirement to obtain informed consent.
The study included all CHS members who were 50 years of age or older on the study start date and had received two doses of BNT162b2 at least 5 months earlier. CHS covers approximately 52% of the Israeli population and is the largest of four health care organizations in Israel that provide mandatory health care. Participants with missing data regarding date of birth or sex were excluded from the study. In addition, participants were excluded if they had been infected with SARS-CoV-2 or had received a booster before August 6, 2021; early administration of the booster was indicated in immunocompromised persons. Finally, participants who received the booster and had a confirmed case of Covid-19 within 3 days before the effective-booster date (defined as 7 days after the booster was administered) were excluded.
The study population was divided into two groups: those who had received a booster during the study period (booster group) and those who had not received a booster (nonbooster group). Participants were included in the booster group on the effective-booster date to allow time for antibodies to build effectively.4,8 Up to 7 days after receiving the booster, participants were still included in the nonbooster group. A description of the transition of participants from the nonbooster group to the booster group is provided in Figure S2.
Data Sources and Organization
We analyzed patient-level data that were extracted from CHS electronic medical records. A specific database was created for this study that integrated patient-level data from two primary sources: the CHS operational database and the CHS Covid-19 database. The CHS operational database includes sociodemographic data and comprehensive clinical information, such as coexisting chronic conditions, community-care visits, hospitalizations, medications, and results of laboratory tests and imaging studies. The CHS Covid-19 database includes information that is collected centrally by the Israeli Ministry of Health and transferred daily to CHS, such as vaccination dates, reverse-transcriptase–quantitative polymerase-chain-reaction (RT-qPCR) test dates and results, and hospitalizations and deaths related to Covid-19.
The CHS databases were used in the primary studies that evaluated the effectiveness1 and safety9 of the BNT162b2 vaccine in a real-world setting. In addition, the Israeli Ministry of Health Covid-19 database was used as the basis of the initial study that evaluated the effectiveness of the BNT162b2 booster among persons 60 years of age or older.10 A description of the CHS data repositories that were used in this study is provided in the Supplementary Appendix.
For each participant in the study, the following sociodemographic data were extracted: age, sex, population sector (general Jewish population, Arab population, or ultra-Orthodox Jewish population), and score for socioeconomic status (scores range from 1 [lowest] to 10 [highest]; details are provided in the Supplementary Appendix). The following clinical data were extracted: vaccination dates (first, second, and booster doses), RT-qPCR test dates and results, death due to Covid-19, and any clinical risk factors for death due to Covid-19 that have been identified in the general population,11 such as diabetes mellitus, chronic obstructive pulmonary disease, asthma, chronic kidney failure, hypertension, ischemic heart disease, chronic heart failure, obesity, lung cancer, or a history of cerebrovascular accident, transient ischemic attack, or smoking.
The primary outcome was death due to Covid-19. In the primary analysis of the effectiveness of the booster with respect to this outcome, we compared the mortality due to Covid-19 in the booster group with that in the nonbooster group.
Because the initial approval of the booster by the Food and Drug Administration was for use in persons 65 years of age or older, we performed a subgroup analysis according to age group. We performed an additional subgroup analysis according to sex.
In a secondary analysis of the effectiveness of the booster in preventing SARS-CoV-2 infection, we compared the frequency of positive RT-qPCR tests in the booster group with that in the nonbooster group.
A chi-square test was used to compare categorical variables according to study group. Given that the independent variable (booster status) varied over time, univariate and multivariate survival analyses were performed with time-dependent covariates, in accordance with the study design.12 A Kaplan–Meier analysis with a log-rank test was used for the univariate analysis. Comparison of the survival curves and Schoenfeld’s global test were used to test the proportional-hazards assumption for each dependent variable. Variables that met the testing criteria served as inputs for multivariate regression analysis.
A Cox proportional-hazards regression model with time-dependent covariates was used to estimate the association of booster status with death due to Covid-19. The regression model was used to estimate the hazard ratio for death due to Covid-19 in the booster group, as compared with the nonbooster group, with the use of sociodemographic and baseline clinical characteristics as independent variables.
The assumption of a 7-day lag time between the administration of the booster and the effective-booster date, during which participants were included in the nonbooster group, was further tested to verify that this grouping did not create any bias. Validation of the lag time used to ensure booster effectiveness was performed through estimation of the hazard ratio for death due to Covid-19 in participants up to 7 days after the administration of the booster, as compared with the nonbooster group. Use of an alternative 14-day lag time was also tested with the same method.
R statistical software, version 3.5.0 (R Foundation for Statistical Computing), was used for the univariate and multivariate survival analyses with time-dependent covariates. SPSS software, version 26 (IBM), was used for all other statistical analyses. A P value of less than 0.05 was considered to indicate significance in all analyses.