Study examines efficacy of Pfizer-BioNTech mRNA COVID-19 vaccine across demographic groups

Scientists across the world are racing against time to develop effective vaccines for severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) to contain the current coronavirus disease 2019 (COVID-19) pandemic. The phase III trial of the mRNA-based BNT162b2 COVID-19 vaccine showed 95% efficacy in preventing SARS-COV-2 infection. In this randomized placebo-controlled trial, the vaccine was found to be effective 12 days following the first dose. Despite several advantages of a controlled trial, its main limitations were the small sample size and certain restrictions that were imposed during the recruitment process. Immunosuppressed patients and patients with unstable chronic conditions were not included in the phase III trials.

Study: Associations of the BNT162b2 COVID-19 vaccine effectiveness with patient age and comorbidities. Image Credit: Rido / Shutterstock

In Israel, the rapid national vaccination rollout has provided an opening to researchers to investigate the efficacy of the vaccine in protecting the diverse population against the SARS-CoV-2 infection. However, estimating the real-world effectiveness of the vaccine is not straightforward. This is so because of the strong temporal and spatial pattern of the epidemic and also the association between testing and vaccination.

Researchers have adopted several strategies to tackle these challenges. For example, models that project disease dynamics in the general population have been developed. The models showed that the vaccinated population was protected with an effectiveness of over 50% after the first dose. These models have shown that after vaccination, there is a decrease in the infection rate by 66-85% and a reduction in hospital admission by more than 90%. Another approach was to study the association of vaccination with population characteristics (age, geographical location, etc.). Researchers have compared the rate of vaccination with COVID-19 incidence and hospitalization in the population. A comprehensive comparative study between infection and disease incidences in a vaccinated population and unvaccinated control group (demographically and clinically matched) has shown a reduction in vaccine effectiveness in patients with comorbidities. Despite these strategies, there is a research gap in quantifying the association of vaccine effectiveness with numerous patient-specific attributes.

A new study that has been published on the medRxiv* preprint server reported the development of multivariate logistic regression analysis by generalizing the above-stated approaches for both daily infections and per-test infections. This regression model would help calculate the risks of COVID-19 for different post-vaccination time ranges. This model has considered different factors such as spatial and temporal patterns of the epidemic and for patient-specific characteristics (age, sex, comorbidities). Scientists believe this method would also help understand the behavioral and biological effects of the vaccine.

The current study was conducted by using anonymized electronic health records that include demographics, geographical locations, age, sex, chronic comorbidities such as type 2 diabetes, chronic kidney disease, immunosuppression, cardiovascular disease, chronic obstructive pulmonary disease (COPD) and high blood pressure, of individuals above 16 years for the period of December 1st, 2020 – February 25th, 2021. Further, the team also considered the SARS-CoV-2 RT-qPCR test results, and individuals inoculated with the first and second dose of the BNT162b2 mRNA COVID-19 vaccine. However, this study excluded individuals who were aged above 90 or below 16 years of age, individuals who were COVID-19 positive before the study period, and individuals who underwent more than twenty tests since March 2020.

This study had several limitations, which are discussed below:

1. Biased data, i.e., the data reflects non-random vaccination and non-random testing, which is strongly biased across the population.
2. The vaccinated group and the unvaccinated group may differ in several aspects. For example, their general health status and the risk of coming in contact with the disease may vary. Some of the differences between the groups may be inherent or pre-existing even before vaccination.
3. Prevalence of several viral variants during the study period. Even though the vaccine was effective against the B.1.1.7 (or UK) variant, the most common variant, the possibility of other variants may introduce biases in the study of the efficacy of the vaccine across subpopulations.

However, the biases in the data were minimized by the rapid pace of freely offered vaccination and free COVID-19 tests in the laboratory to all individuals in Israel. Additionally, the increased rate of SARS-COV-2 disease during the study period has enhanced the weight of the dataset for analysis.

Overall, the study has shown that the Pfizer-BioNTech BNT162b2 vaccine starts preventing individuals from COVID-19 after 12 days following the first-inoculation in a two-dose vaccine regimen. The effectiveness increases gradually and plateaus at 91.2% [CI 88.8%-93.1%] for all infections and 99.3% [CI 95.3%-99.9%] for symptomatic infections.

Further, the effectiveness of the vaccine was found to be similar in both genders. In terms of its effectiveness across age groups, researchers have reported that the vaccine has similar efficacy for age groups between16-80 years. However, statistically, significantly lower effectiveness is observed for older patients. While studying the effectiveness of the vaccine in an individual with chronic comorbidities, scientists have revealed that it is negatively associated with vaccine efficacy.

This research has provided a unified framework to assess the vaccine's effectiveness, revealing the importance of a patient's attributes.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Written by

Dr. Priyom Bose

Priyom holds a Ph.D. in Plant Biology and Biotechnology from the University of Madras, India. She is an active researcher and an experienced science writer. Priyom has also co-authored several original research articles that have been published in reputed peer-reviewed journals. She is also an avid reader and an amateur photographer.