The effectiveness of the first dose of BNT162 b 2 vaccine in reducing SARS-CoV-2 infection 13-24 days after immunization: real-world evidence

BackgroundBNT162b2 vaccines showed high efficacy against COVID-19 in a randomised controlled phase-III trial. A vaccine effectiveness evaluation in real life settings is urgently needed, especially given the global disease surge. Hence, we assessed the short-term effectiveness of the first dose of BNT162b2-vaccine against SARS-CoV-2 infection. Given the BNT162b2 Phase-III results, we hypothesized that the cumulative incidence of SARS-CoV-2 infection among vaccinees will decline after 12 days following immunization compared to the incidence during the preceding days. MethodsWe conducted a retrospective cohort study using data from 2{middle dot}6 million-member state-mandated health provider in Israel. Study population consisted of all members aged 16 or above years who were vaccinated with BNT162b2-vaccine between December/19/2020 and January/15/2021. We collected information regarding medical history and positive SARS-CoV-2 polymerase chain reaction test from days after first dose to January/17/2021. Daily and cumulative infection rates in days 13-24 were compared to days 1-12 after first dose using Kaplan-Meier survival analysis and generalized linear models. FindingsData of 503,875 individuals (mean age 59{middle dot}7 years SD=14{middle dot}7, 47{middle dot}8% males) were analysed, of whom 351,897 had 13-24 days of follow-up. The cumulative incidence of SARS-CoV-2 infection was 0{middle dot}57% (n=2484) during days 1-12 and 0{middle dot}27% (n=614) in days 13-24. A 51{middle dot}4% relative risk reduction (RRR) was calculated in weighted-average daily incidence of SARS-CoV-2 infection from 43{middle dot}41-per-100,000(SE=12{middle dot}07) in days 1-12 to 21{middle dot}08-per-100,000(SE=6{middle dot}16) in days 13-24 following immunization. The decrement in incidence was evident from day 18 after first dose. Similar RRRs were calculated in individuals aged 60 or above (44.5%), younger individuals (50.2%), females (50.0%) and males (52.1%). Findings were similar in sub-populations and patients with various comorbidities. ConclusionsWe demonstrated an effectiveness of 51% of BNT162b2 vaccine against SARS-CoV-2 infection 13-24 days after immunization with the first dose. Immunization with the second dose should be continued to attain the anticipated protection. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for follow-up studies regarding the effectiveness of BNT162b2 mRNA Covid-19 Vaccine without any language restrictions. The search terms were (BNT162b2 OR mRNA Covid-19 Vaccine) AND (effectiveness OR real-world OR phase IV) until Jan 15, 2021. We found no relevant observational studies among humans. We also assessed Phase II and Phase III clinical trials with BNT162b2 mRNA vaccine. Added value of this studyTo our knowledge, this is the first and largest phase IV study on the effectiveness of the BNT162b2 mRNA COVID-19 vaccine in real-world settings. Our findings showed that the first dose of the vaccine is associated with an approximately 51% reduction in the incidence of PCR-confirmed SARS-CoV-2 infections at 13 to 24 days after immunization compared to the rate during the first 12 days. Similar levels of effectiveness were found across age groups, sex, as well as among individuals residing in Arab or ultra-orthodox Jewish communities that display an increased COVID-19 risk. Implications of all the available evidenceThe study results indicate that in real life the first dose of the new BNT162b2 mRNA COVID-19 vaccine confers around 50% protection against overall SARS-CoV-2 infections (symptomatic or asymptomatic). Together our findings and the 95% efficacy shown in the phase III trial, suggest that the BNT162b2 vaccine should be administered in two doses to achieve maximum protection and impact in terms of disease burden reduction and possibly reducing SARS-CoV-2 transmission. COVID-19 vaccines should be urgently deployed globally.

COVID-19 DEATH TOLL: THE ROLE OF THE NATION'S ECONOMIC DEVELOPMENT

BackgroundSeveral months into the novel coronavirus disease (COVID-19) pandemic, there is a limited understanding of the underlying country-specific factors associated with COVID-19 spread and mortality. This study aims to investigate the role of nations economic development in the death toll associated with COVID-19 in Europe and Israel. MethodsNumber of COVID-19 cases, deaths per million, and case fatality rate (CFR) in Israel and 39 countries in Europe were described across quintiles of gross domestic product (GDP) per capita. The association between GDP per capita and COVID-19 incidence, mortality, and CFR was investigated using generalized linear modeling adjusting for the proportion of elderly and density of the population. ResultsIn countries belonging to the three lower GDP quintiles, COVID-19 incidence rates per million (range 708-1134) were substantially lower compared to countries in the fourth (3939) and fifth (3476) quintiles. Major differences were also calculated in COVID-19 mortality rates per million (25-31 vs. 222-268). There was no significant (p=0.19) differences in CFR between GDP quintiles (range: 2.79-7.62%). ConclusionsCOVID-19 had a greater toll in more developed nations. Though comparisons are limited by differences in testing, reporting and lockdown policies, this association likely reflects increased spread from trade and tourism in wealthier countries, whereas limited health system capacity and lack of treatment and vaccination options contributed to higher than expected CFR in wealthier countries. This unique situation will probably encourage the stronger economies to invest the required financial capacity to respond to and recover from the current crisis.

Predicting individual risk for COVID19 complications using EMR data

BackgroundThe global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data. MethodsWe have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n = 1658), or hospitalized and marked as mild (n = 332), or as having moderate (n = 83) or severe (n = 64) complications. FindingsThe AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824-0.879] for the COVID19-based model - 0.872[0.847-0.879]. InterpretationThese models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen. FundingThere was no funding for this study Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe have search PubMed for coronavirus[MeSH Major Topic] AND the following MeSH terms: risk score, predictive analytics, algorithm, predictive analytics. Only few studies were found on predictive analytics for developing COVID19 complications using real-world data. Many of the relevant works were based on self-reported information and are therefore difficult to implement at large scale and without patient or physician participation. Added value of this studyWe have described two models for assessing risk of COVID-19 complications and mortality, based on EMR data. One model was derived by combining a machine-learning model for influenza-complications with epidemiological data for age and sex dependent mortality rates due to COVID-19. The other was directly derived from initial COVID-19 complications data. Implications of all the available evidenceThe developed models may effectively identify patients at high-risk for developing COVID19 complications. Implementing such models into operational data systems may support COVID-19 care workflows and assist in triaging patients.