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Many countries are experiencing a resurgence of COVID-19, driven predominantly by the delta (B.1.617.2) variant of SARS-CoV-2. In response, these countries are considering the administration of a third dose of mRNA COVID-19 vaccine as a booster dose to address potential waning immunity over time and reduced effectiveness against the delta variant. We aimed to use the data repositories of Israel's largest health-care organisation to evaluate the effectiveness of a third dose of the BNT162b2 mRNA vaccine for preventing severe COVID-19 outcomes. Using data from Clalit Health Services, which provides mandatory health-care coverage for over half of the Israeli population, individuals receiving a third vaccine dose between July 30, 2020, and Sept 23, 2021, were matched (1:1) to demographically and clinically similar controls who did not receive a third dose. Eligible participants had received the second vaccine dose at least 5 months before the recruitment date, had no previous documented SARS-CoV-2 infection, and had no contact with the health-care system in the 3 days before recruitment. Individuals who are health-care workers, live in long-term care facilities, or are medically confined to their homes were excluded. Primary outcomes were COVID-19-related admission to hospital, severe disease, and COVID-19-related death. The third dose effectiveness for each outcome was estimated as 1 – risk ratio using the Kaplan-Meier estimator. 1 158 269 individuals were eligible to be included in the third dose group. Following matching, the third dose and control groups each included 728 321 individuals. Participants had a median age of 52 years (IQR 37–68) and 51% were female. The median follow-up time was 13 days (IQR 6–21) in both groups. Vaccine effectiveness evaluated at least 7 days after receipt of the third dose, compared with receiving only two doses at least 5 months ago, was estimated to be 93% (231 events for two doses vs 29 events for three doses; 95% CI 88–97) for admission to hospital, 92% (157 vs 17 events; 82–97) for severe disease, and 81% (44 vs seven events; 59–97) for COVID-19-related death. Our findings suggest that a third dose of the BNT162b2 mRNA vaccine is effective in protecting individuals against severe COVID-19-related outcomes, compared with receiving only two doses at least 5 months ago. The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.

Among more than 1.7 million persons, BNT162b2 vaccination was associated with increased risks of myocarditis (risk ratio, 3.24), lymphadenopathy, appendicitis, and herpes zoster infection; in comparison, Covid-19 increased the risks of myocarditis (risk ratio, 18.28), pericarditis, arrhythmia, deep-vein thrombosis, pulmonary embolism, myocardial infarction, intracranial hemorrhage, and thrombocytopenia.

In Israel, the use of a fourth dose of BNT162b2 vaccine was initiated on January 3, 2022. As of February 18, a fourth dose produced a 45% reduction in the incidence of infection, a 55% reduction in symptomatic infection, a 68% reduction in hospitalization, and a 74% reduction in Covid-19–related death 7 to 30 days after vaccination.

A study involving more than 94,000 vaccinated and unvaccinated adolescents between the ages of 12 and 18 years in Israel showed increasing levels of protection against Covid-19 during the first month after receipt of two vaccine doses. The estimated vaccine efficacy at 7 to 21 days after receipt of two doses was 90% against infection and 93% against symptomatic disease.

Two doses of the BNT162b2 vaccine were associated mainly with low-grade local adverse effects that lasted 2 days or less and afforded nearly 50% protection against omicron infection and symptomatic illness, which was lower than that seen against delta. Greater protection in the youngest group was noted.

Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage - a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) - a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting \"contraindicated\" DDIs (AUROC = 0.92) and less effective for \"minor\" DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.

Several recent studies have applied machine learning techniques to develop risk algorithms that predict subsequent suicidal behavior based on electronic health record data. In this study we used a retrospective cohort study design to test whether developing more tailored predictive models—within specific subpopulations of patients—would improve predictive accuracy. A retrospective cohort of 15,117 patients diagnosed with multiple sclerosis (MS), a diagnosis associated with increased risk of suicidal behavior, was used. The cohort was randomly divided into equal sized training and validation sets. Overall, suicidal behavior was identified among 191 (1.3%) of the patients with MS. A Naïve Bayes Classifier model was trained on the training set to predict future suicidal behavior. With 90% specificity, the model detected 37% of subjects who later demonstrated suicidal behavior, on average 4.6 years before the first suicide attempt. The performance of a model trained only on MS patients was better at predicting suicide in MS patients than that a model trained on a general patient sample of a similar size (AUC of 0.77 vs. 0.66). Unique risk factors for suicidal behavior among patients with MS included pain-related codes, gastroenteritis and colitis, and history of smoking. Future studies are needed to further test the value of developing population-specific risk models.

Effective public health response to novel pandemics relies on accurate and timely surveillance of pandemic spread, as well as characterization of the clinical course of the disease in affected individuals. We sought to determine whether Internet search patterns can be useful for tracking COVID-19 spread, and whether these data could also be useful in understanding the clinical progression of the disease in 32 countries across six continents. Temporal correlation analyses were conducted to characterize the relationships between a range of COVID-19 symptom-specific search terms and reported COVID-19 cases and deaths for each country from January 1 through April 20, 2020. Increases in COVID-19 symptom-related searches preceded increases in reported COVID-19 cases and deaths by an average of 18.53 days (95% CI 15.98–21.08) and 22.16 days (20.33–23.99), respectively. Cross-country ensemble averaging was used to derive average temporal profiles for each search term, which were combined to create a search-data-based view of the clinical course of disease progression. Internet search patterns revealed a clear temporal pattern of disease progression for COVID-19: Initial symptoms of fever, dry cough, sore throat and chills were followed by shortness of breath an average of 5.22 days (3.30–7.14) after initial symptom onset, matching the clinical course reported in the medical literature. This study shows that Internet search data can be useful for characterizing the detailed clinical course of a disease. These data are available in real-time at population scale, providing important benefits as a complementary resource for tracking pandemics, especially before widespread laboratory testing is available.

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.

REGEN-COV, a combination of the monoclonal antibodies casirivimab and imdevimab, has been approved as a treatment for high-risk patients infected with SARS-CoV-2 within five days of their diagnosis. We performed a retrospective cohort study, and used data repositories of Israel’s largest healthcare organization to determine the real-world effectiveness of REGEN-COV treatment against COVID-19-related hospitalization, severe disease, and death. We compared patients infected with Delta variant and treated with REGEN-COV (n = 289) to those infected but not-treated with REGEN-COV (n = 1,296). Demographic and clinical characteristics were used to match patients and for further adjustment as part of the C0x model. Estimated treatment effectiveness was defined as one minus the hazard ratio. Treatment effectiveness of REGEN-COV was 56.4% (95% CI: 23.7–75.1%) in preventing COVID-19 hospitalization, 59.2% (95% CI: 19.9–79.2%) in preventing severe COVID-19, and 93.5% (95% CI: 52.1–99.1%) in preventing COVID-19 death in the 28 days after treatment. In conclusion, REGEN-COV was effective in reducing the risk of severe sequelae in high-risk COVID-19 patients. REGEN-COV is a SARS-CoV-2 combined monoclonal antibody treatment which has been shown to be effective in randomised controlled trials. Here, the authors assess its real-world effectiveness using data from Israel during the Delta wave and find that it reduced the risk of hospitalisation, severe disease and death.