Clinical effectiveness of SARS-CoV-2 vaccines and booster doses in patients with cancer: An analysis from the European OnCovid registry

Background: Immunogenicity and safety of SARS-CoV-2 vaccines have been widely investigated in patients (pts) with cancer. However, their effectiveness against Coronavirus disease 2019 (COVID-19) and the additional protective effect of a booster dose in this population are yet to be defined. Methods: Using OnCovid study data (NCT04393974), a European registry enrolling consecutive pts with cancer and COVID-19, we evaluated morbidity and 14 days case fatality rates (CFR14) from COVID-19 in pts who were unvaccinated, vaccinated (either partially/full vaccinated but not boosted) and those who had received a third dose. Analyses were restricted to pts diagnosed between 17/11/2021 (first breakthrough infection in a boosted pt) and the 31/01/2022. Pts with unknown vaccination status were excluded. Results: By the data lock of 22/02/2022, out of 3820 consecutive pts from 36 institutions, 415 pts from 3 countries (UK, Spain, Italy) were eligible for analysis. Among them, 51 (12.3%) were unvaccinated, 178 (42.9%) were vaccinated and 186 (44.8%) were boosted. Among vaccinated pts, 26 (14.6%) were partially vaccinated (1 dose). Pts with haematological malignancies had more likely received a booster dose prior to infection (25.4% vs 13.6% and 11.8%, p = 0.02). We found no other associations between vaccination status and pts' characteristics including sex, age, comorbidities, smoking history, tumour stage, tumour status and receipt of systemic anticancer therapy. Compared to unvaccinated pts, boosted and vaccinated pts achieved improved CFR14 (6.8% and 7.0% vs 22.4%, p = 0.01), COVID-19-related hospitalization rates (26.1% and 20.6% vs 41.2%, p = 0.01) and COVID-19-related complications rates (14.5% and 15.7% vs 31.4%). Using multivariable Inverse Probability of Treatment Weighting (IPTW) models adjusted for sex, comorbidities, tumour status and country of origin we confirmed that boosted (OR 0.21, 95%CI: 0.05-0.89) and vaccinated pts (OR 0.19, 95%CI: 0.04-0.81) achieved improved CFR14 compared to unvaccinated pts, whilst a significantly reduced risk of COVID-19 complications (OR 0.26, 95%CI: 0.07-0.93) was reported for vaccinated pts only. Conclusions: SARS-CoV-2 vaccines protect from COVID-19 morbidity and mortality in pts with cancer. Accounting for the enrichment of haematologic pts in the boosted group, the observation of comparable mortality outcomes between boosted and vaccinated pts is reassuring and suggests boosting to be associated with reduced mortality in more vulnerable subjects, despite evidence of adverse features in this group.

Major risk factors associated with severe COVID-19 outcomes in patients with multiple myeloma: Report from the National COVID-19 Cohort Collaborative (N3C)

Background: Patients with multiple myeloma (MM), an age-dependent neoplasm of antibody-producing plasma cells, have compromised immune systems due to multiple factors that may increase the risk of severe COVID-19. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multi-center cohort of ∼12M COVID-19 cases and controls nationwide. In this study, we aim to analyze risk factors associated with COVID-19 severity and death in MM patients using the N3C database. Methods: Our cohort included MM patients within the N3C registry diagnosed with COVID-19 based on positive PCR or antigen tests or ICD-10-CM. The outcomes of interest include all-cause mortality (including discharge to hospice) during the index encounter, and clinical indicators of severity (hospitalization/ED visit, use of mechanical ventilation, or extracorporeal membrane oxygenation/ECMO). Results: As of 09/10/2021, the N3C registry included 690371 cancer patients, out of which 17791 were MM patients (4707 were COVID-19+). The mean age at diagnosis was 65.9yrs, 57.6% were >65yo, 46.4% were females, and 21.8% were Blacks. 25.6% had a Charlson Comorbidity Index (CCI) score of ≥2. 55.6% required an inpatient or ED visit, and 3.65% required invasive ventilation. 11.4% developed acute kidney injury during hospitalization. Multivariate logistic regression analysis showed histories of pulmonary disease (OR 2.2;95%CI: 1.7-2.8), renal disease (OR 1.8;95%CI: 1.4-2.4), and black race (p<0.001) were associated with higher risk of severity. Interestingly, smoking status was significantly associated with a lower risk of severity (OR 0.7;95%CI: 0.5-0.9). Further, protective association was also observed between COVID-19 severity and blood or marrow transplant (BMT) (OR 0.52;95%CI: 0.4-0.7), daratumumab therapy (OR 0.64;95%CI: 0.42- 0.99) and COVID-19 vaccination (OR 0.28;95%CI: 0.18-0.44). IMiDs were associated increase in the risk of COVID-19 severity (OR 2.1;95%CI: 1.6-2.7). 2.3% of N3C-myeloma COVID-19+ patients died within the first 10 days, while 4.95% died within 30 days of COVID-19 hospitalization. Overall, the survival probability was 90.5% across the course of the study. Multivariate cox proportional hazard model showed that CCI score ≥2 (HR 4.4;95%CI: 2.2-8.8), hypertension (HR 1.6;95%CI: 1.02- 2.4), IMiD (HR 2.6;95%CI: 1.8-3.8) and proteasome inhibitor (HR 1.6;95%CI: 1.1-2.5) therapy were associated with worse survival. COVID-19 vaccination (HR 0.195;95%CI: 0.09-0.45) and BMT (HR 0.65;95%CI: 0.4-0.995) were associated with lower risk of death. Conclusions: We have identified previously unpublished potential risk factors for COVID-19 severity and death in MM as well as validated some published ones. To the best of our knowledge, this is the largest nationwide study on multiple myeloma patients with COVID-19.

Control Of Epidemic Spreads Via Testing And Lock-Down

Testing and lock-down are interventions that can combat the spread of an infectious disease. Testing is a targeted instrument that permits the isolation of infectious individuals. Lock-down, on the other hand, is blunt and restricts the mobility of all people. In this paper, we present a compartmental epidemic model that accounts for the impact of lock-down and different kinds of testing, motivated by the nature of the ongoing COVID-19 outbreak. We consider the testing of symptomatic, contact traced, and randomly chosen asymptomatic individuals. Using the model, we first characterize static mobility levels and testing requirements that can dampen the spread asymptotically. We then characterize a threshold-type optimal lock-down policy that minimizes the social impact of an epidemic, modeled via a sum of infection and lockdown costs. Our results are contextualized with realistic parameter values for COVID-19.

The acute phase response identifies cancer patients with adverse outcomes from SARS-CoV-2 infection as quantified by the OnCovid Inflammatory Score

We sought to determine parameters of the acute phase response, a feature of innate immunity activated by infectious noxae and cancer, deranged by Covid-19 and establish oncological indices' prognostic potential for patients with concomitant cancer and Covid-19. Between 27/02 and 23/06/2020, OnCovid retrospectively accrued 1,318 consecutive referrals of patients with cancer and Covid-19 aged 18 from the U.K., Spain, Italy, Belgium, and Germany. Patients with myeloma, leukemia, or insufficient data were excluded. The neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), prognostic nutritional index (PNI), modified Glasgow prognostic score (mGPS), and prognostic index (PI) were evaluated for their prognostic potential, with the NLR, PLR, and PNI risk stratifications dichotomized around median values and the pre-established risk categorizations from literature utilized for the mGPS and PI. 1,071 eligible patients were randomly assorted into a training set (TS, n=529) and validation set (VS, n=542) matched for age (67.9±13.3 TS, 68.5±13.5 VS), presence of 1 comorbidity (52.1% TS, 49.8% VS), development of 1 Covid-19 complication (27% TS, 25.9% VS), and active malignancy at Covid-19 diagnosis (66.7% TS, 61.6% VS). Among all 1,071 patients, deceased patients tended to categorize into poor risk groups for the NLR, PNI, mGPS, and PI (P<0.0001) with a return to pre-Covid-19 diagnosis NLR, PNI, and mGPS categorizations following recovery (P<0.01). In the TS, higher mortality rates were associated with NLR>6 (44.6% vs 28%, P<0.0001), PNI<40 (46.6% vs 20.9%, P<0.0001), mGPS (50.6% for mGPS2 vs 30.4% and 11.4% for mGPS1 and 0, P<0.0001), and PI (50% for PI2 vs 40% for PI1 and 9.1% for PI0, P<0.0001). Findings were confirmed in the VS (P<0.001 for all comparisons). Patients in poor risk categories had shorter median overall survival [OS], (NLR>6 30 days 95%CI 1-63, PNI<40 23 days 95%CI 10-35, mGPS2 20 days 95%CI 8-32, PI2 23 days 95%CI 1-56) compared to patients in good risk categories, for whom median OS was not reached (P<0.001 for all comparisons). The PLR was not associated with survival. Analyses of survival in the VS confirmed the NLR (P<0.0001), PNI (P<0.0001), PI (P<0.01), and mGPS (P<0.001) as predictors of survival. In a multivariable Cox regression model including all inflammatory indices and pre-established prognostic factors for severe Covid-19 including sex, age, comorbid burden, malignancy status, and receipt of anti-cancer therapy at Covid-19 diagnosis, the PNI was the only factor to emerge with a significant hazard ratio [HR] in both TS and VS analysis (TS HR 1.97, 95%CI 1.19-3.26, P=0.008;VS HR 2.48, 95%CI 1.47- 4.20, P=0.001). We conclude that systemic inflammation drives mortality from Covid-19 through hypoalbuminemia and lymphocytopenia as measured by the PNI and propose the PNI as the OnCovid Inflammatory Score (OIS) in this context.

SARS-CoV-2 antibody seroprevalence and safety of vaccines in cancer patients who recovered from COVID-19

Background: Little is known about natural anti-SARS-CoV-2 antibody seroprevalence post COVID-19 and safety of vaccines in COVID-19 survivors with cancer. Methods: Among 2795 consecutive patients (pts) with COVID-19 and cancer registered to OnCovid between 01/2020 and 02/2021, we examined natural seroprevalence of anti-SARS-CoV-2 Antibodies (SC2Ab, IgM or IgG) in pts tested post-infection. We analysed prevalence and safety of SARS-Cov-2 vaccine administration in pts who underwent clinical re-assessment at participating institutions. Results: Out of 350 pts tested for SC2Ab, 318 (90.9%) had a positive SC2Ab titre post-convalescence. Neither baseline features (sex, age, comorbidities, smoking history, tumour stage/status, anticancer-therapy and primary tumour) nor COVID-19-specific features (complications, hospitalization, sequelae) were significantly associated SC2Ab status. Receipt of COVID-19 specific therapy was higher among SC2Ab+ pts (62.6% vs 40.6%, p=0.0156). Out of 593 pts with known vaccination status, 178 (30%) had received 1 dose, whilst 38 pts (6.4%) received 2 doses of mRNA based (70.2%) or viral vector vaccine (17.4%). Vaccinated pts were more likely aged ≥65 years (59% vs 48.3%, p=0.0172), with loco-regional tumour stage (56% vs 40.8%, p=0.0014), on anti-cancer therapy at COVID-19 (49.1% vs 38.2%, p=0.0168) and history of prior hospitalisation due to COVID-19 (61.8% vs 48.3%, p=0.0029). Vaccine-related adverse events were reported for 18/56 evaluable pts (32.1%) and included injection site reactions (50%), fever (44.4%), arthralgias (33.3%), fatigue (33.3%) and allergy (5.5%). No long-term vaccine-related morbidity was reported. Conclusions: We report high seroprevalence (>90%) of SC2Ab in convalescent cancer pts who survived COVID-19 irrespective of baseline demographics, oncological characteristics and COVID-19 severity. COVID-19 vaccines appear to be safe in cancer pts with history of prior infection. Clinical trial identification: NCT04393974. Legal entity responsible for the study: Imperial College London. Funding: Has not received any funding. Disclosure: D.J. Pinato: Financial Interests, Personal, Invited Speaker: ViiV Healthcare;Financial Interests, Personal, Invited Speaker: Bayer;Financial Interests, Personal, Advisory Board: Eisai;Financial Interests, Personal, Advisory Board: Amgen;Financial Interests, Personal, Advisory Board: BMS;Financial Interests, Personal, Advisory Board: Pfizer;Financial Interests, Personal, Advisory Board: Nanostring tech. A. Cortellini: Financial Interests, Personal, Advisory Board: MSD;Financial Interests, Personal, Advisory Board: BMS;Financial Interests, Personal, Advisory Board: Roche;Financial Interests, Personal, Invited Speaker: Novartis;Financial Interests, Personal, Advisory Board: SunPharma;Financial Interests, Personal, Invited Speaker: AstraZeneca;Financial Interests, Personal, Invited Speaker: Astellas. All other authors have declared no conflicts of interest.

Pharmacy Impact on Covid-19 Vaccination Progress Using Machine Learning Approach

The novel coronavirus disease (COVID-19) has created immense threats to public health on various levels around the globe. The unpredictable outbreak of this disease and the pandemic situation are causing severe depression, anxiety and other mental as physical health related problems among the human beings. This deadly disease has put social, economic condition of the entire world into an enormous challenge. To combat against this disease, vaccination is essential as it will boost the immune system of human beings while being in the contact with the infected people. The vaccination process is thus necessary to confront the outbreak of COVID-19. The worldwide vaccination progress should be tracked to identify how fast the entire economic as well as social life will be stabilized. The monitor of the vaccination progress, a machine learning based Regressor model is approached in this study. This vaccination tracking process has been applied on the data starting from 14th December, 2020 to 24th April, 2021. A couple of ensemble based machine learning Regressor models such as Random Forest, Extra Trees, Gradient Boosting, AdaBoost and Extreme Gradient Boosting are implemented and their predictive performance are compared. The comparative study reveals that the Extra trees Regressor outperforms with minimized mean absolute error (MAE) of 6.465 and root mean squared error (RMSE) of 8.127. The uniqueness of this study relies on assessing as well as predicting vaccination intake progress by utilizing automated process offered by machine learning techniques. The innovative idea of the method is that the vaccination process and their priority are considered in the paper. Among several existing machine learning approaches, the ensemble based learning paradigms are employed in this study so that improved prediction efficiency can be delivered.

Rainbows, teddy bears and 'others': the cultural politics of children's leisure amidst the COVID-19 pandemic. (Special Issue: Leisure in the time of Covid-19.)

In countries currently under lockdown, schools and leisure facilities have closed their gates to the vast majority of children. Having to stay indoors for most of the day, children's leisurescapes have been radically transformed. In these circumstances, instances have emerged from across the globe of children adapting to the lockdown in creative ways and constructing leisurescapes within the limits of the home, by putting up rainbows and teddy bears on windows and porches. Drawing upon media reports about children's rainbow drawings and teddy bear hunts, in this paper, I deploy a sociological lens to demonstrate how children are using these leisure narratives as tools for participating in the wider conversation around the pandemic. At the same time, however, in pinning romanticized notions of hope and 'national spirit' upon the normative image of the child at play, media narratives are obfuscating the inequalities that fracture lived childhoods in the developed world.