SARS-CoV-2 Vaccines: Much Accomplished, Much to Learn

COVID-19 Vaccine: Promoting Vaccine Acceptance

Donor HLA Class 1 Evolutionary Divergence Is a Major Predictor of Liver Allograft Rejection: A Retrospective Cohort Study: Annals of Internal Medicine: Vol 174, No 10

Background: The HLA evolutionary divergence (HED), a continuous metric quantifying the peptidic differences between 2 homologous HLA alleles, reflects the breadth of the immunopeptidome presented to T lymphocytes. Objective: To assess the potential effect of donor or recipient HED on liver transplant rejection. Design: Retrospective cohort study. Setting: Liver transplant units. Patients: 1154 adults and 113 children who had a liver transplant between 2004 and 2018. Measurements: Liver biopsies were done 1, 2, 5, and 10 years after the transplant and in case of liver dysfunction. Donor-specific anti-HLA antibodies (DSAs) were measured in children at the time of biopsy. The HED was calculated using the physicochemical Grantham distance for class I (HLA-A or HLA-B) and class II (HLA-DRB1 or HLA-DQB1) alleles. The influence of HED on the incidence of liver lesions was analyzed through the inverse probability weighting approach based on covariate balancing, generalized propensity scores. Results: In adults, class I HED of the donor was associated with acute rejection (hazard ratio [HR], 1.09 [95% CI, 1.03 to 1.16]), chronic rejection (HR, 1.20 [CI, 1.10 to 1.31]), and ductopenia of 50% or more (HR, 1.33 [CI, 1.09 to 1.62]) but not with other histologic lesions. In children, class I HED of the donor was also associated with acute rejection (HR, 1.16 [CI, 1.03 to 1.30]) independent of the presence of DSAs. There was no effect of either donor class II HED or recipient class I or class II HED on the incidence of liver lesions in adults and children. Limitation: The DSAs were measured only in children. Conclusion: Class I HED of the donor predicts acute or chronic rejection of liver transplant. This novel and accessible prognostic marker could orientate donor selection and guide immunosuppression. Primary Funding Source: Institut National de la Santé et de la Recherche Médicale.

Impact of Population Growth and Aging on Estimates of Excess U.S. Deaths During the COVID-19 Pandemic, March to August 2020

Background: Excess death estimates quantify the full impact of the coronavirus disease 2019 (COVID-19) pandemic. Widely reported U.S. excess death estimates have not accounted for recent population changes, especially increases in the population older than 65 years. Objective: To estimate excess deaths in the United States in 2020, after accounting for population changes. Design: Surveillance study. Setting: United States, March to August 2020. Participants: All decedents. Measurements: Age-specific excess deaths in the United States from 1 March to 31 August 2020 compared with 2015 to 2019 were estimated, after changes in population size and age were taken into account, by using Centers for Disease Control and Prevention provisional death data and U.S. Census Bureau population estimates. Cause-specific excess deaths were estimated by month and age. Results: From March through August 2020, 1 671 400 deaths were registered in the United States, including 173 300 COVID-19 deaths. An average of 1 370 000 deaths were reported over the same months during 2015 to 2019, for a crude excess of 301 400 deaths (128 100 non–COVID-19 deaths). However, the 2020 U.S. population includes 5.04 million more persons aged 65 years and older than the average population in 2015 to 2019 (a 10% increase). After population changes were taken into account, an estimated 217 900 excess deaths occurred from March through August 2020 (173 300 COVID-19 and 44 600 non–COVID-19 deaths). Most excess non–COVID-19 deaths occurred in April, July, and August, and 34 900 (78%) were in persons aged 25 to 64 years. Diabetes, Alzheimer disease, and heart disease caused the most non–COVID-19 excess deaths. Limitation: Provisional death data are underestimated because of reporting delays. Conclusion: The COVID-19 pandemic resulted in an estimated 218 000 excess deaths in the United States between March and August 2020, and 80% of those deaths had COVID-19 as the underlying cause. Accounting for population changes substantially reduced the excess non–COVID-19 death estimates, providing important information for guiding future clinical and public health interventions. Primary Funding Source: National Cancer Institute.

A Comprehensive Policy Framework to Understand and Address Disparities and Discrimination in Health and Health Care: A Policy Paper From the American College of Physicians

Racial and ethnic minority populations in the United States experience disparities in their health and health care that arise from a combination of interacting factors, including racism and discrimination, social drivers of health, health care access and quality, individual behavior, and biology. To ameliorate these disparities, the American College of Physicians (ACP) proposes a comprehensive policy framework that recognizes and confronts the many elements of U.S. society, some of which are intertwined and compounding, that contribute to poorer health outcomes. In addition to this framework, which includes high-level principles and discusses how disparities are interconnected, ACP offers specific policy recommendations on disparities and discrimination in education and the workforce, those affecting specific populations, and those in criminal justice practices and policies in its 3 companion policy papers. ACP believes that a cross-cutting approach that identifies and offers solutions to the various aspects of society contributing to poor health is essential to achieving its goal of good health care for all, poor health care for none.

Racial Disparities in COVID-19 Testing and Outcomes: Retrospective Cohort Study in an Integrated Health System: Annals of Internal Medicine: Vol 174, No 6

Background: Racial disparities exist in outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objective: To evaluate the contribution of race/ethnicity in SARS-CoV-2 testing, infection, and outcomes. Design: Retrospective cohort study (1 February 2020 to 31 May 2020). Setting: Integrated health care delivery system in Northern California. Participants: Adult health plan members. Measurements: Age, sex, neighborhood deprivation index, comorbid conditions, acute physiology indices, and race/ethnicity; SARS-CoV-2 testing and incidence of positive test results; and hospitalization, illness severity, and mortality. Results: Among 3 481 716 eligible members, 42.0% were White, 6.4% African American, 19.9% Hispanic, and 18.6% Asian; 13.0% were of other or unknown race. Of eligible members, 91 212 (2.6%) were tested for SARS-CoV-2 infection and 3686 had positive results (overall incidence, 105.9 per 100 000 persons; by racial group, White, 55.1; African American, 123.1; Hispanic, 219.6; Asian, 111.7; other/unknown, 79.3). African American persons had the highest unadjusted testing and mortality rates, White persons had the lowest testing rates, and those with other or unknown race had the lowest mortality rates. Compared with White persons, adjusted testing rates among non-White persons were marginally higher, but infection rates were significantly higher; adjusted odds ratios [aORs] for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 2.01 (95% CI, 1.75 to 2.31), 3.93 (CI, 3.59 to 4.30), 2.19 (CI, 1.98 to 2.42), and 1.57 (CI, 1.38 to 1.78), respectively. Geographic analyses showed that infections clustered in areas with higher proportions of non-White persons. Compared with White persons, adjusted hospitalization rates for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 1.47 (CI, 1.03 to 2.09), 1.42 (CI, 1.11 to 1.82), 1.47 (CI, 1.13 to 1.92), and 1.03 (CI, 0.72 to 1.46), respectively. Adjusted analyses showed no racial differences in inpatient mortality or total mortality during the study period. For testing, comorbid conditions made the greatest relative contribution to model explanatory power (77.9%); race only accounted for 8.1%. Likelihood of infection was largely due to race (80.3%). For other outcomes, age was most important; race only contributed 4.5% for hospitalization, 12.8% for admission illness severity, 2.3% for in-hospital death, and 0.4% for any death. Limitation: The study involved an insured population in a highly integrated health system. Conclusion: Race was the most important predictor of SARS-CoV-2 infection. After infection, race was associated with increased hospitalization risk but not mortality. Primary Funding Source: The Permanente Medical Group, Inc.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19

Background: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. Objective: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. Design: Retrospective observational cohort study. Settings: Five hospitals in Maryland and Washington, D.C. Patients: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. Measurements: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. Results: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. Limitation: The SCARP tool was developed by using data from a single health system. Conclusion: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. Primary Funding Source: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Masks and Face Coverings for the Lay Public: A Narrative Update: Annals of Internal Medicine: Vol 174, No 4

Whether and when to mandate the wearing of facemasks in the community to prevent the spread of coronavirus disease 2019 remains controversial. Published literature across disciplines about the role of masks in mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is summarized. Growing evidence that SARS-CoV-2 is airborne indicates that infection control interventions must go beyond contact and droplet measures (such as handwashing and cleaning surfaces) and attend to masking and ventilation. Observational evidence suggests that masks work mainly by source control (preventing infected persons from transmitting the virus to others), but laboratory studies of mask filtration properties suggest that they could also provide some protection to wearers (protective effect). Even small reductions in individual transmission could lead to substantial reductions in population spread. To date, only 1 randomized controlled trial has examined a community mask recommendation. This trial did not identify a significant protective effect and was not designed to evaluate source control. Filtration properties and comfort vary widely across mask types. Masks may cause discomfort and communication difficulties. However, there is no evidence that masks result in significant physiologic decompensation or that risk compensation and fomite transmission are associated with mask wearing. The psychological effects of masks are culturally shaped; they may include threats to autonomy, social relatedness, and competence. Evidence suggests that the potential benefits of wearing masks likely outweigh the potential harms when SARS-CoV-2 is spreading in a community. However, mask mandates involve a tradeoff with personal freedom, so such policies should be pursued only if the threat is substantial and mitigation of spread cannot be achieved through other means.

Preterm Birth and Stillbirth During the COVID-19 Pandemic in Sweden: A Nationwide Cohort Study

Food Insecurity and Weight Loss in an Underserved Primary Care Population: A Post Hoc Analysis of a Cluster Randomized Trial