US COVID-19 Research Articles

Ethical allocation of COVID-19 vaccine in the United States: an evaluation of competing frameworks for the current pandemic and future events.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, created the need for an effective vaccine. Questions arose about allocating the initial limited supplies in the United States. We present four allocation models and compare their characteristics for ethically meeting the health needs of the population. The literature shares broad agreement on guiding ethical principles with those of the four proposed models for vaccine allocation, featuring the concepts of utilitarianism, prioritarianism, equity, and reciprocity. We conclude that the “Interim Framework for COVID-19 Vaccine Allocation and Distribution in the United States” from the Johns Hopkins Bloomberg School of Public Health is the most comprehensive and ethically sound. We recommend government officials and policymakers at all levels consider the principles and objectives in this model as US COVID-19 vaccination distribution efforts continue. This model may serve as an effective framework for initial vaccine distribution efforts during future epidemic and pandemic events.

Hospitalization and mortality trends among patients with confirmed COVID-19 in the United States, April through August 2020.

Background: The United States has experienced high COVID- 19 case counts, hospitalizations, and death rates. This retrospective analysis reports changing trends in the demographics and clinical outcomes of hospitalized US COVID-19 patients between April and August 2020.Design and methods: The Premier Healthcare Database Special Release was used to examine patient demographics of hospitalized COVID-19 patients from all US Census Bureau divisions. Demographics included age, sex, race, and ethnicity. Clinical outcomes included in-hospital mortality, intensive care unit (ICU) admission, and receipt of invasive mechanical ventilation.Results: Overall, 146,491 hospitalized COVID-19 patients were included (mean [SD] age, 61.0 [18.4] years; 51.7% male; 29.6% White non-Hispanic). Monthly total hospitalizations decreased from 44,854 in April to 18,533 in August; ICU admissions increased from 19.8% to 23.6%, and ventilator use and inpatient mortality decreased from 18.6% to 14.5% and 21.0% to 11.4%, respectively. Inpatient mortality was highest in the Middle Atlantic division (20.3%), followed by the New England (19.0%), East North Central (14.2%), and Mountain (13.7%) divisions. Black non-Hispanic patients were overrepresented among hospitalizations (19.0%); this group comprises 12.2% of the US population. Patients aged <65 years made up 53% of hospitalizations and had lower inpatient mortality than those aged ≥65 years.Conclusions: Hospitalizations, ventilator use, and mortality decreased, while ICU admission rates increased from April to August 2020. Older individuals and Black non-Hispanics were found to be at elevated risk of severe outcomes. These trends could inform ongoing patient care and US public health policies to limit the further spread of SARS-CoV-2. Significance for public health The impact of the COVID-19 pandemic on public health in the United States has been significant. Due to the ever-evolving nature of the pandemic, healthcare workers and public health experts require a thorough understanding of the clinical outcomes of hospitalized COVID-19 patients. This study found that despite decreases in overall mortality rates as the pandemic continues, certain demographic groups, including the elderly and Black non-Hispanics remain disproportionately affected. Such information could inform ongoing care of COVID-19 patients, as well as shape public health policies to address health disparities to limit the ongoing spread of SARS-CoV-2.

Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach.

A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.

Lives saved and lost in the first six month of the US COVID-19 pandemic: A retrospective cost-benefit analysis.

In the beginning of the COVID-19 US epidemic in March 2020, sweeping lockdowns and other aggressive measures were put in place and retained in many states until end of August of 2020; the ensuing economic downturn has led many to question the wisdom of the early COVID-19 policy measures in the US. This study's objective was to evaluate the cost and benefit of the US COVID-19-mitigating policy intervention during the first six month of the pandemic in terms of COVID-19 mortality potentially averted, versus mortality potentially attributable to the economic downturn. We conducted a synthesis-based retrospective cost-benefit analysis of the full complex of US federal, state, and local COVID-19-mitigating measures, including lockdowns and all other COVID-19-mitigating measures, against the counterfactual scenario involving no public health intervention. We derived parameter estimates from a rapid review and synthesis of recent epidemiologic studies and economic literature on regulation-attributable mortality. According to our estimates, the policy intervention saved 866,350-1,711,150 lives (4,886,214-9,650,886 quality-adjusted life-years), while mortality attributable to the economic downturn was 57,922-245,055 lives (2,093,811-8,858,444 life-years). We conclude that the number of lives saved by the spring-summer lockdowns and other COVID-19-mitigation was greater than the number of lives potentially lost due to the economic downturn. However, the net impact on quality-adjusted life expectancy is ambiguous.

COVID-19 pandemic experiences of older adults with dementia in community and residential care settings: Results from a US national survey.

Investigations into consequences of the US COVID-19 pandemic for older adults with dementia have been limited. We used the National Health and Aging Trends Study to examine self-reported COVID-19 infection; measures taken to limit its spread; social, behavioral, and emotional responses to the pandemic; and changes in health-care use and provider communication. We compared adults aged ≥70 with and without dementia in community and residential care settings. In residential care settings, infection was substantially higher and social contact less common for those with dementia. In community and residential care settings, those with dementia had 2 to 3 times the odds of reporting sleeping more often. In residential care settings, those with dementia were less likely to put off care and more likely to start telehealth. Findings highlight the disproportionate social and behavioral consequences of the COVID-19 pandemic for those living with dementia, particularly in residential care settings. Data are from the nationally representative National Health and Aging Trends Study.COVID-19 infection was higher in residential care settings for those with dementia.Social contact was less common for those with dementia in residential care.Pandemic-related coping behaviors differed by dementia and residential status.In residential care, those with dementia were less likely to delay health care.

Deviation in the Age Structure of Mortality as an Indicator of COVID-19 Pandemic Severity.

Objectives. To test whether distortions in the age distribution of deaths can track pandemic activity. Methods. We compared weekly distributions of all-cause deaths by age during the COVID-19 pandemic in the United States from March to December 2020 with corresponding prepandemic weekly baseline distributions derived from data for 2015 to 2019. We measured distortions via Kolmogorov-Smirnov (K-S) and χ2 goodness-of-fit statistics as well as deaths among individuals aged 65 years or older as a percentage of total deaths (PERC65+). We computed bivariate correlations between these measures and the number of recorded COVID-19 deaths for the corresponding weeks. Results. Elevated COVID-19-associated fatalities were accompanied by greater distortions in the age structure of mortality. Distortions in the age distribution of weekly US COVID-19 deaths in 2020 relative to earlier years were highly correlated with COVID fatalities (K-S: r = 0.71, P < .001; χ2: r = 0.90, P < .001; PERC65+: r = 0.85, P < .001). Conclusions. A population-representative sample of age-at-death data can serve as a useful means of pandemic activity surveillance when precise cause-of-death data are incomplete, inaccurate, or unavailable, as is often the case in low-resource environments. (Am J Public Health. 2022;112(1):165-168. https://doi.org/10.2105/AJPH.2021.306567).

Stock returns in the time of COVID-19 pandemic

ABSTRACT This paper investigates the dependence between COVID-19 shocks and stock market returns for seven countries: Canada, China, France, Germany, South Korea, the UK and the US. Using a quantile-on-quantile (QQ) regression analysis, we find that all stock markets responded to COVID-19 shocks mostly negatively. However, we also observe instances where an increase in the number of COVID-19 confirmed cases resulted in higher stock returns. This unexpected positive dependence captures the much debated stock market-real economy disconnect at the time of the onset of the COVID-19 pandemic. Furthermore, although stock markets’ responses to both the global and the country-specific COVID-19 shocks are similar across countries, substantial differences also exist. In particular, stock markets in the US and China differed in their responses to the global COVID-19 shocks, with a higher degree of heterogeneity observed for the Chinese market. Finally, Asian markets reacted differently to country-specific COVID-19 shocks when compared to the US market. This finding could be explained by the fact that investors in the US stock markets may form their expectations mostly from the US COVID-19 shock, while investors in Asian countries, including China, may value more the information transmitted through the global COVID-19 shock.

The Great London Plague of 1665 and the US COVID-19 Pandemic Experience Compared

Using Daniel Dafoe's Journal of the Plague Year, Frank O'Hara examines the experience of the plague, the reactions of people, and changes to social cohesion and compares the current COVID-19 pandemic. These isses get at basic human reactions to crisis, and the similarities and differences between the 17th and 21st centuries are instructive.

Expendable to essential? Changing perceptions of gig workers on Twitter in the onset of COVID-19

ABSTRACT While ride-hailing ridership declined in 2020 due to COVID-19 induced restrictions like stay-at-home orders, food/grocery delivery services became quasi-essential. This study investigates if and how public perceptions of gig work related to platform-based ride-hailing and food/grocery delivery services changed during the early stages of the pandemic. We collected a sample of 23,845 Twitter posts (‘tweets’) related to these platform-based services within two-week periods before and after the US COVID-19 emergency declaration. Sentiment analysis on tweets was conducted to investigate changes in public perception of gig work. Tweet content was analyzed by descriptively coding about 10% of the sample of tweets manually along ten different dimensions (e.g., personal experience, informative, and about driver); then we used thematic analysis to gain an understanding about the public’s views towards gig work/workers. We tested supervised machine learning methods to explore their potential to classify the rest of the sample along the ten descriptive dimensions. The number of tweets increased by approximately 150% after the emergency declaration and became more positive in sentiment. Qualitative results indicate that tweets about negative personal experiences with drivers/companies decreased during COVID-19, while tweets exhibiting a sense of community (e.g., sharing information) and concern towards gig workers increased. Findings can inform policy and workforce changes regarding platform-based service companies.

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

The COVID-19 pandemic and unemployment, subsistence needs and mental health among adults with HIV in the United States.

To evaluate whether reported prevalence of unemployment, subsistence needs, and symptoms of depression and anxiety among adults with diagnosed HIV during the COVID-19 pandemic were higher than expected. The Medical Monitoring Project (MMP) is a complex sample survey of adults with diagnosed HIV in the United States. We analyzed 2015-2019 MMP data using linear regression models to calculate expected prevalence, along with corresponding prediction intervals (PI), for unemployment, subsistence needs, depression, and anxiety for June-November 2020. We then assessed whether observed estimates fell within the expected prediction interval for each characteristic, overall and among specific groups. Overall, the observed estimate for unemployment was higher than expected (17% vs. 12%) and exceeded the upper limit of the PI. Those living in households with incomes ≥400% of FPL were the only group where the observed prevalence of depression and anxiety during the COVID-19 period was higher than the PIs; in this group, the prevalence of depression was 9% compared with a predicted value of 5% (75% higher) and the prevalence of anxiety was 11% compared with a predicted value 5% (137% higher). We did not see elevated levels of subsistence needs, although needs were higher among Black and Hispanic compared with White persons. Efforts to deliver enhanced employment assistance to persons with HIV and provide screening and access to mental health services among higher income persons may be needed to mitigate the negative effects of the US COVID-19 pandemic.

Composite Quantile Probability Predictions: Performance and Coherence Analysis of US COVID-19 Confirmed Infection Cases

An analytical framework is presented for the evaluation of composite probability forecasts using empirical quantiles. The framework is demonstrated via the examination of forecasts of the changes in the number of US COVID-19 confirmed infection cases, applying 18 two-week ahead quantile forecasts from four forecasting organisations. The forecasts are analysed individually for each organisation and in combinations of organisational forecasts to ascertain the highest level of performance. It is shown that the relative error reduction achieved by combining forecasts depends on the extent to which the component forecasts contain independent information. The implications of the study are discussed, suggestions are offered for future research and potential limitations are considered.

33. Evaluation of Rural-Urban Differences in Hospitalization and Mortality Rates for US COVID-19 Patients in the United States

BackgroundRural communities are among the most vulnerable and resource-scarce populations in the United States. Rural data is rarely centralized, precluding comparability across regions, and no significant studies have studied this population at scale. The purpose of this study is to present findings from the National COVID Cohort Collaborative (N3C) to provide insight into future research and highlight the urgent need to address health disparities in rural populations.N3C Patient Distribution This figure shows the geospatial distribution of the N3C COVID-19 positive population. N3C contains data from 55 data contributors from across the United States, 40 of whom include sufficient location information to map by ZIP Code centroid spatially. Of those sites, we selected 27 whose data met our minimum robustness qualifications for inclusion in our study. This bubble map is to scale with larger bubbles representing more patients. A. shows all N3C patients. B. shows only urban N3C distribution. C. shows the urban-adjacent rural patient distribution. D. shows the nonurban-adjacent rural patient distribution, representing the most isolated patients in N3C.MethodsThis retrospective cohort of 573,018 patients from 27 hospital systems presenting with COVID-19 between January 2020 and March 2021, of whom 117,897 were admitted (see Data Analysis Plan diagram for inclusion/exclusion criteria), analyzes outcomes and 30-day survival for the hospitalized population by the degree of rurality.Multivariate Cox regression analysis and mixed-effects models were used to estimate the association between rurality, hospitalization, and all-cause mortality, controlling for major risk factors associated with rural-urban health discrepancies and differences in health system outcomes. The difference in distribution by rurality is described as well as supplemented by population-level statistics to confirm representativeness.Data Analysis Plan This data analysis plan includes an overview of study inclusion and exclusion criteria, the matrix for data robustness to determine potential sites to include, and our covariate selection, model building, and residual testing strategy.ResultsThis study demonstrates a significant difference between hospital admissions and outcomes in urban versus urban-adjacent rural (UAR) and nonurban-adjacent rural (NAR) lines. Hospital admissions for UAR (OR 1.41, p< 0.001, 95% CI: 1.37 – 1.45) and NAR (OR 1.42, p< 0.001, 95% CI: 1.35 – 1.50) were significantly higher than their urban counterparts. Similar distributions were present for all-cause mortality for UAR (OR 1.39, p< 0.001, 95% CI: 1.30 – 1.49) and NAR (OR 1.38, p< 0.001, 95% CI: 1.22 – 1.55) compared to urban populations. These associations persisted despite adjustments for significant differences in BMI, Charlson Comorbidity index Score, gender, age, and the quarter of diagnosis for COVID-19.Baseline Characteristics Hospitalized COVID-19 Positive Population by Rurality Category, January 2020 – March 2021 Survival Curves in Hospitalized Patients Over 30 Days from Day of Admission This figure shows a survival plot of COVID-19 positive hospitalized patients in N3C by rural category (A), Charlson Comorbidity Index (B), Quarter of Diagnosis (C), and Age Group (D) from hospital admission through day 30. Events were censored at day 30 based on the incidence of death or transfer to hospice care. These four factors had the highest predictive power of the covariates evaluated in this study.Unadjusted and Adjusted Odds Ratios for Hospitalization and All-Cause Mortality by Rural Category, January 2020 – March 2021 This figure shows the adjusted and unadjusted odds ratios for being hospitalized or dying after hospitalization for the COVID-19 positive population in N3C. Risk is similar between adjusted and unadjusted models, suggesting a real impact of rurality on all-cause mortality. A shows the unadjusted odds ratios for admission to the hospital after a positive COVID-19 diagnosis for all N3C patients. B shows the unadjusted odds ratios for all-cause mortality at any point after hospitalization for COVID-19 positive patients. C shows the adjusted odds ratios for being admitted to the hospital after a positive COVID-19 diagnosis for all N3C patients. D shows the adjusted odds ratios for all-cause mortality for all-cause mortality at any point after hospitalization for COVID-19 positive patients. Adjusted models include adjustments for gender, race, ethnicity, BMI, age, Charlson Comorbidity Index (CCI) composite score, rurality, and quarter of diagnosis. The data provider is included as a random effect in all models.ConclusionIn N3C, we found that hospitalizations and all-cause mortality were greater among rural populations when compared to urban populations after adjustment for several factors, including age and co-morbidities. This study also identified key demographic and clinical disparities among rural patients that require further investigation.Disclosures Sally L. Hodder, M.D., Gilead (Advisor or Review Panel member)Merck (Grant/Research Support, Advisor or Review Panel member)Viiv Healthcare (Grant/Research Support, Advisor or Review Panel member)

571. Safety and Immunogenicity of INO-4800, a COVID-19 DNA Vaccine as a Primary Series and Booster

BackgroundDNA vaccines are safe, tolerable, elicit humoral and cellular responses, allow for repeated dosing over time, are thermostable at room temperature, and are easy to manufacture. We present a compilation of Phase 1 and Phase 2 data of Inovio’s US COVID-19 DNA Vaccine (INO-4800) targeting the full-length Spike antigen of SARS-CoV-2. A South Korean Phase 2 study is ongoing.MethodsParticipants in the open-label Phase 1 trial received 0.5 mg, 1.0 mg or 2.0 mg intradermally (ID) followed by electroporation (EP) at Days 0 and 28. An optional booster dose was administered >6 months post-dose 2. The Phase 2 further compared the 1.0 mg and 2.0 mg doses against placebo in a total of 401 participants randomized at a 3:3:1:1 ratio. ClinicalTrials.gov identifiers: NCT04336410 and NCT04642638 ResultsThe majority of adverse events (AEs) related to INO-4800 across both trials were mild in severity and did not increase in frequency with age and subsequent doses. In Phase 1, 78% (14/18) and 84% (16/19) of subjects generated neutralizing antibody responses with geometric mean titers (GMTs) of 17.4 (95%CI 8.3, 36.5) and 62.3 (95% CI 36.4, 106.7) in the 1.0 and 2.0 groups, respectively (Figure 1). By week 8, 74% (14/19) and 100% (19/19) subjects generated T cell responses by Th1- associated IFNγ ELISPOT assay . Following a booster dose, neutralizing GMTs rose to 82.2 (95% CI 38.2, 176.9) and 124.7 (95% CI 62.8, 247.7) in the 1.0 mg and 2.0 mg groups, respectively, demonstrating the ability of INO-4800 to boost (Figure 2). In Phase 2, neutralizing antibody responses demonstrated GMTs of 93.6 (95%CI 77.3, 113.4) in the 1.0 mg dose group and 150.6 (95%CI 123.8, 183.1) in the 2.0 mg dose group (Figure 3). ConclusionINO-4800 appears safe and tolerable as a primary series and as a booster with the induction of both humoral and cellular immune responses. In addition to eliciting neutralizing antibodies, INO-4800 also induced T cell immune responses as demonstrated by IFNγ ELISpot. Finally, as a homologous booster, INO-4800, when administered 6-10.5 months following the primary series, resulted in an increased immune response without increase in reactogenicity. The 2.0 mg dose was selected for Phase 3 evaluation. Disclosures Joseph Agnes, PhD, Inovio (Employee, Shareholder) Mary Giffear, BS, Inovio Pharmaceuticals, Inc. (Employee) Kimberly A. Kraynyak, PhD, Inovio Pharmaceuticals (Employee, Other Financial or Material Support, Stock options) Dinah Amante, BS, Inovio (Employee) Emma Reuschel, PhD, Inovio Pharmaceuticals (Employee) Aaron Christensen-Quick, PhD, Inovio Pharmaceuticals, Inc (Employee) Viviane M. Andrade, PhD, Inovio Pharmaceuticals Inc. (Employee) Gabriella Garufi, PhD, Inovio Pharmaceuticals, Inc. (Employee) Albert Sylvester, MS, Inovio (Employee, Shareholder) Matthew P. Morrow, PhD, Inovio Pharmaceuticals (Employee) Patrick P. Pezzoli, BS, Inovio Pharmaceuticals, Inc. (Employee) Jan Pawlicki, PhD, Inovio Pharmaceuticals (Employee) Elisabeth Gillespie, PhD, Inovio Pharmaceuticals, Inc. (Employee) Katherine Schultheis, MSc, Inovio Pharmaceuticals (Employee) Hedieh Badie, PhD, INOVIO Pharmaceuticals (Employee) Timothy A. Herring, MPH, Inovio Pharmaceuticals, Inc. (Employee, Other Financial or Material Support, Own stock in the company) Keiko O. Simon, PhD, Inovio Pharmaceuticals (Employee) Trevor R. F. Smith, PhD, Inovio (Employee, Shareholder) Stephanie Ramos, PhD, Inovio Pharmaceuticals (Employee) Jessica Lee, MPH, Inovio Pharmaceuticals (Employee) Michael Dallas, PhD, Inovio Pharmaceuticals, Inc. (Employee, Shareholder) Ami Shah Brown, PhD, Abbot Laboratories (Shareholder)IBB Biotech ETF (Shareholder)Inovio Pharmaceuticals (Employee)J & J (Shareholder)Moderna (Shareholder) Jacqueline E. Shea, PhD, Inovio Pharmaceuticals (Employee, Shareholder) J Joseph Kim, PhD, Inovio (Employee) David Weiner, PhD, Inovio (Board Member, Grant/Research Support, Shareholder, I serve on the SAB in addition to the above activities) Kate Broderick, PhD, Inovio (Employee) Trevor McMullan, MSc, Inovio (Shareholder) Jean Boyer, PhD, Inovio (Employee) Laurent Humeau, PhD, Inovio Pharmaceuticals (Employee) Mammen P. Mammen Jr., MD, Inovio Pharmaceuticals (Employee)

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

Estimating the early impact of the US COVID-19 vaccination programme on COVID-19 cases, emergency department visits, hospital admissions, and deaths among adults aged 65 years and older: an ecological analysis of national surveillance data

BackgroundIn the USA, COVID-19 vaccines became available in mid-December, 2020, with adults aged 65 years and older among the first groups prioritised for vaccination. We estimated the national-level impact of the initial phases of the US COVID-19 vaccination programme on COVID-19 cases, emergency department visits, hospital admissions, and deaths among adults aged 65 years and older.MethodsWe analysed population-based data reported to US federal agencies on COVID-19 cases, emergency department visits, hospital admissions, and deaths among adults aged 50 years and older during the period Nov 1, 2020, to April 10, 2021. We calculated the relative change in incidence among older age groups compared with a younger reference group for pre-vaccination and post-vaccination periods, defined by the week when vaccination coverage in a given age group first exceeded coverage in the reference age group by at least 1%; time lags for immune response and time to outcome were incorporated. We assessed whether the ratio of these relative changes differed when comparing the pre-vaccination and post-vaccination periods.FindingsThe ratio of relative changes comparing the change in the COVID-19 case incidence ratio over the post-vaccine versus pre-vaccine periods showed relative decreases of 53% (95% CI 50 to 55) and 62% (59 to 64) among adults aged 65 to 74 years and 75 years and older, respectively, compared with those aged 50 to 64 years. We found similar results for emergency department visits with relative decreases of 61% (52 to 68) for adults aged 65 to 74 years and 77% (71 to 78) for those aged 75 years and older compared with adults aged 50 to 64 years. Hospital admissions declined by 39% (29 to 48) among those aged 60 to 69 years, 60% (54 to 66) among those aged 70 to 79 years, and 68% (62 to 73), among those aged 80 years and older, compared with adults aged 50 to 59 years. COVID-19 deaths also declined (by 41%, 95% CI –14 to 69 among adults aged 65–74 years and by 30%, –47 to 66 among those aged ≥75 years, compared with adults aged 50 to 64 years), but the magnitude of the impact of vaccination roll-out on deaths was unclear.InterpretationThe initial roll-out of the US COVID-19 vaccination programme was associated with reductions in COVID-19 cases, emergency department visits, and hospital admissions among older adults.FundingNone.

Unexplained mortality during the US COVID-19 pandemic: retrospective analysis of death certificate data and critical assessment of excess death calculations

ObjectivesCause-of-death discrepancies are common in respiratory illness-related mortality. A standard epidemiological metric, excess all-cause death, is unaffected by these discrepancies but provides no actionable policy information when increased all-cause mortality...

A Guide to Observable Differences in Stated Preference Evidence.

Background and ObjectiveIn health preference research, studies commonly hypothesize differences in parameters (i.e., differential or joint effects on attribute importance) and/or in choice predictions (marginal effects) by observable factors. Discrete choice experiments may be designed and conducted to test and estimate these observable differences. This guide covers how to explore and corroborate various observable differences in health preference evidence.MethodsThe analytical process has three steps: analyze the exploratory data, analyze the confirmatory data, and interpret and disseminate the evidence. In this guide, we demonstrate the process using dual samples (where exploratory and confirmatory samples were collected from different sources) on 2020 US COVID-19 vaccination preferences; however, investigators may apply the same approach using split samples (i.e., single source).ResultsThe confirmatory analysis failed to reject ten of the 17 null hypotheses generated by the exploratory analysis (p < 0.05). Apart from demographic, socioeconomic, and geographic differences, political independents and persons who have never been vaccinated against influenza are among those least likely to be vaccinated (0.838 and 0.872, respectively).ConclusionsFor all researchers in health preference research, it is essential to know how to identify and corroborate observable differences. Once mastered, this skill may lead to more complex analyses of latent differences (e.g., latent classes, random parameters). This guide concludes with six questions that researchers may ask themselves when conducting such analyses or reviewing published findings of observable differences.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40271-021-00551-x.

US COVID-19 Vaccine Regulatory and Safety Practices

US COVID-19 Vaccine Regulatory and Safety Practices

Racial and Ethnic Disparities in Rates of COVID-19–Associated Hospitalization, Intensive Care Unit Admission, and In-Hospital Death in the United States From March 2020 to February 2021

Racial and ethnic minority groups are disproportionately affected by COVID-19. To evaluate whether rates of severe COVID-19, defined as hospitalization, intensive care unit (ICU) admission, or in-hospital death, are higher among racial and ethnic minority groups compared with non-Hispanic White persons. This cross-sectional study included 99 counties within 14 US states participating in the COVID-19-Associated Hospitalization Surveillance Network. Participants were persons of all ages hospitalized with COVID-19 from March 1, 2020, to February 28, 2021. Laboratory-confirmed COVID-19-associated hospitalization, defined as a positive SARS-CoV-2 test within 14 days prior to or during hospitalization. Cumulative age-adjusted rates (per 100 000 population) of hospitalization, ICU admission, and death by race and ethnicity. Rate ratios (RR) were calculated for each racial and ethnic group compared with White persons. Among 153 692 patients with COVID-19-associated hospitalizations, 143 342 (93.3%) with information on race and ethnicity were included in the analysis. Of these, 105 421 (73.5%) were 50 years or older, 72 159 (50.3%) were male, 28 762 (20.1%) were Hispanic or Latino, 2056 (1.4%) were non-Hispanic American Indian or Alaska Native, 7737 (5.4%) were non-Hispanic Asian or Pacific Islander, 40 806 (28.5%) were non-Hispanic Black, and 63 981 (44.6%) were White. Compared with White persons, American Indian or Alaska Native, Latino, Black, and Asian or Pacific Islander persons were more likely to have higher cumulative age-adjusted rates of hospitalization, ICU admission, and death as follows: American Indian or Alaska Native (hospitalization: RR, 3.70; 95% CI, 3.54-3.87; ICU admission: RR, 6.49; 95% CI, 6.01-7.01; death: RR, 7.19; 95% CI, 6.47-7.99); Latino (hospitalization: RR, 3.06; 95% CI, 3.01-3.10; ICU admission: RR, 4.20; 95% CI, 4.08-4.33; death: RR, 3.85; 95% CI, 3.68-4.01); Black (hospitalization: RR, 2.85; 95% CI, 2.81-2.89; ICU admission: RR, 3.17; 95% CI, 3.09-3.26; death: RR, 2.58; 95% CI, 2.48-2.69); and Asian or Pacific Islander (hospitalization: RR, 1.03; 95% CI, 1.01-1.06; ICU admission: RR, 1.91; 95% CI, 1.83-1.98; death: RR, 1.64; 95% CI, 1.55-1.74). In this cross-sectional analysis, American Indian or Alaska Native, Latino, Black, and Asian or Pacific Islander persons were more likely than White persons to have a COVID-19-associated hospitalization, ICU admission, or in-hospital death during the first year of the US COVID-19 pandemic. Equitable access to COVID-19 preventive measures, including vaccination, is needed to minimize the gap in racial and ethnic disparities of severe COVID-19.