Initial economic damage from the COVID-19 pandemic in the United States is more widespread across ages and geographies than initial mortality impacts

Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic

Research on the COVID-19 pandemic in the United States has consistently found disproportionately high mortality among ethnoracial minorities, but reports differ with respect to the magnitude of mortality disparities and reach different conclusions regarding which groups were most impacted. We suggest that these variations stem from differences in the temporal scope of the mortality data used and difficulties inherent in measuring race and ethnicity. To circumvent these issues, we link Social Security Administration death records for 2010 through 2021 to decennial census and American Community Survey race and ethnicity responses. We use these linked data to estimate excess all-cause mortality for age-, sex-, race-, and ethnicity-specific subgroups and examine ethnoracial variation in excess mortality across states and over the course of the pandemic's first year. Results show that non-Hispanic American Indians and Alaska Natives experienced the highest excess mortality of any ethnoracial group in the first year of the pandemic, followed by Hispanics and non-Hispanic Blacks. Spatiotemporal and age-specific ethnoracial disparities suggest that the socioeconomic determinants driving health disparities prior to the pandemic were amplified and expressed in new ways in the pandemic's first year to disproportionately concentrate excess mortality among racial and ethnic minorities.

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems.

Socioeconomic Disparities In Mortality And Health Care Use During The COVID-19 Pandemic: Evidence From Germany

Across the globe, populations with low socioeconomic status have borne a disproportionate burden of the COVID-19 pandemic. This article examines the relationship between two socioeconomic factors (education and income) and all-cause mortality and health care use to improve understanding of the impact of the pandemic on socioeconomic disparities in Germany, a high-income country with a universal health care system. We used mortality rates from the period 2011-21 and hospitalizations from the period 2014-21. We examined rates of all-cause mortality and all hospital admissions as well as admissions for respiratory, emergency, cancer surgery, elective, and ambulatory care-sensitive care. Although the use of some health care services was affected by the pandemic, our findings suggest that Germany endured COVID-19 without amplifying socioeconomic disparities in all-cause mortality and large segments of inpatient utilization.

The Rates of Postmastectomy Immediate Breast Reconstruction during the Initial Months of the COVID-19 Pandemic

Background

Coronavirus disease 2019 (COVID-19) pandemic-related changes may have led to changes in immediate breast reconstruction (IBR) rates. We aimed to evaluate these changes before, during, and after the initial wave of COVID-19.

Methods

We retrospectively reviewed women who underwent mastectomy with or without IBR from January 1 to September 30, 2019 and from January 1 to September 30, 2020, and compared demographic, clinical, and surgical variables between defined time periods.

Results

A total of 202 mastectomies were included. Fewer patients underwent IBR during the initial surge of COVID-19 (surge period) compared with the months before (presurge period; 38.46% versus 70.97%, P = 0.0433). When comparing the postsurge period with a year before (postsurge control), fewer patients underwent reconstruction even after the initial surge had passed (53.13% versus 81.25%, P = 0.0007). Those who underwent IBR were older than the year before (59.34 versus 53.06, P = 0.0181). The median number of postoperative visits in the postsurge period was 8.50 (interquartile range: 6-12) compared with 14 (interquartile range: 8-20.50) in the year before (P = 0.0017). The overall incidences of complications and unanticipated resource utilization were also significantly lower in the postsurge period compared with the year before [5.88% versus 30.77% (P = 0.0055), and 14.71% versus 28.85% (P = 0.0103), respectively].

Conclusions

IBR rates were lower even after the initial surge than at the year before. Furthermore, during the pandemic, IBR patients were older, had fewer follow-up visits, and fewer reported complications.

Do We Learn From Errors? The Economic Impact of Differentiated Policy Restrictions in Italy

This paper investigates the economic impact of the three tiers risk framework implemented in Italy against the COVID-19 pandemic during the Autumn of 2020. Exploiting a large-scale dataset encompassing daily credit card transactions mediated by a large Italian bank, we estimate a set of panel event study models to disentangle the impact of restrictions with low, medium and high stringency levels in terms of consumption reduction. We show that space-time differentiated policies tend to produce stronger welfare losses for progressively more stringent restrictions in specific sectors targeted by these policies such as Retail and Restaurants. However, when we compare provinces implementing the same level of policy stringency, we show that territories with higher income per capita and larger concentration of manufacturing and service activities experience both significantly worse economic and epidemiological performances. Overall, our results suggest that policy makers should properly account for local socio-economic characteristics when designing tailored restrictions entailing an equal and homogeneous impact across territories.

A Preliminary Study of Stress, Mental Health, and Pain Related to the COVID-19 Pandemic and Odds of Persistent Prescription Opioid Use

BACKGROUND

The COVID-19 pandemic has been associated with increased opioid prescribing. It is not known if perceived COVID-19 related stress is associated with increased odds of long-term opioid use.

OBJECTIVE

To determine if greater COVID-19-related stress and worsening pain attributed to the pandemic was associated with LTOT over a 6-month observation period.

DESIGN

Longitudinal cohort.

PARTICIPANTS

Patients (n=477) from two midwestern health care systems, with any acute or chronic non-cancer pain, starting a new period of 30-90-day prescription opioid use, were invited to participate in the Prescription Opioids and Depression Pathways Cohort Study, a longitudinal survey study of pain, opioid use, and mental health outcomes.

MAIN MEASURES

Baseline and 6-month follow-up assessments were used to measure the association between perceived COVID-19 stressors, the perception that pain was made worse by the pandemic and the odds of persistent opioid use, i.e., remaining a prescription opioid user at 6-month follow-up. Multivariate models controlled for demographics, opioid dose, and change in pain characteristics, mental health measures, and social support.

KEY RESULTS

Participants were, on average, 53.9 (±11.4) years of age, 67.1% White race, and 70.9% female. The most frequently endorsed COVID-19 stressor was "worry about health of self/others" (85.7% endorsed) and the least endorsed was "worsened pain due to pandemic" (26.2%). After adjusting for all covariates, "worsened pain due to pandemic" (OR=2.88; 95%CI: 1.33-6.22), change in pain interference (OR=1.20; 95%CI: 1.04-1.38), and change in vital exhaustion (OR=0.90; 95%CI: 0.82-0.99) remained significantly associated with persistent opioid use.

CONCLUSIONS

Patients who attribute worsening pain to the COVID-19 pandemic are more likely to be persistent opioid users. Further research is warranted to identify mechanisms underlying this association. Clinicians may consider discussing pain in the context of the pandemic to identify patients at high risk for persistent opioid use.

Targeting COVID-19 vaccine-hesitancy in college students: An audience-centered approach

Objective: The study tested potential factors that differentiated the COVID-19 vaccine-hesitant and -inclined college students and, based on these factors, identified subgroups of the vaccine-hesitant students. Participants: Participants were 1,183 U.S. college students attending four-year universities or community colleges recruited through Qualtrics between January 25 and March 3, 2021. Methods: Participants completed an online survey assessing their COVID-19 vaccination intention, perceived risks of COVID-19 and the COVID-19 vaccines, efficacy beliefs regarding COVID-19 and the COVID-19 vaccines, and emotions toward taking the COVID-19 vaccines. Results: Vaccine-hesitant and -inclined college students varied in their emotions, risk perceptions, and efficacy beliefs regarding the virus and the vaccines. Using these factors as indicators, vaccine-hesitant college students were classified into five latent subgroups with distinct characteristics. Conclusions: In identifying subgroups of the vaccine-hesitant college students, the study has important insights to offer regarding the design of vaccine-promotion messaging strategies targeting the college student population.

The prevalence of metabolic conditions before and during the COVID-19 pandemic and its association with health and sociodemographic factors

BACKGROUND

There is a dearth of evidence on the relationship between COVID-19 and metabolic conditions among the general U.S. population. We examined the prevalence and association of metabolic conditions with health and sociodemographic factors before and during the COVID-19 pandemic.

METHODS

Data were drawn from the 2019 (N = 5,359) and 2020 (N = 3,830) Health Information National Trends Surveys on adults to compare observations before (2019) and during (2020) the COVID-19 pandemic. We conducted weighted descriptive and multivariable logistic regression analyses to assess the study objective.

RESULTS

During the pandemic, compared to pre-pandemic, the prevalence of diabetes (18.10% vs. 17.28%) has increased, while the prevalence of hypertension (36.38% vs. 36.36%) and obesity (34.68% vs. 34.18%) has remained similar. In general, the prevalence of metabolic conditions was higher during the pandemic (56.09%) compared to pre-pandemic (54.96%). Compared to never smokers, former smokers had higher odds of metabolic conditions (AOR = 1.38, 95% CI = 1.01, 1.87 and AOR = 1.57, 95% CI = 1.10, 2.25) before and during the pandemic, respectively. People with mild anxiety/depression symptoms (before: AOR = 1.52, 95% CI = 1.06, 2.19 and during: AOR = 1.55, 95% CI = 1.01, 2.38) had higher odds of metabolic conditions relative to those with no anxiety/depression symptoms.

CONCLUSION

This study found increased odds of metabolic conditions among certain subgroups of US adults during the pandemic. We recommend further studies and proper allocation of public health resources to address these conditions.

Pricing extreme mortality risk in the wake of the COVID-19 pandemic

In pricing extreme mortality risk, it is commonly assumed that interest rate and mortality rate are independent. However, the COVID-19 pandemic calls this assumption into question. In this paper, we employ a bivariate affine jump-diffusion model to describe the joint dynamics of interest rate and excess mortality, allowing for both correlated diffusions and joint jumps. Utilizing the latest U.S. mortality and interest rate data, we find a significant negative correlation between interest rate and excess mortality, and a much higher jump intensity when the pandemic experience is considered. Moreover, we construct a risk-neutral pricing measure that accounts for both diffusion and jump risk premia, and we solve for the market prices of risk based on mortality bond prices. Our results show that the pandemic experience can drastically change investors' perception of the mortality risk market in the post-pandemic era.

A Hitchhiker's Guide to Worldwide COVID-19 Vaccinations: A Detailed Review of Monovalent and Bivalent Vaccine Schedules, COVID-19 Vaccine Side Effects, and Effectiveness Against Omicron and Delta Variants

For the primary prevention of coronavirus disease 2019 (COVID-19), there are currently four different vaccines available in the USA. These are Pfizer (messenger RNA [mRNA]), Moderna (mRNA), Novavax (recombinant protein), and Jansen/Johnson & Johnson (adenoviral vector). All individuals should get vaccinated, and the Centers for Disease Control and Prevention (CDC) has provided comprehensive guidelines on recommended doses, their frequency by age group, and vaccine types, all discussed in detail in this article. Vaccines are a critical and cost-effective tool for preventing the disease. Prior to receiving a vaccine, patients should get adequate counseling regarding any potential adverse effects post vaccination. Appropriate safety precautions must be taken for those more likely to experience adverse consequences. Healthcare professionals should be aware of the symptoms, indicators, and treatment of any adverse event post-vaccination. We have provided a comprehensive review of the different characteristics of COVID-19 vaccines available in the United States, including their effectiveness against various variants, adverse effects, and precautions necessary for healthcare professionals and the general population. This article also briefly covers COVID-19 vaccines available worldwide, specifically their mode of action and effectiveness.

The relationship between health worry, work distress, and affective symptoms during the COVID-19 pandemic: The mediating role of hopelessness and helplessness

OBJECTIVES

The COVID-19 pandemic presented both serious health threats and economic hardships, which were reflected in increased rates of mood and anxiety symptoms. We examined two separate distress domains, health worries and work distress, as predictors of mood and anxiety symptoms. Additionally, we considered whether these two domains might be uniquely associated with the development of dysfunctional beliefs, as a proposed mechanism to account for increased symptoms during the pandemic. Two separate models were considered to examine if associations remained stable through the first year of the pandemic.

METHODS

Participants (N = 2152) were a representative sample of Florida adults. They completed online surveys at three waves: Wave 1 (April-May 2020), Wave 2 (May-June 2020), and Wave 3 (December-February 2021). Participants completed measures of COVID-19 health worry and work distress, anxiety, and depression. They also reported their level of hopelessness and helplessness (indices of dysfunctional beliefs).

RESULTS

In an early pandemic model (Wave 1-Wave 2), health worry directly and indirectly predicted anxiety and depression via dysfunctional beliefs. In contrast, work distress only indirectly predicted both outcomes. In a longer-term model (Wave 2-Wave 3), health worry had direct and indirect effects on downstream anxiety but not depression. Pandemic work distress had no effect on depression or dysfunctional beliefs; however, it was associated with less anxiety.

CONCLUSIONS

Although health worry and work distress predicted later symptoms of anxiety and depression, they appeared to operate through different pathways. These findings provide guidance for the development of more effective interventions to reduce the impact of pandemics.

Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies

While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties. Understanding the transmission process and quantifying the costs of local policies are essential to the task of tackling this challenge. Here, we investigate the dynamic contact patterns of the populations from anonymized, geo-localized mobility data and census and demographic data to create data-driven, agent-based contact networks. We then simulate the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States and evaluate a combination of mobility reduction, mask use, and reopening policies. We find that our model captures the spatial-temporal and heterogeneous case trajectory within various counties based on dynamic population behaviors. Our results show that a decision-making tool that considers both economic cost and infection outcomes of policies can be informative in making decisions of local containment strategies for optimal balancing of economic slowdown and virus spread.

Subjective socioeconomic disadvantage is indirectly associated with food portion selection through perceived disruption of personal resources during a nationwide COVID-19 stay-at-home order

In addition to its public health implications, the global COVID-19 pandemic has also produced significant disruptions to individuals' socioeconomic resources and opportunities. Prior research has suggested that low subjective socioeconomic status (SSES) may stimulate appetite and motivate increased energy intake. Here, we tested whether individuals experiencing lower levels of SSES (SSES disadvantage) during a nationwide stay-at-home order for COVID-19 exhibited preferences for larger food portion sizes through perceived disruptions to personal financial and material resources. Data was collected near the conclusion of a nationwide partial lockdown (Singapore's “Circuit-Breaker” from April to June 2020). Participants (N = 295) completed an online survey involving a measure of SSES, the Coronavirus Impacts Questionnaire, and a food portion selection task where participants estimated the portion size they prefer to consume for a range of common foods. SSES disadvantage was associated with selection of smaller average portion sizes. Yet, a significant indirect effect of coronavirus impact was observed in this relationship, such that participants experiencing greater SSES disadvantage selected larger portion sizes through the effect of greater perceived impacts of COVID-19 to one's financial/material resources (controlling for one's actual level of income). These findings further support the idea that perceived deprivation and insecurity of important resources (financial, social, material) may influence intentions to consume greater amounts of energy. Consequently, systematic societal disruptions to such resources may reinforce and perpetuate potentially obesogenic eating behaviors of populations that are especially vulnerable to such shocks (i.e., people experiencing SSES disadvantage).

Viability of compact cities in the post-COVID-19 era: subway ridership variations in Seoul Korea

COVID-19 exposed the vulnerability of compact cities against shock events. As the impact of COVID-19 not only persists, but also expands throughout the world, this study questions whether the compact city model would be sustainable in the post-COVID-19 era. As such, this study examines the dynamics among major COVID-19 outbreak events, government interventions, and subway ridership in two compact cities, Seoul and New York City. Then, to gain thorough understanding of the impact of risks on compact urban form, it narrows the scope to Seoul in comparing subway ridership patterns in 2019 and 2020, and identifying characteristics that affect the volatility of subway ridership levels. The results affirm that individual mobility, COVID-19 outbreaks, and government interventions are closely related, and reveal that the extent of social distancing measures in compact cities is limited. This finding aligns with existing literature that link diseases transmission with dense population and mixed land use, accentuating the vulnerability of the compact city model against shocks. As a result, a multidimensional urban planning approach that incorporates polycentric and decentralized urban form is recommended to effectively and sustainably control disease outbreaks in compact cities.

Population perspectives and demographic methods to strengthen CRVS systems: introduction

Civil registration and vital statistics (CRVS) systems and legal identity systems have become increasingly recognized as catalytic both for inclusive development and for monitoring population dynamics spanning the entire life course. Population scientists have a long history of contributing to the strengthening of CRVS and legal identity systems and of using vital registration data to understand population and development dynamics. This paper provides an overview of the Genus thematic series on CRVS systems. The series spans 11 research articles that document new insights on the registration of births, marriages, separations/divorces, deaths and legal residency. This introductory article to the series reviews the importance of population perspectives and demographic methods in strengthening CRVS systems and improving our understanding of population dynamics across the lifecourse. The paper highlights the major contributions from this thematic series and discusses emerging challenges and future research directions on CRVS systems for the population science community.

The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China

The COVID-19 pandemic has brought unprecedented disruptions to many industries, and the transportation industry is among the most disrupted ones. We seek to address, in the context of a ride-sharing platform, the response of drivers to the pandemic and the post-pandemic recovery. We collected comprehensive trip data from one of the leading ride-sharing companies in China from September 2019 to August 2020, which cover pre-, during-, and post-pandemic phases in three major Chinese cities, and investigate the causal effect of the COVID-19 pandemic on driver behavior. We find that drivers only slightly reduce their number of shifts in response to increased COVID-19 cases, likely because they have to make a living from providing ride-sharing services. Nevertheless, conditional on working, drivers exhibit strong risk aversion: As the number of new cases increases, drivers strategically adjust the scope of their search for passengers, complete fewer trips, and as a result, make lower daily earnings. Finally, our heterogeneity analyses indicate that the effects appear to vary both across drivers and over time, with generally stronger effects on drivers who are older, more experienced, more active before the pandemic, and higher-status within the firm. Our findings have strong policy implications: These drivers tend to contribute more to the focal company, and also rely more on providing ride-sharing services to make a living. Therefore, they should be prioritized in stimulus plans offered by the government or the ride-sharing company.

Nafamostat in hospitalized patients with moderate to severe COVID-19 pneumonia: a randomised Phase II clinical trial

BACKGROUND

Nafamostat, a serine protease inhibitor, has been used for the treatment of disseminated intravascular coagulation and pancreatitis. In vitro studies and clinical reports suggest its beneficial effect in the treatment of COVID-19 pneumonia.

METHODS

This phase 2 open-label, randomised, multicentre, controlled trial evaluated nafamostat (4.8 mg/kg/day) plus standard-of-care (SOC) in hospitalised patients with COVID-19 pneumonia (i.e., those requiring nasal high-flow oxygen therapy and/or non-invasive mechanical ventilation). The primary outcome was the time to clinical improvement. Key secondary outcomes included the time to recovery, rates of recovery and National Early Warning Score (NEWS). The trial is registered with ClinicalTrials.gov Identifier: NCT04623021.

FINDINGS

A total of 104 patients, mean age 58.6 years were enrolled in 13 clinical centres in Russia between 25/9/2020 and 14/11/2020 and randomised to nafamostat plus SOC (n=53) or SOC alone (n=51). There was no significant difference in time to clinical improvement (primary endpoint) between the nafamostat and SOC groups (median 11 [interquartile range (IQR) 9 to 14) vs 11 [IQR 9 to 14] days; Rate Ratio [RR; the ratio for clinical improvement], 1.00; 95% CI, 0.65 to 1.57; p=0.953). In 36 patients with baseline NEWS ≥7, nafamostat was superior to SOC alone in median time to clinical improvement (11 vs 14 days; RR, 2.89; 95% CI, 1.17 to 7.14; p=0.012). Patients receiving nafamostat in this subgroup had a significantly higher recovery rate compared with SOC alone (61.1% (11/18) vs 11.1 % (2/18) by Day 11, p=0.002). The 28-day mortality was 1.9% (1/52) for nafamostat and 8.0% (4/50) for SOC (95% CI, -17.0 to 3.4; p=0.155). No case of COVID-19 related serious adverse events leading to death was recorded in the patients receiving nafamostat.

INTERPRETATION

Our study found no significant difference in time to clinical improvement between the nafamostat and SOC groups, but a shorter median time to clinical improvement in a small group of high-risk COVID-19 patients requiring oxygen treatment. To assess the efficacy further, a larger Phase 3 clinical trial is warranted.

FUNDING

Korea Research Institute of Bioscience and Biotechnology [2020M3A9H5108928] and Chong Kun Dang (CKD) Pharm (Seoul, Korea).

Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents' behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs' effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.

Socioeconomic differences and persistent segregation of Italian territories during COVID-19 pandemic

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.

The economic burden of COVID-19 in the United States: Estimates and projections under an infection-based herd immunity approach

OBJECTIVES

To assess the economic burden of COVID-19 that would arise absent behavioral or policy responses under the herd immunity approach in the United States and compare it to the total burden that also accounts for estimates of the value of lives lost.

METHODS

We use the trajectories of age-specific human and physical capital in the production process to calculate output changes based on a human capital-augmented production function. We also calculate the total burden that results when including the value of lives lost as calculated from mortality rates of COVID-19 and estimates for the value of a statistical life in the United States based on studies assessing individual's willingness to pay to avoid risks.

RESULTS

Our results indicate that the GDP loss associated with unmitigated COVID-19 would amount to a cumulative US$1.4 trillion by 2030 assuming that 60 percent of the population is infected over three years. This is equivalent to around 7.7 percent of GDP in 2019 (in constant 2010 US$) or an average tax on yearly output of 0.6 percent. After applying the value of a statistical life to account for the value of lives lost, our analyses show that the total burden can mount to between US$17 and 94 trillion over the next decade, which is equivalent to an annual tax burden between 8 and 43 percent.

CONCLUSION

Our results show that the United States would incur a sizeable burden if it adopted a non-interventionist herd immunity approach.

FUNDING

Research reported in this paper was supported by the Alexander von Humboldt Foundation, the Bill & Melinda Gates Foundation (Project INV-006261), and the Sino-German Center for Research Promotion (Project C-0048), which is funded by the German Research Foundation (DFG) and the National Natural Science Foundation of China (NSFC). Preparation of this article was also supported by the Value of Vaccination Research Network (VoVRN) through a grant from the Bill & Melinda Gates Foundation (Grant OPP1158136). The content is solely the responsibility of the authors.

COVID-19 Outbreak Management and Vaccination Strategy in The United States of America

Four months after the first case of COVID-19 was reported in the United States, the SARS-CoV-2 virus had spread to more than 90% of all counties. Although the transmission of the virus can be grossly mitigated through non-pharmaceutical interventions and public health measures, risks of future outbreaks, emergence of more infectious variants, and disruptions to socio-economic life will probably remain until effective vaccines are administered to large portions of the global population. An exceptional collaboration between governments and the scientific community has led to the authorization of eight vaccines globally for full use, four of which were funded and developed in the United States. In this paper, we contextualize epidemiological, political, and economic impacts of the COVID-19 vaccination strategy in the United States of America between 20 January 2020, to 5 May 2021, with a key focus on vaccine hesitancy and public-private partnerships.

Impacts of COVID-19 lockdowns and stimulus payments on low-income population's spending in the United States

The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments on this population's spending along the inter-temporal, geo-spatial, and cross-categorical dimensions. Leveraging the difference-in-differences analyses at the per card and zip code levels, we uncover three key findings. (1) Inter-temporally, the state lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code. (2) Spatial heterogeneity prevailed: Democratic zip codes displayed much more volatile dynamics, with an initial decline three times that of Republican zip codes, followed by a higher rebound and a net gain after the stimulus payments; also, Southwest exhibited the highest initial decline whereas Southeast had the largest net gain after the stimulus payments. (3) Across 26 categories, the stimulus payments promoted spending in those categories that enhanced public health and charitable donations, reduced food insecurity and digital divide, while having also stimulated non-essential and even undesirable categories, such as liquor and cigar. In addition, spatial association analysis was employed to identify spatial dependency and local hot spots of spending changes at the county level. Overall, these analyses reveal the imperative need for more geo- and category-targeted stimulus programs, as well as more effective and strategic policy communications, to protect and promote the well-being of the low-income population during public health and economic crises.

Brief exposure of skin to near-infrared laser augments early vaccine responses

Rapid establishment of herd immunity with vaccination is effective to combat emerging infectious diseases. Although the incorporation of adjuvant and intradermal (ID) injection could augment early responses to the vaccine, the current chemical or biological adjuvants are inappropriate for this purpose with their side effects and high reactogenicity in the skin. Recently, a near-infrared (NIR) laser has been shown to augment the immune response to ID vaccination and could be alternatively used for mass vaccination programs. Here, we determined the effect of NIR laser as well as licensed chemical adjuvants on the immunogenicity 1, 2, and 4 weeks after ID influenza vaccination in mice. The NIR laser adjuvant augmented early antibody responses, while the widely used alum adjuvant induced significantly delayed responses. In addition, the oil-in-water and alum adjuvants, but not the NIR laser, elicited escalated T_H2 responses with allergenic immunoglobulin E (IgE) responses. The effect of the NIR laser was significantly suppressed in the basic leucine zipper transcription factor ATF-like 3 (Batf3) knockout mice, suggesting a critical role of the cluster of differentiation 103⁺ (CD103)⁺ dendritic cells. The current preliminary study suggests that NIR laser adjuvant is an alternative strategy to chemical and biological agents to timely combat emerging infectious diseases. Moreover, its immunomodulatory property could be used to enhance the efficacy of immunotherapy for allergy and cancer.

COVID-19 Vaccination Acceptance and Hesitancy among Healthcare Workers in Germany

Vaccination hesitancy is a threat to herd immunity. Healthcare workers (HCWs) play a key role in promoting Coronavirus disease 2019 (COVID-19) vaccination in the general population. We therefore aimed to provide data on COVID-19 vaccination acceptance/hesitancy among German HCWs. For this exploratory, cross-sectional study, an online survey was conducted in February 2021. The survey included 54 items on demographics; previous vaccination behavior; trust in vaccines, physicians, the pharmaceutical industry and health politics; fear of adverse effects; assumptions regarding the consequences of COVID-19; knowledge about vaccines; and information seeking behavior. Odds ratios with 95% confidence intervals were calculated and chi-square tests were performed. Four thousand five hundred surveys were analyzed. The overall vaccination acceptance was 91.7%. The age group ≤20 years showed the lowest vaccination acceptance. Factors associated with vaccination hesitancy were lack of trust in authorities and pharmaceutical companies. Attitudes among acquaintances were associated with vaccination hesitancy too. Participants with vaccination hesitancy more often obtained information about COVID-19 vaccines via messenger services or online video platforms and underperformed in the knowledge test. We found high acceptance amongst German HCWs. Several factors associated with vaccination hesitancy were identified which could be targeted in HCW vaccination campaigns.

Mobility-based real-time economic monitoring amid the COVID-19 pandemic

Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries' economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities.

Mental Health Status, Life Satisfaction, and Mood State of Elite Athletes During the COVID-19 Pandemic: A Follow-Up Study in the Phases of Home Confinement, Reopening, and Semi-Lockdown Condition

Scientific reports notified that the pandemic caused by the Coronavirus disease 2019 (COVID-19) has raised an unprecedented mental health emergency worldwide. Abrupt changes in daily routine, environmental constraints, adopted home confinement measures, and uncertainty about a date for returning to usual activities can potentially affect mental health and sports activities in athletes. Hence, we designed a cross-sectional study with a within-subjects design to investigate the impact of the pandemic on mental health, mood states, and life satisfaction of elite athletes. During the three phases of home confinement (April 14-24, n = 525), reopening (May 9-19, n = 464), and current semi-lockdown (July 20-31, n = 428), elite athletes voluntarily responded to an online survey. The self-report questionnaire was prepared to collect demographic and epidemiological variables of interest and the COVID-19-related information. All participants also completed the Profile of Mood State (POMS), General Health Questionnaire-28 (GHQ-28), and Satisfaction with Life Scale (SWLS). The main result is that the training rate, mental health, life satisfaction, and positive mood have decreased during the home confinement period as compared with the reopening and semi-lockdown phases. However, the need for psychosocial services has increased during the pandemic period. The present study provides the first preliminary evidence that home confinement conditions during the COVID-19 pandemic might have negatively influenced elite athlete's mood state, mental health, and life satisfaction, as well as training rates. Monitoring the psychological parameters of elite athletes and developing strategies to improve their mental health during the COVID-19 pandemic should be on the agenda. Next studies, therefore, seem reasonable to focus on active interventions for athletes during the ongoing COVID-19 pandemic.

International Wildlife Trafficking: A perspective on the challenges and potential forensic genetics solutions

International wildlife trafficking (IWT) is a thriving and pervasive illegal enterprise that adversely affects modern societies. Yet, despite being globally recognized as a threat to biodiversity, national security, economy, and biosecurity, IWT remains largely unabated and is proliferating at an alarming rate. The increase in IWT is generally attributed to a lack of prioritization to curb wildlife crime through legal and scientific infrastructure. This review: (1) lays out the damaging scope and influence of IWT; (2) discusses the potential of DNA marker systems, barcodes, and emerging molecular technologies, such as long-read portable sequencing, to facilitate rapid, in situ identification of species and individuals; and (3) encourages initiatives that promote quality and innovation. Interdisciplinary collaboration promises to be one of the most effective ways forward to surmounting the complex scientific and legal challenges posed by IWT.

Identifying US County-level characteristics associated with high COVID-19 burden

BACKGROUND

Identifying county-level characteristics associated with high coronavirus 2019 (COVID-19) burden can help allow for data-driven, equitable allocation of public health intervention resources and reduce burdens on health care systems.

METHODS

Synthesizing data from various government and nonprofit institutions for all 3142 United States (US) counties, we studied county-level characteristics that were associated with cumulative and weekly case and death rates through 12/21/2020. We used generalized linear mixed models to model cumulative and weekly (40 repeated measures per county) cases and deaths. Cumulative and weekly models included state fixed effects and county-specific random effects. Weekly models additionally allowed covariate effects to vary by season and included US Census region-specific B-splines to adjust for temporal trends.

RESULTS

Rural counties, counties with more minorities and white/non-white segregation, and counties with more people with no high school diploma and with medical comorbidities were associated with higher cumulative COVID-19 case and death rates. In the spring, urban counties and counties with more minorities and white/non-white segregation were associated with increased weekly case and death rates. In the fall, rural counties were associated with larger weekly case and death rates. In the spring, summer, and fall, counties with more residents with socioeconomic disadvantage and medical comorbidities were associated greater weekly case and death rates.

CONCLUSIONS

These county-level associations are based off complete data from the entire country, come from a single modeling framework that longitudinally analyzes the US COVID-19 pandemic at the county-level, and are applicable to guiding government resource allocation policies to different US counties.

Computational Mutagenesis at the SARS-CoV-2 Spike Protein/Angiotensin-Converting Enzyme 2 Binding Interface: Comparison with Experimental Evidence

The coronavirus disease-2019 (COVID-19) pandemic, caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), started in China during late 2019 and swiftly spread worldwide. Since COVID-19 emergence, many therapeutic regimens have been relentlessly explored, and although two vaccines have just received emergency use authorization by different governmental agencies, antiviral therapeutics based neutralizing antibodies and small-drug inhibitors can still be vital viable options to prevent and treat SARS-CoV-2 infections. The viral spike glycoprotein (S-protein) is the key molecular player that promotes human host cellular invasion via recognition of and binding to the angiotensin-converting enzyme 2 gene (ACE2). In this work, we report the results obtained by mutating in silico the 18 ACE2 residues and the 14 S-protein receptor binding domain (S-RBD_CoV-2) residues that contribute to the receptor/viral protein binding interface. Specifically, each wild-type protein-protein interface residue was replaced by a hydrophobic (isoleucine), polar (serine and threonine), charged (aspartic acid/glutamic acid and lysine/arginine), and bulky (tryptophan) residue, respectively, in order to study the different effects exerted by nature, shape, and dimensions of the mutant amino acids on the structure and strength of the resulting binding interface. The computational results were next validated a posteriori against the corresponding experimental data, yielding an overall agreement of 92%. Interestingly, a non-negligible number of mis-sense variations were predicted to enhance ACE2/S-RBD_CoV-2 binding, including the variants Q24T, T27D/K/W, D30E, H34S7T/K, E35D, Q42K, L79I/W, R357K, and R393K on ACE2 and L455D/W, F456K/W, Q493K, N501T, and Y505W on S-RBD_CoV-2, respectively.

Association of Human Mobility Restrictions and Race/Ethnicity-Based, Sex-Based, and Income-Based Factors With Inequities in Well-being During the COVID-19 Pandemic in the United States

Importance

An accurate understanding of the distributional implications of public health policies is critical for ensuring equitable responses to the COVID-19 pandemic and future public health threats.

Objective

To identify and quantify the association of race/ethnicity-based, sex-based, and income-based inequities of state-specific lockdowns with 6 well-being dimensions in the United States.

Design, Setting, and Participants

This pooled, repeated cross-sectional study used data from 14 187 762 households who participated in phase 1 of the population-representative US 2020 Household Pulse Survey (HPS). Households were invited to participate by email, text message, and/or telephone as many as 3 times. Data were collected via an online questionnaire from April 23 to July 21, 2020, and participants lived in all 50 US states and the District of Columbia.

Exposures

Indicators of race/ethnicity, sex, and income and their intersections.

Main Outcomes and Measures

Unemployment; food insufficiency; mental health problems; no medical care received for health problems; default on last month's rent or mortgage; and class cancellations with no distance learning. Race/ethnicity, sex, income, and their intersections were used to measure distributional implications across historically marginalized populations; state-specific, time-varying population mobility was used to measure lockdown intensity. Logistic regression models with pooled repeated cross-sections were used to estimate risk of dichotomous outcomes by social group, adjusted for confounding variables.

Results

The 1 088 314 respondents (561 570 [51.6%; 95% CI, 51.4%-51.9%] women) were aged 18 to 88 years (mean [SD], 51.55 [15.74] years), and 826 039 (62.8%; 95% CI, 62.5%-63.1%) were non-Hispanic White individuals; 86 958 (12.5%; 95% CI, 12.4%-12.7%), African American individuals; 86 062 (15.2%; 95% CI, 15.0%-15.4%), Hispanic individuals; and 50 227 (5.6%; 95% CI, 5.5%-5.7%), Asian individuals. On average, every 10% reduction in mobility was associated with higher odds of unemployment (odds ratio [OR], 1.3; 95% CI, 1.2-1.4), food insufficiency (OR, 1.1; 95% CI, 1.1-1.2), mental health problems (OR, 1.04; 95% CI, 1.0-1.1), and class cancellations (OR, 1.1; 95% CI, 1.1-1.2). Across most dimensions compared with White men with high income, African American individuals with low income experienced the highest risks (eg, food insufficiency, men: OR, 3.3; 95% CI, 2.8-3.7; mental health problems, women: OR, 1.9; 95% CI, 1.8-2.1; medical care inaccessibility, women: OR, 1.7; 95% CI, 1.6-1.9; unemployment, men: OR, 2.8; 95% CI, 2.5-3.2; rent/mortgage defaults, men: OR, 5.7; 95% CI, 4.7-7.1). Other high-risk groups were Hispanic individuals (eg, unemployment, Hispanic men with low income: OR, 2.9; 95% CI, 2.5-3.4) and women with low income across all races/ethnicities (eg, medical care inaccessibility, non-Hispanic White women: OR, 1.8; 95% CI, 1.7-2.0).

Conclusions and Relevance

In this cross-sectional study, African American and Hispanic individuals, women, and households with low income had higher odds of experiencing adverse outcomes associated with the COVID-19 pandemic and stay-at-home orders. Blanket public health policies ignoring existing distributions of risk to well-being may be associated with increased race/ethnicity-based, sex-based, and income-based inequities.

Four propositions on integrated sustainability: toward a theoretical framework to understand the environment, peace, and sustainability nexus

The sustainability agenda has evolved around a set of interconnected dilemmas regarding economic, social, and environmental goals. Progress has been made in establishing thresholds and targets that must be achieved to enable life to continue to thrive on the planet. However, much work remains to be done in articulating coherent theoretical frameworks that adequately describe the mechanisms through which sustainability outcomes are achieved. This paper reviews core concepts in the sustainability agenda to develop four propositions on integrated sustainability that collectively describe the underlying mechanisms of sustainable development. We then advance a framework for integrated sustainability and assess its viability through linear regression and principal components analysis of key selected indicators. The results provide preliminary evidence that countries with institutions that enable cooperation and regulate competition perform better in attaining integrated sustainability indicators. Our findings suggest that institutional design is important to sustainability outcomes and that further research into process-oriented mechanisms and institutional characteristics can yield substantial dividends in enabling effective sustainability policy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11625-021-00925-y.

Racial Disparities In Excess All-Cause Mortality During The Early COVID-19 Pandemic Varied Substantially Across States

The impact of the coronavirus disease 2019 (COVID-19) pandemic has been starkly unequal across race and ethnicity. We examined the geographic variation in excess all-cause mortality by race and ethnicity to better understand the impact of the pandemic. We used individual-level administrative data on the US population between January 2011 and April 2020 to estimate the geographic variation in excess all-cause mortality by race and Hispanic origin. All-cause mortality allows a better understanding of the overall impact of the pandemic than mortality attributable to COVID-19 directly. Nationwide, adjusted excess all-cause mortality during that period was 6.8 per 10,000 for Black people, 4.3 for Hispanic people, 2.7 for Asian people, and 1.5 for White people. Nationwide averages mask substantial geographic variation. For example, despite similar excess White mortality, Michigan and Louisiana had markedly different excess Black mortality, as did Pennsylvania compared with Rhode Island. Wisconsin experienced no significant White excess mortality but had significant Black excess mortality. Further work understanding the causes of geographic variation in racial and ethnic disparities-the relevant roles of social and environmental factors relative to comorbidities and of the direct and indirect health effects of the pandemic-is crucial for effective policy making.

Identifying US Counties with High Cumulative COVID-19 Burden and Their Characteristics

Identifying areas with high COVID-19 burden and their characteristics can help improve vaccine distribution and uptake, reduce burdens on health care systems, and allow for better allocation of public health intervention resources. Synthesizing data from various government and nonprofit institutions of 3,142 United States (US) counties as of 12/21/2020, we studied county-level characteristics that are associated with cumulative case and death rates using regression analyses. Our results showed counties that are more rural, counties with more White/non-White segregation, and counties with higher percentages of people of color, in poverty, with no high school diploma, and with medical comorbidities such as diabetes and hypertension are associated with higher cumulative COVID-19 case and death rates. We identify the hardest hit counties in US using model-estimated case and death rates, which provide more reliable estimates of cumulative COVID-19 burdens than those using raw observed county-specific rates. Identification of counties with high disease burdens and understanding the characteristics of these counties can help inform policies to improve vaccine distribution, deployment and uptake, prevent overwhelming health care systems, and enhance testing access, personal protection equipment access, and other resource allocation efforts, all of which can help save more lives for vulnerable communities.

SIGNIFICANCE STATEMENT

We found counties that are more rural, counties with more White/non-White segregation, and counties with higher percentages of people of color, in poverty, with no high school diploma, and with medical comorbidities such as diabetes and hypertension are associated with higher cumulative COVID-19 case and death rates. We also identified individual counties with high cumulative COVID-19 burden. Identification of counties with high disease burdens and understanding the characteristics of these counties can help inform policies to improve vaccine distribution, deployment and uptake, prevent overwhelming health care systems, and enhance testing access, personal protection equipment access, and other resource allocation efforts, all of which can help save more lives for vulnerable communities.

Rural-urban differences in the labor-force impacts of COVID-19 in the United States

COVID-19 has had dramatic impacts on economic outcomes across the United States, yet most research on the pandemic's labor-market impacts has had a national or urban focus. We overcome this limitation using data from the U.S. Current Population Survey's COVID-19 supplement to study pandemic-related labor-force outcomes in rural and urban areas from May 2020 through February 2021. We find the pandemic has generally had more severe labor-force impacts on urban adults than their rural counterparts. Urban adults were more often unable to work, go unpaid for missed hours, and be unable to look for work due to COVID-19. However, rural workers were less likely to work remotely than urban workers. These differences persist even when adjusting for adults' socioeconomic characteristics and state-level factors. Our results suggest rural-urban differences in the nature of work during the pandemic cannot be explained by well-known demographic and political differences between rural and urban America.