Since 2013, the Robert Wood Johnson Foundation (RWJF) has led the development of a pioneering national action framework to advance a "culture that enables all in our diverse society to lead healthier lives now and for generations to come." Accomplishing these principles requires a national paradigm shift from a traditionally disease and health care-centric view of health toward one that focuses on well-being. Recognizing that paradigm shifts require intentional actions, RWJF worked with RAND researchers to design an actionable path to fulfill the Culture of Health (CoH) vision. A central piece of this work is the development of measures to assess constructs underlying a CoH. The National Survey of Health Attitudes (NSHA) is a survey that RWJF and RAND analysts developed and conducted as part of the foundation's CoH strategic framework. The foundation undertook this survey to measure key constructs that could not be measured in other data sources. Thus, the survey was not meant to capture the full action framework that informs CoH, but rather just selected measure areas. The questions in this survey primarily addressed the action area: making health a shared value. The survey covers a variety of topics, including views regarding what factors influence health, such as the notion of health interdependence (peer, family, neighborhood, and workplace drivers of health), values related to national and community investment for health and well-being; behaviors around health and well-being, including civic engagement on behalf of health, and the role of community engagement and sense of community in relation to health attitudes and values. This study includes the results from the 2023 RWJF National Survey of Health Attitudes. The 2023 survey is the third wave of the NSHA. The first wave was conducted in 2015 (ICPSR 37405) and the second wave in 2018 (ICPSR 37633). The 2023 report complements the overview of the 2015 survey described in the RAND report Development of the Robert Wood Johnson Foundation National Survey of Health Attitudes (Carman et al., 2016), and its subsequent topline 2018 Survey of National Health Attitudes: Description and Top-Line Summary (Carman et al., 2019) and is organized similarly for consistency. A companion set of longitudinal surveys during the COVID-19 pandemic was fielded between 2020 and 2021 and is further described in four top-line reports, COVID-19 and the Experiences of Populations at Greater Risk (Carman et al., 2020-2021). The questions in the 2023 survey uniquely capture aspects of American mindset about health, health equity, structural racism, and wellbeing in ways that are not present in other surveys. This version of the NSHA can be viewed in three main sections: (1) individual health experiences, perspectives, and knowledge (making health a shared value); (2) health equity perspectives; and (3) community wellbeing, including climate views and barriers to community engagement. Insights from the surveys referenced above, including this one, have established a baseline and set of cross-sectional pulse checks on where the American public is regarding their recognition of social determinants of health, their understanding of health inequities including structural racism, their willingness to address those inequities and their indication of who in society should be responsible for solving health inequities.

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

This preregistration documents an experimental intervention implemented in the second and third waves of a three-wave panel survey measuring public opinion about COVID-19 and the novel coronavirus in the United Kingdom (specifically Great Britain). (This study also includes observational survey data and data on web browsing behavior that will be analyzed as descriptive/exploratory rather than confirmatory.)

Respondents will be randomly assigned to read four articles adapted from U.S. and U.K. fact-checkers debunking four myths about COVID-19 and the novel coronavirus in wave 2 (independent random assignment; probability .5) and/or wave 3 (independent random assignment; probability .5). As a result, they will be exposed to the fact-check articles in wave 2, wave 3, both waves, or neither wave. Respondents who do not receive the fact-check articles in a given wave are instead shown four placebo articles.

This preregistration, which describes the analysis of our experiment, was written with the wave 1 baseline data (which did not include any experimental manipulations) in hand.

North America General Aviation Market Outlook 2031

The North America General Aviation Market size was valued at USD 17 Billion in 2022 and is likely to reach USD 21.17 Billion by 2031 expanding at a CAGR of 2.47 % during the forecast period, 2023 – 2031. General aviation includes usage of various aircrafts and jets by mass for travelling to one place to another. The Covid-19 pandemic affected the demand and supply chain of North America general aviation market.



Between Q2 2020 and Q1 2021, the US faced considerable issues as a result of the COVID-19 pandemic, particularly in the commercial and general aviation sectors. According to IATA, North American airlines' full-year passenger traffic plummeted 75.4% in 2019 compared to the previous year. In 2020, the closedown of international borders and local borders resulted in disruption in the general aviation market.

North America General Aviation Market Trends, Drivers, Restraints, and Opportunities

• The regulatory authorities' increased attention on operational safety, as well as ongoing advancements targeted at building light sports aircraft is projected to drive the market growth.


• Technology breakthroughs in avionics systems, new product introductions, and long-term procurement and service agreements are some factors that can spur the market.


• The presence of routes and charter services is fostering to spur the market growth.


• The third wave of COVID-19 pandemic can create challenges for the market.


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BackgroundThe South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead.MethodsOur tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary.ResultsGiven the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely.ConclusionThe SACMC’s models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

Incidence-Rate Ratios (IRR) & Average Marginal Effect (AME).

We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from USAFacts (usafacts.org). We considered Mar 25 to Jun 3 as the “1st wave”, Jun 4 to Sep 2 as the “2nd wave”, and Sep 3 to Nov 12 as the “3rd wave” of COVID-19. For the 2nd and 3rd waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.

Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the University of Washington Global Health Data Exchange (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).

This is a Phase 1/2/3, randomized, placebo-controlled, observer-blind, dose-finding, vaccine candidate-selection, and efficacy study in healthy individuals.

The study consists of 2 parts: Phase 1: to identify preferred vaccine candidate(s) and dose level(s); Phase 2/3: an expanded cohort and efficacy part.

The study will evaluate the safety, tolerability, and immunogenicity of 3 different SARS-CoV-2 RNA vaccine candidates against COVID-19 and the efficacy of 1 candidate:

• As a 2-dose (separated by 21 days) schedule;

• At various different dose levels in Phase 1;

• As a booster;

• In 3 age groups (Phase 1: 18 to 55 years of age, 65 to 85 years of age; Phase 2/3: ≥12 years of age [stratified as 12-15, 16-55 or >55 years of age]).

  The candidate selected for efficacy evaluation in Phase 2/3 is BNT162b2 at a dose of 30 µg.

  Participants who originally received placebo will be offered the opportunity to receive BNT162b2 at defined points as part of the study.

  In order to describe the boostability of BNT162, and potential heterologous protection against emerging SARS-CoV-2 VOCs, an additional dose of BNT162b2 at 30 µg will be given to Phase 1 participants approximately 6 to 12 months after their second dose of BNT162b1 or BNT162b2. This will provide an early assessment of the safety of a third dose of BNT162, as well as its immunogenicity.

  The assessment of boostability will be further expanded in a subset of Phase 3 participants at selected sites in the US who will receive a third dose of BNT162b2 at 30 µg or a third and potentially a fourth dose of prototype BNT162b2VOC at 30 µg (BNT162b2s01, based upon the South African variant and hereafter referred to as BNT162b2SA). A further subset of Phase 3 participants will receive a third, lower, dose of BNT162b2 at 5 or 10 µg.

  To further describe potential homologous and heterologous protection against emerging SARS-CoV-2 VOCs, a new cohort of participants will be enrolled who are COVID-19 vaccine-naïve (ie, BNT162b2-naïve) and have not experienced COVID-19. They will receive BNT162b2SA given as a 2-dose series, separated by 21 days.

  To reflect current and anticipated recommendations for COVID 19 vaccine boosters, participants in C4591001 who meet specified recommendations and have not already received one, will be offered a third dose of BNT162b2 after their second dose of BNT162.

Marginal effect of housing quality at mean racial/ethnic composition.

We present Regional Psychologically Valid Agents (R-PVAs) as a modeling approach to predicting transmission-reducing behaviors and epidemiology. The approach builds upon computational cognitive theory and formalizes aspects of theories of individual-level behavior change. We present R-PVA models of social distancing and mask wearing in response to dynamics in the physical and information environments in the 50 U.S. states. The models achieve strong goodness-of-fits for predicting day-to-day mask-wearing (R2 = 0.93) and social distancing (R2 = 0.62) for the first three waves of COVID-19, prior to the rollout of vaccines.

Descriptive measures of teachers’ reported school experiences during the COVID-19 pandemic.

Regression models predicting quality of life and psychological distress amongst a sample of BC teachers during the COVID-19 pandemic.

Regression models predicting job-related positive affect and turnover intentions amongst a sample of BC teachers during the COVID-19 pandemic.