The COVID-19 Pandemic, Rising Inflation, and Their Influence on Dining Out Frequency and Spending.

BACKGROUND: High intake of food away from home is associated with poor diet quality. This study examines how the COVID-19 pandemic period and Food Away from Home (FAFH) inflation rate fluctuations influenced dining out behaviors. METHODS: Approximately 2800 individuals in Texas reported household weekly dining out frequency and spending. Responses completed prior to the COVID-19 pandemic (2019 to early 2020) were compared to the post-COVID-19 period (2021 through mid-2022). Multivariate analysis with interaction terms was used to test study hypotheses. RESULTS AND CONCLUSION: From the COVID-19 period (before vs. after), the unadjusted frequency of dining out increased from 3.4 times per week to 3.5 times per week, while the amount spent on dining out increased from $63.90 to $82.20. Once the relationship between dining out (frequency and spending) was adjusted for FAFH interest rate and sociodemographic factors, an increase in dining out frequency post-COVID-19 remained significant. However, the unadjusted increase in dining out spending did not remain significant. Further research to understand the demand for dining out post-pandemic is warranted.

Taking a Shot: The Impact of Information Frames and Channels on Vaccination Willingness in a Pandemic.

The reluctance of people to receive safe and recommended available vaccines is a well-documented public health challenge. As information and communication technologies evolve, this challenge gets more complex and even harder to manage during complex public health situations. In this experimental study, we examine the relationship between vaccine information frames (with scientific information vs. without scientific information) and channels (through government vs. religious organizations) and vaccination willingness in the U.S. in the context of a pandemic. Additionally, we evaluate the interaction between vaccine skepticism, vaccine information frames, and vaccine information channels on vaccination willingness. This experimental study uses data from Amazon Mechanical Turk (MTURK) to evaluate the relationships between vaccine skepticism, vaccine information frames, and channels on vaccination willingness. We find that contrary to our hypothesis, a vaccine advisory framed with scientific information decreases people's vaccination willingness compared to one framed without scientific information. Additionally, the impact of framing on vaccination willingness is conditioned on participants' skepticism-participants who hold skepticism toward the vaccine but received information framed with scientific information score significantly higher in vaccination willingness compared to participants who do not hold skepticism toward a vaccine. The results suggest that the factors impacting vaccination willingness are complex and nuanced. Thus, policymakers should be more strategic with the delivery of vaccination information, especially during complex health crises.

Social media use in public health and policy study

The Internet plays a significant role in health information searching, sharing, and emotional support. Chapter 1 explores the complementary and substitutive value of online health information from diseases, especially chronic diseases, health insurance, barriers to health resources, and their interaction effects with income. Furthermore, social networks such as Twitter enable people to interact with each other and share health-related concerns and emotions in an effective and novel way, as evidenced during the COVID-19 pandemic when in-person communication became more inconvenient under the stay-at-home policy. Public emotions from these social network data have increasingly attracted scholars' attention because of their significant value in predicting public behaviors and public opinions. Chapter 2 examines 1) the spatial-temporal clustering trends of negative emotions (or spillover effects);2) whether health policies such as stay-at-home policy and political ideology are associated with spatiotemporal emotion patterns towards COVID-19.During COVID-19, public mobility experienced a significant reduction as many people's work environment shifted from workplace to home or offline to online, especially under policies like the stay-at-home policy (Wen, Sheng, & Sharp, 2021). However, little has been done to examine the relationships between public emotions mined from social networks and the public behavioral responses to the COVID-19 crisis, especially considering the interaction effects between public emotions and public policy and political leaders' political ideology. Chapter 3 fills these gaps by examining the relationships between public emotions and working modes, and the interaction effects between public emotion, public policy, and political leaders' political ideology on working modes. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Associations between Public Fear of COVID-19 and Number of COVID-19 Vaccinations: A County-Level Longitudinal Analysis.

BACKGROUND AND PURPOSE: A large number of COVID-19 infections and deaths and the ensuing socioeconomic problems created widespread public fear around COVID-19. Fear around COVID-19 greatly influences people's attitudes towards receiving the COVID-19 vaccines. The purpose of this study is examining (a) the impact of the public fear of COVID-19 (PFC) on the number of COVID-19 vaccinations at the county level; (b) the interaction effect between the PFC and per capita income, unemployment rates, and COVID-19 vaccines incentive policies, on the number of COVID-19 vaccinations at the county level. METHOD: This is a longitudinal analysis across states in the U.S. by using county-level data of 2856 counties from 1 February to 1 July. Random-effects models were adopted to analyze the associations between the PFC and the number of COVID-19 vaccinations. RESULT: the PFC was positively associated with the number of COVID-19 vaccinations at county-level, as PFC increases from 0 to 300, the predicted vaccination number increases from 10,000 to 230,000. However, the associations were divergent when the PFC interacts with county-level per capita income, unemployment rates, and incentive policies. CONCLUSION: public fear is an important indicator for the county-level vaccination numbers of COVID-19. However, it is critical to consider public fear and socioeconomic factors when making policies that aim to increase COVID-19 vaccination rates.

Impact of Short-Term Air Pollution on Respiratory Infections: A Time-Series Analysis of COVID-19 Cases in California during the 2020 Wildfire Season.

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

Associations between Bonus and Lottery COVID-19 Vaccine Incentive Policies and Increases in COVID-19 Vaccination Rates: A Social Epidemiologic Analysis.

The objectives of this longitudinal study were to analyze the impact of COVID-19 vaccine incentive policies (e.g., bonuses and lottery entries) on county-level COVID-19 vaccination rates, and to examine the interactive effects between COVID-19 vaccine incentive policies and socioeconomic factors on COVID-19 vaccination rates. Using publicly available data, county-level COVID-19 vaccination rates and socioeconomic data between January 2021 and July 2021 were extracted and analyzed across counties in the United States (US)-an analysis of 19,992 observations over time. Pooled ordinary least squares (OLS) analysis was employed to longitudinally examine associations with COVID-19 vaccination rates, and four random-effects models were developed to analyze interaction effects. Bonus incentive policies were effective in counties with a high per capita income, high levels of education, and a high percentage of racial minorities, but not in counties with high unemployment. Lottery incentive policies were effective in counties with a high percentage of racial minorities, but not in counties with high per capita income, high levels of education, and high unemployment. County-level socioeconomic factors should be considered ahead of implementing incentive policies, versus a blanket approach, to avoid the unintentional misuse of economic resources for futile COVID-19 vaccination outcomes.

Impact of Short-Term Air Pollution on Respiratory Infections: A Time-Series Analysis of COVID-19 Cases in California during the 2020 Wildfire Season

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

An Epidemiologic Analysis of Associations between County-Level Per Capita Income, Unemployment Rate, and COVID-19 Vaccination Rates in the United States.

The purpose of this longitudinal study was to examine associations between per capita income, unemployment rates, and COVID-19 vaccination rates at the county-level across the United States (U.S.), as well as to identify the interaction effects between county-level per capita income, unemployment rates, and racial/ethnic composition on COVID-19 vaccination rates. All counties in the U.S. that reported COVID-19 vaccination rates from January 2021 to July 2021 were included in this longitudinal study (n = 2857). Pooled ordinary least squares (OLS) with fixed-effects were employed to longitudinally examine economic impacts on racial/ethnic disparities on county-level COVID-19 vaccination rates. County-level per capita income and county-level unemployment rates were both positively associated with county-level COVID-19 vaccination rates across the U.S. However, the associations were divergent in the context of race/ethnicity. Public health efforts to bolster COVID-19 vaccination rates are encouraged to consider economic factors that are associated with decreases in COVID-19 vaccination rates.

Death by political party: The relationship between COVID-19 deaths and political party affiliation in the United States.

This study explored social factors that are associated with the US deaths caused by COVID-19 after the declaration of economic reopening on May 1, 2020 by President Donald Trump. We seek to understand how county-level support for Trump interacted with social distancing policies to impact COVID-19 death rates. Overall, controlling for several potential confounders, counties with higher levels of Trump support do not necessarily experience greater mortality rates due to COVID-19. The predicted weekly death counts per county tended to increase over time with the implementation of several key health policies. However, the difference in COVID-19 outcomes between counties with low and high levels of Trump support grew after several weeks of the policy implementation as counties with higher levels of Trump support suffered relatively higher death rates. Counties with higher levels of Trump support exhibited lower percentages of mobile staying at home and higher percentages of people working part time or full time than otherwise comparable counties with lower levels of Trump support. The relative negative performance of Trump-supporting counties is robust after controlling for these measures of policy compliance. Counties with high percentages of older (aged 65 and above) persons tended to have greater death rates, as did more populous counties in general. This study indicates that policymakers should consider the risks inherent in controlling public health crises due to divisions in political ideology and confirms that vulnerable communities are at particularly high risk in public health crises.