Category: Public Health

Our paper on American public support for COVID-19 risk mitigation

6/16/2021

Our paper entitled "Understanding American public support for COVID-19 risk mitigation: The role of political orientation, socio-demographic characteristics, personal concern, and experience" has been published the International Journal of Public Health (Impact factor: 3.38). Below please find the abstract:

"Objectives: COVID-19 is the most challenging public health crisis in decades in the United States. It is imperative to enforce social distancing rules before any safe and effective vaccines are widely available. Policies without public support are destined to fail. This study aims to reveal factors that determine the American public support for six mitigation measures (e.g., cancel gatherings, close schools, restrict non-essential travel).
Methods: Based on a nationally representative survey, this study uses Structural Equation Modelling to reveal the relationships between various factors and public support for COVID-19 mitigation.
Results: 1). Democrats are more likely than Republicans to support mitigation measures; 2).Favorability towards the political leader (Biden or Trump) can slant public support for COVID-19 mitigation measures among different segments of the public; 3). Indirect experience, rather than direct experience with COVID-19 can motivate people to support mitigation; 4). Concern for COVID-19 is a strong motivator of support for mitigation.
Conclusion: Political polarization poses an enormous challenge to societal well-being during a pandemic. Indirect experience renders COVID-19 an imminent threat."

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A new paper in American Journal of Health Promotion

4/23/2021

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Our new paper entitled "Understanding the effects of individual and state-level factors on American public response to COVID-19" has been accepted for publication in American Journal of Health Promotion (Impact factor: 2.87). Below please find the abstract:

"Purpose: To examine multilevel predictors on American public response to COVID–19.
Design: Multilevel study.
Setting: A national survey was conducted by Qualtrics from August 24 to September 11, 2020. The state-level variables were constructed on data from multiple sources.
Subjects: 2,440 respondents 18 years and older from all 50 states and D.C.
Measures: The outcome variable is the public response to COVID–19 measured by threat perception, behavioral adjustment, and policy support. The predictors include individual-level sociodemographic factors and state-level indicators about public health conditions, political context, and economic recovery.
Analysis: Multilevel structural equation modeling is used for statistical estimation.
Results: People from states with more COVID–19 cases (β=0.020, p<0.1), mandatory face mask policies (β=0.069, p<0.05), and liberal governments (β=0.002, p<0.05) are more likely to respond while people from states whose economies have recovered closer to the pre-pandemic level are less likely to do so (β=-0.005, p<0.05). Regarding individual-level predictors, older people (β=0.005, p<0.001) and people with better education (β=0.029, p<0.01), leaning toward the Democrat Party (β=0.066, p<0.001) and liberal political ideology (β=0.094, p<0.001), and have stronger generalized trust (β=0.033, p<0.001) are more likely to respond than their counterparts.

Conclusion: Differences in the public response to the pandemic stem from variations in individual characteristics and contextual factors of states where people live. These findings contribute to the rapidly growing literature and have implications for public health policies."

Figure 1. Multilevel Structural Equation Modeling Diagram (Hao and Shao 2021b)

Figure 2. The Influence of State-Level Factors on Public Response to COVID–19 (Hao and Shao 2021b)

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Our new paper in Health and Place

2/15/2021

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Our new paper entitled "Understanding the influence of contextual factors and individual social capital on American public mask wearing in response to COVID-19" has been published in Health and Place (Impact Factor: 4.078) . Below please find the abstract:

"The COVID–19 pandemic poses unprecedented risks to the health and well-being of the entire population in the U.S. To control the pandemic, it is imperative for individuals to take precautionary behaviors (e.g., wearing a mask, keeping social distance, washing hands frequently, etc.). The factors that influence individual behavioral response thus warrants a close examination. Using survey data for respondents from 10 states merged with state-level data, our study represents a pioneering effort to reveal contextual and individual social capital factors that explain public mask wearing in response to COVID–19. Findings of logistic multilevel regression show that the COVID–19 death rate and political control of government at the state level along with one’s social capital at the individual level altogether influence whether people decide to wear face masks. These findings contribute to the rapidly growing literature and have policy implications for mitigating the pandemic’s devastating impact on the American public."

Fig. 2. Estimated Odds of Wearing a Face Mask predicted by State-Level COVID–19 Death Rate and Political Control of Government (Hao et al. 2021)

Fig. 3. Estimated Odds of Wearing a Face Mask predicted by Social Capital Indicators (Hao et al. 2021).

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Our new paper is published in the International Journal of Environmental Health Research

9/16/2019

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Our new paper has been published in the International Journal of Environmental Health Research. In this paper, we examined potential factors that could affect community-level mental health across the United States. Here is the the summary:

"Mental health studies have underscored the hazardous conditions of each phase of life, from youth and pre-adulthood through adulthood in the United States. This situation calls for increased public awareness of the mental health issue and better understanding of the significant factors associated with mental health hazard. The main objective of this spatial epidemiological research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Using Behavioural Risk Factor Surveillance System (BRFSS) data in 2014, we examine main factors of mental health hazard including health behaviour, clinical care, socioeconomic and physical environment, demographic, community resilience, and extreme climatic conditions. In this study, we take complex design factors such as clustering, stratification and sample weight in the BRFSS data into account by using Complex Samples General Linear Model (CSGLM). Then, spatial regression models, spatial lag and error models, are applied to examine spatial dependencies and heteroscedasticity. Econometric analysis underscores that all categories of air pollution, community resilience, and sunlight variables tested are significant push factors of mentally unhealthy days (MUDs) duration. Results of the geographic analyses indicate that counties with lower air pollution (PM2.5), higher community resilience (social, economic, infrastructure, and institutional resilience), and higher sunlight exposure had significantly lower average number of MUDs reported in the past 30 days. These findings suggest that policy makers should take air pollution, community resilience, and sunlight exposure into account when designing environmental and health policies and allocating resources to more effectively manage mental health problems."

The following figure is from this paper (Ha and Shao 2019)

Figure 1. County-level annual average number of age-adjusted mentally unhealthy days (MUDs) reported in the past 30 days, 2014 (source: Ha and Shao, forthcoming)

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Our paper on American public support for COVID-19 risk mitigation

A new paper in American Journal of Health Promotion

Our new paper in Health and Place

Our new paper is published in the International Journal of Environmental Health Research

Wanyun Shao, Ph.D

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