A spatial epidemiology case study of mentally unhealthy days (MUDs): air pollution, community resilience, and sunlight perspectives.

The main objective of this spatial epidemiologic research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Mentally unhealthy days (MUDs) are studied in entire cross counties for year of 2014. 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. 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.

Assessing community vulnerability to floods and hurricanes along the Gulf Coast of the United States.

Hurricanes and flooding have affected millions of people and generated massive economic losses over the past several decades. Geographic information system (GIS) methods are employed in this paper to analyse coastal communities' vulnerability to these two hazards along the Gulf Coast of the United States. Specifically, two types of quantitative indicators are developed: (i) exposure to hurricanes and flooding, based on information from multiple sources; and a social vulnerability index, constructed using census data. These indices are combined to depict the spatial patterns of overall community vulnerability to hurricanes and flooding along the US Gulf Coast. The results of this study can potentially inform disaster management agencies, county governments, and municipalities in areas at heightened risk. Furthermore, the demonstration of the geographic distribution of community vulnerability can assist decision-makers in prioritising to-do items and designing policies and plans for the more effective allocation of resources. The paper ends by discussing the study's limitations and its practical implications.

Seeing is Believing? An Examination of Perceptions of Local Weather Conditions and Climate Change Among Residents in the U.S. Gulf Coast.

What role do objective weather conditions play in coastal residents' perceptions of local climate shifts and how do these perceptions affect attitudes toward climate change? While scholars have increasingly investigated the role of weather and climate conditions on climate-related attitudes and behaviors, they typically assume that residents accurately perceive shifts in local climate patterns. We directly test this assumption using the largest and most comprehensive survey of Gulf Coast residents conducted to date supplemented with monthly temperature data from the U.S. Historical Climatology Network and extreme weather events data from National Climatic Data Center. We find objective conditions have limited explanatory power in determining perceptions of local climate patterns. Only the 15- and 19-year hurricane trends and decadal summer temperature trend have some effects on perceptions of these weather conditions, while the decadal trend of total number of extreme weather events and 15- and 19-year winter temperature trends are correlated with belief in climate change. Partisan affiliation, in contrast, plays a powerful role affecting individual perceptions of changing patterns of air temperatures, flooding, droughts, and hurricanes, as well as belief in the existence of climate change and concern for future consequences. At least when it comes to changing local conditions, "seeing is not believing." Political orientations rather than local conditions drive perceptions of local weather conditions and these perceptions-rather than objectively measured weather conditions-influence climate-related attitudes.

Social network, political climate, income inequality, and Americans uptake of monovalent COVID-19 booster.

The COVID-19 pandemic has posed an unprecedented impact on Americans for over three years. To control the damage, a booster shot becomes increasingly necessary because the efficacy of the initial vaccine is waning and new variants of the virus are emerging. This study aims to understand factors at both individual and state levels that influence one's decision to take the monovalent booster. We merged data from a national survey administered in the Spring of 2022 with state-level indicators of the political climate, income inequality, and public health conditions. Multilevel logistic regression is adopted for statistical estimation. Findings show contrasting effects of the social network. More vaccinated people in one's network promote booster uptake, while more family members and close friends who contracted the virus in one's network inhibit booster uptake. In addition, residents of states with more votes for the Democratic candidate in the 2020 general election are more likely to take the booster. Meanwhile, residents from states with high income inequality are less likely to become boosted. This study identified multilevel determinants of the individual decision to receive the monovalent COVID-19 booster. The results imply the need to leverage the social network, weaken partisanship salience, and reduce income inequality to encourage booster uptake.

Monitoring the impact of climate extremes and COVID-19 on statewise sentiment alterations in water pollution complaints.

The COVID-19 pandemic and associated prevention policies can directly or indirectly alter the sentiment of individuals while registering water pollution complaints, but observational evidence remains limited. Here, we conducted a sentiment analysis on over 10,000 water pollution complaints from residents in Alabama, USA (2012-2021) to better understand how and to what extent COVID-19 has altered emotion (polarity score-based) and attitude (subjectivity) of water pollution complaints. We found that the 2017 state-wise drought significantly increased the percentage of negative water pollution complaints by +35%, with no significant alternation in attitude before the COVID-19 pandemic. Since COVID-19, the percentage of negative and subjective water pollution complaints significantly decreased and increased by -30 and +20%, respectively, and these sentiment alternations were maintained by 2021. This study provides a new direction for environmental governance and management, requiring a timely response to changes in the public's emotions and attitudes during the next climate extremes and pandemics.

Understanding the influence of political orientation, social network, and economic recovery on COVID-19 vaccine uptake among Americans.

The COVID-19 pandemic poses unprecedented risks to the well-being of Americans. To control the pandemic, a sufficient proportion of the population needs to be vaccinated promptly. Despite the proven efficacy and widespread availability, vaccine distribution and administration rates remain low. Thus, it is important to understand the public behavior of COVID-19 vaccination. This study aims to identify determinants at multiple levels that promote or inhibit one's vaccine uptake. We combine individual-level data from a national survey conducted in the summer of 2021 with corresponding state-level indicators. Findings of multilevel logistic regression show that political orientation, social network, and economic recovery altogether have significant influence. We articulate that individual decision to take the vaccine are a function of their personal characteristics and are also rooted in their home state's political, public health, and economic contexts. These findings contribute to the literature and have policy implications. Knowledge of the profiles among people who take/refuse the vaccine provides essential information to leverage certain factors and maximize vaccine uptake to mitigate the pandemic's devastating impact.

Understanding the Effects of Individual and State-Level Factors on American Public Response to COVID-19.

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 (ß = 0.020, p < 0.1), mandatory face mask policies (ß = 0.069, p < 0.05), and liberal governments (ß = 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 (ß = -0.005, p < 0.05). Regarding individual-level predictors, older people (ß = 0.005, p < 0.001) and people with better education (ß = 0.029, p < 0.01), leaning toward the Democrat Party (ß = 0.066, p < 0.001) and liberal political ideology (ß = 0.094, p < 0.001), and have stronger generalized trust (ß = 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.

Understanding American Public Support for COVID-19 Risk Mitigation: The Role of Political Orientation, Socio-Demographic characteristics, Personal Concern, and Experience, the United States, 2020.

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.

Understanding the influence of contextual factors and individual social capital on American public mask wearing in response to COVID-19.

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.

Retrospective and prospective evaluations of drought and flood.

Climate extremes will be intensified and become more frequent. One of the regions where this is the case is the U.S. Gulf coast region. This region is susceptible to the impacts of climate extremes. This region has recently experienced large amounts of economic damages caused by high-impact hurricanes and floods. Meanwhile, drought can also pose serious risks once it occurs. By using a 2019 U.S. Gulf Coast survey combined with Standard Precipitation Index, we closely examined retrospective and prospective evaluations of drought and flood among coastal residents. Drawing upon literature on human-environment system, we were interested in how the objective conditions of past drought and flood influenced individual's perceptions of these hazards and how their retrospective evaluations were correlated with their prospective evaluations of future trends of these hazards. Coastal residents' retrospective evaluations of past drought and flood were found to be influenced by historic objective conditions. Higher drought frequencies were found to increase the probability of perceiving increasing trend of drought number in the past. Higher flood frequencies were found to decrease the probability of perceiving increasing trend of flood number in the past. Higher intensities of drought and flood were found to increase the probabilities of perceiving increasing trends of drought duration and flood amount in the past. Coastal residents' prospective evaluations of future drought and flood were found to be influenced by retrospective evaluations of these hazards, suggesting the temporal continuity in human judgment. Moreover, those who relied on a longer time span in reference to the future were found to be more likely to perceive increasing trends of drought and flood. We ended this paper by proposing a theoretical framework to guide future studies and discussing policy implications.

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States.

Flash flood is among the most catastrophic natural hazards which causes disruption in the environment and societies. Flash flood is mainly initiated by intense rainfall, and due to its rapid onset (within six hours of rainfall), taking action for effective response is challenging. Building resilience to flash floods require understanding of the socio-economic characteristics of the societies and their vulnerability to these extreme events. This study provides a comprehensive assessment of socio-economic vulnerability to flash floods and investigates the main characteristics of flash flood hazard, i.e. frequency, duration, severity, and magnitude. A socio-economic vulnerability index is developed at the county level across the Contiguous United States (CONUS). For this purpose, an ensemble of social and economic variables from the US Census and the Bureau of Economic Analysis were analyzed. Then, the coincidence of socio-economic vulnerability and flash flood hazard were investigated to identify the critical and non-critical regions. Results show that the southwest U.S. experienced severe flash flooding with high magnitude, whereas the Northern Great Plains experience lower severity and frequency. Critical counties (high-vulnerable-hotspot) are mostly located in the southern and southwestern parts of the U.S. The majority of counties in the Northern Great Plains indicate a non-critical status.

A sequential model to link contextual risk, perception and public support for flood adaptation policy.

The economic damage from coastal flooding has dramatically increased over the past several decades, owing to rapid development in shoreline areas and possible effects of climate change. To respond to these trends, it is imperative for policy makers to understand individuals' support for flood adaptation policy. Using original survey data for all coastal counties of the United States Gulf Coast merged with contextual data on flood risk, this study investigates coastal residents' support for two adaptation policy measures: incentives for relocation and funding for educational programs on emergency planning and evacuation. Specifically, this study explores the interactive relationships among contextual flood risks, perceived flood risks and policy support for flood adaptation, with the effects of social-demographic variables being controlled. Age, gender, race and partisanship are found to significantly affect individuals' policy support for both adaptation measures. The contextual flooding risks, indicated by distance from the coast, maximum wind speed and peak height of storm surge associated with the last hurricane landfall, and percentage of high-risk flood zone per county, are shown to impact one's perceptions of risk, which in turn influence one's support for both policy measures. The key finding -risk perception mediates the impact of contextual risk conditions on public support for flood management policies - highlights the need to ensure that the public is well informed by the latest scientific, engineering and economic knowledge. To achieve this, more information on current and future flood risks and options available for mitigation as well as risk communication tools are needed.

Understanding the effects of past flood events and perceived and estimated flood risks on individuals' voluntary flood insurance purchase behavior.

Over the past several decades, the economic damage from flooding in the coastal areas has greatly increased due to rapid coastal development coupled with possible climate change impacts. One effective way to mitigate excessive economic losses from flooding is to purchase flood insurance. Only a minority of coastal residents however have taken this preventive measure. Using original survey data for all coastal counties of the United States Gulf Coast merged with contextual data, this study examines the effects of external influences and perceptions of flood-related risks on individuals' voluntary behaviors to purchase flood insurance. It is found that the estimated flood hazard conveyed through the U.S. Federal Emergency Management Agency's (FEMA's) flood maps, the intensities and consequences of past storms and flooding events, and perceived flood-related risks significantly affect individual's voluntary purchase of flood insurance. This behavior is also influenced by home ownership, trust in local government, education, and income. These findings have several important policy implications. First, FEMA's flood maps have been effective in conveying local flood risks to coastal residents, and correspondingly influencing their decisions to voluntarily seek flood insurance in the U.S. Gulf Coast. Flood maps therefore should be updated frequently to reflect timely and accurate information about flood hazards. Second, policy makers should design strategies to increase homeowners' trust in the local government, to better communicate flood risks with residents, to address the affordability issue for the low-income, and better inform less educated homeowners through various educational programs. Future studies should examine the voluntary flood insurance behavior across countries that are vulnerable to flooding.

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.