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Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events. 

COVID-19 variants continue to create public health danger impacting mortality and morbidity across the United States. The spillover effects of COVID-19 on the economy and social institutions pose a significant threat to broader wellbeing, including the food security of millions across the country. We aim to explore whether the context of place matters above and beyond individual and social vulnerabilities for food insecurity. To do so, we employ a multi-level framework using data from a survey of over 10,000 U.S. adults from March 2020 with American Community Survey (ACS) and John Hopkins COVID Dashboard county-level data. We find nearly two in five respondents were food insecure by March of 2020 with disparities across race, nativity, the presence of children in the home, unemployment, and age. Furthermore, we note that individuals living in more disadvantaged communities were more likely to report food insecurity above and beyond individual and social vulnerabilities. Overall, food insecurity is driven by complex, multi-level dynamics that remain a pressing public health concern for the current—but also future—public health crisis. 

Background: The current study explores how characteristics of individuals, their communities, and their relative exposure to nearby Covid-19 cases are associated with specific fears or perceived threat/risk of the virus itself during the early stages of the pandemic in March 2020. Aims: Drawing from research emphasizing the intersectional relationships between individual social vulnerabilities, community characteristics, and Covid-19 outbreak locales, we test several hypotheses predicting fear. Method: Using data from a large-scale survey of 10,368 U.S. adults from March 2020, we construct a series of hierarchical linear and logistic regression models that nest individuals within their residential counties in order to account for key socio-demographic characteristics of individuals, communities, and each respondent’s geographic proximity to Covid-19 cases. Results: Results show that individual fear and perceived risk to oneself and family is predicted by individual social vulnerabilities, the type of community in which respondents live, and the relative presence of the virus in nearby places. Conclusion: Our findings highlight the importance of understanding fear, particularly as a possible mediator for both mental and physical health outcomes. Likewise, we emphasize ongoing efforts aimed at understanding how different groups and communities respond to fear and/or concern over Covid-19 as the pandemic remains ongoing. 

The United States' response to the COVID-19 pandemic has relied heavily on personal mitigation behaviors versus centralized governmental prevention strategies, especially early in the virus's outbreak. This study examines how family structure shapes mitigation, focusing on the intersectional effects of gender, marital status, and the presence of children while accounting for differences in worry about infection from the virus. Using data from a national survey of 10,368 United States adults early in the pandemic (March 2020), survey-weighted logistic regression models show important differences in the likelihood of personal mitigation adoption across family structures. Unmarried women with children were most likely to report personal mitigation behaviors, including washing hands more frequently and avoiding social gatherings. Our findings highlight the differential impacts of the pandemic on those living in specific family circumstances.