Purpose: This ecological analysis investigates the spatial patterns of the COVID‐19 epidemic in the United States in relation to socioeconomic variables that characterize US counties.Methods: Data on confirmed cases and deaths from COVID‐19 for 2,814 US counties were obtained from Johns Hopkins University. We used Geographic Information Systems (GIS) to map the spatial aspects of this pandemic and investigate the disparities between metropolitan and nonmetropolitan communities. Multiple regression models were used to explore the contextual risk factors of infections and death across US counties. We included population density, percent of population aged 65+, percent population in poverty, percent minority population, and percent of the uninsured as independent variables. A state‐level measure of the percent of the population that has been tested for COVID‐19 was used to control for the impact of testing.Findings: The impact of COVID‐19 in the United States has been extremely uneven. Although densely populated large cities and their surrounding metropolitan areas are hotspots of the pandemic, it is counterintuitive that incidence and mortality rates in some small cities and nonmetropolitan counties approximate those in epicenters such as New York City. Regression analyses support the hypotheses of positive correlations between COVID‐19 incidence and mortality rates and socioeconomic factors including population density, proportions of elderly residents, poverty, and percent population tested.Conclusions: Knowledge about the spatial aspects of the COVID‐19 epidemic and its socioeconomic correlates can inform first responders and government efforts. Directives for social distancing and to “shelter‐in‐place” should continue to stem the spread of COVID‐19.

Ovarian cancer is the fifth leading cause of female cancer mortality in the U.S. and accounts for five percent of all cancer deaths among women. No environmental risk factors for ovarian cancer have been confirmed. We previously reported that ovarian cancer incidence rates at the state level were significantly correlated with the extent of pulp and paper manufacturing. We evaluated that association using county-level data and advanced geospatial methods. Specifically, we investigated the relationship of spatial patterns of ovarian cancer incidence rates with toxic emissions from pulp and paper facilities using data from the Environmental Protection Agency’s Toxic Release Inventory (TRI). Geospatial analysis identified clusters of counties with high ovarian cancer incidence rates in south-central Iowa, Wisconsin, New York, Pennsylvania, Alabama, and Georgia. A bivariate local indicator of spatial autocorrelation (LISA) analysis confirmed that counties with high ovarian cancer rates were associated with counties with large numbers of pulp and paper mills. Regression analysis of state level data indicated a positive correlation between ovarian cancer and water pollutant emissions. A similar relationship was identified from the analysis of county-level data. These data support a possible role of water-borne pollutants from pulp and paper mills in the etiology of ovarian cancer.

Prior studies of neighborhood crime have largely overlooked a border issue in the aggregation of criminal incidents to small areal units and a denominator problem arising from the use of population to normalize crime counts and have failed to discern racial disparities in crime victimization due to the use of non-race-specific crime data. This article explores the relationships between neighborhood characteristics and robbery victimization by applying an improved method for neighborhood crime aggregation and normalization to race-disaggregated incident data. Results suggest that controlling for above issues helps better explain the spatial variations of robbery victimization across urban neighborhoods. The methods and findings have implications for improving the measurement accuracy of neighborhood crime and disentangling the effects of race and other socioeconomic factors on urban crime.

Introduction Fly ash is a waste product generated from burning coal for electricity. It is comprised of spherical particles ranging in size from 0.1 µm to over 100 µm in diameter that contain trace levels of heavy metals. Large countries such as China and India generate over 100 million tons per year while smaller countries like Italy and France generate 2 to 3 million tons per year. The USA generates over 36 million tons of ash, making it one of the largest industrial waste streams in the nation. Fly ash is stored in landfills and surface impoundments exposing communities to fugitive dust and heavy metals that leach into the groundwater. Limited information exists on the health impact of exposure to fly ash. This protocol represents the first research to assess children’s exposure to coal fly ash and neurobehavioural outcomes. Methods We measure indoor exposure to fly ash and heavy metals, and neurobehavioural symptoms in children aged 6 to 14 years old. Using air pollution samplers and lift tape samples, we collect particulate matter ≤10 µm that is analysed for fly ash and heavy metals. Toenails and fingernails are collected to assess body burden for 72 chemical elements. Using the Behavioural Assessment and Research System and the Child Behaviour Checklist, we collect information on neurobehavioural outcomes. Data collection began in September 2015 and will continue until February 2021. Ethics and dissemination This study was approved by the Institutional Review Boards of the University of Louisville (#14.1069) and the University of Alabama at Birmingham (#300003807). We have collected data from 267 children who live within 10 miles of two power plants. Children are at a greater risk for environmental exposure which justifies the rationale for this study. Results of this study will be distributed at conferences, in peer-reviewed journals and to the participants of the study.