The spread of coronavirus in the United States with nearly five and half million confirmed cases and over 170,000 deaths has strained public health and health care systems. While many have focused on clinical outcomes, less attention has been paid to vulnerability and risk of infection. In this study, we developed a planning tool that examines factors that affect vulnerability to COVID-19. Across 46 variables, we defined five broad categories: 1) access to medical services, 2) underlying health conditions, 3) environmental exposures, 4) vulnerability to natural disasters, and 5) sociodemographic, behavioral, and lifestyle factors. The developed tool was validated by comparing the estimated overall vulnerability with the real-time reported normalized confirmed cases of COVID-19. A principal component analysis was undertaken to reduce the dimensions. In order to identify vulnerable census tracts, we conducted rank-based exceedance and K-means cluster analyses. All of the 5 vulnerability categories, as well as the overall vulnerability, showed significant (P-values <<0.05) and relatively strong correlations (0.203<ρ<0.57) with the normalized confirmed cases of COVID-19 at the census tract level. Our study showed a total of 722,357 (~17% of the County population) people, including 171,403 between the ages of 45–65 (~4% of County’s population), and 76,719 seniors (~2% of County population), are at a higher risk based on the aforementioned categories. The exceedance and K-means cluster analysis demonstrated that census tracts in the northeastern, eastern, southeastern and northwestern regions of the County are at highest risk. Policymakers can use this planning tool to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions.

Superfund sites could affect life expectancy (LE) via increasing the likelihood of exposure to toxic chemicals. Here, we assess to what extent such presence could alter the LE independently and in the context of sociodemographic determinants. A nationwide geocoded statistical modeling at the census tract level was undertaken to estimate the magnitude of impact. Results showed a significant difference in LE among census tracts with at least one Superfund site and their neighboring tracts with no sites. The presence of a Superfund site could cause a decrease of −0.186 ± 0.027 years in LE. This adverse effect could be as high as −1.22 years in tracts with Superfund sites and high sociodemographic disadvantage. Specific characteristics of Superfund sites such as being prone to flooding and the absence of a cleanup strategy could amplify the adverse effect. Furthermore, the presence of Superfund sites amplifies the negative influence of sociodemographic factors at lower LEs.

A pathogen impaired watershed in Houston, Tex., was studied to assess the spatial and temporal nature of point and nonpoint bacterial load contributions. End-of-pipe sampling at wastewater treatment plant effluent and storm sewers discharging under dry weather conditions was undertaken. Relatively low concentrations of E. coli were found in wastewater treatment effluent, with a geometric mean of 5 MPN/dL, while dry weather storm sewer discharges exhibited a geometric mean concentration of 212 MPN/dL. Loads from both point and nonpoint sources of E. coli were calculated and compared to in-stream bacteria loads. Nonpoint loads were estimated using an event mean concentration approach on an annual basis. Nonpoint source (NPS) loads were the primary source of bacteria loading to the bayou. Wastewater treatment plant and dry weather storm sewer loads, however, dominated in dry weather conditions. While NPS loads remained relatively constant from headwaters to the mouth of the bayou, point source loads exhibite...