Rethinking access to care: A spatial-economic analysis of the potential impact of pharmacy closures in the United States.

Data chronicling the geo-locations of all 61,589 pharmacies in the U.S. (from the Homeland Infrastructure Foundation-Level Data (HIFLD) Open Data interface, updated on April 2018) across 215,836 census block groups were combined with Medically Underserved Areas (MUAs) information, and the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI). Geospatial techniques were applied to calculate the distance between the center of each census block and the nearest pharmacy. We then modeled the expected additional travel distance if the nearest pharmacy to the center of a census block closed and estimated additional travel costs, CO2 emissions, and lost labor productivity costs associated with the additional travel. Our findings revealed that MUA residents have almost two times greater travel distances to pharmacies than non-MUAs (4,269 m (2.65 mi) vs. 2,388 m (1.48 mi)), and this disparity is exaggerated with pharmacy closures (107% increase in travel distance in MUAs vs. 75% increase in travel distance in non-MUAs). Similarly, individuals living in MUAs experience significantly greater average annual economic costs than non-MUAs ($34,834 ± $668 vs. $22,720 ± $326). Our findings suggest the need for additional regulations to ensure populations are not disproportionately affected by these closures and that there is a significant throughput with community stakeholders before any pharmacy decides to close.

Measuring Historic and Longitudinal Social Vulnerability in Disaster-Prone Communities: A Modification to the Centers for Disease Control and Prevention Social Vulnerability Index (CDC-SVI).

OBJECTIVE: Researchers have developed numerous indices to identify vulnerable sub-populations. The Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) is the most common and highly serviceable, but it has some temporal limitations considering that some variables used in calculating the CDC-SVI were not available before 1980. Changes in societal composition over time can impact social vulnerability. This study defines an alternate, but similar, index that could serve as a surrogate for the CDC-SVI without the temporal limitations. METHODS: An inventory analysis of the historical census data (1960-2018) was used to develop a Modified SVI that allows for historic analyses. To consider the chronic effect of social vulnerabilities, a longitudinal SVI was introduced to elucidate how a community's multidimensional experiences exacerbate vulnerability to disaster events, such as the COVID-19 pandemic. We use Harris County, Texas, in this case study to examine how the Modified SVI performs against the original CDC-SVI. RESULTS: This Modified SVI was used to generate historical maps, find temporal patterns, and inform a longitudinal SVI measure. The results showed a good agreement among the developed indices and the CDC-SVI. We also observed satisfactory performance in identifying the areas that are most vulnerable to the COVID-19 pandemic. CONCLUSIONS: The Modified SVI overcomes temporal limitations associated with the CDC-SVI, and the longitudinal SVI captures a community's multidimensional experiences that exacerbate a community's vulnerability to disaster events over time.

Source apportionment of polychlorinated dibenzo-p-dioxins and dibenzofurans in the sediments of an urban estuary.

Polychlorinated dibenzo-p-dioxins and dibenzofurans in the sediments of aquatic systems are a persistent global problem that poses serious health risks. Identifying the sources of dioxins in natural water systems and the extent of their contributions to observed sediment concentrations is important from a health advisory and mitigation perspective. This paper proposes novel distribution-based qualitative and quantitative methods as source apportionment techniques and alternatives to conventional source attribution methods. Using sampled data, air, runoff, industrial effluent, and industrial paper and pulp wastes were identified as four distinct dioxin contributors to concentrations found in the sediments of the test bed region: the Houston Ship Channel-San Jacinto River-Galveston Bay (HSC-SJR-GB) estuarine system that also includes 2 Superfund sites with dioxin contamination. Two qualitative methods, the Kullback-Leibler divergence (K-L divergence) and the Bhattacharya measure (BM), and a quantitative method, the L2 norm, were used to investigate the spatial and temporal sourcing patterns of dioxins in the system sediments. The results indicated a global contribution from air and runoff sources across the estuarine system and over time with more localized impacts of the Superfund sites and the industrial sources. The results using the developed methodologies were compared with the output from the more conventional positive matrix factorization (PMF) method. Statistically significant correlations were observed among source contributions from the proposed methods and the PMF method, with Spearman's ρ ranging between - 0.596 to - 0.963 and 0.652 to 0.719, demonstrating the utility of the sourcing approaches used in the study. Additionally, the proposed methods were found to be rigorous in terms of elucidating spatial and temporal changes in the sourcing of dioxin to the estuary, indicating their suitability for use for other contaminants and other estuarine systems.

Examining longitudinal disparities in COVID-19 prevalence in the U.S.: a county level growth rate perspective.

Background: The objectives of the present study are to understand the longitudinal variability in COVID-19 reported cases at the county level and to associate the observed rates of infection with the adoption and lifting of stay-home orders.Materials and Methods: The study uses the trajectory of the pandemic in a county and controls for social and economic risk factors, physical environment, and health behaviors to elucidate the social determinants contributing to the observed rates of infection.Results and conclusion: Results indicated that counties with higher percentages of young individuals, racial and ethnic minorities and, higher population densities experienced greater difficulty suppressing transmission.Except for Education and the Gini Index, all factors were influential on the rate of COVID-19 spread before and after stay-home orders. However, after lifting the orders, six of the factors were not influential on the rate of spread; these included: African-Americans, Population Density, Single Parent Households, Average Daily PM2.5, HIV Prevalence Rate, and Home Ownership. It was concluded that different factors from the ones controlling the initial spread of COVID-19 are at play after stay-home orders are lifted.KEY MESSAGESObserved rates of COVID-19 infection at the County level in the U.S. are not directly associated with adoption and lifting of stay-home orders.Disadvantages in sociodemographic determinants negatively influence the rate of COVID-19 spread.Counties with more young individuals, racial and ethnic minorities, and higher population densities have greater difficulty suppressing transmission.

Sediment and their bacterial communities in an industrialized estuary after Hurricane Harvey.

Estuaries experience variable physicochemical conditions, especially after hurricanes and due to anthropogenic sources of pollution. Their microbial communities are not as well understood in terms of community structure and diversity, particularly in response to stresses from pollution and severe events. This study presents a 16S rRNA-based description of sediment microbial communities in the Houston Ship Channel-Galveston Bay estuary after Hurricane Harvey in 2017. A total of 11 sites were sampled, and microbial genomic DNA was isolated from sediment. The presence and abundance of specific bacterial and archaeal taxa in the sediment indicated pollutant inputs from identified legacy sources. The abundance of certain microbial groups was explained by the mobilization of contaminated sediment and sediment transport due to Harvey. Several microorganisms involved in the biodegradation of xenobiotics were observed. The spatial occurrence of Dehalococcoidia, a degrader of persistent polychlorinated compounds, was explained in relation to sediment properties and contaminant concentrations.

Longitudinal patterns in sediment type and quality during daily flow regimes and following natural hazards in an urban estuary: a Hurricane Harvey retrospective.

Understanding the transport of sediments in urban estuaries and their effects on water quality and microorganisms is a convergent challenge that has yet to be addressed especially as a result of natural hazards that affect the hydrodynamics of estuarine systems. This study provides a holistic view of the longitudinal nature and character of sediment in an urban estuary, the Galveston Bay Estuary System (GBES), under daily and extreme flow regimes and presents the results of water and sediment sampling after Hurricane Harvey. The sediment sampling quantified total suspended sediment (TSS) concentrations, metal concentrations, and the diversity of microbial communities. The results revealed the impact of the substantial sediment loads that were transported into the GBES in terms of sediment grain type, the spatial distribution of trace metals, and the diversity of microbial communities. A measurable shift in the percentage of silt relative to historical norms was noted in the GBES after Hurricane Harvey. Not only did sediment metal data confirms this shift and its ensuing impact on metal concentrations; microbial data provided ample evidence of the effect of leaks and spills from wastewater treatment plants, superfund sites, and industrial runoff on microbial diversity. The research demonstrates the importance of understanding longitudinal sediment transport and deposition in estuarine systems under daily flow regimes but more critically, following natural hazard events to ensure sustainability and resilience of systems such as the GBES that encounter numerous acute and chronic stresses.

The presence of Superfund sites as a determinant of life expectancy in the United States.

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.

Assessing COVID-19 risk, vulnerability and infection prevalence in communities.

BACKGROUND: 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. METHODS: 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. ANALYSIS: 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. RESULTS: 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. CONCLUSION: 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.

Modeling water quality impacts from hurricanes and extreme weather events in urban coastal systems using Sentinel-2 spectral data.

Conventional water quality measurements are nearly impossible during and immediately after extreme storms due to dangerous conditions. In this study, remotely sensed reflectance is used to develop a regression equation that quantifies total suspended solids (TSS) in near real-time after Hurricane Harvey. The application focused specifically on sediment loading and deposition and its potential impacts on the Houston Ship Channel and Galveston Bay riverine-estuarine system. The European Space Agency's Sentinel-2 satellite captured images at critical points in the storm's progression, necessitating the development of a new algorithm for this relatively new satellite mission. Several linear regressions were analyzed with the goal of developing a simple one- or two-band equation, and the final model uses the red and near infrared bands (R2 = 0.74). Results show that record flows during Harvey delivered unprecedented suspended sediment loads to the Gulf of Mexico at concentrations above 125 mg/L with a mean concentration of 43 mg/L across the bay. The study findings demonstrated that it took up to 11 days after the storm for sediment transport to abate.

Natural attenuation of indicator bacteria in coastal streams and estuarine environments.

One of the most significant causes of poor water quality is the presence of pathogens. To reduce the cost of human exposure to microbial contamination, monitoring of Fecal Indicator Bacteria (FIB), as a surrogate for the presence of pathogens in natural waters, has become the norm. A total maximum daily load (TMDL) framework is used to establish limits for microbial concentrations in impaired waterbodies. In order to meet microbial loads determined by the TMDLs, reductions in microbial sources varying from 50% to almost complete elimination are required. Such targets are fairly difficult, if not impossible, to achieve. A natural attenuation (NA) framework is proposed that takes into account the connectivity between freshwater streams and their receiving coastal estuaries. The framework accounts for destructive and non-destructive mechanisms and defines three regimes: NA 1 - reaction-dilution mixing at the freshwater-tidal interface, NA 2 - advection-reactions within the tidally influenced coastal stream, and NA 3 - dilution-discharge at the interface with the estuary. The framework was illustrated using the Houston Metropolitan area freshwater streams, their discharge into the Houston Ship Channel (HSC) and into Galveston Bay. FIB concentrations in Galveston Bay were much lower when compared to FIB concentrations in Houston streams. Lower enterococci concentrations in tributary tidal waters were found compared to their counterparts in fresh waters (NA1 regime). Additionally, 70% reduction in FIB loads within the HSC was demonstrated as well as a decreasing trend in enterococci geometric means, from upstream to downstream, on the order of 0.092 day-1 (NA2 regime). Lower enterococci concentrations in Galveston Bay at the confluence with the HSC were also demonstrated (NA3 regime). Statistical testing showed that dilution, tide-associated processes, and salinity are the most important NA mechanisms and indicated the significant effect of ambient temperature and rainfall patterns on FIB concentrations and the NA mechanisms.

Physical, Chemical, and Microbial Quality of Floodwaters in Houston Following Hurricane Harvey.

Hurricane Harvey brought more than 50 in. of rainfall to some areas of the Greater Houston Metro area (GHMA) starting on August 25, 2017; the Hurricane was also associated with damage to environmental infrastructure such as wastewater facilities, superfund sites, and leaks and spills from industrial and municipal facilities. This study collected post-Harvey water quality data in multiple streams for several weeks after the Hurricane. In addition to measuring impact, the study compared the observed concentrations of several physical, chemical, and microbial constituents and water properties to their historical counterparts in an effort to understand the water quality impacts of Harvey on the natural water systems within the GHMA. Unusual water quality findings such as low pH were observed that likely had acute and chronic effects on ecosystems including the loss of oyster populations in Galveston Bay. In-stream microbial concentrations, using E. coli as the indicator, were within historical norms typically reported for the GHMA. The observed levels of measured dissolved metals post Harvey, while relatively low, when multiplied by the significant volume of water discharged from bayous to Galveston Bay, meant the delivery of a substantial load of trace metals to the estuary. Specifically, the load in the particulate phase would be expected to accumulate and gradually repartition to the dissolved phase for a long period of time. Total metal concentrations, when elevated relative to their historical counterparts, could be associated with the presence of industrial activities. Overall, anthropogenic activities including the presence of hydraulic flood control structures, local runoff from industrialized areas, and active superfund sites were recognized as key factors affecting short-term acute water quality impacts. Watersheds with very little human alterations experienced minimal water quality changes and had relatively rapid recoveries post-Harvey.

Development of a storm surge driven water quality model to simulate spills during hurricanes.

Hurricanes can cause widespread environmental pollution that has yet to be fully articulated. This study develops a predictive water quality model to forecast potential contamination resulting from buckled or ruptured storage tanks in coastal industrialized areas when subjected to storm surge. The developed EFDC-Storm Surge model (EFDC-SS) couples EPA's EFDC code with the SWAN-ADCIRC hurricane simulation model. EFDC-SS is demonstrated using the Houston Ship Channel in Texas as a testbed and hurricane Ike as a model hurricane. Conservative and decaying dye runs evaluated various hurricane scenarios, combined with spills released at different locations and release times. Results showed that tank locations with shorter distances to the main waterbody and lower ground elevations have a higher risk of inundation and rapid spill mass transport. It was also determined that hurricane strength and landfall location, the location of the spill, and the spill release time relative to peak surge were interdependent.