Environmental Factors Associated with Incidence and Distribution of V. parahaemolyticus and V. vulnificus in Chesapeake Bay, Maryland, USA: A three-year case study.

Members of the genus Vibrio are ecologically significant bacteria native to aquatic ecosystems globally, and a few can cause diseases in humans. Vibrio-related illnesses have increased in recent years, primarily attributed to changing environmental conditions. Therefore, understanding the role of environmental factors in the occurrence and growth of pathogenic strains is crucial for public health. Water, oyster, and sediment samples were collected between 2009 and 2012 from Chester River and Tangier Sound sites in Chesapeake Bay, Maryland, USA, to investigate the relationship between water temperature, salinity, and chlorophyll with the incidence and distribution of Vibrio parahaemolyticus (VP) and Vibrio vulnificus (VV). Odds ratio analysis was used to determine association between the likelihood of VP and VV presence and these environmental variables. Results suggested that water temperature threshold of 20°C or higher was associated with an increased risk, favoring the incidence of Vibrio spp. A significant difference in salinity was observed between the two sampling sites, with distinct ranges showing high odds ratio for Vibrio incidence, especially in water and sediment, emphasizing the impact of salinity on VP and VV incidence and distribution. Notably, salinity between 9-20 PPT consistently favored the Vibrio incidence across all samples. Relationship between chlorophyll concentrations and VP and VV incidence varied depending on sample type. However, chlorophyll range of 0-10 µg/L was identified as critical in oyster samples for both vibrios. Analysis of odds ratios for water samples demonstrated consistent outcomes across all environmental parameters, indicating water samples offer a more reliable indicator of Vibrio spp. incidence.

Assessment of pathogens in flood waters in coastal rural regions: Case study after Hurricane Michael and Florence.

The severity of hurricanes, and thus the associated impacts, is changing over time. One of the understudied threats from damage caused by hurricanes is the potential for cross-contamination of water bodies with pathogens in coastal agricultural regions. Using microbiological data collected after hurricanes Florence and Michael, this study shows a dichotomy in the presence of pathogens in coastal North Carolina and Florida. Salmonella typhimurium was abundant in water samples collected in the regions dominated by swine farms. A drastic decrease in Enterococcus spp. in Carolinas is indicative of pathogen removal with flooding waters. Except for the abundance presence of Salmonella typhimurium, no significant changes in pathogens were observed after Hurricane Michael in the Florida panhandle. We argue that a comprehensive assessment of pathogens must be included in decision-making activities in the immediate aftermath of hurricanes to build resilience against risks of pathogenic exposure in rural agricultural and human populations in vulnerable locations.

Exploring the impact of socio-demographic characteristics, health concerns, and product type on home delivery rates and expenditures during a strict COVID-19 lockdown period: A case study from Portland, OR.

E-commerce volumes and home deliveries have experienced steady growth in the last two decades. Strict COVID-19 lockdowns made home delivery an essential service and a lifeline for many households that, for travel restrictions or health concerns, were not able to utilize traditional shopping methods. This research studies the impact of socio-demographic variables and e-commerce attitudes on household deliveries for seven product categories (groceries, meals, electronics, household and office goods, recreational items, and fashion, beauty and personal care products, and medicine/health-related products) during the lockdown period in the greater Portland metropolitan region. To understand these impacts, exploratory factor analysis and choice models with latent variables are estimated utilizing data collected from an online survey representing the population in the greater Portland metropolitan region. The results indicate that each factor has a unique profile in terms of significant socio-demographic variables. A novel contribution of this research is to study the impact on home deliveries of non-traditional variables like health and safety concerns and the presence of household members with disabilities during a pandemic. The results show that health concerns are very influential and that there are substantial differences across factors on delivery rate and expenditure levels. Key findings and perspectives regarding future delivery rates and implications for transportation agencies and logistics companies are discussed.

Exploratory analysis of factors affecting levels of home deliveries before, during, and post- COVID-19.

The COVID-19 pandemic has significantly affected shopping behavior and has accelerated the adoption of online shopping and home deliveries. We administered an online survey among the population in the Portland-Vancouver-Hillsboro Metropolitan area on household and demographic characteristics, e-commerce preferences and factors, number of deliveries made before and during the COVID-19 lockdown, and number of deliveries expected to make post-pandemic. In this research, we conduct an exploratory analysis of the factors that affect home delivery levels before, during, and post-COVID-19. There was a significant increase in home deliveries during the COVID-19 lockdown relative to the before COVID-19 period. A high proportion of the households that made less than three deliveries per month before the pandemic stated they would order more online post-pandemic. A majority of the households that ordered more than three deliveries per month before COVID-19 are expected to revert to their original levels post-pandemic. The two variables most positively affecting the likelihood of online shopping were access to delivery subscriptions and income. Tech-savvy individuals are expected to make more home delivery orders post-pandemic compared to before and during COVID-19. Health concerns positively increase the likelihood of ordering online during the pandemic and post-pandemic. Older and retired individuals are less likely to use online deliveries. However, the likelihood of older and retired individuals ordering more home deliveries increased during the pandemic lockdown. Households with disabled members, single workers, and respondents concerned about online experience and health are more likely to be first-time online shoppers during the pandemic.

Distribution and Antibiotic Resistance Profiles of Salmonella enterica in Rural Areas of North Carolina After Hurricane Florence in 2018.

In this study, water samples were analyzed from a rural area of North Carolina after Hurricane Florence in 2018 and the distribution of the ttrC virulence gene of Salmonella enterica were investigated. We also examined the distribution of culturable S. enterica and determined their antibiotic resistance profiles. Antibiotic resistance genes (ARGs) in the classes of aminoglycoside, beta-lactam, and macrolide-lincosamide-streptogramin B (MLSB) were targeted in this study. The ttrC gene was detected in 23 out of 25 locations. There was a wider and higher range of the ttrC gene in flooded water versus unflooded water samples (0-2.12 × 105 copies/L vs. 0-4.86 × 104 copies/L). Culturable S. enterica was isolated from 10 of 25 sampling locations, which was less prevalent than the distribution of the ttrC gene. The antibiotic resistance profiles were not distinct among the S. enterica isolates. The aminoglycoside resistance gene aac(6')-Iy had the highest relative abundance (around 0.05 copies/16S rRNA gene copy in all isolates) among all ARGs. These findings suggested that the 2018 flooding event led to higher copy numbers of the ttrC genes of S. enterica in some flooded water bodies compared to those in unflooded water bodies. The high ARG level and similar ARG profiles were observed in all S. enterica isolates from both flooded and unflooded samples, suggesting that the antibiotic resistance was prevalent in S. enterica within this region, regardless of flooding.

Home-deliveries before-during COVID-19 lockdown: Accessibility, environmental justice, equity, and policy implications.

During the COVID-19 lockdowns, home deliveries have changed from being a desirable luxury or comfortable solution to a health-supporting and essential service for many COVID-19 at-risk populations. However, not all households are equal in terms of access to home deliveries. The onset of COVID-19 has brought to light access inequalities that preceded the pandemic and that the COVID-19 lockdown has exacerbated and made visible. The concept of home-based accessibility (HBA) is introduced, and novel research questions are addressed: (i) What type of households had zero home deliveries before COVID-19 lockdown? (ii) How the COVID-19 lockdown affected the type of households that receive home deliveries? and (iii) What are the implications of no access to home delivery services in terms of equity and environmental justice? To answer the first two questions, exploratory and confirmatory models with latent variables are estimated utilizing data collected from an online survey representative of the population in the Portland metropolitan region. Policy and environmental equity implications are discussed using the concept of home-based accessibility (HBA). The results indicate that traditionally underserved populations are less likely to benefit from home-based delivery services and that COVID-19 has worsened home delivery inequalities for underserved populations.

Predictive Time Series Analysis Linking Bengal Cholera with Terrestrial Water Storage Measured from Gravity Recovery and Climate Experiment Sensors.

Outbreaks of diarrheal diseases, including cholera, are related to floods and droughts in regions where water and sanitation infrastructure are inadequate or insufficient. However, availability of data on water scarcity and abundance in transnational basins, are a prerequisite for developing cholera forecasting systems. With more than a decade of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment, conditions favorable for predicting cholera occurrence may now be determined. We explored lead-lag relationships between TWS in the Ganges-Brahmaputra-Meghna basin and endemic cholera in Bangladesh. Since bimodal seasonal peaks in cholera in Bangladesh occur during spring and autumn seasons, two separate logistical models between TWS and disease time series (2002-2010) were developed. TWS representing water availability showed an asymmetrical, strong association with cholera prevalence in the spring (τ = -0.53; P < 0.001) and autumn (τ = 0.45; P < 0.001) up to 6 months in advance. One unit (centimeter of water) decrease in water availability in the basin increased odds of above normal cholera by 24% (confidence interval [CI] = 20-31%; P < 0.05) in the spring, while an increase in regional water by 1 unit, through floods, increased odds of above average cholera in the autumn by 29% (CI = 22-33%; P < 0.05).

A time of day analysis of crashes involving large trucks in urban areas.

Previous studies have looked at different factors that contribute to large truck-involved crashes, however a detailed analysis considering the specific effects of time of day is lacking. Using the Crash Records Information System (CRIS) database in Texas, large truck-involved crashes occurring on urban freeways between 2006 and 2010 were separated into five time periods (i.e., early morning, morning, mid-day, afternoon and evening). A series of log likelihood ratio tests were conducted to validate that five separate random parameters logit models by time of day were warranted. The outcomes of each time of day model show major differences in both the combination of variables included in each model and the magnitude of impact of those variables. These differences show that the different time periods do in fact have different contributing factors to each injury severity further highlighting the importance of examining crashes based on time of day. Traffic flow, light conditions, surface conditions, time of year and percentage of trucks on the road were found as key differences between the time periods.

Analysis of large truck crash severity using heteroskedastic ordered probit models.

Long-combination vehicles (LCVs) have significant potential to increase economic productivity for shippers and carriers by decreasing the number of truck trips, thus reducing costs. However, size and weight regulations, triggered by safety concerns and, in some cases, infrastructure investment concerns, have prevented large-scale adoption of such vehicles. Information on actual crash performance is needed. To this end, this work uses standard and heteroskedastic ordered probit models, along with the United States' Large Truck Crash Causation Study, General Estimates System, and Vehicle Inventory and Use Survey data sets, to study the impact of vehicle, occupant, driver, and environmental characteristics on injury outcomes for those involved in crashes with heavy-duty trucks. Results suggest that the likelihood of fatalities and severe injury is estimated to rise with the number of trailers, but fall with the truck length and gross vehicle weight rating (GVWR). While findings suggest that fatality likelihood for two-trailer LCVs is higher than that of single-trailer non-LCVs and other trucks, controlling for exposure risk suggest that total crash costs of LCVs are lower (per vehicle-mile traveled) than those of other trucks.