Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies.

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio's Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

Public transit cuts during COVID-19 compound social vulnerability in 22 US cities.

The COVID-19 pandemic has severely impacted public transit services through plummeting ridership during the lockdown and subsequent budget cuts. This study investigates the equity impacts of reductions in accessibility due to transit service cuts during COVID-19 and their association with urban sprawl. We evaluated transit access to food and health care services across 22 US cities in three phases during 2020. We found stark socio-spatial disparities in access to basic services and employment in food and health care. Transit service cuts worsened accessibility for communities with multiple social vulnerabilities, such as neighborhoods with high rates of poverty, low-income workers, and zero-vehicle households, as well as poor neighborhoods with high concentrations of black residents. Moreover, sprawled cities experienced greater access loss during COVID-19 than compact cities. Our results point to policies and interventions to maintain social equity and sustainable urban development while benefiting diverse social groups during disruptions.

COVID-19 exacerbates unequal food access.

Inequality to food access has always been a serious problem, yet it became even more critical during the COVID-19 pandemic, which exacerbated social inequality and reshaped essential travel. This study provides a holistic view of spatio-temporal changes in food access based on observed travel data for all grocery shopping trips in Columbus, Ohio, during and after the state-wide stay-at-home period. We estimated the decline and recovery patterns of store visits during the pandemic to identify the key socio-economic and built environment determinants of food shopping patterns. The results show a disparity: during the lockdown, store visits to dollar stores declined the least, while visits to big-box stores declined the most and recovered the fastest. Visits to stores in low-income areas experienced smaller changes even during the lockdown period. A higher percentage of low-income customers was associated with lower store visits during the lockdown period. Furthermore, stores with a higher percentage of white customers declined the least and recovered faster during the reopening phase. Our study improves the understanding of the impact of the COVID-19 crisis on food access disparities and business performance. It highlights the role of COVID-19 and similar disruptions on exposing underlying social problems in the US.

Network analysis of intra-hospital transfers and hospital onset clostridium difficile infection.

OBJECTIVES: To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment. METHODS: A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS. RESULTS: The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor. DISCUSSION: This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals. CONCLUSION: Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.

Evaluating the attractiveness of a new light rail extension: Testing simple change and displacement change hypotheses.

Many communities in the United States have been adding new light rail to bus-predominant public transit systems. However, there is disagreement as to whether opening light rail lines attracts new ridership or merely draws ridership from existing transit users. We study a new light rail line in Salt Lake City, Utah, USA, which is part of a complete street redevelopment. We utilize a pre-test post-test control group quasi-experimental design to test two different measures of ridership change. The first measure is calculated from stops along the light rail route; the second assumes that nearby bus stops might be displaced by the rail and calculates ridership change with those stops included as baseline. Both the simple measure (transit use changes on the complete street light rail corridor) and the "displacement" measure (transit use changes in the one-quarter mile catchment areas around new light rail stops) showed significant (p < .01) and substantial (677%) increases in transit passengers compared to pre-light rail bus users. In particular, the displacement analysis discredits a common challenge that when a new light rail line opens, most passengers are simply former bus riders whose routes were canceled in favor of light rail. The study suggests that light rail services can attract additional ridership to public transit systems. In addition, although pre-post control-group designs require time and effort, this project underscores the benefits of such quasi-experimental designs in terms of the strength of the inferences that can be drawn about the impacts of new transit infrastructure and services.

Transit Use, Physical Activity, and Body Mass Index Changes: Objective Measures Associated With Complete Street Light-Rail Construction.

OBJECTIVES: We assessed effects on physical activity (PA) and weight among participants in a complete street intervention that extended a light-rail line in Salt Lake City, Utah. METHODS: Participants in the Moving Across Places Study resided within 2 kilometers of the new line. They wore accelerometers and global positioning system (GPS) loggers for 1 week before and after rail construction. Regression analyses compared change scores of participants who never rode transit with continuing, former, and new riders, after adjustment for control variables (total n = 537). RESULTS: New riders had significantly more accelerometer-measured counts per minute than never-riders (P < .01), and former riders had significantly fewer (P < .01). New riders lost (P < .05) and former riders gained (P < .01) weight. Former riders lost 6.4 minutes of moderate-to-vigorous PA (MVPA) per 10 hours of accelerometer wear (P < .01) and gained 16.4 minutes of sedentary time (P < .01). New riders gained 4.2 MVPA minutes (P < .05) and lost 12.8 (P < .05) sedentary minutes per 10 hours accelerometer wear. CONCLUSIONS: In light of the health benefits of transit ridership in the complete street area, research should address how to encourage more sustained ridership.

Orally delivered water soluble Coenzyme Q10 (Ubisol-Q10) blocks on-going neurodegeneration in rats exposed to paraquat: potential for therapeutic application in Parkinson's disease.

BACKGROUND: Paraquat, still used as an herbicide in some parts of the world, is now regarded as a dangerous environmental neurotoxin and is linked to the development Parkinson's disease (PD). Paraquat interacts with cellular redox systems and causes mitochondrial dysfunction and the formation of reactive oxygen species, which in turn, plays a crucial role in the pathophysiology of PD. Various antioxidant therapies have been explored with the expectations that they deliver health benefits to the PD patients, however, no such therapies were effective. Here we have tested the neuroprotective efficacy of a novel water-soluble CoQ10 (Ubisol-Q10), in a rat model of paraquat-induced neurodegeneration in order to evaluate its potential application in the management of PD. RESULTS: We have developed a rat model of progressive nigrostriatal degeneration by giving rats five intraperitoneal injections of paraquat (10 mg/kg/injection), once every five days. Neuronal death occurred over a period of 8 weeks with close to 50% reduction in the number of tyrosine hydroxylase-positive cells. Ubisol-Q10, at 6 mg CoQ10/kg body weight/day, was delivered as a supplement in drinking water. The intervention begun after the completion of paraquat injections when the neurodegenerative process had already began and about 20% of TH-positive neurons were lost. Ubisol-Q10 treatment halted the progression of neurodegeneration and remaining neurons were protected. The outcomes were evaluated based on the number of surviving tyrosine hydroxylase-positive neurons in the substantia nigra region and improved motor skills in response to the Ubisol-Q10 intervention. To maintain this neuroprotection, however, continuous Ubisol- Q10 supplementation was required, if withdrawn, the neuronal death pathway resumed, suggesting that the presence of CoQ10 was essential for blocking the pathway. CONCLUSION: The CoQ10, given orally as Ubisol-Q10 in drinking solution, was effective in blocking the progression of neurodegeneration when administered therapeutically (post-toxin injection), at a much lower concentration than other previously tested oil soluble formulations and well within the acceptable daily intake of 12 mg/kg/day. Such unprecedented neuroprotection has never been reported before. These results are very encouraging and suggest that Ubisol-Q10 should be further tested and developed as a therapy for halting the progression of PD.

Physical activity mediates the relationship between perceived crime safety and obesity.

OBJECTIVE: The current cross-sectional study tests whether low perceived crime safety is associated with body mass index (BMI) and obesity risk and whether less moderate-to-vigorous physical activity (MVPA) accounts for part of this relationship. METHOD: Adults (n=864) from a relatively low-income and ethnically mixed neighborhood in Salt Lake City UT (2012) were assessed for perceived crime safety, objective physical activity, and BMI measures. RESULTS: This neighborhood had lower perceived safety than for other published studies utilizing this safety measure. In a mediation test, lower perceived crime safety was significantly associated with higher BMI and greater risk of obesity, net of control variables. Residents with lower perceived safety had less MVPA. Lower MVPA partially explained the relationship between less safety and both elevated BMI and higher obesity risk, suggesting that perceiving less crime safety limits MVPA which, in turn, increases weight. CONCLUSION: In this neighborhood, with relatively low perceived safety from crime, residents' low perceived safety is related to more obesity and higher BMI; lower MVPA among residents explained part of this relationship. If residents are to become more active in their neighborhood it may be important to address perceived crime safety as part of broader efforts to enhance active living.

Adding maps (GPS) to accelerometry data to improve study participants' recall of physical activity: a methodological advance in physical activity research.

OBJECTIVE: Obtaining the 'when, where and why' of healthy bouts of moderate-to-vigorous physical activity (MVPA) provides insights into natural PA. DESIGN: In Salt Lake City, Utah, adults wore accelerometer and Global Positioning System (GPS) loggers for a week in a cross-sectional study to establish baseline travel and activity patterns near a planned Complete Street intervention involving a new rail line, new sidewalks and a bike path. RESULTS: At the end of the week, research assistants met with the 918 participants who had at least three 10 h days of good accelerometer readings. Accelerometer and GPS data were uploaded and integrated within a custom application, and participants were provided with maps and time information for past MVPA bouts of ≥3 min to help them recall bout details. Participants said that 'getting someplace' was, on average, a more important motivation for their bouts than leisure or exercise. A series of recall tests showed that participants recalled most bouts they were asked about, regardless of the duration of the bout, suggesting that participant perceptions of their shorter lifestyle bouts can be studied with this methodology. Visual prompting with a map depicting where each bout took place yielded more accurate recall than prompting with time cues alone. CONCLUSIONS: These techniques provide a novel way to understand participant memories of the context and subjective assessments associated with healthy bouts of PA. Prompts with time-stamped maps that illustrate places of MVPA offer an effective method to improve understanding of activity and its supportive sociophysical contexts.

Transportation Factors and Postoperative Attendance and Weight Loss Through 24 Months.

PURPOSE: Transportation, access to follow-up care, and association with weight loss are understudied in the bariatric population. The objective of this study was to determine how transportation variables associate with postoperative attendance and weight loss through 24 months. MATERIALS AND METHODS: Seven hundred eighty-seven patients (81.3% female; 59.1% White) who had primary surgery (48.6% gastric bypass) from 2015 to 2019 were included. Sidewalk coverage and number of bus stops from patients' homes, driving distance in miles and minutes from patients' homes to the nearest bus stop and the clinic were measured. Bivariate analyses were conducted with the transportation variables and attendance and %TWL at 2 or 3, 6, 12, and 24 months. One mixed multilevel model was conducted with dependent variable %TWL over 24 months with visits as the between-subjects factor and covariates: race, insurance, surgical procedure, and driving distance to the clinic in minutes, attendance, and %TWL over 24 months; an interaction between distance, attendance, and visits. RESULTS: There were no significant differences between the majority of the transportation variables and postoperative attendance or %TWL. Patients who had perfect attendance had improved %TWL at 12 months [t(534)=-1.92, p=0.056] and 24 months [t(393)=-2.69, p=0.008] compared to those who missed at least one appointment. Patients with perfect attendance and who had shorter driving times (under 20 min) to the clinic had greater weight loss through 24 months [F(10, 1607.50)=2.19, p=0.016)]. CONCLUSIONS: Overall, transportation factors were not associated with attendance and weight loss, with the exception of the interaction between shorter driving minutes to follow-up and perfect attendance.

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis.

BACKGROUND: Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. METHODS: This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. RESULTS: By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. CONCLUSIONS: The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning.

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods.

The BARI-hoods Project: neighborhood social determinants of health and postoperative weight loss using integrated electronic health record, census, and county data.

BACKGROUND: While social determinants of health (SDoH) have gained attention for their role in weight loss following bariatric surgery, electronic health record (EHR) data provide limited information beyond demographics associated with disparities in weight loss. OBJECTIVE: To integrate EHR, census, and county data to explore disparities in SDoH and weight loss among patients in the largest populous county of Ohio. SETTING: Seven hundred seventy-two patients (82.1% female; 37.0% Black) who had primary bariatric surgery (48.7% gastric bypass) from 2015 to 2019 at Ohio State University. METHODS: EHR variables included race, insurance, procedure, and percent total weight lost (%TWL) at 2/3, 6, 12, and 24 months. Census variables included poverty and unemployment rates. County variables included food stores, fitness/recreational facilities, and open area within a 5- and 10-minute walk from home. Two mixed multilevel models were conducted with %TWL over 24 months, with visits as the between-subjects factor; race, census, county, insurance, and procedure variables were covariates. Two additional sets of models determined within-group differences for Black and White patients. RESULTS: Access to more food stores within a 10-minute walk was associated with greater %TWL over 24 months (P = .029). Black patients with access to more food stores within a 10-minute (P = .017) and White patients with more access within a 5-minute walk (P = .015) had greater %TWL over 24 months. Black patients who lived in areas with higher poverty rates (P = .036) experienced greater %TWL over 24 months. No significant differences were found for unemployment rate or proximity to fitness/recreational facilities and open areas. CONCLUSIONS: Close proximity to food stores is associated with better weight loss 2 years after bariatric surgery. Lower poverty levels did not negatively affect weight loss in Black patients.

Proximity to High/Moderate vs Low Diversity Selection Food Stores and Patient Weight loss through 24 Months.

PURPOSE: Explorations into the neighborhood food environment have not adequately extended to adults with obesity who undergo bariatric surgery. The objective of this study is to determine how diversity of food selection at food retail stores within proximities of 5- and 10-min walks associate with patient postoperative weight loss over 24 months. MATERIALS AND METHODS: Eight hundred eleven patients (82.1% female; 60.0% White) who had primary bariatric surgery (48.6% gastric bypass) from 2015 to 2019 at The Ohio State University were included. EHR variables included race, insurance, procedure, and percent total weight loss (%TWL) at 2, 3, 6, 12, and 24 months. Proximity from patients' home addresses to food stores within a 5- (0.25 mile)- and 10-min (0.50 mile) walk were totaled for low (LD) and moderate/high (M/HD) diversity food selections. Bivariate analyses were conducted with %TWL at all visits and LD and M/HD selections within 5- (0, ≥ 1) and 10-min (0, 1, ≥ 2) walk proximities. Four mixed multilevel models were conducted with dependent variable %TWL over 24 months with visits as the between subjects factor and covariates: race, insurance, procedure, and interaction between proximity to type of food store selections with visits to determine association with %TWL over 24 months. RESULTS: There were no significant differences for patients living within a 5- (p = 0.523) and 10-min (p = 0.580) walk in proximity to M/HD food selection stores and weight loss through 24 months. However, patients living in proximity to at least 1 LD selection store within a 5- (p = 0.027) and 1 or 2 LD stores within a 10-min (p = 0.015) walk had less weight loss through 24 months. CONCLUSION: Overall, living in proximity to LD selection stores was a better predictor of postoperative weight loss over 24 months than living within proximity of M/HD selection stores.

Realizable accessibility: evaluating the reliability of public transit accessibility using high-resolution real-time data.

The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space-time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedule-based and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data.

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county.

OBJECTIVES: An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data. METHODS: We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types. RESULTS: A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26-35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race. CONCLUSIONS: Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.

Mode of ethanol administration influences the severity of hemorrhage-induced lactic acidemia in conscious guinea pigs.

Mechanisms that limit metabolic acidemia during shock are limited by ethanol (EtOH). This may be due to (1) loss of respiratory compensation, (2) a greater fall in cardiac output, (3) altered removal of plasma lactate by the liver, and (4) alterations in central nervous system orchestration of compensatory responses. We have previously shown that loss of metabolic compensation during hemorrhage is correlated with plasma EtOH concentrations. The present study determines if the mode of ethanol administration influences compensation during hemorrhage. Male guinea pigs were administered EtOH (1 g/kg, 30% wt/vol) via intraperitoneal (IP) or intragastric (IG) routes. After 30 minutes, 60% of the estimated blood volume was removed. Animals remained in shock for 30 minutes were resuscitated with lactated Ringer solution and monitored for 3 hours. Plasma EtOH levels were similar in the 2 groups at the initiation of, and during, hemorrhage and resuscitation. Animals given EtOH IP exhibited more severe acidemia. The mode of EtOH administration may affect hepatic ethanol and lactate metabolism, thus exacerbating acidemia. An altered central nervous system response may impact compensatory responses during shock. Our results indicate that the "history" of the EtOH episode may be an important determinant in the compensation for hemorrhage and resuscitation.

The impacts of COVID-19 pandemic on public transit demand in the United States.

The COVID-19 pandemic and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic.