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.

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.

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.

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.

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.

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.

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.

311 service requests as indicators of neighborhood distress and opioid use disorder.

Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as "311" requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008-2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.

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.

Towards an Integrated Science of Movement: Converging Research on Animal Movement Ecology and Human Mobility Science.

There is long-standing scientific interest in understanding purposeful movement by animals and humans. Traditionally, collecting data on individual moving entities was difficult and time-consuming, limiting scientific progress. The growth of location-aware and other geospatial technologies for capturing, managing and analyzing moving objects data are shattering these limitations, leading to revolutions in animal movement ecology and human mobility science. Despite parallel transitions towards massive individual-level data collected automatically via sensors, there is little scientific cross-fertilization across the animal and human divide. There are potential synergies from converging these separate domains towards an integrated science of movement. This paper discusses the data-driven revolutions in the animal movement ecology and human mobility science, their contrasting worldviews and, as examples of complementarity, transdisciplinary questions that span both fields. We also identify research challenges that should be met to develop an integrated science of movement trajectories.

Geographic regions for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs.

There is growing international evidence that supportive built environments encourage active travel such as walking. An unsettled question is the role of geographic regions for analyzing the relationship between the built environment and active travel. This paper examines the geographic region question by assessing walking trip models that use two different regions: walking activity spaces and self-defined neighborhoods. We also use two types of built environment metrics, perceived and audit data, and two types of study design, cross-sectional and longitudinal, to assess these regions. We find that the built environment associations with walking are dependent on the type of metric and the type of model. Audit measures summarized within walking activity spaces better explain walking trips compared to audit measures within self-defined neighborhoods. Perceived measures summarized within self-defined neighborhoods have mixed results. Finally, results differ based on study design. This suggests that results may not be comparable among different regions, metrics and designs; researchers need to consider carefully these choices when assessing active travel correlates.

Analyzing Walking Route Choice through Built Environments using Random Forests and Discrete Choice Techniques.

Walking is a form of active transportation with numerous benefits, including better health outcomes, lower environmental impacts and stronger communities. Understanding built environmental associations with walking behavior is a key step towards identifying design features that support walking. Human mobility data available through GPS receivers and cell phones, combined with high resolution walkability data, provide a rich source of georeferenced data for analyzing environmental associations with walking behavior. However, traditional techniques such as route choice models have difficulty with highly dimensioned data. This paper develops a novel combination of a data-driven technique with route choice modeling for leveraging walkability audits. Using data from a study in Salt Lake City, Utah, USA, we apply the data-driven technique of random forests to select variables for use in walking route choice models. We estimate data-driven route choice models and theory-driven models based on predefined walkability dimensions. Results indicate that the random forest technique selects variables that dramatically improve goodness of fit of walking route choice models relative to models based on predefined walkability dimensions. We compare the theory-driven and data-driven walking route choice models based on interpretability and policy relevance.

Environmental, behavioral, and psychological predictors of transit ridership: Evidence from a community intervention.

Understanding who takes advantage of new transit (public transportation) interventions is important for personal and environmental health. We examine transit ridership for residents living near a new light rail construction as part of "complete street," pedestrian-friendly improvements. Adult residents (n=536) completed surveys and wore accelerometer and GPS units that tracked ridership before and after new transit service started. Transit riders were more physically active. Those from environments rated as more walkable were likely to be continuing transit riders. Place attachment, but not perceived physical incivilities on the path to transit, was associated with those who continued to ride or became new riders of transit. This effect was mediated through pro-city attitudes, which emphasize how the new service makes residents eager to explore areas around transit. Thus, place attachment, along with physical and health conditions, may be important predictors and promoters of transit use.

Changes in bicycling over time associated with a new bike lane: relations with kilocalories energy expenditure and body mass index.

Although bicycling has been related to positive health indicators, few studies examine health-related measures associated with non-competitive community cycling before and after cycling infrastructure improvements. This study examined cycling changes in a neighborhood receiving a bike lane, light rail, and other "complete street" improvements. Participants wore accelerometers and global positioning system (GPS) data loggers for one week in both 2012 and 2013, pre- and post- construction completion. Participants sampled within 2 km of the complete street improvements had the following patterns of cycling: never cyclists (n=434), continuing cyclists (n= 29), former cyclists (n=33, who bicycled in 2012 but not 2013), and new cyclists (n=40, who bicycled in 2013 but not 2012). Results show that all three cycling groups, as identified by GPS/accelerometry data, expended more estimated kilocalories (kcal) of energy per minute during the monitoring week than those who were never detected cycling, net of control variables. Similar but attenuated results emerged when cycling self-report measures were used. BMI was not related to cycling group but those who cycled longer on the new path had lower BMI. Although cyclists burn more calories than non-cyclists across the week, among cyclists, their cycling days involved more calories expended than their non-cycling days. The new cyclists account for 39% of the cyclists identified in this study and former cyclists account for 32% of cyclists. These results suggest that cycling is healthy, but that sustaining rates of cycling will be an important goal for future policy and research.

A Complete Street Intervention for Walking to Transit, Nontransit Walking, and Bicycling: A Quasi-Experimental Demonstration of Increased Use.

BACKGROUND: Complete streets require evaluation to determine if they encourage active transportation. METHODS: Data were collected before and after a street intervention provided new light rail, bike lanes, and better sidewalks in Salt Lake City, Utah. Residents living near (<800 m) and far (≥801 to 2000 m) from the street were compared, with sensitivity tests for alternative definitions of near (<600 and <1000 m). Dependent variables were accelerometer/global positioning system (GPS) measures of transit trips, nontransit walking trips, and biking trips that included the complete street corridor. RESULTS: Active travel trips for Near-Time 2 residents, the group hypothesized to be the most active, were compared with the other 3 groups (Near-Time 1, Far-Time 1, and Far-Time 2), net of control variables. Near-Time 2 residents were more likely to engage in complete street transit walking trips (35%, adjusted) and nontransit walking trips (50%) than the other 3 groups (24% to 25% and 13% to 36%, respectively). Bicycling was less prevalent, with only 1 of 3 contrasts significant (10% of Near-Time 2 residents had complete street bicycle trips compared with 5% of Far-Time 1 residents). CONCLUSIONS: Living near the complete street intervention supported more pedestrian use and possibly bicycling, suggesting complete streets are also public health interventions.