Spatial analysis of the association of alcohol outlets and alcohol-related pedestrian/bicyclist injuries in New York City

Background Pedestrian and bicyclist injury is an important public health issue. The retail environment, particularly the presence of alcohol outlets, may contribute the the risk of pedestrian or bicyclist injury, but this association is poorly understood. Methods This study quantifies the spatial risk of alcohol-related pedestrian injury in New York City at the census tract level over a recent 10-year period using a Bayesian hierarchical spatial regression model with Integrated Nested Laplace approximations. The analysis measures local risk, and estimates the association between the presence of alcohol outlets in a census tract and alcohol-involved pedestrian/bicyclist injury after controlling for social, economic and traffic-related variables. Results Holding all other covariates to zero and adjusting for both random and spatial variation, the presence of at least one alcohol outlet in a census tract increased the risk of a pedestrian or bicyclist being struck by a car by 47 % (IDR = 1.47, 95 % Credible Interval (CrI) 1.13, 1.91). Conclusions The presence of one or more alcohol outlets in a census tract in an urban environment increases the risk of bicyclist/pedestrian injury in important and meaningful ways. Identifying areas of increased risk due to alcohol allows the targeting of interventions to prevent and control alcohol-related pedestrian and bicyclist injuries

The Impact of Built and Social Environment on Physical Activity among Older Adults

Physical activity, defined as bodily movement produced by skeletal muscles that results in energy expenditure, has many known mental and physical health benefits for older adults. However, as of 2008, only 22.6% of older adults in the United States reported meeting recommended physical activity guidelines. This dissertation examines the role of the built and social environment on physical activity among older adults, with particular focus on physical disorder, or the visual indications of neighborhood deterioration. All empirical analyses use data from the New York City Neighborhood and Mental Health in the Elderly Study (NYCNAMES-II), a three-wave longitudinal study of about 3,500 older adults living in New York City. We first systematically review the existing literature concerning physical disorder as an influence on physical activity among adults of all ages. We find that most prior studies of disorder and activity have been cross-sectional and that disorder has not consistently been associated with less activity across all studies. However, we also find indications that older adults’ activity levels may be more negatively impacted by disorder than younger adults’ activity levels. Next, we use a longitudinal analysis to estimate the association between neighborhood disorder and total physical activity among the NYCNAMES-II cohort. In multivariable mixed regression models accounting for individual and neighborhood factors, for missing data, and for loss to follow-up, we find that each standard deviation increase in neighborhood disorder was associated with an estimated 3.0 units (95% CI: 1.9, 4.2) lower PASE score at baseline, or the equivalent of about 10 minutes of walking per day. There was no significant interaction between physical disorder and changes in PASE score over two years of follow-up. We next apply a latent transition analysis to identify patterns of types of physical activity the same cohort, identifying seven latent classes of activity. Of these seven classes, three pairs of classes were roughly equivalent except for participation in exercise. About three quarters of subjects remained within each latent class between waves; most transitions that did occur were between classes defined by exercise to the parallel class without exercise or vice-versa. More neighborhood disorder was modestly associated with moving out of a sports and recreation class (Relative Risk = 1.27, 95% CI = 1.00, 1.61 between waves 1 and 2, Relative Risk = 1.28, 95% CI = 0.85, 1.93 between waves 2 and 3). Finally, we develop the Neighborhood Environment-Wide Association Study (NE-WAS), an agnostic approach to systematically explore the plethora of neighborhood measures available to modern researchers equipped with geographic information systems (GIS) software. We find that only neighborhood socioeconomic status and disorder measures were associated with total activity and gardening, whereas a broader range of measures was associated with walking. Substantively, we conclude that more physical disorder was associated with less physical activity, potentially due to decreases in sports and recreation among those living amidst physical disorder, though latent transition analysis estimates were too imprecise to rule out chance. Future longitudinal research on physical disorder as an influence on physical activity would benefit from longer periods of follow-up in which more subjects moved between neighborhoods. Methodologically, the NE-WAS approach appears to be a promising way to systematize neighborhood research as the scale of available spatially located administrative data continues to grow. Future NE-WASes might profitably focus on comparing the spatial scale of neighborhood measures

Mathematical Modelling of Metabolic Regulation in Aging

The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area

Cause and context: place-based approaches to investigate how environments affect mental health

Objectives Our surroundings affect our mood, our recovery from stress, our behavior, and, ultimately, our mental health. Understanding how our surroundings influence mental health is central to creating healthy cities. However, the traditional observational methods now dominant in the psychiatric epidemiology literature are not sufficient to advance such an understanding. In this essay we consider potential alternative strategies, such as randomizing people to places, randomizing places to change, or harnessing natural experiments that mimic randomized experiments. Methods We discuss the strengths and weaknesses of these methodological approaches with respect to (1) defining the most relevant scale and characteristics of context, (2) disentangling the effects of context from the effects of individuals’ preferences and prior health, and (3) generalizing causal effects beyond the study setting. Results Promising alternative strategies include creating many small-scale randomized place-based trials, using the deployment of place-based changes over time as natural experiments, and using fluctuations in the changes in our surroundings in combination with emerging data collection technologies to better understand how surroundings influence mood, behavior, and mental health. Conclusions Improving existing research strategies will require interdisciplinary partnerships between those specialized in mental health, those advancing new methods for place effects on health, and those who seek to optimize the design of local environments

A Pilot Study of Sidewalk Equity in Seattle Using Crowdsourced Sidewalk Assessment Data

We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work.Comment: Workshop paper presented at "The 1st ASSETS'22 Workshop on The Future or urban Accessibility (UrbanAccess'22)

A Robust Solution Procedure for Hyperelastic Solids with Large Boundary Deformation

Compressible Mooney-Rivlin theory has been used to model hyperelastic solids, such as rubber and porous polymers, and more recently for the modeling of soft tissues for biomedical tissues, undergoing large elastic deformations. We propose a solution procedure for Lagrangian finite element discretization of a static nonlinear compressible Mooney-Rivlin hyperelastic solid. We consider the case in which the boundary condition is a large prescribed deformation, so that mesh tangling becomes an obstacle for straightforward algorithms. Our solution procedure involves a largely geometric procedure to untangle the mesh: solution of a sequence of linear systems to obtain initial guesses for interior nodal positions for which no element is inverted. After the mesh is untangled, we take Newton iterations to converge to a mechanical equilibrium. The Newton iterations are safeguarded by a line search similar to one used in optimization. Our computational results indicate that the algorithm is up to 70 times faster than a straightforward Newton continuation procedure and is also more robust (i.e., able to tolerate much larger deformations). For a few extremely large deformations, the deformed mesh could only be computed through the use of an expensive Newton continuation method while using a tight convergence tolerance and taking very small steps.Comment: Revision of earlier version of paper. Submitted for publication in Engineering with Computers on 9 September 2010. Accepted for publication on 20 May 2011. Published online 11 June 2011. The final publication is available at http://www.springerlink.co

Residents in Seattle, WA Report Differential Use of Free-Floating Bikeshare by Age, Gender, Race, and Location

Bikesharing may have numerous urban health, sustainability, and mobility benefits. Bikesharing systems that do not require stations (i.e., “dockless,” or “free-floating” bikeshare) launched in North America in 2017. While this novel model may enhance access to and use of bikeshare by diverse populations, to date no work has examined equity in free-floating bikeshare use. This brief report uses a web-based panel survey (n = 601) to provide sociodemographic characteristics of adult Seattle residents reporting bikeshare use during the first 6 months of a pilot free-floating program. One-third of Seattle adults surveyed reported trying free-floating bikeshare. These users were disproportionately young, male, White, resided closer to the city center, and already more likely to have or use a bicycle. Safety, social, spatial access, physical size, operation, technology, and cost barriers remained, particularly for males and non-White respondents. Almost half of non-users were open to trying free-floating bikeshare. However, these respondents hold limited potential to diversify the user population: while more likely to be female, like current riders, they were young and already using bicycles. If cities, researchers, and operators work together in the rapidly-shifting mobility landscape, they may be able to remove inequitably distributed barriers to transportation technology

Reproducibility and scientific integrity of big data research in urban public health and digital epidemiology: a call to action

The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level