Building CapaCITY/É for sustainable transportation: protocol for an implementation science research program in healthy cities.

INTRODUCTION: Improving sustainable transportation options will help cities tackle growing challenges related to population health, congestion, climate change and inequity. Interventions supporting active transportation face many practical and political hurdles. Implementation science aims to understand how interventions or policies arise, how they can be translated to new contexts or scales and who benefits. Sustainable transportation interventions are complex, and existing implementation science frameworks may not be suitable. To apply and adapt implementation science for healthy cities, we have launched our mixed-methods research programme, CapaCITY/É. We aim to understand how, why and for whom sustainable transportation interventions are successful and when they are not. METHODS AND ANALYSIS: Across nine Canadian municipalities and the State of Victoria (Australia), our research will focus on two types of sustainable transportation interventions: all ages and abilities bicycle networks and motor vehicle speed management interventions. We will (1) document the implementation process and outcomes of both types of sustainable transportation interventions; (2) examine equity, health and mobility impacts of these interventions; (3) advance implementation science by developing a novel sustainable transportation implementation science framework and (4) develop tools for scaling up and scaling out sustainable transportation interventions. Training activities will develop interdisciplinary scholars and practitioners able to work at the nexus of academia and sustainable cities. ETHICS AND DISSEMINATION: This study received approval from the Simon Fraser University Office of Ethics Research (H22-03469). A Knowledge Mobilization Hub will coordinate dissemination of findings via a website; presentations to academic, community organisations and practitioner audiences; and through peer-reviewed articles.

Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases.

Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.

Using Real-Time Dashboards to Monitor the Impact of Disruptive Events on Real Estate Market. Case of COVID-19 Pandemic in Australia.

Real estate markets are complex both in terms of structure and dynamics: they are both influenced by and influence almost all aspects of the economy and are equally vulnerable to the shocks experienced by the broader economy. Therefore, understanding the extent and nature of the impact of large-scale disruptive events such as natural disasters and economic financial downturns on the real estate market is crucial to policy makers and market stakeholders. In addition to anticipating and preparing for long-term effects, it has become imperative for stakeholders to monitor and manage the short-term effects as well due to the emergence of 'PropTech' and 'platform real estate'. In this work, we explore the use of online, real-time dashboards which have been used extensively in the context of urban management, policymaking, citizen engagement and disaster response as an appropriate tool for the purpose of monitoring real estate markets. We describe the process of designing, building, and maintaining an operational dashboard for monitoring the residential real estate market in Australia during the COVID-19 pandemic in 2020. We detail the techniques and methods used in creating the dashboard and critically evaluate their feasibility and usefulness. Finally, we identify the major challenges in the process, such as the spatial and temporal availability and veracity of the real estate market data, and we identify possible avenues for consistent, high-quality data; methodology; and outputs for further research.

Examining associations between area-level spatial measures of housing with selected health and wellbeing behaviours and outcomes in an urban context.

Adequate and affordable housing is a major social determinant of health; yet no work has attempted to conceptually map and spatially test area-level measures of housing with selected health and wellbeing outcomes. Sourcing data from 7,753 adults from Melbourne, Australia, we tested associations between area-level measures of housing density, tenure, and affordability with individual-level measures of neighbourhood safety, community satisfaction, and self-rated health. Compared with the reference groups, the odds of: feeling unsafe was higher for residents living in areas with less affordable housing; community dissatisfaction was ~30% higher in those living in areas with >36% residential properties assigned as rentals, and was significantly higher in the least affordable areas (OR =1.57). Compared with the reference groups, as dwelling density, proportion of rental properties, and housing unaffordability increased, the odds of reporting poorer self-rated health increased; however these associations did not always reach statistical significance. This work highlights the benefits of evidenced-based planning spatial measures to support health and wellbeing.

Using simple agent-based modeling to inform and enhance neighborhood walkability.

BACKGROUND: Pedestrian-friendly neighborhoods with proximal destinations and services encourage walking and decrease car dependence, thereby contributing to more active and healthier communities. Proximity to key destinations and services is an important aspect of the urban design decision making process, particularly in areas adopting a transit-oriented development (TOD) approach to urban planning, whereby densification occurs within walking distance of transit nodes. Modeling destination access within neighborhoods has been limited to circular catchment buffers or more sophisticated network-buffers generated using geoprocessing routines within geographical information systems (GIS). Both circular and network-buffer catchment methods are problematic. Circular catchment models do not account for street networks, thus do not allow exploratory 'what-if' scenario modeling; and network-buffering functionality typically exists within proprietary GIS software, which can be costly and requires a high level of expertise to operate. METHODS: This study sought to overcome these limitations by developing an open-source simple agent-based walkable catchment tool that can be used by researchers, urban designers, planners, and policy makers to test scenarios for improving neighborhood walkable catchments. A simplified version of an agent-based model was ported to a vector-based open source GIS web tool using data derived from the Australian Urban Research Infrastructure Network (AURIN). The tool was developed and tested with end-user stakeholder working group input. RESULTS: The resulting model has proven to be effective and flexible, allowing stakeholders to assess and optimize the walkability of neighborhood catchments around actual or potential nodes of interest (e.g., schools, public transport stops). Users can derive a range of metrics to compare different scenarios modeled. These include: catchment area versus circular buffer ratios; mean number of streets crossed; and modeling of different walking speeds and wait time at intersections. CONCLUSIONS: The tool has the capacity to influence planning and public health advocacy and practice, and by using open-access source software, it is available for use locally and internationally. There is also scope to extend this version of the tool from a simple to a complex model, which includes agents (i.e., simulated pedestrians) 'learning' and incorporating other environmental attributes that enhance walkability (e.g., residential density, mixed land use, traffic volume).