White Working Class Communities in Lyon: French

This report is part of a six-city research series, Europe's White Working Class Communities, which examines the realities of people from majority populations in Aarhus, Amsterdam, Berlin, Lyon, Manchester, and Stockholm.White Working Class Communities in Lyon explores the views and experiences of the majority population in the 8th arrondissement (borough) of Lyon, a diverse and dense area, and socially and economically one of the most challenged areas in the city. Given that in France it is not permitted to define people based on ethnic or racial characteristics, and it is very difficult to talk explicitly about ethnicity, the term "majority population" was used as an open category to recruit focus groups' participants. This study is the largest, and to our knowledge, only empirical study on the majority population that has been conducted in France.Lyon is considered to be a role model in France for working actively on inclusion and cohesion issues. This report analyzes six areas of its local policy—education, employment, housing, health and social protection, policing and security, and civil and political and participation—as well as broader themes of belonging and identity and the role of the media.The findings, permeated by a changing socio-economic environment and anxiety over perceived and real differences, are complex. For instance, residents felt their French identity was under pressure but had strong local identities. Likewise, they had serious hardships in different areas and felt they were ignored by the state and the media but were also generally positive about their future and did not feel particularly disempowered.White Working Class Communities in Lyon is part of a six-city series by the Open Society Foundations' At Home in Europe project providing groundbreaking research on the realities of a section of the population whose lives are often caricatured and whose voices are rarely heard in public debates on integration, social cohesion, and social inclusion. Through a comparative lens, the project seeks to highlight parallels and differences in policies, practices and experiences across the European cities of Aarhus, Amsterdam, Berlin, Lyon, Manchester, and Stockholm

The role of health impact assessment in Phase V of the Healthy Cities European Network

Health impact assessment (HIA) is a prospective decision-making aid tool that aims to improve the quality of policies, programmes or projects through recommendations that promote health. It identifies how and through which pathways a decision can impact a wide range of health determinants and seeks to define the distribution of effects within populations, thereby raising the issue of equity. HIA was introduced to the WHO European Healthy Cities Network as one of its four core themes during the Phase IV (2004-08). Here we present an evaluation of the use of HIA during Phase V (2009-13), where HIA was linked with the overarching theme of health and health equity in all local policies and a requirement regarding capacity building. The evaluation was based on 10 case studies contributed by 9 Healthy Cities in five countries (France, Hungary, Italy, Spain and the UK). A Realist Evaluation framework was used to collect and aggregate data obtained through three methods: an HIA factors analysis, a case-study template analysis using Nvivo software and a detailed questionnaire. The main conclusion is that HIA significantly helps promote Health in All Policies (HiAP) and sustainability in Healthy Cities. It is recommended that all Healthy City candidates to Phase VI (2014-18) of the WHO Healthy Cities European Network effectively adopt HIA and HiA

White Working Class Communities in Lyon

White Working Class Communities in Lyon explores the views and experiences of the majority population in the 8th arrondissement (borough) of Lyon, a diverse and dense area, and socially and economically one of the most challenged areas in the city. This study is the largest and probably only empirical study on the majority population that has been conducted in France. Lyon is considered a role model in France for working actively on inclusion and cohesion issues. This report analyzes six areas of its local policy—education, employment, housing, health and social protection, policing and security, and civil and political and participation—as well as broader themes of belonging and identity and the role of the media

Frequent walkers: from healthy individual behaviours to sustainable mobility futures

Walking is often taken for granted or considered as an ancillary activity. Little is known about the distribution of walking in contemporary populations, and even less about the few people who walk for an hour or more in public space on most days of the week, for whom we coined the term "frequent walkers". Because they have succeeded in acquiring and maintaining this behaviour over time, frequent walkers may constitute a pioneer population with the potential to inspire change towards a sustainable and healthy mobility system. This project seeks to understand how and why people become frequent walkers, how they integrate walking into their schedules, and what they perceive as facilitators or hindrances to frequent walking. To answer these questions, we undertook a mixed-methods study with a trans-disciplinary approach. In a quantitative phase, we analysed the Swiss mobility and transport micro-census, finding that the walking is distributed in an unequal manner: over one third of all people aged 6-99 do not travel by foot on a given day, while around 13% walk for 5 km or more. Semi-structured interviews with 41 adult frequent walkers, mostly from the Geneva-Lausanne area, show that concern with personal health, pleasure and well-being are key motivators for walking. Time-management strategies such as getting up earlier in the morning or using alternative routes on the way out and on the way back home are common. Walking is facilitated - but not decisively - by parks or green spaces. Hindrances include road traffic, narrow or missing pavements (sidewalks), slow traffic lights, and exposure to traffic noise, air pollution or tobacco smoke. Environmental motivation is rarely mentioned and we find no trace of an informal community of frequent walkers, who do not know each other and tend to switch off while walking, operating in a socially closed mode. Individual rather than collective motivations emerge from the analysis. We then equipped 48 volunteers with a GPS tracker, for a duration of 8-10 days and carried out computer-assisted follow-up interviews concentrating on the details of walking routes. In an additional phase presented in the Appendix, we enabled a subset of 27 volunteers to have a check-up in the Health Bus of Geneva University Hospitals, to determine their glycaemia, total cholesterol, blood pressure, resting heart rate, body-mass index and waist-to-hip ratio. This phase aimed at acquiring preliminary data for a follow-up project to investigate the health effects of frequent walking. From the pooled analysis, there emerged a group of frequent walkers whose walking was mainly for transport and was integrated into their daily transportation routines. Another group walked for leisure but not for transportation, leading to less favourable impacts on the environment. In our general discussion, we consider frequent walking to be an embodied, situated and inconspicuous practice, with limited instrumental advantages due to the time and effort involved. So-called symbolic attributes, related to perceived status and self-identity, are likely to play an important role and are worthy of future study. We conclude with a research agenda and recommendations for promoting frequent walking at population level

Build your own Monte Carlo spreadsheet

This article gives instructions for building a simple but general purpose spreadsheet for collating Monte Carlo (MC) outputs using a VBA macro. Two user defined functions for sampling with and without replacement give the spreadsheet the facility to do a variety of resampling and permutation tests, while a third user defined function generates random values from a triangular distribution to be used in risk analysis. The spreadsheet is suitable for classroom use, and a collection of ten simple but interesting examples gives students an introduction to MC simulation, MC integration, MC confidence intervals, randomization and permutation tests, and MC risk analysis. Supplementary material includes the full Monte Carlo spreadsheet, along with further examples in each topic and a file containing the data used

A High-Resolution Earth Observations and Machine Learning-Based Approach to Forecast Waterborne Disease Risk in Post-Disaster Settings

Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches