Gentrification pathways and their health impacts on historically marginalized residents in Europe and North America: Global qualitative evidence from 14 cities.

As global cities grapple with the increasing challenge of gentrification and displacement, research in public health and urban geography has presented growing evidence about the negative impacts of those unequal urban changes on the health of historically marginalized groups. Yet, to date comprehensive research about the variety of health impacts and their pathways beyond single case sites and through an international comparative approach of different gentrification drivers and manifestations remains scarce. In this paper, we analyze qualitative data on the pathways by which gentrification impacts the health of historically marginalized residents in 14 cities in Europe and North America. We build on 77 interviews with key neighborhood stakeholders. Data analysis indicates four main concurrent processes: Threats to housing and financial security; Socio-cultural displacement; Loss of services and amenities through institutional gentrification; and Increased risks of criminal behavior and compromised public safety. Gentrification is experienced as a chain of physical and emotional community and individual traumas - an overall shock for historically marginalized groups - because of permanent pressures of insecurity, loss, state of displaceability, and the associated exacerbation of socio-environmental disadvantages.

The rise of West Nile Virus in Southern and Southeastern Europe: A spatial-temporal analysis investigating the combined effects of climate, land use and economic changes.

West Nile Virus (WNV) has recently emerged as a major public health concern in Europe; its recent expansion also coincided with some remarkable socio-economic and environmental changes, including an economic crisis and some of the warmest temperatures on record. Here we empirically investigate the drivers of this phenomenon at a European wide scale by constructing and analyzing a unique spatial-temporal data-set, that includes data on climate, land-use, the economy, and government spending on environmental related sectors. Drivers and risk factors of WNV were identified by building a conceptual framework, and relationships were tested using a Generalized Additive Model (GAM), which could capture complex non-linear relationships and also account for spatial and temporal auto-correlation. Some of the key risk factors identified in our conceptual framework, such as a higher percentage of wetlands and arable land, climate factors (higher summer rainfall and higher summer temperatures) were positive predictors of WNV infections. Interestingly, winter temperatures of between 2 °C and 6 °C were among some of the strongest predictors of annual WNV infections; one possible explanation for this result is that successful overwintering of infected adult mosquitoes (likely Culex pipiens) is key to the intensity of outbreaks for a given year. Furthermore, lower surface water extent over the summer is also associated with more intense outbreaks, suggesting that drought, which is known to induce positive changes in WNV prevalence in mosquitoes, is also contributing to the upward trend in WNV cases in affected regions. Our indicators representing the economic crisis were also strong predictors of WNV infections, suggesting there is an association between austerity and cuts to key sectors, which could have benefited vector species and the virus during this crucial period. These results, taken in the context of recent winter warming due to climate change, and more frequent droughts, may offer an explanation of why the virus has become so prevalent in Europe.

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico.

BACKGROUND: This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue. METHODS: A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation. RESULTS: The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income. CONCLUSIONS: Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.