How Does the Urban Environment Affect Health and Well-Being? A Systematic Review

Krefis, Anne Caroline; Augustin, Matthias; Schlünzen, K. Heinke; Oßenbrügge, Jürgen; Augustin, Jobst

doi:10.3390/urbansci2010021

Cited by 115 publications

(78 citation statements)

References 65 publications

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“…The approach of smart sensing can also be applied in many other demographic groups to raise awareness, especially for target risk groups with diseases (e.g., asthma, allergies, or circulation problems), as environmental stressors are expected to rise in frequency and more people are becoming vulnerable [10,46]. These groups might also be more motivated to change their everyday routes than people who may be used to a certain stressor.…”

Section: Harnessing Smart Sensing For Research In Health Geographymentioning

confidence: 99%

“…Evidence from several health studies highlights the impact of these stressors on mortality and morbidity [7][8][9]. Even though there is no direct link between personal exposure and health outcomes, it has clearly been proven that multiple environmental stressors are significantly associated with health effects and the well-being of citizens when controlling for socio-economic factors [10]. Diseases caused by air pollution, for example, were responsible for around 9 million premature deaths in 2015, and the health effects of pollution are still underestimated in calculations of the global burden of disease [11].…”

Section: Introductionmentioning

confidence: 99%

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Cyclists’ Multiple Environmental Urban Exposures—Comparing Subjective and Objective Measurements

et al. 2019

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Citizens in urban areas are exposed to multiple environmental stressors like noise, heat, and air pollution, with impact on human health. There is a great deal of evidence that connects human health, objective environmental exposure, and place of residence. However, little is known about subjective and objective multiple personal exposures while being mobile. To address this research gap, this paper presents results from a mixed-methods exploratory study with cyclists in the City of Leipzig, Germany. In the summer of 2017, cyclists (n = 66) wore a unique combination of sensors that measured particle number counts (PNC), noise, humidity, temperature, geolocation, and the subjective perception of each exposure on everyday routes for one week (n = 730). A smartphone application was developed to question participants about their perception of subjective exposure. The data were analyzed with three aims: (i) to compare the multiple exposure profiles of the cyclists, (ii) to contrast the objective data and subjective individual perception, and (iii) to examine the role of route decision-making and awareness of health impacts for healthier route choices. The results indicate distinct differences between the exposure profiles of cyclists. Over 80% of the cyclists underestimated their exposure to noise and air pollution. Except for heat, no significant associations between the objective and subjective data were found. This reveals an exposure awareness gap that needs to be considered in urban health planning and risk communication. It is argued that knowledge about health impacts and route characteristics plays a crucial role in decision-making about route choices. The paper concludes with suggestions to harness smart sensing for exposure mitigation and research in health geography.

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Section: Harnessing Smart Sensing For Research In Health Geographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cyclists’ Multiple Environmental Urban Exposures—Comparing Subjective and Objective Measurements

et al. 2019

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“…Worldwide, cities contain 55% of the population, a proportion that is expected to increase to 68% by 2050, with an estimated 83% of the global urban population living in LMICs [8]. The health benefits of living in urban as opposed to rural areas are well documented [9], where urban dwellers benefit from improved access to healthcare and other public services [10]. However, cities also concentrate industry, traffic, and waste at unprecedented scales, which can lead to high levels of population exposures to contaminated soil, water, and air [1,11].…”

Section: Introductionmentioning

confidence: 99%

The role of cities in reducing the cardiovascular impacts of environmental pollution in low- and middle-income countries

Baumgartner

Bräuer

Ezzati

2020

BMC Med

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Background: As low-and middle-income countries urbanize and industrialize, they must also cope with pollution emitted from diverse sources. Main text: Strong and consistent evidence associates exposure to air pollution and lead with increased risk of cardiovascular disease occurrence and death. Further, increasing evidence, mostly from high-income countries, indicates that exposure to noise and to both high and low temperatures may also increase cardiovascular risk. There is considerably less research on the cardiovascular impacts of environmental conditions in low-and middleincome countries (LMICs), where the levels of pollution are often higher and the types and sources of pollution markedly different from those in higher-income settings. However, as such evidence gathers, actions to reduce exposures to pollution in low-and middle-income countries are warranted, not least because such exposures are very high. Cities, where pollution, populations, and other cardiovascular risk factors are most concentrated, may be best suited to reduce the cardiovascular burden in LMICs by applying environmental standards and policies to mitigate pollution and by implementing interventions that target the most vulnerable. The physical environment of cities can be improved though municipal processes, including infrastructure development, energy and transportation planning, and public health actions. Local regulations can incentivize or inhibit the polluting behaviors of industries and individuals. Environmental monitoring can be combined with public health warning systems and publicly available exposure maps to inform residents of environmental hazards and encourage the adoption of pollution-avoiding behaviors. Targeted individual or neighborhood interventions that identify and treat high-risk populations (e.g., lead mitigation, portable air cleaners, and preventative medications) can also be leveraged in the very near term. Research will play a key role in evaluating whether these approaches achieve their intended benefits, and whether these benefits reach the most vulnerable. Conclusion: Cities in LMICs can play a defining role in global health and cardiovascular disease prevention in the next several decades, as they are well poised to develop innovative, multisectoral approaches to pollution mitigation, while also protecting the most vulnerable.

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“…The link between environment and health is well documented, and some literature focused on urban areas, health and environment has been recently published. For instance, Krefis et al wrote a systematic review in 2018 [9], showing that the link between green areas and health have been addressed by several studies, but also pointing out a lack of interdisciplinary studies that approach the complexity of urban structure, its dynamics and links with wellbeing. Some socio-technical studies have been performed as well, such as the one conducted by Tavano Blessi et al in Milan, Italy [10], in which they analyzed survey data to determine the influence of urban green areas in the precepted wellbeing of the population.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Learning to Unveil Correlations between Urban Landscape and Population Health

Pala

Caldarone

Franzini³

et al. 2020

Sensors

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The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.

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How Does the Urban Environment Affect Health and Well-Being? A Systematic Review

Cited by 115 publications

References 65 publications

Cyclists’ Multiple Environmental Urban Exposures—Comparing Subjective and Objective Measurements

Cyclists’ Multiple Environmental Urban Exposures—Comparing Subjective and Objective Measurements

The role of cities in reducing the cardiovascular impacts of environmental pollution in low- and middle-income countries

Deep Learning to Unveil Correlations between Urban Landscape and Population Health

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