A novel land use approach for assessment of human health: The relationship between urban structure types and cardiorespiratory disease risk

Urban structure types and students' academic performance

In this study, we propose a novel approach for estimating the relationship between neighborhood characteristics and students' academic performance. We propose the concept of urban morphology by Urban Structure Types (USTs). USTs are spatial indicators that describe the urban system through its physical, environmental, and functional characteristics. Our academic performance data includes 344,175 students from 256 public schools in the Federal District (FD), Brazil. This is student-level academic achievement data from 2017 to 2020. We performed the UST mapping in the FD by using visual interpretation. We classified 21 different types of UST. We fit mixed-effects regression models with a student-specific random intercept and slope. The model was adjusted for temporal factors, SES factors, and variables representing the characteristics and the location of each school. Our findings suggest associations between several types of USTs surrounding schools and academic performance. Overall, areas characterized as low population density, with high green index, and high standard residences were associated with an increase in student performance. In contrast, areas that include old buildings near streets, with significant traffic density, and areas with significant exposed soil (areas devasted) were associated with a decrease in student performance. The results of our study support the creation of effective educational and urban planning policies for local interventions. These interventions are likely to translate into healthier schools and improvements in children's behavioral development and learning performance.

The Associations Between Urban Form and Major Non-communicable Diseases: a Systematic Review

In the current century, non-communicable diseases (NCDs), particularly cardiovascular diseases, diabetes, cancer, and chronic respiratory diseases, are the most important cause of mortality all over the world. Given the effect of the built environment on people's health, the present study seeks to conduct a systematic review in order to investigate the relationship between urban form and these four major NCDs as well as their main risk factors. Two independent reviewers in November 2020 after an extensive search through PubMed and Scopus identified 77 studies. Studies published in English were included if they addressed one or more attributes of urban form in relation to any major NCDs and their main risk factors. Publication date, country, geographical scale, study design, methods of built environment measurement, and findings of the relationships among variables were extracted from eligible studies. The findings suggest that the elements of urban form (density, transportation and accessibility, characteristics of building and streetscape, land use, spatial layouts and configuration) could increase or inhibit these diseases through their effect on physical activity, diet, air pollution, blood pressure, and obesity. However, there are study shortages, contradictions, and ambiguities in these relationships which are mainly due to methodological and conceptual challenges. As a result, more in-depth research is needed to achieve solid and consistent results that could be made into clear guidelines for planning and designing healthier cities.

Land Use Impacts on Particulate Matter Levels in Seoul, South Korea: Comparing High and Low Seasons

Seoul, a city in South Korea, experiences high particulate matter (PM) levels well above the recommended standards suggested by the World Health Organization. As concerns about public health and everyday lives are being raised, this study investigates the effects of land use on PM levels in Seoul. Specifically, it attempts to identify which land use types increase or decrease PM10 and PM2.5 levels and compare the effects between high and low seasons using two sets of land use classifications: one coarser and the other finer. A series of partial least regression models identifies that industrial land use increases the PM levels in all cases. It is also reported that residential and commercial land uses associated with lower density increase these levels. Other uses, such as green spaces and road, show mixed or unclear effects. The findings of this study may inform planners and policymakers about how they can refine future land use planning and development practice in cities that face similar challenges.

A Generic Classification Scheme for Urban Structure Types

This paper presents a proposal for a generic urban structure type (UST) scheme. Initially developed in the context of urban ecology, the UST approach is increasingly popular in the remote sensing community. However, there is no consistent and standardized UST framework. Until now, the terms land use and certain USTs are often used and described synonymously, or components of structure and use are intermingled. We suggest a generic nomenclature and a respective UST scheme that can be applied worldwide by stakeholders of different disciplines. Based on the insights of a rigorous literature analysis, we formulate a generic structural- and object-based typology, allowing for the generation of hierarchically and terminologically consistent USTs. The developed terminology exclusively focuses on morphology, urban structures and the general exterior appearance of buildings. It builds on the delimitation of spatial objects at several scales and leaves out all social aspects and land use aspects of an urban area. These underlying objects or urban artefacts and their structure- and object-related features, such as texture, patterns, shape, etc. are the core of the hierarchically structured UST scheme. Finally, the authors present a generic framework for the implementation of a remote sensing-based UST classification along with the requirements regarding sensors, data and data types.

Mapping distance-decay of premature mortality attributable to PM2.5-related traffic congestion

Although several air pollution studies have examined the relationship between people living close to roadways and human health, we are unaware of studies that have examined the distance-decay of this effect based on a snapshot of congestion and focused on a micro-level traffic emission inventory. In this paper we estimate the distance-decay of premature mortality risk related to PM_2.5 emitted by traffic congestion in Hamilton, Canada, in 2011 We employ the Stochastic User Equilibrium (SUE) traffic assignment algorithm to estimate congested travel times for each road link in our study area. Next, we used EPA's MOVES model to estimate mass of PM_2.5, and then R-line dispersion model to predict concentration of PM_2.5. Finally, we apply Integrated Exposure Response Function (IERF) to estimate PM_2.5-related premature mortality at 100 m × 100 m grid resolution. We estimated total premature mortality over Hamilton to be 73.10 (95%CI: 39.05; 82.11) deaths per year. We observed that the proximity to a roadway increases the risk of premature mortality and the strength of this risk decreases as buffer sizes are increased. For example, we estimated that the premature mortality risk within buffer 0-100 m is 29.5% higher than for the buffer 101-200 m, 179.3% higher than for the buffer 201-300 m, and 566% higher than for the buffer 301-400 m. Our study provides a new perspective on exposure increments from traffic congestion. In particular, our findings show health effects gradients across neighborhoods, capturing microscale near-road exposure up to 2000 m of the roadway. Results from this research can be useful for policymakers to develop new strategies for the challenges of regulating transportation, land use, and air pollution.

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM_2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM_2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables

BACKGROUND

Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors.

OBJECTIVES

We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality.

SEARCH METHODS

We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016.

SELECTION CRITERIA

We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender.

DATA COLLECTION AND ANALYSIS

We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies.

MAIN RESULTS

We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and respiratory diseases ( Figure 1 , we observed a risk equal to 2.7% (95% confidence interval = 0.9%, 7.7%). Our results showed statistical significance in the test of moderators for all pollutants, suggesting that the modifier variables influence the average cardiorespiratory disease risk and may explain the varying effects of air pollution.

CONCLUSIONS

Variables related to aspects of energy, transportation, and socioeconomic status may explain the varying effect size of the association between air pollution and cardiorespiratory diseases. Public Health Implications. Our study provides a transferable model to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention.

Mapping distance-decay of cardiorespiratory disease risk related to neighborhood environments

Neighborhood characteristics affect an individual's quality of life. Although several studies have examined the relationship between neighborhood environments and human health, we are unaware of studies that have examined the distance-decay of this effect and then presented the risk results spatially. Our study is unique in that is explores the health effects in a less developed country compared to most studies that have focused on developed countries. The objective of our study is to quantify the distance-decay cardiorespiratory diseases risk related to 28 neighborhood aspects in the Federal District, Brazil and present this information spatially through risk maps of the region. Toward this end, we used a quantile regression model to estimate risk and GIS modeling techniques to create risk maps. Our analysis produced the following findings: i) a 2500 m increase in highway length was associated with a 46% increase in cardiorespiratory diseases; ii) 46,000 light vehicles in circulation (considering a buffer of ≤500 m from residences) was associated with 6 hospital admissions (95% CI: 2.6, 14.6) per cardiorespiratory diseases; iii) 74,000 m² of commercial areas (buffer ≤1700 m) was associated with 12 hospital admissions (95% CI: 2.2, 20.8); iv) 1km² increase in green areas intra urban was associated with less two hospital admissions, and; vi) those who live ≤500 m from the nearest point of wildfire are more likely to have cardiorespiratory diseases that those living >500 m. Our findings suggest that the approach used in this study can be an option to improve the public health policies.

Is Living in a High-Rise Building Bad for Your Self-Rated Health?

While the construction of high-rise buildings is a popular policy strategy for accommodating population growth in cities, there is still much debate about the health consequences of living in high flats. This study examines the relationship between living in high-rise buildings and self-rated health in Belgium. We use data from the Belgian Census of 2001, merged with the National Register of Belgium (N = 6,102,820). Results from multilevel, binary logistic regression analyses show that residents living in high-rise buildings have considerable lower odds to have a good or very good self-rated health in comparison with residents in low-rise buildings (OR 0.67; 95 % CI 0.67-0.68). However, this negative relationship disappears completely after adjusting for socioeconomic and demographic variables (OR 1.04; 95 % CI 1.03-1.05), which suggests that residents' worse self-rated health in high-rise buildings can be explained by the strong demographic and socioeconomic segregation between high- and low-rise buildings in Belgium. In addition, there is a weak, but robust curvilinear relationship between floor level and self-rated health within high-rise buildings. Self-rated health increases until the sixth floor (OR 1.19; 95 % CI 1.15-1.24) and remains stable from the seventh floor and upwards. These findings refute one of the central ideas in architectural sciences that living in high buildings is bad for one's health.

Association between vehicular emissions and cardiorespiratory disease risk in Brazil and its variation by spatial clustering of socio-economic factors

Many studies have suggested that socio-economic factors are strong modifiers of human vulnerability to air pollution effects. Most of these studies were performed in developed countries, specifically in the US and Europe. Only a few studies have been performed in developing countries, and analyzed small regions (city level) with no spatial disaggregation. The aim of this study was to assess the association between vehicle emissions and cardiorespiratory disease risk in Brazil and its modification by spatial clustering of socio-economic conditions. We used a quantile regression model to estimate the risk and a geostatistical approach (K means) to execute spatial cluster analysis. We performed the risk analysis in three stages. First, we analyzed the entire study area (primary analysis), and then we conducted a spatial cluster analysis based on various municipal-level socio-economic factors, followed by a sensitivity analysis. We studied 5444 municipalities in Brazil between 2008 and 2012. Our findings showed a significant association between cardiorespiratory disease risk and vehicular emissions. We found that a 15% increase in air pollution is associated with a 6% increase in hospital admissions rates. The results from the spatial cluster analysis revealed two groups of municipalities with distinct sets of socio-economic factors and risk levels of cardiorespiratory disease related to exposure to vehicular emissions. For example, for vehicle emissions of PM in 2008, we found a relative risk of 4.18 (95% CI: 3.66, 4.93) in the primary analysis; in Group 1, the risk was 0.98 (95% CI: 0.10, 2.05) while in Group 2, the risk was 5.56 (95% CI: 4.46, 6.25). The risk in Group 2 was 480% higher than the risk in Group 1, and 35% higher than the risk in the primary analysis. Group 1 had higher values (3rd quartile) for urbanization rate, highway density, and GDP; very high values (≥3rd quartile) for population density; median values for distance from the capital; and lower values (1st quartile) for rural population density. Group 2 had lower values (1st quartile) urbanization rate; median values for highway density, GDP, and population density; between median and third quartile values for distance from the capital; and higher values (3rd quartile) for rural population density. Our findings suggest that socio-economic factors are important modifiers of the human risk of cardiorespiratory disease due to exposure to vehicle emissions in Brazil. Our study provides support for creating effective public policies related to environmental health that are targeted to high-risk populations.