A prospective exploration of the urban exposome in relation to headache in the Dutch population-based Occupational and environmental health cohort study (AMIGO).

OBJECTIVE: Headache is one of the most prevalent and disabling health conditions globally. We prospectively explored the urban exposome in relation to weekly occurrence of headache episodes using data from the Dutch population-based Occupational and Environmental Health Cohort Study (AMIGO). MATERIAL AND METHODS: Participants (N = 7,339) completed baseline and follow-up questionnaires in 2011 and 2015, reporting headache frequency. Information on the urban exposome covered 80 exposures across 10 domains, such as air pollution, electromagnetic fields, and lifestyle and socio-demographic characteristics. We first identified all relevant exposures using the Boruta algorithm and then, for each exposure separately, we estimated the average treatment effect (ATE) and related standard error (SE) by training causal forests adjusted for age, depression diagnosis, painkiller use, general health indicator, sleep disturbance index and weekly occurrence of headache episodes at baseline. RESULTS: Occurrence of weekly headache was 12.5 % at baseline and 11.1 % at follow-up. Boruta selected five air pollutants (NO2, NOX, PM10, silicon in PM10, iron in PM2.5) and one urban temperature measure (heat island effect) as factors contributing to the occurrence of weekly headache episodes at follow-up. The estimated causal effect of each exposure on weekly headache indicated positive associations. NO2 showed the largest effect (ATE = 0.007 per interquartile range (IQR) increase; SE = 0.004), followed by PM10 (ATE = 0.006 per IQR increase; SE = 0.004), heat island effect (ATE = 0.006 per one-degree Celsius increase; SE = 0.007), NOx (ATE = 0.004 per IQR increase; SE = 0.004), iron in PM2.5 (ATE = 0.003 per IQR increase; SE = 0.004), and silicon in PM10 (ATE = 0.003 per IQR increase; SE = 0.004). CONCLUSION: Our results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.

Associations between urban exposome and recurrence risk among survivors of acute myocardial infarction in Beijing, China.

Few previous studies have investigated the impacts of coexposure to multiple urban environmental factors on the prognosis of acute myocardial infarction (AMI) events. This study aimed to evaluate the associations between the urban exposome and AMI recurrence. We used data from 88,509 AMI patients from a large cohort obtained from the Beijing Cardiovascular Disease Surveillance System between 2013 and 2019. Twenty-six types of urban exposures were assessed within 300-m, 500-m, and 1000-m buffers of patients' home addresses in the baseline and cumulative average levels. We used the Cox proportional hazard model along with the Elastic Net (ENET) algorithm to estimate the hazard ratios (HRs) of recurrent AMI per interquartile range increase in each selected urban exposure. The increased risk of AMI recurrence was significantly associated with lower urban function diversity in the 500-m buffer, longer distance to subway stations and higher PM2.5 for both baseline and cumulative average exposure. The cumulative averages of two urban factors, including the distance to parks, and the density of fruit and vegetable shops in the 1000-m buffer, were also identified as significant factors affecting the risk of AMI recurrence. These findings can help improve the urban design for promoting human cardiovascular health.

Mixtures of long-term exposure to ambient air pollution, built environment and temperature and stroke incidence across Europe.

INTRODUCTION: The complex interplay of multiple environmental factors and cardiovascular has scarcely been studied. Within the EXPANSE project, we evaluated the association between long-term exposure to multiple environmental indices and stroke incidence across Europe. METHODS: Participants from three traditional adult cohorts (Germany, Netherlands and Sweden) and four administrative cohorts (Catalonia [region Spain], Rome [city-wide], Greece and Sweden [nationwide]) were followed until incident stroke, death, migration, loss of follow-up or study end. We estimated exposures at residential addresses from different exposure domains: air pollution (nitrogen dioxide (NO2), particulate matter < 2.5 µm (PM2.5), black carbon (BC), ozone), built environment (green/blue spaces, impervious surfaces) and meteorology (seasonal mean and standard deviation of temperatures). Associations between environmental exposures and stroke were estimated in single and multiple-exposure Cox proportional hazard models, and Principal Component (PC) Analyses derived prototypes for specific exposures domains. We carried out random effects meta-analyses by cohort type. RESULTS: In over 15 million participants, increased levels of NO2 and BC were associated with increased higher stroke incidence in both cohort types. Increased Normalized Difference Vegetation Index (NDVI) was associated with a lower stroke incidence in both cohort types, whereas an increase in impervious surface was associated with an increase in stroke incidence. The first PC of the air pollution domain (PM2.5, NO2 and BC) was associated with an increase in stroke incidence. For the built environment, higher levels of NDVI and lower levels of impervious surfaces were associated with a protective effect [%change in HR per 1 unit = -2.0 (95 %CI, -5.9;2.0) and -1.1(95 %CI, -2.0; -0.3) for traditional adult and administrative cohorts, respectively]. No clear patterns were observed for distance to blue spaces or temperature parameters. CONCLUSIONS: We observed increased HRs for stroke with exposure to PM2.5, NO2 and BC, lower levels of greenness and higher impervious surface in single and combined exposure models.

Multivariate associations between cognition and neighborhood geospatial characteristics in schizophrenia.

Cognitive impairment contributes to functional impairment in schizophrenia. Yet, little is known about how environmental characteristics are related to cognition in schizophrenia. By examining how cognition and the environment are intertwined, it may be possible to identify modifiable risk and protective factors that can improve cognitive outcomes in schizophrenia. We aimed to identify multivariate associations between cognition and three geospatial characteristics (built-space density, habitable green spaces, and public spaces for social interaction) within one's immediate neighborhood among individuals with schizophrenia. We recruited participants with schizophrenia from three sites - an urban metropolitan and two towns in southern India. We administered standard cognitive assessments and performed a principal axis factoring to identify episodic memory, cognitive control, and social inference-making factors for use in further analyses. We estimated geospatial characteristics of an individual's neighborhood, i.e., up to 1 km2 around the residence, by sourcing data from Google Earth. We performed unconditional and conditional (to examine the effect of clinical covariates) canonical correlation analyses to understand the multivariate relationship between cognition and geospatial characteristics. We analyzed data from 208 participants; the first canonical cognitive variate (higher social inference-making and poorer cognitive control) shared 24% of the variance (r = 0.49; P < 0.001) with the first geospatial variate (lower built density and poorer access to public spaces). Years of education, age at onset, and place of residence significantly modulated this relationship. We observe differential associations of the built environment with social and non-social cognition in schizophrenia, and highlight the clinical and demographic characteristics that shape these associations.

Effect of residential relocation on environmental exposures in European cohorts: An exposome-wide approach.

Residential relocation is increasingly used as a natural experiment in epidemiological studies to assess the health impact of changes in environmental exposures. Since the likelihood of relocation can be influenced by individual characteristics that also influence health, studies may be biased if the predictors of relocation are not appropriately accounted for. Using data from Swedish and Dutch adults (SDPP, AMIGO), and birth cohorts (BAMSE, PIAMA), we investigated factors associated with relocation and changes in multiple environmental exposures across life stages. We used logistic regression to identify baseline predictors of moving, including sociodemographic and household characteristics, health behaviors and health. We identified exposure clusters reflecting three domains of the urban exposome (air pollution, grey surface, and socioeconomic deprivation) and conducted multinomial logistic regression to identify predictors of exposome trajectories among movers. On average, 7 % of the participants relocated each year. Before relocating, movers were consistently exposed to higher levels of air pollution than non-movers. Predictors of moving differed between the adult and birth cohorts, highlighting the importance of life stages. In the adult cohorts, moving was associated with younger age, smoking, and lower education and was independent of cardio-respiratory health indicators (hypertension, BMI, asthma, COPD). Contrary to adult cohorts, higher parental education and household socioeconomic position were associated with a higher probability of relocation in birth cohorts, alongside being the first child and living in a multi-unit dwelling. Among movers in all cohorts, those with a higher socioeconomic position at baseline were more likely to move towards healthier levels of the urban exposome. We provide new insights into predictors of relocation and subsequent changes in multiple aspects of the urban exposome in four cohorts covering different life stages in Sweden and the Netherlands. These results inform strategies to limit bias due to residential self-selection in epidemiological studies using relocation as a natural experiment.

City-scale assessment of long-term air quality impacts on the respiratory and cardiovascular health.

Background: The impact of the urban environment on human health is a contemporary subject of environmental research. Air pollution is often considered a leading environmental driver. However, a plethora of other factors within the urban exposome may be involved. At the same time, the resolution of spatial data is also an important facet to consider. Generally, systematic tools for accurate health risk assessment in the urban environment are missing or are not implemented. Methods: The long-term impact of air quality (PM10, PM2.5, NO2, benzene, and SO2) on respiratory and cardiovascular health was assessed with a log-linear model. We used the most accurate health data in high city scale spatial resolution over the period 2010 to 2018. Selected external exposome parameters were also included in the analysis. Results: Statistically significant associations between air pollution and the health of the urban population were found. The strongest association was between benzene and the incidence of bronchitis in the adult population [RR 1.552 95% CI (1.415-1.704) per 0.5 µg/m3 change in benzene concentration]. A similar relation was observed between NO2 and the same health condition [RR 1.483 95% CI (1.227-1.792) per 8.9 µg/m3 of change in NO2]. Other weaker associations were also found between asthma in children and PMs, NO2, or benzene. Cardiovascular-related hospitalizations in the general population were linked with NO2 [RR 1.218 95% CI (1.119-1.325) per 9.7 µg/m3 change in NO2]. The remaining pollutants were slightly less but still significantly associated with cardiovascular-related hospitalizations. Conclusion: Our findings are mostly highly statistically significant (p ≤ 0.001) and are in line with current literature on the adverse effects of air pollution on the human population. The results highlight the need for continual improvements in air quality. We propose the implementation of this approach as a systematic tool for the investigation of possible health risks over a long period of time. However, further research involving other variables is an essential step toward understanding the complex urban exposome and its implications for human health. An increase in data spatial resolution is especially important in this respect as well as for improving city health risk management.

Using an Exposome-Wide Approach to Explore the Impact of Urban Environments on Blood Pressure among Adults in Beijing-Tianjin-Hebei and Surrounding Areas of China.

Existing studies mostly explored the association between urban environmental exposures and blood pressure (BP) in isolation, ignoring correlations across exposures. This study aimed to systematically evaluate the impact of a wide range of urban exposures on BP using an exposome-wide approach. A multicenter cross-sectional study was conducted in ten cities of China. For each enrolled participant, we estimated their urban exposures, including air pollution, built environment, surrounding natural space, and road traffic indicator. On the whole, this study comprised three statistical analysis steps, that is, single exposure analysis, multiple exposure analysis and a cluster analysis. We also used deletion-substitution-addition algorithm to conduct variable selection. After considering multiple exposures, for hypertension risk, most significant associations in single exposure model disappeared, with only neighborhood walkability remaining negatively statistically significant. Besides, it was observed that SBP (systolic BP) raised gradually with the increase in PM2.5, but such rising pattern slowed down when PM2.5 concentration reached a relatively high level. For surrounding natural spaces, significant protective associations between green and blue spaces with BP were found. This study also found that high population density and public transport accessibility have beneficially significant association with BP. Additionally, with the increase in the distance to the nearest major road, DBP (diastolic BP) decreased rapidly. When the distance was beyond around 200 m, however, there was no obvious change to DBP anymore. By cluster analysis, six clusters of urban exposures were identified. These findings reinforce the importance of improving urban design, which help promote healthy urban environments to optimize human BP health.

Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges.

With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors, and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions (UEIs) before implementing them. Spatial agent-based modeling (ABM) can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This article discusses model architectures and methodological challenges for successfully modeling UEIs using spatial ABM. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; and strategies for model calibration. Major challenges for a successful application of ABM to UEI assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research.

Developing the building blocks to elucidate the impact of the urban exposome on cardiometabolic-pulmonary disease: The EU EXPANSE project.

By 2030, more than 80% of Europe's population will live in an urban environment. The urban exposome, consisting of factors such as where we live and work, where and what we eat, our social network, and what chemical and physical hazards we are exposed to, provides important targets to improve population health. The EXPANSE (EXposome Powered tools for healthy living in urbAN SEttings) project will study the impact of the urban exposome on the major contributors to Europe's burden of disease: Cardio-Metabolic and Pulmonary Disease. EXPANSE will address one of the most pertinent questions for urban planners, policy makers, and European citizens: "How to maximize one's health in a modern urban environment?" EXPANSE will take the next step in exposome research by (1) bringing together exposome and health data of more than 55 million adult Europeans and OMICS information for more than 2 million Europeans; (2) perform personalized exposome assessment for 5,000 individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution of the exposome and health through the life course; and (5) evaluating the impact of changes in the urban exposome on the burden of cardiometabolic and pulmonary disease. EXPANSE will translate its insights and innovations into research and dissemination tools that will be openly accessible via the EXPANSE toolbox. By applying innovative ethics-by-design throughout the project, the social and ethical acceptability of these tools will be safeguarded. EXPANSE is part of the European Human Exposome Network.