Toxicity Weighting for Human Biomonitoring Mixture Risk Assessment: A Proof of Concept.

Chemical mixture risk assessment has, in the past, primarily focused on exposures quantified in the external environment. Assessing health risks using human biomonitoring (HBM) data provides information on the internal concentration, from which a dose can be derived, of chemicals to which human populations are exposed. This study describes a proof of concept for conducting mixture risk assessment with HBM data, using the population-representative German Environmental Survey (GerES) V as a case study. We first attempted to identify groups of correlated biomarkers (also known as 'communities', reflecting co-occurrence patterns of chemicals) using a network analysis approach (n = 515 individuals) on 51 chemical substances in urine. The underlying question is whether the combined body burden of multiple chemicals is of potential health concern. If so, subsequent questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this, a biomonitoring hazard index was developed by summing over hazard quotients, where each biomarker concentration was weighted (divided) by the associated HBM health-based guidance value (HBM-HBGV, HBM value or equivalent). Altogether, for 17 out of the 51 substances, health-based guidance values were available. If the hazard index was higher than 1, then the community was considered of potential health concern and should be evaluated further. Overall, seven communities were identified in the GerES V data. Of the five mixture communities where a hazard index was calculated, the highest hazard community contained N-Acetyl-S-(2-carbamoyl-ethyl)cysteine (AAMA), but this was the only biomarker for which a guidance value was available. Of the other four communities, one included the phthalate metabolites mono-isobutyl phthalate (MiBP) and mono-n-butyl phthalate (MnBP) with high hazard quotients, which led to hazard indices that exceed the value of one in 5.8% of the participants included in the GerES V study. This biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies. Future mixture risk assessment using HBM data will benefit from additional HBM health-based guidance values based on population studies. Additionally, accounting for different biomonitoring matrices would provide a wider range of exposures. Future hazard index analyses could also take a common mode of action approach, rather than the more agnostic and non-specific approach we have taken in this proof of concept.

Application of human biomonitoring data to support policy development, raise awareness and environmental public health protection among countries within the HBM4EU project.

Most countries have acknowledged the importance of assessing and quantifying their population's internal exposure from chemicals in air, water, soil, food and other consumer products due to the potential health and economic impact. Human biomonitoring (HBM) is a valuable tool which can be used to quantify such exposures and effects. Results from HBM studies can also contribute to improving public health by providing evidence of individuals' internal chemical exposure as well as data to understand the burden of disease and associated costs thereby stimulating the development and implementation of evidence-based policy. To have a holistic view on HBM data utilisation, a multi-case research approach was used to explore the use of HBM data to support national chemical regulations, protect public health and raise awareness among countries participating in the HBM4EU project. The Human Biomonitoring for Europe (HBM4EU) Initiative (https://www.hbm4eu.eu/) is a collaborative effort involving 30 countries, the European Environment Agency (EEA) and the European Commission (contracting authority) to harmonise procedures across Europe and advance research into the understanding of the health impacts of environmental chemical exposure. One of the aims of the project was to use HBM data to support evidence based chemical policy and make this information timely and directly available for policy makers and all partners. The main data source for this article was the narratives collected from 27 countries within the HBM4EU project. The countries (self-selection) were grouped into 3 categories in terms of HBM data usage either for public awareness, policy support or for the establishment HBM programme. Narratives were analysed/summarised using guidelines and templates that focused on ministries involved in or advocating for HBM; steps required to engage policy makers; barriers, drivers and opportunities in developing a HBM programme. The narratives reported the use of HBM data either for raising awareness or addressing environmental/public health issues and policy development. The ministries of Health and Environment were reported to be the most prominent entities advocating for HBM, the involvement of several authorities/institutions in the national hubs was also cited to create an avenue to interact, discuss and gain the attention of policy makers. Participating in European projects and the general population interest in HBM studies were seen as drivers and opportunities in developing HBM programmes. A key barrier that was cited by countries for establishing and sustaining national HBM programmes was funding which is mainly due to the high costs associated with the collection and chemical analysis of human samples. Although challenges and barriers still exist, most countries within Europe were already conversant with the benefits and opportunities of HBM. This article offers important insights into factors associated with the utilisation of HBM data for policy support and public awareness.

Identification of Real-Life Mixtures Using Human Biomonitoring Data: A Proof of Concept Study.

Human health risk assessment of chemical mixtures is complex due to the almost infinite number of possible combinations of chemicals to which people are exposed to on a daily basis. Human biomonitoring (HBM) approaches can provide inter alia information on the chemicals that are in our body at one point in time. Network analysis applied to such data may provide insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called "communities", within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from Belgium, Czech Republic, Germany, and Spain, with the aim to explore its added value for exposure and risk assessment. The datasets varied in study population, study design, and chemicals analysed. Sensitivity analysis was performed to address the influence of different approaches to standardise for creatinine content of urine. Our approach demonstrates that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated. This information is relevant for regulatory risk assessment, as well as for the design of relevant mixture exposure experiments.

Mixture risk assessment and human biomonitoring: Lessons learnt from HBM4EU.

Unintentional chemical mixtures that are present in the environment are of societal concern as the (environmental) chemicals contained therein, either singly or in combination, may possess properties that are hazardous (toxic) for human health. The current regulatory practice, however, is still largely based on evaluating single chemical substances one-by-one. Over the years various research efforts have delivered tools and approaches for risk assessment of chemical mixtures, but many of these were not considered sufficiently mature for regulatory implementation. This is (partly) due to mixture risk assessment (MRA) being very complex because of the large number of chemicals present in the environment. A key element in risk assessment is information on actual exposures in the population of interest. To date, information on actual personal (internal) mixture exposures is largely absent, severely limiting MRA. The use of human biomonitoring data may improve this situation. Therefore, we investigated within the European Human Biomonitoring Initiative (HBM4EU) various approaches to assess combined exposures and MRA. Based on the insights and lessons learnt in the context of the HBM4EU project, conclusions as well as recommendations for policy development regarding chemical mixtures and for further research were drafted. These conclusions and recommendations relate to both exposure and adverse health effects in humans. The recommendations were discussed with stakeholders in a workshop held in October 2021. There was considerable support and agreement with the spirit, scope and intention of the draft recommendations. Here we describe the lessons learnt on mixture risk assessment through the HBM4EU project and present the final recommendations. Overall, HBM4EU results demonstrated the potential of human biomonitoring as an instrument to obtain insight into the real-life mixtures the human population is exposed to. Also, HBM4EU results demonstrated that chemical mixtures are of public health concern. In the majority of the cases, it was possible to identify risk drivers, i.e. chemicals that contribute more strongly than others to the health risk. The novel approaches to identify co-occurrence patterns demonstrated clusters of co-occurring chemicals; chemicals in these mixture clusters are regulated independently under different legislative frameworks. Moreover, HBM4EU data and expertise can support a science-based derivation of a Mixture Assessment Factor and gauge potential impacts on the population's exposure to chemicals. While further expansion is needed on various aspects of the mixture activities carried out in the context of HBM4EU, application of available methodologies for mixture risk assessment should already be implemented to the degree possible.

Assessment of exposure to pesticide mixtures in five European countries by a harmonized urinary suspect screening approach.

Humans are exposed to a mixture of pesticides through diet as well as through the environment. We conducted a suspect-screening based study to describe the probability of (concomitant) exposure to a set of pesticide profiles in five European countries (Latvia, Hungary, Czech Republic, Spain and the Netherlands). We explored whether living in an agricultural area (compared to living in a peri-urban area), being a a child (compared to being an adult), and the season in which the urine sample was collected had an impact on the probability of detection of pesticides (-metabolites). In total 2088 urine samples were collected from 1050 participants (525 parent-child pairs) and analyzed through harmonized suspect screening by five different laboratories. Fourty pesticide biomarkers (either pesticide metabolites or the parent pesticides as such) relating to 29 pesticides were identified at high levels of confidence in samples across all study sites. Most frequently detected were biomarkers related to the parent pesticides acetamiprid and chlorpropham. Other biomarkers with high detection rates in at least four countries related to the parent pesticides boscalid, fludioxonil, pirimiphos-methyl, pyrimethanil, clothianidin, fluazifop and propamocarb. In 84% of the samples at least two different pesticides were detected. The median number of detected pesticides in the urine samples was 3, and the maximum was 13 pesticides detected in a single sample. The most frequently co-occurring substances were acetamiprid with chlorpropham (in 62 urine samples), and acetamiprid with tebuconazole (30 samples). Some variation in the probability of detection of pesticides (-metabolites) was observed with living in an agricultural area or season of urine sampling, though no consistent patterns were observed. We did observe differences in the probability of detection of a pesticide (metabolite) among children compared to adults, suggesting a different exposure and/or elimination patterns between adults and children. This survey demonstrates the feasibility of conducting a harmonized pan-European sample collection, combined with suspect screening to provide insight in the presence of exposure to pesticide mixtures in the European population, including agricultural areas. Future improvements could come from improved (harmonized) quantification of pesticide levels.

Green space, air pollution, traffic noise and mental wellbeing throughout adolescence: Findings from the PIAMA study.

BACKGROUND: Green space, air pollution and traffic noise exposure may be associated with mental health in adolescents. We assessed the associations of long-term exposure to residential green space, ambient air pollution and traffic noise with mental wellbeing from age 11 to 20 years. METHODS: We included 3059 participants of the Dutch PIAMA birth cohort who completed the five-item Mental Health Inventory (MHI-5) at ages 11, 14, 17 and/or 20 years. We estimated exposure to green space (the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m, 1000 m and 3000 m), ambient air pollution (particulate matter (PM10 and PM2.5), nitrogen dioxide, PM2.5 absorbance and the oxidative potential of PM2.5) and road traffic and railway noise (Lden) at the adolescents' home addresses at the times of completing the MHI-5. Associations with poor mental wellbeing (MHI-5 score ≤ 60) were assessed by generalized linear mixed models with a logit link, adjusting for covariates. RESULTS: The odds of poor mental wellbeing at age 11 to 20 years decreased with increasing exposure to green space in a 3000 m buffer (adjusted odds ratio (OR) 0.78 [95% CI 0.68-0.88] per IQR increase in the average NDVI; adjusted OR 0.77 [95% CI 0.67-0.88] per IQR increase in the total percentage of green space). These associations persisted after adjustment for air pollution and road traffic noise. Relationships between mental wellbeing and green space in buffers of 300 m and 1000 m were less consistent. Higher air pollution exposure was associated with higher odds of poor mental wellbeing, but these associations were strongly attenuated after adjustment for green space in a buffer of 3000 m, traffic noise and degree of urbanization. Traffic noise was not related to mental wellbeing throughout adolescence. CONCLUSIONS: Residential exposure to green space may be associated with a better mental wellbeing in adolescents.

Effects of exposure to surrounding green, air pollution and traffic noise with non-accidental and cause-specific mortality in the Dutch national cohort.

BACKGROUND: Everyday people are exposed to multiple environmental factors, such as surrounding green, air pollution and traffic noise. These exposures are generally spatially correlated. Hence, when estimating associations of surrounding green, air pollution or traffic noise with health outcomes, the other exposures should be taken into account. The aim of this study was to evaluate associations of long-term residential exposure to surrounding green, air pollution and traffic noise with mortality. METHODS: We followed approximately 10.5 million adults (aged ≥ 30 years) living in the Netherlands from 1 January 2013 until 31 December 2018. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 and 1000 m), annual average ambient air pollutant concentrations [including particulate matter (PM2.5), nitrogen dioxide (NO2)] and traffic noise with non-accidental and cause-specific mortality, adjusting for potential confounders. RESULTS: In single-exposure models, surrounding green was negatively associated with all mortality outcomes, while air pollution was positively associated with all outcomes. In two-exposure models, associations of surrounding green and air pollution attenuated but remained. For respiratory mortality, in a two-exposure model with NO2 and NDVI 300 m, the HR of NO2 was 1.040 (95%CI: 1.022, 1.059) per IQR increase (8.3 µg/m3) and the HR of NDVI 300 m was 0.964 (95%CI: 0.952, 0.976) per IQR increase (0.14). Road-traffic noise was positively associated with lung cancer mortality only, also after adjustment for air pollution or surrounding green. CONCLUSIONS: Lower surrounding green and higher air pollution were associated with a higher risk of non-accidental and cause-specific mortality. Studies including only one of these correlated exposures may overestimate the associations with mortality of that exposure.

Green space, air pollution, traffic noise and saliva cortisol in children: The PIAMA study.

Green space, air pollution, and traffic noise exposure may be associated with stress levels in children. A flattened diurnal cortisol slope (the decline in cortisol concentrations from awakening to evening) is an indicator of chronic stress. We examined associations of green space, ambient air pollution, and traffic noise with the diurnal cortisol slope in children 12 years of age. METHODS: At age 12 years, 1,027 participants of the Dutch PIAMA birth cohort collected three saliva samples during 1 day. We estimated residential exposure to green space (i.e., the average Normalized Difference Vegetation Index [NDVI] and percentages of green space in circular buffers of 300 m and 3,000 m), air pollution, and traffic noise. Associations of these exposures with the diurnal cortisol slope (in nmol/L per hour) were assessed by multiple linear regression, adjusting for potential confounders. RESULTS: Higher average NDVI and total percentage of green space in a 3,000 m buffer were associated with a larger diurnal decrease in cortisol levels (adjusted difference [95% confidence interval] = -0.11 nmol/L/hr [-0.21, 0.00 nmol/L/hr] per interquartile range increase in the average NDVI; -0.13 nmol/L/hr [-0.26, 0.00 nmol/L/hr] per interquartile range increase in the total percentage of green space). These associations were largely driven by associations with the percentage of agricultural green space and by associations in children living in nonurban areas. We observed no relationships between air pollution or traffic noise and the diurnal cortisol slope. CONCLUSIONS: Residential exposure to green space in a buffer of 3,000 m may be associated with lower stress levels in children 12 years of age.

Network Analysis to Identify Communities Among Multiple Exposure Biomarkers Measured at Birth in Three Flemish General Population Samples.

Introduction: Humans are exposed to multiple environmental chemicals via different sources resulting in complex real-life exposure patterns. Insight into these patterns is important for applications such as linkage to health effects and (mixture) risk assessment. By providing internal exposure levels of (metabolites of) chemicals, biomonitoring studies can provide snapshots of exposure patterns and factors that drive them. Presentation of biomonitoring data in networks facilitates the detection of such exposure patterns and allows for the systematic comparison of observed exposure patterns between datasets and strata within datasets. Methods: We demonstrate the use of network techniques in human biomonitoring data from cord blood samples collected in three campaigns of the Flemish Environment and Health Studies (FLEHS) (sampling years resp. 2002-2004, 2008-2009, and 2013-2014). Measured biomarkers were multiple organochlorine compounds, PFAS and metals. Comparative network analysis (CNA) was conducted to systematically compare networks between sampling campaigns, smoking status during pregnancy, and maternal pre-pregnancy BMI. Results: Network techniques offered an intuitive approach to visualize complex correlation structures within human biomonitoring data. The identification of groups of highly connected biomarkers, "communities," within these networks highlighted which biomarkers should be considered collectively in the analysis and interpretation of epidemiological studies or in the design of toxicological mixture studies. Network analyses demonstrated in our example to which extent biomarker networks and its communities changed across the sampling campaigns, smoking status during pregnancy, and maternal pre-pregnancy BMI. Conclusion: Network analysis is a data-driven and intuitive screening method when dealing with multiple exposure biomarkers, which can easily be upscaled to high dimensional HBM datasets, and can inform mixture risk assessment approaches.

Proximity to livestock farms and exposure to livestock-related particulate matter are associated with lower probability of medication dispensing for obstructive airway diseases.

OBJECTIVES: The aim of this study is to assess whether medication use for obstructive airway diseases is associated with environmental exposure to livestock farms. Previous studies in the Netherlands at a regional level suggested that asthma and chronic obstructive pulmonary disease (COPD) are less prevalent among persons living near livestock farms. METHODS: A nationwide population-based cross-sectional study was conducted among 7,735,491 persons, with data on the dispensing of drugs for obstructive airway diseases in the Netherlands in 2016. Exposure was based on distances between home addresses and farms and on modelled atmospheric particulate matter (PM10) concentrations from livestock farms. Data were analysed for different regions by logistic regression analyses and adjusted for several individual-level variables, as well as modelled PM10 concentration of non-farm-related air pollution. Results for individual regions were subsequently pooled in meta-analyses. RESULTS: The probability of medication for asthma or COPD being dispensed to adults and children was lower with decreasing distance of their homes to livestock farms, particularly cattle and poultry farms. Increased concentrations of PM10 from cattle were associated with less dispensing of medications for asthma or COPD, as well (meta-analysis OR for 10th-90th percentile increase in concentration of PM10 from cattle farms, 95%CI: 0.92, 0.86-0.97 for adults). However, increased concentrations of PM10 from non-farm sources were positively associated (meta-analysis OR for 10th-90th percentile increase in PM10-concentration, 95%CI: 1.29, 1.09-1.52 for adults). CONCLUSIONS: The results show that the probability of dispensing medication for asthma or COPD is inversely associated with proximity to livestock farms and modelled exposure to livestock-related PM10 in multiple regions within the Netherlands. This finding implies a notable prevented risk: under the assumption of absence of livestock farms in the Netherlands, an estimated 2%-5% more persons (an increase in tens of thousands) in rural areas would receive asthma or COPD medication.

Analysis of different preferences for the EU's regulatory options for endocrine disruptor identification criteria using argumentation theory.

What criteria are most suitable to identify endocrine disrupting substances (EDSs) for regulatory purposes in the EU? The results of the European Commission's public consultation, as part of the process to establish identification criteria for EDSs, show that different regulatory options are supported. Some respondents prefer an option including hazard characterization considerations, whereas others prefer an option that avoids these considerations and introduces several hazard-identification based weight-of-evidence categories. In this study, the argumentation underlying the different preferences for identification criteria are analyzed and compared using pragma-dialectical argumentation theory (PDAT). All responses of non-anonymous, national governments that submitted a response in English (n = 17) were included. Responses of other stakeholder organizations were included if a Google News search returned an opinionated presence in the media on the subject (n = 9). Five topical themes and 21 underlying issues were identified. The themes are 1) mechanistic understanding of EDSs, 2) regulatory considerations related to the identification of EDSs, 3) consistency with existing regulatory frameworks, and 4) evaluations of specific issues related to a category approach and 5) related to including potency. We argue that two overarching (implicit) 'advocacy coalitions' can be discerned, that adopted contrasting positions towards the identified themes and issues. Among these 'coalitions', there appears to be consensus about the necessity of having 'science-based' criteria, though different perspectives exist as to what the most accurate mechanistic understanding of EDSs entails. To move the discussion forward, we argue that a societal dialogue would be beneficial, where EDS science and regulation are discussed as interrelated themes.

Effects of Dutch livestock production on human health and the environment.

Observed multiple adverse effects of livestock production have led to increasing calls for more sustainable livestock production. Quantitative analysis of adverse effects, which can guide public debate and policy development in this area, is limited and generally scattered across environmental, human health, and other science domains. The aim of this study was to bring together and, where possible, quantify and aggregate the effects of national-scale livestock production on 17 impact categories, ranging from impacts of particulate matter, emerging infectious diseases and odor annoyance to airborne nitrogen deposition on terrestrial nature areas and greenhouse gas emissions. Effects were estimated and scaled to total Dutch livestock production, with system boundaries including feed production, manure management and transport, but excluding slaughtering, retail and consumption. Effects were expressed using eight indicators that directly express Impact in the sense of the Drivers-Pressures-State-Impact-Response framework, while the remaining 14 express Pressures or States. Results show that livestock production may contribute both positively and negatively to human health with a human disease burden (expressed in disability-adjusted life years) of up to 4% for three different health effects: those related to particulate matter, zoonoses, and occupational accidents. The contribution to environmental impact ranges from 2% for consumptive water use in the Netherlands to 95% for phosphorus transfer to soils, and extends beyond Dutch borders. While some aggregation across impact categories was possible, notably for burden of disease estimates, further aggregation of disparate indicators would require normative value judgement. Despite difficulty of aggregation, the assessment shows that impacts receive a different contribution of different animal sectors. While some of our results are country-specific, the overall approach is generic and can be adapted and tuned according to specific contexts and information needs in other regions, to allow informed decision making across a broad range of impact categories.

Statement on advancing the assessment of chemical mixtures and their risks for human health and the environment.

The number of anthropogenic chemicals, manufactured, by-products, metabolites and abiotically formed transformation products, counts to hundreds of thousands, at present. Thus, humans and wildlife are exposed to complex mixtures, never one chemical at a time and rarely with only one dominating effect. Hence there is an urgent need to develop strategies on how exposure to multiple hazardous chemicals and the combination of their effects can be assessed. A workshop, "Advancing the Assessment of Chemical Mixtures and their Risks for Human Health and the Environment" was organized in May 2018 together with Joint Research Center in Ispra, EU-funded research projects and Commission Services and relevant EU agencies. This forum for researchers and policy-makers was created to discuss and identify gaps in risk assessment and governance of chemical mixtures as well as to discuss state of the art science and future research needs. Based on the presentations and discussions at this workshop we want to bring forward the following Key Messages.

Surrounding green, air pollution, traffic noise exposure and non-accidental and cause-specific mortality.

BACKGROUND: Most previous studies that investigated associations of surrounding green, air pollution or traffic noise with mortality focused on single exposures. OBJECTIVES: The aim of this study was to evaluate combined associations of long-term residential exposure to surrounding green, air pollution and traffic noise with total non-accidental and cause-specific mortality. METHODS: We linked a national health survey (Public Health Monitor, PHM) conducted in 2012 to the Dutch longitudinal mortality database. Subjects of the survey who were 30 years or older on 1 January 2013 (n = 339,633) were followed from 1 January 2013 till 31 December 2017. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 m and 1000 m), annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), nitrogen dioxide (NO2)) and traffic noise with non-accidental, circulatory disease, respiratory disease, lung cancer and neurodegenerative disease mortality. RESULTS: We observed 26,886 non-accidental deaths over 1.627.365 person-years of follow-up. Surrounding green, air pollution and traffic noise exposure were not significantly associated with non-accidental or cause-specific mortality. For non-accidental mortality, we found a hazard ratio (HR) of 0.99 (0.98, 1.01) per IQR increase in NDVI 300 m, a HR of 0.99 (95% CI: 0.97, 1.01) per IQR increase in NO2, a HR of 0.98 (0.97, 1.00) per IQR increase in PM2.5 and a HR of 0.99 (95% CI: 0.97, 1.01) per IQR increase in road-traffic noise. Analyses restricted to non-movers or excluding subjects aged 85+ years did not change the findings. CONCLUSION: We found no evidence for associations of long-term residential exposures to surrounding green, air pollution and traffic noise with non-accidental or cause-specific mortality in a large population based survey in the Netherlands, possibly related to the relatively short follow-up period.

Risk of pneumonia among residents living near goat and poultry farms during 2014-2016.

In the Netherlands, an association was found between the prevalence of pneumonia and living near goat and poultry farms in 2007-2013. This association then led to regulatory decisions to restrict the building of new goat farms and to reduce emissions of poultry farms. Confirmation of these results, however, is required because the period of previous analyses overlapped a Q-fever epidemic in 2007-2010. To confirm the association, we performed a population-based study during 2014-2016 based on general practitioner (GP) data. Electronic medical records of 90,183 persons were used to analyze the association between pneumonia and the population living in the proximity (within 500-2000 m distance) of goat and poultry farms. Data were analyzed with three types of logistic regression (with and without GP practice as a random intercept and with stratified analyses per GP practice) and a kernel model to discern the influence of different statistical methods on the outcomes. In all regression analyses involving adults, a statistically significant association between pneumonia and residence within 500 meters of goat farms was found (odds ratio [OR] range over all analyses types: 1.33-1.60), with a decreasing OR for increasing distances. In kernel analyses (including all ages), a population-attributable risk between 6.0 and 7.8% was found for a distance of 2000 meters in 2014-2016. The associations were consistent across all years and robust for mutual adjustment for proximity to other animals and for several other sensitivity analyses. However, associations with proximity to poultry farms are not supported by the present study. As the causes of the elevated pneumonia incidence in persons living close to goat farms remain unknown, further research into potential mechanisms is required for adequate prevention.

Residential surrounding green, air pollution, traffic noise and self-perceived general health.

Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental exposures on SGH. Our aim was to evaluate associations of combined residential exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined exposures with poor SGH. In single-exposure models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-exposure models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-exposure models. Studies including only one of these correlated exposures may overestimate the risk of poor SGH attributed to the studied exposure, while underestimating the risk of combined exposures.

Associations of Combined Exposures to Surrounding Green, Air Pollution, and Road Traffic Noise with Cardiometabolic Diseases.

BACKGROUND: Surrounding green, air pollution, and noise have been associated with cardiometabolic diseases, but most studies have assessed only one of these correlated exposures. OBJECTIVES: We aimed to evaluate associations of combined exposures to green, air pollution, and road traffic noise with cardiometabolic diseases. METHODS: In this cross-sectional study, we studied associations between self-reported physician-diagnosed diabetes, hypertension, heart attack, and stroke from a Dutch national health survey of 387,195 adults and residential surrounding green, annual average air pollutant concentrations [including particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text]), PM with aerodynamic diameter [Formula: see text] ([Formula: see text]), nitrogen dioxide ([Formula: see text]), and oxidative potential (OP) with the dithiothreitol (DTT) assay ([Formula: see text])] and road traffic noise. Logistic regression models were used to analyze confounding and interaction of surrounding green, air pollution, and noise exposure. RESULTS: In single-exposure models, surrounding green was inversely associated with diabetes, while air pollutants ([Formula: see text], [Formula: see text]) and road traffic noise were positively associated with diabetes. In two-exposure analyses, associations with green and air pollution were attenuated but remained. The association between road traffic noise and diabetes was reduced to unity when adjusted for surrounding green or air pollution. Air pollution and surrounding green, but not road traffic noise, were associated with hypertension in single-exposure models. The weak inverse association of surrounding green with hypertension attenuated and lost significance when adjusted for air pollution. Only [Formula: see text] was associated with stroke and heart attack. CONCLUSIONS: Studies including only one of the correlated exposures surrounding green, air pollution, and road traffic noise may overestimate the association of diabetes and hypertension attributed to the studied exposure. https://doi.org/10.1289/EHP3857.

Green space, air pollution, traffic noise and cardiometabolic health in adolescents: The PIAMA birth cohort.

BACKGROUND: Green space has been hypothesized to improve cardiometabolic health of adolescents, whereas air pollution and traffic noise may negatively impact cardiometabolic health. OBJECTIVES: To examine the associations of green space, air pollution and traffic noise with cardiometabolic health in adolescents aged 12 and 16 years. METHODS: Waist circumference, blood pressure, cholesterol and glycated hemoglobin (HbA1c) were measured in subsets of participants of the Dutch PIAMA birth cohort, who participated in medical examinations at ages 12 (n = 1505) and/or 16 years (n = 797). We calculated a combined cardiometabolic risk score for each participant, with a higher score indicating a higher cardiometabolic risk. We estimated exposure to green space (i.e. the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m and 3000 m), air pollution (by land-use regression models) and traffic noise (using the Standard Model Instrumentation for Noise Assessments (STAMINA) model) at the adolescents' home addresses at the time of the medical examinations. We assessed associations of these exposures with cardiometabolic health outcomes at ages 12 and 16 by multiple linear regression, adjusting for potential confounders. RESULTS: We did not observe consistent patterns of associations of green space, air pollution and traffic noise with the cardiometabolic risk score, blood pressure, total cholesterol levels, the total/HDL cholesterol ratio and HbA1c. We found inverse associations of air pollution with waist circumference at both age 12 and 16. These associations weakened after adjustment for region, except for particulate matter with a diameter of <2.5 µm (PM2.5) at age 12. The association of PM2.5 with waist circumference at age 12 remained after adjustment for green space and road traffic noise (adjusted difference - 1.42 cm [95% CI -2.50, -0.35 cm] per 1.16 µg/m3 increase in PM2.5). CONCLUSION: This study does not provide evidence for beneficial effects of green space or adverse effects of air pollution and traffic noise on cardiometabolic health in adolescents.

Associations of combined exposures to surrounding green, air pollution and traffic noise on mental health.

BACKGROUND: Evidence is emerging that poor mental health is associated with the environmental exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single exposures with poor mental health. OBJECTIVES: To evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health. METHODS: In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise exposure with poor mental health. RESULTS: In single exposure models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single exposure models. Associations of environmental exposures with poor mental health differed somewhat by age. CONCLUSIONS: Studies including only one of these three correlated exposures may overestimate the influence of poor mental health attributed to the studied exposure, while underestimating the influence of combined environmental exposures.