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Article ; Online: Exploring associations between residential exposure to pesticides and birth outcomes using the Dutch birth registry

Mariana Simões / Roel Vermeulen / Lützen Portengen / Nicole Janssen / Anke Huss

Environment International, Vol 178, Iss , Pp 108085- (2023)

2023

Abstract: Background: Maternal occupational exposure to pesticides has been linked to adverse birth outcomes but associations with residential pesticide exposures are inconclusive. Objectives: To explore associations between residential exposure to specific ... ...

Abstract	Background: Maternal occupational exposure to pesticides has been linked to adverse birth outcomes but associations with residential pesticide exposures are inconclusive. Objectives: To explore associations between residential exposure to specific pesticides and birth outcomes using individual level exposure and pregnancy/birth data. Methods: From all 2009–2013 singleton births in the Dutch birth registry, we selected mothers > 16 years old living in non-urban areas, who had complete address history and changed addresses at most once during pregnancy (N = 339,947). We estimated amount (kg) of 139 active ingredients (AI) used within buffers of 50, 100, 250 and 500 m around each mother's home during pregnancy. We used generalized linear models to investigate associations between 12 AIs with evidence of reproductive toxicity and gestational age (GA), birth weight (BW), perinatal mortality, child́s sex, prematurity, low birth weight (LBW), small for gestational age (SGA) and large for gestational age (LGA), adjusting for individual and area-level confounders. For the remainder 127 AIs, we used minimax concave penalty with a stability selection step to identify those that could be related to birth outcomes. Results: Regression analyses showed that maternal residential exposure to fluroxypyr-meptyl was associated with longer GA, glufosinate-ammonium with higher risk of LBW, linuron with higher BW and higher odds of LGA, thiacloprid with lower odds of perinatal mortality and vinclozolin with longer GA. Variable selection analysis revealed that picoxystrobin was associated with higher odds of LGA. We found no evidence of associations with other AIs. Sensitivity and additional analysis supported these results except for thiacloprid. Discussion: In this exploratory study, pregnant women residing near crops where fluroxypyr-meptyl, glufosinate-ammonium, linuron, vinclozolin and picoxystrobin were applied had higher risk for certain potentially adverse birth outcomes. Our findings provide leads for confirmatory ...
Keywords	Pesticides ; Residential exposure ; Pre-natal exposure ; General population ; Birth outcomes ; Birth registry ; Environmental sciences ; GE1-350
Subject code	300
Language	English
Publishing date	2023-08-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring

Jules Kerckhoffs / Gerard Hoek / Ulrike Gehring / Roel Vermeulen

Environment International, Vol 154, Iss , Pp 106569- (2021)

2021

Abstract: Background: Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), ...

Abstract	Background: Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models. Method: We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016–2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country. Results: We found similar predictive performance for the six models, with validation R2 values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random ...
Keywords	Ultrafine particles ; National LUR model ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Network Analysis to Identify Communities Among Multiple Exposure Biomarkers Measured at Birth in Three Flemish General Population Samples

Ilse Ottenbros / Eva Govarts / Erik Lebret / Roel Vermeulen / Greet Schoeters / Jelle Vlaanderen

Frontiers in Public Health, Vol

2021 Volume 9

Abstract: 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 ... ...

Abstract	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.
Keywords	network analysis ; human biomonitoring ; multiple exposure biomarkers ; mixtures ; risk assessment ; community detection ; Public aspects of medicine ; RA1-1270
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Associations between the urban exposome and type 2 diabetes

Haykanush Ohanyan / Lützen Portengen / Oriana Kaplani / Anke Huss / Gerard Hoek / Joline W.J. Beulens / Jeroen Lakerveld / Roel Vermeulen

Environment International, Vol 170, Iss , Pp 107592- (2022)

Results from penalised regression by least absolute shrinkage and selection operator and random forest models

2022

Abstract: Background: Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety ... ...

Abstract	Background: Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies. Methods: Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables. Results: Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in the multivariable models (LASSO, RF). Selected variables differed between the two multi-variable approaches, especially for weaker predictors. Some established risk factors (air pollutants) appeared in univariate analysis but were not among the most important factors in multivariable analysis. Other established factors (green space) did not appear in univariate, but appeared in multivariable analysis (RF). Average estimates of the prediction error (logLoss) from nested cross-validation showed that the LASSO outperformed both RF and ANN approaches. Conclusions: Neighbourhood socio-economic and socio-demographic characteristics and surface temperature were consistently associated with the risk of T2D. For other physical-chemical factors associations differed per analytical approach.
Keywords	Neighbourhood socio-economic position ; Neighbourhood socio-demographic characteristics ; Temperature ; Machine learning ; Deep learning ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning approaches to characterize the obesogenic urban exposome

Haykanush Ohanyan / Lützen Portengen / Anke Huss / Eugenio Traini / Joline W.J. Beulens / Gerard Hoek / Jeroen Lakerveld / Roel Vermeulen

Environment International, Vol 158, Iss , Pp 107015- (2022)

2022

Abstract: Background: Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously. Objectives: We aimed to explore what environmental factors ... ...

Abstract	Background: Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously. Objectives: We aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches. Methods: A cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations. Results: The most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5 km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house ...
Keywords	Exposome ; Random forest ; Extreme gradient boosting (XGBoost) ; Shapley values ; Socioeconomic position ; Air pollution ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Setting the European environment and health research agenda –under-researched areas and solution-oriented research

Anke Huss / Annette Peters / Tianyu Zhao / Robert Barouki / Manolis Kogevinas / Roel Vermeulen / Franziska Matthies-Wiesler

Environment International, Vol 163, Iss , Pp 107202- (2022)

2022

Abstract: Background: The aim of the EU-funded HERA (health and environment research agenda) project is to set priorities for the future European research agenda in the environment, climate and health nexus. We report results from a European researcher’s ... ...

Abstract	Background: The aim of the EU-funded HERA (health and environment research agenda) project is to set priorities for the future European research agenda in the environment, climate and health nexus. We report results from a European researcher’s perspective and identify research areas that have been inadequately investigated to date. Methods: An online survey was completed by European researchers to assess, evaluate and visualise research gaps. These research gaps were identified for 21 predefined areas within 3 main categories: i) classical environment and health paradigm; ii) problem or sector-based research areas and approaches and iii) holistic research areas and concepts. All research gaps were then evaluated by expert groups with the pre-defined criteria and systematically summarized. For areas identified within the survey as under-reported, additional input was sought from a range of key selected experts. The EU project database Cordis was utilized to verify that these areas were under-researched. Results: Between May and July 2019, 318 respondents from 38 countries reported 624 research gaps. The main areas for attention identified were: urban environments; chemicals; and climate change, (combined n = 313 gaps). Biodiversity loss and health; transport, mobility, sustainable solutions and health; energy transition and health; waste and the circular economy and health; ethics and philosophy and health were areas that were acknowledged as under-researched (combined n = 27 gaps). These under-researched areas were identified as having certain commonalities, they: i) mostly fell in the category “problem or sector based approaches“; ii) they are essential for developing and implementing solutions; and iii) require trans-disciplinary and cross-sectoral collaboration. Conclusions: Currently attention is given to topical and highly researched areas in environmental health. In contrast, this paper identifies key topics and approaches that are under-researched, yet, are critical for the implementation of the EU Green ...
Keywords	Research agenda ; Environment health under-researched green deal ; Environmental sciences ; GE1-350
Subject code	300
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Quantile regression to examine the association of air pollution with subclinical atherosclerosis in an adolescent population

Adjani A. Peralta / Joel Schwartz / Diane R. Gold / Judith M. Vonk / Roel Vermeulen / Ulrike Gehring

Environment International, Vol 164, Iss , Pp 107285- (2022)

2022

Abstract: Background: Air pollution has been associated with carotid intima-media thickness test (CIMT), a marker of subclinical atherosclerosis. To our knowledge, this is the first study to report an association between ambient air pollution and CIMT in a younger ...

Abstract	Background: Air pollution has been associated with carotid intima-media thickness test (CIMT), a marker of subclinical atherosclerosis. To our knowledge, this is the first study to report an association between ambient air pollution and CIMT in a younger adolescent population. Objective: To investigate the associations beyond standard mean regression by using quantile regression to explore if associations occur at different percentiles of the CIMT distribution. Methods: We measured CIMT cross-sectionally at the age of 16 years in 363 adolescents participating in the Dutch PIAMA birth cohort. We fit separate quantile regressions to examine whether the associations of annual averages of nitrogen dioxide (NO2), fine particulate matter (PM2.5), PM2.5 absorbance (a marker for black carbon), PMcoarse and ultrafine particles up to age 14 assigned at residential addresses with CIMT varied across deciles of CIMT. False discovery rate corrections (FDR, p < 0.05 for statistical significance) were applied for multiple comparisons. We report quantile regression coefficients that correspond to an average change in CIMT (µm) associated with an interquartile range increase in the exposure. Results: PM2.5 absorbance exposure at birth was statistically significantly (FDR < 0.05) associated with a 6.23 µm (95% CI: 0.15, 12.3) higher CIMT per IQR increment in PM2.5 absorbance in the 10th quantile of CIMT but was not significantly related to other deciles within the CIMT distribution. For NO2 exposure we found similar effect sizes to PM2.5 absorbance, but with much wider confidence intervals. PM2.5 exposure was weakly positively associated with CIMT while PMcoarse and ultrafine did not display any consistent patterns. Conclusions: Early childhood exposure to ambient air pollution was suggestively associated with the CIMT distribution during adolescence. Since CIMT increases with age, mitigation strategies to reduce traffic-related air pollution early in life could possibly delay atherosclerosis and subsequently CVD development ...
Keywords	Quantile regression ; Environmental Epidemiology ; Air pollution ; Cardiovascular disease ; Atherosclerosis ; Adolescents ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Residential proximity to livestock animals and mortality from respiratory diseases in The Netherlands

Mariana Simões / Nicole Janssen / Dick J.J. Heederik / Lidwien A.M. Smit / Roel Vermeulen / Anke Huss

Environment International, Vol 161, Iss , Pp 107140- (2022)

A prospective census-based cohort study

2022

Abstract: Background: There is increasing evidence of associations between residential proximity to livestock farms and respiratory morbidity, but less is known about potential effects on respiratory mortality among residents. Objectives: We aimed to assess ... ...

Abstract	Background: There is increasing evidence of associations between residential proximity to livestock farms and respiratory morbidity, but less is known about potential effects on respiratory mortality among residents. Objectives: We aimed to assess potential associations between respiratory mortality and residential proximity to (intensive) livestock farming. Methods: In DUELS, a national census-based cohort, we selected all inhabitants from rural and semi-urban areas of the Netherlands, aged ≥30 years and living at the same address for five years up to baseline (2004). We followed these ∼4 million individuals for respiratory mortality (respiratory system diseases, chronic lower respiratory diseases, pneumonia) from 2005 to 2012. We computed the average number of cattle, pigs, chicken, and mink present in 500 m, 1000 m, 1500 m and 2000 m of each individual’s residence in the period 1999–2003. Analyses were conducted using Cox proportional hazards regression, adjusting for potential confounders at individual and neighbourhood level. Results: We found evidence that living up to 2000 m of pig farms was associated with respiratory mortality, namely from chronic lower respiratory diseases, with Hazard Ratios ranging from 1.06 (1.02, 1.10) in people living close to low numbers (<median number of animals) of pigs in 1000 m and 1.18 (1.13, 1.24) in those living near high numbers (≥median) of pigs in 2000 m. We also found indications of higher pneumonia mortality in people living near mink farms. Conclusion: Our results are in line with previous findings of adverse respiratory effects in people living near livestock farms. Little is known about the physical, chemical, and biological exposures leading to respiratory morbidity and mortality warranting further explorations of air contaminants in the vicinity of livestock farms.<br />
Keywords	Livestock farming ; Public health ; Respiratory health effects ; Residential exposure ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Impact of COVID-19 containment measures on perceived health and health-protective behavior

Warner van Kersen / Myrna M. T. de Rooij / Lützen Portengen / Nekane Sandoval Diez / Inka Pieterson / Marjan Tewis / Jolanda M. A. Boer / Gerard Koppelman / Judith M. Vonk / Roel Vermeulen / Ulrike Gehring / Anke Huss / Lidwien A. M. Smit

Scientific Reports, Vol 14, Iss 1, Pp 1-

a longitudinal study

2024 Volume 9

Abstract: Abstract This longitudinal study aimed to assess the impact of COVID-19 containment measures on perceived health, health protective behavior and risk perception, and investigate whether chronic disease status and urbanicity of the residential area modify ...

Abstract	Abstract This longitudinal study aimed to assess the impact of COVID-19 containment measures on perceived health, health protective behavior and risk perception, and investigate whether chronic disease status and urbanicity of the residential area modify these effects. Participants (n = 5420) were followed for up to 14 months (September 2020-October 2021) by monthly questionnaires. Chronic disease status was obtained at baseline. Urbanicity of residential areas was assessed based on postal codes or neighborhoods. Exposure to containment measures was assessed using the Containment and Health Index (CHI). Bayesian multilevel-models were used to assess effect modification of chronic disease status and urbanicity by CHI. CHI was associated with higher odds for worse physical health in people with chronic disease (OR = 1.09, 95% credibility interval (CrI) = 1.01, 1.17), but not in those without (OR = 1.01, Crl = 0.95, 1.06). Similarly, the association of CHI with higher odds for worse mental health in urban dwellers (OR = 1.31, Crl = 1.23, 1.40) was less pronounced in rural residents (OR = 1.20, Crl = 1.13, 1.28). Associations with behavior and risk perception also differed between groups. Our study suggests that individuals with chronic disease and those living in urban areas are differentially affected by government measures put in place to manage the COVID-19 pandemic. This highlights the importance of considering vulnerable subgroups in decision making regarding containment measures.
Keywords	Medicine ; R ; Science ; Q
Subject code	360
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modelling of daily radiofrequency electromagnetic field dose for a prospective adolescent cohort

Marloes Eeftens / Chen Shen / Jana Sönksen / Claudia Schmutz / Luuk van Wel / Ilaria Liorni / Roel Vermeulen / Elisabeth Cardis / Joe Wiart / Mireille Toledano / Martin Röösli

Environment International, Vol 172, Iss , Pp 107737- (2023)

2023

Abstract: Introduction: Radiofrequency electromagnetic fields originate from a variety of wireless communication sources operating near and far from the body, making it challenging to quantify daily absorbed dose. In the framework of the prospective cohort SCAMP ( ... ...

Abstract	Introduction: Radiofrequency electromagnetic fields originate from a variety of wireless communication sources operating near and far from the body, making it challenging to quantify daily absorbed dose. In the framework of the prospective cohort SCAMP (Study of Cognition, Adolescents and Mobile Phones), we aimed to characterize RF-EMF dose over a 2-year period. Methods: The SCAMP cohort included 6605 children from greater London, UK at baseline (age 12.1 years; 2014–2016) and 5194 at follow-up (age 14.2; 2016–2018). We estimated the daily dose of RF-EMF to eight tissues including the whole body and whole brain, using dosimetric algorithms for the specific absorption rate transfer into the body. We considered RF-EMF dose from 12 common usage scenarios such as mobile phone calls or data transmission. We evaluated the association between sociodemographic factors (gender, ethnicity, phone ownership and socio-economic status), and the dose change between baseline and follow-up. Results: Whole body dose was estimated at an average of 170 mJ/kg/day at baseline and 178 mJ/kg/day at follow-up. Among the eight tissues considered, the right temporal lobe received the highest daily dose (baseline 1150 mJ/kg/day, follow-up 1520 mJ/kg/day). Estimated daily dose [mJ/kg/day] increased between baseline and follow-up for head and brain related tissues, but remained stable for the whole body and heart. Doses estimated at baseline and follow-up showed low correlation among the 3384 children who completed both assessments. Asian ethnicity (compared to white) and owning a bar phone or no phone (as opposed to a smartphone) were associated with lower estimated whole-body and whole-brain RF-EMF dose, while black ethnicity, a moderate/low socio-economic status (compared to high), and increasing age (at baseline) were associated with higher estimated RF-EMF dose. Conclusion: This study describes the first longitudinal exposure assessment for children in a critical period of development. Dose estimations will be used in further ...
Keywords	Mobile phones ; Personal exposure ; Radiofrequency electromagnetic Fields ; Smart Phones ; WiFi ; SCAMP ; Environmental sciences ; GE1-350
Subject code	600
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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