Periodic Reporting for period 1 - URBINEX (The relationship between social inequalities, child mental health and exposure to urban environmental stressors: an epidemiological analysis)
Période du rapport: 2021-11-01 au 2023-10-31
Summary: Mental health disorders are one of the primary sources of ill health for children and adolescents. Children from more disadvantaged backgrounds are up to three times more likely to experience mental health problems than those more privileged. However, we still do not fully understand the mechanisms through which childhood socio-economic circumstances (SEC) affect children’s mental health. With an ever-increasing proportion of EU citizens living in cities, attention is turning to the effects of environmental stressors such as air pollution, traffic noise, and natural space on children’s development. In many cities, children from lower socio-economic backgrounds are disproportionately exposed to more harmful aspects of the urban environment. However, we do not know to what degree greater exposure or vulnerability to these environmental stressors contribute to social inequalities in child mental health. Understanding this is vital to inform cost-effective public health interventions which prioritise modifiable risk factors. The overall objective of URBINEX was to use data from the EU Child Cohort Network on over 200,000 children to better understand how exposure to urban environmental stressors contributes to social inequalities in mental health. The aim of WP1 was to describe social inequalities in child internalising and externalising problems across multiple countries and to explore how these inequalities change as children age. The aim of WP2 was to explore associations between prenatal exposure to multiple domains of the urban exposome (built environment, natural spaces, air pollution, and road traffic noise) and child mental health. WP3 is onging, and aim will be to study how the degree of urbanization in pregnancy characterises the personal exposome pattern during fetal life, infancy and early childhood in different countries.
Work Package 1
Methods: I used longitudinal data from eight birth cohorts containing participants from twelve countries (Australia, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Poland, Norway, Spain and the United Kingdom). The number of included children in each cohort ranged from N=584 (Greece) to N=73,042 (Norway), with a total sample of N=149,604. Child socio‐economic circumstances (SEC) were measured using self‐reported maternal education at birth. Child mental health outcomes were internalising and externalising problems measured using either the Strengths and Difficulties Questionnaire or the Child Behavior Checklist. I modelled the slope index of inequality (SII) using sex‐stratified multi‐level models.
Results: For almost all cohorts, at the earliest age of measurement children born into more deprived SECs had higher internalising and externalising scoress than children born to less deprived SECs. For example, in Norway at age 2 years, boys born to mothers of lower education had an estimated 0.3 (95% CI 0.3 0.4) standard deviation higher levels of internalising problems (SII) compared to children born to mothers with high education. The exceptions were for boys in Australia (age 2) and both sexes in Greece (age 6), where we observed minimal social inequalities. In UK, Denmark and Netherlands inequalities decreased as children aged, however for other countries (France, Norway, Australia and Crete) inequalities were heterogeneous depending on child sex and outcome. For all countries except France inequalities remained at the oldest point of measurement.
Work Package 2
Methods: I used data from 13 EU birth cohorts including up to 36,819 children. Exposure to up to 27 aspects of the urban exposome in pregnancy were estimated (built environment, natural spaces, ambient air pollution, noise) using land-use regression models or other established methods. Three mental health outcomes were used: internalising, aggressive behaviour and ADHD symptoms, square-root transformed to achieve normality and converted to within-cohort z-scores. Associations between each exposure and outcome were estimated using separate linear regression models within each cohort and combined using 2-stage meta-analysis. False-discovery rate was used to correct for multiple testing. In order to test which were the most influential exposures, I also conducted LASSO regression as a variable selection tool. As in work package 1, all analysis was conducted using the DataSHIELD platform.
Results: For child internalising, we found no associations between any environmental exposures and child mental health outcomes. For aggressive behaviour, we found associations between the following aspects of the urban environment and aggressive behaviour: transport land use, connectivity density, facility density, number of bus lines, number of bus stops, NDVI, sive of green & blue space and road traffic noise. Analysis with LASSO regression retained one variable: access to blue spaces. Finally, we found associations between two aspects of the urban environmemt and ADHD symptoms: connectivity density and PM2.5 (air pollution). Analysis with LASSO regression also retained one variable: access to blue spaces.
Work Package 3
Methods: Eight EU cohorts will be included with available data on urbanicity and the personal exposome stressors. The exposure will be level of urbanization at birth, based on the Global Human Settlement Layer (GHSL-SMOD) which stratifies the household residence of participants according to the three levels of the degrees of urbanisation. The outcomes will be exposome variables classified in five subgroups: (i) diet, (ii) lifestyle, (iii) maternal health (fetal period only), (iv) childcare (pre-school period only) and (v) exposure to mental health disorders. The personal exposome will be analysed in three different periods: (i) fetal life, (ii) pre-school age (0-4 years) and (iii) early childhood (5-8 years). For each time period, separate logistic regression models will be fit for each exposome variable with the degree of urbanisation variable as the exposure. Associations will be visualised using volcano plots and/or tabular format.
Methods: I used longitudinal data from eight birth cohorts containing participants from twelve countries (Australia, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Poland, Norway, Spain and the United Kingdom). The number of included children in each cohort ranged from N=584 (Greece) to N=73,042 (Norway), with a total sample of N=149,604. Child socio‐economic circumstances (SEC) were measured using self‐reported maternal education at birth. Child mental health outcomes were internalising and externalising problems measured using either the Strengths and Difficulties Questionnaire or the Child Behavior Checklist. I modelled the slope index of inequality (SII) using sex‐stratified multi‐level models.
Results: For almost all cohorts, at the earliest age of measurement children born into more deprived SECs had higher internalising and externalising scoress than children born to less deprived SECs. For example, in Norway at age 2 years, boys born to mothers of lower education had an estimated 0.3 (95% CI 0.3 0.4) standard deviation higher levels of internalising problems (SII) compared to children born to mothers with high education. The exceptions were for boys in Australia (age 2) and both sexes in Greece (age 6), where we observed minimal social inequalities. In UK, Denmark and Netherlands inequalities decreased as children aged, however for other countries (France, Norway, Australia and Crete) inequalities were heterogeneous depending on child sex and outcome. For all countries except France inequalities remained at the oldest point of measurement.
Work Package 2
Methods: I used data from 13 EU birth cohorts including up to 36,819 children. Exposure to up to 27 aspects of the urban exposome in pregnancy were estimated (built environment, natural spaces, ambient air pollution, noise) using land-use regression models or other established methods. Three mental health outcomes were used: internalising, aggressive behaviour and ADHD symptoms, square-root transformed to achieve normality and converted to within-cohort z-scores. Associations between each exposure and outcome were estimated using separate linear regression models within each cohort and combined using 2-stage meta-analysis. False-discovery rate was used to correct for multiple testing. In order to test which were the most influential exposures, I also conducted LASSO regression as a variable selection tool. As in work package 1, all analysis was conducted using the DataSHIELD platform.
Results: For child internalising, we found no associations between any environmental exposures and child mental health outcomes. For aggressive behaviour, we found associations between the following aspects of the urban environment and aggressive behaviour: transport land use, connectivity density, facility density, number of bus lines, number of bus stops, NDVI, sive of green & blue space and road traffic noise. Analysis with LASSO regression retained one variable: access to blue spaces. Finally, we found associations between two aspects of the urban environmemt and ADHD symptoms: connectivity density and PM2.5 (air pollution). Analysis with LASSO regression also retained one variable: access to blue spaces.
Work Package 3
Methods: Eight EU cohorts will be included with available data on urbanicity and the personal exposome stressors. The exposure will be level of urbanization at birth, based on the Global Human Settlement Layer (GHSL-SMOD) which stratifies the household residence of participants according to the three levels of the degrees of urbanisation. The outcomes will be exposome variables classified in five subgroups: (i) diet, (ii) lifestyle, (iii) maternal health (fetal period only), (iv) childcare (pre-school period only) and (v) exposure to mental health disorders. The personal exposome will be analysed in three different periods: (i) fetal life, (ii) pre-school age (0-4 years) and (iii) early childhood (5-8 years). For each time period, separate logistic regression models will be fit for each exposome variable with the degree of urbanisation variable as the exposure. Associations will be visualised using volcano plots and/or tabular format.
WP1 contained the largest study of its kind which used rich harmonized data from multiple EU countries and Australia. It extends on previous studies both in this breadth of geographical coverage and also through modelling social inequalities over the entirety of childhood, rather than a smaller period. This study was also completed using the innovative software platform DataSHIELD, which allows non-disclosive federated analysis of data. WP2 contributed to the state of the art in two main ways. Using DataSHIELD as a platform to conduct these analyses is methodologically innovative: this is the first project to perform an exposome-wide association analysis using DataSHIELD or use machine learning techniques such as LASSO. From a scientific point of view, it extends previous studies by exploring associations between multiple environmental exposures, multiple outcomes using data from a large number of children across different EU countries.