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The analysis of time series studies linking daily counts of a health indicator with environmental variables (e.g., mortality or hospital admissions with air pollution concentrations or temperature; or motor vehicle crashes with temperature) is usually conducted with Poisson regression models controlling for long-term and seasonal trends using temporal strata. When the study includes multiple zones, analysts usually apply a two-stage approach: first, each zone is analyzed separately, and the resulting zone-specific estimates are then combined using meta-analysis. This approach allows zone-specific control for trends. A one-stage approach uses spatio-temporal strata and could be seen as a particular case of the case–time series framework recently proposed. However, the number of strata can escalate very rapidly in a long time series with many zones. A computationally efficient alternative is to fit a conditional Poisson regression model, avoiding the estimation of the nuisance strata. To allow for zone-specific effects, we propose a conditional Poisson regression model with a random slope, although available frequentist software does not implement this model. Here, we implement our approach in the Bayesian paradigm, which also facilitates the inclusion of spatial patterns in the effect of interest. We also provide a possible extension to deal with overdispersed data. We first introduce the equations of the framework and then illustrate their application to data from a previously published study on the effects of temperature on the risk of motor vehicle crashes. We provide R code and a semi-synthetic dataset to reproduce all analyses presented., We acknowledge support from the grant CEX2018- 000806-S funded by MCIN/AEI/ 10.13039/501100011033, support from Ministry of Research and Universities of the Government of Catalonia (2021 SGR 01563) and support from the Generalitat de Catalunya through the CERCA Program., Peer Reviewed, Postprint (published version)

Acord transformatiu CRUE-CSIC, Background: Evidence on validation of surrogates applied to evaluate the personal exposure levels of solar ultraviolet radiation (UVR) in epidemiological studies is scarce. Objectives: To determine and compare the validity of three approaches, including (i) ambient UVR levels, (ii) time spent outdoors and (iii) a modelling approach integrating the aforementioned parameters, to estimate personal UVR exposure over a period of 6 months among indoor and outdoor workers and in different seasons (summer/winter). Methods: This validation study was part of the European Commission-funded ICEPURE project and was performed between July 2010 and January 2011 in a convenience sample of indoor and outdoor workers in Catalunya, Spain. We developed linear regression models to quantify the variation in the objectively measured personal UVR exposure that could be explained, separately, by the ambient UVR, time spent outdoors and modelled UVR levels. Results: Our 39 participants - mostly male and with a median age of 35 years - presented a median daily objectively measured UVR of 0·37 standard erythemal doses. The UVR dose was statistically significantly higher in summer and for outdoor workers. The modelled personal UVR exposure and self-reported time spent outdoors could reasonably predict the variation in the objectively measured personal UVR levels (R range 0·75-0·79), whereas ambient UVR was a poor predictor (R = 0·21). No notable differences were found between seasons or occupation. Conclusions: Time outdoors and our modelling approach were reliable predictors and of value to be applied in epidemiological studies of the health effects of current exposure to UVR.

Background: Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. Methods: We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. Results: All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. Conclusions: Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both

Background: The recent evidence of the short-term impact of air pollution on youth cognitive functions is based primarily on observational studies. Objectives: We conducted a randomized controlled trial to assess whether purifying the air of the classrooms produced short-term changes in attention processes of adolescents. Methods: We recruited a total of 2,123 adolescents (13–16 years old) in 33 high schools in Barcelona metropolitan area (Spain). In each school, adolescents from each class were randomly split into two equal-sized groups and assigned to two different classrooms. A set of two air cleaner devices with the same appearance (one recirculating and filtrating the air and the other only recirculating the air) was used. Each one of the devices was placed at random at one of the two classrooms. Students were masked to intervention allocation and had to complete several computerized activities for 1.5 h, including an attention test (Flanker task) to be performed at baseline and at the end of the intervention. The response speed consistency, expressed as hit reaction time standard error (HRT-SE, in ms), was measured as the primary outcome. Analyses were conducted using conditional linear regressions with classroom as strata, adjusted for variables that may differ from one class to another such as temperature, humidity and carbon dioxide concentration. Results: Average levels of PM2.5 and black carbon throughout the 1.5 h of experiment were 89% and 87%, respectively, lower in the classrooms with air cleaner than in the control classrooms. No differences were found in the median of HRT-SE between classrooms with cleaned air and normal air (percent change: 1.37%, 95% confidence interval: −2.81%, 5.56%). Sensitivity analyses with secondary attention outcomes resulted in similar findings. Conclusions: Cleaning the air of a classroom to reduce exposure to air pollutants for 1.5 h did not have an impact on the attention function of adolescents. Still, in

Studies on factors that can influence attention in healthy adolescents are recent and focus on recurrent topics. Students’ contribution to public health research often revolves around collecting data but rarely around creating data collection instruments. The ATENC!Ó project reunited secondary students and scientists to create a questionnaire including factors that students thought could affect their attention. We conducted a cross‐sectional study to assess whether the factors included in this questionnaire had an effect on attention in adolescents. A total of 1667 students (13–16 years old) from 28 schools in Barcelona performed a validated attention test and answered the questionnaire. The response speed consistency (attentiveness), expressed as hit reaction time standard error (HRT‐SE, in ms), was used as the primary outcome. Analyses were conducted using conditional linear regression with school as strata, adjusted for sociodemographic characteristics and further stratified by gender and maternal social class. Some factors showed a negative influence on attention, including taking medication and not reading regularly. We found a significant 14.3% (95% confidence interval: 3.4%, 25.3%) higher median of HRT‐SE (increase inattentiveness) among students who reported not having a good relationship with classmates. Students’ input into research is relevant for advancing the knowledge production in public health.

There is limited suggestive evidence of relationships between public transport strikes and either increased air pollution or worse population health. In this study we aimed to assess whether public transport strikes were associated with increases in health events (overall, cardiovascular and respiratory mortality, and cardiovascular and respiratory hospitalizations). We also explored whether air pollution mediated those associations. We used data from the city of Barcelona (Spain) for the period 2005–2016 on strikes, health events, and ambient air pollution (nitrogen dioxide, nitrogen monoxide, particulate matter (PM) with an aerodynamic diameter =10 µm, PM with an aerodynamic diameter =2.5µm, PM with an aerodynamic diameter =1µm, number of particles with a diameter greater than 5 nm per cm3 (particle number concentration), and black carbon). We used linear and quasi-Poisson regression models to explore the associations between air pollution and public transport strikes and between public transport strikes and health outcomes. We also investigated potential causal mediation by air pollution. Overall, this study suggested that public transport strikes are associated with increased overall mortality, respiratory mortality, and respiratory hospitalizations. However, our findings suggest that such increases are not mediated by the increase in air pollution. Our results indicate the need to further investigate these relationships and potential mechanisms., Peer Reviewed, Postprint (published version)

En el contexto de los estudios longitudinales observacionales, hemos obtenido los valores óptimos para el número de participantes y el número de medidas repetidas que maximizan la potencia para detectar el hipotético efecto, sin rebasar un presupuesto económico determinado, o que minimizan el coste total del estudio, alcanzando una potencia determinada. Hemos considerado una variable respuesta continua, cuya estructura de covarianza se supone exponencialmente amortiguada, y una exposición binaria variable en el tiempo, tanto para un efecto agudo y transitorio de la exposición, a través de una diferencia constante en la media entre expuestos y no expuestos, como para un efecto acumulativo, a través de una diferencia linealmente divergente. Hemos derivado expresiones cerradas para la solución del problema en el caso particular en que se supone que la estructura de covarianza de la respuesta es de simetría compuesta. Los resultados mostraron la importancia de la correlación intraclase de la exposición sobre la determinación de la combinación óptima del número de participantes y el número de medidas repetidas y, por tanto, sobre la potencia o el coste optimizados. Así, la incorrecta asunción de una exposición constante conduce a resultados sesgados en el cálculo del tamaño de la muestra y de la potencia o el coste del estudio. También analizamos la sensibilidad de los resultados a la presencia de datos faltantes debido al abandono de participantes durante el seguimiento, al cambio de la estructura de covarianza de la respuesta y a una prevalencia de la exposición variable en el tiempo. Como ejemplo, ilustramos algunos de estos resultados aplicando nuestros métodos en un estudio real. Además, proporcionamos el paquete de R optimalAllocation para realizar todos los cálculos necesarios para obtener la combinación óptima del número de participantes y el número de medidas repetidas, In the context of observational longitudinal studies, the aim of this work is to obtain optimal values of the number of participants and the number of repeated measurements that maximize the power to detect the hypothesized effect, given the total financial cost of the study