Indoor environmental quality in offices and risk of health and productivity complaints at work: A literature review

Many service jobs are carried out in modern offices, with individual offices being increasingly replaced by open-plan settings. The high number of adult people working in office buildings, in most situations sharing the work-place with many others during a considerable part of their daily time, highlights the importance of providing adequate guidance to ensure the quality of office environments. This paper aims to summarize existing data on modern offices' indoor environmental quality (IEQ) conditions in terms of air pollution (volatile organic compounds (VOC), particulate matter and inorganic pollutants), thermal comfort, lighting and acoustics and the respective associations with health and productivity-related outcomes in workers. Evidence shows that al-though many offices present acceptable IEQ, some office settings can have levels of air pollutants, hygrothermal conditions/thermal comfort and illuminance that do not comply with the existing international standards and recommendations. In addition, findings suggest the existence of significant associations between the assessed IEQ indicators and the risk of detrimental effects on health and productivity of office workers. In particular, airborne particles, CO2, O 3 and thermal comfort were linked with the prevalence of sick building syndrome symptoms. Poor lighting and acoustical quality have also been associated with malaise and physiological stress among office workers. Similarly, better productivity levels have been registered for good indoor air quality conditions, in terms of VOC, airborne particles and CO2. Overall, the evidence revised in this work suggests that for promoting health and productivity recommendations for office building managers include actions to ensure that: i) all relevant IEQ indicators are periodically controlled to ensure that levels comply with recommended limit values; ii) declared in-door pollution sources are avoided; iii) adequate ventilation and acclimatization strategies are implemented; and iv) there is the possibility of conduct personalized adjustments to environmental conditions (following workers' preferences).The authors gratefully acknowledge Fundacao para a Ciencia e nologia (FCT) for the financial support of FF through the PhD BD/6521/2020

Adjusting ROC curve for Covariates with AROC R package

The ability of a medical test to differentiate between diseased and non-diseased states is of vital importance and must be screened by statistical analysis for reliability and improvement. The receiver operating characteristic (ROC) curve remains a popular method of marker analysis, disease screening and diagnosis. Covariates in this field related to the subject’s characteristics are incorporated in the analysis to avoid bias. The covariate adjusted ROC (AROC) curve was proposed as a method of incorporation. The AROC R-package was recently released and brings various methods of estimation based on multiple authors work. The aim of this study was to explore the AROC package functionality and usability using real data noting its possible limitations. The main methods of the package were capable of incorporating different and multiple variables, both categorical and continuous, in the AROC curve estimation. When tested for the same data, AROC curves are generated with no statistical differences, regardless of method. The package offers a variety of methods to estimate the AROC curve complemented with predictive checks and pooled ROC estimation. The package offers a way to conduct a more thorough ROC and AROC analysis, making it available for any R user.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Indoor environmental quality in households of families with infant twins under 1 year of age living in Porto

Exposure to air pollution in early years can exacerbate the risk of noncommunicable diseases throughout childhood and the entire life course. This study aimed to assess temperature, relative humidity (RH), carbon dioxide (CO2) and monoxide (CO), particulate matter (PM2.5, PM10), ultrafine particles, nitrogen dioxide (NO2), ozone (O3), formaldehyde, acetaldehyde and volatile organic compounds (VOC) levels in the two rooms where infant twins spend more time at home (30 dwellings, Northern Portugal). Findings showed that, in general, the worst indoor environmental quality (IEQ) settings were found in bedrooms. In fact, although most of the bedrooms surveyed presented adequate comfort conditions in terms of temperature and RH, several children are sleeping in a bedroom with improper ventilation and/or with a significant degree of air pollution. In particular, mean concentrations higher than recommended limits were found for CO2, PM2.5, PM10 and total VOC. Additionally, terpenes and decamethylcyclopentasiloxane were identified as main components of emissions from indoor sources. Overall, findings revealed that factors related to behaviors of the occupants, namely related to a conscientious use of cleaning products, tobacco and other consumer products (air-fresheners, incenses/candles and insecticides) and promotion of ventilation are essential for the improvement of air quality in households and for the promotion of children's health.The authors gratefully acknowledge the funding of Project HEALS – ‘Health and Environment-wide Associations based on Large Population Surveys’, through the European Union's Seventh Program for research, technological development and demonstration under grant agreement No info:eu-repo/grantAgreement/EC/FP7/603946/EU. MFG, FF and ZM also gratefully acknowledge the funding of Project NORTE-01-0145-FEDER-000010, Health, Comfort and Energy in the Built Environment (HEBE), cofinanced by Programa Operacional Regional do Norte (NORTE2020), through Fundo Europeu de Desenvolvimento Regional (FEDER). The co-author CR was supported by a PhD Grant PD/BD/135925/2018 funded by the Foundation for Science and Technology (FCT) - Portuguese Ministry of Science, Technology and Higher Education

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample