Impact of environmental factors in predicting daily severity scores of atopic dermatitis

Mapping Intimacies ◽

10.1101/2020.10.27.20220947 ◽

2020 ◽

Author(s):

Guillem Hurault ◽

Valentin Delorieux ◽

Young-Min Kim ◽

Kangmo Ahn ◽

Hywel C. Williams ◽

...

Keyword(s):

Atopic Dermatitis ◽

Environmental Factors ◽

Air Pollutants ◽

Panel Study ◽

Predictive Performance ◽

Statistical Machine Learning ◽

Short Term ◽

Mixed Effect ◽

Severity Scores ◽

Term Basis

ABSTRACTBackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Although environmental factors including weather and air pollutants have been shown to be associated with AD symptoms, the time-dependent nature of such a relationship has not been adequately investigated.ObjectiveThis paper aims to assess the short-term impact of weather and air pollutants on AD severity scores.MethodsUsing longitudinal data from a published panel study of 177 paediatric patients followed up for 17 months, we developed statistical machine learning models to predict daily AD severity scores for individual study participants. Exposures consisted of daily meteorological variables and concentrations of air pollutants and outcomes were daily recordings of scores for six AD signs. We developed a mixed effect autoregressive ordinal logistic regression model, validated it in a forward-chaining setting, and evaluated the effects of the environmental factors on the predictive performance.ResultsOur model outperformed benchmark models for daily prediction of the AD severity scores. The predictive performance of AD severity scores was not improved by the addition of measured environmental factors. Any potential short-term influence of environmental exposures on AD severity scores was outweighed by the underlying persistence of preceding scores.ConclusionsOur data does not offer enough evidence to support a claim that AD symptoms are associated with weather or air pollutants on a short-term basis. Inferences about the magnitude of the effect of environmental factors require consideration of the time-dependence of the AD severity scores.

Can serum biomarkers predict the outcome of systemic therapy for atopic dermatitis?

10.1101/2020.12.02.20242404 ◽

2020 ◽

Author(s):

Guillem Hurault ◽

Evelien Roekevisch ◽

Mandy E. Schram ◽

Krisztina Szegedi ◽

Sanja Kezic ◽

...

Keyword(s):

Atopic Dermatitis ◽

Systemic Therapy ◽

Measurement Errors ◽

Treatment Strategies ◽

Predictive Performance ◽

Historical Record ◽

Statistical Machine Learning ◽

Minimum Detectable Change ◽

Severity Scores ◽

Personalised Treatment

SUMMARYBackgroundAtopic dermatitis (AD or eczema) is a most common chronic skin disease. Designing personalised treatment strategies for AD based on patient stratification, rather than the “one-size-fits-all” treatments, is of high clinical relevance. It has been hypothesised that the measurement of biomarkers could help predict therapeutic response for individual patients.ObjectiveWe aim to assess whether biomarkers can predict the outcome of systemic therapy.MethodsWe developed a statistical machine learning predictive model using the data of an already published longitudinal study of 42 patients who received systemic therapy. The data contained 26 serum cytokines measured before the therapy. The model described the dynamics of the latent disease severity and measurement errors to predict AD severity scores (EASI, (o)SCORAD and POEM) two-weeks ahead. We conducted feature selection to identify the most important biomarkers for predicting the AD severity scores.ResultsWe validated our model and confirmed that it outperformed standard time-series forecasting models. Adding biomarkers did not improve predictive performance. Our estimates of the minimum detectable change for the AD severity scores were larger than already published estimates of the minimal clinically important difference.ConclusionsBiomarkers had a negligible and non-significant effect for predicting the future AD severity scores and the outcome of the systemic therapy. Instead, a historical record of severity scores provides rich and insightful dynamical information required for prediction of therapeutic responses.

Short-term effects of weather and air pollution on atopic dermatitis symptoms in children: A panel study in Korea

PLoS ONE ◽

10.1371/journal.pone.0175229 ◽

2017 ◽

Vol 12 (4) ◽

pp. e0175229 ◽

Cited By ~ 25

Author(s):

Young-Min Kim ◽

Jihyun Kim ◽

Youngshin Han ◽

Byoung-Hak Jeon ◽

Hae-Kwan Cheong ◽

...

Keyword(s):

Air Pollution ◽

Atopic Dermatitis ◽

Panel Study ◽

Short Term ◽

Short Term Effects

Exploring Cross-Modal Influences on the Evaluation of Indoor-Environmental Conditions

Frontiers in Built Environment ◽

10.3389/fbuil.2021.676607 ◽

2021 ◽

Vol 7 ◽

Author(s):

Christiane Berger ◽

Ardeshir Mahdavi

Keyword(s):

Environmental Factors ◽

Experimental Approach ◽

Past Research ◽

Energy Performance ◽

Short Term ◽

Energy Performance Of Buildings ◽

Parametric Studies ◽

Term Basis ◽

Human Participants ◽

Office Rooms

The comfort requirements of occupants influence indoor-environmental factors and energy performance of buildings. Occupants are typically exposed to a multitude of indoor-environmental factors, including a variety of different thermal, auditory, visual, and air quality conditions. However, the bulk of past research and derivative indoor-environmental codes and standards concerning the comfort of occupants address the multiple indoor-environmental stimuli in isolation. Starting from a brief review of past research on multi-perceptual indoor-environmental assessments of occupants, the present study pursues an experimental approach to explore the potential cross-modal effects on the evaluation of indoor-environmental thermal, visual, and acoustic aspects. In this context, a laboratory space including two adjacent identical mock-up office rooms was used to conduct multi-aspect parametric studies with human participants. Different thermal, visual, and auditory conditions were maintained in these two units. In the course of the present study, 296 participants were exposed, on a short-term basis, to different combinations of thermal, visual, and auditory conditions. The experiments were intended to explore if the evaluation of one aspect of the indoor environment could be influenced by differences in the values pertaining to the other aspects. The experimental results are presented and discussed, including their limitations.

Effects of Short-Term Exposure to Particulate Air Pollutants on the Inflammatory Response and Respiratory Symptoms: A Panel Study in Schoolchildren from Rural Areas of Japan

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph13100983 ◽

2016 ◽

Vol 13 (10) ◽

pp. 983 ◽

Cited By ~ 6

Author(s):

Masanari Watanabe ◽

Hisashi Noma ◽

Jun Kurai ◽

Hiroyuki Sano ◽

Degejirihu Hantan ◽

...

Keyword(s):

Inflammatory Response ◽

Rural Areas ◽

Air Pollutants ◽

Respiratory Symptoms ◽

Panel Study ◽

Short Term ◽

Short Term Exposure

The Effects of Short-term Pm 2.5 Exposure on Pulmonary Function Among Children With Asthma——A Panel Study in Shanghai, China

10.21203/rs.3.rs-1187744/v1 ◽

2022 ◽

Author(s):

Ji Zhou ◽

Ruoyi Lei ◽

Jianming Xu ◽

Peng Li ◽

Xiaofang Ye ◽

...

Keyword(s):

Risk Factor ◽

Lung Function ◽

Panel Study ◽

Maximum Effect ◽

Short Term ◽

Mixed Effect ◽

Short Term Exposure ◽

Children With Asthma ◽

Hot Season ◽

The Relationship

Abstract BackgroundFine particulate matter with aerodynamic diameter ≤ 2.5 mm (PM2.5) has been reported to be an important risk factor for asthma. Our study was designed to evaluate the relationship between air PM2.5 and lung function among children with asthma in Shanghai, China. MethodsFrom 2016 to 2019, a total of 70 Chinese children aged 4 to 14 in Pudong, Shanghai were recruited for this panel study. Upon entry to the group, questionnaire was used to collect basic information, and the lung function covering forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1) and peak expiratory flow (PEF) were carried out for each child more than two times during follow-up. Meanwhile, the simultaneous daily air atmospheric pollutants and meteorological data were collected. The linear mixed effect (LME) model was used to assess the relationship between air pollutants and lung function adjusting other covariates like sex, age, season and so on. ResultsA significantly negative association was found between PM2.5 level and lung function in children with asthma. In the single-pollutant model, the largest effect of PM2.5 on lung function were found on lag 0-2, FVC and FEV1 decreased by 0.91% [95% confidence interval (CI): -1.75, -0.07] and 1.05% (95% CI: -2.09, 0.00) for every 10 mg/m3 increase of PM2.5. In the multi-pollution model (adjusted PM2.5+SO2+O3), the maximum effect of PM2.5 on FVC and FEV1 also appeared on lag 0-2, FVC and FEV1 decreased by 1.57% (95% CI: -2.69, -0.44) and 1.67% (95% CI: -3.05, -0.26) for every 10 mg/m3 increase of PM2.5, respectively. In the subgroup analysis, boys, children and hot season more were sensitive. ConclusionThe short-term exposure of ambient PM2.5 is a risk factor for the lung function of children with asthma, particularly in boys, preschoolers (<6 years old) and in the hot season.

Environmental Exposure and Childhood Atopic Dermatitis in Shanghai: A Season-Stratified Time-Series Analysis

Dermatology ◽

10.1159/000514685 ◽

2021 ◽

pp. 1-8

Author(s):

Yabin Hu ◽

Fan Jiang ◽

Jianguo Tan ◽

Shijian Liu ◽

Shenghui Li ◽

...

Keyword(s):

Atopic Dermatitis ◽

Low Temperature ◽

Environmental Factors ◽

Environmental Exposure ◽

Air Pollutants ◽

Warm Season ◽

Cold Season ◽

Seasonal Effects ◽

Outpatient Visits ◽

Increased Risk

Background: Childhood atopic dermatitis (AD) is an inflammatory skin disease which sometimes predisposes to allergies. Environmental factors (low humidity, irritants, etc.) are prominent causative triggers of AD. Objectives: This study aims to explore the effects of both meteorological factors and air pollutants on childhood AD, and the modification effects by season in Shanghai, China. Methods: Quasi-Poisson generalized linear regression model, combined with a distributed lag nonlinear model was used to examine the nonlinear and lagged effects of environmental factors on childhood AD from 2009 to 2017 in Shanghai. We also performed a season-stratified analysis to determine the modification effects of environmental exposure by season on childhood AD. Results: There were 1,043,240 outpatient visits for childhood AD in total, at 3 major pediatric hospitals. Low temperature and relative humidity (RH), and high daily temperature difference (DTD) and air pollutants (i.e., NO2) increased the relative risks (RRs) of outpatient visits for childhood AD in the whole year. In the cold season, an increased risk of outpatient visits for childhood AD was associated with low RH (RR 2.26, 95% CI 1.69–3.02) and high NO2 (1.11, 95% CI 1.06–1.17). In the warm season, outpatient visits for childhood AD were associated with low temperature (3.49, 95% CI 3.22–3.77), low RH (1.89, 95% CI 1.74–2.06), high DTD (1.41, 95% CI 1.31–1.53), and high NO2 (1.05, 95% CI 1.03–1.06). Conclusions: This study suggests that environmental exposure may be a key trigger for outpatient visits for childhood AD with apparent seasonal effects. Tailored preventive strategies to avoid environmental triggers of childhood AD should be developed.