scholarly journals Ambient air pollution exposure and risk and progression of interstitial lung abnormalities: the Framingham Heart Study

Thorax ◽  
2019 ◽  
Vol 74 (11) ◽  
pp. 1063-1069 ◽  
Author(s):  
Mary B Rice ◽  
Wenyuan Li ◽  
Joel Schwartz ◽  
Qian Di ◽  
Itai Kloog ◽  
...  
Keyword(s):  
Air Pollution ◽  
Logistic Regression ◽  
Regression Models ◽  
Cardiac Ct ◽  
Prediction Models ◽  
Ambient Air ◽  
Chest Ct ◽  
Ambient Air Pollution ◽  
Logistic Regression Models ◽  
Lung Abnormalities

BackgroundAmbient air pollution accelerates lung function decline among adults, however, there are limited data about its role in the development and progression of early stages of interstitial lung disease.AimsTo evaluate associations of long-term exposure to traffic and ambient pollutants with odds of interstitial lung abnormalities (ILA) and progression of ILA on repeated imaging.MethodsWe ascertained ILA on chest CT obtained from 2618 Framingham participants from 2008 to 2011. Among 1846 participants who also completed a cardiac CT from 2002 to 2005, we determined interval ILA progression. We assigned distance from home address to major roadway, and the 5-year average of fine particulate matter (PM2.5), elemental carbon (EC, a traffic-related PM2.5 constituent) and ozone using spatio-temporal prediction models. Logistic regression models were adjusted for age, sex, body mass index, smoking status, packyears of smoking, household tobacco exposure, neighbourhood household value, primary occupation, cohort and date.ResultsAmong 2618 participants with a chest CT, 176 (6.7%) had ILA, 1361 (52.0%) had no ILA, and the remainder were indeterminate. Among 1846 with a preceding cardiac CT, 118 (6.4%) had ILA with interval progression. In adjusted logistic regression models, an IQR difference in 5-year EC exposure of 0.14 µg/m3 was associated with a 1.27 (95% CI 1.04 to 1.55) times greater odds of ILA, and a 1.33 (95% CI 1.00 to 1.76) times greater odds of ILA progression. PM2.5 and O3 were not associated with ILA or ILA progression.ConclusionsExposure to EC may increase risk of progressive ILA, however, associations with other measures of ambient pollution were inconclusive.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

scholarly journals Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach

2016 ◽  
Vol 49 (1) ◽  
pp. 1502127 ◽  
Author(s):  
Yutong Cai ◽  
Wilma L. Zijlema ◽  
Dany Doiron ◽  
Marta Blangiardo ◽  
Paul R. Burton ◽  
...  
Keyword(s):  
Air Pollution ◽  
Noise Exposure ◽  
Traffic Noise ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Asthma Prevalence ◽  
Pool Data ◽  
Cross Sectional ◽  
Adult Asthma ◽  
Logistic Regression Models

We investigated the effects of both ambient air pollution and traffic noise on adult asthma prevalence, using harmonised data from three European cohort studies established in 2006–2013 (HUNT3, Lifelines and UK Biobank).Residential exposures to ambient air pollution (particulate matter with aerodynamic diameter ≤10 µm (PM10) and nitrogen dioxide (NO2)) were estimated by a pan-European Land Use Regression model for 2007. Traffic noise for 2009 was modelled at home addresses by adapting a standardised noise assessment framework (CNOSSOS-EU). A cross-sectional analysis of 646 731 participants aged ≥20 years was undertaken using DataSHIELD to pool data for individual-level analysis via a “compute to the data” approach. Multivariate logistic regression models were fitted to assess the effects of each exposure on lifetime and current asthma prevalence.PM10 or NO2 higher by 10 µg·m−3 was associated with 12.8% (95% CI 9.5–16.3%) and 1.9% (95% CI 1.1–2.8%) higher lifetime asthma prevalence, respectively, independent of confounders. Effects were larger in those aged ≥50 years, ever-smokers and less educated. Noise exposure was not significantly associated with asthma prevalence.This study suggests that long-term ambient PM10 exposure is associated with asthma prevalence in western European adults. Traffic noise is not associated with asthma prevalence, but its potential to impact on asthma exacerbations needs further investigation.

scholarly journals P.2.39 Periconceptional exposure to ambient air pollution and congenital hypospadias

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A97.2-A97
Author(s):  
Ching-chun Huang ◽  
Yue Leon Guo
Keyword(s):  
Air Pollution ◽  
Ambient Air ◽  
Maternal Diabetes ◽  
Full Term ◽  
Ambient Air Pollution ◽  
Annual Household Income ◽  
Birth Season ◽  
Birth Registry ◽  
Logistic Regression Models ◽  
Term Births

BackgroundEvidence regarding whether prenatal exposure to air pollution increases the risk of hypospadias is limited.ObjectivesThe aim of the study is to evaluate the association between exposure to ambient air pollution during early pregnancy and occurrence of hypospadias.MethodsWe conducted a 1:10 case-control study using the Taiwanese Birth Registry database. Those male births reported to have hypospadias were defined as cases; while controls were randomly, matched by birth year, selected from those male births without any congenital anomaly. Monthly average of ambient air pollutants, including PM10, PM2.5, NO2, NOx, and O3, from three months pre- to six months post-conception were retrieved from the 76 air quality monitoring stations and interpolated to the level of township using empirical bayesian kriging. Potential covariates to be adjusted included gestational age, birth weight, birth season, maternal age, maternal diabetes and hypertension, maternal smoking, annual household income and population density of the residential township.ResultsDuring 2007–2014, a total of 265 hypospadias was reported, and 230 (87%) of them were full-term births. Results of multivariate logistic regression models revealed that for per IQR increase of O3 (8.0 p.p.b) exposure during the first months after conception increased the risk of hypospadias (aOR=1.38, 95% CI=1.07–1.78). In subgroup analysis of full-term births, we further found that PM2.5 exposure during the first three months post-conception significantly increased the risk of developing hypospadias (aOR=1.29, 95% CI=1.01–1.65, per IQR=15.4 ug/m3).ConclusionsThe results of the study suggested that early gestational exposure to ambient air pollution increased the risk of hypospadias occurrence.

scholarly journals Association of Ambient Air Pollution with Perceived Stress in Pregnant Women: A Prospective Cohort Study

2021 ◽  
Author(s):  
Dirga Kumar Lamichhane ◽  
Dal-Young Jung ◽  
Yee-Jin Shin ◽  
Kyung-Sook Lee ◽  
So-Yeon Lee ◽  
...  
Keyword(s):  
Air Pollution ◽  
Pregnant Women ◽  
Perceived Stress ◽  
Regression Models ◽  
Ambient Air ◽  
Air Pollutant ◽  
Ambient Air Pollution ◽  
Linear Regression Models ◽  
Third Trimester ◽  
The Third

Abstract Background: Air pollution is associated with perceived stress in the general population, but its influence on maternal perceived stress during pregnancy has not been investigated.We aimed to investigate the relationship between air pollution and non-specific perceived stress among pregnant women.Methods: Our analysis included2162 pregnant women who had participated in the cohort for childhood origin of asthma and allergic disease study between 2008 and 2015. Maternal exposures to particulate matter with an aerodynamic diameter < 2.5 µm (PM2.5) and < 10 µm (PM10), as well as to nitrogen dioxide (NO2) and ozone (O3) for each trimesterand the entire pregnancy were determined using land-use regression models. Maternal perceived stress during the third trimester was assessed using the 14-item Perceived Stress Scale (PSS): scores ranged from 0-56 with higher scores indicating increased stress. Linear regression models were applied to estimate associations between PSS scores and each air pollutant, after adjusting for socio-demographic and behavioral covariates.Results: In single-pollutant models,after adjustment, an IQR increase in the whole pregnancy exposure to PM2.5 and PM10 and O3 in the third trimester was related to 0.37 (95% confidence interval [CI]: 0.01, 0.74) and 0.55 (95% CI: 0.12, 0.98) and 0.29 (95% CI: 0.05, 0.52) points increase in the PSS score, respectively.This association was more evident in women with child-bearing age and lower levelofeducation, and the association of PM10was stronger in thespring season.In multi-pollutant models, exposures to PM10 and O3 were associated with higher perceived stress. Conclusion:Our findings suggest that pregnancy exposure to PM2.5, PM10and O3 is positively associated with maternal PSS score during the third trimester.

scholarly journals Acute effects of ambient air pollution on lower respiratory infections in Guangzhou children of China

2021 ◽  
Author(s):  
Huawen Zhong ◽  
Linlin Yang ◽  
Wei Qiang ◽  
Yongxian Zhou ◽  
Lintong Wei ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Regression Models ◽  
Hospital Admissions ◽  
Respiratory Infections ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Time Lags ◽  
Pathogenic Microorganisms ◽  
Guangzhou City

Abstract Background Daily concentrations of air pollution are associated with lower respiratory diseases. This study investigated the short-term association of ambient air pollution with daily hospital admissions due to pneumonia among children aged 0–17 in Guangzhou city of China.Methods Daily ambient air pollution concentrations were extracted from the databases of the Chinese Environmental Monitoring Center. Children hospital admission counts for pneumonia during 2013–2018 were sourced from the Guangdong Maternal and Child Healthcare Hospital. Associations between outdoor air pollution levels and hospital admissions were estimated for time lags of zero up to seven days using Quasi-Poisson regression models, adjusted for seasonal variations, meteorological variables, day of week and holidays. The associations between clinical pathogenic microorganism inspection results for pneumonia and air pollutants were calculated using Lasso regression models.Results Ambient air pollutants were all positively associated with children hospital admissions due to pneumonia of all ages. Significant associations were found for air pollutants except for inhalable particulate matter (PM) \(\le\)10 µm in aerodynamic diameter (PM10) in children aged 0–17 years. Increments of an interquartile range (IQR) (279.10µg/m3 and 28.42µg/m3, respectively) in the 7-day-average level of carbon monoxide (CO) and nitrogen dioxide (NO2) were associated with a 26.17% (95% confidence interval (CI) 1.40%-56.98%) and 25.09% (95%CI 0.54%-55.64%) increase in pneumonia hospitalizations for children aged 6–17, respectively. An IQR increase in CO concentrations (279.10µg/m3) was associated with a 15.15% (95%CI 4.34%-27.08%) increase in pneumonia hospitalizations for children aged 1–5. Estimates for CO were statistically significant among children aged 1–5 years in summer. The associations remained stable in two-pollutant models. Daily cases of microbial detection for pneumonia were positively associated with daily NO2 concentration. The pneumonia hospitalizations due to Mycoplasma pneumonia, Flu A virus and Flu B virus, the predominant pathogenic microorganisms detected in children aged 0–5 are apparently associated with levels of PMs, CO, NO2 and O3.Conclusions Strong associations among hospital admissions for lower respiratory infections, pathogenic microorganisms and daily levels of air pollution confirm the urgent need to adopt sustainable improving ambient air quality policies in Guangzhou city to protect children's health.

scholarly journals Interstitial lung abnormalities and self-reported health and functional status

Thorax ◽  
2018 ◽  
Vol 73 (9) ◽  
pp. 884-886 ◽  
Author(s):  
Gisli Thor Axelsson ◽  
Rachel K Putman ◽  
Tetsuro Araki ◽  
Sigurdur Sigurdsson ◽  
Elias Freyr Gudmundsson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Functional Status ◽  
Daily Living ◽  
Regression Models ◽  
General Health Status ◽  
Logistic Regression Models ◽  
Gene Environment ◽  
Lung Abnormalities ◽  
Reported Health

We investigated the association between interstitial lung abnormalities (ILA) and self-reported measures of health and functional status in 5764 participants from the Age, Gene/Environment Susceptibility-Reykjavik study. The associations of ILA to activities of daily living (ADLs), general health status and physical activity were explored using logistic regression models. Participants with ILA were less likely to be independent in ADLs (OR 0.70; 95% CI 0.55 to 0.90) to have good or better self-reported health (OR 0.66; 95% CI 0.52 to 0.82) and to participate in physical activity (OR 0.72; CI 0.56 to 0.91). The results demonstrate ILA’s association with worsening self-reported health and functional status.

scholarly journals Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

Open Heart ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. e001297
Author(s):  
Nicklas Vinter ◽  
Anne Sofie Frederiksen ◽  
Andi Eie Albertsen ◽  
Gregory Y H Lip ◽  
Morten Fenger-Grøn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Sinus Rhythm ◽  
Regression Models ◽  
Prediction Models ◽  
Electrical Cardioversion ◽  
High Recurrence Rate ◽  
Logistic Regression Models ◽  
Specific Prediction

ObjectiveElectrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is difficult. The objective was to develop sex-specific prediction models for successful electrical cardioversion and assess the potential of machine learning methods in comparison with traditional logistic regression.MethodsIn a retrospective cohort study, we examined several candidate predictors, including comorbidities, biochemistry, echocardiographic data, and medication. The outcome was successful cardioversion, defined as normal sinus rhythm immediately after the electrical cardioversion and no documented recurrence of AF within 3 months after. We used random forest and logistic regression models for sex-specific prediction.ResultsThe cohort comprised 332 female and 790 male patients with persistent AF who underwent electrical cardioversion. Cardioversion was successful in 44.9% of the women and 49.9% of the men. The prediction errors of the models were high for both women (41.0% for machine learning and 48.8% for logistic regression) and men (46.0% for machine learning and 44.8% for logistic regression). Discrimination was modest for both machine learning (0.59 for women and 0.56 for men) and logistic regression models (0.60 for women and 0.59 for men), although the models were well calibrated.ConclusionsSex-specific machine learning and logistic regression models showed modest predictive performance for successful electrical cardioversion. Identifying patients who will benefit from cardioversion remains challenging in clinical practice. The high recurrence rate calls for thoroughly informed shared decision-making for electrical cardioversion.

scholarly journals Ambient air pollution exposure and chronic bronchitis in the Lifelines cohort

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-216142
Author(s):  
Dany Doiron ◽  
Jean Bourbeau ◽  
Kees de Hoogh ◽  
Anna L Hansell
Keyword(s):  
Air Pollution ◽  
Chronic Bronchitis ◽  
Smoking Status ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Air Pollution Exposure ◽  
Pollution Exposure

BackgroundFew large studies have assessed the relationship of long-term ambient air pollution exposure with the prevalence and incidence of symptoms of chronic bronchitis and cough.MethodsWe leveraged Lifelines cohort data on 132 595 (baseline) and 65 009 (second assessment) participants linked to ambient air pollution estimates. Logistic regression models adjusted for sex, age, educational attainment, body mass index, smoking status, pack-years smoking and environmental tobacco smoke at home were used to assess associations of air pollution with prevalence and incidence of chronic bronchitis (winter cough and sputum almost daily for ≥3 months/year), chronic cough (winter cough almost daily for ≥3 months/year) and prevalence of cough and sputum symptoms, irrespective of duration.ResultsAssociations were seen for all pollutants for prevalent cough or sputum symptoms. However, for prevalent and incident chronic bronchitis, statistically significant associations were seen for nitrogen dioxide (NO2) and black carbon (BC) but not for fine particulate matter (PM2.5). For prevalent chronic bronchitis, associations with NO2 showed OR: 1.05 (95% CI: 1.02 to 1.08) and with BC OR: 1.06 (95% CI: 1.03 to 1.09) expressed per IQR; corresponding results for incident chronic bronchitis were NO2 OR: 1.07 (95% CI: 1.02 to 1.13) and BC OR: 1.07 (95% CI: 1.02 to 1.13). In subgroup analyses, slightly stronger associations were observed among women, never smokers and younger individuals.ConclusionThis is the largest analysis to date to examine cross-sectional and longitudinal associations between ambient air pollution and chronic bronchitis. NO2 and BC air pollution was associated with increased odds of prevalent and incident chronic bronchitis.

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