Traffic-related Air Pollution, Dust Mite Allergen, and Childhood Asthma in Puerto Ricans

Prediction of Asthma Exacerbations in Children

Asthma exacerbations are common in asthmatic children, even among those with good disease control. Asthma attacks result in the children and their parents missing school and work days; limit the patient's social and physical activities; and lead to emergency department visits, hospital admissions, or even fatal events. Thus, the prompt identification of asthmatic children at risk for exacerbation is crucial, as it may allow for proactive measures that could prevent these episodes. Children prone to asthma exacerbation are a heterogeneous group; various demographic factors such as younger age, ethnic group, low family income, clinical parameters (history of an exacerbation in the past 12 months, poor asthma control, poor adherence to treatment, comorbidities), Th2 inflammation, and environmental exposures (pollutants, stress, viral and bacterial pathogens) determine the risk of a future exacerbation and should be carefully considered. This paper aims to review the existing evidence regarding the predictors of asthma exacerbations in children and offer practical monitoring guidance for promptly recognizing patients at risk.

Biodiesel exhaust particle airway toxicity and the role of polycyclic aromatic hydrocarbons

Renewable alternatives to fossil diesel (FD) including fatty acid methyl ester (FAME) biodiesel have become more prevalent. However, toxicity of exhaust material from their combustion, relative to the fuels they are displacing has not been fully characterised. This study was carried out to examine particle toxicity within the lung epithelium and the role for polycyclic aromatic hydrocarbons (PAHs). Exhaust particles from a 20% (v/v) blend of FAME biodiesel had little impact on primary airway epithelial toxicity compared to FD derived particles but did result in an altered profile of PAHs, including an increase in particle bound carcinogenic B[a]P. Higher blends of biodiesel had significantly increased levels of more carcinogenic PAHs, which was associated with a higher level of stress response gene expression including CYP1A1, NQO1 and IL1B. Removal of semi-volatile material from particulates abolished effects on airway cells. Particle size difference and toxic metals were discounted as causative for biological effects. Finally, combustion of a single component fuel (Methyl decanoate) containing the methyl ester molecular structure found in FAME mixtures, also produced more carcinogenic PAHs at the higher fuel blend levels. These results indicate the use of FAME biodiesel at higher blends may be associated with an increased particle associated carcinogenic and toxicity risk.

Monitoring ambient air pollution and pulmonary function in asthmatic children by mobile applications in COVID-19 pandemic

BACKGROUND

Several public health measures were implemented during the COVID-19 pandemic. However, little is known about the real-time assessment of environmental exposure on the pulmonary function of asthmatic children. Therefore, we developed a mobile phone application for capturing real-time day-to-day dynamic changes in ambient air pollution during the pandemic. We aim to explore the change in ambient air pollutants between pre-lockdown, lockdowns, and lockdowns and analyze the association between pollutants and PEF mediated by mite sensitization and seasonal change.

METHOD

A prospective cohort study was conducted among 511 asthmatic children from January 2016 to February 2022. Smartphone-app used to record daily ambient air pollution, particulate matter (PM2.5, PM10) Ozon (O₃), nitrogen dioxide (NO₂), Carbon Monoxide (CO), sulfur dioxide (SO₂), average temperature, and relative humidity, which measured and connected from 77 nearby air monitoring stations by linking to Global Positioning System (GPS)-based software. The outcome of pollutants' effect on peak expiratory flow meter (PEF) and asthma is measured by a smart peak flow meter from each patient or caregiver's phone for real-time assessment.

RESULTS

The lockdown (May 19th, 2021, to July 27th, 2021) was associated with decreased levels of all ambient air pollutants aside from SO₂ after adjusting for 2021. NO₂ and SO₂ were constantly associated with decreased levels of PEF across lag 0 (same day when the PEF was measured), lag 1 (one day before PEF was measured), and lag 2 (two days prior when the PEF was measured. Concentrations of CO were associated with PEF only in children who were sensitized to mites in lag 0, lag 1, and lag 2 in the stratification analysis for a single air pollutant model. Based on the season, spring has a higher association with the decrease of PEF in all pollutant exposure than other seasons.

CONCLUSION

Using our developed smartphone apps, we identified that NO₂, CO, and PM10 were higher at the pre-and post-COVID-19 lockdowns than during the lockdown. Our smartphone apps may help collect personal air pollution data and lung function, especially for asthmatic patients, and may guide protection against asthma attacks. It provides a new model for individualized care in the COVID era and beyond.

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models

BACKGROUND

Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations.

METHODS

We extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30-180 day time horizons, and evaluated the contributions of different data types to model performance.

RESULTS

Models using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730-0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes.

CONCLUSIONS

EHR data-based models perform moderately wellover a 30-180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful.

TRIAL REGISTRATION

Not applicable.

Diet, Asthma, and Severe Asthma Exacerbations in a Prospective Study of Puerto Rican Youth

BACKGROUND

Poor diet quality may contribute to the disproportionate asthma burden in Puerto Rican youth.

OBJECTIVE

To examine whether an unhealthy diet at one or two study visits conducted over about 5 years was associated with asthma, severe asthma exacerbations, and worse lung function in Puerto Rican youth.

METHODS

This was a prospective study of 406 Puerto Rican youth aged 6 to 14 years at a baseline visit and 9 to 20 years at a follow-up visit. As in prior work, diet was assessed using a dietary score ranging from -2 to +2. The exposure of interest was an unhealthy diet, defined as a nonpositive dietary score (0 to -2) at one or both visits. Outcomes of interest were asthma (defined as physician-diagnosed asthma and one of more episode of wheeze in the year before the second visit), one or more severe asthma exacerbation in the year before the second visit, and change in percent predicted lung function measures (FEV₁, FVC, and FEV₁/FVC) between the first and second visits.

RESULTS

In a multivariable analysis, an unhealthy diet at both visits was associated with increased odds of asthma (adjusted odds ratio = 3.38; 95% confidence interval, 1.74-6.57) and severe asthma exacerbations (adjusted odds ratio = 2.65; 95% confidence interval, 1.16-6.03), but not with change in lung function.

CONCLUSIONS

An unhealthy diet at both visits was associated with increased odds of asthma and severe asthma exacerbations, compared with a healthy diet at both visits. Our findings support health policies promoting a healthy diet in Puerto Rican youth, a population at high risk for asthma.

Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow

Severe asthma exacerbations are the primary cause of morbidity and mortality in children with asthma. Accurate prediction of children at risk for severe exacerbations, defined as those requiring systemic corticosteroids, emergency department visit, and/or hospitalization, would considerably reduce health care utilization and improve symptoms and quality of life. Substantial progress has been made in identifying high-risk exacerbation-prone children. Known risk factors for exacerbations include demographic characteristics (ie, low income, minority race/ethnicity), poor asthma control, environmental exposures (ie, aeroallergen exposure/sensitization, concomitant viral infection), inflammatory biomarkers, genetic polymorphisms, and markers from other "omic" technologies. The strongest risk factor for a future severe exacerbation remains having had one in the previous year. Combining risk factors into composite scores and use of advanced predictive analytic techniques such as machine learning are recent methods used to achieve stronger prediction of severe exacerbations. However, these methods are limited in prediction efficiency and are currently unable to predict children at risk for impending (within days) severe exacerbations. Thus, we provide a commentary on strategies that have potential to allow for accurate and reliable prediction of children at risk for impending exacerbations. These approaches include implementation of passive, real-time monitoring of impending exacerbation predictors, use of population health strategies, prediction of severe exacerbation responders versus nonresponders to conventional exacerbation management, and considerations for preschool-age children who can be especially high risk. Rigorous prediction and prevention of severe asthma exacerbations is needed to advance asthma management and improve the associated morbidity and mortality.