Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications

A Multipollutant Approach to Estimating Causal Effects of Air Pollution Mixtures on Overall Mortality in a Large, Prospective Cohort

BACKGROUND

Several studies have confirmed associations between air pollution and overall mortality, but it is unclear to what extent these associations reflect causal relationships. Moreover, few studies to our knowledge have accounted for complex mixtures of air pollution. In this study, we evaluate the causal effects of a mixture of air pollutants on overall mortality in a large, prospective cohort of Dutch individuals.

METHODS

We evaluated 86,882 individuals from the LIFEWORK study, assessing overall mortality between 2013 and 2017 through national registry linkage. We predicted outdoor concentration of five air pollutants (PM2.5, PM10, NO2, PM2.5 absorbance, and oxidative potential) with land-use regression. We used logistic regression and mixture modeling (weighted quantile sum and boosted regression tree models) to identify potential confounders, assess pollutants' relevance in the mixture-outcome association, and investigate interactions and nonlinearities. Based on these results, we built a multivariate generalized propensity score model to estimate the causal effects of pollutant mixtures.

RESULTS

Regression model results were influenced by multicollinearity. Weighted quantile sum and boosted regression tree models indicated that all components contributed to a positive linear association with the outcome, with PM2.5 being the most relevant contributor. In the multivariate propensity score model, PM2.5 (OR=1.18, 95% CI: 1.08-1.29) and PM10 (OR=1.02, 95% CI: 0.91-1.14) were associated with increased odds of mortality per interquartile range increase.

CONCLUSION

Using novel methods for causal inference and mixture modeling in a large prospective cohort, this study strengthened the causal interpretation of air pollution effects on overall mortality, emphasizing the primary role of PM2.5 within the pollutant mixture.

Is short-term and long-term exposure to black carbon associated with cardiovascular and respiratory diseases? A systematic review and meta-analysis based on evidence reliability

OBJECTIVE

Adverse health effects of fine particles (particulate matter_2.5) have been well documented by a series of studies. However, evidences on the impacts of black carbon (BC) or elemental carbon (EC) on health are limited. The objectives were (1) to explored the effects of BC and EC on cardiovascular and respiratory morbidity and mortality, and (2) to verified the reliability of the meta-analysis by drawing p value plots.

DESIGN

The systematic review and meta-analysis using adapted Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach and p value plots approach.

DATA SOURCES

PubMed, Embase and Web of Science were searched from inception to 19 July 2021.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Time series, case cross-over and cohort studies that evaluated the associations between BC/EC on cardiovascular or respiratory morbidity or mortality were included.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcomes were analysed via a random effects model and reported as relative risk (RR) with 95% CI. The certainty of evidences was assessed by adapted GRADE. The reliabilities of meta-analyses were analysed by p value plots.

RESULTS

Seventy studies met our inclusion criteria. (1) Short-term exposure to BC/EC was associated with 1.6% (95% CI 0.4% to 2.8%) increase in cardiovascular diseases per 1 µg/m³ in the elderly; (2) Long-term exposure to BC/EC was associated with 6.8% (95% CI 0.4% to 13.5%) increase in cardiovascular diseases and (3) The p value plot indicated that the association between BC/EC and respiratory diseases was consistent with randomness.

CONCLUSIONS

Both short-term and long-term exposures to BC/EC were related with cardiovascular diseases. However, the impact of BC/EC on respiratory diseases did not present consistent evidence and further investigations are required.

PROSPERO REGISTRATION NUMBER

CRD42020186244.

Using self-organising maps to predict and contain natural disasters and pandemics

The unfolding coronavirus (COVID-19) pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID-19 continues to cause widespread economic and social turmoil, and while the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon turn to future proofing. In line with this, this paper proposes a prediction and containment model that could be used for pandemics and natural disasters. It combines selective lockdowns and protective cordons to rapidly contain the hazard while allowing minimally impacted local communities to conduct "business as usual" and/or offer support to highly impacted areas. A flexible, easy to use data analytics model, based on Self Organising Maps, is developed to facilitate easy decision making by governments and organisations. Comparative tests using publicly available data for Great Britain (GB) show that through the use of the proposed prediction and containment strategy, it is possible to reduce the peak infection rate, while keeping several regions (up to 25% of GB parliamentary constituencies) economically active within protective cordons.

Identifying impacts of air pollution on subacute asthma symptoms using digital medication sensors

BACKGROUND

Objective tracking of asthma medication use and exposure in real-time and space has not been feasible previously. Exposure assessments have typically been tied to residential locations, which ignore exposure within patterns of daily activities.

METHODS

We investigated the associations of exposure to multiple air pollutants, derived from nearest air quality monitors, with space-time asthma rescue inhaler use captured by digital sensors, in Jefferson County, Kentucky. A generalized linear mixed model, capable of accounting for repeated measures, over-dispersion and excessive zeros, was used in our analysis. A secondary analysis was done through the random forest machine learning technique.

RESULTS

The 1039 participants enrolled were 63.4% female, 77.3% adult (>18) and 46.8% White. Digital sensors monitored the time and location of over 286 980 asthma rescue medication uses and associated air pollution exposures over 193 697 patient-days, creating a rich spatiotemporal dataset of over 10 905 240 data elements. In the generalized linear mixed model, an interquartile range (IQR) increase in pollutant exposure was associated with a mean rescue medication use increase per person per day of 0.201 [95% confidence interval (CI): 0.189-0.214], 0.153 (95% CI: 0.136-0.171), 0.131 (95% CI: 0.115-0.147) and 0.113 (95% CI: 0.097-0.129), for sulphur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3), respectively. Similar effect sizes were identified with the random forest model. Time-lagged exposure effects of 0-3 days were observed.

CONCLUSIONS

Daily exposure to multiple pollutants was associated with increases in daily asthma rescue medication use for same day and lagged exposures up to 3 days. Associations were consistent when evaluated with the random forest modelling approach.

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.

Assessment of Multipollutant Exposures During Pregnancy Using Silicone Wristbands

Silicone wristbands can assess multipollutant exposures in a non-invasive and minimally burdensome manner, which may be suitable for use among pregnant women. We investigated silicone wristbands as passive environmental samplers in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. We used wristbands to assess exposure to a broad range of organic chemicals, identified multipollutant exposure profiles using self-organizing maps (SOMs), and assessed temporal consistency and determinants of exposures during pregnancy. Participants (n = 255) wore wristbands for 1 week at 12 gestational weeks. Of 1,530 chemicals assayed, 199 were detected in at least one wristband and 16 were detected in >60% of wristbands. A median of 23 (range: 12,37) chemicals were detected in each wristband, and chemicals in commerce and personal care products were most frequently detected. A subset of participants (n=20) wore a second wristband at 24 gestational weeks, and concentrations of frequently detected chemicals were moderately correlated between time points (median intraclass correlation: 0.22; range: 0.00,0.69). Women with higher educational attainment had fewer chemicals detected in their wristbands and the total number of chemicals detected varied seasonally. Triphenyl phosphate concentrations were positively associated with nail polish use, and benzophenone concentrations were highest in summer. No clear associations were observed with other a priori relations, including certain behaviors, season, and socioeconomic factors. SOM analyses revealed 12 profiles, ranging from 2 to 149 participants, captured multipollutant exposure profiles observed in this cohort. The most common profile (n = 149) indicated that 58% of participants experienced relatively low exposures to frequently detected chemicals. Less common (n ≥ 10) and rare (n < 10) profiles were characterized by low to moderate exposures to most chemicals and very high and/or very low exposure to a subset of chemicals. Certain covariates varied across SOM profile membership; for example, relative to women in the most common profile who had low exposures to most chemicals, women in the profile with elevated exposure to galaxolide and benzyl benzoate were younger, more likely to be single, and more likely to report nail polish use. Our study illustrates the utility of silicone wristbands for measurement of multipollutant exposures in sensitive populations, including pregnant women.

Effect modifiers of lung function and daily air pollutant variability in a panel of schoolchildren

BACKGROUND

Acute pollutant-related lung function changes among children varies across pollutants and lag periods. We examined whether short-term air pollutant fluctuations were related to daily lung function among a panel of children and whether these effects are modified by airway hyperresponsiveness, location and asthma severity.

METHODS

Students from randomly selected grade 4 classrooms at seven primary schools in Durban, participated, together with asthmatic children from grades 3-6 (n=423). The schools were from high pollutant exposed communities (south) and compared with schools from communities with lower levels of pollution (north), with similar socioeconomic profiles. Interviews, spirometry and methacholine challenge testing were conducted. Bihourly lung function measurements were performed over a 3-week period in four phases. During all schooldays, students blew into their personal digital monitors every 1.5-2 hours. Nitrogen dioxide (NO₂), nitrogen oxide (NO), sulphur dioxide and particulate matter (<10 μm diameter) (PM₁₀) were measured at each school. Generalised estimating equations assessed lag effects, using single-pollutant (single or distributed lags) models.

RESULTS

FEV₁ declines ranged from 13 to 18 mL per unit increase in IQR for NO and 14-23 mL for NO₂. Among the 5-day average models, a 20 mL and 30 mL greater drop in FEV₁ per IQR for NO₂ and NO, respectively, among those with airway hyperresponsiveness compared with those without. Effects were seen among those with normal airways.

CONCLUSIONS

This first panel study in sub-Saharan Africa, showed significant declines in lung function, in response to NO and NO₂ with effects modified by airway hyperresponsiveness or persistent asthma.

Characterizing the joint effects of pesticide exposure and criteria ambient air pollutants on pediatric asthma morbidity in an agricultural community

Supplemental Digital Content is available in the text.

Background:

Environmental contributions to pediatric asthma morbidity have been studied extensively in urban settings; exposures characteristic of agricultural and rural communities have received less attention despite a comparable burden of morbidity.

Methods:

We obtained repeated urine samples (n = 139) from 16 school-age children with asthma in the Yakima Valley of Washington State between July and October 2012. Biomarkers of organophosphate (OP) pesticide exposure (dialkyl phosphates [DAPs]) and asthma exacerbation (leukotriene E4 [LTE4]) were analyzed in samples. Corresponding 24-hour average particulate matter <2.5 μg (PM_2.5) and maximum 8-hour ozone concentration data for the study period were available from local monitoring stations. We evaluated the independent and multi-pollutant associations between LTE4 and exposure to ambient air pollutants and DAPs using generalized estimating equations. For multi-domain and multi-pollutant models, we created categorized pollution combination levels and estimated the relative health impact of exposure to pollutant mixtures.

Results:

In single-pollutant models, an interquartile range increase in exposures to DAPs was associated with increase in LTE4 levels (β: 4.1 [0.6–7.6] pg/mg). PM_2.5 and ozone were also associated with increase in LTE4, though confidence intervals contained the null value. Increase in LTE4 levels was consistently associated with increase in median-dichotomized multi-pollutant combination exposures; the highest effect estimates were observed with joint highest (vs. the lowest) category of the three-pollutant exposure (PM_2.5, ozone, and OP; β: 53.5, 95% confidence interval = 24.2, 82.8 pg/mg).

Conclusion:

Concurrent short-term exposure to criteria air pollutants and OPs in an agricultural community was associated with an increase in a marker of asthma morbidity.

Fine Particulate Matter-Induced Exacerbation of Allergic Asthma via Activation of T-cell Immunoglobulin and Mucin Domain 1

Background:

Fine particulate matter (PM_2.5) exacerbates airway inflammation and hyperreactivity in patients with asthma, but the mechanism remains unclear. The aim of this study was to observe the effects of prolonged exposure to high concentrations of PM_2.5 on the pathology and airway hyperresponsiveness (AHR) of BALB/c mice undergoing sensitization and challenge with ovalbumin (OVA) and to observe the effects of apoptosis and T-cell immunoglobulin and mucin domain 1 (TIM-1) in this process.

Methods:

Forty female BALB/c mice were divided into four groups: control group, OVA group, OVA/PM group, and PM group (n = 10 in each group). Mice in the control group were exposed to filtered clean air. Mice in the OVA group were sensitized and challenged with OVA. Mice in the OVA/PM group were sensitized and challenged as in the OVA group and then exposed to PM_2.5 for 4 h per day and 5 days per week for a total of 8 weeks using a nose-only “PM_2.5 online enrichment system” in The Second Hospital of Hebei Medical University. Mice in the PM group were exposed to the PM_2.5 online enrichment system only. AHR was detected. Bronchoalveolar lavage fluid (BALF) was collected for cell classification. The levels of interleukin-4 (IL-4), IL-5, and IL-33 in BALF were measured using enzyme-linked immunosorbent assay. Changes in histological structures were examined by light microscopy, and changes in ultramicrostructures were detected by electron microscopy. Apoptosis was determined by terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) assay in the lung tissues. Western blotting and immunohistochemistry were utilized to analyze the expression of Bcl-2, Bax, and TIM-1 in the lungs.

Results:

The results showed that AHR in the OVA/PM group was significantly more severe than that in the OVA and PM groups (P < 0.05). AHR in the PM group was also considerably more severe than that in the control group (P < 0.05). The BALF of OVA/PM group (28.00 ± 6.08 vs. 12.33 ± 4.51, t = 4.631, P = 0.002) and PM group (29.00 ± 3.00 vs. 12.33 ± 4.51, t = 4.927, P = 0.001) had more lymphocytes than the BALF of the control group. The number of neutrophils in the BALF of the OVA/PM group (6.67 ± 1.53 vs. 3.33 ± 1.53, t = 2.886, P = 0.020) and PM group (6.67 ± 1.53 vs. 3.33 ± 1.53, t = 2.886, P = 0.020) was much higher than those in the BALF of OVA group (P < 0.05). TUNEL assays showed that the number of apoptotic cells in the OVA/PM group was significantly higher than that in the OVA group (Tunel immunohistochemical scores [IHS%], 1.20 ± 0.18 vs. 0.51 ± 0.03, t = 8.094, P < 0.001) and PM group (Tunel IHS%, 1.20 ± 0.18 vs. 0.51 ± 0.09, t = 8.094, P < 0.001), and that the number of apoptotic cells in the PM group was significantly higher than that in the control group (Tunel IHS%, 0.51 ± 0.09 vs. 0.26 ± 0.03, t = 2.894, P = 0.020). The concentrations of IL-4 (77.44 ± 11.19 vs. 48.02 ± 10.02 pg/ml, t = 4.595, P = 0.002) and IL-5 (15.65 ± 1.19 vs. 12.35 ± 0.95 pg/ml, t = 3.806, P = 0.005) and the Bax/Bcl-2 ratio (1.51 ± 0.18 vs. 0.48 ± 0.10, t = 9.654, P < 0.001) and TIM-1/β-actin ratio (0.78 ± 0.11 vs. 0.40 ± 0.06, t = 6.818, P < 0.001) in the OVA/PM group were increased compared to those in the OVA group. The concentrations of IL-4 (77.44 ± 11.19 vs. 41.47 ± 3.40 pg/ml, t = 5.617, P = 0.001) and IL-5 (15.65 ± 1.19 vs. 10.99 ± 1.40 pg/ml, t = 5.374, P = 0.001) and the Bax/Bcl-2 ratio (1.51 ± 0.18 vs. 0.97 ± 0.16, t = 5.000, P = 0.001) and TIM-1/β-actin ratio (0.78 ± 0.11 vs. 0.31 ± 0.06, t = 8.545, P < 0.001) in the OVA/PM group were increased compared to those in the PM group. The concentration of IL-4 (41.47 ± 3.40 vs. 25.46 ± 2.98 pg/ml, t = 2.501, P = 0.037) and the Bax/Bcl-2 ratio (0.97 ± 0.16 vs. 0.18 ± 0.03, t = 7.439, P < 0.001) and TIM-1/β-actin ratio (0.31 ± 0.06 vs. 0.02 ± 0.01, t = 5.109, P = 0.001) in the PM group were also higher than those in the control group.

Conclusions:

Exacerbated AHR associated with allergic asthma caused by PM_2.5 is related to increased apoptosis and TIM-1 activation. These data might provide insights into therapeutic targets for the treatment of acute exacerbations of asthma induced by PM_2.5.

Associations between multipollutant day types and select cardiorespiratory outcomes in Columbia, South Carolina, 2002 to 2013

Background

Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.

Objective

Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.

Methods

We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 microns (PM_2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).

Results

We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O₃ and PM_2.5. We also found that cooler days with higher NO₂ pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).

Conclusion

Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Addressing unmet needs in understanding asthma mechanisms: From the European Asthma Research and Innovation Partnership (EARIP) Work Package (WP)2 collaborators

Asthma is a heterogeneous, complex disease with clinical phenotypes that incorporate persistent symptoms and acute exacerbations. It affects many millions of Europeans throughout their education and working lives and puts a heavy cost on European productivity. There is a wide spectrum of disease severity and control. Therapeutic advances have been slow despite greater understanding of basic mechanisms and the lack of satisfactory preventative and disease modifying management for asthma constitutes a significant unmet clinical need. Preventing, treating and ultimately curing asthma requires co-ordinated research and innovation across Europe. The European Asthma Research and Innovation Partnership (EARIP) is an FP7-funded programme which has taken a co-ordinated and integrated approach to analysing the future of asthma research and development. This report aims to identify the mechanistic areas in which investment is required to bring about significant improvements in asthma outcomes.

Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures

PURPOSE

Air pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions.

METHODS

New and innovative statistical methods to examine multipollutant exposures were identified through a broad literature search, with a specific focus on those statistical approaches currently used in epidemiologic studies of short-term exposures to criteria air pollutants (i.e., particulate matter, carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone).

RESULTS

Five broad classes of statistical approaches were identified for examining associations between short-term multipollutant exposures and health outcomes, specifically additive main effects, effect measure modification, unsupervised dimension reduction, supervised dimension reduction, and nonparametric methods. These approaches are characterized including advantages and limitations in different epidemiologic scenarios.

DISCUSSION

By highlighting the characteristics of various studies in which multipollutant statistical methods have been used, this review provides epidemiologists and biostatisticians with a resource to aid in the selection of the most optimal statistical method to use when examining multipollutant exposures.

Pediatric emergency department visits and ambient Air pollution in the U.S. State of Georgia: a case-crossover study

BACKGROUND

Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures.

METHODS

Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year.

RESULTS

In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O₃, NO₂, and SO₂); the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O₃ and the PM_2.5 components SO₄^2-, NO^3-, and NH⁴⁺) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the "criteria pollutants" (O₃, NO₂, CO, SO₂, and PM_2.5).

CONCLUSIONS

Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases.

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

BACKGROUND

Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality.

METHODS

We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics.

RESULTS

We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution.

CONCLUSION

Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.