Lagged Influence of Fine Particulate Matter and Geographic Disparities on Clinic Visits for Children's Asthma in Taiwan

Temperature modification of air pollutants and their synergistic effects on respiratory diseases in a semi-arid city in Northwest China

Air pollutants and temperature are significant threats to public health, and the complex linkages between the environmental factors and their interactions harm respiratory diseases. This study is aimed to analyze the impact of air pollutants and meteorological factors on respiratory diseases and their synergistic effects in Dingxi, a city in northwestern China, from 2018 to 2020 using a generalized additive model (GAM). Relative risk (RR) was employed to quantitatively evaluate the temperature modification on the short-term effects of PM2.5 and O3 and the synergistic effects of air pollutants (PM2.5 and O3) and meteorological elements (temperature and relative humidity) on respiratory diseases. The results indicated that the RRs per inter-quatile range (IQR) rise in PM2.5 and O3 concentrations were (1.066, 95% CI: 1.009-1.127, lag2) and (1.037, 95% CI: 0.975-1.102, lag4) for respiratory diseases, respectively. Temperature stratification suggests that the influence of PM2.5 on respiratory diseases was significantly enhanced at low and moderate temperatures, and the risk of respiratory diseases caused by O3 was significantly increased at high temperatures. The synergy analysis demonstrated significant a synergistic effect of PM2.5 with low temperature and high relative humidity and an antagonistic effect of high relative humidity and O3 on respiratory diseases. The findings would provide a scientific basis for the impact of pollutants on respiratory diseases in Northwest China.

Seasonal extreme temperatures and short-term fine particulate matter increases child respiratory hospitalizations in a sparsely populated region of the intermountain western United States

Background

Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Few studies have evaluated these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health.

Methods

We explored short-term exposure to air pollution on childhood respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated hospitalizations. The main outcome measure included all respiratory-related hospital admissions for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for all individuals aged 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis and distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 15 prior-days modified by temperature or season.

Results

Short-term exposure increases of 1 μg/m3 in PM2.5 were associated with elevated odds of all three respiratory hospital admission categories. PM2.5 was associated with the largest increased odds of hospitalizations for asthma at lag 7-13 days [1.87(1.17-2.97)], for LRTI at lag 6-12 days [2.18(1.20-3.97)], and for URTI at a cumulative lag of 13 days [1.29(1.07-1.57)]. The impact of PM2.5 varied by temperature and season for each respiratory outcome scenario. For asthma, PM2.5 was associated most strongly during colder temperatures [3.11(1.40-6.89)] and the winter season [3.26(1.07-9.95)]. Also in colder temperatures, PM2.5 was associated with increased odds of LRTI hospitalization [2.61(1.15-5.94)], but no seasonal effect was observed. Finally, 13 days of cumulative PM2.5 prior to admissions date was associated with the greatest increased odds of URTI hospitalization during summer days [3.35(1.85-6.04)] and hotter temperatures [1.71(1.31-2.22)].

Conclusions

Children's respiratory-related hospital admissions were associated with short-term exposure to PM2.5. PM2.5 associations with asthma and LRTI hospitalizations were strongest during cold periods, whereas associations with URTI were largest during hot periods.

Classification

environmental public health, fine particulate matter air pollution, respiratory infections.

Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Air pollutants are major risk factors for respiratory diseases, particularly asthma, socially and spatially correlated. Many existing environment-asthma-related studies, however, have evaluated the impact of crude trends at the largest district level, which accounts only for temporal effects and may produce biased results with spatial autocorrelation. This study aimed to investigate how the spatial autocorrelation affects the air pollution effect estimations (sulfur dioxide [SO₂], nitrogen dioxide [NO₂], carbon monoxide [CO], and particulate matter [PM₁₀]) on daily asthma emergency department (ED) visits in two metropolitan areas in Korea (Seoul Metropolitan Area [SMA] and Busan Metropolitan City, Ulsan Metropolitan City, Gyeongsangnamdo [BUG]). We applied eigenvector spatial filter (ESF) to the spatio-temporal model to remove spatial autocorrelation and distributed lag nonlinear model (DLNM) to explore nonlinear patterns between air pollutant concentration and lagged days on the three models including aggregated model (a temporal model), spatial model without ESF, and spatial model with ESF (both are spatio-temporal models). The effect of SO₂ was not statistically significant for asthma ED visits in the aggregated model for SMA (cumulative relative risks [CRR] = 0.99, confidence intervals [CI]: 0.93-1.05), while the effect was statistically significant in the spatial model with ESF (CRR = 1.10, CI: 1.08-1.12). NO₂ and CO were positively correlated to asthma ED visits in the spatial model without ESF (CRR = 0.84, CI: 0.81-0.86; 0.91, 0.89-0.94, respectively), but the spatial model with ESF showed significant risks (CRR = 1.21, CI: 1.18-1.24; 1.13, 1.11-1.16). Moreover, the spatial model with ESF successfully removed spatial autocorrelation (P-values for Moran's I 0.83-0.98) and demonstrated the highest model fit (McFadden's pseudo R² 0.42-0.43 for SMA and 0.26-0.27 for BUG) among the three models. Our findings demonstrate how ESF can be introduced into spatial correlation to remove bias and construct more reliable models.

A review on the role of dispersion and receptor models in asthma research

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

A study on the short-term effect of particulate matter pollution on hospital visits for asthma in children in Shanghai, China

Recently, particulate matter pollution has been worsening, which has been affecting the asthma visits in children. In this study, we assessed the short-term effects of PM₁₀ and PM_2.5 on asthma visits in children in Shanghai, China from January 1, 2009 and December 31, 2010, using a generalized additive model. We controlled the confounding factors, such as long-term trends, week day effect, and weather elements. The lag effects of different age subgroups (≤ 2 yr, 3-5 yr, and 6-18 yr subgroups) were performed. The results showed significant effects of PM₁₀ and PM_2.5 on asthma visits in children, though the seasonal lags varied for the three age subgroups. In general, the effect of PM_2.5 on asthma visits in children was stronger and more acute than that of PM₁₀. PM_2.5 showed the highest relative risk of 1.192 at lag 0 day in summer; and PM₁₀ showed the highest relative risk of 1.073 at lag 3 day in autumn. Overall, particulate matter pollution showed a greater effect on relatively younger children. In particular, the ≤ 2 yr subgroup showed the highest seasonal relative risk of PM₁₀. Especially, seasonal relative risk of PM₁₀ in autumn for the ≤ 2 yr subgroup was much higher than that for the other two subgroups. The 3-5 yr and 6-18 yr subgroups showed the highest seasonal relative risk of PM_2.5 in summer and winter, respectively. But the pediatric visits data we obtained cannot reflect the true prevalence of asthma and multiple visits. Thus, selection bias may exist in our analysis.

The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru

BACKGROUND

Asthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 μm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5.

OBJECTIVE

We conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016.

METHODS

We used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification.

RESULTS

There were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 μg/m3) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender.

DISCUSSION

Results from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.

The short-term association of ambient fine particulate air pollution with hypertension clinic visits: A multi-community study in Guangzhou, China

BACKGROUND

The association of ambient fine particulate pollution with daily outpatient clinic visits (OCV) for hypertension in China remains to be investigated.

OBJECTIVES

This study aimed to examine short-term impacts of exposure to fine particulate matter of aerodynamic diameter < 2.5μm (PM_2.5) on daily OCV for hypertension, using a large-scale multi-center community database in Guangzhou, one of the most densely-populated cities in Southern China.

METHODS

We collected a total of 28,548 individual records of OCV from 22 community healthcare facilities in Guangzhou from January 1st to May 7th 2020. Hourly data on air pollutants and daily information on meteorological factors were obtained. According to the World Health Organization air-quality guidelines, daily excessive concentration hours (DECH) was calculated. PM_2.5 daily mean, hourly-peak concentration and DECH were used as the exposure variables. Based on a case-time-control design, the Cox regression model was applied to evaluate the short-term relative risks (RR) of daily OCV for hypertension. Sensitivity analyses were conducted, with nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone being adjusted.

RESULTS

Daily mean and hourly-peak of PM_2.5 were significantly associated with daily OCV for hypertension, while weaker associations were observed for DECH. The estimated RRs at lag day 0 were 1.039 (95% confidence interval [CI]: 1.037, 1.040), 1.851 (95%CI: 1.814, 1.888), and 1.287 (95%CI: 1.276, 1.298), respectively, in association with a 1-unit increase in DECH, daily mean, and hourly-peak concentration of PM_2.5. For the lagged effect, lag₄ models estimated the greatest RRs for PM_2.5 DECH and hourly-peak, whereas a lag₂ model produced the highest for PM_2.5 daily mean.

DISCUSSION

This study consolidates the evidence for a positive correlation between ambient PM_2.5 exposure and risks of hypertensive OCV. It also provides profound insight regarding planning for health services needs and establishing early environmental responses to the worsening air pollution in the communities.

Short-term effect of PM2.5 on pediatric asthma incidence in Shanghai, China

Recent epidemiological studies pointed out that air pollution has a significant impact on pediatric asthma. Shanghai is one of the biggest cities in China, and the short-term effect of atmospheric particulate matter on the incidence of pediatric asthma has become a hot topic. From January 1, 2009, to December 31, 2010, we used daily measurements of pollutant concentrations, daily weather data, and daily records of pediatric asthma hospital visits from local authorities to evaluate the short-term effect of air pollution on pediatric asthma incidence in Shanghai, China. We used a generalized additive model (GAM) in the analysis, and the controlled confounding factors include long-term trends, day-of-the-week effects, and weather elements. We divided the entire study group into different age-subgroups. In addition, we took a variety of lag models into consideration. The results showed a strong connection between concentrations of fine particulate matter (PM_2.5) and pediatric asthma hospital visits from 2009 to 2010 in Shanghai, China. For the entire study group, the greatest relative risk (RR) of PM_2.5 on pediatric asthma hospital visits was 1.060 on a lag of 4 days. As for the three different age-subgroups, the greatest RR of PM_2.5 on pediatric asthma hospital visits was 1.061 (at a lag of 5 days), 1.071 (at a lag of 4 days), and 1.052 (at a lag of 2 days), for the under-2-year-olds, 3-to-5-year-olds, and the 6-to-18-year-olds, respectively. The overall short-term effect of PM_2.5 on pediatric asthma hospital visits was relatively stronger in younger children. Within the year, we detected the strongest seasonal effect of PM_2.5 on pediatric asthma hospital visits in Summer. When adding other air pollutants in the analysis model, RR of PM_2.5 on pediatric asthma hospital visits would be increased.

Air pollution, respiratory illness and behavioral adaptation: Evidence from South Korea

Air pollution is closely associated with the development of respiratory illness. Behavioral adaptations of people to air pollution may influence its impact, yet this has not been investigated in the literature. Our hypothesis is that people experience and learn the underlying air quality to decide their adaptation, and they have a stronger incentive to behaviorally adapt to the air quality as it deteriorates. We tested our hypothesis on a sample of approximately 25,700 individuals from South Korea from 2002 to 2013 that contained information on daily doctor's visits due to respiratory disease. We matched individuals to the mean of the past seven-day concentration of the particulate matter of size between 2.5 and 10 micrometers (PM10) in their county of residence. We examined whether people living in counties with greater air pollution suffer less from respiratory disease when the concentration increases. For the analysis, we separated counties into quintiles based on their mean seven-day PM10, and regressed the binary indicator of a daily doctor's visit with a resulting diagnosis of respiratory disease on the seven-day PM10 concentration of the county of residence interacted with the quintile dummies. The key findings are that a 1-standard-deviation increase in the seven-day PM10 concentration in the two lowest quintiles is associated with an increase of 0.054 percentage points in the likelihood of a doctor's visit with a resulting diagnosis of respiratory disease, which is about 40% larger than the effect in higher quintiles, and the size of 1-standard-deviation gradually increases from 0.037 percentage points in the third quintile to 0.040 percentage points in the fifth quintile. The smaller increase in the likelihood of respiratory disease in more polluted locations can be explained by the behavioral adaptation to the environment, but the effectiveness of the adaptation seems limited among the highly polluted locations.