The modification of air particulate matter on the relationship between temperature and childhood asthma hospitalization: An exploration based on different interaction strategies

Ambient temperature and the risk of childhood epilepsy hospitalizations: Potentially neglected risk of temperature extremes and modifying effects of air pollution

PURPOSE

Extreme temperatures and air pollution are increasingly important risk factors for human health in the background of climate change, with limited evidence available for neurological disorders. This study intended to investigate the short-term effects of extreme temperatures on childhood epilepsy and explore the potential modifying effect of air pollution.

METHODS

Daily childhood epilepsy hospitalization, meteorological and air pollution data were collected from 10 cities in Anhui Province of China during 2016-2018. We firstly employed a space-time-stratified case-crossover design and conditional logistic regression model to fit the short-term relationship between temperature and epilepsy. Then, we conducted stratified analyses by the level of air pollution and individual characteristics.

RESULTS

Both extreme heat and extreme cold increased the risk of hospitalization for childhood epilepsy. The effect of extreme heat [97.5th vs. minimum hospitalization temperature (MHT)] on hospitalization was acute and emerged at lag0 [OR: 1.229 (95 %CI: 1.035 to 1.459)], while the effect of extreme cold (2.5th vs. MHT) was delayed and appeared at lag5 [OR: 1.098 (95 %CI: 1.043 to 1.156)]. We also found children aged 6-18 years were more susceptible to extreme cold than children aged 0-5 years. Besides, extreme heat and cold effects differed by the level of air pollutants.

CONCLUSION

This study suggests that extreme temperatures might be the novel but currently neglected risk factor for childhood epilepsy, and air pollution could further amplify the adverse effect of temperature.

The correlation between daily temperature, diurnal temperature range, and asthma hospital admissions in Lanzhou city, 2013-2020

BACKGROUND

With the backdrop of global climate change, the impact of climate change on respiratory diseases like asthma is receiving increasing attention. However, the effects of temperature and diurnal temperature range (DTR) on asthma are complex, and understanding these effects across different seasons, age groups, and sex is of utmost importance.

METHODS

This study utilized asthma hospitalization data from Lanzhou, China, and implemented a distributed lag nonlinear model (DLNM) to investigate the relationship between temperature and DTR and asthma hospitalizations. It considered differences in the effects across various seasons and population subgroups.

RESULTS

The study revealed that low temperatures immediately increase the risk of asthma hospitalization (RR = 1.2010, 95% CI: 1.1464, 1.2580), and this risk persists for a period of time. Meanwhile, both high and low DTR were associated with an increased risk of asthma hospitalization. Lower temperatures (RR = 2.9798, 95% CI: 1.1154, 7.9606) were associated with higher asthma risk in the warm season, while in the cold season, the risk significantly rose for the general population (RR = 3.6867, 95% CI: 1.7494, 7.7696), females (RR = 7.2417, 95% CI: 2.7171, 19.3003), and older individuals (RR = 18.5425, 95% CI: 5.1436, 66.8458). In the warm season, low DTR conditions exhibited a significant association with asthma hospitalization risk in males (RR = 7.2547, 95% CI: 1.2612, 41.7295) and adults aged 15-64 (RR = 9.9494, 95% CI: 2.2723, 43.5643). Children also exhibited noticeable risk within specific DTR ranges. In the cold season, lower DTR increases the risk of asthma hospitalization for the general population (RR = 3.1257, 95% CI: 1.4004, 6.9767). High DTR significantly increases the risk of asthma hospitalization in adults (RR = 5.2563, 95% CI: 2.4131, 11.4498).

CONCLUSION

This study provides crucial insights into the complex relationship between temperature, DTR, and asthma hospitalization, highlighting the variations in asthma risk across different seasons and population subgroups.

Time series analysis of the interaction between ambient temperature and air pollution on hospitalizations for AECOPD in Ganzhou, China

This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure-response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO2, PM2.5, and PM10 were - 0.428 (95% CI - 0.637, - 0.218), -- 0.227 (95% CI - 0.293, - 0.161), and - 0.119 (95% CI - 0.159, - 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO2 (> 33 μg/m3) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM10 in men and high PM2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

A unified framework for assessing interaction effects among environmental exposures in epidemiologic studies: A case study on temperature, air pollution, and kidney-related conditions in New York state

BACKGROUND

There are various methods to assess interaction effects. However, current methods have limitations, and quantification of interaction effects is rarely performed. This study aimed to develop a unified quantitative framework for assessing interaction effects.

METHODS

We proposed a novel framework using log-linear models with a product term(s) across the exposures that generates parametric bi-variate association and interaction effect surfaces and allows flexible functional forms for exposures in the interaction term(s). In a case study, we assessed the interaction effects between temperature and air pollution (i.e., PM2.5, NO2, and O3) on risk for kidney-related conditions in New York State (2007-2016) using a case-crossover design with conditional logistic models. Our measures of exposure were the moving averages at lag 0-5 days for air pollution (linear) and daytime mean outdoor wet-bulb globe temperature (WBGT; using a natural cubic spline).

RESULTS

We derived closed-form expressions for the magnitude of multiplicative interaction effects (the joint relative risk divided by the product of the two conditional relative risks) and their uncertainties. In the case study, we found a Bonferroni-corrected significant multiplicative interaction effect (IE) between outdoor WBGT at the 99th percentile (median as the reference) and (1) PM2.5 (per 5 μg/m3 increase, IE = 1.052; 95 % confidence interval [CI]: 1.019, 1.087) for acute kidney failure and (2) O3 (per 5 ppb increase; IE = 1.022; 95 % CI: 1.008, 1.036) for urolithiasis (the latter being inconclusive based on the sensitivity analysis).

CONCLUSIONS

Our framework allows different functional forms of exposure variables in the interaction term, quantifies the magnitudes of entire-exposure-range (in addition to discrete exposure level) multiplicative interaction effects and their uncertainties in a categorical or continuous (linear or non-linear) manner, and harmonizes the two-way evaluation of effect modification. The case study underscores co-consideration of heat and air pollution when estimating health burden and designing heat/pollution alert systems.

Bidirectional modification effects on nonlinear associations of summer temperature and air pollution with first-ever stroke morbidity

High temperature and air pollution may induce stroke morbidity. However, whether associations between high temperature and air pollution with stroke morbidity are modified by each other is still unclear. Data on 23,578 first-ever stroke patients in Shenzhen, China, during the summers of 2014-2018 were collected. Distributed lag nonlinear models were used to assess the modifying effects of air pollution stratified by the median for the associations between summer temperature and stroke morbidity at 0-3 lag days; modifying effects of temperature stratified by the minimum morbidity temperature on the associations between air pollution and stroke morbidity at the same lags were also estimated. The attributable risks of high temperature and high pollution on stroke morbidity were quantified. Stratified analyses of gender, age, migration type, and complication type were conducted to assess vulnerable population characteristics. Summer high temperature may induce stroke morbidity at high-level PM2.5, PM10, O3, SO2, and NO2 conditions, with attributable fraction (AF) of 2.982% (95% empirical confidence interval [eCI]: 0.943, 4.929), 3.113% (0.948, 5.200), 2.841% (0.943, 4.620), 3.617% (1.539, 5.470), and 2.048% (0.279, 3.637), respectively. High-temperature effects were statistically insignificant at corresponding low-level air pollution conditions. High-level PM2.5, PM10, and O3 may induce stroke morbidity at high-temperature conditions, with AF of 3.664% (0.036, 7.196), 4.129% (0.076, 7.963), and 4.574% (1.009, 7.762), respectively. High-level PM2.5, PM10, and O3 were not associated with stroke morbidity at low-temperature conditions. The effects of high temperature and high pollution on stroke morbidity were statistically significant among immigrants and patients with hypertension, dyslipidemia, or diabetes but insignificant among natives and patients without complications. The associations of summer temperature and air pollution with first-ever stroke morbidity may be enhanced bidirectionally. Publicity on the health risks of combined high temperature and high pollution events should be strengthened to raise protection awareness of relevant vulnerable populations.

Methods to Advance Climate Science in Respiratory Health: Satellite-Based Environmental Modeling for Temperature Exposure Assessment in Epidemiological Studies

Climate change is a major concern with significant impacts on human health including respiratory outcomes, particularly through changes in air temperature. The rise in global temperature has led to an increase in heat waves and extreme weather events, which pose serious risks to respiratory health. Accurately assessing the effects of air temperature on respiratory health requires a comprehensive approach that incorporates fine-scale exposure assessment to characterize the geospatial environment impacting population health. Recent advances in open-source earth observation data have allowed for improved exposure assessment through temperature modeling.

The association between PM2.5 concentration and the severity of acute asthmatic exacerbation in hospitalized children: A retrospective study in Chongqing, China

BACKGROUND

Ambient PM2.5 is associated with asthma exacerbation. The association between the concentration of PM2.5 and the severity of asthma exacerbation has yet to be thoroughly clarified. The study aims to explore the association between the piror 30 days average concentration of PM2.5 and the severity of acute asthma exacerbation in hospitalized children.

METHODS

A total of 269 children with acute exacerbation of asthma were enrolled and divided into three groups according to the PM2.5 exposure concentrations: group 1 (PM2.5: <37.5 μg/m3 ), group 2 (PM2.5: 37.5-75 μg/m3 ), group 3 (PM2.5: ≥75 μg/m3 ), respectively. The ordered logistic regression modeling was conducted to explore the influence of daily PM2.5 concentration on the clinical severity of children's asthma exacerbation. Multiple linear regression was conducted to explore the association between the concentration of PM2.5 and the length of stay in the hospital (LOS). We also conducted a receiver operating characteristic (ROC) curve analysis to explore the cutoff value of PM2.5 to predict the children's asthma exacerbation.

RESULTS

There was no statistical difference among the three groups of children in gender, age, body mass index, ethnicity, the first diagnosis of asthma, allergic history, passive smoke exposure, or family history of asthma. There was a statistically significant difference in many hospitalization characteristics (p < 0.05) among the three groups of children. Significant differences were found in terms of accessory muscles of respiration (p = 0.005), respiratory failure (p = 0.012), low respiratory tract infectious (p = 0.020), and the severity of asthma exacerbation (p < 0.001) among the three groups. PM2.5 concentration was primarily positively correlated to neutrophile inflammation. The ordered multivariate logistic regression model showed that higher PM2.5 concentrations were significantly associated with greater odds of more severe asthma exacerbation in one and two-pollutant models. The adjusted odds ratio of severe asthma exacerbation was 1.029 (1.009, 1.049) in the one-pollutant model. The most significant odds ratio of severe asthma exacerbation was 1.050 (1.027, 1.073) when controlling NO2 in the two-pollutant models. Multiple linear regression showed that PM2.5 concentration was significantly associated with longer LOS in both one-pollutant and two-pollutant models. By performing ROC analysis, the average daily concentration of 44.5 µg/m3 of PM2.5 (AUC = 0.622, p = 0.002) provided the best performance to predict severe asthma of children exacerbation with a sensitivity of 59.2% and a specificity of 63.8%.

CONCLUSION

The increased prior 30 days average concentration of PM2.5 was associated with greater asthma exacerbation severity and longer length of stay in the hospital of children with asthma exacerbation.

Asthma triggered by extreme temperatures: From epidemiological evidence to biological plausibility

BACKGROUND

There is rapidly growing evidence indicating that extreme temperature is a crucial trigger and potential activator of asthma; however, the effects of extreme temperature on asthma are inconsistently reported and the its potential mechanisms remain undefined.

OBJECTIVES

This review aims to estimate the impacts of extreme heat, extreme cold, and temperature variations on asthma by systematically summarizing the existing studies from epidemiological evidence to biological plausibility.

METHODS

We conducted a systematic search in PubMed, Embase, and Web of Science from inception to June 30, 2022, and we retrieved articles of epidemiology and biological studies which assessed associations between extreme temperatures and asthma. This protocol was registered with PROSPERO (CRD42021273613).

RESULTS

From 12,435 identified records, 111 eligible studies were included in the qualitative synthesis, and 37 articles were included in the meta-analysis (20 for extreme heat, 16 for extreme cold, and 15 for temperature variations). For epidemiological evidence, we found that the synergistic effects of extreme temperatures, indoor/outdoor environments, and individual vulnerabilities are important triggers for asthma attacks, especially when there is extreme heat or cold. Meta-analysis further confirmed the associations, and the pooled relative risks for asthma attacks in extreme heat and extreme cold were 1.07 (95%CI: 1.03-1.12) and 1.20 (95%CI: 1.12-1.29), respectively. Additionally, this review discussed the potential inflammatory mechanisms behind the associations between extreme temperatures and asthma exacerbation, and highlighted the regulatory role of immunological pathways and transient receptor potential ion channels in asthma triggered by extreme temperatures.

CONCLUSIONS

We concluded that both extreme heat and cold could significantly increase the risk of asthma. Additionally, we proposed a potential mechanistic framework, which is important for understanding the disease pathogenesis that uncovers the complex mechanisms of asthma triggered by extreme temperatures and protects the sensitive individuals from impacts of extreme weather events and climate change.