Changes in ambient temperature increase hospital outpatient visits for allergic rhinitis in Xinxiang, China

Associations of heat with diseases and specific symptoms in Flanders, Belgium: An 8-year retrospective study of general practitioner registration data

INTRODUCTION

Global temperature rise has become a major health concern. Most previous studies on the impact of heat on morbidity have used hospital data.

OBJECTIVE

This study aimed to quantify the association between ambient temperature and a variety of potentially heat-related medical conditions and symptoms using general practitioner (GP) data, in Flanders, Belgium.

METHODS

We used eight years (2012-2019) of aggregated data of daily GP visits during the Belgian summer period (May-September). A distributed lag nonlinear model (DLNM) with time-stratified conditional quasi-Poisson regression was used to account for the non-linear and delayed effect of temperature indicators (minimum, mean and maximum). We controlled for potential confounders such as particulate matter, humidity, and ozone.

RESULTS

The overall (lag0-14) association between heat and most of the outcomes was J-shaped, with an increased risk of disease observed at higher temperatures. The associations were more pronounced using the minimum temperatures indicator. Comparing the 99th (20 °C) to the minimum morbidity temperature (MMT) of the minimum temperature distribution during summer, the relative risk (RR) was significantly higher for heat-related general symptoms (RR = 1.30 [95 % CI: 1.07, 1.57]), otitis externa (RR = 4.87 [95 % CI:2.98, 7.98]), general heart problems (RR = 2.43 [95 % CI: 1.33, 4.42]), venous problems (RR = 2.48 [95 % CI:1.55, 3.96]), respiratory complaints (RR = 1.97 [95 % CI: 1.25, 3.09]), skin problems (RR = 3.26 [95 % CI: 2.51, 4.25]), and urinary infections (RR = 1.37 [95 % CI: 1.11, 1.69]). However, we did not find evidence for heat-related increases in gastrointestinal problems, cerebrovascular events, cardiovascular events, arrhythmia, mental health problems, upper respiratory problems and lower respiratory problems. An increased risk of allergy was observed when the minimum temperature reached 17.8 °C (RR = 1.50 [95 % CI: 1.23, 1.83]). Acute effects of heat were observed (largest effects at the first few lags).

SUMMARY

Our findings indicated that the occurrence of certain symptoms and illnesses during summer season is associated to high temperature or environmental exposures that are augmented by elevated temperatures. Overall, unlike hospitalization data, GP visits data provide broader population coverage, revealing a more accurate representation of heat-health association.

Pollen effects in a changing climate: Ragweed pollen exposure and sleepiness in immunotherapy patients of a Southeastern Michigan allergy clinic

Allergic rhino-conjunctivitis (AR) is a globally relevant health disorder characterized by sneezing, rhinorrhea and sleep disturbance. Ragweed (Ambrosia artemisiifolia) is a plant common to North America and an important allergen. Coarse methods of measuring airborne pollen counts are used to predict seasonal allergy symptoms. This research used a longitudinal study design with a novel, model-based raster of predicted pollen counts to test associations with self-reported symptoms of AR collected from patients receiving immunotherapy for pollen allergies at an allergy clinic. Researchers visited a clinic six times over three weeks. Immunotherapy patients were asked to fill out a brief intake survey on allergic and symptomatic profiles, daytime sleepiness, housing quality, and demographics. Participants responded to a daily, emailed survey on sleepiness and asthma symptoms for 21 days. Using the date and location of responses, ragweed pollen counts were extracted from a prognostic, model based raster (25km pixels). Lag associations of pollen counts with sleepiness were tested using a logistic regression model , adjusted for housing and demographic characteristics, in a distributed lag non-linear model (DLNM) framework. 49 people participated in the study. 26 (52%) were female. The mean age was 37.9 years. Asthma/allergy symptoms were not associated with ragweed pollen but sleepiness was highest two days after exposure (Estimate: 0.33 [0.04,0.62]). Subjects traveled widely during the study period. Intense exposures to ragweed pollen may be associated with daytime sleepiness within small exposure windows. Model-based predicted pollen counts could be used to study health impacts of pollen in people with disease severe enough to receive immunotherapy. Daytime sleepiness can affect productivity and injury risk, and pollen season length and allergenicity may be increasing with climate change. Thus our results may have important implications for population health.

Concurrent ozone and high temperature exacerbates nasal epithelial barrier damage in allergic rhinitis mice: Insights from the nasal transcriptome and nasal microbiota

The global ambient temperature has been rising in recent decades and high temperature is usually accompanied by ozone (O3) pollution. Environmental change is an underlying factor for the increased prevalence of respiratory allergic disease. However, the potential mechanisms are complex and remain elusive. This study was performed to reveal toxic effects and molecular mechanisms of O3 or/and high temperature induced allergic rhinitis (AR) deterioration. The results indicated that O3 and high temperature co-exposure exacerbated rhinitis symptoms, destroyed ultrastructure of nasal mucosa and down-regulated the expression of nasal epithelial barrier structural proteins ZO-1 and occludin. Moreover, the levels of total protein and lactate dehydrogenase (LDH) in nasal lavage fluid and the levels of IL-1β and TNF-α in serum also exhibited a significant upward trend. Transcriptomic analysis revealed that immune and inflammatory signaling pathways such as IL-17 signaling pathway was involved in the combined toxicity of O3 and high temperature. Microbiome examination showed that Prevotella and Elizabethkingia were linked to nasal injury. What's more, spearman correlation analysis revealed correlations among nasal microbiota dysbiosis, inflammation and injury. To sum up, the present study assessed the combined toxicity of O3 and high temperature and found potential mechanisms, which provided important experimental evidence for making preventive intervention strategies and protecting vulnerable populations.

The impact of temperature on the skin barrier and atopic dermatitis

Climate change is a global threat to public health and causes or worsens various diseases including atopic dermatitis (AD), allergic, infectious, cardiovascular diseases, physical injuries, and mental disorders. The incidence of allergy, such as AD, has increased over the past several decades, and environmental factors such as climate change have been implicated as a potential mechanism. A substantial amount of literature has been published on the impact of climate factors, including cold and hot temperatures, on the skin barrier and AD. Studies in several countries have found a greater incidence of AD in children born in the colder seasons of fall and winter. The effect of cold and warm temperatures on itch, skin flares, increased outpatient visits, skin barrier dysfunction, development of AD, and asthma exacerbations have been reported. Understanding mechanisms by which changes in temperature influence allergies is critical to the development of measures for the prevention and treatment of allergic disorders, such as AD and asthma. Low and high temperatures induce the production of proinflammatory cytokines and lipid mediators such as interleukin-1β, thymic stromal lymphopoietin, and prostaglandin E2, and cause itch and flares by activation of TRPVs such as TRPV1, TRPV3, and TRPV4. TRPV antagonists may attenuate temperature-mediated itch, skin barrier dysfunction, and exacerbation of AD.

Exposure to ambient air pollutants and short-term risk for exacerbations of allergic rhinitis: a time-stratified, case-crossover study in the three largest urban agglomerations in Poland

Allergic rhinitis (AR) affects 10% of the world population, with an increased prevalence in regions with substantial air pollution, but the association between exposure to air pollutants and the short-term risk of AR exacerbations is unclear. We used a time-series approach to analyze the risk of hospital admissions due to AR over 8 days from exposure to various air pollutants. Distributed lag nonlinear models were used to analyze data gathered between 2012 and 2018 in the three largest urban agglomerations in Poland. The analyses were carried out separately for the warm (April - September) and cold seasons (October - March). Overall, there were 1407 admissions due to AR. In the warm season, the rate ratio (95% confidence interval) for admission per 10µg/m3 was 1.202 (1.044, 1.384) for particulate matter less than 10 microns (PM10); 1.094 (0.896, 1.335) for particulate matter less than 2.5 microns (PM2.5); 0.946 (0.826, 1.085) for nitrogen dioxide (NO₂); 0.837 (0.418, 1.677) for sulfur dioxide (SO₂); and 1.112 (1.011, 1.224) for ozone (O₃). In the cold season, the rate ratio for admission per 10µg/m3 was 1.035 (0.985, 1.088) for PM10; 1.041 (0.977, 1.108) for PM2.5; 1.252 (1.122, 1.398) for NO₂; 0.921 (0.717, 1.181) for SO₂; and 1.030 (1.011, 1.050) for O₃. In conclusion, the risk of admission due to AR increased significantly after exposure to O₃ in the warm and cold seasons. Exposure to PM10 was associated with a significantly increased risk of AR hospitalizations in the warm season only, whereas exposure to NO₂ was associated with a significantly increased risk of AR admission in the cold season.

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

The Association Between Air Pollutants and Daily Outpatient Visits for Allergic Rhinitis: A Time-Series Analysis Based on Distribution Lag Nonlinear Model in Chongqing, China

Background

Allergic rhinitis (AR) is a severe and the most common chronic allergic disease, affecting 10-40% of the world population. The effect of air pollutants on AR has been confirmed in clinical experiments.

Purpose

This study aimed to quantify the association between air pollutants and daily outpatient visits for AR in Chongqing, China.

Methods

Based on the data of AR outpatients in the primary urban area of Chongqing from 2016 to 2017, along with the atmospheric pollutants and meteorological data in the same period, the distributed lag nonlinear model (DLNM) and generalized additive model (GAM) were used to analyze the time-series. We examined the effects of the single and double pollutant models with a maximum lag day of 30 days. Effect estimates were described as relative risk (RR) and 95% confidence intervals (CIs) in daily outpatient visits for AR per 10 μg/m3 increases in PM2.5, PM10, SO2, NO2, O3, and per 1 mg/m3 increase in CO.

Results

A single pollutant's O3 level had an immediate positive effect on AR within two days, the relative risks (RR, 95% CI) were 1.066 (1.008-1.127), 1.057 (1.005-1.112) and 1.048 (1.002-1.097). PM2.5 had a lag effect within 11-18 days, the max relative risks (RR, 95% CI) were 1.083 (1.010-1.160). Moreover, O3, PM2.5, PM10, SO2 and NO2 had significant effects on AR in the two-pollutant model. The RR cumulative effect of PM2.5 became more pronounced as the concentration increased. The cumulative effect of NO2 was lesser than PM2.5.

Conclusion

Air pollutants were associated with the daily outpatient visits for AR, which may have considerable implications for developing tailored health policies and services to prevent AR in Chongqing and even all over the world.

Association between air pollution and outpatient visits for allergic rhinitis: Effect modification by ambient temperature and relative humidity

Mounting evidence indicated the associations between air pollution and outpatient visits for allergic rhinitis (AR), while few studies assessed the effect modification of these associations by ambient temperature and relative humidity (RH). In this study, dataset of AR outpatients was obtained from Chinese People's Liberation Army Strategic Support Force Characteristic Medical Center in Beijing during 2014 to 2019, and the average concentrations of air pollutants including particulate matter ≤2.5 μm in diameter (PM_2.5) and ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and meteorological factors (temperature and RH) at the same period were collected from one nearby air monitoring station. We performed a time-series study with Poisson regression model to examine the effects of air pollutants on AR outpatients after adjustment for potential confounders. And the effects modification analysis was further conducted by stratifying temperature and RH by tertiles into three groups of low, middle and high. In total of 33,599 outpatient visits for AR were recorded during the study period. Results found that a 10 μg/m³ increase in PM_2.5, PM₁₀, NO₂ and SO₂ was associated with significant increases in AR outpatients of 1.24% (95% confidence interval (CI): 0.69%, 1.78%), 0.79% (95% CI: 0.43%, 1.15%), 3.05% (95% CI: 1.72%, 4.40%) and 5.01% (95% CI: 1.18%, 8.96%), respectively. Stronger associations were observed in males than those in females, as well as in young adults (18-44 years) than those in other age groups. Air pollution effects on AR outpatients increased markedly at low temperature (<33.3th percentile) and high RH (>66.7th percentile). Findings in this study indicate that air pollution is associated with increased risk of AR outpatients, and the effects of air pollution on AR could be enhanced at low temperature and high RH.

Association of temperature variability with the risk of initial outpatient visits for allergic rhinitis: a time-series study in Changchun

Epidemiological studies have revealed associations between several temperature parameters and allergic rhinitis (AR). However, few studies have reported the association of AR with daily temperature variability, which indicates both short-term intra- and interday temperature changes. This study aimed to analyze associations between temperature variability and initial outpatient visits for AR. The analysis was conducted with an over-dispersed Poisson model using daily time-series data on temperature and the number of initial AR outpatients from 2013 to 2015 in Changchun, China. The composite index of temperature variability was derived by calculating the standard deviation of daily minimum temperature and maximum temperature over exposure days. Stratified analysis by season was also conducted. There were 23,344 AR outpatients during the study period. In the total period, per 1 °C increase in temperature variability at 0-2 days (TV_0-2), 0-3 days (TV_0-3), and 0-4 days (TV_0-4) was associated with a 4.03% (95% CI: 0.91-7.25%), 4.40% (95% CI: 0.95-7.97%), and 4.12% (95% CI: 0.38-8.01%) increase in the number of AR outpatients, respectively. When stratified by season, the strongest effect was shown in spring. Our results suggested that temperature variability was associated with increased initial outpatient visits for AR, which may provide helpful implications for formulating public health policies to reduce adverse health impacts of unstable temperature.

The Short-term Effects of Temperature on Infectious Diarrhea among Children under 5 Years Old in Jiangsu, China: A Time-series Study (2015-2019)

The association between meteorological factors and infectious diarrhea has been widely studied in many countries. However, investigation among children under 5 years old in Jiangsu, China remains quite limited. Data including infectious diarrhea cases among children under five years old and daily meteorological indexes in Jiangsu, China from 2015 to 2019 were collected. The lag-effects up to 21 days of daily maximum temperature (Tmax) on infectious diarrhea were explored using a quasi-Poisson regression with a distributed lag non-linear model (DLNM) approach. The cases number of infectious diarrhea was significantly associated with seasonal variation of meteorological factors, and the burden of disease mainly occurred among children aged 0–2 years old. Moreover, when the reference value was set at 16.7°C, Tmax had a significant lag-effect on cases of infectious diarrhea among children under 5 years old in Jiangsu Province, which was increased remarkably in cold weather with the highest risk at 8°C. The results of DLNM analysis implicated that the lag-effect of Tmax varied among the 13 cities in Jiangsu and had significant differences in 8 cities. The highest risk of Tmax was presented at 5 lag days in Huaian with a maximum RR of 1.18 (95% CI: 1.09, 1.29). Suzhou which had the highest number of diarrhea cases (15830 cases), had a maximum RR of 1.04 (95% CI:1.03, 1.05) on lag 15 days. Tmax is a considerable indicator to predict the epidemic of infectious diarrhea among 13 cities in Jiangsu, which reminds us that in cold seasons, more preventive strategies and measures should be done to prevent infectious diarrhea.