Correction to: The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China.
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ABSTRACT:
SUBMITTER: Zhang R
PROVIDER: S-EPMC8422696 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlthough exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China.MethodsThe data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted.ResultsOverall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7-17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0-6 years and 18-64 years were more sensitive to air pollution.ConclusionTemperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.
| S-EPMC8138986 | biostudies-literature
Project description:BackgroundExposure to air pollutants has been related to preterm birth, but little evidence can be available for PM2.5, O3 and CO in China. This study aimed to investigate the short-term effect of exposure to air pollutants on risk preterm birth during 2014-2016 in Ningbo, China.MethodsWe conducted a time-series study to evaluate the associations between daily preterm birth and major air pollutants (including PM2.5, PM10, SO2, NO2, O3 and CO) in Ningbo during 2014-2016. A General Additive Model extend Poisson regression was used to evaluate the relationship between preterm birth and air pollution with adjustment for time-trend, meteorological factors and day of the week (DOW). We also conducted a subgroup analysis by season and age.ResultsIn this study, a total of 37,389 birth occurred between 2014 and 2016 from the Electronic Medical Records System of Ningbo Women and Children's Hospital, of which 5428 were verified as preterm birth. The single pollutant model suggested that lag effect of PM2.5, PM10, NO2 reached a peak at day 3 before delivery and day 6 for SO2, and no relationships were observed for O3 and preterm birth. Excess risks (95% confidence intervals) for an increase of IQR of air pollutant concentrations were 4.84 (95% CI: 1.77, 8.00) for PM2.5, 3.56 (95% CI: 0.07, 7.17) for PM10, 3.65 (95% CI: 0.86, 6.51) for SO2, 6.49 (95% CI: 1.86, 11.34) for NO2, -?0.90 (95% CI: -4.76, 3.11) for O3, and 3.36 (95% CI: 0.50, 6.30) for CO. Sensitivity analyses by exclusion of maternal age?<?18 or?>?35 years did not materially alter our results.ConclusionsThis study indicates that short-term exposure to air pollutants (including PM2.5, PM10, SO2, NO2) are positively associated with risk of preterm birth in Ningbo, China.
| S-EPMC6147039 | biostudies-literature
Project description:BackgroundHand, foot, and mouth disease (HFMD) remains a significant public health issue, especially in developing countries. Many studies have reported the association between environmental temperature and HFMD. However, the results are highly heterogeneous in different regions. In addition, there are few studies on the attributable risk of HFMD due to temperature.ObjectivesThe study aimed to assess the association between temperature and HFMD incidence and to evaluate the attributable burden of HFMD due to temperature in Ningbo China.MethodsThe research used daily incidence of HFMD from 2014 to 2017 and distributed lag non-linear model (DLNM) to investigate the effects of daily mean temperature (Tmean) on HFMD incidence from lag 0 to 30 days, after controlling potential confounders. The lag effects and cumulative relative risk (CRR) were analyzed. Attributable fraction (AF) of HFMD incidence due to temperature was calculated. Stratified analysis by gender and age were also conducted.ResultsThe significant associations between Tmean and HFMD incidence were observed in Ningbo for lag 0-30. Two peaks were observed at both low (5-11 °C) and high (16-29 °C) temperature scales. For low temperature scale, the highest CRR was 2.22 (95% CI: 1.61-3.07) at 7 °C on lag 0-30. For high temperature scale, the highest CRR was 3.54 (95% CI: 2.58-4.88) at 24 °C on lag 0-30. The AF due to low and high temperature was 5.23% (95% CI: 3.10-7.14%) and 39.55% (95% CI: 30.91-45.51%), respectively. There was no significant difference between gender- and age-specific AFs, even though the school-age and female children had slightly higher AF values.ConclusionsThe result indicates that both high and low temperatures were associated with daily incidence of HFMD, and more burdens were caused by heat in Ningbo.
| S-EPMC8263339 | biostudies-literature
Project description:The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air-water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.
| S-EPMC5994338 | biostudies-literature
Project description:BackgroundAir pollution leads to many adverse health conditions, mainly manifested by respiratory or cardiac symptoms. Previous studies are limited as to whether air pollutants were associated to influenza-like illness (ILI). This study aimed to explore the association between air pollutants and outpatient visits for ILI, especially during an outbreak of influenza.MethodsDaily counts of hospital visits for ILI were obtained from Peking University Third Hospital between January 1, 2015, and March 31, 2018. A generalized additive Poisson model was applied to examine the associations between air pollutants concentrations and daily outpatient visits for ILI when adjusted for the meteorological parameters.ResultsThere were 35862 outpatient visits at the fever clinic for ILI cases. Air quality index (AQI), PM2.5, PM10, CO and O3 on lag0 days, as well as nitrogen dioxide (NO2) and sulfur dioxide (SO2) on lag1 days, were significantly associated with an increased risk of outpatient visits for ILI from January 2015 to November 2017. From December 2017 to March 2018, on lag0 days, air pollutants PM2.5 [risk ratio (RR) = 0.971, 95% CI: 0.963-0.979], SO2 (RR = 0.892, 95% CI: 0.840-0.948) and CO (RR = 0.306, 95% CI: 0.153-0.612) were significantly associated with a decreased risk of outpatient visits for ILI. Interestingly, on the lag2 days, all the pollutants were significantly associated with a reduced risk of outpatient visits for ILI except for O3. We did not observe the linear correlations between the outpatient visits for ILI and any of air pollutants, which were instead associated via a curvilinear relationship.ConclusionsWe found that the air pollutants may be associated with an increased risk of outpatient visits for ILI during the non-outbreak period and with a decreased risk during the outbreak period, which may be linked with the use of disposable face masks and the change of outdoor activities. These findings expand the current knowledge of ILI outpatient visits correlated with air pollutants during an influenza pandemic.
| S-EPMC8179240 | biostudies-literature
Project description:Dengue is a climate-sensitive disease with an increasing global burden. Although the relationship between meteorological conditions and dengue incidence is well established, less is known about the modifying nature of sociodemographic variables on that relationship. We assess the strength and direction of sociodemographic effect modification of the temperature-dengue relationship in the second largest city of the Peruvian Amazon to identify populations that may have heightened vulnerability to dengue under varying climate conditions. We used weekly dengue counts and averaged meteorological variables to evaluate the association between disease incidence, meteorological exposures, and sociodemographic effect modifiers (gender, age, and district) in negative binomial regression models. District was included to consider geographical effect modification. We found that being a young child or elderly, being female, and living in the district of Manantay increased dengue's incidence rate ratio (IRR) as a result of 1°C increase in weekly mean temperature (IRR = 2.99, 95% CI: 1.99-4.50 for women less than 5 years old and IRR = 2.86, 95% CI: = 1.93-4.22 for women older than 65 years, both estimates valid for the rainy season). The effect of temperature on dengue depended on season, with stronger effects during rainy seasons. Sociodemographic variables can provide options for intervention to mitigate health impacts with a changing climate. Our results indicate that patterns of baseline risk between regions and sociodemographic conditions can differ substantially from trends in climate sensitivity. These results challenge the assumption that the distribution of climate change impacts will be patterned similarly to existing social gradients in health.
| S-EPMC6947766 | biostudies-literature
Project description:AIM:To investigate the effect of temperature, humidity and the concentration of ambient air pollution on the hospitalization of AECOPD. METHOD:Hospitalization record was obtained from Shenyang Medical Insurance Bureau, concluding patient's age, gender, income hospital time, outcome hospital; Generalized additive model was used to analyze the relationship between temperature, humidity, the concentration of ambient air pollution and the hospitalization of AECOPD. RESULT:The effect of ozone on admission rate in male group was higher than that in female group. Ambient air pollution had a weak influence on age≤50 group. It was found that the optimal lag day for daily relative 40 humidity to age≤50 group, 50<age≤60, 60<age≤70 group and age>70 group was on lag5, lag4, lag4 and lag5, respectively. CONCLUSION:Air pollution, relative humidity and temperature can increase the risk of admission for acute exacerbation of COPD, and in this process there was a lag effect.
| S-EPMC6879126 | biostudies-literature
Project description:The subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.
| S-EPMC7892886 | biostudies-literature
Project description:BackgroundA large number of studies about effects of air pollutants on cardiovascular mortality have been conducted; however, those investigating association between air pollutants and cardiovascular morbidity are limited, especially in developing countries.MethodsA time-series analysis on the short-term association between outdoor air pollutants including particulate matter (PM) with diameters of 10 µm or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) and cardiovascular morbidity was conducted in Tianjin, China based on 4 years of daily data (2008-2011). The morbidity data were stratified by sex and age. The effects of air pollutants during the warm season and the cool season were also analyzed separately.ResultsEach increase in PM10, SO2, and NO2 by increments of 10 µg/m3 in a 2-day average concentration was associated with increases in the cardiovascular morbidity of 0.19% with 95 percent confidence interval (95% CI) of 0.08-0.31, 0.43% with 95% CI of 0.03-0.84, and 0.52% with 95% CI of -0.09-1.13, respectively. The effects of air pollutants were more evident in the cool season than those in the warm season, females and the elderly were more vulnerable to outdoor air pollution.ConclusionsAll estimated coefficients of PM10, SO2 and NO2 are positive but only the effect of SO2 implied statistical significance at the 5% level. Moreover, season, sex and age might modify health effects of outdoor air pollutants. This work may bring inspirations for formulating local air pollutant standards and social policy regarding cardiovascular health of residents.
| S-EPMC4172570 | biostudies-literature
Project description:Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade(-1)) and a widening of the synchronized period (29 d decade(-1)). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (?0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (?0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.
| S-EPMC4782734 | biostudies-literature