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 | 2021 Sep
REPOSITORIES: biostudies-literature
Publications
Correction to: The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China.
Zhang Rui R Meng Yujie Y Song Hejia H Niu Ran R Wang Yu Y Li Yonghong Y Wang Songwang S
Respiratory research 20210907 1
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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 complexity of transmission of COVID-19 in the human population cannot be overstated. Although major transmission routes of COVID-19 remain as human-to-human interactions, understanding the possible role of climatic and weather processes in accelerating such interactions is still a challenge. The majority of studies on the transmission of this disease have suggested a positive association between a decrease in ambient air temperature and an increase in human cases. Using data from 19 early epicenters, we show that the relationship between the incidence of COVID-19 and temperature is a complex function of prevailing climatic conditions influencing human behavior that govern virus transmission dynamics. We note that under a dry (low-moisture) environment, notably at dew point temperatures below 0°C, the incidence of the disease was highest. Prevalence of the virus in the human population, when ambient air temperatures were higher than 24°C or lower than 17°C, was hypothesized to be a function of the interaction between humans and the built or ambient environment. An ambient air temperature range of 17 to 24°C was identified, within which virus transmission appears to decrease, leading to a reduction in COVID-19 human cases.
| S-EPMC8922505 | 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: Not available
| S-EPMC10862499 | 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:Knowledge about local air temperature variations and extremes in Antarctica is of large interest to many polar disciplines such as climatology, glaciology, hydrology, and ecology and it is a key variable to understand climate change. Due to the remote and harsh conditions of Antarctica's environment, the distribution of air temperature observations from Automatic Weather Stations is notably sparse across the region. Previous studies have shown that satellite-derived land and ice surface temperatures can be used as a suitable proxy for air temperature. Here, we developed a daily near-surface air temperature dataset, AntAir ICE for terrestrial Antarctica and the surrounding ice shelves by modelling air temperature from MODIS skin temperature for the period 2003 to 2021 using a linear model. AntAir ICE has a daily temporal resolution and a gridded spatial resolution of 1 km2. AntAir ICE has a higher accuracy in reproducing in-situ measured air temperature when compared with the well-established climate re-analysis model ERA5 and a higher spatial resolution which highlights its potential for monitoring temperature patterns in Antarctica.
| S-EPMC10681983 | biostudies-literature
Project description:BackgroundSeveral studies have suggested a significant association of hand, foot, and mouth disease (HFMD) with ambient air pollutants. Existing studies have characterized the role of air pollutants on HFMD using only risk ratio measures while ignoring the attributable burden. And whether the geographical context (i.e., diverse topographic features) could modulate the relationships is unclear.MethodsDaily reported childhood HFMD counts, ambient air pollution, and meteorological data during 2015-2017 were collected for each of 21 cities in Sichuan Province. A multistage analysis was carried out in different populations based on geographical context to assess effect modification by topographic conditions. We first constructed a distributed lag nonlinear model (DLNM) for each city to describe the relationships with risk ratio measures. Then, we applied a multivariate meta-regression to estimate the pooled effects of multiple air pollutants on HFMD from the exposure and lagged dimensions. Finally, attributable risks measures were calculated to quantify HFMD burden by air pollution.ResultsBased on 207554 HFMD cases in Sichuan Province, significant associations of HFMD with ambient air pollutants were observed mainly at relatively high exposure ranges. The effects of ambient air pollutants on HFMD are most pronounced on lag0 or around lag7, with relative risks gradually approaching the reference line thereafter. The attributable risks of O3 were much greater than those of other air pollutants, particularly in basin and mountain regions.ConclusionsThis study revealed significant pooled relationships between multiple air pollutants and HFMD incidence from both exposure and lag dimensions. However, the specific effects, including RRs and ARs, differ depending on the air pollution variable and geographical context. These findings provide local authorities with more evidence to determine key air pollutants and regions for devising and implementing targeted interventions.
| S-EPMC10804470 | biostudies-literature