The association between ambient particulate matters, nitrogen dioxide, and childhood scarlet fever in Hangzhou, Eastern China, 2014-2018

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Scaling features in high-concentrations PM2.5 evolution: the Ignored factor affecting scarlet fever incidence

As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM2.5 (HC-PM2.5) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM2.5. It has been found that index D can quantify the time distribution of HC-PM2.5, and lower D values indicate more cluster distribution of HC-PM2.5. Moreover, scale-invariance in HC-PM2.5 at different time scales has been discovered, which indicates that HC-PM2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM2.5. This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM2.5 evolution.

An association between PM2.5 components and respiratory infectious diseases: A China's mainland-based study

The particulate matter with diameter of less than 2.5 µm (PM2.5) is an important risk factor for respiratory infectious diseases, such as scarlet fever, tuberculosis, and similar diseases. However, it is not clear which component of PM2.5 is more important for respiratory infectious diseases. Based on data from 31 provinces in mainland China obtained between 2013 and 2019, this study investigated the effects of different PM2.5 components, i.e., sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and organic matter (OM), and black carbon (BC), on respiratory infectious diseases incidence [pulmonary tuberculosis (PTB), scarlet fever (SF), influenza, hand, foot, and mouth disease (HFMD), and mumps]. Geographical probes and the Bayesian kernel machine regression (BKMR) model were used to investigate correlations, single-component effects, joint effects, and interactions between components, and subgroup analysis was used to assess regional and temporal heterogeneity. The results of geographical probes showed that the chemical components of PM2.5 were associated with the incidence of respiratory infectious diseases. BKMR results showed that the five components of PM2.5 were the main factors affecting the incidence of respiratory infectious diseases (PIP>0.5). The joint effect of influenza and mumps by co-exposure to the components showed a significant positive correlation, and the exposure-response curve for a single component was approximately linear. And single-component modelling revealed that OM and BC may be the most important factors influencing the incidence of respiratory infections. Moreover, respiratory infectious diseases in southern and southwestern China may be less affected by the PM2.5 component. This study is the first to explore the relationship between different components of PM2.5 and the incidence of five common respiratory infectious diseases in 31 provinces of mainland China, which provides a certain theoretical basis for future research.

Impact of the coronavirus disease 2019 pandemic on the diversity of notifiable infectious diseases: a case study in Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) has not only posed significant challenges to public health but has also impacted every aspect of society and the environment. In this study, we propose an index of notifiable disease outbreaks (NDOI) to assess the impact of COVID-19 on other notifiable diseases in Shanghai, China. Additionally, we identify the critical factors influencing these diseases using multivariate statistical analysis. We collected monthly data on 34 notifiable infectious diseases (NIDs) and corresponding environmental and socioeconomic factors (17 indicators) from January 2017 to December 2020. The results revealed that the total number of cases and NDOI of all notifiable diseases decreased by 47.1% and 52.6%, respectively, compared to the period before the COVID-19 pandemic. Moreover, the COVID-19 pandemic has led to improved air quality as well as impacted the social economy and human life. Redundancy analysis (RDA) showed that population mobility, particulate matter (PM2.5), atmospheric pressure, and temperature were the primary factors influencing the spread of notifiable diseases. The NDOI is beneficial in establishing an early warning system for infectious disease epidemics at different scales. Furthermore, our findings also provide insight into the response mechanisms of notifiable diseases influenced by social and environmental factors.

Ambient Temperature Is an Independent Risk Factor for Acute Tonsillitis Incidence

OBJECTIVE

Acute tonsillitis is a common disease in otorhinolaryngology. Meteorological factors can affect the incidence of many infectious diseases. This study aims to analyze the correlation between acute tonsillitis and meteorological conditions.

MATERIALS AND METHODS

We collected the meteorological data, including daily temperature, humidity, and fine particulate matter (PM_2.5) of Shanghai, China, from 2014 to 2015. The monthly number of acute tonsillitis cases in our hospital was also calculated and used as the outcome variable. The associations between them were evaluated, respectively.

RESULTS

The average number of patients diagnosed with acute tonsillitis in our hospital per month was 68.67 ± 18.67 from 2014 to 2015. The average temperature, humidity, and PM_2.5 of Shanghai during the defined period was 16.84 °C ± 7.80 °C, 75.93% ± 5.45%, and 52.38 ± 14.23 μg/m³, respectively. The temperature was significantly positively associated with the acute tonsillitis cases number both in Pearson correlation analysis (R = 0.423, P = .039) and in multivariate regression analysis (coefficient =2.194, P = .012). However, no correlation between the acute tonsillitis cases number and relative humidity or PM_2.5 was found through a multivariate regression model (P = .225 and P = .243), respectively.

CONCLUSION

The high temperature was associated with an increased incidence of acute tonsillitis.

The effect of air pollution exposure on risk of outpatient visits for Sjogren's syndrome: A time-series study

BACKGROUND

Emerging evidence showed that air pollutants are associated with development and recurrence of autoimmune disorders, but there is scarce evidence regarding the relationship between air pollutants and Sjogren's syndrome (SS). We sought to investigate whether air pollutants affect the risk of outpatient visits for SS and to quantify the burden of SS visits attributable to air pollution exposure in Hefei, China.

METHODS

Daily data on outpatient visits for SS, air pollutants and meteorological data in Hefei, China, from January 1, 2015 to December 31, 2020 were obtained. A distributed lag non-linear model in conjunction with a generalized linear model were employed to assess the relationship between air pollution and SS outpatient visits. Stratified analyses were further performed by gender, age and season. Attributable fraction (AF) and attributable number (AN) were used to reflect disease burden.

RESULTS

There were 4501 records of outpatient visits for SS. Exposure to PM_2.5 was associated with increased risk of SS outpatient visits (relative risk (RR) = 1.218, 95% confidence interval (CI): 1.017-1.458, lag 0-14 day). An increase of 24 μg/m³ (interquartile range) in NO₂ concentration was associated with 26.3% increase in the risk of SS outpatient visits (RR = 1.263, 95%CI: 1.105-1.445, lag 0-10 day). In contrast, exposure to O₃ was associated with decreased risk of SS outpatient visits (RR = 0.692, 95%CI: 0.510-0.939, per 63 μg/m³ in O₃ exposure, lag 0-27 day). Stratified analyses showed that females (vs. males) was more vulnerable to SS outpatient visits associated with NO₂ and O₃ exposure. SS patients aged ≥65 years (vs. aged <65 years) were susceptible to PM_2.5 exposure. Exposure to PM_2.5 or NO₂ in the cold season was associated with higher risk of SS outpatient visits than that in the warm season. In addition, the AN (232, 95%CI: 119, 324) and AF (5.16%, 95%CI: 2.55%, 7.21%) of NO₂ exposure were higher than those of PM_2.5 exposure.

CONCLUSION

PM_2.5 and NO₂ exposure are associated with increased risk of SS outpatient visits, while O₃ exposure appears to be associated with decreased risk of SS outpatient visits. The effect of air pollutants exposure on risk of SS outpatients can be modified by age, gender and season. The burden of SS outpatient visits attributable to NO₂ exposure is higher than those attributable to PM_2.5 exposure.

Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model

The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R²) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)₁₂. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.

Acute associations between air pollution on premature rupture of membranes in Hefei, China

Numerous studies had focused on the association between air pollution and health outcomes in recent years. However, little evidence is available on associations between air pollutants and premature rupture of membranes (PROM). Therefore, we performed time-series analysis to evaluate the association between PROM and air pollution. The daily average concentrations of PM_2.5, SO₂ and NO₂ were 54.58 μg/m³, 13.06 μg/m³ and 46.09 μg/m³, respectively, and daily maximum 8-h average O₃ concentration was 95.67 μg/m³. The strongest effects of SO₂, NO₂ and O₃ were found in lag4, lag06 and lag09, and an increase of 10 μg/m³ in SO₂, NO₂ and O₃ was corresponding to increase in incidence of PROM of 8.74% (95% CI 2.12-15.79%), 3.09% (95% CI 0.64-5.59%) and 1.68% (95% CI 0.28-3.09%), respectively. There were no significant effects of PM_2.5 on PROM. Season-specific analyses found that the effects of PM_2.5, SO₂ and O₃ on PROM were more obvious in cold season, but the statistically significant effect of NO₂ was observed in warm season. We also found the modifying effects by maternal age on PROM, and we found that the effects of SO₂ and NO₂ on PROM were higher among younger mothers (< 35 years) than advanced age mothers (≥ 35 years); however, ≥ 35 years group were more vulnerable to O₃ than < 35 years group. This study indicates that air pollution exposure is an important risk factor for PROM and we wish this study could provide evidence to local government to take rigid approaches to control emissions of air pollutants.

Spatiotemporal clustering and meteorological factors affected scarlet fever incidence in mainland China from 2004 to 2017

OBJECTIVE

To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease.

METHODS

The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord G_i^⁎ hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence.

RESULTS

Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively.

CONCLUSION

The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.

Limited role for meteorological factors on the variability in COVID-19 incidence: A retrospective study of 102 Chinese cities

While many studies have focused on identifying the association between meteorological factors and the activity of COVID-19, we argue that the contribution of meteorological factors to a reduction of the risk of COVID-19 was minimal when the effects of control measures were taken into account. In this study, we assessed how much variability in COVID-19 activity is attributable to city-level socio-demographic characteristics, meteorological factors, and the control measures imposed. We obtained the daily incidence of COVID-19, city-level characteristics, and meteorological data from a total of 102 cities situated in 27 provinces/municipalities outside Hubei province in China from 1 January 2020 to 8 March 2020, which largely covers almost the first wave of the epidemic. Generalized linear mixed effect models were employed to examine the variance in the incidence of COVID-19 explained by different combinations of variables. According to the results, including the control measure effects in a model substantially raised the explained variance to 45%, which increased by >40% compared to the null model that did not include any covariates. On top of that, including temperature and relative humidity in the model could only result in < 1% increase in the explained variance even though the meteorological factors showed a statistically significant association with the incidence rate of COVID-19. In conclusion, we showed that very limited variability of the COVID-19 incidence was attributable to meteorological factors. Instead, the control measures could explain a larger proportion of variance.

COVID-19 has a great impact worldwide, especially in some rural settings where healthcare resources are not sufficient. While control measures in these area may be limited, scholars have been discussing the potential effects of meteorological factors on mitigating COVID-19 transmission. Unfortunately, the majority of literatures only looked at the association between COVID-19 and environmental factors in which their findings could mislead readers that certain environmental conditions could be ‘protective’. In this study, we argue that the impact of the meteorological factors was very limited by using the incidence data from 102 Chinese cities in the first epidemic period when control measures have been taken into account. As what we expected, once the control measures have been incorporated in the modelling analysis, the meteorological factors could only explain < 1% increase in variability of COVID-19 while control measure explained the variance for more than 40% in total. Because of it, we suggest stringent control measures are necessary to control COVID-19 regardless the meteorological conditions of an area. Given that no vaccine is available to date, our investigation provides an additional evidence, as advocated by World Meteorological Organization rather than relying on changes in the natural environment for mitigation, active non-pharmaceutical interventions are necessary to curb the COVID-19 pandemic.

A intermediate concentration of atmospheric nitrogen dioxide enhances PSII activity and inhibits PSI activity in expanded leaves of tobacco seedlings

Nitrogen dioxide (NO2) is a major air pollutant that affects plant growth, development and yields. Previous studies have found that atmospheric NO2 changes plant photosynthesis in a concentration-dependent manner. Low concentrations of NO2 (4.0 μL L-1) can increase photosynthetic rates, while high concentrations of NO2 (16.0 μL L-1) can have an inhibitory effect. However, the specific effects of a critical intermediate concentration of NO2 on the photosynthetic apparatus of plants has remained unknown. Therefore, in this study, tobacco seedlings at three-leaf ages were fumigated with a intermediate concentration of 8.0 μL L-1 NO2 for 15 days to determine the effects on leaf weight, leaf number per plant, chlorophyll content, net photosynthetic rate, the reaction center activity of photosystems I and II (PSI and PSII, respectively) and core protein gene expression (PsbA and PsaA). Fumigation with 8.0 μL L-1 NO2 increased the number of leaves per plant and the weight of leaves, and the leaves became dark green and curly after 10 days of fumigation. During NO2 fumigation for 15 days, the chlorophyll content, PSII maximum photochemical efficiency (Fv/Fm), electron transfer rate (ETR) and non-photochemical quenching (NPQ) increased most in the oldest leaves (Lmax leaves), but decreased PSI activity (∆I/Io). The Fv/Fm, ETR and NPQ in the youngest leaves (Lmin leaves) were lower than those of Lmax leaves, but the actual photochemical efficiency (ΦPSII) of PSII increased most and ∆I/Io was the highest in these samples. The Fv/Fm, ETR, NPQ and ΦPSII in the leaves at the middle leaf age (Lmid leaves) were lower than those of Lmin and Lmax leaves, but the relative fluorescence intensity of point L (VL) and the relative fluorescence intensity of point K (VK) decreased the most in these samples. Thus, this critical concentration of atmospheric NO2 increased the activity of PSII and inhibited PSI activity in expanded leaves of tobacco seedlings.

Atmospheric fine particulate matter exposure exacerbates atherosclerosis in apolipoprotein E knockout mice by inhibiting autophagy in macrophages via the PI3K/Akt/mTOR signaling pathway

Fine particulate matter (PM_2.5) exposure is intimately linked to atherosclerosis. Defective macrophages autophagy plays an accelerated role in advanced atherosclerosis, however, whether macrophages autophagy has been implicated in the development of PM_2.5-induced atherosclerosis has not been analyzed in full detail. Here we aimed to investigate the association between macrophages autophagy and PM_2.5-induced atherosclerosis, as well as the underlying mechanisms. ApoE^-/- mice were randomly exposed to PM_2.5 or filtered air for 3 months, macrophage RAW264.7 cells were isolated and were stimulated with PM_2.5 sample, selective inhibitors of PI3K/Akt/mTOR pathway LY294002, triciribine, and rapamycin were used in vitro and in vivo to detect the potential mechanisms. We found that PM_2.5 could significantly accelerate atherosclerotic plaque formation in ApoE^-/- mice, increase serum levels of TC and LDL-C, accelerate lipid accumulation in RAW264.7 cells, elevate serum and supernatant levels of IL-6, TNF-α and hs-CRP, decrease the number of autophagosomes in aortic plaque and RAW264.7 cells, reduce the expressions of autophagy-related genes LC3-I, LC3-II and Beclin1 in aortic tissues and RAW264.7 cells but increase the expression of autophagy regulator p62, elevate PI3K, Akt and mTOR distributions in aorta, and increase p-PI3K, p-Akt and p-mTOR protein expressions in aorta and RAW264.7 cells. However, these effects of PM_2.5 were aggravated with the administration of LY294002, triciribine, or rapamycin. This study indicated that the PI3K/Akt/mTOR pathway is involved in the suppression of autophagy induced by PM_2.5 in macrophages, the accelerated effect of PM_2.5 on atherosclerosis was mediated by down-regulation of macrophages autophagy via activating the PI3K/Akt/mTOR signaling pathway.

Improving graph-based detection of singular events for photochemical smog agents

Recently, a set of graph-based tools have been introduced for the identification of singular events of O₃, NO₂ and temperature time series, as well as description of their dynamics. These are based on the use of the Visibility Graphs (VG). In this work, an improvement of the original approach is proposed, being called Upside-Down Visibility Graph (UDVG). It adds the possibility of investigating the singular lowest episodes, instead of the highest. Results confirm the applicability of the new method for describing the multifractal nature of the underlying O₃, NO₂, and temperature. Asymmetries in the NO₂ degree distribution are observed, possibly due to the interaction with different chemicals. Furthermore, a comparison of VG and UDVG has been performed and the outcomes show that they describe opposite subsets of the time series (low and high values) as expected. The combination of the results from the two networks is proposed and evaluated, with the aim of obtaining all the information at once. It turns out to be a more complete tool for singularity detection in photochemical time series, which could be a valuable asset for future research.