The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

Does environmental pollution reduce residents' income? Evidence from CFPS in China

Understanding the relationship between environmental pollution and residents' income is extremely important for promoting sustained progress and high-quality economic growth. This research examines the impact, mechanism, and heterogeneity of environmental contamination on residents' earnings by fusing the micro data from China Family Panel Survey with the macro data of government statistics. The results reveal that environmental degradation has a significantly negative impact on residents' individual income. Further research on the intermediary mechanism finds that environmental pollution plays a harmful role in residents' income by reducing residents' subjective well-being and labor employment. Besides, the income effect of environmental pollution is significantly heterogeneous among different regions and differentiated groups. The deteriorating environmental quality widens urban-rural income gap and increases wage inequality of inhabitants in eastern, central, and western regions of China. The gender income gap and the income disparity between different income brackets also expand with environmental deterioration. These findings not only prove that long-term development at the cost of the ecological environment is undesirable, but also demonstrate the important role of the improvement of ecological environmental quality in promoting human well-being.

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.

Distributed lag effects and vulnerable groups of PM and active pulmonary TB in Qingdao, China

There has been a gap regarding current knowledge of the effect of PM on pulmonary TB, such as the exposure-time-response between them. This study aimed to explore the distributed lag effects of particulate matter (PM) on active pulmonary tuberculosis (TB) and identify the vulnerable groups. A generalized additive mixed model combined with a distributed lag non-linear model was applied to quantify the association between PM and active pulmonary TB with adjustment for potential confounders. Relative risk (RR) and cumulative RR with 95% confidence interval (CI) were calculated to quantify the exposure-time-response. A total of 16,486 cases of active pulmonary TB were notified. Results suggested that a unit 10 µg/m3 increase of daily PM2.5 concentration was positively associated with active pulmonary TB morbidity at 36-115 lag day and RR reached maximum at 66 lag day (1.0076; 95%CI, 1.0031-1.0122), and the cumulative RR was 2.1940 (95%CI, 1.2292-3.9161). For PM10, this association was significantly positive at 73-117 lag day, and RR reached maximum at 100 lag day (1.0036; 95%CI, 1.0003-1.0067), and the cumulative RR was not significant. This study provides evidence that PM significantly associate with active pulmonary TB. Vulnerability to PM2.5 was identified in male, female, 0-18 ages, 19-64 ages, workers, and students. Our findings have significant implications for developing local strategies to prevent and reduce health impact in PM polluted areas.

The effect of ambient temperature on hand, foot and mouth disease in Qingdao, China, 2014-2018

Hand, foot, and mouth disease (HFMD) is a kind of infection gastrointestinal disease. The present study aims to explore the association between ambient temperature and HFMD in Qingdao. A distributed lag nonlinear model with Poisson distribution was adopted to explore the effects of daily mean temperature on HFMD incidence. Our results found that the high temperature had acute and short-term effects and then declined rapidly along the lag days, with the maximum risk occurring 0 day of exposure. Compared with low temperature, higher effects were observed for high-temperature exposure. Overall, we found that the association between temperature and HFMD incidence was non-linear, exhibiting an approximate "J" shape, with peak value occurring at 30.5℃ (RR = 2.208, 95% CI: 1.995-2.444). Our findings suggest that ambient temperature is significantly associated with the incidence of HFMD in Qingdao. Monitoring ambient temperature changes is an appropriate recommendation to prevent HFMD.

Patterns in the Incidence of Scarlet Fever Among Children Aged 0-9 Years - China, 2010-2019

Introduction

This study investigates the patterns of scarlet fever among Chinese children aged 0-9 years from 2010 to 2019. The objective is to provide insights that may inform potential adjustments to China's current prevention and control tactics for this illness.

Methods

The present study utilized data on the occurrence of scarlet fever in children from 2010 to 2019, sourced from the National Notifiable Disease Reporting System database, managed by the Chinese Center for Disease Control and Prevention. This research implemented SAS9.4 software to construct trajectory models representing the temporal incidence of scarlet fever, accounting for key variables such as sex, geographic region, urban versus rural dwellings, and various age brackets.

Results

From 2010 to 2019, a total of 554,695 scarlet fever cases were reported among children aged 0-9 years in the 31 mainland Chinese provincial-level administrative divisions, signifying a rate of 35.36 per 100,000 individuals. An inconsistent yet generally rising trend was observed, evidenced by a 3.17-fold increase in reported cases and a 3.02-fold escalation in incidence rate over this period. Examination of these trends revealed three distinctive developmental patterns for both males and females, with the lowest prevalence in the first trajectory and the highest in the third. The incidence was consistently higher among males than females in all trajectories. The urban and northern regions displayed equal or greater trajectory rates than their rural and southern counterparts, respectively. In terms of age groups, the lowest incidence was observed in the 0-1-year age group, while the highest was recorded in the 4-5 and 6-7-year age groups.

Conclusions

Between 2010 and 2019, there was a marked increase in the incidence of scarlet fever among children in China. The disease predominantly impacts urban-dwelling children, ranging from 4 to 7 years old, in the northern regions of the country. The incidence is reported to be higher among boys compared to girls.

Effects of Ambient Air Pollution on Precocious Puberty: A Case-Crossover Analysis in Nanjing, China

BACKGROUND

Ambient air pollution is closely related to a variety of health outcomes. Few studies have focused on the correlations between air pollution exposure and children's sexual development. In this study, we investigated the potential effects of exposure to air pollution on precocious puberty among children using real-world evidence.

METHODS

We conducted a case-crossover study (n = 2201) to investigate the effect of ambient air pollution exposure on precocious puberty from January 2016 to December 2021. Average exposure levels of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ before diagnosis were calculated by using the inverse distance weighting (IDW) method. Distributed lag nonlinear model (DLNM) was used to assess the effect of air pollutants exposure on precocious puberty.

RESULTS

The mean age of the children who were diagnosed with precocious puberty was 7.47 ± 1.24 years. The average concentration of PM_2.5 and PM₁₀ were 38.81 ± 26.36 μg/m³ and 69.77 ± 41.07 μg/m³, respectively. We found that exposure to high concentrations of PM_2.5 and PM₁₀ might increase the risk of precocious puberty using the DLNM model adjusted for the age, SO₂, NO₂, CO, and O₃ levels. The strongest effects of the PM_2.5 and PM₁₀ on precocious puberty were observed in lag 27 (OR = 1.72, 95% CI: 1.01-2.92) and lag 16 (OR = 1.95, 95% CI: 1.33-2.85), respectively.

CONCLUSION

Our findings supported that short-term exposure to air pollution was the risk factor for precocious puberty. Every effort should be made to protect children from air pollution.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis

Objective

Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China.

Methods

Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods.

Results

From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (β2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447).

Conclusions

The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

Spatiotemporal dynamics and potential ecological drivers of acute respiratory infectious diseases: an example of scarlet fever in Sichuan Province

OBJECT

Scarlet fever is an acute respiratory infectious disease that endangers public health and imposes a huge economic burden. In this paper, we systematically studied its spatial and temporal evolution and explore its potential ecological drivers. The goal of this research is to provide a reference for analysis based on surveillance data of scarlet fever and other acute respiratory infectious illnesses, and offer suggestions for prevention and control.

METHOD

This research is based on a spatiotemporal multivariate model (Endemic-Epidemic model). Firstly, we described the epidemiology status of the scarlet fever epidemic in Sichuan Province from 2016 to 2019. Secondly, we used spatial autocorrelation analysis to understand the spatial pattern. Thirdly, we applied the endemic-epidemic model to analyze the spatiotemporal dynamics by quantitatively decomposing cases into endemic, autoregressive, and spatiotemporal components. Finally, we explored potential ecological drivers that could influence the spread of scarlet fever.

RESULTS

From 2016 to 2019, the incidence of scarlet fever in Sichuan Province varied much among cities. In terms of temporal distribution, there were 1-2 epidemic peaks per year, and they were mainly concentrated from April to June and October to December. In terms of transmission, the endemic and temporal spread were predominant. Our findings imply that the school holiday could help to reduce the spread of scarlet fever, and a standard increase in Gross Domestic Product (GDP) was associated with 2.6 folds contributions to the epidemic among cities.

CONCLUSION

Scarlet fever outbreaks are more susceptible to previous cases, as temporal spread accounted for major transmission in many areas in Sichuan Province. The school holidays and GDP can influence the spread of infectious diseases. Given that covariates could not fully explain heterogeneity, adding random effects was essential to improve accuracy. Paying attention to critical populations and hotspots, as well as understanding potential drivers, is recommended for acute respiratory infections such as scarlet fever. For example, our study reveals GDP is positively associated with spatial spread, indicating we should consider GDP as an important factor when analyzing the potential drivers of acute infectious disease.