Epidemiological study of scarlet fever in Shenyang, China

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.

Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study

BACKGROUND

This study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005-2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China.

METHODS

Descriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025.

RESULTS

Between 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3-7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing's urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015-2019) and the dynamic zero-COVID period (2020-2022), respectively, compared to the pre-upsurge period (2005-2014) (P < 0.001). During the dynamic zero-COVID period, the incidence of scarlet fever decreased by 68.61%, 25.66%, and 10.59% (P < 0.001) in 2020, 2021, and 2022, respectively, compared to the predicted incidence. In 2023, after the dynamic zero-COVID period, the reported cases decreased to 1.5168 per 100,000 people unexpectedly instead of increasing. The cases of scarlet fever are predicted to increase in 2024 (675 cases) and 2025 (705 cases).

CONCLUSIONS

Children aged 3-7 years are the most affected population, particularly males, and kindergartens and primary schools serving as transmission hotspots. It is predicted that the high incidence of scarlet fever in Chongqing will persist in 2024 and 2025, and the outer districts (counties) beyond urban zone would bear the brunt of the impact. Therefore, imminent public health planning and resource allocation should be focused within those areas.

Epidemiological Characteristics and Trends of Scarlet Fever in Zhejiang Province of China: Population-Based Surveillance during 2004-2022

Background

Over the past two decades, scarlet fever has resurged in some countries or areas. Nationwide nonpharmaceutical interventions changed the patterns of other infectious diseases, but its effects on the spread of scarlet fever were rarely studied. This study aimed to evaluate the changes in scarlet fever incidence in Zhejiang Province, China, before and during the COVID-19 pandemic periods and to provide references for scarlet fever prevention and control.

Methods

Scarlet fever surveillance data in Zhejiang, China (2004-2022), were analyzed in three stages. Two-sample z test, ANOVA, and Tukey's test were used to compare and analyze the characteristics of disease spread at different stages. The ARIMA model was used to predict the overall trend. The data were obtained from the National Infectious Disease Reporting Information System.

Results

A total of 28,652 cases of scarlet fever were reported across Zhejiang Province during the study period, with the lowest average monthly incidences in 2020 (0.111/100,000). The predominant areas affected were the northern and central regions of Zhejiang, and all regions of Zhejiang experienced a decrease in incidence in 2020. The steepest decline in incidence in 2020 was found in children aged 0-4 years (67.3% decrease from 23.8/100,000 to 7.8/100,000). The seasonal pattern changed, with peak occurrences in April to June and November to January during 2004-2019 and 2021 and a peak in January in 2020. The median duration from diagnosis to confirmation was highest before COVID-19 (4 days); however, it decreased to 1 day in 2020-2022, matching the other two medians.

Conclusions

In 2020, Zhejiang experienced an unprecedented decrease in scarlet fever, with the lowest incidence in nearly 18 years, but it rebounded in 2021 and 2022. The seasonal epidemiologic characteristics of scarlet fever also changed with the COVID-19 outbreaks. This suggested that nationwide nonpharmaceutical interventions greatly depressed the spread of scarlet fever. With the relaxation of non-pharmaceutical intervention restrictions, scarlet fever may reappear. Government policymakers should prioritize the control of future scarlet fever outbreaks for public health.

Epidemiological characteristics and spatiotemporal clustering of scarlet fever in Liaoning Province, China, 2010-2019

BACKGROUND

To explore the epidemiological characteristics and spatiotemporal distribution of scarlet fever in Liaoning Province, which could provide scientific evidence for the formulation and improvement of prevention and control strategies and measures.

METHODS

Data on scarlet fever cases and population were obtained from the China Information System for Disease Control and Prevention in Liaoning Province between 2010 and 2019. We examined the spatial and spatiotemporal clusters of scarlet fever across Liaoning Province using the Moran's I, local indicators of spatial association, local Gi* hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis.

RESULTS

Between 1st January 2010 and 31st December 2019, 46,652 cases of scarlet fever were reported in Liaoning Province, with an annual average incidence of 10.67 per 100,000. The incidence of scarlet fever had obvious seasonality with high incidence in early summer June and early winter December. The male-to-female ratio was 1.53:1. The highest incidence of cases occurred in 3-9 year old children. The most likely spatiotemporal cluster and the secondary clusters were detected in urban regions of Shenyang and Dalian, Liaoning Province.

CONCLUSIONS

The incidence of scarlet fever has obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in urban area of Shenyang and Dalian, Liaoning Province. Control strategies need to focus on high-risk season, high-risk areas and high-risk populations in order to reduce the incidence of scarlet fever.

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.

Epidemiological Characteristics of Norovirus Outbreaks in Shenyang from 2017 to 2021

Norovirus is one of the leading causes of acute gastroenteritis outbreaks worldwide. This study aimed to identify the epidemiological characteristics of norovirus outbreaks and to provide evidence for public health entities. Specimens and epidemiological survey data were collected to determine if there were differences in the attack rate of norovirus in terms of the year, season, transmission route, exposure setting, and region and to determine whether there were relationships between the reporting interval, the number of illnesses in a single outbreak and the duration of the outbreak. Norovirus outbreaks were reported throughout the year, with seasonal characteristics (i.e., high rates in spring and winter). Among all regions in Shenyang with the exception of Huanggu and Liaozhong, norovirus outbreaks had been reported, and the primary genotype was GII.2[P16]. Vomiting was the most common symptom. The main places of occurrence were childcare institutions and schools. The person-to-person route was the main transmission route. The median duration of norovirus was 3 days (IQR [interquartile range]: 2-6 days), the median reporting interval was 2 days (IQR: 1-4 days), the median number of illnesses in a single outbreak was 16 (IQR: 10-25); there was a positive correlation between these parameters. Norovirus surveillance and genotyping studies still need to be further strengthened to increase knowledge regarding the pathogens and their variant characteristics, to better characterize the patterns of norovirus outbreaks and to provide information for outbreak prevention. Norovirus outbreaks should be detected, reported and handled early. Public health entities and the government should develop corresponding measures for different seasons, transmission routes, exposure settings, and regions.

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.

Spatio-temporal evolution and trend prediction of the incidence of Class B notifiable infectious diseases in China: a sample of statistical data from 2007 to 2020

BACKGROUND

With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies.

METHODS

Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA).

RESULTS

From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year.

CONCLUSIONS

The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases.

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.

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.