Impact of meteorological factors on scarlet fever in Jiangsu province, China

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.

Application of logistic differential equation models for early warning of infectious diseases in Jilin Province

Background

There is still a relatively serious disease burden of infectious diseases and the warning time for different infectious diseases before implementation of interventions is important. The logistic differential equation models can be used for predicting early warning of infectious diseases. The aim of this study is to compare the disease fitting effects of the logistic differential equation (LDE) model and the generalized logistic differential equation (GLDE) model for the first time using data on multiple infectious diseases in Jilin Province and to calculate the early warning signals for different types of infectious diseases using these two models in Jilin Province to solve the disease early warning schedule for Jilin Province throughout the year.

Methods

Collecting the incidence of 22 infectious diseases in Jilin Province, China. The LDE and GLDE models were used to calculate the recommended warning week (RWW), the epidemic acceleration week (EAW) and warning removed week (WRW) for acute infectious diseases with seasonality, respectively.

Results

Five diseases were selected for analysis based on screening principles: hemorrhagic fever with renal syndrome (HFRS), shigellosis, mumps, Hand, foot and mouth disease (HFMD), and scarlet fever. The GLDE model fitted the above diseases better (0.80 ≤ R2 ≤ 0.94, P <  0. 005) than the LDE model. The estimated warning durations (per year) of the LDE model for the above diseases were: weeks 12–23 and 40–50; weeks 20–36; weeks 15–24 and 43–52; weeks 26–34; and weeks 16–25 and 41–50. While the durations of early warning (per year) estimated by the GLDE model were: weeks 7–24 and 36–51; weeks 13–37; weeks 11–26 and 39–54; weeks 23–35; and weeks 12–26 and 40–50.

Conclusions

Compared to the LDE model, the GLDE model provides a better fit to the actual disease incidence data. The RWW appeared to be earlier when estimated with the GLDE model than the LDE model. In addition, the WRW estimated with the GLDE model were more lagged and had a longer warning time.

The dynamics of scarlet fever in The Netherlands, 1906-1920: a historical analysis

Background. Scarlet fever, an infectious disease caused by Streptococcus pyogenes, largely disappeared in developed countries during the twentieth century. In recent years, scarlet fever is on the rise again, and there is a need for a better understanding of possible factors driving transmission. Methods. Using historical case notification data from the three largest cities in The Netherlands (Amsterdam, Rotterdam and The Hague) from 1906 to 1920, we inferred the transmission rate for scarlet fever using time-series susceptible-infected-recovered (TSIR) methods. Through additive regression modelling, we investigated the contributions of meteorological variables and school term times to transmission rates. Results. Estimated transmission rates varied by city, and were highest overall for Rotterdam, the most densely populated city at that time. High temperature, seasonal precipitation levels and school term timing were associated with transmission rates, but the roles of these factors were limited and not consistent over all three cities. Conclusions. While weather factors alone can only explain a small portion of the variability in transmission rates, these results help understand the historical dynamics of scarlet fever infection in an era with less advanced sanitation and no antibiotic treatment and may offer insights into the driving factors associated with its recent resurgence.

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.

Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China

The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL-NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.

A review of the impact of outdoor and indoor environmental factors on human health in China

The Intergovernmental Panel on Climate Change (IPCC) reported that global climate change has led to the increased occurrence of extreme weather events. In the context of global climate change, more evidence indicates that abnormal meteorological conditions could increase the risk of epidemiological mortality and morbidity. In this study, using a systematic review, we evaluated a total of 175 studies (including 158 studies on outdoor environment and 17 studies on indoor environment) to summarize the impact of outdoor and indoor environment on human health in China using the database of PubMed, Web of Science, the Cochrane Library, and Embase. In particular, we focused on studies about cardiovascular and respiratory mortality and morbidity, the prevalence of digestive system diseases, infectious diseases, and preterm birth. Most of the studies we reviewed were conducted in three of the metropolises of China, including Beijing, Guangzhou, and Shanghai. For the outdoor environment, we summarized the effects of climate change-related phenomena on health, including ambient air temperature, diurnal temperature range (DTR), temperature extremes, and so on. Studies on the associations between temperature and human health accounted for 79.7% of the total studies reviewed. We also screened out 19 articles to explore the effect of air temperature on cardiovascular diseases in different cities in the final meta-analysis. Besides, modern lifestyle involves a large amount of time spent indoors; therefore, indoor environment also plays an important role in human health. Nevertheless, studies on the impact of indoor environment on human health are rarely reported in China. According to the limited reports, adverse indoor environment could impose a high health risk on children.

Epidemiology of infectious diarrhoea and the relationship with etiological and meteorological factors in Jiangsu Province, China

We depicted the epidemiological characteristics of infectious diarrhoea in Jiangsu Province, China. Generalized additive models were employed to evaluate the age-specific effects of etiological and meteorological factors on prevalence. A long-term increasing prevalence with strong seasonality was observed. In those aged 0–5 years, disease risk increased rapidly with the positive rate of virus (rotavirus, norovirus, sapovirus, astrovirus) in the 20–50% range. In those aged > 20 years, disease risk increased with the positive rate of adenovirus and bacteria (Vibrio parahaemolyticus, Salmonella, Escherichia coli, Campylobacter jejuni) until reaching 5%, and thereafter stayed stable. The mean temperature, relative humidity, temperature range, and rainfall were all related to two-month lag morbidity in the group aged 0–5 years. Disease risk increased with relative humidity between 67–78%. Synchronous climate affected the incidence in those aged >20 years. Mean temperature and rainfall showed U-shape associations with disease risk (with threshold 15 °C and 100 mm per month, respectively). Meanwhile, disease risk increased gradually with sunshine duration over 150 hours per month. However, no associations were found in the group aged 6–19 years. In brief, etiological and meteorological factors had age-specific effects on the prevalence of infectious diarrhoea in Jiangsu. Surveillance efforts are needed to prevent its spread.

Incidence of Scarlet Fever in Children in Jeju Province, Korea, 2002-2016: An Age-period-cohort Analysis

Objectives:

Outbreaks of scarlet fever in Mexico in 1999, Hong Kong and mainland China in 2011, and England in 2014-2016 have received global attention, and the number of notified cases in Korean children, including in Jeju Province, has also increased since 2010. To identify relevant hypotheses regarding this emerging outbreak, an age-period-cohort (APC) analysis of scarlet fever incidence was conducted among children in Jeju Province, Korea.

Methods:

This study analyzed data from the nationwide insurance claims database administered by the Korean National Health Insurance Service. The inclusion criteria were children aged ≤14 years residing in Jeju Province, Korea who received any form of healthcare for scarlet fever from 2002 to 2016. The age and year variables were categorized into 5 groups, respectively. After calculating the crude incidence rate (CIR) for age and calendar year groups, the intrinsic estimator (IE) method was applied to conduct the APC analysis.

Results:

In total, 2345 cases were identified from 2002 to 2016. Scarlet fever was most common in the 0-2 age group, and boys presented more cases than girls. Since the CIR decreased with age between 2002 and 2016, the age and period effect decreased in all observed years. The IE coefficients suggesting a cohort effect shifted from negative to positive in 2009.

Conclusions:

The results suggest that the recent outbreak of scarlet fever among children in Jeju Province might be explained through the cohort effect. As children born after 2009 showed a higher risk of scarlet fever, further descriptive epidemiological studies are needed.