Re-emergence of scarlet fever: old players return?

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.

Epidemiologic changes of infectious diseases in the post-SARS era in China, 2004-2018

OBJECTIVES

To outline 44 major infectious diseases in the post-SARS (severe acute respiratory syndrome) in China and describe their long-term trends and changes by age, sex, epidemic season, and province.

BACKGROUND

After the outbreak of severe acute respiratory syndrome (SARS) in 2003, with the change of infectious disease prevention and control system and the improvement of residents' quality of life, the incidence and mortality of infectious diseases have undergone major changes.

METHODS

The data of 44 major infectious diseases in China from 2004 to 2018 were obtained from the monthly analysis report of the China Information System for Disease Control and Prevention (CISDCP) and the Public Health Science Data Center. Joinpoint r regression models were used to examine trends in incidence and mortality for 44 major and important infectious diseases from 2004 to 2018.

RESULTS

From 2004 to 2018, 20,105, 500, 772 patients (10, 306, 546, 523 males and 9, 798, 954, 249 females) were diagnosed with 44 major infectious diseases. The overall incidence of 44 infectious diseases increased significantly from 294.6 per 100,000 people in 2004 to 479.1 per 100,000 people in 2010, with 7.9% APC (95% CI 5.2% -10.7%, P < 0.001), then slowed, and then increased to 561.2 per 100,000 people in 2018, with 1.5% APC (-0.1%-3.2%, P = 0.070). The overall mortality rose significantly, from 0.49 to 1.13 per 100,000 people between 2004 and 2011, with an APC increase of 11.6% (7.7% -15.6%, P < 0.001), and then remained stable until 2018. Among these, the prevalence of vaccine-preventable diseases and gastrointestinal & enteroviral diseases remained high and increased year by year. Patients with zoonotic diseases have the greatest risk of death, while patients with sexually transmitted and blood-borne diseases have the greatest number of deaths. Incidence rates vary considerably across geographic regions. Western China has a disproportionate burden of infectious diseases compared with eastern regions.

CONCLUSIONS

After the event of SARS in 2003, infectious disease preventing and controlling model has undergone major changes in China, and certain achievements have been made in this field. Although overall morbidity and case fatality rates are still rising, they have leveled off. In reducing the disproportionate disease burden in the western region, expanding vaccination programs, preventing further increases in rates of sexually transmitted diseases, renewing efforts for emerging and persistent infectious diseases, and addressing seasonal and unpredictable outbreaks (such as the COVID-19 pandemic), there are still remain many challenges.

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.

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.

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

Background

We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014–2018.

Methods

A distributed lag non-linear model (DLNM) combined with a generalized additive mixed model (GAMM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders.

Results

A total of 6316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different relative risk (RR) of air pollution degrees [1.172, 95% confidence interval (CI): 1.038–1.323 in mild air pollution; 1.374, 95% CI 1.078–1.749 in moderate air pollution; 1.610, 95% CI 1.163–2.314 in severe air pollution; 1.887, 95% CI 1.163–3.061 in most severe air pollution].

Conclusions

Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06674-8.

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.

Exposure to air pollution and scarlet fever resurgence in China: a six-year surveillance study

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011-2018 was twice that in 2004-2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29-2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO₂ (r = 0.21) and O₃ (r = 0.11). A 10 μg/m³ increase of NO₂ and O₃ was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02-1.10) and 1.04 (95% CI: 1.01-1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO₂ and O₃ may be associated with an increased risk of scarlet fever incidence, but direct causality is not established.

Increase of emm1 isolates among group A Streptococcus strains causing scarlet fever in Shanghai, China

OBJECTIVE

Scarlet fever epidemics caused by group A Streptococcus (GAS) have been ongoing in China since 2011. However, limited data are available on the dynamic molecular characterizations of the epidemic strains.

METHOD

Epidemiological data of scarlet fever in Shanghai were obtained from the National Notifiable Infectious Disease Surveillance System. Throat swabs of patients with scarlet fever and asymptomatic school-age children were cultured. Illumina sequencing was performed on 39emm1 isolates.

RESULTS

The annual incidence of scarlet fever was 7.5-19.4/100,000 persons in Shanghai during 2011-2015, with an average GAS carriage rate being 7.6% in school-age children. The proportion ofemm1 GAS strains increased from 3.8% in 2011 to 48.6% in 2014; they harbored a superantigen profile similar to emm12 isolates, except for the speA gene. Two predominant clones, SH001-emm12, and SH002-emm1, circulated in 66.9% of scarlet fever cases and 44.8% of carriers. Genomic analysis showed emm1 isolates throughout China constituted distinct clades, enriched by the presence of mobile genetic elements carrying the multidrug-resistant determinants ermB and tetM and virulence genes speA, speC, and spd1.

CONCLUSION

A significant increase in the proportion ofemm1 strains occurred in the GAS population, causing scarlet fever in China. Ongoing surveillance is warranted to monitor the dynamic changes of GAS clones.

Spatial-temporal analysis and visualization of scarlet fever in mainland China from 2004 to 2017

This study retrospectively analyzed the spatio-temporal distribution and spatial clustering of scarlet fever in mainland China from 2004 to 2017. In recent years, the incidence of scarlet fever is increasing. Previous studies on the spatial distribution of scarlet fever in China are mainly focused at the provincial and municipal levels, and there is few systematic report on the spatial and temporal distribution characteristics of scarlet fever on the national level. Based on the incidence information of scarlet fever in mainland China between 2004 and 2017 collected from the China Center for Disease Control, this paper systematically explored the Spatio-temporal distribution of scarlet fever by three methods, contains spatial autocorrelation analysis, Spatio-temporal scanning analysis, and trend surface analysis. The results demonstrate that the incidence of scarlet fever varies by seasons, which is in line with double-peak distribution.The first peak generally occurs from May to June and the second one from November to December, while February and August is the lowest period of incidence. Trend surface analysis indicates that the incidence of scarlet fever in northern China is higher than the south, slightly higher in western compared to the east, and lower in the central part. Additionally, the results show that the clustering regions of scarlet fever centrally distributed in the northeast, northwest, north china and some provinces in the east, such as Zhejiang, Shanghai, Shandong, and Jiangsu.

Epidemiological study of scarlet fever in Shenyang, China

Background

Since 2011, there has been an increase in the incidence of scarlet fever across China. The main objective of this study was to depict the spatiotemporal epidemiological characteristics of the incidence of scarlet fever in Shenyang, China, in 2018 so as to provide the scientific basis for effective strategies of scarlet control and prevention.

Methods

Excel 2010 was used to demonstrate the temporal distribution at the month level and ArcGIS10.3 was used to demonstrate the spatial distribution at the district/county level. Moran’s autocorrelation coefficient was used to examine the spatial autocorrelation and the Getis-Ord statistic was used to determine the hot-spot areas of scarlet fever.

Results

A total of 2314 scarlet fever cases were reported in Shenyang in 2018 with an annual incidence of 31.24 per 100,000. The incidence among males was higher than that among females(p<0.001). A vast majority of the cases (96.89%) were among children aged 3 to 11 years. The highest incidence was 625.34/100,000 in children aged 5–9 years. In 2018 there were two seasonal peaks of scarlet fever in June (summer-peak) and December (winter-peak). The incidence of scarlet fever in urban areas was significantly higher than that in rural areas(p<0.001). The incidence of scarlet fever was randomly distributed in Shenyang. There are hotspot areas located in seven districts.

Conclusions

Urban areas are the hot spots of scarlet fever and joint prevention and control measures between districts should be applied. Children aged 3–11 are the main source of scarlet fever and therefore the introduction of prevention and control into kindergarten and primary schools may be key to the control of scarlet fever epidemics.

Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017

OBJECTIVE

To explore the relationship between meteorological factors and scarlet fever incidence from 2006 to 2017 in Guangzhou, the largest subtropical city of Southern China, and assist public health prevention and control measures.

METHODS

Data for weekly scarlet fever incidence and meteorological variables from 2006 to 2017 in Guangzhou were collected from the National Notifiable Disease Report System (NNDRS) and the Guangzhou Meteorological Bureau (GZMB). Distributed lag nonlinear models (DLNMs) were conducted to estimate the effect of meteorological factors on weekly scarlet fever incidence in Guangzhou.

RESULTS

We observed nonlinear effects of temperature, relative humidity, and wind velocity. The risk was the highest when the weekly mean temperature was 31 °C during lag week 14, yielding a relative risk (RR) of 1.48 (95% CI: 1.01-2.17). When relative humidity was 43.5% during lag week 0, the RR was 1.49 (95% CI: 1.04-2.12); the highest RR (1.55, 95% CI: 1.20-1.99) was reached when relative humidity was 93.5% during lag week 20. When wind velocity was 4.4 m/s during lag week 13, the RR was highest at 3.41 (95% CI: 1.57-7.44). Positive correlations were observed among weekly temperature ranges and atmospheric pressure with scarlet fever incidence, while a negative correlation was detected with aggregate rainfall. The cumulative extreme effect of meteorological variables on scarlet fever incidence was statistically significant, except for the high effect of wind velocity.

CONCLUSION

Weekly mean temperature, relative humidity, and wind velocity had double-trough effects on scarlet fever incidence; high weekly temperature range, high atmospheric pressure, and low aggregate rainfall were risk factors for scarlet fever morbidity. Our findings provided preliminary, but fundamental, information that may be useful for a better understanding of epidemic trends of scarlet fever and for developing an early warning system. Laboratory surveillance for scarlet fever should be strengthened in the future.

Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model

Background

Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease.

Methods

Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error.

Results

The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)₁₂, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year.

Conclusions

The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.

Resurgence of scarlet fever in China: a 13-year population-based surveillance study

Background

A re-emergence of scarlet fever has been noted in Hong Kong, South Korea, and England, UK, since 2008. China also had a sudden increase in the incidence of the disease in 2011. In this study, we aimed to assess the epidemiological changes before and after the upsurge. We also aimed to explore the reasons for the upsurge in disease in 2011, the epidemiological factors that contributed to it, and assess how these could be managed to prevent future epidemics.

Methods

In this observational study, we extracted the epidemiological data for all cases of scarlet fever between 2004 and 2016 in China from the Chinese Public Health Science Data Center, the official website of National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System. These data had been collected from 31 provinces and regions in China and included geographical, seasonal, and patient demographic information. We used descriptive statistical methods and joinpoint regression to examine the spatiotemporal patterns and annual percentage change in incidence of the upsurge of disease across China.

Findings

Between Jan 1, 2004, and Dec 31, 2016, 502 723 cases of scarlet fever, with ten fatalities, were reported in China, resulting in an annualised average incidence of 2·8807 per 100 000 people. The annual average incidence increased from 1·457 per 100 000 people in 2004 to 4·7638 per 100 000 people in 2011 (incidence rate ratio [IRR] 3·27, 95% CI 3·22–3·32; p<0·0001), peaking in 2015 (5·0092 per 100 000 people). The annual incidence after the 2011 upsurge of scarlet fever, between 2011 and 2016, was twice the average annual incidence reported between 2004 and 2010 (4·0125 vs 1·9105 per 100 000 people; IRR 2·07, 95% CI 2·06–2·09; p<0·0001). Most cases were distributed in the north, northeast, and northwest of the country. Semi-annual patterns were observed in May–June and November–December. The median age at onset of disease was 6 years, with the annual highest incidence observed in children aged 6 years (49·4675 per 100 000 people). The incidence among boys and men was 1·54 greater than that among girls and women before the upsurge, and 1·51 times greater after the upsurge (p<0·0001 for both). The median time from disease onset to reporting of the disease was shorter after the upsurge in disease than before (3 days vs 4 days; p=0·001).

Interpretation

To our knowledge, this is the largest epidemiological study of scarlet fever worldwide. The patterns of infection across the country were similar before and after the 2011 upsurge, but the incidence of disease was substantially higher after 2011. Prevention and control strategies being implemented in response to this threat include improving disease surveillance and emergency response systems. In particular, the school absenteeism and symptom monitoring and early-warning system will contribute to the early diagnosis and report of the scarlet fever. This approach will help combat scarlet fever and other childhood infectious diseases in China.

Funding

National Key R&D Plan of China Science and key epidemiological disciplines of Zhejiang Provincial Health of China.

Immunogenicity and Safety of a Chemically Synthesized Divalent Group A Streptococcal Vaccine

BACKGROUND

Group A streptococcus (GAS) infections and poststreptococcal sequelae remain a health problem worldwide, which necessitates searching for an effective vaccine, while no licensed GAS vaccine is available. We have developed a divalent peptide vaccine composed of 84 amino acids to cover the main GAS serotypes (M1 and M12 streptococci) in China, and herein, we aimed to evaluate immunogenicity and safety of this vaccine.

METHODS

Mice were immunized with the vaccine. ELISA, indirect bactericidal test, and immunofluorescent assay were used to study immunogenicity. GAS challenge assay was used to test the protective effect. Safety was tested by histopathological analysis.

RESULTS

Immunized group mice (n=16) developed higher titer antibody after immunization than nonimmunized group mice (n=16) did. This antibody can deposit on the surface of GAS and promote killing of GAS, resulting in 93.1% decrease of M1 GAS and 89.5% of M12 GAS. When challenged with M1 and M12 streptococci, immunized group mice had a higher survival rate (87.5% and 75%) than nonimmunized group mice (37.5% and 25%). No autoimmune reactions were detected on organs of mice.

CONCLUSION

The results suggest that this vaccine shows fair immunogenicity and safety, which will lead our research on GAS vaccine into clinical trial.

Managing scarlet fever

Scarlet fever, an infection caused by toxin-producing strains of Streptococcus pyogenes, was associated with high levels of morbidity and mortality when epidemics were common in the 18^th and 19^th centuries throughout Europe and the USA.¹ Although this disease nearly disappeared during the 20^th century, several countries, including the UK, have recently experienced a re-emergence of scarlet fever.^1-3 However, the reason for these new outbreaks remains unclear.^1,4 Despite a general move to reduce the use of antibiotics for many mild self-limiting infections (e.g. tonsillitis, sinusitis), national guidance recommends treating people with scarlet fever with antibiotics regardless of severity of illness to speed recovery, to reduce the length of time the infection is contagious and to reduce the risk of complications.^5,6 Here, we discuss the management of scarlet fever in the UK.

Effects of meteorological factors on incidence of scarlet fever during different periods in different districts of China

OBJECTIVE

To reveal the difference of meteorological effect on scarlet fever in Beijing and Hong Kong, China, during different periods among 2004-2014.

METHODS

The data of monthly incidence of scarlet fever and meteorological variables from 2004 to 2014 in Beijing and Hong Kong were collected from Chinese science data center of public health, meteorological data website and Hong Kong observatory website. The whole study period was separated into two periods by the outbreak year 2011 (Jan 2004-Dec 2010 and Jan 2011-Dec 2014). A generalized additive Poisson model was conducted to estimate the effect of meteorological variables on monthly incidence of scarlet fever during two periods in Beijing and Hong Kong, China.

RESULTS

Incidence of scarlet fever in two districts were compared and found the average incidence during period of 2004-2010 were significantly different (Z=203.973, P<0.001) while average incidence became generally equal during 2011-2014 (Z=2.125, P>0.05). There was also significant difference in meteorological variables between Beijing and Hong Kong during whole study period, except air pressure (Z=0.165, P=0.869). After fitting GAM model, it could be found monthly mean temperature showed a negative effect (RR=0.962, 95%CI: 0.933, 0.992) on scarlet fever in Hong Kong during the period of 2004-2010. By comparison, for data in Beijing during the period of 2011-2014, the RRs of monthly mean temperature range growing 1°C and monthly sunshine duration growing 1h was equal to 1.196(1.022, 1.399) and 1.006(1.001, 1.012), respectively. The changes of meteorological effect on scarlet fever over time were not significant both in Beijing and Hong Kong.

CONCLUSION

This study suggests that meteorological variables were important factors for incidence of scarlet fever during different period in Beijing and Hong Kong. It also support that some meteorological effects were opposite in different period although these differences might not completely statistically significant.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.