Report and analysis of a scarlet fever outbreak among adults through food-borne transmission in China

Yang, Shigui; Dong, Hongjun; Li, Furong; Xie, Shuyun; Cao, Hong; Xia, Shichang; Zhao, Yu; Li, Lanjuan

doi:10.1016/j.jinf.2007.07.011

Cited by 31 publications

(23 citation statements)

References 11 publications

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“…Lacking of specific protective measure, scarlet fever outbreaks still occurred in some areas during the twenty-first century, such as the scarlet fever outbreak with 50 cases in two nurseries in southwest England in 2006 (Hoek et al 2006), 45 cases in a secondary school in China in 2006 (Yang et al 2007), and a large outbreak in mainland China and Hong Kong in 2011 (Chen et al 2012;Davies et al 2015). If effect of meteorological factors on the incidence of scarlet fever was demonstrated, prevention and controlling of it would be promoted greatly.…”

Section: Introductionmentioning

confidence: 99%

Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China

et al. 2016

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Studies on scarlet fever with meteorological factors included were few. We aimed to illustrate meteorological factors' effects on monthly incidence of scarlet fever. Cases of scarlet fever were collected from the report of legal infectious disease in Hefei City from 1985 to 2006; the meteorological data were obtained from the weather bureau of Hefei City. Monthly incidence and corresponding meteorological data in these 22 years were used to develop the model. The model of auto regressive integrated moving average with covariates was used in statistical analyses. There was a highest peak from March to June and a small peak from November to January. The incidence of scarlet fever ranges from 0 to 0.71502 (per 10 population). SARIMAX (1,0,0)(1,0,0) model was fitted with monthly incidence and meteorological data optimally. It was shown that relative humidity (β = -0.002, p = 0.020), mean temperature (β = 0.006, p = 0.004), and 1 month lag minimum temperature (β = -0.007, p < 0.001) had effect on the incidence of scarlet fever in Hefei. Besides, the incidence in a previous month (AR(β) = 0.469, p < 0.001) and in 12 months before (SAR(β) = 0.255, p < 0.001) was positively associated with the incidence. This study shows that scarlet fever incidence was negatively associated with monthly minimum temperature and relative humidity while was positively associated with mean temperature in Hefei City, China. Besides, the ARIMA model could be useful not only for prediction but also for the analysis of multiple correlations.

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Section: Introductionmentioning

confidence: 99%

Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China

et al. 2016

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“…Generally, it spreads by the aerosol route (inhalation), but may also be able to spread through skin contact or by fomites [1,2,3,4]. So far, it is not normally considered as a food- or milk-borne illness, as an outbreak noted in China due to infected chicken meat [5,6]. There are currently no vaccines available to protect against S. pyogenes infection (the vaccine developed by George and Gladys Dick in 1924 was discontinued due to its poor efficacy) [2,3].…”

Section: Introductionmentioning

confidence: 99%

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

Mahara

Wang

Yang

et al. 2016

IJERPH

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(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.

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“…16 A total of 47 cases including 45 adults and two students were finally identified in this outbreak. From the 47 patients, 10 were diagnosed as having scarlet fever, 37 acute pharyngitis and/or tonsillopharyngitis.…”

Section: Correction Of Epidemiological and Clinical Datamentioning

confidence: 99%

β-Haemolytic group A streptococci emm75 carrying altered pyrogenic exotoxin A linked to scarlet fever in adults

Dong

et al. 2008

Journal of Infection

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Report and analysis of a scarlet fever outbreak among adults through food-borne transmission in China

Cited by 31 publications

References 11 publications

Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China

Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China

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

β-Haemolytic group A streptococci emm75 carrying altered pyrogenic exotoxin A linked to scarlet fever in adults

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