Scarlet Fever Epidemic, Hong Kong, 2011

Luk, Emma Y.Y.; Lo, Janice Y.C.; Li, Amy Z.L.; Lau, Michael C.K.; Cheung, Terence K.M.; Wong, Alice Y.M.; Wong, M.; Wong, Christine; Chuang, Shuk-Kwan; Tsang, Thomas

doi:10.3201/eid1810.111900

Cited by 57 publications

(46 citation statements)

References 5 publications

(5 reference statements)

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“…In the Far East, emm 1 and emm 4 isolates were the leading causes of scarlet fever in the late 1990s ( 6 ), although more recently, antimicrobial drug–resistant emm 12 S. pyogenes has dominated in this region ( 7 – 9 ). We found that the proportion of emm 12 isolates fell during the scarlet fever surge and found no antimicrobial drug resistance among emm 3 or emm 4 isolates associated with scarlet fever.…”

Section: Discussionmentioning

confidence: 99%

Scarlet Fever Upsurge in England and Molecular-Genetic Analysis in North-West London, 2014

Turner¹,

Pyzio²,

Song³

et al. 2016

Emerg. Infect. Dis.

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Scarlet fever notifications surged across the United Kingdom in spring 2014. Molecular epidemiologic investigation of Streptococcus pyogenes infections in North-West London highlighted increased emm4 and emm3 infections coincident with the upsurge. Unlike outbreaks in other countries, antimicrobial resistance was uncommon, highlighting an urgent need to better understand the drivers of scarlet fever activity.

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Section: Discussionmentioning

confidence: 99%

Scarlet Fever Upsurge in England and Molecular-Genetic Analysis in North-West London, 2014

Turner¹,

Pyzio²,

Song³

et al. 2016

Emerg. Infect. Dis.

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“…If both LM error and lag statistics reject the null hypothesis, the robust LM test can be applied. When both robust LM error and lag test statistics are highly significant, the analysis may select the model with the larger value of the statistics [52,53,54,55]. …”

Section: Methodsmentioning

confidence: 99%

“…It should be underlined that the choice of spatial lag or error models should not be based on statistical analysis, and it is actual previous theoretical knowledge [54,55]. If the omitted parameters are expected in a study due to unobservable spatially-correlated factors, then SEM models may be suitable.…”

Section: Methodsmentioning

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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“…Treatment for 10 days with beta lactam antibiotic is enough even if the swollen tonsils and lymph nodes may take weeks to return to normal [14]. Scarlet fever attack in Hong Kong in 2011 was resistant to macrolides but still sensitive to beta lactams [15]. The most common associated complication of scarlet fever is Glomerulonephritis.…”

Section: Scarlet Fevermentioning

confidence: 99%

Upper Respiratory Tract Infections: Hidden Complications and Management Plan

Alsaeed¹

2017

JPNC

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The individual type of bacteria that causes bacterial URTI in a previously healthy child depends on: a. Immunization history.b. Previous antimicrobial therapy. AbstractUpper Respiratory Tract Infection (URTI) or acute nasopharyngitis is the most common disease in pediatrics. However, it is not always simple and transient as its causative pathogens are changing and challenging. Local and Systemic Septic complications of bacterial URTI are common and systemic aseptic complications of streptococcal nasopharyngitis are not uncommon. However, there is a great debate about the management plan because of two facts: It is the most common cause of antibiotic use and misuse [1] and all septic and aseptic complications are actually preventable if bacterial URTI was properly treated with antibiotics [2]. The aim of this article is to summarize facts about septic and aseptic complications of URTI and to illustrate the best management plan including wise use of antibiotics to save pediatric patients not only from overtreatment, but also from under treatment.

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Scarlet Fever Epidemic, Hong Kong, 2011

Cited by 57 publications

References 5 publications

Scarlet Fever Upsurge in England and Molecular-Genetic Analysis in North-West London, 2014

Scarlet Fever Upsurge in England and Molecular-Genetic Analysis in North-West London, 2014

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

Upper Respiratory Tract Infections: Hidden Complications and Management Plan

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