The relationship between meteorological factors and mumps incidence in Guangzhou, China, 2005–2012:

Yang, Qiongying; Yang, Zhicong; Ding, Haiyuan; Xiao, Zhang; Dong, Zhiqiang; Hu, Wensui; Liu, Xiangyi; Wang, Ming; Hu, Guifang; Fu, Chuanxi

doi:10.4161/hv.29286

Cited by 41 publications

(62 citation statements)

References 41 publications

(50 reference statements)

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“…There are more male patients than female. Prior studies have shown that males have a higher morbidity rate than females6. During 2005–2014, mumps cases aged 5–9 years old increased-more than 40% of the overall cases.…”

Section: Discussionmentioning

confidence: 96%

Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005–2014

Cheng

Luo

et al. 2017

Sci Rep

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Mumps presents a serious threat to public health in China. We conducted a descriptive analysis to identify the epidemiological characteristics of mumps in Shandong Province. Spatial autocorrelation and space-time scan analyses were utilized to detect spatial-temporal clusters. From 2005 to 2014, 115745 mumps cases were reported in Shandong, with an average male-to-female ratio of 1.94. Mumps occurred mostly in spring (32.17% of all reported cases) and in children aged 5 to 9 (40.79% of all reported cases). The Moran’s I test was significant and local indicators of spatial autocorrelation (LISA) analysis revealed significant spatial clusters with high incidence. The results showed that the mid-west of Shandong Province and some coastal regions (Qingdao City and Weihai City) were high-risk areas, particularly in the center of the Jining City and the junction of Dongying City, Binzhou City and Zibo City. The results could assist local and national public health agencies in formulating better public health strategic planning and resource allocation.

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Section: Discussionmentioning

confidence: 96%

Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005–2014

Cheng

Luo

et al. 2017

Sci Rep

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“…where Y t is the number of EDVDBs on day t; α is the intercept; cb is the "cross-basis" function for generating bi-dimensional exposure-lagresponse relationship with 3 degrees of freedom (df) for the exposure and lag spaces, respectively (Yang et al, 2014a;Yang et al, 2014b); Temp t is the mean temperature on day t; "lag = 14" refers a maximum lag of 14 days being used to present the lagged effect of temperature; ns(Time, df = 7 * 3) is the natural cubic spline of time with 7 df per year for controlling the seasonality and long-term trend as suggested by previous studies (Goldberg et al, 2011;Guo et al, 2014;Muggeo and Hajat, 2009); DOW t, is a categorical variable for adjusting day of the week on day t; Holiday t is a binary variable for adjusting public holidays in China; τ t is an autoregressive term for explaining the autocorrelations of the residuals by using the log scale of daily EDVDBs at lag days; autocorrelation function is used to determine which lag days will be added into τ t ; β, γ and ε are the coefficients. In this study, the relative risk (RR) of EDVDBs associated with temperature was calculated by setting the reference value at the minimum-EDVDB temperature.…”

Section: Discussionmentioning

confidence: 99%

Are hospital emergency department visits due to dog bites associated with ambient temperature? A time-series study in Beijing, China

Zhang

Zhao

Zhang³

et al. 2017

Science of The Total Environment

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“…However, our investigation found that low atmospheric pressure protected preschool children aged 0‐6 years old. Guangdong is a coastal province frequented by tropical cyclones, mostly typhoons, which bring low pressure and rich rainfall . Low pressure can also reduce children's outdoor activities and interpersonal body contacts.…”

Section: Discussionmentioning

confidence: 99%

“…The relative risks (RRs) of influenza brought by low‐temperature (cold effect), low relative humidity (dry effect), and low‐pressure (low‐pressure effect) were calculated by comparing the 5 th percentiles to the median value. To estimate the cumulative extreme effects of mean temperature, on the basis of Akaike Information Criterion (AIC) and other references, a maximum lag of 10 days was selected for hot effect, of 27 days for cold effect, for humidity, a maximum lag of 27 days was selected for humid effect, of 20 days for dry effect, and for atmospheric pressure, a maximum lag of 15 days was selected for high‐pressure effect, of 20 days for low‐pressure effect.…”

Section: Methodsmentioning

confidence: 99%

The effects of meteorological factors on influenza among children in Guangzhou, China

Guo

Dong

Zeng

et al. 2018

Influenza Resp Viruses

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Background Influenza seriously affects the health of children, yet little evidence is available on the association between meteorological factors and the occurrence of influenza among children in subtropical regions. The current study aimed to explore the effects of meteorological factors on influenza among children in Guangzhou, a subtropical city in China. Methods The distributed lag nonlinear model ( DLNM ) was used to assess the effects of meteorological factors on children influenza occurrence in Guangzhou, China. Daily number of influenza cases among children aged 0‐17 years from 2013 to 2017 were obtained from the National Information System for Disease Control and Prevention. Results Mean temperature, relative humidity, and atmospheric pressure were associated with influenza cases. The relative risks ( RR s) increased as temperature fell below 20°C. The relationship between relative humidity and influenza cases could be described with a U‐shaped curve, and the RR s increased if relative humidity was lower than 50% or higher than 80%. The risk of influenza increased with rising atmospheric pressure with 1005 hP a as the break point. The cold effect, humid effect, dry effect, high‐pressure effect, and low‐pressure effect showed statistical significance both in female and male. The cold effect increased with age. The humid‐effect affects all age ranges of children, but dry effect mainly affected 4‐14 years old. High‐pressure effect mainly affected the 0‐3 years old, whereas low‐pressure effect protected preschool children aged 0‐6 years old. Conclusion Mean temperature, relative humidity, and atmospheric pressure might be important predictors of the influenza occurrence among children in Guangzhou.

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The relationship between meteorological factors and mumps incidence in Guangzhou, China, 2005–2012:

Cited by 41 publications

References 41 publications

Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005–2014

Epidemiological Characteristics and Spatial-Temporal Clusters of Mumps in Shandong Province, China, 2005–2014

Are hospital emergency department visits due to dog bites associated with ambient temperature? A time-series study in Beijing, China

The effects of meteorological factors on influenza among children in Guangzhou, China

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