Assessing the vaccine effectiveness for hand, foot, and mouth disease in Guangzhou, China: a time-series analysis

AbstractFebrile seizure (FS) in children is a common complication of infections with respiratory viruses and hand, foot and mouth disease (HFMD). We conducted a retrospective ecological time-series analysis to determine the temporal relationship between hospital attendances for FS and HFMD or respiratory virus infections. Epilepsy attendance was used as a control. Data from 2004 to 2012 FS and epilepsy hospital attendance, HFMD notifications to the Ministry of Health and from laboratory-confirmed viral respiratory infections among KK Women's and Children's Hospital inpatients were used. A multivariate linear regression analysis was conducted to evaluate the relationship between FS and the virus time series. Relative risks of FS by age were calculated using Bayesian statistical methods. Paediatric accident and emergency (A&E) attendances for FS were found to be associated with influenza A (extra 0.47 FS per influenza A case), B (extra 0.32 per influenza B case) and parainfluenza 3 (extra 0.35 per parainfluenza type 3 case). However, other viruses were not significantly associated with FS. None of the viruses were associated with epileptic seizure attendance. Influenza A, B and parainfluenza 3 viruses contributed to the burden of FS resulting in A&E attendance. Children at risk of FS should be advised to receive seasonal influenza vaccination.

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Short-term effects of meteorological factors on pediatric hand, foot, and mouth disease in Guangdong, China: a multi-city time-series analysis

BMC Infectious Diseases ◽

10.1186/s12879-016-1846-y ◽

2016 ◽

Vol 16 (1) ◽

Cited By ~ 31

Author(s):

Cui Guo ◽

Jun Yang ◽

Yuming Guo ◽

Qiao-Qun Ou ◽

Shuang-Quan Shen ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Meteorological Factors ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Short Term ◽

Series Analysis ◽

Foot And Mouth ◽

Short Term Effects

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The application of time series analysis to determine the pattern of foot-and-mouth disease in cattle in Paraguay

Preventive Veterinary Medicine ◽

10.1016/0167-5877(82)90005-8 ◽

1982 ◽

Vol 1 (1) ◽

pp. 27-36 ◽

Cited By ~ 9

Author(s):

Eduardo Alvarez Peralta ◽

Tim E. Carpenter ◽

Thomas B. Farver

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Series Analysis ◽

Foot And Mouth

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Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

Epidemiology and Infection ◽

10.1017/s095026881800362x ◽

2019 ◽

Vol 147 ◽

Cited By ~ 4

Author(s):

C. W. Tian ◽

H. Wang ◽

X. M. Luo

Keyword(s):

Time Series ◽

Seasonal Variation ◽

Moving Average ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Annual Peak ◽

Time Series Modelling ◽

Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

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Early Evidence of Inactivated Enterovirus 71 Vaccine Impact Against Hand, Foot, and Mouth Disease in a Major Center of Ongoing Transmission in China, 2011–2018: A Longitudinal Surveillance Study

Clinical Infectious Diseases ◽

10.1093/cid/ciz1188 ◽

2019 ◽

Cited By ~ 4

Author(s):

Jennifer R Head ◽

Philip A Collender ◽

Joseph A Lewnard ◽

Nicholas K Skaff ◽

Ling Li ◽

...

Keyword(s):

Time Series ◽

Incidence Rate ◽

Enterovirus 71 ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Asia Pacific ◽

Full Time ◽

Study Region ◽

Foot And Mouth ◽

Vaccine Impact

Abstract Background Enterovirus 71 (EV71) is a major causative agent of hand, foot, and mouth disease (HFMD), associated with severe manifestations of the disease. Pediatric immunization with inactivated EV71 vaccine was initiated in 2016 in the Asia-Pacific region, including China. We analyzed a time series of HFMD cases attributable to EV71, coxsackievirus A16 (CA16), and other enteroviruses in Chengdu, a major transmission center in China, to assess early impacts of immunization. Methods Reported HFMD cases were obtained from China’s notifiable disease surveillance system. We compared observed postvaccination incidence rates during 2017–2018 with counterfactual predictions made from a negative binomial regression and a random forest model fitted to prevaccine years (2011–2015). We fit a change point model to the full time series to evaluate whether the trend of EV71 HFMD changed following vaccination. Results Between 2011 and 2018, 279 352 HFMD cases were reported in the study region. The average incidence rate of EV71 HFMD in 2017–2018 was 60% (95% prediction interval [PI], 41%–72%) lower than predicted in the absence of immunization, corresponding to an estimated 6911 (95% PI, 3246–11 542) EV71 cases averted over 2 years. There were 52% (95% PI, 42%–60%) fewer severe HFMD cases than predicted. However, the incidence rate of non-CA16 and non-EV71 HFMD was elevated in 2018. We identified a significant decline in the trend of EV71 HFMD 4 months into the postvaccine period. Conclusions We provide the first real-world evidence that programmatic vaccination against EV71 is effective against childhood HFMD and present an approach to detect early vaccine impact or intended consequences from surveillance data.

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