2022
DOI: 10.1016/j.vaccine.2022.09.030
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Effect of different vaccine strategies for the control of Japanese encephalitis in mainland China from 1961 to 2020: A quantitative analysis

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Cited by 8 publications
(18 citation statements)
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References 36 publications
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“…China included JE vaccine in the national immunization program in 2008. Therefore, most people born before 2008 also had not received JE vaccine and their antibody positivity rate for JE was low 10 . In this study, we also observed that the proportion of JE cases in adults aged ≥20 years in Gansu Province began to reverse with JE cases in children since 2013, with the incidence of JE in adults increasing and surpassing the JE incidence of children and adolescents.…”
Section: Discussionsupporting
confidence: 55%
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“…China included JE vaccine in the national immunization program in 2008. Therefore, most people born before 2008 also had not received JE vaccine and their antibody positivity rate for JE was low 10 . In this study, we also observed that the proportion of JE cases in adults aged ≥20 years in Gansu Province began to reverse with JE cases in children since 2013, with the incidence of JE in adults increasing and surpassing the JE incidence of children and adolescents.…”
Section: Discussionsupporting
confidence: 55%
“…In China, Gansu Province is one of the poorest economic regions 14 . Therefore, the coverage rate of JE vaccine in Gansu Province was not very high before China included JE vaccine in the national immunization program in 2008 10 . In addition, Gansu Province has a lot of rural villages and farmers, and the poorer rural sanitation and a large number of farms provide very favorable conditions for the breeding of JEV hosts 15 .…”
Section: Introductionmentioning
confidence: 99%
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“…The pre-intervention time variable was X 1 (taking values of 1, 2, 3, ..., n), the intervention variable was X 2 (0 prior to January 2020, 1 from January 2020), the post-intervention time variable was X 3 (0 prior to January 2020, with values of 0, 1, 2, …, n in order by month from January 2020), and ε was the random error term not explained by the outcome variable. The ITS model was: where β 0 is the rate at the beginning, β 1 represents the trend in the rate before COVID-19, β 2 is the level change in the rate in the short term (within January 2020), which reflects the immediate impact of COVID-19 on the rate, β 3 is the slope change in the rate after the onset of the COVID-19 epidemic (from January 2020 to December 2020), which reflects the long-term impact of COVID-19 on the rate, and β 1 +β 3 represents the trend in the rate after COVID-19 [ 2 , 22 , 32 ].…”
Section: Methodsmentioning
confidence: 99%
“…where β 0 is the rate at the beginning, β 1 represents the trend in the rate before COVID-19, β 2 is the level change in the rate in the short term (within January 2020), which reflects the immediate impact of COVID-19 on the rate, β 3 is the slope change in the rate after the onset of the COVID-19 epidemic (from January 2020 to December 2020), which reflects the long-term impact of COVID-19 on the rate, and β 1 +β 3 represents the trend in the rate after COVID-19 [2,22,32].…”
Section: Interrupted Time Series Designmentioning
confidence: 99%