Influenza vaccination rate and its association with chronic diseases in China: Results of a national cross-sectional study

Fan, Jing; Cong, S; Wang, Ning; Bao, Heling; Wang, Baohua; Feng, Yanping; Lv, Xueli; Zhang, Yang; Zha, Zhenqiu; Yu, Lei; Yang, Ting; Wang, Lınhong; Fang, Liwen

doi:10.1016/j.vaccine.2020.01.093

Cited by 61 publications

(48 citation statements)

References 28 publications

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“…It is estimated that annual influenza vaccination coverage is 3.8% (95% CI 1.6-5.9%) in the population aged !60 years in China (Fan et al, 2020), which is far behind the coverage in the USA and Europe (Smetana et al, 2018). Great efforts should be made to increase the uptake of influenza vaccine in the older population in China, as the excess mortality and years of life lost are comparable between the Chinese population and Western population.…”

Section: Yearmentioning

confidence: 99%

Influenza-attributable years of life lost in older adults in a subtropical city in China, 2012–2017: A modeling study based on a competing risks approach

Hao

Huang

Liu

et al. 2020

International Journal of Infectious Diseases

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The aim of this study was to estimate influenza-attributable years of life lost (YLL) in older adults in subtropical Hefei, China during the years 2012-2017, based on a competing risks approach. Methods: The quasi-Poisson model was fitted to weekly numbers of all-cause deaths by 5-year age groups for older adults !60 years of age. The product of the weekly influenza-like illness consultation rate and the proportion of specimens that tested positive for influenza was taken as the measurement of influenza activity, which was incorporated into the model as an exploratory variable. Excess deaths associated with influenza were calculated by subtracting baseline deaths (setting influenza activity to zero) from fitted deaths. Influenza-attributable YLL accounting for competing risks was estimated using restricted mean lifetime survival analysis. Results: The annual influenza-attributable YLL was highest in the 75-79 years age group (565 per 100,000 persons, 95% confidence interval 550-580), followed by the 80-84, 70-74, 85-89, 65-69, and 60-64 years age groups. Influenza A(H3N2) virus was associated with higher YLL than A(H1N1) and B viruses. Influenza-attributable YLL accounted for 1.03-1.53% of total YLL, and the proportion would be overestimated to 2.91-7.34% if the traditional Kaplan-Meier method ignoring competing risks was used. Conclusions: Although influenza-associated mortality increased with age, influenza-attributable YLL was found to be highest in the 75-79 years age group.

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Section: Yearmentioning

confidence: 99%

Influenza-attributable years of life lost in older adults in a subtropical city in China, 2012–2017: A modeling study based on a competing risks approach

Hao

Huang

Liu

et al. 2020

International Journal of Infectious Diseases

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“…Another potential reason for the counterintuitive result is related to the vaccine rollout process in China. Rather than first vaccinating the most vulnerable, 18–59-year-old adults were the first to be vaccinated [ 33 ], giving the impression that elderly people and those who are sick should not be vaccinated. These may be the reasons for the decreasing vaccination rate for participants with poor subjective health status and chronic diseases.…”

Section: Discussionmentioning

confidence: 99%

Subjective health status: an easily available, independent, robust and significant predictive factor at the prometaphase of vaccination programs for the vaccination behavior of Chinese adults

et al. 2022

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Background The World Health Organization (WHO) proposed COVID-19 vaccination as an emergent and important method to end the COVID-19 pandemic. Since China started vaccination programs in December 2020, vaccination has spread to provinces and municipalities nationwide. Previous research has focused on people's vaccination willingness and its influencing factors but has not examined vaccination behavior. We examine the effectiveness of psychosocial factors in predicting vaccination behavior. Methods A cross-sectional online survey was performed among Chinese adults on 8 May and 4 June 2021. The statistical analysis of the data included univariate analysis, receiver operator characteristics (ROC) analysis and ordinal multiclassification logistic regression model analysis. Results Of the 1300 respondents, 761 (58.5%) were vaccinated. Univariate analysis showed that a high education level and good subjective health status were protective factors for vaccination behavior, while suffering from chronic diseases was a risk factor. ROC analysis showed that subjective health status (AUC = 0.625, 95% CI: 0.594–0.656, P < 0.001) was the best predictor of vaccination behavior. Logistic regression analysis with subjective health status as a dependent variable indicated that older age, female sex, depression, neurasthenia, obsession, hypochondriasis and chronic disease were significant risk factors, while positive coping tendencies were a significant protective factor. Conclusion Our study found a simple and effective marker, subjective health status, that can predict vaccination behavior. This finding can guide future epidemic prevention work.

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“…Further, testing was expanded state-by-state to include all symptomatic patients as well as asymptomatic contacts of con rmed cases. In early March-April 2020, many cases were overlooked due to limited testing availability and testing algorithms (19).…”

Section: Review Expansion Of Sars-cov-2mentioning

confidence: 99%

“…Till date, there is no primary study on COVID-19 transmission dynamics and clinical outcome is available to validate these models and inform intervention strategies (16)(17)(18). The phylogenetic tree and Entropy plot are used to show the strain variation with root difference or Boot strip changes among various nucleotide plots with mutational changes (19).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Study of COVID-19 Disease Severity in India for the Genetic Variation and Prevention of Disease Progression

Datt

Akhtar

Datt

et al. 2021

Preprint

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Introduction:Coronaviruses (CoV) have been responsible for three epidemics in the 21st century, including Severe Acute Respiratory Syndrome (SARS) in 2003. Corona virus is a single strand ribonucleic acid which is genotypically and phenotypically diverse.Method:The phylogenetic tree and Entropy plot are used to show the strain variation with root difference or Boot strip changes among various nucleotide plots with mutational changes (19).Therefore, we would like to explain evolutionary changes in other countries compared to India and in different states of India. To evaluate the strain variation with their mutation and adaptation of the environment with the virus or may be due to several passages occurring over a period of time.Results:For phylogenetic tree analysis, we have selected 8-8 isolates from each state, Assam, Chhattisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Orissa, Maharashtra, Punjab, Telangana, Uttar Pradesh and West Bengal. Total 74 isolates were selected from given states for making of phylogenetic tree and mutational study. There were many point mutations found.Discussion:According to our study, we found a several point mutation occurred among the sequence submitted in gene bank database that is same as China which has been reported. This study was conducted to systematic analysis on the genomic data of NCBI. This work is also significant in terms of mutational research. Evolutionary changes and approach a new strain through the mutational study done by BioEdit and MEGA 6.0 software. The bioinformatics and the statistical genetic analysis may contribute for study of COVID-19 as other infection and complex disease. This study is used understand the host–pathogen interaction and to contain the COVID-19 outbreak.Conclusion:This study highlighted the evolutionary changes occurred in the COVID-19 disease severity variation in strain for the prevention and mutation. We reported the genetic study of COVID-19 disease with their association in already submitted strain of database for the study of their genetic variation as well as disease progression.

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Influenza vaccination rate and its association with chronic diseases in China: Results of a national cross-sectional study

Cited by 61 publications

References 28 publications

Influenza-attributable years of life lost in older adults in a subtropical city in China, 2012–2017: A modeling study based on a competing risks approach

Influenza-attributable years of life lost in older adults in a subtropical city in China, 2012–2017: A modeling study based on a competing risks approach

Subjective health status: an easily available, independent, robust and significant predictive factor at the prometaphase of vaccination programs for the vaccination behavior of Chinese adults

Evolutionary Study of COVID-19 Disease Severity in India for the Genetic Variation and Prevention of Disease Progression

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