6,237 research outputs found

    Vaccine Myths: Setting the Record Straight

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    Despite their standing as one of the most remarkable public health achievements, vaccines have been surrounded by dangerous myths since the development of the smallpox vaccine in the 18th century. In recent decades, with the publication of a fraudulent article linking vaccines to autism, the involvement of celebrities in the debate, and the rise of the internet and social media as sources for information for patients, these myths have become more widespread. This paper reviews four common vaccine myths: vaccines cause autism, vaccines are not safe, too many vaccines are given too soon, and the influenza vaccine is not necessary. For each of these myths, we review the origin and spread of misinformation. The authors then present the scientific evidence against each myth. Extensive research has found no link between vaccines, and particularly the MMR vaccine or the preservative thimerosal, and autism. The U.S. and world health agencies have effective mechanisms in place to review and monitor vaccine safety. These systems have worked to detect and evaluate even rare vaccine adverse events. The recommended vaccine schedule is safe for infants’ immune systems. The flu vaccine is an essential tool in the fight against the seasonal influenza deaths. A consequence of these myths is that parents are choosing to delay or refuse recommended vaccines for themselves and their children. This has resulted in outbreaks of measles, pertussis, H. influenza type b, varicella, and pneumococcal disease in the United States. Unvaccinated and undervaccinated children risk contracting the disease themselves, and pose a risk to their community as herd immunity decreases. It is important to explore and refute the myths leading to decreased vaccination rates, so health care providers and parents can make educated decisions to protect children and ensure public health

    Laypersons' perception of common cold and influenza prevention : a qualitative study in Austria, Belgium and Croatia

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    Background: Common cold and influenza result in an increased number of primary care consultations, significant work/school absences and cause a socio-economic burden. Laypeople's perceptions and knowledge regarding common cold and influenza prevention is poorly understood and under-researched. Objectives: Our study explores laypeople's knowledge of prevention of common cold and influenza across three European countries. Furthermore, it investigates if there is any distinction between prevention activities focussing on reasons impacting the attitude towards influenza vaccination as well as investigating cross-country variation. Methods: In total, 85 semi-structured individual interviews were performed across three European countries (Austria n = 31, Belgium n = 30, Croatia n = 24). Qualitative thematic content analysis was performed. Results: Most participants across all three countries made no distinction between the prevention of the common cold and influenza and referenced the same preventative measures for both conditions. They mainly expressed negative attitudes towards influenza vaccination possibly effective but only intended for high-risk groups (bedridden/older people, chronic patients or health workers). There were very few cross-country differences in results. Conclusion: The perception of health risk of contracting influenza and a primary healthcare physicians' recommendation played an important role in shaping participants' decisions towards vaccination. Primary healthcare physicians are invited to assess and if necessary adjust inappropriate prevention behaviour through their everyday patient consultations as well as add to the knowledge about influenza severity and influenza vaccination benefits to their patients

    Impact of information on intentions to vaccinate in a potential epidemic: swine-origin Influenza A (H1N1)

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    Vaccination campaigns to prevent the spread of epidemics are successful only if the targeted populations subscribe to the recommendations of health authorities. However, because compulsory vaccination is hardly conceivable in modern democracies, governments need to convince their populations through efficient and persuasive information campaigns. In the context of the swine-origin A (H1N1) 2009 pandemic, we use an interactive study among the general public in the South of France, with 175 participants, to explore what type of information can induce change in vaccination intentions at both aggregate and individual levels. We find that individual attitudes to vaccination are based on rational appraisal of the situation, and that it is information of a purely scientific nature that has the only significant positive effect on intention to vaccinate.France; experiment; interactive; information; vaccination; influenza A (H1N1); attitudes

    A Comparative Analysis of Influenza Vaccination Programs

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    The threat of avian influenza and the 2004-2005 influenza vaccine supply shortage in the United States has sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus. We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high risk populations and morbidity-based that target high prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions. We find that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies. If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities

    Impact of information on intentions to vaccinate in a potential epidemic : swine-origin Influenza A (H1N1)

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    Vaccination campaigns to prevent the spread of epidemics are successful only if the targeted populations subscribe to the recommendations of health authorities. However, because compulsory vaccination is hardly conceivable in modern democracies, governments need to convince their populations through efficient and persuasive information campaigns. In the context of the swine-origin A (H1N1) 2009 pandemic, we use an interactive study among the general public in the South of France, with 175 participants, to explore what type of information can induce change in vaccination intentions at both aggregate and individual levels. We find that individual attitudes to vaccination are based on rational appraisal of the situation, and that it is information of a purely scientific nature that has the only significant positive effect on intention to vaccinate.France, experiment, interactive, information, vaccination, influenza A (H1N1), attitudes.

    Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining

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    The aim of this study was to compare impressions of COVID-19 vaccination and influenza vaccination in Japan by analyzing social media (Twitter®) using a text-mining method. We obtained 10,000 tweets using the keywords “corona vaccine” and “influenza vaccine” on 15 December 2022 and 19 February 2023. We then counted the number of times the words were used and listed frequency of these words by a text-mining method called KH Coder. We also investigated concepts in the data using groups of words that often appeared together or groups of documents that contained the same words using multi-dimensional scaling (MDS). “Death” in relation to corona vaccine and “severe disease” for influenza vaccine were frequently used on 15 December 2022. The number of times the word “death” was used decreased, “after effect” was newly recognized for corona vaccine, and “severe disease” was not used in relation to influenza vaccine. Through this comprehensive analysis of social media data, we observed distinct variations in public perceptions of corona vaccination and influenza vaccination in Japan. These findings provide valuable insights for public health authorities and policymakers to better understand public sentiment and tailor their communication strategies accordingly

    Comparison of contact patterns relevant for transmission of respiratory pathogens in Thailand and the Netherlands using respondent-driven sampling

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    Understanding infection dynamics of respiratory diseases requires the identification and quantification of behavioural, social and environmental factors that permit the transmission of these infections between humans. Little empirical information is available about contact patterns within real-world social networks, let alone on differences in these contact networks between populations that differ considerably on a socio-cultural level. Here we compared contact network data that were collected in the Netherlands and Thailand using a similar online respondent-driven method. By asking participants to recruit contact persons we studied network links relevant for the transmission of respiratory infections. We studied correlations between recruiter and recruited contacts to investigate mixing patterns in the observed social network components. In both countries, mixing patterns were assortative by demographic variables and random by total numbers of contacts. However, in Thailand participants reported overall more contacts which resulted in higher effective contact rates. Our findings provide new insights on numbers of contacts and mixing patterns in two different populations. These data could be used to improve parameterisation of mathematical models used to design control strategies. Although the spread of infections through populations depends on more factors, found similarities suggest that spread may be similar in the Netherlands and Thailand

    Comparison of Voluntary versus Mandatory Vaccine Discussions in Online Health Communities: A Text Analytics Approach

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    Vaccines are vital health interventions. However, they are controversial and some people support them while others reject them. Social media discussion and big data are a rich source to understand people’s insights about different vaccines and the related topics that concern most of them. This study aims to explore the online discussions about mandatory and voluntary vaccines using text analysis techniques. Reddit social platform is popular in online health discussion and thus data from Reddit is analyzed. The results show that different aspects are discussed for different types of vaccines. The discussion of mandatory vaccines is more interactive and is focused on the risks associated with them. Voluntary vaccines’ discussion is focused on their effectiveness and whether to get them or not. The study have important implications for health agencies and researchers as well as for healthcare providers and caregivers

    Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

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    Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

    Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

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    Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201
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