54 research outputs found

    What the Public Was Saying about the H1N1 Vaccine: Perceptions and Issues Discussed in On-Line Comments during the 2009 H1N1 Pandemic

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    During the 2009 H1N1 pandemic, a vaccine was made available to all Canadians. Despite efforts to promote vaccination, the public's intent to vaccinate remained low. In order to better understand the public's resistance to getting vaccinated, this study addressed factors that influenced the public's decision making about uptake. To do this, we used a relatively novel source of qualitative data – comments posted on-line in response to news articles on a particular topic. This study analysed 1,796 comments posted in response to 12 articles dealing with H1N1 vaccine on websites of three major Canadian news sources. Articles were selected based on topic and number of comments. A second objective was to assess the extent to which on-line comments can be used as a reliable data source to capture public attitudes during a health crisis. The following seven themes were mentioned in at least 5% of the comments (% indicates the percentage of comments that included the theme): fear of H1N1 (18.8%); responsibility of media (17.8%); government competency (17.7%); government trustworthiness (10.7%); fear of H1N1 vaccine (8.1%); pharmaceutical companies (7.6%); and personal protective measures (5.8%). It is assumed that the more frequently a theme was mentioned, the more that theme influenced decision making about vaccination. These key themes for the public were often not aligned with the issues and information officials perceived, and conveyed, as relevant in the decision making process. The main themes from the comments were consistent with results from surveys and focus groups addressing similar issues, which suggest that on-line comments do provide a reliable source of qualitative data on attitudes and perceptions of issues that emerge in a health crisis. The insights derived from the comments can contribute to improved communication and policy decisions about vaccination in health crises that incorporate the public's views

    Concordant association of insulin degrading enzyme gene (IDE) variants with IDE mRNA, abeta, and alzheimer's disease.

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    Background: The insulin-degrading enzyme gene (IDE) is a strong functional and positional candidate for late onset Alzheimer's disease (LOAD). Methodology/Principal findings: We examined conserved regions of IDE and its 10 kb flanks in 269 AD cases and 252 controls thereby identifying 17 putative functional polymorphisms. These variants formed eleven haplotypes that were tagged with ten variants. Four of these showed significant association with IDE transcript levels in samples from 194 LOAD cerebella. The strongest, rs6583817, which has not previously been reported, showed unequivocal association (p = 1.5x10(-8), fold-increase = 2.12,); the eleven haplotypes were also significantly associated with transcript levels (global p = 0.003). Using an in vitro dual luciferase reporter assay, we found that rs6583817 increases reporter gene expression in Be(2)-C (p = 0.006) and HepG2 (p = 0.02) cell lines. Furthermore, using data from a recent genome-wide association study of two Croatian isolated populations (n = 1,879), we identified a proxy for rs6583817 that associated significantly with decreased plasma Abeta40 levels (ss = -0.124, p = 0.011) and total measured plasma Abeta levels (b = -0.130, p = 0.009). Finally, rs6583817 was associated with decreased risk of LOAD in 3,891 AD cases and 3,605 controls. (OR = 0.87, p = 0.03), and the eleven IDE haplotypes (global p = 0.02) also showed significant association. Conclusions: Thus, a previously unreported variant unequivocally associated with increased IDE expression was also associated with reduced plasma Ass40 and decreased LOAD susceptibility. Genetic association between LOAD and IDE has been difficult to replicate. Our findings suggest that targeted testing of expression SNPs (eSNPs) strongly associated with altered transcript levels in autopsy brain samples may be a powerful way to identify genetic associations with LOAD that would otherwise be difficult to detect
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