Global prevalence and content of information about alcohol use as a cancer risk factor on Twitter.

OBJECTIVES: Alcohol use is a major risk factor for several forms of cancer, though many people have limited knowledge of this link. Public health communicators and cancer advocates desire to increase awareness of this link with the long-term goal of reducing cancer burden. The current study is the first to examine the prevalence and content of information about alcohol use as a cancer risk on social media internationally. METHODS: We used a three-phase process (hashtag search, dictionary-based auto-identification of content, and human coding of content) to identify and evaluate information from Twitter posts between January 2019 and December 2021. RESULTS: Our hashtag search retrieved a large set of cancer-related tweets (N = 1,122,397). The automatic search process using an alcohol dictionary identified a small number of messages about cancer that also mentioned alcohol (n = 9061, 0.8%), a number that got small after adjusting for human coded estimates of the dictionary precision (n = 5927, 0.5%). When cancer-related messages also mentioned alcohol, 82% (n = 1003 of 1225 examined through human coding) indicated alcohol use as a risk factor. Coding found rare instances of problematic information (e.g., promotion of alcohol, misinformation) in messages about alcohol use and cancer. CONCLUSIONS: Few social media messages about cancer types that can be linked to alcohol mention alcohol as a cancer risk factor. If public health communicators and cancer advocates want to increase knowledge and understanding of alcohol use as a cancer risk factor, efforts will need to be made on social media and through other communication platforms to increase exposure to this information over time.

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube.

BACKGROUND: Common methods for extracting content in health communication research typically involve using a set of well-established queries, often names of medical procedures or diseases, that are often technical or rarely used in the public discussion of health topics. Although these methods produce high recall (ie, retrieve highly relevant content), they tend to overlook health messages that feature colloquial language and layperson vocabularies on social media. Given how such messages could contain misinformation or obscure content that circumvents official medical concepts, correctly identifying (and analyzing) them is crucial to the study of user-generated health content on social media platforms. OBJECTIVE: Health communication scholars would benefit from a retrieval process that goes beyond the use of standard terminologies as search queries. Motivated by this, this study aims to put forward a search term identification method to improve the retrieval of user-generated health content on social media. We focused on cancer screening tests as a subject and YouTube as a platform case study. METHODS: We retrieved YouTube videos using cancer screening procedures (colonoscopy, fecal occult blood test, mammogram, and pap test) as seed queries. We then trained word embedding models using text features from these videos to identify the nearest neighbor terms that are semantically similar to cancer screening tests in colloquial language. Retrieving more YouTube videos from the top neighbor terms, we coded a sample of 150 random videos from each term for relevance. We then used text mining to examine the new content retrieved from these videos and network analysis to inspect the relations between the newly retrieved videos and videos from the seed queries. RESULTS: The top terms with semantic similarities to cancer screening tests were identified via word embedding models. Text mining analysis showed that the 5 nearest neighbor terms retrieved content that was novel and contextually diverse, beyond the content retrieved from cancer screening concepts alone. Results from network analysis showed that the newly retrieved videos had at least one total degree of connection (sum of indegree and outdegree) with seed videos according to YouTube relatedness measures. CONCLUSIONS: We demonstrated a retrieval technique to improve recall and minimize precision loss, which can be extended to various health topics on YouTube, a popular video-sharing social media platform. We discussed how health communication scholars can apply the technique to inspect the performance of the retrieval strategy before investing human coding resources and outlined suggestions on how such a technique can be extended to other health contexts.

Emergence of GII.Pg norovirus in gastroenteritis outbreaks in Victoria, Australia.

The ORF 1 GII.Pg genotype represents an obligatory recombinant comprising the ORF 1 GII.Pg genotype and a number of ORF 2 genotypes. The emergence, incidence, and molecular features of GII.Pg norovirus have never been considered in detail and are the subject of the current study. Over the period 2002-2013, GII.Pg norovirus was detected in 16 outbreaks in Victoria, Australia. It was first identified in 2009 and thereafter was detected at low level in each year of the study. GII.Pg norovirus outbreaks occurred in both healthcare and non-healthcare settings and could be found in individuals with a broad range of ages. The seasonality of GII.Pg norovirus outbreaks was significantly different from that of all other (non-GII.Pg) norovirus outbreaks. For the 15 GII.Pg norovirus outbreaks where ORF 2 sequencing data were available, two ORF 2 recombinant genotypes were found: GII.1 in 5 (33%) outbreaks and GII.12 in 10 (67%) outbreaks. The ORF 1 phylogenetic tree shows that the GII.Pg ORF 1 genotype fell into two distinct groups. The ORF 2 phylogenetic tree indicates that the GII.1 and GII.12 clusters each corresponded to one of the groups in the ORF 1 tree. This indicates the two recombinant forms were evolving in parallel and not one from the other. Analysis of age data indicates the GII.1 and GII.12 recombinant forms circulated in different ways in the community. J. Med. Virol. 88:1521-1528, 2016. © 2016 Wiley Periodicals, Inc.

Evaluation of the updated RIDA®QUICK (Version N1402) immunochromatographic assay for the detection of norovirus in clinical specimens.

The sensitivity and specificity of the R-Biopharm RIDA(®)QUICK (N1402) immunochromatography assay for norovirus detection was examined using fecal material from Australian gastroenteritis incidents. The study involved the analysis of 3 groups of specimens; group 1 comprised 100 norovirus open reading frame (ORF) 1 RT-PCR positive specimens; group 2 comprised 100 ORF 1 RT-PCR norovirus negative specimens and group 3 comprised 12 specimens containing common gastroenteritis viruses other than norovirus. The RIDA(®)QUICK (N1402) assay detected both GI and GII norovirus and had an overall sensitivity of 87%. Genotype analysis of the capsid region of the genome (ORF 2) indicated the RIDA(®)QUICK (N1402) assay could detect a range of genotypes including GI.1, GI.2, GI.3, GI.4, GI.5, GII.3, GII.4 (including variants GII.4 (2009-like), GII.4 (2012), GII.4 (2012-like) and GII.4 (unknown)), GII.6, GII.13 and GII.21. The assay had good sensitivity for both GI and GII norovirus. The assay had a specificity of 97% and did not cross react with a number of common fecal viruses. However, one of eight rotavirus positive, norovirus negative specimens gave a positive result; rotavirus cannot be taken as the cause of such a false positive but cannot be excluded either. The kit was quick and easy to use and would be valuable in point-of-care testing.

Norovirus genotype diversity associated with gastroenteritis outbreaks in Victoria in 2013.

The noroviruses are now considered a leading cause of outbreaks of non-bacterial gastroenteritis worldwide. Vaccine strategies against norovirus are currently under consideration but depend on a detailed knowledge of the capsid genotypes. This study examined the incidence of norovirus outbreaks in Victoria over 1 year (2013) and documented the genotypes occurring in the different outbreak settings (healthcare and non-healthcare) and age groups. It was found that 63.1% of gastroenteritis outbreaks were associated with norovirus, thereby showing norovirus to be the major viral cause of illness in gastroenteritis outbreaks. Sixteen capsid genotypes were identified and included GI.2, GI.3, GI.4, GI.6, GI.7, GI.8, GI.9, GII.1, GII.2, GII.3, GII.4, GII.5, GII.6, GII.7, GII.13 and an as yet unclassified GII genotype. All genotypes found in the study, with the exception of GI.9, were detected in the elderly, indicating prior exposure to a norovirus genotype did not appear to confer long term immunity in many cases. The incidence of genotypes GII.1, GII.4 and GII.7 was linked with setting and age. As setting and age were correlated it was not possible to determine which variable was critical with the exception of GII.7, which appeared to be linked to age. The findings indicate that norovirus vaccine strategies should encompass a broad range of genotypes and, as setting or age may be important in determining genotype incidence, this should be taken into account as well.

Norovirus genotype diversity in community-based sporadic gastroenteritis incidents: a five-year study.

Although norovirus is a known cause of sporadic gastroenteritis, the incidence and genotypes of norovirus associated with sporadic community-based gastroenteritis are poorly understood. The current study examined this issue by using material from alleged food poisoning incidents in the state of Victoria, Australia, for the period 2008-2012. Norovirus was identified, by either ORF (open reading frame) 1 or ORF 2 RT-PCR methodology, in 159 of 379 (42.0%) sporadic gastroenteritis incidents, thereby showing that norovirus was an important cause of sporadic gastroenteritis. The number of sporadic norovirus incidents did not vary significantly from year to year, indicating that the pool of circulating norovirus remained constant. Norovirus ORF 1 genotypes identified included GI.1, GI.2, GI.3, GI.4, GI.b, GI.d, GII.2, GII.4 (including variants 2006a, 2006b, 2007, and 2009), GII.16, GII.22, GII.b, GII.e, and GII.g. Norovirus ORF 2 genotypes identified included GI.1, GI.2, GI.3, GI.4, GI.6, GII.2, GII.3, GII.4 (variants 2006b, 2009, 2009-like, 2012, and "unknown"), GII.6, GII.7, GII.9, GII.12, and GII.13. Five ORF 1/ORF 2 norovirus recombinant forms were confirmed: GII.b/GII.3, GII.e/GII.4 (2012), GII.e/GII.4 (unknown), GII.g/GII.12 and GII.16/GII.2. Although the incidence of ORF 2 GI.3 was significantly higher in children than in adults, this was not the case for other major ORF 2 genotypes (GII.2, GII.4, and GII.6) which occurred equally in all age groups. The findings demonstrate the importance and diverse nature of norovirus in sporadic community-based gastroenteritis incidents and indicate that the development of successful vaccine strategies may be difficult.