JMIR infodemiology最新文献

{"title":"Development of a Medical Social Media Ethics Scale and Assessment of #IRad, #CardioTwitter, and #MedTwitter Posts: Mixed Methods Study.","authors":"Vongai Christine Mlambo, Eric Keller, Caroline Mussatto, Gloria Hwang","doi":"10.2196/47770","DOIUrl":"10.2196/47770","url":null,"abstract":"<p><strong>Background: </strong>Social media posts by clinicians are not bound by the same rules as peer-reviewed publications, raising ethical concerns that have not been extensively characterized or quantified.</p><p><strong>Objective: </strong>We aim to develop a scale to assess ethical issues on medical social media (SoMe) and use it to determine the prevalence of these issues among posts with 3 different hashtags: #MedTwitter, #IRad, and #CardioTwitter.</p><p><strong>Methods: </strong>A scale was developed based on previous descriptions of professionalism and validated via semistructured cognitive interviewing with a sample of 11 clinicians and trainees, interrater agreement, and correlation of 100 posts. The final scale assessed social media posts in 6 domains. This was used to analyze 1500 Twitter posts, 500 each from the 3 hashtags. Analysis of posts was limited to original Twitter posts in English made by health care professionals in North America. The prevalence of potential issues was determined using descriptive statistics and compared across hashtags using the Fisher exact and χ² tests with Yates correction.</p><p><strong>Results: </strong>The final scale was considered reflective of potential ethical issues of SoMe by participants. There was good interrater agreement (Cohen κ=0.620, P<.01) and moderate to strong positive interrater correlation (=0.602, P<.001). The 6 scale domains showed minimal to no interrelation (Cronbach α=0.206). Ethical concerns across all hashtags had a prevalence of 1.5% or less except the conflict of interest concerns on #IRad, which had a prevalence of 3.6% (n=18). Compared to #MedTwitter, posts with specialty-specific hashtags had more patient privacy and conflict of interest concerns.</p><p><strong>Conclusions: </strong>The SoMe professionalism scale we developed reliably reflects potential ethical issues. Ethical issues on SoMe are rare but important and vary in prevalence across medical communities.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e47770"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11007602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140295528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Government-Nongovernmental Organization (NGO) Collaboration in Macao's COVID-19 Vaccine Promotion: Social Media Case Study.","authors":"Xuechang Xian, Rostam J Neuwirth, Angela Chang","doi":"10.2196/51113","DOIUrl":"10.2196/51113","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic triggered unprecedented global vaccination efforts, with social media being a popular tool for vaccine promotion.</p><p><strong>Objective: </strong>This study probes into Macao's COVID-19 vaccine communication dynamics, with a focus on the multifaceted impacts of government agendas on social media.</p><p><strong>Methods: </strong>We scrutinized 22,986 vaccine-related Facebook posts from January 2020 to August 2022 in Macao. Using automated content analysis and advanced statistical methods, we unveiled intricate agenda dynamics between government and nongovernment entities.</p><p><strong>Results: </strong>\"Vaccine importance\" and \"COVID-19 risk\" were the most prominent topics co-occurring in the overall vaccine communication. The government tended to emphasize \"COVID-19 risk\" and \"vaccine effectiveness,\" while regular users prioritized vaccine safety and distribution, indicating a discrepancy in these agendas. Nonetheless, the government has limited impact on regular users in the aspects of vaccine importance, accessibility, affordability, and trust in experts. The agendas of government and nongovernment users intertwined, illustrating complex interactions.</p><p><strong>Conclusions: </strong>This study reveals the influence of government agendas on public discourse, impacting environmental awareness, public health education, and the social dynamics of inclusive communication during health crises. Inclusive strategies, accommodating public concerns, and involving diverse stakeholders are paramount for effective social media communication during health crises.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e51113"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.","authors":"Ryuichiro Ueda, Feng Han, Hongjian Zhang, Tomohiro Aoki, Katsuhiko Ogasawara","doi":"10.2196/57880","DOIUrl":"10.2196/57880","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/37881.].</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e57880"},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Social Listening for Digital Public Health Surveillance of Human Papillomavirus Vaccine Misinformation Online: Exploratory Study.","authors":"Dannell Boatman, Abby Starkey, Lori Acciavatti, Zachary Jarrett, Amy Allen, Stephenie Kennedy-Rea","doi":"10.2196/54000","DOIUrl":"10.2196/54000","url":null,"abstract":"<p><p>Despite challenges related to the data quality, representativeness, and accuracy of artificial intelligence-driven tools, commercially available social listening platforms have many of the attributes needed to be used for digital public health surveillance of human papillomavirus vaccination misinformation in the online ecosystem.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e54000"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10960215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140061465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Social Media in Knowledge, Perceptions, and Self-Reported Adherence Toward COVID-19 Prevention Guidelines: Cross-Sectional Study.","authors":"Camryn Garrett, Shan Qiao, Xiaoming Li","doi":"10.2196/44395","DOIUrl":"10.2196/44395","url":null,"abstract":"<p><strong>Background: </strong>Throughout the COVID-19 pandemic, social media has served as a channel of communication, a venue for entertainment, and a mechanism for information dissemination.</p><p><strong>Objective: </strong>This study aims to assess the associations between social media use patterns; demographics; and knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines, due to growing and evolving social media use.</p><p><strong>Methods: </strong>Quota-sampled data were collected through a web-based survey of US adults through the Qualtrics platform, from March 15, 2022, to March 23, 2022, to assess covariates (eg, demographics, vaccination, and political affiliation), frequency of social media use, social media sources of COVID-19 information, as well as knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines. Three linear regression models were used for data analysis.</p><p><strong>Results: </strong>A total of 1043 participants responded to the survey, with an average age of 45.3 years, among which 49.61% (n=515) of participants were men, 66.79% (n=696) were White, 11.61% (n=121) were Black or African American, 13.15% (n=137) were Hispanic or Latino, 37.71% (n=382) were Democrat, 30.21% (n=306) were Republican, and 25% (n=260) were not vaccinated. After controlling for covariates, users of TikTok (β=-.29, 95% CI -0.58 to -0.004; P=.047) were associated with lower knowledge of COVID-19 guidelines, users of Instagram (β=-.40, 95% CI -0.68 to -0.12; P=.005) and Twitter (β=-.33, 95% CI -0.58 to -0.08; P=.01) were associated with perceiving guidelines as strict, and users of Facebook (β=-.23, 95% CI -0.42 to -0.043; P=.02) and TikTok (β=-.25, 95% CI -0.5 to -0.009; P=.04) were associated with lower adherence to the guidelines (R² 0.06-0.23).</p><p><strong>Conclusions: </strong>These results allude to the complex interactions between online and physical environments. Future interventions should be tailored to subpopulations based on their demographics and social media site use. Efforts to mitigate misinformation and implement digital public health policy must account for the impact of the digital landscape on knowledge, perceptions, and level of adherence toward prevention guidelines for effective pandemic control.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e44395"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10907931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.","authors":"Ryuichiro Ueda, Feng Han, Hongjian Zhang, Tomohiro Aoki, Katsuhiko Ogasawara","doi":"10.2196/37881","DOIUrl":"10.2196/37881","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan's early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions.</p><p><strong>Objective: </strong>This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period.</p><p><strong>Methods: </strong>We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords \"corona,\" \"COVID-19,\" and \"new pneumonia\" from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups.</p><p><strong>Results: </strong>The analysis revealed a mix of positive and negative sentiments, with \"joy\" significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as \"worry\" and \"disgust,\" were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group.</p><p><strong>Conclusions: </strong>This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e37881"},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10849083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study.","authors":"Shuhua Yin, Shi Chen, Yaorong Ge","doi":"10.2196/49756","DOIUrl":"10.2196/49756","url":null,"abstract":"<p><strong>Background: </strong>Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC's social media communications and the actual epidemic metrics to improve public health agencies' communication strategies during health emergencies.</p><p><strong>Objective: </strong>This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC's digital health communication strategies for future health emergencies.</p><p><strong>Methods: </strong>Two types of data were collected: (1) a total of 17,524 COVID-19-related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19-related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data.</p><p><strong>Results: </strong>Four major topics from the CDC's COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC's topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data.</p><p><strong>Conclusions: </strong>Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. ","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e49756"},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10848128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139522381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Social Media to Express and Manage Medical Uncertainty in Dyskeratosis Congenita: Content Analysis.","authors":"Emily Pearce, Hannah Raj, Ngozika Emezienna, Melissa B Gilkey, Allison J Lazard, Kurt M Ribisl, Sharon A Savage, Paul Kj Han","doi":"10.2196/46693","DOIUrl":"10.2196/46693","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Social media has the potential to provide social support for rare disease communities; however, little is known about the use of social media for the expression of medical uncertainty, a common feature of rare diseases.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the expression of medical uncertainty on social media in the context of dyskeratosis congenita, a rare cancer-prone inherited bone marrow failure and telomere biology disorder (TBD).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We performed a content analysis of uncertainty-related posts on Facebook and Twitter managed by Team Telomere, a patient advocacy group for this rare disease. We assessed the frequency of uncertainty-related posts, uncertainty sources, issues, and management and associations between uncertainty and social support.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Across all TBD social media platforms, 45.98% (1269/2760) of posts were uncertainty related. Uncertainty-related posts authored by Team Telomere on Twitter focused on scientific (306/434, 70.5%) or personal (230/434, 53%) issues and reflected uncertainty arising from probability, ambiguity, or complexity. Uncertainty-related posts in conversations among patients and caregivers in the Facebook community group focused on scientific (429/511, 84%), personal (157/511, 30.7%), and practical (114/511, 22.3%) issues, many of which were related to prognostic unknowns. Both platforms suggested uncertainty management strategies that focused on information sharing and community building. Posts reflecting response-focused uncertainty management strategies (eg, emotional regulation) were more frequent on Twitter compared with the Facebook community group (χ&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;1&lt;/sub&gt;=3.9; P=.05), whereas posts reflecting uncertainty-focused management strategies (eg, ordering information) were more frequent in the Facebook community group compared with Twitter (χ&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;1&lt;/sub&gt;=55.1; P&lt;.001). In the Facebook community group, only 36% (184/511) of members created posts during the study period, and those who created posts did so with a low frequency (median 3, IQR 1-7 posts). Analysis of post creator characteristics suggested that most users of TBD social media are White, female, and parents of patients with dyskeratosis congenita.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Although uncertainty is a pervasive and multifactorial issue in TBDs, our findings suggest that the discussion of medical uncertainty on TBD social media is largely limited to brief exchanges about scientific, personal, or practical issues rather than ongoing supportive conversation. The nature of uncertainty-related conversations also varied by user group: patients and caregivers used social media primarily to discuss scientific uncertainties (eg, regarding prognosis), form social connections, or exchange advice on accessing and organizing medical care, whereas Team Telomere used social media to express scientific and personal issu","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"4 ","pages":"e46693"},"PeriodicalIF":3.5,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10825764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139467364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study.","authors":"Nekabari Sigalo, Vanessa Frias-Martinez","doi":"10.2196/43700","DOIUrl":"10.2196/43700","url":null,"abstract":"<p><strong>Background: </strong>Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect.</p><p><strong>Objective: </strong>This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data.</p><p><strong>Methods: </strong>COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS.</p><p><strong>Results: </strong>The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey.</p><p><strong>Conclusions: </strong>These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e43700"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring SARS-CoV-2 Using Infoveillance, National Reporting Data, and Wastewater in Wales, United Kingdom: Mixed Methods Study.","authors":"Jordan P Cuff, Shrinivas Nivrutti Dighe, Sophie E Watson, Rafael A Badell-Grau, Andrew J Weightman, Davey L Jones, Peter Kille","doi":"10.2196/43891","DOIUrl":"10.2196/43891","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized.</p><p><strong>Objective: </strong>This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers.</p><p><strong>Methods: </strong>Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models.</p><p><strong>Results: </strong>Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time.</p><p><strong>Conclusions: </strong>Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":"e43891"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}