Infodemiology and Infoveillance: Scoping Review

Tracking Public Interest in Rare Diseases and Eosinophilic Disorders in Germany: Web Search Analysis

Background

Eosinophilia and hypereosinophilic syndrome (HES) are rare disorders grouped under the term hypereosinophilic disorders. They are diagnosed based on an increased number of eosinophils. They can also cause serious symptoms, including skin, lung, and gastrointestinal problems. These disorders are very rarely recognized due to their rarity and misdiagnosis.

Objective

This study analyzes public interest in hypereosinophilic disorders using data on internet search volume in Germany between 2020 and 2023. Objectives include identifying frequently searched terms, evaluating temporal trends, analyzing seasonal patterns, evaluating geographic differences in search behavior, and identifying unmet information needs and frequently searched risk factors.

Methods

A retrospective analysis using Google Ads Keyword Planner gathered monthly search volume data for 12 German terms related to hypereosinophilic disorders. These terms were selected based on their medical relevance and common usage identified from medical literature. Data were analyzed descriptively, with trends, seasonal variations, and geographical distributions examined. Chi-square tests and correlation analysis assessed statistical significance.

Results

A total of 178 keywords were identified, resulting in a search volume of 1,745,540 queries. The top keyword was "eosophile," a misspelling, followed by "eosinophilia" and "HES." The main categories included "Eosinophilia," "Eosinophils," and "Churg-Strauss syndrome." Temporal analysis showed seasonal growth in search volumes, peaking in January 2023, with higher interest during winter. Geographical analysis showed regional variations.

Conclusions

This research shows a growing public interest in eosinophilic diseases, reflected by a steadily increasing search volume over time. This is particularly evident in searches for basic definitions and diagnostic criteria, such as "eosinophils" or "symptoms of eosinophilic diseases." This increase in search volume, which peaked in January 2023, indicates an increased interest in accurate and readily available information for rare conditions.

Exploring home fall events among infants and toddlers using social media information: an infodemiology study in China

BACKGROUND

Practical interventions of fall prevention are challenging for infants and toddlers. This study aimed to explore specific details of falls that occurred at home for kids 0-3 years old using key information from social media platforms, which provided abundant data sources for fall events.

METHODS

We used internet-based search techniques to collect fall events information from 2013 to 2023. The search was restricted and implemented between 1 and 12 April 2023. Online platforms included Baidu, Weibo, WeChat, TikTok, Toutiao and Little Red Book. A qualitative descriptive approach was used to analyse the fall events and major factors, including the fall event time, child age, environmental factors and behavioural characteristics of children and caregivers.

RESULTS

We identified 1005 fall injury cases among infants and toddlers. Fall mechanisms included falls from household furniture (71.2%), falls from height (21.4%) and falls on the same level (7.4%). Environmental risk factors mainly consisted of not using or installing bed rails incorrectly, a gap between beds, unstable furniture, slippery ground and windows without guardrails. Behavioural factors included caregivers leaving a child alone, lapsed attention, turning around to retrieve something, misusing baby products, inadequately holding the child and falling asleep with children. Child behavioural factors included walking or running while holding an object in hand or mouth and underdeveloped walking skills.

CONCLUSION

Interventions for preventing falls should be designed specifically for Chinese families, especially considering family function in the context of Chinese culture. Social media reports could provide rich information for researchers.

AD-AutoGPT: An autonomous GPT for Alzheimer's disease infodemiology

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT, which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022, leading to the autonomous execution of robust trend analyses, intertopic distance map visualization, and identification of salient terms pertinent to Alzheimer's Disease. This approach has yielded not only a quantifiable metric of relevant discourse but also valuable insights into public focus on Alzheimer's Disease. This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives like Alzheimer's Disease in an autonomous manner, setting the groundwork for future AI-driven investigations in global health landscapes. Code, a demo video, and other information are available at https://github.com/levyisthebest/AD-AutoGPT.

Infodemic Versus Viral Information Spread: Key Differences and Open Challenges

Unlabelled

As we move beyond the COVID-19 pandemic, the risk of future infodemics remains significant, driven by emerging health crises and the increasing influence of artificial intelligence in the information ecosystem. During periods of apparent stability, proactive efforts to advance infodemiology are essential for enhancing preparedness and improving public health outcomes. This requires a thorough examination of the foundations of this evolving discipline, particularly in understanding how to accurately identify an infodemic at the appropriate time and scale, and how to distinguish it from other processes of viral information spread, both within and outside the realm of public health. In this paper, we integrate expertise from data science and public health to examine the key differences between information production during an infodemic and viral information spread. We explore both clear and subtle distinctions, including context and contingency (ie, the association of an infodemic and viral information spread with a health crisis); information dynamics in terms of volume, spread, and predictability; the role of misinformation and information voids; societal impact; and mitigation strategies. By analyzing these differences, we highlight challenges and open questions. These include whether an infodemic is solely associated with pandemics or whether it could arise from other health emergencies; if infodemics are limited to health-related issues or if they could emerge from crises initially unrelated to health (like climate events); and whether infodemics are exclusively global phenomena or if they can occur on national or local scales. Finally, we propose directions for future quantitative research to help the scientific community more robustly differentiate between these phenomena and develop tailored management strategies.

Trends of public interest in chronic traumatic encephalopathy (CTE) from 2004 to 2022

CONTEXT

Public interest in sport-related medical conditions is known to be affected by social media and pop cultural coverage. The purpose of this project was to assess the relationship between popular culture concerning chronic traumatic encephalopathy (CTE) and analyze of how often this topic was searched on the internet.

OBJECTIVES

The objective of this study was to investigate deviations in public interest following player incidents of CTE and the effects that the media has had on public interest in CTE.

METHODS

To determine our primary objective, we utilized Google Trends to extract the monthly relative search interest (RSI) in CTE between January 2002 and October 2022. To assess the increase in RSI following a major event, an autoregressive integrated moving average (ARIMA) to predict RSI from March 2012 was created through the end of the period, and calculated the differences between the actual and forecasted values.

RESULTS

Data indicate that RSI increased over time, specifically following the release of the movie Concussion. The peak in RSI (100) over this timespan was following the release of Aaron Hernandez's autopsy results in 2017, which was 87.8 (95 % CI: 8.7-15.7) higher than forecasted, showing a 720.3 % increase in RSI. While research was published regarding CTE in 2005, the first major spike in search interest occurred after Junior Seau died in 2012. Increasing public interest in CTE continued when media exposure conveyed autopsies of former NFL players, the movie Concussion, and the release of The Killer Inside: The Mind of Aaron Hernandez. Given this increased interest in CTE, we recommend that media broadcasters become more educated on brain injuries, as well as the movement of Brain Injury Awareness Month and Concussion Awareness Day.

CONCLUSIONS

There has been an increase in public interest in CTE from 2004 through 2022 with surges following media releases of events involving NFL players. Therefore, physicians and media broadcasters must create partnerships to better educate the public about head injuries and the effects of CTE.

The COVID-19 pandemic and the worldwide online interest in telepsychiatry: an infodemiological study from 2004 to 2022

Introduction

Studies call for the further assessment and understanding of public interests and concerns about telepsychiatry, especially during the COVID-19 pandemic. Since telepsychiatry services are accessed through the Internet, this study analyzed online searches and queries to determine telepsychiatry-related interests and concerns over time. The findings can inform the development and customization of online telepsychiatry resources and services, enabling a more effective response to public needs.

Materials and methods

This study determined public concerns and interests in telepsychiatry using data from Google Trends and Wikipedia from 2004 to 2022. These platforms were selected for their large global market share. After describing the data, bootstrap for independent sample tests of search volumes and Wikipedia page views before and during the COVID-19 pandemic.

Results

The highest interest in telepsychiatry was observed in high-income countries. Search volumes for telepsychiatry increased, while Wikipedia page views decreased during the COVID-19 pandemic. The top and rising queries that can be incorporated into telepsychiatry websites include telepsychiatry concepts, jobs, services, costs, and locations.

Discussion

The findings support that the use of the Internet for telepsychiatry information increased compared to previous years, especially during the start of the COVID-19 pandemic. There may also be a higher interest in telepsychiatry among high-income nations compared to low and middle-income countries. Furthermore, the study also supports that digital information should be tailored to respond to public needs and expectations by incorporating telepsychiatry-related concepts, jobs, services, costs, and locations.

A call for government-driven health promotion solutions surveillance in public health: Nests and networks

The Health Promotion Solutions Surveillance (HPSS) framework is proposed to enhance public health systems by systematically identifying, cataloguing, and supporting community-driven public health solutions. Developed in response to limited public health resources and the dominance of problem-focused health protection surveillance systems, this framework can complement current infrastructure using a proactive and solutions-oriented approach. Designed for implementation by government public health systems, it focuses on interdisciplinary collaboration, equity, and engagement with communities and their grassroots initiatives. Rooted in health promotion principles, it seeks to address systemic inequities and bridge public health gaps that fail to adequately resource people and communities to address identified public health issues. Although the framework has not been implemented in its intended context, its ability to recognize various methodologies and use diverse methods, including community network identification, digital ethnography, and participatory action research, would position public health systems to promote innovation, collaboration, and sustainable public health outcomes. An accompanying visual guide outlines practical steps and principles for operationalizing this framework, offering public health professionals and policymakers a tool to align local interventions with public health goals.

Assessing Public Interest in Mammography, Computed Tomography Lung Cancer Screening, and Computed Tomography Colonography Screening Examinations Using Internet Search Data: Cross-Sectional Study

Background

The noninvasive imaging examinations of mammography (MG), low-dose computed tomography (CT) for lung cancer screening (LCS), and CT colonography (CTC) play important roles in screening for the most common cancer types. Internet search data can be used to gauge public interest in screening techniques, assess common screening-related questions and concerns, and formulate public awareness strategies.

Objective

This study aims to compare historical Google search volumes for MG, LCS, and CTC and to determine the most common search topics.

Methods

Google Trends data were used to quantify relative Google search frequencies for these imaging screening modalities over the last 2 decades. A commercial search engine tracking product (keywordtool.io) was used to assess the content of related Google queries over the year from May 1, 2022, to April 30, 2023, and 2 authors used an iterative process to agree upon a list of thematic categories for these queries. Queries with at least 10 monthly instances were independently assigned to the most appropriate category by the 2 authors, with disagreements resolved by consensus.

Results

The mean 20-year relative search volume for MG was approximately 10-fold higher than for LCS and 25-fold higher than for CTC. Search volumes for LCS have trended upward since 2011. The most common topics of MG-related searches included nearby screening locations (60,850/253,810, 24%) and inquiries about procedural discomfort (28,970/253,810, 11%). Most common LCS-related searches included CT-specific inquiries (5380/11,150, 48%) or general inquiries (1790/11,150, 16%), use of artificial intelligence or deep learning (1210/11,150, 11%), and eligibility criteria (1020/11,150, 9%). For CTC, the most common searches were CT-specific inquiries (1800/5590, 32%) or procedural details (1380/5590, 25%).

Conclusions

Over the past 2 decades, Google search volumes have been significantly higher for MG than for either LCS or CTC, although search volumes for LCS have trended upward since 2011. Knowledge of public interest and queries related to imaging-based screening techniques may help guide public awareness efforts.

Exploring infodemiology: unraveling the intricate relationships among stress, headaches, migraines, and suicide through Google Trends analysis

Introduction

Google Trends has emerged as a vital resource for understanding public information-seeking behavior. This study investigates the interconnected search trends of stress, headaches, migraines, and suicide, highlighting their relevance to public health and mental well-being. By employing infodemiology, the study explores temporal and geographical patterns in search behavior and examines the impact of global events like the COVID-19 pandemic.

Methods

Data mining was conducted using Google Trends for the search terms "stress," "headache," "migraine," and "suicide." Relative Search Volume (RSV) data from October 2013 to October 2023 was collected and adjusted for time and location. Statistical analyses, including Pearson correlation tests, linear regression, and seasonal Mann-Kendall tests, were applied to identify correlations, trends, and seasonal variations. Geographical differences were also analyzed to understand regional disparities.

Results

Significant correlations were observed among the search terms, with "migraine" and "suicide" showing the strongest association. Seasonal variations revealed a peak in search volumes during winter months. Geographical analysis highlighted consistently high RSV in the Philippines for all terms. During the COVID-19 pandemic, searches for stress, headaches, and migraines showed notable increases, reflecting heightened public interest in mental health-related topics during this period.

Discussion

The study underscores the interconnected nature of stress, headaches, migraines, and suicide in public search behavior. Seasonal patterns and regional variations emphasize the need for targeted interventions. The observed surge in search volume during the COVID-19 pandemic highlights the profound impact of global crises on mental health and the importance of timely public health responses.

Conclusion

Google Trends provides valuable insights into the public's interest in health-related topics, demonstrating the intricate relationship between stress, headaches, migraines, and suicide. The findings highlight the need for increased mental health awareness and interventions, particularly during times of heightened stress. Further research is essential to develop strategies that mitigate the impact of these stressors on public health.

Comparing public health-related material in print and web page versions of legacy media

Objectives

The objectives of this study were to create a database of public health content from a sample of legacy media, and to compare the prevalence of public health themes in print and web-based versions over time.

Materials and Methods

A database was created from eleven nationally published magazines as a sample of legacy media content. Relevant material was extracted and coded by the title of the article, periodical, print or web edition, month of publication, item type, and 1-3 public health theme codes.

Results

Theme codes emerged as the documents were reviewed based on the primary discussion in each piece. A total of 2558 unique documents were extracted from print issues and 6440 from web-based issues. Seventeen public health themes were identified. Individual coded documents were saved with file names identical to the code string, thus creating a searchable database.

Discussion

Legacy media are those that existed before the internet and social media. Publishers target readership groups defined by age, gender, race, sexual orientation, and other commonalities. Although legacy media have been identified as trusted sources of health information, they have not been examined as sources of public health communication. Because both print and web-based versions exist as unstructured textual data, these are rarely examined with informatics methods.

Conclusion

The process described can serve as a model for application of informatics approaches to similar data and assist development of targeted public health communications. Having a better understanding of what types of health content is distributed through legacy media can help to target health messages to specific demographic and interest groups in ways that are understandable and appealing to them.

It's late, but not too late to transform health systems: a global digital citizen science observatory for local solutions to global problems

A key challenge in monitoring, managing, and mitigating global health crises is the need to coordinate clinical decision-making with systems outside of healthcare. In the 21st century, human engagement with Internet-connected ubiquitous devices generates an enormous amount of big data, which can be used to address complex, intersectoral problems via participatory epidemiology and mHealth approaches that can be operationalized with digital citizen science. These big data - which traditionally exist outside of health systems - are underutilized even though their usage can have significant implications for prediction and prevention of communicable and non-communicable diseases. To address critical challenges and gaps in big data utilization across sectors, a Digital Citizen Science Observatory (DiScO) is being developed by the Digital Epidemiology and Population Health Laboratory by scaling up existing digital health infrastructure. DiScO's development is informed by the Smart Framework, which leverages ubiquitous devices for ethical surveillance. The Observatory will be operationalized by implementing a rapidly adaptable, replicable, and scalable progressive web application that repurposes jurisdiction-specific cloud infrastructure to address crises across jurisdictions. The Observatory is designed to be highly adaptable for both rapid data collection as well as rapid responses to emerging and existing crises. Data sovereignty and decentralization of technology are core aspects of the observatory, where citizens can own the data they generate, and researchers and decision-makers can re-purpose digital health infrastructure. The ultimate aim of DiScO is to transform health systems by breaking existing jurisdictional silos in addressing global health crises.

Mental Health Care Navigation Tools in Australia: Infoveillance Study

Background

In response to the well-documented fragmentation within its mental health system, Australia has witnessed recently rapid expansion in the availability of digital mental health care navigation tools. These tools focus on assisting consumers to identify and access appropriate mental health care services, the proliferation of such varied web-based resources risks perpetuating further fragmentation and confusion for consumers. There is a pressing need to systematically assess the characteristics, comprehensiveness, and validity of these navigation tools, especially as demand for digital resources continues to escalate.

Objective

This study aims to identify and describe the current landscape of Australian digital mental health care navigation tools, with a focus on assessing their comprehensiveness, identifying potential gaps, and the extent to which they meet the needs of various stakeholders.

Methods

A comprehensive infoveillance approach was used to identify Australian digital mental health care navigation tools. This process involved a systematic web-based search complemented by consultations with subject matter experts. Identified navigation tools were independently screened by 2 authors, while data extraction was conducted by 3 authors. Extracted data were mapped to key domains and subdomains relevant to navigation tools.

Results

From just a handful in 2020, by February 2024 this study identified 102 mental health care navigation tools across Australia. Primary Health Networks (n=37) and state or territory governments (n=21) were the predominant developers of these tools. While the majority of navigation tools were primarily designed for consumer use, many also included resources for health professionals and caregivers. Notably, no navigation tools were specifically designed for mental health care planners. Nearly all tools (except one) featured directories of mental health care services, although their functionalities varied: 27% (n=27) provided referral information, 20% (n=21) offered geolocated service maps, 12% (n=12) included diagnostic screening capabilities, and 7% (n=7) delineated care pathways.

Conclusions

The variability of navigation tools designed to facilitate consumer access to mental health services could paradoxically contribute to further confusion. Despite the significant expansion of digital navigation tools in recent years, substantial gaps and challenges remain. These include inconsistencies in tool formats, resulting in variable information quality and validity; a lack of regularly updated service information, including wait times and availability for new clients; insufficient details on program exclusion criteria; and limited accessibility and user-friendliness. Moreover, the inclusion of self-assessment screening tools is infrequent, further limiting the utility of these resources. To address these limitations, we propose the development of a national directory of mental health navigation tools as a centralized resource, alongside a system to guide users toward the most appropriate tool for their individual needs. Addressing these issues will enhance consumer confidence and contribute to the overall accessibility, reliability, and utility of digital navigation tools in Australia's mental health system.

US Public Interest in Merkel Cell Carcinoma Following Jimmy Buffett's Death and Implications for Continued Health Advocacy: Infodemiology Study of Google Trends

Unlabelled

Through Jimmy Buffett's unfortunate battle with lymphoma originating from Merkel cell carcinoma and subsequent media coverage of his death, public interest in skin cancer, Merkel cell carcinoma, and the health effects of sunlight exposure increased, as evidenced by our results.

Health Locus of Control and Medical Behavioral Interventions: Systematic Review and Recommendations

BACKGROUND

Health locus of control (HLOC) is a theory that describes how individuals perceive different forces that influence their lives. The concept of a locus of control can affect an individual's likelihood to commit to behaviors related to their health. This study explores the literature on the relationships between HLOC and medical behavioral interventions.

OBJECTIVE

This study aims to better understand how HLOC constructs can potentially affect patient responses to health behavioral interventions and to propose a series of guidelines for individuals interested in designing medical behavioral interventions related to HLOC.

METHODS

We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and performed an analysis of 50 papers related to the topic of HLOC and medical behavioral interventions. Inclusion criteria were studies that had a behavioral intervention involving patients and contained a metric of at least 1 of the constructs related to HLOC. The initial screening and search were conducted by 2 researchers (AY and SM) separately. The results were then combined and compared.

RESULTS

Our findings explore the influence of different levels of HLOC along with the importance of both patient- and health-related context when assessing the relationships between HLOC constructs and the likelihood of health behavior change. The findings show that different constructs related to HLOC can act as reliable predictors for patient responses to medical behavioral interventions. Patients who score higher on internal HLOC measures are more likely to exhibit behaviors that are consistent with positive health outcomes. Patients who score higher on chance HLOC are more likely to exhibit behaviors that may lead to adverse health outcomes. These conclusions are supported by most of the 50 studies surveyed.

CONCLUSIONS

We propose guidelines for individuals designing medical behavioral interventions so that they can make use of these relationships linked to HLOC. The three guidelines suggested are as follows: (1) in most situations, improving internal HLOC will improve health outcomes for patients; (2) patients with high external HLOC should be further studied to determine the source of the external HLOC; and (3) patients with a high chance HLOC are less likely to follow preventative behaviors or be responsive to interventions. Limitations of the study are that the primary search and analysis were conducted by 2 principal researchers (AY and SM). Interpretation and development of the guidelines are subject to individual interpretation of results and may not be applicable to all contexts.

Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies

BACKGROUND

The mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control.

OBJECTIVE

This review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research.

METHODS

The scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic.

RESULTS

A total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization's declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15%), and Twitter was the most used social media app (32/61, 52%) for researchers. Researchers from 33 countries were concerned about mpox infodemic-related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic.

CONCLUSIONS

This comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future.

Underreporting of Cases in the COVID-19 Outbreak of Borriana (Spain) during Mass Gathering Events in March 2020: A Cross-Sectional Study

Determining the number of cases of an epidemic is the first function of epidemiological surveillance. An important underreporting of cases was observed in many locations during the first wave of the COVID-19 pandemic. To estimate this underreporting in the COVID-19 outbreak of Borriana (Valencia Community, Spain) in March 2020, a cross-sectional study was performed in June 2020 querying the public health register. Logistic regression models were used. Of a total of 468 symptomatic COVID-19 cases diagnosed in the outbreak through anti-SARS-CoV-2 serology, 36 cases were reported (7.7%), resulting in an underreporting proportion of 92.3% (95% confidence interval [CI], 89.5-94.6%), with 13 unreported cases for every reported case. Only positive SARS-CoV-2 polymerase chain reaction cases were predominantly reported due to a limited testing capacity and following a national protocol. Significant factors associated with underreporting included no medical assistance for COVID-19 disease, with an adjusted odds ratio [aOR] of 10.83 (95% CI 2.49-47.11); no chronic illness, aOR = 2.81 (95% CI 1.28-6.17); middle and lower social classes, aOR = 3.12 (95% CI 1.42-6.85); younger age, aOR = 0.97 (95% CI 0.94-0.99); and a shorter duration of illness, aOR = 0.98 (95% CI 0.97-0.99). To improve the surveillance of future epidemics, new approaches are recommended.

The early-stage impacts of shock events on adult sleep: Lessons from the COVID-19 pandemic

The present study aims to estimate the early-stage association of the coronavirus disease 2019 (COVID-19) pandemic with sleep duration, 4 months after the initial outbreak, at a national level. Using the China Family Panel Studies in 2018 and its follow-up in 2020, 16,563 adult participants were analyzed in our study. The first wave transmissibility of the COVID-19 pandemic in mainland China was used as a quasi-natural experiment. The difference in difference method was employed to compare variations across participants interviewed before or after the pandemic and between groups from provinces with different COVID-19 severity. The study observed a decline in workday sleep duration among adults, regardless of their employment status. Specifically, working adults experienced a significant decrease in sleep duration on work days (-1.54, 95% confidence interval -2.16 to -0.92), alongside an increase on work-free days (2.41, 1.56-3.24), leading to a wider sleep discrepancy in sleep patterns (3.95, 3.31-4.59). Noteworthy, working-age adults (-2.00, -2.79 to -1.22) and males (-3.31, -4.31 to -2.30) exhibited greater sleep decreases on work days, whereas females exhibited a more pronounced disparity in sleep patterns (6.18, 4.73-7.63) between work and work-free days. The pandemic is significantly associated with prolonged changes in adults' sleep duration, including sleep decreases on work days and catch-up sleep on work-free days for working adults. To prepare for future global emergencies, the government may need to promote resilience to mitigate the pandemic's adverse impacts on the working population. Guaranteeing adequate sleep among working adults and reducing sleep debt should be prioritized in such efforts.

Transformers and large language models in healthcare: A review

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.

Detecting epinephrine auto-injector shortages in Finland 2016-2022: Log-data analysis of online information seeking

INTRODUCTION

Medicine shortages prevail as a worldwide problem causing life-threatening situations for adults and children. Epinephrine auto-injectors are used for serious allergic reactions called anaphylaxis, and alternative auto-injectors are not always available in pharmacies. Healthcare professionals in Finland use the dedicated internet source, Physician's Database (PD), when seeking medical information in practice, while Health Library (HL) provides health information for citizens (S1 Data). The objectives were to assess whether (1) professionals' searches for epinephrine auto-injectors and (2) citizens' anaphylaxis article openings relate to epinephrine shortages in Finland.

METHODS

Monthly log data on epinephrine auto-injectors (EpiPen®, Jext®, Emerade®) from PD and on openings of anaphylaxis articles from HL were collected during 2016-2022. Professionals' searches of seven auto-injectors and citizens' openings of four anaphylaxis articles were compared to information on epinephrine shortages reported by Finnish Medicines Agency. Professionals' auto-injector prescriptions provided by Social Insurance Institution were also assessed.

RESULTS

Total searches in EpiPen® (N = 111,740), Jext® (N = 25,631), and Emerade® (N = 18,329) could be analyzed during 2016-2022. EpiPen® only could visually show seasonal patterns during summertime, peaking vigorously in the summer of 2018 when the major EpiPen® shortage appeared worldwide. Anaphylaxis articles equaled 2,030,855 openings altogether. Openings of one anaphylaxis article ("Bites and Stings") peaked during summertime, while another article ("Anaphylactic Reaction") peaked only once (three-fold increase) at the end of 2020 when COVID-19 vaccinations started, and auto-injector prescriptions were lowest. Fifty EpiPen®, one Jext®, and twelve Emerade® shortages were reported. Almost a two-fold increase in peaks of auto-injector prescriptions was found during summertime.

CONCLUSION

This study shows that (1) epinephrine shortages related to professionals' searching for auto-injectors, and (2) citizens' information seeking on anaphylaxis related to summertime and shortages with lesser prescriptions. Therefore, the dedicated internet databases aimed at professionals and citizens could be used as additional information sources to detect anaphylactic reactions and auto-injector shortages.

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

BACKGROUND

Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited.

OBJECTIVE

This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security.

METHODS

A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years.

RESULTS

In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): "Global production," "Food insecurity and health," "Use of food banks," "Giving to food banks," "Family poverty," "Food relief provision," "Global food insecurity," "Climate change," "Australian food insecurity," and "Human rights." The topic "Giving to food banks," which focused on support and donation, had the highest proportion of positive sentiment, and "Global food insecurity," which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues.

CONCLUSIONS

In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area.

Analysis of the hikikomori phenomenon - an international infodemiology study of Twitter data in Portuguese

BACKGROUND

Hikikomori refers to the extreme isolation of individuals in their own homes, lasting at least six months. In recent years social isolation has become an important clinical, social, and public health problem, with increased awareness of hikikomori around the globe. Portuguese is one of the six most spoken languages in the world, but no studies have analysed the content regarding this phenomenon expressed in Portuguese.

OBJECTIVE

To explore the hikikomori phenomenon on Twitter in Portuguese, utilising a mixed-methods approach encompassing content analysis, emotional analysis, and correlation analysis.

METHODS

A mixed methods analysis of all publicly available tweets in the Portuguese language using a specific keyword (hikikomori) between 1st January 2008 and 19th October 2022. The content analysis involved categorising tweets based on tone, content, and user types, while correlation analysis was used to investigate user engagement and geographical distribution. Statistical analysis and artificial intelligence were employed to classify and interpret the tweet data.

RESULTS

Among the total of 13,915 tweets generated, in terms of tone 10,731 were classified as "negative", and 3184 as "positive". Regarding content, "curiosities" was the most posted, as well as the most retweeted and liked topic. Worldwide, most of the hikikomori related tweets in Portuguese were posted in Europe, while "individuals with hikikomori" were the users most active posting. Regarding emotion analysis, the majority of tweets were "neutral".

CONCLUSIONS

These findings show the global prevalence of the discourse on hikikomori phenomenon among Portuguese speakers. It also indicates an increase in the number of tweets on this topic in certain continents over the years. These findings can contribute to developing specific interventions, support networks, and awareness-raising campaigns for affected individuals.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Worldwide internet trends in the public interest related to skin whitening and bleaching creams

Skin whitening is a practice that is used to obtain lighter skin tone and is most prevalent in Africa and Asia. Substances used for this procedure, such as hydroquinone or mercury have a variety of side effects and are banned in several countries. This study examined the popularity of internet searches for terms related to skin whitening and bleaching creams with the use of GoogleTrends (GT). GT was searched globally for the topic "skin whitening" and two terms "hydroquinone cream" and "mercury cream" throughout a 10-year period (01.09.2013-31.08.2023). The popularity of searches increased during the analyzed period. The topic "skin whitening" was most popular in Sudan, Vietnam, and Sri Lanka. The searches were higher for "hydroquinone cream" than "mercury cream" in almost all countries, besides the Philippines and Indonesia. Our study confirms that skin whitening practices are popular, especially among populations with darker skin tone. Despite potentially toxic side effects, creams with hydroquinone and mercury are increasingly searched worldwide. Education about skin whitening and the usage of bleaching substances should be implemented, especially in the regions of Africa and Asia.

Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study

BACKGROUNDS

Previous studies have indicated that users' health information-seeking behavior can serve as a reflection of current health issues within a community. This study aimed to investigate the online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP).

METHODS

Google Trends (GTr) was utilized to collect big data from the internet searches conducted by Iranian web users. A focus group discussion was employed to identify users' selected keywords when searching for HSP. Additionally, keywords related to the disease's symptoms were selected based on recent clinical studies. All keywords were queried in GTr from January 1, 2012 to October 30, 2022. The outputs were saved in an Excel format and analyzed using SPSS.

RESULTS

The highest and lowest search rates of HSP were recorded in winter and summer, respectively. There was a significant positive correlation between HSP search rates and the terms "joint pain" (P = 0.007), "vomiting" (P = 0.032), "hands and feet swelling" (P = 0.041) and "seizure" (P < 0.001).

CONCLUSION

The findings were in accordance with clinical facts about HSP, such as its seasonal pattern and accompanying symptoms. It appears that the information-seeking behavior of Iranian users regarding HSP can provide valuable insights into the outbreak of this disease in Iran.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

Maximum generable interest: A universal standard for Google Trends search queries

The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different works is desirable to simplify the comparison. The coronavirus pandemic's enormous impact and media coverage have triggered an exceptionally high search interest. Consequently, the maximum generable interest (MGI) on coronavirus is proposed as a universal reference for objectifying and comparing relative search interest in the future. This search interest can be explored with search engine data such as Google Trends data. Additional standards for medium and low search volumes can also be used to reflect the search interest of topics at different levels. Size standards, such as reference to MGI, may help make research more comparable and better evaluate relative search volumes. This study presents a framework for this purpose using the example of stroke.

Topic and Trend Analysis of Weibo Discussions About COVID-19 Medications Before and After China's Exit from the Zero-COVID Policy: Retrospective Infoveillance Study

BACKGROUND

After 3 years of its zero-COVID policy, China lifted its stringent pandemic control measures with the announcement of the 10 new measures on December 7, 2022. Existing estimates suggest 90%-97% of the total population was infected during December. This change created a massive demand for COVID-19 medications and treatments, either modern medicines or traditional Chinese medicine (TCM).

OBJECTIVE

This study aimed to explore (1) how China's exit from the zero-COVID policy impacted media and the public's attention to COVID-19 medications; (2) how social COVID-19 medication discussions were related to existing model estimates of daily cases during that period; (3) what the diversified themes and topics were and how they changed and developed from November 1 to December 31, 2022; and (4) which topics about COVID-19 medications were focused on by mainstream and self-media accounts during the exit. The answers to these questions could help us better understand the consequences of exit strategies and explore the utilities of Sina Weibo data for future infoveillance studies.

METHODS

Using a scrapper for data retrieval and the structural topic modeling (STM) algorithm for analysis, this study built 3 topic models (all data, before a policy change, and after a policy change) of relevant discussions on the Chinese social media platform Weibo. We compared topic distributions against existing estimates of daily cases and between models before and after the change. We also compared proportions of weibos published by mainstream versus self-media accounts over time on different topics.

RESULTS

We found that Weibo discussions shifted sharply from concerns of social risks (case tracking, governmental regulations, etc) to those of personal risks (symptoms, purchases, etc) surrounding COVID-19 infection after the exit from the zero-COVID policy. Weibo topics of "symptom sharing" and "purchase and shortage" of modern medicines correlated more strongly with existing susceptible-exposed-infected-recovered (SEIR) model estimates compared to "TCM formulae" and other topics. During the exit, mainstream accounts showed efforts to specifically engage in topics related to worldwide pandemic control policy comparison and regulations about import and reimbursement of medications.

CONCLUSIONS

The exit from the zero-COVID policy in China was accompanied by a sudden increase in social media discussions about COVID-19 medications, the demand for which substantially increased after the exit. A large proportion of Weibo discussions were emotional and expressed increased risk concerns over medication shortage, unavailability, and delay in delivery. Topic keywords showed that self-medication was sometimes practiced alone or with unprofessional help from others, while mainstream accounts also tried to provide certain medication instructions. Of the 16 topics identified in all 3 STM models, only "symptom sharing" and "purchase and shortage" showed a considerable correlation with SEIR model estimates of daily cases. Future studies could consider topic exploration before conducting predictive infoveillance analysis, even with narrowly defined search criteria with Weibo data.

Clarity of publications on HPV in Instagram profiles of official health agencies in Brazil

The study analyzed the clarity of publications on human papillomavirus (HPV) in the Instagram profiles of official Brazilian health agencies. An infodemiological study analyzed publications on HPV in the 81 Instagram profiles selected from the Health Ministry, States' Health Departments, and dental councils and associations. The following data were collected: classification of content, type of profiles, type of media, how the content was addressed, number of posts, frequency, likes, comments, viewings, and hashtags, and how the HPV vaccine was addressed. The clarity of the educational publications was assessed using the Brazilian version of the Clear Communication Index (BR-CDC-CCI). Data analysis was performed with Spearman's correlation and the Mann-Whitney test (p < 0.05). A total of 504 publications on HPV were found. The average number of likes was 528.3 (SD = 2388.2) and the average BR-CDC-CCI score was 67.1 (SD = 14.1). The quality and clarity of the information was considered adequate (BR-CDC-CCI score ≥ 90) in 6.9% of the publications. A weak positive correlation was found between the number of likes and both the BR-CDC-CCI score (r = 0.195) and number of posts (r = 0.124). Publications from the Health Ministry had a significantly higher BR-CDC-CCI score (72.9) compared to the other profiles analyzed (p = 0.01). Most publications concerned government actions, had low engagement, and written educational information was of low clarity and quality. However, the effort to reach the population was evident, with an increase in publications over the years.

GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach

The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide and others) now promises effective, non-invasive treatment of obesity for individuals with and without diabetes. Social media platforms' users started promoting semaglutide/Ozempic as a weight-loss treatment, and the associated increase in demand has contributed to an ongoing worldwide shortage of the drug associated with levels of non-prescribed semaglutide intake. Furthermore, recent reports emphasized some GLP-1 RA-associated risks of triggering depression and suicidal thoughts. Consistent with the above, we aimed to assess the possible impact of GLP-1 RAs on mental health as being perceived and discussed in popular open platforms with the help of a mixed-methods approach. Reddit posts yielded 12,136 comments, YouTube videos 14,515, and TikTok videos 17,059, respectively. Out of these posts/entries, most represented matches related to sleep-related issues, including insomnia (n = 620 matches); anxiety (n = 353); depression (n = 204); and mental health issues in general (n = 165). After the initiation of GLP-1 RAs, losing weight was associated with either a marked improvement or, in some cases, a deterioration, in mood; increase/decrease in anxiety/insomnia; and better control of a range of addictive behaviors. The challenges of accessing these medications were a hot topic as well. To the best of our knowledge, this is the first study documenting if and how GLP-1 RAs are perceived as affecting mood, mental health, and behaviors. Establishing a clear cause-and-effect link between metabolic diseases, depression and medications is difficult because of their possible reciprocal relationship, shared underlying mechanisms and individual differences. Further research is needed to better understand the safety profile of these molecules and their putative impact on behavioral and non-behavioral addictions.

Mining Google Trends data for nowcasting and forecasting colorectal cancer (CRC) prevalence

Background

Colorectal cancer (CRC) is the third most prevalent and second most lethal form of cancer in the world. Consequently, CRC cancer prevalence projections are essential for assessing the future burden of the disease, planning resource allocation, and developing service delivery strategies, as well as for grasping the shifting environment of cancer risk factors. However, unlike cancer incidence and mortality rates, national and international agencies do not routinely issue projections for cancer prevalence. Moreover, the limited or even nonexistent cancer statistics for large portions of the world, along with the high heterogeneity among world nations, further complicate the task of producing timely and accurate CRC prevalence projections. In this situation, population interest, as shown by Internet searches, can be very important for improving cancer statistics and, in the long run, for helping cancer research.

Methods

This study aims to model, nowcast and forecast the CRC prevalence at the global level using a three-step framework that incorporates three well-established univariate statistical and machine-learning models. First, data mining is performed to evaluate the relevancy of Google Trends (GT) data as a surrogate for the number of CRC survivors. The results demonstrate that population web-search interest in the term "colonoscopy" is the most reliable indicator to nowcast CRC disease prevalence. Then, various statistical and machine-learning models, including ARIMA, ETS, and FNNAR, are trained and tested using relevant GT time series. Finally, the updated monthly query series spanning 2004-2022 and the best forecasting model in terms of out-of-sample forecasting ability (i.e., the neural network autoregression) are utilized to generate point forecasts up to 2025.

Results

Results show that the number of people with colorectal cancer will continue to rise over the next 24 months. This in turn emphasizes the urgency for public policies aimed at reducing the population's exposure to the principal modifiable risk factors, such as lifestyle and nutrition. In addition, given the major drop in population interest in CRC during the first wave of the COVID-19 pandemic, the findings suggest that public health authorities should implement measures to increase cancer screening rates during pandemics. This in turn would deliver positive externalities, including the mitigation of the global burden and the enhancement of the quality of official statistics.

Population Interest in Information on Obesity, Nutrition, and Occupational Health and Its Relationship with the Prevalence of Obesity: An Infodemiological Study

OBJECTIVE

To identify and analyze population interest in obesity, nutrition, and occupational health and safety and its relationship with the worldwide prevalence of obesity through information search trends.

METHOD

In this ecological study, data were obtained through online access to Google Trends using the topics "obesity", "nutrition", and "occupational health and safety". Obesity data were obtained from the World Health Organization (WHO) website for crude adult prevalence and estimates by region. The variables studied were relative search volume (RSV), temporal evolution, milestone, trend, and seasonality. The temporal evolution of the search trends was examined by regression analysis (R2). To assess the relationship between quantitative variables, the Spearman correlation coefficient (Rho) was used. Seasonality was verified using the augmented Dickey-Fuller (ADF) test.

RESULTS

The RSV trends were as follows: obesity (R2 = 0.04, p = 0.004); nutrition (R2 = 0.42, p < 0.001); and occupational health and safety (R2 = 0.45, p < 0.001). The analysis of seasonality showed the absence of a temporal pattern (p < 0.05 for all terms). The associations between world obesity prevalence (WOP) and the different RSVs were as follows: WOP versus RSV obesity, Rho = -0.79, p = 0.003; WOP versus RSV nutrition, Rho = 0.57, p = 0.044; and WOP versus RSV occupational health and safety, Rho = -0.93, p = 0.001.

CONCLUSIONS

Population interest in obesity continues to be a trend in countries with the highest prevalence, although there are clear signs popularity loss in favor of searches focused on possible solutions and treatments, with a notable increase in searches related to nutrition and diet. Despite the fact that most people spend a large part of their time in the workplace and that interventions including various strategies have been shown to be useful in combating overweight and obesity, there has been a decrease in the population's interest in information related to obesity in the workplace. This information can be used as a guide for public health approaches to obesity and its relationship to nutrition and a healthy diet, approaches that are of equal utility and applicability in occupational health.

Expert Credibility and Sentiment in Infodemiology of Hydroxychloroquine's Efficacy on Cable News Programs: Empirical Analysis

BACKGROUND

Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread information about hydroxychloroquine, whereas cable television was a vital source. To exemplify, experts discussed in cable television broadcasts about hydroxychloroquine for treating COVID-19. However, how the experts' comments influenced airtime allocation on cable television to help in public health communication, either during COVID-10 or at other times, is not understood.

OBJECTIVE

This study aimed to examine how 3 factors, that is, the credibility of experts as doctors (DOCTOREXPERT), the credibility of government representatives (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in discussions and comments, influence the allocation of airtime (AIRTIME) in cable television broadcasts. SENTIMENT pertains to the information credibility conveyed through the tone and language of experts' comments during cable television broadcasts, in contrast to the individual credibility of the doctor or government representatives because of the degree or affiliations.

METHODS

We collected transcriptions of relevant hydroxychloroquine-related broadcasts on cable television between March 2020 and October 2020. We coded the experts as DOCTOREXPERT or GOVTEXPERT using publicly available data. To determine the sentiments expressed in the broadcasts, we used a machine learning algorithm to code them as POSITIVE, NEGATIVE, NEUTRAL, or MIXED sentiments.

RESULTS

The analysis revealed a counterintuitive association between the expertise of doctors (DOCTOREXPERT) and the allocation of airtime, with doctor experts receiving less airtime (P<.001) than the nonexperts in a base model. A more nuanced interaction model suggested that government experts with a doctorate degree received even less airtime (P=.03) compared with nonexperts. Sentiments expressed during the broadcasts played a significant role in airtime allocation, particularly for their direct effects on airtime allocation, more so for NEGATIVE (P<.001), NEUTRAL (P<.001), and MIXED (P=.03) sentiments. Only government experts expressing POSITIVE sentiments during the broadcast received a more extended airtime (P<.001) than nonexperts. Furthermore, NEGATIVE sentiments in the broadcasts were associated with less airtime both for DOCTOREXPERT (P<.001) and GOVTEXPERT (P<.001).

CONCLUSIONS

Source credibility plays a crucial role in infodemics by ensuring the accuracy and trustworthiness of the information communicated to audiences. However, cable television media may prioritize likeability over credibility, potentially hindering this goal. Surprisingly, the findings of our study suggest that doctors did not get good airtime on hydroxychloroquine-related discussions on cable television. In contrast, government experts as sources received more airtime on hydroxychloroquine-related discussions. Doctors presenting facts with negative sentiments may not help them gain airtime. Conversely, government experts expressing positive sentiments during broadcasts may have better airtime than nonexperts. These findings have implications on the role of source credibility in public health communications.

Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study

BACKGROUND

Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests.

OBJECTIVE

This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time.

METHODS

A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software.

RESULTS

We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward.

CONCLUSIONS

Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.

Tweeting for Health Using Real-time Mining and Artificial Intelligence-Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter

BACKGROUND

Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time.

OBJECTIVE

This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics.

METHODS

U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis.

RESULTS

U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave-related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately.

CONCLUSIONS

The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics.

Impact on online research on celebrities' uncommon diseases: the curious case of Justin Bieber and Ramsay Hunt syndrome

Aim

We investigated how to use Internet user searches to gauge the impact of a celebrity illness on global public interest.

Methods

The study design is cross-sectional. Data on Internet searches were obtained from Google Trends (GT) for the period between 2017-2022 using the search words "Ramsay Hunt syndrome" (RHS), "Ramsay Hunt syndrome type 2," "Herpes zoster," and "Justin Bieber." The frequency of specific page views for "Ramsay Hunt syndrome," "Ramsay Hunt syndrome type 1," Ramsay Hunt syndrome type 2," Ramsay Hunt syndrome type 3," "Herpes zoster," and "Justin Bieber" were collected via a Wikipedia analysis tool that shows the number of times a specific page is viewed. Statistical analyses were performed using the Pearson (r) and Spearman's rank correlation coefficient (rho).

Results

GT data, in 2022, show a strong correlation for Justin Bieber and RHS or RHS type 2 (r = 0.75); similarly, Wikipedia data show a strong correlation for Justin Bieber and the others explored terms (r > 0.75). Furthermore, the correlation was strong between GT and Wikipedia for RHS (rho = 0.89) and RHS type 2 (rho = 0.88).

Conclusions

The peak search times for the GT and Wikipedia pages were during the same period. Useful new tools and analyses of Internet traffic data may be effective in assessing the impact of announced celebrity uncommon illnesses on global public interest.

Impact of COVID-19 on Public Interest in Breast Cancer Screening and Related Symptoms: A Google Trends Analysis

BACKGROUND

The COVID-19 Pandemic has led to a decrease in cancer screening due to redeployment of healthcare resources and public avoidance of healthcare facilities. Breast Cancer is the most common cancer diagnosed in women, with improved survival rates with early detection. An avoidance of screening, resulting in late detection, greatly affects survival and increases healthcare resource burden and costs.

OBJECTIVE

To evaluate if a sustained decrease in public interest in screening occurred, and to evaluate other search terms, and hence interest, associated with that.

METHODS

This study utilized Google Trends to analyze public interest in breast cancer screening and symptoms. We queried search data for four keyword terms ("mammogram", "breast pain", "breast lump", and "nipple discharge") from 2019-01-01 to 2022-01-01. The relative search frequency (RSF) metric was used to assess interest in these terms, and related queries were retrieved for each keyword to evaluate trends in search patterns.

RESULTS

Despite an initial drastic drop in interest in mammography from March-April 2020, this quickly recovered by July 2020. After this period, alongside recovery of interest in screening there was a rapid increase in interest for arranging for mammography. Relative search frequencies of perceived Breast Cancer related symptoms such as Breast Lump, Nipple Discharge and Breast Pain remained stable. There was increase public interest in natural and alternative therapy of breast lumps despite recovery of interest in mammography and breast biopsy. There was a significant correlation between search activity and Breast Cancer Awareness Month (BCAM) in October.

CONCLUSIONS

Online search interest in breast cancer screening experienced a sharp decline at the beginning of the COVID-19 pandemic, with a subsequent return to baseline interest in arranging mammography followed this short period of decreased interest.

CLINICALTRIAL

Google Trends for health research: Its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology

The high utilization of infodemiological tools for psychiatric and mental health topics signals the emergence of a new discipline. Drawing on the definition of infodemiology by Eysenbach, this emerging field can be termed "psychiatric and mental health infodemiology," defined as the science of distribution and determinants of information in an electronic medium, including the internet, or in a population to inform mental health services and policies. Since Google Trends is one of its popular tools, this minireview describes its advantages, application, methodological considerations, and limitations in psychiatric and mental health research. The advantage of Google Trends is the nature of its data, which may represent the actual behavior rather than their users' stated preferences in real-time through automatic anonymization. As such, it can provide readily available data about sensitive health topics like mental disorders. Therefore, Google Trends has been used to explore public concerns, interests, and behaviors about psychiatric and mental health phenomena, service providers, and specific disciplines. In this regard, several methodological can be considered by studies using Google Trends, including documenting their exact keywords, query category, time range, location, and date of retrieval. Likewise, its limitations should be accounted for in its interpretation, including restricted representation of people who use the Google search engine, limited validity in areas with low internet penetration or freedom of speech, does not provide absolute search volumes, unknown sampled queries, and limited transparency in its algorithm, especially the terms and idioms it subsumes under its "topic" keywords.

Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom

BACKGROUND

Health research using commercial data is increasing. The evidence on public acceptability and sociodemographic characteristics of individuals willing to share commercial data for health research is scarce.

OBJECTIVE

This survey study investigates the willingness to share commercial data for health research in the United Kingdom with 3 different organizations (government, private, and academic institutions), 5 different data types (internet, shopping, wearable devices, smartphones, and social media), and 10 different invitation methods to recruit participants for research studies with a focus on sociodemographic characteristics and psychological predictors.

METHODS

We conducted a web-based survey using quota sampling based on age distribution in the United Kingdom in July 2020 (N=1534). Chi-squared tests tested differences by sociodemographic characteristics, and adjusted ordered logistic regressions tested associations with trust, perceived importance of privacy, worry about data misuse and perceived risks, and perceived benefits of data sharing. The results are shown as percentages, adjusted odds ratios, and 95% CIs.

RESULTS

Overall, 61.1% (937/1534) of participants were willing to share their data with the government and 61% (936/1534) of participants were willing to share their data with academic research institutions compared with 43.1% (661/1534) who were willing to share their data with private organizations. The willingness to share varied between specific types of data-51.8% (794/1534) for loyalty cards, 35.2% (540/1534) for internet search history, 32% (491/1534) for smartphone data, 31.8% (488/1534) for wearable device data, and 30.4% (467/1534) for social media data. Increasing age was consistently and negatively associated with all the outcomes. Trust was positively associated with willingness to share commercial data, whereas worry about data misuse and the perceived importance of privacy were negatively associated with willingness to share commercial data. The perceived risk of sharing data was positively associated with willingness to share when the participants considered all the specific data types but not with the organizations. The participants favored postal research invitations over digital research invitations.

CONCLUSIONS

This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.

A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

BACKGROUND

The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare.

OBJECTIVE

This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs.

METHODS

We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis.

RESULTS

The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment.

CONCLUSIONS

Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination.

A Google Trends analysis revealed global public interest and awareness of nasal polyps

INTRODUCTION

Nasal polyps (NPs) is a common upper airway inflammatory disorder with a huge negative burden on both the quality of life and costs to patients. However, NPs patients remain undiagnosed and untreated in a timely, which may be due to a lack of disease-related awareness. Google Trends (GT) is an online and open tool, which can provide real-world data on health informatics worldwide.

OBJECTIVES

This study aimed to explore global public interest and awareness in nasal polyps (NPs) by performing a GT analysis.

METHODS

Data on relative search volume (RSV) for NPs globally were collected by the public website Google Trends from January 2007 to December 2021. Top-related topics, rising-related topics, and regions were extracted for further analysis. Seasonal variation analysis, the latitude difference analysis, and the rising-related topics between the developed countries and the developing countries were analyzed. A P value less than 0.05 was considered statistically significant.

RESULTS

The average searching strength showed an overall increasing trend, although with slight fluctuation. The public interest of NPs focuses on the symptoms and treatment for NPs and changes with time. For seasonal variation countries, the peak for the RSV occurred in winter and the bottom in summer. A region in higher latitudes may yield more RSV than that in lower latitudes. The rising-related topics in the recent 5 years reflected the significant differences in treatment and public interest of NPs between the developed and developing countries.

CONCLUSIONS

Google Trends analysis revealed global public interest and awareness of the evolution of trends and related topics in nasal polyps over time. Geographic distribution and seasonal variation may be potential trigger factors for NPs, and the public's interest in treatment especially biologics is rising.

Trends in public interest in child abuse in the United States: An infodemiology study of Google Trends from 2004 to 2022

INTRODUCTION

More than 1 in 7 children in the United States experience abuse annually with rates remaining consistent over the past 2 decades. During this timeframe, several high-profile cases of child abuse and neglect were publicized in national media in addition to multiple investigations uncovering Indigenous children dying from abuse at Indian Boarding Schools. Increased media attention among other public health and medical topics has been linked to increased public interest, thus, our objective was to investigate trends in public interest from 2004 to 2022.

METHODS

To assess trends in public interest, we extracted monthly relative search interest in child abuse from Google Trends. We constructed linear regression to determine the long-term trajectory of interest, and also compared the slope of the trend to other topics, such as domestic violence. Further, we compared mean relative search interest (RSI) from Child Abuse Awareness Month (April) to other months via t-test. Lastly, we assess by-state correlations of RSI and number of children abused.

RESULTS

Since 2004, search interest in child abuse has significantly declined in the United States-more than other related search terms. Child Abuse Awareness Month showed spikes in RSI which were greater than other months. By-state correlations of RSI and abuse were moderate to weak.

CONCLUSION

Despite heavy media attention covering stories of child abuse during the past 2 decades, search interest in child abuse has significantly declined. This trend may be related to aversion to secondary traumatic stress as news broadcasts often include stories of violence-of which child abuse stories may be most provoking. Following journalism guidance from the Centers for Disease Control and Prevention, reporting with focus on resiliency and prevention, rather than the individuals who perpetrated the crime, may provide more community support and increased public interest.

Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review

BACKGROUND

Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear.

OBJECTIVE

This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers.

METHODS

We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention.

RESULTS

We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented.

CONCLUSIONS

Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed.

TRIAL REGISTRATION

PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

Using Natural Language Processing to Explore "Dry January" Posts on Twitter: Longitudinal Infodemiology Study

BACKGROUND

Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants' experiences. One means through which to gain insights into individuals' Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January.

OBJECTIVE

We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets?

METHODS

We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term "dry january" or "dryjanuary" posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest.

RESULTS

We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals' experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters.

CONCLUSIONS

The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

Paracetamol Use in Patients With Osteoarthritis and Lower Back Pain: Infodemiology Study and Observational Analysis of Electronic Medical Record Data

Background

Lower back pain (LBP) and osteoarthritis (OA) are common musculoskeletal disorders and account for around 17.0% of years lived with disability worldwide; however, there is a lack of real-world data on these conditions. Paracetamol brands are frequently prescribed in France for musculoskeletal pain and include Doliprane, Dafalgan, and Ixprim (tramadol-paracetamol).

Objective

The objective of this retrospective study was to understand the journey of patients with LBP or OA when treated with paracetamol.

Methods

Three studies were undertaken. Two studies analyzed electronic medical records from general practitioners (GPs) and rheumatologists of patients with OA or LBP, who had received at least one paracetamol prescription between 2013 and 2018 in France. Data were extracted, anonymized, and stratified by gender, age, and provider specialty. The third study, an infodemiology study, analyzed associations between terms used on public medical forums and Twitter in France and the United States for OA only.

Results

In the first 2 studies, among patients with LBP (98,998), most (n=92,068, 93.0%) saw a GP, and Doliprane was a first-line therapy for 87.0% (n=86,128) of patients (71.0% [n=61,151] in combination with nonsteroidal anti-inflammatory drugs [NSAIDs] or opioids). Among patients with OA (99,997), most (n=84,997, 85.0%) saw a GP, and Doliprane was a first-line therapy for 83.0% (n=82,998) of patients (62.0% [n=51,459] in combination). Overall, paracetamol monotherapy prescriptions decreased as episodes increased. In the third study, in line with available literature, the data confirmed that the prevalence of OA increases with age (91.5% [212,875/232,650] above 41 years), OA is more predominant in females (46,530/232,650, 20.0%), and paracetamol use varies between GPs and rheumatologists.

Conclusions

This health surveillance analysis provides a better understanding of the journey for patients with LBP or OA. These data confirmed that although paracetamol remains the most common first-line analgesic for patients with LBP and OA, usage varies among patients and health care specialists, and there are concerns over efficacy.

Public Interest and Accessibility of Telehealth in Japan: Retrospective Analysis Using Google Trends and National Surveillance

Background

Recently, the use of telehealth for patient treatment under the COVID-19 pandemic has gained interest around the world. As a result, many infodemiology and infoveillance studies using web-based sources such as Google Trends were reported, focusing on the first wave of the COVID-19 pandemic. Although public interest in telehealth has increased in many countries during this time, the long-term interest has remained unknown among people living in Japan. Moreover, various mobile telehealth apps have become available for remote areas in the COVID-19 era, but the accessibility of these apps in epidemic versus nonepidemic regions is unknown.

Objective

We aimed to investigate the public interest in telehealth during the first pandemic wave and after the wave in the first part of this study, and the accessibility of medical institutions using telehealth in the epidemic and nonepidemic regions, in the second part.

Methods

We examined and compared the first wave and after the wave with regards to severe cases, number of deaths, relative search volume (RSV) of telehealth and COVID-19, and the correlation between RSV and COVID-19 cases, using open sources such as Google Trends and the Japanese Ministry of Health, Labour and Welfare (JMHLW) data. The weekly mean and the week-over-week change rates of RSV and COVID-19 cases were used to examine the correlation coefficients. In the second part, the prevalence of COVID-19 cases, severe cases, number of deaths, and the telehealth accessibility rate were compared between epidemic regions and nonepidemic regions, using the JMHLW data. We also examined the regional correlation between telehealth accessibility and the prevalence of COVID-19 cases.

Results

Among the 83 weeks with 5 pandemic waves, the overall mean for the RSV of telehealth and COVID-19 was 11.3 (95% CI 8.0-14.6) and 30.7 (95% CI 27.2-34.2), respectively. The proportion of severe cases (26.54% vs 18.16%; P<.001), deaths (5.33% vs 0.99%; P<.001), RSV of telehealth (mean 33.1, 95% CI 16.2-50.0 vs mean 7.3, 95% CI 6.7-8.0; P<.001), and RSV of COVID-19 (mean 52.1, 95% CI 38.3-65.9 vs mean 26.3, 95% CI 24.4-29.2; P<.001) was significantly higher in the first wave compared to after the wave. In the correlation analysis, the public interest in telehealth was 0.899 in the first wave and –0.300 overall. In Japan, the accessibility of telehealth using mobile apps was significantly higher in epidemic regions compared to nonepidemic regions in both hospitals (3.8% vs 2.0%; P=.004) and general clinics (5.2% vs 3.1%; P<.001). In the regional correlation analysis, telehealth accessibility using mobile apps was 0.497 in hospitals and 0.629 in general clinics.

Conclusions

Although there was no long-term correlation between the public interest in telehealth and COVID-19, there was a regional correlation between mobile telehealth app accessibility in Japan, especially for general clinics. We also revealed that epidemic regions had higher mobile telehealth app accessibility. Further studies about the actual use of telehealth and its effect after the COVID-19 pandemic are necessary.

Discussion of waterpipe tobacco smoking on reddit

Objectives

This study aimed to examine public discussions of waterpipe tobacco smoking, as well as the potential associations of different waterpipe flavors with health symptoms, using Reddit data.

Study design

This is an observational infodemiology study.

Methods

Using keywords such as “waterpipe”, “hookah”, and “shisha”, Reddit posts were extracted from Reddit Archive (pushshift.io) between February 1, 2016, and December 31, 2020. Temporal analysis was used to understand the longitudinal trend of the discussions about waterpipe tobacco smoking. Topic modeling using the Latent Dirichlet Allocation (LDA) model was performed to examine the topics on waterpipe tobacco. We estimated the conditional probability of having each health problem for each given waterpipe flavor.

Results

The discussion of waterpipe on Reddit was slowly decreasing from 2016 to 2018 and surged until May 2020. The fruit was the most popular waterpipe flavor and the neurological symptom was the most mentioned health category in waterpipe-related Reddit posts. The most popular topics included “Friends spending time together at night with waterpipe smoking”, “Playing games and smoking waterpipe at a hookah bar”, and “Discussing waterpipe flavors and related products”. Some waterpipe flavors were more associated with certain health problems than others. For example, mint/menthol flavor had a high probability to be associated with symptoms related to throat and mouth.

Conclusion

This study provided longitudinal surveillance of waterpipe tobacco smoking discussed on Reddit. We showed the potential relationship between waterpipe flavors and health symptoms, which provides preliminary evidence about the potential health effects of waterpipe tobacco smoking.