Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet

Systematic review of infodemiology studies using artificial intelligence: social media posts on HIV preexposure prophylaxis

OBJECTIVES

To explore how artificial intelligence (AI) can enhance infodemiology, which distributes and scans information in the electronic medium, to process social media posts for HIV preexposure prophylaxis (PrEP).

DESIGN

Systematic review.

METHODS

We searched in the U.S. Centers for Disease Control and Prevention's Prevention Research Synthesis database through June 2024 (PROSPERO: CRD42023458870). We included infodemiology studies published in English and reported using AI to process social media posts on PrEP. Two reviewers independently screened citations, extracted data, and conducted a risk of bias assessment using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. Findings are narratively summarized.

RESULTS

Of the 135 citations screened, eight infodemiology studies were identified, analyzing over 58.9 million posts. Infodemiology studies found the PrEP topics commonly discussed in communities (e.g., barriers of uptake), rumors that may raise public health concerns (e.g., PrEP is a prevention method against COVID-19 infection), geographic locations where concerns regarding risk of acquiring HIV were raised (e.g., most HIV-related posts were from the 10 states with the highest numbers of new HIV diagnoses), and predicted HIV trends (e.g., HIV-related tweets were negatively correlated with the county-level HIV incidence rate in the following year).

CONCLUSIONS

Despite the limitations of this review including a small number of studies reviewed, our review suggests social media posts may provide information on real-time PrEP-related concerns, and AI can accelerate and enhance the processing of mass data to identify the information that communities need and the areas/locations that may need HIV prevention intervention.

Mapping the landscape of mental health research through Google Trends: Bibliometric and thematic insights

Aim

The rise of Internet usage has introduced innovative methods for public health research, particularly using Google Trends to understand mental health issues. This study aims to conduct a comprehensive bibliometric and content analysis of research utilizing Google Trends for mental health.

Methods

We conducted a literature search on Scopus focusing on peer-reviewed articles from January 2010 to May 2024. Bibliometric analysis included descriptive statistics, bibliographic coupling, keyword co-occurrence, and coauthorship networks. Qualitative content analysis identified themes in the study objectives.

Results

The bibliometric analysis revealed an increase in publications post-pandemic. The bibliographic coupling analysis identified the Journal of Medical Internet Research as central, with significant connections to journals like JMIR Public Health and Surveillance and BMC Public Health. Keyword co-occurrence highlighted themes such as "mental health," "COVID-19," "anxiety," and "social media." Author co-citation and coauthorship analyses showed strong closely linked collaborations, with a few central authors leading the research. Coauthor country analysis revealed limited international collaborations, particularly involving the United States and the United Kingdom. Content analysis identified six major themes: economic and social impacts, mental health during public health emergencies, online behavior, specific conditions and treatments, public health policies, and psychological and social impacts.

Conclusion

This study underscores the importance of Google Trends in mental health research, revealing key trends and thematic focuses. The findings contribute to understanding the current research landscape and offer a foundation for future studies leveraging digital tools for mental health insights.

Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence

BACKGROUND

Long COVID, the persistent illness following COVID-19 infection, has emerged as a major public health concern since the outbreak of the pandemic. Effective disease surveillance is crucial for policymaking and resource allocation.

METHODS

We investigated the potential of utilizing Google Trends data to enhance long COVID symptoms surveillance. Though Google Trends provides freely available search popularity data, limitations in data normalization and retrieval restrictions have hindered its predictive capabilities. In our study, we carefully selected 33 search terms and 20 related topics from the long COVID symptoms list provided by the Centers for Disease Control and Prevention and the database "scite", and calculated their merged search volumes from Google Trends data using our developed statistical method for analysis.

RESULTS

We identify four related topics (ageusia, anosmia, chest pain, and headaches) that consistently exhibit increased search popularity before that of "long COVID." Additionally, nine related topics (aching muscle pain, anxiety, chest pain, clouding of consciousness, dizziness, fatigue, myalgia, shortness of breath, and hypochondriasis) show increased search popularity following that of "long COVID." We demonstrate that the merged search volume (MSV), derived from the relative search volume data downloaded from Google, can be used to forecast the prevalence of long COVID in a prediction study, supporting the use of the methodology in risk management regarding the prevalence of long COVID.

CONCLUSIONS

By utilizing a comprehensive list of search terms and sophisticated statistical analytics, our study contributes to exploring the potential of Google Trends data for forecasting and monitoring long COVID prevalence. These findings and methodologies can be used as prior knowledge to inform future infodemiological and epidemiological investigations.

Insight into public sentiment and demand in China's public health emergency response: a weibo data analysis

BACKGROUND

During the COVID-19 pandemic, public sentiment and demands have been prominently reflected on social media platforms like Weibo. Understanding these sentiments and demands is crucial for governments, health officials, and policymakers to make effective responses and adjustments.

OBJECTIVE

The study aims to analyze public sentiment and identify key demands concerning COVID-19 policies and social issues using Weibo data, providing insights to improve China's policies and legal systems in public health emergencies.

METHODS

The study used Python tools to collect public opinion data from Weibo regarding policy adjustments, social issues, and livelihood concerns. A total of 50,249 valid comments on 100 blog posts were collected from December 2019 to October 2023 in China. The SnowNLP algorithm was employed for sentiment analysis, Latent Dirichlet Allocation was used for topic clustering, and sampling coding was applied to further explore public demands by condensing the comment texts.

RESULTS

The study categorized 100 blog posts into 23 important topics, with average sentiment scores ranging from 0.24 to 0.66. These scores ranging from 0 to 1 reflect sentiment polarity, where lower values indicate more negative public sentiment. The topics of material safety and information security management had the lowest scores, at 0.24 and 0.34, respectively. The analysis further revealed that the 23 topics could be classified into 57 subtopics, and a total of 101 concepts were identified through coding. The study found that public demands fall into five key categories: transportation and travel security, epidemic protection and health security, law building and policy implementation, social services and public demand, and education demand.

CONCLUSIONS

The study underscores the complexity of public sentiment during the epidemic, with significant concerns about material safety and information security management. Public demands span basic survival needs to higher-order concerns such as education and legal protections. The findings suggest that policy-making processes must become more responsive, transparent, and equitable, incorporating real-time public feedback and ensuring comprehensive policies and legal systems are in place to address multifaceted public demands effectively.

Infoveillance of COVID-19 Infections in Dentistry Using Platform X: Descriptive Study

BACKGROUND

The effect of the COVID-19 pandemic on the well-being of dental professionals and patients has been difficult to track and quantify. X (formerly known as Twitter) proved to be a useful infoveillance tool for tracing the impact of the COVID-19 pandemic worldwide.

OBJECTIVE

This study aims to investigate the use of X to track COVID-19 infections and deaths associated with dental practices.

METHODS

English Tweets reporting infections or deaths associated with the dental practice were collected from January 1, 2020, to March 31, 2021. Tweets were searched manually using the X Pro search engine (previously known as TweetDeck [X Corp], Twitter Inc, and TweetDeck Ltd) and automatically using a tweet crawler on the X Academic Research application programming interface. Queries included keywords on infection or death of dental staff and patients caused by COVID-19. Tweets registering events on infection or death of dentists, dental staff, and patients as part of their conversation were included.

RESULTS

A total of 5641 eligible tweets were retrieved. Of which 1583 (28.1%) were deemed relevant after applying the inclusion and exclusion criteria. Of the relevant tweets, 311 (19.6%) described infections at dental practices, where 1168 (86.9%) infection cases were reported among dentists, 134 (9.9%) dental staff, and 41 (3.1%) patients. The majority of reported infections occurred in the United States, India, and Canada, affecting individuals aged 20-51 years. Among the 600 documented deaths, 253 (42.2%) were dentists, 22 (3.7%) were dental staff, and 7 (1.2%) were patients. The countries with the highest number of deaths were the United States, Pakistan, and India, with an affected age range of 23-83 years.

CONCLUSIONS

The data suggest that analyses of X information in populations of affected areas may provide useful information regarding the impact of a pandemic on the dental profession and demonstrate a correlation with suspected and confirmed infection or death cases. Platform X shows potential as an early predictor for disease spread. However, further research is required to confirm its validity.

Google infodemiology and temporal trends in benign prostatic hyperplasia surgery in Australia

OBJECTIVE

To contextualise the national population-based temporal surgical trends in the management of benign prostatic hyperplasia (BPH) in Australia.

METHODS

Google Trends (GT) was used to analyse the level of patients' online interest in various surgical options for the treatment of BPH. Analysis of variance was performed and the average rates of change in GT popularity score was calculated for the period from 1 January 2010 to 31 October 2022. Data regarding surgical volume were extracted from the Medicare Benefits Schedule and the Australian Institute of Health and Welfare databases, and population-adjusted rates of BPH procedures performed were calculated accordingly.

RESULTS

The level of online interest in Australia was highest for transurethral resection of the prostate (TURP) and population data also showed that TURP remained the most utilised procedure across the study period. Nationwide, there was an increased uptake of photovaporisation of the prostate, accounting for 7.6% of BPH procedures in 2020-2021, with correspondingly lower rates of TURP and simple prostatectomies performed. Holmium laser enucleation of the prostate (HoLEP) appeared to be underutilised and had the lowest mean popularity score, while prostatic urethral lift (PUL) and transurethral water vapour ablation (TUWA) showed strong uptake in the more recent years. The average rates of change were highest for TURP at +1.59/year (P < 0.001), followed by PUL at +0.56/year (P < 0.001) and TUWA at +0.50/year (P = 0.004), while the lowest was for HoLEP at +0.09/year (P = 0.116).

CONCLUSION

Differences were found between GT analysis and actual BPH procedures performed at a population level. This study highlights that clinicians need to adapt and provide better patient counselling to ensure a shared decision-making process when choosing the treatment procedure for BPH/male LUTS.

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.

Analysis of the Influence of the SARS-CoV-2 (COVID-19) Pandemic on the Search for Facial Aesthetic Procedures

The SARS-CoV-2 (COVID-19) pandemic caused, in 2020, a serious and urgent health issue worldwide, establishing social distancing rules, which changed daily life in all its aspects. Work relationships and socialization became possible only through virtual networks. As a result, there has been an extreme increase in the exposure of people in general to their own image for many hours a day through cameras and video conferencing applications. This exacerbated exposure to self-image and greater criticism regarding own appearance raised hypotheses that the pandemic state generated in the population the desire to seek improvements in appearance and, consequently, would have caused an increase in searches for aesthetic procedures on the face and upper region of the chest. Therefore, this study aimed to elucidate these questions, using the virtual search trend analysis tool on the Google platform, called Google Trends. Analyzes of the main surgical and non-surgical facial aesthetic procedures carried out by dentistry professionals were done through statistical tests, tables, and observation of graphs generated by Google Trends. With the analysis of the results, a constant growth trend in searches by the public in relation to aesthetic procedures was observed, which may have been triggered by the pandemic, but is not related to it. The growth in Google searches continued after the pandemic state and some showed greater growth years after the declaration of social isolation.

Analyzing Online Search Trends for Kidney, Prostate, and Bladder Cancers in China: Infodemiology Study Using Baidu Search Data (2011-2023)

BACKGROUND

Cancers of the bladder, kidney, and prostate are the 3 major genitourinary cancers that significantly contribute to the global burden of disease (GBD) and continue to show increasing rates of morbidity and mortality worldwide. In mainland China, understanding the cancer burden on patients and their families is crucial; however, public awareness and concerns about these cancers, particularly from the patient's perspective, remain predominantly focused on financial costs. A more comprehensive exploration of their needs and concerns has yet to be fully addressed.

OBJECTIVE

This study aims to analyze trends in online searches and user information-seeking behaviors related to bladder, kidney, and prostate cancers-encompassing descriptive terms (eg, "bladder cancer," "kidney cancer," "prostate cancer") as well as related synonyms and variations-on both national and regional scales. This study leverages data from mainland China's leading search engine to explore the implications of these search patterns for addressing user needs and improving health management.

METHODS

The study analyzed Baidu Index search trends for bladder, kidney, and prostate cancers (from January 2011 to August 2023) at national and provincial levels. Search volume data were analyzed using the joinpoint regression model to calculate annual percentage changes (APCs) and average APCs (AAPCs), identifying shifts in public interest. User demand was assessed by categorizing the top 10 related terms weekly into 13 predefined topics, including diagnosis, treatment, and traditional Chinese medicine. Data visualization and statistical analyses were performed using Prism 9. Results revealed keyword trends, demographic distributions, and public information needs, offering insights into health communication and management strategies based on online information-seeking behavior.

RESULTS

Three cancer topics were analyzed using 39 search keywords, yielding a total Baidu Search Index (BSI) of 43,643,453. From 2011 to 2015, the overall APC was 15.2% (P<.05), followed by -2.8% from 2015 to 2021, and 8.9% from 2021 to 2023, with an AAPC of 4.9%. Bladder, kidney, and prostate cancers exhibited AAPCs of 2.8%, 3.9%, and 6.8%, respectively (P<.05). The age distribution of individuals searching for these cancer topics varied across the topics. Geographically, searches for cancer were predominantly conducted by people from East China, who accounted for approximately 30% of each cancer search query. Regarding user demand, the total BSI for relevant user demand terms from August 2022 to August 2023 was 676,526,998 out of 2,570,697,380 (15.74%), representing only a limited total cancer-related search volume.

CONCLUSIONS

Online searches and inquiries related to genitourinary cancers are on the rise. The depth of users' information demands appears to be influenced by regional economic levels. Cancer treatment decision-making may often involve a family-centered approach. Insights from internet search data can help medical professionals better understand public interests and concerns, enabling them to provide more targeted and reliable health care services.

Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations

BACKGROUND

Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health.

OBJECTIVE

In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations.

METHODS

We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients' age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years.

RESULTS

Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman ρ=0.70 vs ρ=0.56; Spain: ρ=0.88 vs ρ=0.76; Brazil: ρ=0.83 vs ρ=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: ρ=0.75 vs ρ=0.52; Spain: ρ=0.83 vs ρ=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals (ρ=0.92 vs ρ=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities (ρ=0.68 vs ρ=0.66).

CONCLUSIONS

We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users' health-seeking behaviors.

Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis

BACKGROUND

While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collection times.

OBJECTIVE

Harnessing drug-related data posted on social media in real-time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study aimed to develop a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19-related drugs.

METHODS

This study constructed a full pipeline for COVID-19-related drug tweet analysis, using pretrained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of 4 core modules: named entity recognition and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names for time trend analysis, target sentiment analysis to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to dig potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to the COVID-19 pandemic and drug therapies between February 1, 2020, and April 30, 2022.

RESULTS

From a dataset comprising 169,659,956 COVID-19-related tweets from 103,682,686 users, our named entity recognition model identified 2,124,757 relevant tweets sourced from 1,800,372 unique users, and the top 5 most-discussed drugs: ivermectin, hydroxychloroquine, remdesivir, zinc, and vitamin D. Time trend analysis revealed that the public focused mostly on repurposed drugs (ie, hydroxychloroquine and ivermectin), and least on remdesivir, the only officially approved drug among the 5. Sentiment analysis of the top 5 most-discussed drugs revealed that public perception was predominantly shaped by celebrity endorsements, media hot spots, and governmental directives rather than empirical evidence of drug efficacy. Topic analysis obtained 15 general topics of overall drug-related tweets, with "clinical treatment effects of drugs" and "physical symptoms" emerging as the most frequently discussed topics. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use.

CONCLUSIONS

This study shows that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media-based public health analytics.

Sepsis in silico: definition, development and application of an electronic phenotype for sepsis

Repurposing electronic health record (EHR) or electronic medical record (EMR) data holds significant promise for evidence-based epidemic intelligence and research. Key challenges include sepsis recognition by physicians and issues with EHR and EMR data. Recent advances in data-driven techniques, alongside initiatives like the Surviving Sepsis Campaign and the Severe Sepsis and Septic Shock Management Bundle (SEP-1), have improved sepsis definition, early detection, subtype characterization, prognostication and personalized treatment. This includes identifying potential biomarkers or digital signatures to enhance diagnosis, guide therapy and optimize clinical management. Machine learning applications play a crucial role in identifying biomarkers and digital signatures associated with sepsis and its sub-phenotypes. Additionally, electronic phenotyping, leveraging EHR and EMR data, has emerged as a valuable tool for evidence-based sepsis identification and management. This review examines methods for identifying sepsis cohorts, focusing on two main approaches: utilizing health administrative data with standardized diagnostic coding via the International Classification of Diseases and integrating clinical data. This overview provides a comprehensive analysis of current cohort identification and electronic phenotyping strategies for sepsis, highlighting their potential applications and challenges. The accuracy of an electronic phenotype or signature is pivotal for precision medicine, enabling a shift from subjective clinical descriptions to data-driven insights.

Discussion of the public interest in arthroscopy based on the Baidu index and its implications for nursing care

BACKGROUND

Despite the widespread application of big data in topic analysis, the public's attention and nursing requirements for arthroscopy remain inadequate.

AIM

To understand netizens' concerns and spatial distributions regarding arthroscopy and to provide customized nursing strategies.

METHODS

The Baidu index was employed to gather and analyze the search index, demand graph, keyword popularity, and regional distribution data for the keywords "arthroscopy," "knee arthroscopy," and "arthroscopy surgery" from 2018 to 2023.

RESULTS

A total of 254692 items of information were searched for these keywords, with 59.86% from mobile terminals. Netizens' interest in arthroscopy showed a fluctuating pattern, which was consistent with fluctuations in the elasticity coefficient, and was primarily concentrated in the provinces of Guangdong, Jiangsu, and Shandong.

CONCLUSION

The Baidu index provides new avenues for exploring public demand for arthroscopy. Nursing personnel can utilize these data to develop more precise health education plans and guidance, enhancing the quality and satisfaction of patient care.

Public interest in online searching of asthma information: insights from a Google trends analysis

BACKGROUND

Google Trends (GT) is a free tool that provides insights into the public's interest and information-seeking behavior on specific topics. In this study, we utilized GT data on patients' search history to better understand their questions and information needs regarding asthma.

METHODS

We extracted the relative GT search volume (RSV) for keywords associated with asthma to explore information-seeking behaviors and assess internet search patterns regarding asthma disease from 2004 to 2024 in both English and Persian languages. In addition, a correlation analysis was conducted to assess terms correlated with asthma searches. Then, the AutoRegressive predictive models were developed to estimate future patterns of asthma-related searches and the information needs of individuals with asthma.

RESULTS

The analysis revealed that the mean total RSV for asthma-related keywords over the 20-year period was 41.79 ± 6.07. The researchers found that while asthma-related search volume has shown a consistent upward trend in Persian-speaking countries over the last decade, English-speaking countries have experienced less variability in such searches except for a spike during the COVID-19 pandemic. The correlation analysis of related subjects showed that "air pollution", "infection", and "insomnia" have a positive correlation with asthma. Developing AutoRegressive predictive models on retrieved Google Trends data revealed a seasonal pattern in global asthma-related search interest. In contrast, the models forecasted a growing increase in information-seeking behaviors regarding asthma among Persian-speaking patients over the coming decades.

CONCLUSIONS

There are significant differences in how people search for and access asthma information based on their language and regional context. In English-speaking countries, searches tend to focus on broader asthma-related topics like pollution and infections, likely due to the availability of comprehensive asthma resources. In contrast, Persian speakers prioritize understanding specific aspects of asthma-like symptoms, medications, and complementary treatments. To address these divergent information needs, health organizations should tailor content to these divergent needs.

The Complex Interaction Between Sleep-Related Information, Misinformation, and Sleep Health: Call for Comprehensive Research on Sleep Infodemiology and Infoveillance

The complex interplay between sleep-related information-both accurate and misleading-and its impact on clinical public health is an emerging area of concern. Lack of awareness of the importance of sleep, and inadequate information related to sleep, combined with misinformation about sleep, disseminated through social media, nonexpert advice, commercial interests, and other sources, can distort individuals' understanding of healthy sleep practices. Such misinformation can lead to the adoption of unhealthy sleep behaviors, reducing sleep quality and exacerbating sleep disorders. Simultaneously, poor sleep itself impairs critical cognitive functions, such as memory consolidation, emotional regulation, and decision-making. These impairments can heighten individuals' vulnerability to misinformation, creating a vicious cycle that further entrenches poor sleep habits and unhealthy behaviors. Sleep deprivation is known to reduce the ability to critically evaluate information, increase suggestibility, and enhance emotional reactivity, making individuals more prone to accepting persuasive but inaccurate information. This cycle of misinformation and poor sleep creates a clinical public health issue that goes beyond individual well-being, influencing occupational performance, societal productivity, and even broader clinical public health decision-making. The effects are felt across various sectors, from health care systems burdened by sleep-related issues to workplaces impacted by decreased productivity due to sleep deficiencies. The need for comprehensive clinical public health initiatives to combat this cycle is critical. These efforts must promote sleep literacy, increase awareness of sleep's role in cognitive resilience, and correct widespread sleep myths. Digital tools and technologies, such as sleep-tracking devices and artificial intelligence-powered apps, can play a role in educating the public and enhancing the accessibility of accurate, evidence-based sleep information. However, these tools must be carefully designed to avoid the spread of misinformation through algorithmic biases. Furthermore, research into the cognitive impacts of sleep deprivation should be leveraged to develop strategies that enhance societal resilience against misinformation. Sleep infodemiology and infoveillance, which involve tracking and analyzing the distribution of sleep-related information across digital platforms, offer valuable methodologies for identifying and addressing the spread of misinformation in real time. Addressing this issue requires a multidisciplinary approach, involving collaboration between sleep scientists, health care providers, educators, policy makers, and digital platform regulators. By promoting healthy sleep practices and debunking myths, it is possible to disrupt the feedback loop between poor sleep and misinformation, leading to improved individual health, better decision-making, and stronger societal outcomes.

Health-Related Messages About Herbs, Spices, and Other Botanicals Appearing in Print Issues and Websites of Legacy Media: Content Analysis and Evaluation

BACKGROUND

Legacy media are publications that existed before the internet. Many of these have migrated to a web format, either replacing or in parallel to their print issues. Readers place an economic value on access to the information presented as they pay for subscriptions and place a higher degree of trust in their content. Much has been written about inaccurate and misleading health information in social media; however, the content and accuracy of information contained in legacy media has not been examined in detail. Discussion of herbs, spices, and other botanicals has been absent from this context.

OBJECTIVE

The objectives of this study were to (1) identify the health associations of botanical products mentioned in legacy media targeted to a range of demographic groups and (2) evaluate these health associations for accuracy against published scientific studies.

METHODS

In total, 10 popular magazines targeting a range of gender, race/ethnicity, and sexual orientation demographic groups were selected for analysis. Relevant content was extracted and coded over 1 year. Associations between specific botanical products and health factors were identified. For the most frequent botanical-health application associations, a PubMed search was conducted to identify reviews corresponding to each item's indicated applications. Where no systematic reviews were available, single research studies were sought.

RESULTS

A total of 237 unique botanical products were identified. There were 128 mentions of these in the print issues and 1215 on the websites. In total, 18 health applications were identified and used to categorize the indicated uses for the various products individually and as general categories. The most frequently mentioned applications were skin care, with 913 mentions, immunity enhancement, with 705 mentions, gastrointestinal health and probiotics, with 184 mentions, and cognitive function (stress and mental health), with 106 mentions. Comparison to published literature evaluating the efficacy of these functions identified positive support for aloe vera, argan oil, chamomile, jojoba oil, lavender, rosemary, and tea tree oil in skin care. Berries, ginger, turmeric, and green tea had the strongest evidence for a role in immunity enhancement. Ginger and oats were supported as having a role in gastrointestinal health. Finally, berries, lavender, ashwagandha, and cannabidiol were supported as having a role in managing stress. Other frequently mentioned items such as aloe vera, ashwagandha, or mushrooms for immunity were less strongly supported.

CONCLUSIONS

Comparison of the most prevalent associations between botanical products and health applications to published literature indicates that, overall, these associations were consistent with current scientific reports about the health applications of botanical products. While some products had a greater degree of research support than others, truly egregious falsehoods were absent. Therefore, legacy media may be considered a credible source of information to readers about these topics.

eHealth in pediatric respiratory allergy

PURPOSE OF REVIEW

This review explores the relevance of eHealth technologies to address unmet needs in pediatric respiratory allergies, particularly allergic rhinitis (AR) and asthma. Given the increasing burden of these conditions, there is a pressing need for effective solutions to enhance disease surveillance, diagnosis, and management.

RECENT FINDINGS

Recent literature highlights the potential of eHealth tools to transform pediatric respiratory allergy care. The use of digital data for infodemiology, application of machine learning models to improve diagnostic sensitivity, smartphone apps with digital patient reported outcome measure (PROMs) and embedded sensors to monitor disease, healthcare professional dashboards with real-time data monitoring and clinical decision support systems (CDSS) are advances emerging to optimize pediatric respiratory allergy care.

SUMMARY

Integrating eHealth technologies into the pediatric respiratory allergy care pathway is a potential solution for current healthcare challenges to better meet the needs of children with AR and asthma. However, while the potential of eHealth is evident, its widespread implementation in real-world practice requires continued research, collaboration, and efforts to overcome existing barriers.

Public interest in drug-related problems reflected in information search trends: an infodemiological study

BACKGROUND

The analysis of how people search and "navigate" the internet to obtain health-related information and how they communicate and share this information can provide valuable knowledge about the disease patterns behaviour and health habits of populations.

OBJECTIVE

To determine the population's interest in drug-related problems through information search trends.

METHOD

A descriptive ecological correlational study, based on obtaining Google Trends data.

VARIABLES STUDIED

relative search volume (RSV), evolution over time, milestones and seasonality.

RESULTS

The most searched topic was drug overdose, with mean RSV of 56.25 ± 0.65. The highest increase occurred in the contraindication topic (R2 = 0.87, p < 0.001). The main milestone was observed in the drug overdose topic in July 2018 (RSV = 100). A very close relationship was found between adverse drug reaction and contraindication (R = 0.89, p < 0.001). Slight seasonality was noted in the adverse drug reaction (augmented Dickey-Fuller test [ADF] = -1.96), contraindication (ADF = -2.66) and drug interaction (ADF = -1.67) topics, but did not show an epidemiological trend.

CONCLUSIONS

The greatest public interest was found in the drug overdose and contraindication topics, which showed a stronger upward trend, although the seasonality study did not show any very notable data or demonstrate epidemiological information search behaviour. The main milestone observed was due to media factors related to the consumption of narcotics. There was a clear difference in English-speaking countries in the use of the drug overdose topic. A correlation between the adverse drug reaction and contraindication topics was confirmed.

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.

Health Information Disorders Models: A Scoping Review

Background

The purpose of this scoping review is to identify the models of Health Information Disorders (HIDs), the components of these models, their study setting, and their designing approaches.

Materials and Methods

In this study, PubMed, Web Of Science (WOS), Scopus, ProQuest, and Embase databases were searched to identify relevant articles. After screening the identified studies, 22 studies were selected. Data was extracted based on objectives and was combined and summarized by a narrative method.

Results

The analysis of articles showed most of the included studies presented conceptual models or frameworks that provide a more structured and comprehensive view of a topic. The elements and components of the HID models were categorized into five main components, including information issues, communication issues, psychology issues, social issues and theories. Most studies employed, existing theories, evidence, or principles to design their approaches. The main setting of studies were COVID-19 and related topics such as vaccination.

Conclusions

By synthesizing the HID models we tried to find the gap among types, components, designing approaches and setting of models. It seems we need some HID models based on contextual frameworks to understand deeply the way of being born, spread and death of HIDs in society. Also, future advancements in HID models should focus on other diseases rather than COVID-19 to provide a holistic approach in diverse healthcare landscapes.

Artificial Intelligence-Supported Development of Health Guideline Questions

BACKGROUND

Guideline questions are typically proposed by experts.

OBJECTIVE

To assess how large language models (LLMs) can support the development of guideline questions, providing insights on approaches and lessons learned.

DESIGN

Two approaches for guideline question generation were assessed: 1) identification of questions conveyed by online search queries and 2) direct generation of guideline questions by LLMs. For the former, the researchers retrieved popular queries on allergic rhinitis using Google Trends (GT) and identified those conveying questions using both manual and LLM-based methods. They then manually structured as guideline questions the queries that conveyed relevant questions. For the second approach, they tasked an LLM with proposing guideline questions, assuming the role of either a patient or a clinician.

SETTING

Allergic Rhinitis and its Impact on Asthma (ARIA) 2024 guidelines.

PARTICIPANTS

None.

MEASUREMENTS

Frequency of relevant questions generated.

RESULTS

The authors retrieved 3975 unique queries using GT. From these, they identified 37 questions, of which 22 had not been previously posed by guideline panel members and 2 were eventually prioritized by the panel. Direct interactions with LLMs resulted in the generation of 22 unique relevant questions (11 not previously suggested by panel members), and 4 were eventually prioritized by the panel. In total, 6 of 39 final questions prioritized for the 2024 ARIA guidelines were not initially thought of by the panel. The researchers provide a set of practical insights on the implementation of their approaches based on the lessons learned.

LIMITATION

Single case study (ARIA guidelines).

CONCLUSION

Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels.

PRIMARY FUNDING SOURCE

Fraunhofer Cluster of Excellence for Immune-Mediated Diseases.

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.

Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis

BACKGROUND

Allergy disorders caused by biological particles, such as the proteins in some airborne pollen grains, are currently considered one of the most common chronic diseases, and European Academy of Allergy and Clinical Immunology forecasts indicate that within 15 years 50% of Europeans will have some kind of allergy as a consequence of urbanization, industrialization, pollution, and climate change.

OBJECTIVE

The aim of this study was to monitor and analyze the dissemination of information about pollen symptoms from December 2006 to January 2022. By conducting a comprehensive evaluation of public comments and trends on Twitter, the research sought to provide valuable insights into the impact of pollen on sensitive individuals, ultimately enhancing our understanding of how pollen-related information spreads and its implications for public health awareness.

METHODS

Using a blend of large language models, dimensionality reduction, unsupervised clustering, and term frequency-inverse document frequency, alongside visual representations such as word clouds and semantic interaction graphs, our study analyzed Twitter data to uncover insights on respiratory allergies. This concise methodology enabled the extraction of significant themes and patterns, offering a deep dive into public knowledge and discussions surrounding respiratory allergies on Twitter.

RESULTS

The months between March and August had the highest volume of messages. The percentage of patient tweets appeared to increase notably during the later years, and there was also a potential increase in the prevalence of symptoms, mainly in the morning hours, indicating a potential rise in pollen allergies and related discussions on social media. While pollen allergy is a global issue, specific sociocultural, political, and economic contexts mean that patients experience symptomatology at a localized level, needing appropriate localized responses.

CONCLUSIONS

The interpretation of tweet information represents a valuable tool to take preventive measures to mitigate the impact of pollen allergy on sensitive patients to achieve equity in living conditions and enhance access to health information and services.

Mapping the Evolution of Digital Health Research: Bibliometric Overview of Research Hotspots, Trends, and Collaboration of Publications in JMIR (1999-2024)

BACKGROUND

While bibliometric studies of individual journals have been conducted, to the best of our knowledge, bibliometric mapping has not yet been utilized to analyze the literature published by the Journal of Medical Internet Research (JMIR).

OBJECTIVE

In celebration of the journal's 25th anniversary, this study aimed to review the entire collection of JMIR publications from 1999 to 2024 and provide a comprehensive overview of the main publication characteristics.

METHODS

This study included papers published in JMIR during the 25-year period from 1999 to 2024. The data were analyzed using CiteSpace, VOSviewer, and the "Bibliometrix" package in R. Through descriptive bibliometrics, we examined the dynamics and trend patterns of JMIR literature production and identified the most prolific authors, papers, institutions, and countries. Bibliometric maps were used to visualize the content of published articles and to identify the most prominent research terms and topics, along with their evolution. A bibliometric network map was constructed to determine the hot research topics over the past 25 years.

RESULTS

This study revealed positive trends in literature production, with both the total number of publications and the average number of citations increasing over the years. And the global COVID-19 pandemic induced an explosive rise in the number of publications in JMIR. The most productive institutions were predominantly from the United States, which ranked highest in successful publications within the journal. The editor-in-chief of JMIR was identified as a pioneer in this field. The thematic analysis indicated that the most prolific topics aligned with the primary aims and scope of the journal. Currently and in the foreseeable future, the main themes of JMIR include "artificial intelligence," "patient empowerment," and "victimization."

CONCLUSIONS

This bibliometric study highlighted significant contributions to digital health by identifying key research trends, themes, influential authors, and collaborations. The findings underscore the necessity to enhance publications from developing countries, improve gender diversity among authors, and expand the range of research topics explored in the journal.

Identifying Marijuana Use Behaviors Among Youth Experiencing Homelessness Using a Machine Learning-Based Framework: Development and Evaluation Study

BACKGROUND

Youth experiencing homelessness face substance use problems disproportionately compared to other youth. A study found that 69% of youth experiencing homelessness meet the criteria for dependence on at least 1 substance, compared to 1.8% for all US adolescents. In addition, they experience major structural and social inequalities, which further undermine their ability to receive the care they need.

OBJECTIVE

The goal of this study was to develop a machine learning-based framework that uses the social media content (posts and interactions) of youth experiencing homelessness to predict their substance use behaviors (ie, the probability of using marijuana). With this framework, social workers and care providers can identify and reach out to youth experiencing homelessness who are at a higher risk of substance use.

METHODS

We recruited 133 young people experiencing homelessness at a nonprofit organization located in a city in the western United States. After obtaining their consent, we collected the participants' social media conversations for the past year before they were recruited, and we asked the participants to complete a survey on their demographic information, health conditions, sexual behaviors, and substance use behaviors. Building on the social sharing of emotions theory and social support theory, we identified important features that can potentially predict substance use. Then, we used natural language processing techniques to extract such features from social media conversations and reactions and built a series of machine learning models to predict participants' marijuana use.

RESULTS

We evaluated our models based on their predictive performance as well as their conformity with measures of fairness. Without predictive features from survey information, which may introduce sex and racial biases, our machine learning models can reach an area under the curve of 0.72 and an accuracy of 0.81 using only social media data when predicting marijuana use. We also evaluated the false-positive rate for each sex and age segment.

CONCLUSIONS

We showed that textual interactions among youth experiencing homelessness and their friends on social media can serve as a powerful resource to predict their substance use. The framework we developed allows care providers to allocate resources efficiently to youth experiencing homelessness in the greatest need while costing minimal overhead. It can be extended to analyze and predict other health-related behaviors and conditions observed in this vulnerable community.

Seasonal and Weekly Patterns of Korean Adolescents' Web Search Activity on Insomnia: Retrospective Study

BACKGROUND

Sleep deprivation in adolescents is a common but serious public health issue. Adolescents often have a progressive circadian delay and suffer from insufficient sleep during weekdays due to the school schedule. Temporal patterns in internet search activity data can provide relevant information for understanding the characteristic sleep problems of the adolescent population.

OBJECTIVE

We aimed to reveal whether adolescents exhibit distinct temporal seasonal and weekly patterns in internet search activity on insomnia compared to adults.

METHODS

We hypothesized that adolescents exhibit larger variations in the internet search volume for insomnia, particularly in association with the school schedule (e.g., academic vacations and weekends). We extracted the daily search volume for insomnia in South Korean adolescents (13-18 years old), adults (19-59 years old), and young adults (19-24 years old) during the years 2016-2019 using NAVER DataLab, the most popular search engine in South Korea. The daily search volume data for each group were normalized with the annual median of each group. The time series of the search volume was decomposed into slow fluctuation (over a year) and fast fluctuation (within a week) using fast Fourier transform. Next, we compared the normalized search volume across months in a year (slow fluctuation) and days in a week (fast fluctuation).

RESULTS

In the annual trend, 2-way ANOVA revealed a significant (group) × (month) interaction (P<.001). Adolescents exhibited much greater seasonal variations across a year than the adult population (coefficient of variation=0.483 for adolescents vs 0.131 for adults). The search volume for insomnia in adolescents was notably higher in January, February, and August, which are academic vacation periods in South Korea (P<.001). In the weekly pattern, 2-way ANOVA revealed a significant (group) × (day) interaction (P<.001). Adolescents showed a considerably increased search volume on Sunday and Monday (P<.001) compared to adults. In contrast, young adults demonstrated seasonal and weekly patterns similar to adults.

CONCLUSIONS

Adolescents demonstrate distinctive seasonal and weekly patterns in internet searches on insomnia (ie, increased search in vacation months and weekend-weekday transitions), which are closely associated with the school schedule. Adolescents' sleep concerns might be potentially affected by the disrupted daily routine and the delayed sleep phase during vacations and weekends. As we demonstrated, comparing various age groups in infodemiology and infoveillance data might be helpful in identifying distinctive features in vulnerable age groups.

The cardiovascular scamdemic: The epidemic spread of cardiovascular treatment scams and misinformation

Recently, some cardiovascular surgeons have been increasingly using social media for marketing, often employing misleading terminology. This trend, which we termed the "cardiovascular scamdemic," involves the widespread dissemination of deceptive advertisements for cardiovascular treatments, resembling an epidemic. Exposure to such misinformation not only endangers patients, who naturally rely on information from professional sources, but also erodes public trust in medical ethics and scientific integrity. Additionally, it contributes to treatment refusal and adverse health outcomes. The lack of comprehensive global regulations addressing these issues highlights the urgent need for more effective enforcement measures.

Factors driving misinformation production and user engagement with toothache content on Facebook

Objectives: This study characterized toothache-related Portuguese Facebook posts, identifying factors driving misinformation production and user engagement. Methods: Investigators qualitatively analyzed 500 posts published between August 2018 and August 2022, screening on language and theme. Posts were selected using CrowdTangle and assessed for motivation, author profile, content, sentiment, facticity, and format. The interaction metrics (total interactions/overperforming scores) were compared between groups of dichotomized characteristics, including time of publication. Data were evaluated by descriptive analysis, the Mann-Whitney U test, and the path analysis by generalized structural equation modeling. Results: 39.6% of posts (n = 198) contained misinformation, significantly linked to noncommercial posts with positive sentiment, links, and videos from regular users motivated by financial motivation. Additionally, user engagement was only positively associated with business/health authors' profiles and the time of publication. Conclusion: Toothache-related posts often contain misinformation, shared by regular users in links and video formats, tied to positive sentiments, and generally with financial motivation.

Google trends in infodemiology: Methodological steps to avoid irreproducible results and invalid conclusions

BACKGROUND

Google Trends is a widely used tool for infodemiological surveys. However, irregularities in the random sampling and aggregation algorithms compromise the reliability of the relative search volume (RSV) and the regional online interest (ROI).

OBJECTIVE

The study aims to unmask methodological criticalities commonly ignored in carrying out infodemiological surveys via Google Trends. A guide to avoiding these shortcomings is also provided.

MATERIAL AND METHODS

The Google Topic "Coronavirus disease 2019" has been investigated using different timelapses, categories, and IP addresses. The same samples were manually collected multiple times to evaluate the RSV and ROI stability. Stability was estimated through indicators of variability (e.g., coefficient of percentage variation "CV%" and its 4-surprisal interval "4-I"). The content aggregation capacity of the algorithms relating to topics and categories was evaluated through the quantitative analysis of RSV and ROI and the qualitative examination of the related queries.

RESULTS

The stability of Google Trends' RSV and ROI is not linked exclusively to the dataset dimension or the IP address. Subregional datasets can be highly unstable (e.g., CV% = 10, 4-I: [8,13]). Google Trends categories and topics can exclude relevant queries or include unnecessary queries. The statistical scenario is consistent with the following hypotheses: i) datasets containing too few queries are highly unstable, ii) the "interest over time" data format is generally reliable for evaluating trends and correlations, iii) Google Trends improvements have altered the RSV historical trends.

CONCLUSIONS

Google Trends can be an effective and efficient infodemiological tool as long as the reliability of web search indexes is appropriately analyzed and weighted for the scientific goal. The methodological steps discussed in this study are critical to drawing valid and relevant scientific conclusions.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review

BACKGROUND

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.

OBJECTIVE

We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.

METHODS

We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations.

RESULTS

In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines.

CONCLUSIONS

This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan

BACKGROUND

The COVID-19 pandemic has spurred the growth of a global infodemic. In order to combat the COVID-19 infodemic, it is necessary to understand what kinds of misinformation are spreading. Furthermore, various local factors influence how the infodemic manifests in different countries. Therefore, understanding how and why infodemics differ between countries is a matter of interest for public health. This study aims to elucidate and compare the types of COVID-19 misinformation produced from the infodemic in the US and Japan.

METHODS

COVID-19 fact-checking articles were obtained from the two largest publishers of fact-checking articles in each language. 1,743 US articles and 148 Japanese articles in their respective languages were gathered, with articles published between 23 January 2020 and 4 November 2022. Articles were analyzed using the free text mining software KH Coder. Exploration of frequently-occurring words and groups of related words was carried out. Based on agglomeration plots and prior research, eight categories of misinformation were created. Lastly, coding rules were created for these eight categories, and a chi-squared test was performed to compare the two datasets.

RESULTS

Overall, the most frequent words in both languages were related to health-related terms, but the Japan dataset had more words referring to foreign countries. Among the eight categories, differences with chi-squared p ≤ 0.01 were found after Holm-Bonferroni p value adjustment for the proportions of misinformation regarding statistics (US 40.0% vs. JP 25.7%, ϕ 0.0792); origin of the virus and resultant discrimination (US 7.0% vs. JP 20.3%, ϕ 0.1311); and COVID-19 disease severity, treatment, or testing (US 32.6% vs. JP 45.9%, ϕ 0.0756).

CONCLUSIONS

Local contextual factors were found that likely influenced the infodemic in both countries; representations of these factors include societal polarization in the US and the HPV vaccine scare in Japan. It is possible that Japan's relative resistance to misinformation affects the kinds of misinformation consumed, directing attention away from conspiracy theories and towards health-related issues. However, more studies need to be done to verify whether misinformation resistance affects misinformation consumption patterns this way.

Tweeting about fatphobia and body shaming: A retrospective infodemiological study

BACKGROUND

We aimed to analyze reactions to tweets that employed weight-based discrimination terms and to compare them to others posted by the same users on X (formerly Twitter).

METHODS

We collected tweets featuring weight-based discrimination terms (the study group) and created a control group by randomly selecting up to five tweets from each account that did not mention any such terms. Descriptive statistics, sentiment analysis, and logistic regression modeling were used to compare the numbers of likes and retweets of the study and control groups, and to understand the emotions associated with these tweets.

RESULTS

Our analysis included 22,075 study group tweets and 50,341 control group tweets. Sentiment analysis was conducted on 41,403 (57.2%) tweets, with 65.7% of the study group tweets being found to contain negative sentiments. The study group had a higher median of likes (1 [0-4]) and retweets (0 [0-0]) than the control group (1 [0-2] and 0 [0-0], respectively, with the study group obtaining higher mean ranks in both comparisons, P < 0.001). Multivariable logistic regression analysis revealed that tweets using weight-based discrimination terms gained more likes (OR = 1.22; 95% CI: 1.16-1.28) and retweets (OR = 1.61; 95% CI: 1.49-1.74), independent of, for example, verification status, follower count, year and season of the tweet, and emotional expression of the tweet.

CONCLUSIONS

Tweets concerning fatphobia, body shaming, and similar terms gain more reactions than others posted by the same accounts.

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.

Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study

BACKGROUND

Manually analyzing public health-related content from social media provides valuable insights into the beliefs, attitudes, and behaviors of individuals, shedding light on trends and patterns that can inform public understanding, policy decisions, targeted interventions, and communication strategies. Unfortunately, the time and effort needed from well-trained human subject matter experts makes extensive manual social media listening unfeasible. Generative large language models (LLMs) can potentially summarize and interpret large amounts of text, but it is unclear to what extent LLMs can glean subtle health-related meanings in large sets of social media posts and reasonably report health-related themes.

OBJECTIVE

We aimed to assess the feasibility of using LLMs for topic model selection or inductive thematic analysis of large contents of social media posts by attempting to answer the following question: Can LLMs conduct topic model selection and inductive thematic analysis as effectively as humans did in a prior manual study, or at least reasonably, as judged by subject matter experts?

METHODS

We asked the same research question and used the same set of social media content for both the LLM selection of relevant topics and the LLM analysis of themes as was conducted manually in a published study about vaccine rhetoric. We used the results from that study as background for this LLM experiment by comparing the results from the prior manual human analyses with the analyses from 3 LLMs: GPT4-32K, Claude-instant-100K, and Claude-2-100K. We also assessed if multiple LLMs had equivalent ability and assessed the consistency of repeated analysis from each LLM.

RESULTS

The LLMs generally gave high rankings to the topics chosen previously by humans as most relevant. We reject a null hypothesis (P<.001, overall comparison) and conclude that these LLMs are more likely to include the human-rated top 5 content areas in their top rankings than would occur by chance. Regarding theme identification, LLMs identified several themes similar to those identified by humans, with very low hallucination rates. Variability occurred between LLMs and between test runs of an individual LLM. Despite not consistently matching the human-generated themes, subject matter experts found themes generated by the LLMs were still reasonable and relevant.

CONCLUSIONS

LLMs can effectively and efficiently process large social media-based health-related data sets. LLMs can extract themes from such data that human subject matter experts deem reasonable. However, we were unable to show that the LLMs we tested can replicate the depth of analysis from human subject matter experts by consistently extracting the same themes from the same data. There is vast potential, once better validated, for automated LLM-based real-time social listening for common and rare health conditions, informing public health understanding of the public's interests and concerns and determining the public's ideas to address them.

Exploring the Thoughts, Needs and Fears of Chemotherapy Patients-An Analysis Based on Google Search Behavior

Chemotherapy poses both physical and psychological challenges for patients, prompting many to seek answers independently through online resources. This study investigates German Google search behavior regarding chemotherapy-related terms using Google AdWords data from September 2018 to September 2022 to gain insights into patient concerns and needs. A total of 1461 search terms associated with "chemotherapy" were identified, representing 1,749,312 to 28,958,400 search queries. These terms were categorized into four groups based on frequency and analyzed. Queries related to "adjuvant" and "neoadjuvant" chemotherapy, as well as "immunotherapy", suggest potential confusion among patients. Breast cancer emerged as the most searched tumor type, with hair loss, its management, and dermatological issues being the most searched side effects. These findings underscore the role of search engines such as Google in facilitating access to healthcare information and provide valuable insights into patient thoughts and needs. Healthcare providers can leverage this information to deliver patient-centric care and optimize treatment outcomes.

A Comprehensive Analysis of COVID-19 Misinformation, Public Health Impacts, and Communication Strategies: Scoping Review

BACKGROUND

The COVID-19 pandemic was marked by an infodemic, characterized by the rapid spread of both accurate and false information, which significantly affected public health. This infodemic led to confusion, mistrust in health authorities, noncompliance with health guidelines, and engagement in risky health behaviors. Understanding the dynamics of misinformation during the pandemic is crucial for developing effective public health communication strategies.

OBJECTIVE

This comprehensive analysis aimed to examine the complexities of COVID-19 misinformation. Specifically, it sought to identify the sources and themes of misinformation, the target audiences most affected, and the effectiveness of various public health communication strategies in mitigating misinformation.

METHODS

This scoping review used the MEDLINE (PubMed), Embase, and Scopus databases to identify relevant studies. An established, methodical framework for scoping reviews was used to review literature published between December 2019 and September 2023. The inclusion criteria focused on peer-reviewed studies published in English that address COVID-19 misinformation and its sources, themes, and target audiences, as well as the effectiveness of public health communication strategies.

RESULTS

The scoping review identified that misinformation significantly impacted mental health, vaccine hesitancy, and health care decision-making. Social media and traditional media were major conduits for spreading misinformation. Key misinformation themes included the origins of the virus, ineffective treatments, and misunderstandings about public health measures. Misinformation sources ranged from social media platforms to traditional media outlets and informal networks. The impact of misinformation was found to vary across different regions and demographic groups, with vulnerable populations being disproportionately affected. Effective strategies to counter misinformation included enhancing health literacy; using digital technology; promoting clear, authoritative communication; and implementing fact-checking mechanisms. In addition, community engagement and targeted health campaigns played a crucial role in addressing misinformation.

CONCLUSIONS

The review emphasizes the critical need for accurate and consistent messaging to combat misinformation. Cooperative efforts among policy makers, health professionals, and communication experts are essential for developing effective interventions. Addressing the infodemic is vital for building a well-informed, health-literate society capable of handling misinformation in future global health crises. The study provides valuable insights into the dynamics of misinformation and highlights the importance of robust public health communication strategies. These findings can guide future efforts to mitigate the impact of misinformation during health emergencies.

Identifying sarcoidosis trends using web search and real-world data in Sweden: a retrospective longitudinal study

Web search data are associated with disease incidence, population interest, and seasonal variations. This study aimed to investigate seasonal and geographical variations of web search data for sarcoidosis and to explore its association with external factors and sarcoidosis incidence in Sweden. Therefore, sarcoidosis-related data from Google Ads Keyword Planer (2017-2020) were generated for Sweden according to its 21 counties. The relationship between search volume and season, region, population demographics, environmental factors, and the sarcoidosis incidence listed in the National Patient Register was assessed. Analyses revealed seasonal variations for Sweden with an overall peak in the spring and autumn. Geographical differences were observed, with a higher search volume for north-western counties and the lowest search volume for Stockholm County. At the country level, the search volume was positively associated with the sarcoidosis incidence. Higher male proportion and older mean age were associated with a higher search volume, while a higher proportion of foreign-born residents, humidity, and mean temperature were associated with a lower search volume. Our analyses detected correlations between web search data, sarcoidosis incidence, and external factors. Analyses of sarcoidosis web search data therefore appear to be a valuable approach to disease surveillance to address medical needs and public interest.

Burden of atopic dermatitis in Europe: A population-centred approach leveraging web search data in 21 European countries

BACKGROUND

The complexity, high prevalence, and substantial personal and socioeconomic burden collectively render atopic dermatitis (AD) a major public health concern. Using crowdsourced Internet data has the potential to provide unique insights into this concern, as demonstrated by several previous studies. However, a comprehensive comparison across European countries remains lacking.

OBJECTIVES

The study aimed to investigate AD-related web searches across Europe to assess spatiotemporal variations and associations between disease-related and external factors.

METHODS

AD-related web search data were extracted for 21 European countries between February 2019 and January 2023. Descriptive analysis and autocorrelation functions were performed to examine spatiotemporal patterns. Correlations (r) were used to evaluate the associations between web searches and disease-related, socioeconomic and meteorological data.

RESULTS

Over 241 million AD-related web searches were identified, with search volume varying substantially among European countries (p < 0.001) and correlating with AD prevalence and disease burden (both r = 0.51, p = 0.019). Search volume increased between 2019 and 2023 in all countries and seasonally peaked in January and March. Negative correlations with median population age (r = -0.46, p = 0.039), number of general practitioners (r = -0.29, p = 0.226) and specialists (r = -0.27, p = 0.270) were observed. Moderate to strong correlations were found between search volume and cold, humid and windy weather with fewer sunshine hours, while higher online interest typically occurred 1-3 months after such weather conditions.

CONCLUSION

The study highlights the great potential of online crowdsourced data analysis, for example, to investigate the impact of climate change or to identify unmet needs at a population level. Furthermore, the growing online interest in AD and the corresponding seasonal peaks emphasize the necessity of adapting treatment plans, intensifying public health campaigns, and disseminating reliable online information by governments and healthcare providers, especially during these periods.

The impact of fintech development on the entrepreneurial behavior of farmers: evidence from the China Household Finance Survey

Fintech is disrupting the traditional financial models with the power of technology. Will it solve the problems faced by farmers in starting their own businesses and stimulate the vitality of rural entrepreneurship? In this paper, we focus on the impact of fintech development on the entrepreneurial behavior of farmers in China. First, we theoretically analyze the effects of fintech development on the entrepreneurial behavior of farmers and put forward the corresponding theoretical hypotheses. Second, using data from Baidu search index and the China Household Finance Survey (CHFS), we employ a Probit model to verify our theoretical hypotheses. Our findings suggest that (1) fintech development encourages farmer entrepreneurial behavior, (2) it achieves this by alleviating credit constraints, information constraints, and risk aversion effects, (3) in terms of the type of entrepreneurship, the development of fintech mainly promotes survival entrepreneurship among farmers, while the entrepreneurial effect of fintech is more significant among farmers with low education level, low income level and in the central and western regions. Overall, our study provides timely insights into boosting farmer entrepreneurship in emerging countries such as China by leveraging the potential of fintech.

Understanding public perceptions and discussions on diseases involving chronic pain through social media: cross-sectional infodemiology study

BACKGROUND

Chronic pain is a highly prevalent medical condition that negatively impacts quality of life and is associated with considerable functional disability. Certain diseases, such as fibromyalgia, headache, paraplegia, neuropathy, and multiple sclerosis, manifest with chronic pain.

OBJECTIVE

The aim of this study is to examine the number and type of tweets (original or retweet) related to chronic pain, as well as to analyze the emotions and compare the societal impact of the diseases under study.

METHODS

We investigated tweets posted between January 1, 2018, and December 31, 2022, by Twitter users in English and Spanish, as well as the generated retweets. Additionally, emotions were extracted from these tweets and their diffusion was analyzed. Furthermore, the topics most frequently discussed by users were collected.

RESULTS

A total of 72,874 tweets were analyzed, including 44,467 in English and 28,407 in Spanish. Paraplegia represented 23.3% with 16,461 of the classified tweets, followed by headache and fibromyalgia with 15,337 (21.7%) and 15,179 (21.5%) tweets, respectively. Multiple sclerosis generated 14,781 tweets (21%), and the fewest tweets were related to neuropathy with 8,830 tweets (12.5%). The results showed that the primary emotions extracted were "fear" and "sadness." Additionally, the reach and impact of these tweets were investigated through the generated retweets, with those related to headaches showing the highest interest and interaction among users.

CONCLUSION

Our results underscore the potential of leveraging social media for a better understanding of patients suffering from chronic pain and its impact on society. Among the most frequently encountered topics are those related to treatment, symptoms, or causes of the disease. Therefore, it is relevant to inform the patient to prevent misconceptions regarding their illness.

Public interest and seasonal peaks for wisdom teeth related web inquiries - A google trends analysis

Objective: The study aimed to analyze the public interest in wisdom teeth-related search terms as well as regional and seasonal trends based on information from the Google search engine.

METHODS

With the help of the online search query tool, Google Trends, the public interest in the primary search term "wisdom teeth" for the timeframe between January 1st, 2004 and September 31st, 2021 was analyzed. To do so, a country-specific search was conducted in English-speaking countries (the USA, the UK, Canada, and Australia) in the northern and southern hemispheres. The extracted time series was examined for reliability, and a Cosinor analysis evaluated the statistical significance of seasonal interest peaks.

RESULTS

The reliability of averaged time series data on the search term "wisdom teeth" was excellent in all examined countries. In all countries analyzed, "wisdom teeth removal" was one of the most common related search terms. Significant interest peaks for wisdom teeth-related search terms were found in Canada and the USA during summer (p < .001). In Canada and the USA, significant seasonal patterns with the highest interest during the summer months, could be displayed.

CONCLUSION

This phenomenon could be caused by increased wisdom teeth-related complaints induced by seasonal climate changes.

English tweets on allergy: Content analysis and association with surveillance data

BACKGROUND

Analysis of X (formerly Twitter) posts can inform on the interest/perceptions that social media users have on health subjects. In this study, we aimed to analyse tweets on allergic conditions, comparing them with surveillance data.

METHODS

We retrieved tweets from England on "allergy," "asthma," and "allergic rhinitis," published between 2016 and 2021. We estimated the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English surveillance data on the incidence of asthma and allergic rhinitis medical visits. We performed sentiment analysis, computing a score informing on the emotional tone of assessed tweets. We applied a topic modelling approach to identify topics (clusters of words frequently occurring together) for tweets on each assessed condition.

RESULTS

We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food-related words were preponderant on tweets on "allergy," while "eyes" was the most frequent meaningful word on "allergy rhinitis" tweets. We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency and sentiment - the incidence of allergic rhinitis medical visits was moderately to strongly correlated with the frequency (ρ = 0.866) and sentiment (ρ = -0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were observed. The average sentiment score was negative for all assessed conditions, ranging from -0.004 ("asthma") to -0.083 ("allergic rhinitis").

CONCLUSIONS

Tweets on "allergic rhinitis" displayed a seasonal pattern regarding their frequency and sentiment, which correlated with surveillance data. No such patterns were observed for "asthma."

Search Engine Use for Health-Related Purposes: Behavioral Data on Online Health Information-Seeking in Germany

Internet searches for health-related purposes are common, with search engines like Google being the most popular starting point. However, results on the popularity of health information-seeking behaviors are based on self-report data, often criticized for suffering from incomplete recall, overreporting, and low reliability. Therefore, the current study builds on user-centric tracking of Internet use to reveal how individuals actually behave online. We conducted a secondary analysis of passively recorded Internet use logs to examine the prevalence of health-related search engine use, the types of health information searched for, and the sources visited after the searches. The analysis revealed two key findings. 1) We largely support earlier survey-based findings on the prevalence of online health information seeking with search engines and the relatively minor differences in information-seeking behaviors between socio-demographic groups. 2) We provide a more granular picture of the process of HISB using search engines by identifying different selection patterns depending on the scope of the searches.

Regional insights on tobacco-related tweets: unveiling user opinions and usage patterns

Introduction

Tobacco consumption and its impact on health remain high worldwide. Additionally, it is a contentious issue generating significant controversy. Twitter has proven to be a useful platform for evaluating public health topics related to population health behaviors, and tobacco consumption.

Objective

The objective of this study is to analyze the content of tweets related to tobacco. Moreover, geolocation data will be considered to understand regional differences.

Methods

Tweets published between 2018 and 2022, in both English and Spanish, containing the keyword "tobacco," were analyzed. A total of 56,926 tweets were obtained. The tweets were classified into different categories. 550 tweets were manually analyzed, and an automated and computerized classification was performed for the remaining and largest subset of tweets.

Results

The analysis yielded 30,812 classifiable tweets. Healthcare professionals were the most frequent contributors to the topic (50.2%), with the most common theme being general information about the toxic effects of tobacco. 57.9% of the tweets discussed the harmful effects of tobacco on health, with fear being the predominant emotion. The largest number of tweets were located in America.

Conclusions

Our study revealed a substantial number of tweets highlighting the health risks and negative perceptions of tobacco consumption. Africa showed the lowest percentage of tweets discussing the health risks associated with tobacco, coinciding with the continent having the least developed anti-tobacco policies. Healthcare professionals emerged as the most prominent users discussing the topic, which is encouraging as they play a crucial role in disseminating accurate and scientific health information.

Representation of skin carcinomas in public awareness - Awareness worldwide and in Germany

BACKGROUND AND OBJECTIVES

In recent years, there has been an increase in skin cancer. The aim of this study was therefore to investigate the representation of skin cancer in public awareness worldwide and in Germany, and to determine whether Skin Cancer Awareness Month is represented in the search interests of the Internet-using population in the same way as Breast Cancer Awareness Month worldwide.

DATA AND METHODS

In this study, Google Trends data were used to track levels of public awareness for different tumor entities and skin cancer types worldwide and for Germany.

RESULTS

The results of this analysis clearly showed a high level of relative public search interest in breast cancer worldwide in the awareness month of October. Worldwide and in Germany, there was a certain increase in search interest and a certain seasonal effect around the May awareness month for skin cancer. For example, the analysis showed a search interest in May and during the summer months in Germany.

CONCLUSIONS

It is likely that the population, for example in Germany, may benefit further from an even greater emphasis on the topic of skin cancer.

Repräsentation von Hautkarzinomen im öffentlichen Bewusstsein – Aufmerksamkeit weltweit und in Deutschland

Hintergrund und Zielsetzung

In den letzten Jahren ist eine Zunahme von Hauttumoren zu verzeichnen. Ziel dieser Studie war es daher, die Repräsentation von Hautkrebs im öffentlichen Bewusstsein weltweit und in Deutschland zu untersuchen und festzustellen, ob der Skin Cancer Awareness Month in den Suchinteressen der internetnutzenden Bevölkerung in gleicher Weise wie der Breast Cancer Awareness Month weltweit repräsentiert ist.

Daten und Methodik

In dieser Studie wurden die Daten von Google Trends verwendet, um den Grad der öffentlichen Aufmerksamkeit für verschiedene Tumorentitäten und Hautkrebsarten weltweit und in Deutschland zu ermitteln.

Ergebnisse

Die Ergebnisse dieser Analyse zeigten deutlich ein hohes Niveau für das relative öffentliche Suchinteresse am Thema Brustkrebs weltweit im Sensibilisierungsmonat Oktober. Weltweit und in Deutschland war ein gewisser Anstieg des Suchinteresses beziehungsweise ein gewisser saisonaler Effekt um den Sensibilisierungsmonat Mai für Hautkrebs festzustellen. So zeigte die Analyse beispielsweise ein Suchinteresse im Mai und während der Sommermonate in Deutschland.

Schlussfolgerungen

Es ist wahrscheinlich, dass die Bevölkerung, zum Beispiel in Deutschland, weiterhin von einer noch stärkeren Betonung des Themas Hautkrebs profitieren könnte.

Infodemiology and infoveillance: framework for contagious exanthematous diseases, of childhood in Italy

BACKGROUND

Contagious exanthematous diseases are becoming a major public health problem. The purpose of this study was to evaluate the potential epidemiological trend of four infectious exanthematous diseases in Italy through the searches on the internet.

METHODS

We used the following Italian search term: 'Sesta malattia' (Sixth Disease, in English), 'Eritema Infettivo' (also knows 'Quinta malattia' in Italian; Fifth Disease in English), 'Quarta malattia' (Fourth Disease in English) and 'Scarlattina' (Scarlet fever in English). We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). The study period is between July 2015 and December 2022.

RESULTS

The diseases considered have a seasonal trend and the search peaks between GT and Wikipedia overlap. A temporal correlation was observed between GT and Wikipedia search trends. Google Trends Internet search data showed strong correlation with Wikipedia with a rho statistically significant for Fifth disease (rho = 0.78), Fourth disease (rho = 0.76) and Scarlet-fever (rho = 0.77), moderate correlation for Sixth disease (rho = 0.32).

CONCLUSIONS

Infectious disease searches using Google and Wikipedia can be useful for public health surveillance and help policy makers implement prevention and information programs for the population, in addition to the fact that increases in searches could represent an early warning in the detection of outbreaks.