Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis.

BACKGROUND: Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. OBJECTIVE: The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. METHODS: Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. RESULTS: Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78%. The estimated overall change in average search volume was 627%. In comparison, the average annual percent change was 9.23% for lone star tick, 7.34% for meat allergy, and 2.45% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years (ρ=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. CONCLUSIONS: The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome.

The Representation of Skin Tones in Google Images of Skin Cancers.

Skin self-examinations play a vital role in skin cancer detection and are often aided by online resources. Available reference photos must display the full spectrum of skin tones so patients may visualize how skin lesions can appear. This study investigated the portrayal of skin tones in skin cancer-related Google Images, discovering a significant underrepresentation of darker skin tones. J Drugs Dermatol. 2024;23(5):e132-e133.     doi:10.36849/JDD.7886e.

Public and Research Interest in Telemedicine From 2017 to 2022: Infodemiology Study of Google Trends Data and Bibliometric Analysis of Scientific Literature.

BACKGROUND: Telemedicine offers a multitude of potential advantages, such as enhanced health care accessibility, cost reduction, and improved patient outcomes. The significance of telemedicine has been underscored by the COVID-19 pandemic, as it plays a crucial role in maintaining uninterrupted care while minimizing the risk of viral exposure. However, the adoption and implementation of telemedicine have been relatively sluggish in certain areas. Assessing the level of interest in telemedicine can provide valuable insights into areas that require enhancement. OBJECTIVE: The aim of this study is to provide a comprehensive analysis of the level of public and research interest in telemedicine from 2017 to 2022 and also consider any potential impact of the COVID-19 pandemic. METHODS: Google Trends data were retrieved using the search topics "telemedicine" or "e-health" to assess public interest, geographic distribution, and trends through a joinpoint regression analysis. Bibliographic data from Scopus were used to chart publications referencing the terms "telemedicine" or "eHealth" (in the title, abstract, and keywords) in terms of scientific production, key countries, and prominent keywords, as well as collaboration and co-occurrence networks. RESULTS: Worldwide, telemedicine generated higher mean public interest (relative search volume=26.3%) compared to eHealth (relative search volume=17.6%). Interest in telemedicine remained stable until January 2020, experienced a sudden surge (monthly percent change=95.7%) peaking in April 2020, followed by a decline (monthly percent change=-22.7%) until August 2020, and then returned to stability. A similar trend was noted in the public interest regarding eHealth. Chile, Australia, Canada, and the United States had the greatest public interest in telemedicine. In these countries, moderate to strong correlations were evident between Google Trends and COVID-19 data (ie, new cases, new deaths, and hospitalized patients). Examining 19,539 original medical articles in the Scopus database unveiled a substantial rise in telemedicine-related publications, showing a total increase of 201.5% from 2017 to 2022 and an average annual growth rate of 24.7%. The most significant surge occurred between 2019 and 2020. Notably, the majority of the publications originated from a single country, with 20.8% involving international coauthorships. As the most productive country, the United States led a cluster that included Canada and Australia as well. European, Asian, and Latin American countries made up the remaining 3 clusters. The co-occurrence network categorized prevalent keywords into 2 clusters, the first cluster primarily focused on applying eHealth, mobile health (mHealth), or digital health to noncommunicable or chronic diseases; the second cluster was centered around the application of telemedicine and telehealth within the context of the COVID-19 pandemic. CONCLUSIONS: Our analysis of search and bibliographic data over time and across regions allows us to gauge the interest in this topic, offer evidence regarding potential applications, and pinpoint areas for additional research and awareness-raising initiatives.

Misinformation does not reduce trust in accurate search results, but warning banners may backfire.

People rely on search engines for information in critical contexts, such as public health emergencies-but what makes people trust some search results more than others? Can search engines influence people's levels of trust by controlling how information is presented? And, how does the presence of misinformation influence people's trust? Research has identified both rank and the presence of misinformation as factors impacting people's search behavior. Here, we extend these findings by measuring the effects of these factors, as well as misinformation warning banners, on the perceived trustworthiness of individual search results. We conducted three online experiments (N = 3196) using Covid-19-related queries, and found that although higher-ranked results are clicked more often, they are not more trusted. We also showed that misinformation does not damage trust in accurate results displayed below it. In contrast, while a warning about unreliable sources might decrease trust in misinformation, it significantly decreases trust in accurate information. This research alleviates some concerns about how people evaluate the credibility of information they find online, while revealing a potential backfire effect of one misinformation-prevention approach; namely, that banner warnings about source unreliability could lead to unexpected and nonoptimal outcomes in which people trust accurate information less.

Does the Internet Provide Quality Information on Metoidioplasty? Using the Modified Ensuring Quality Information for Patients Tool to Evaluate Artificial Intelligence-Generated and Online Information on Metoidioplasty.

BACKGROUND: Public interest in gender affirmation surgery has surged over the last decade. This spike in interest, combined with extensive free online medical knowledge, has led many to the Internet for more information on this complicated procedure. This study aimed to evaluate the quality of online information on metoidioplasty. METHODS: Google Trends in searches on "metoidioplasty" from 2004 to present were assessed. "metoidioplasty" was searched on three popular search engines (Google, Yahoo, and Bing), and the first 100 websites from each search were extracted for inclusion (Fig. 1). Exclusion criteria included duplicates, websites requiring fees, photo libraries, and irrelevant websites. Websites were assigned a score (out of 36) using the modified Ensuring Quality Information for Patients (EQIP) instrument, which grades patient materials based on content (18), identification (6), and structure (12). ChatGPT was also queried for metoidioplasty-related information and responses were analyzed using EQIP. RESULTS: Google Trends analysis indicated relative search interest in "metoidioplasty" has more than quadrupled since 2013(Fig. 2). Of the 93 websites included, only 2 received an EQIP score greater than 27 (6%). Website scores ranged from 7 to 33, with a mean of 18.6 ± 4.8. Mean scores were highest for websites made by health departments (22.3) and lowest for those made by encyclopedias and academic institutions (16.0). Websites with the highest frequency were research articles, web portals, hospital websites, and private practice sites, which averaged scores of 18.2, 19.7, 19.0, and 17.8, respectively. Health department sites averaged the highest content points (11.25), and academic institutions averaged the lowest (5.5). The average content point across all websites was 7.9 of 18. ChatGPT scored a total score of 29: 17 content, 2 identification, and 10 structures. The artificial intelligence chatbot scored the second highest score among all included online resources. CONCLUSIONS: Despite the continued use of search engines, the quality of online information on metoidioplasty remains exceptionally poor across most website developers. This study demonstrates the need to improve these resources, especially as interest in gender-affirming surgery continues to grow. ChatGPT and other artificial intelligence chatbots may be efficient and reliable alternatives for those seeking to understand complex medical information.

Investigating child sexual abuse material availability, searches, and users on the anonymous Tor network for a public health intervention strategy.

Tor is widely used for staying anonymous online and accessing onion websites; unfortunately, Tor is popular for distributing and viewing illicit child sexual abuse material (CSAM). From 2018 to 2023, we analyse 176,683 onion domains and find that one-fifth share CSAM. We find that CSAM is easily available using 21 out of the 26 most-used Tor search engines. We analyse 110,133,715 search sessions from the Ahmia.fi search engine and discover that 11.1% seek CSAM. When searching CSAM by age, 40.5% search for 11-year-olds and younger; 11.0% for 12-year-olds; 8.2% for 13-year-olds; 11.6% for 14-year-olds; 10.9% for 15-year-olds; and 12.7% for 16-year-olds. We demonstrate accurate filtering for search engines, introduce intervention, show a questionnaire for CSAM users, and analyse 11,470 responses. 65.3% of CSAM users first saw the material when they were children themselves, and half of the respondents first saw the material accidentally, demonstrating the availability of CSAM. 48.1% want to stop using CSAM. Some seek help through Tor, and self-help websites are popular. Our survey finds commonalities between CSAM use and addiction. Help-seeking correlates with increasing viewing duration and frequency, depression, anxiety, self-harming thoughts, guilt, and shame. Yet, 73.9% of help seekers have not been able to receive it.

'Just Google it'-A scoping review of online mental health resources for survivors of breast cancer.

OBJECTIVE: As the Internet is a ubiquitous resource for information, we aimed to replicate a patient's Google search to identify and assess the quality of online mental health/wellbeing materials available to support women living with or beyond cancer. METHODS: A Google search was performed using a key term search strategy including search strings 'cancer', 'wellbeing', 'distress' and 'resources' to identify online resources of diverse formats (i.e., factsheet, website, program, course, video, webinar, e-book, podcast). The quality evaluation scoring tool (QUEST) was used to analyse the quality of health information provided. RESULTS: The search strategy resulted in 283 resources, 117 of which met inclusion criteria across four countries: Australia, USA, UK, and Canada. Websites and factsheets were primarily retrieved. The average QUEST score was 10.04 (highest possible score is 28), indicating low quality, with 92.31% of resources lacking references to sources of information. CONCLUSIONS: Our data indicated a lack of evidence-based support resources and engaging information available online for people living with or beyond cancer. The majority of online resources were non-specific to breast cancer and lacked authorship and attribution.

Assessment of using Google Trends for real-time monitoring of infectious disease outbreaks: a measles case study.

Measles remains a significant threat to children worldwide despite the availability of effective vaccines. The COVID-19 pandemic exacerbated the situation by leading to the postponement of supplementary measles immunization activities. Along with this postponement, measles surveillance also deteriorated, with the lowest number of submitted specimens in over a decade. In this study, we focus on measles as a challenging case study due to its high vaccination coverage, which leads to smaller outbreaks and potentially weaker signals on Google Trends. Our research aimed to explore the feasibility of using Google Trends for real-time monitoring of infectious disease outbreaks. We evaluated the correlation between Google Trends searches and clinical case data using the Pearson correlation coefficient and Spearman's rank correlation coefficient across 30 European countries and Japan. The results revealed that Google Trends was most suitable for monitoring acute disease outbreaks at the regional level in high-income countries, even when there are only a few weekly cases. For example, from 2017 to 2019, the Pearson correlation coefficient was 0.86 (p-value< 0.05) at the prefecture level for Okinawa, Japan, versus 0.33 (p-value< 0.05) at the national level for Japan. Furthermore, we found that the Pearson correlation coefficient may be more suitable than Spearman's rank correlation coefficient for evaluating the correlations between Google Trends search data and clinical case data. This study highlighted the potential of utilizing Google Trends as a valuable tool for timely public health interventions to respond to infectious disease outbreaks, even in the context of diseases with high vaccine coverage.

Exploring Opportunities for Clinical Data Warehouse Enhancement Through Data Catalog Integration.

Secondary use of clinical health data implies a prior integration of mostly heterogenous and multidimensional data sets. A clinical data warehouse addresses the technological and organizational framework conditions required for this, by making any data available for analysis. However, users of a data warehouse often do not have a comprehensive overview of all available data and only know about their own data in their own systems - a situation which is also referred to as 'data siloed state'. This problem can be addressed and ultimately solved by implementation of a data catalog. Its core function is a search engine, which allows for searching the metadata collected from different data sources and thereby accessing all data there is. With this in mind, we conducted an explorative online market survey followed by vendor comparison as a pre-requisite for system selection of a data catalog. Assessment of vendor performance was based on seven predetermined and weighted selection criteria. Although three vendors achieved the highest score, results were lying closely together. Detailed investigations and test installations are needed for further narrowing down the selection process.

Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks.

It is likely that individuals are turning to Large Language Models (LLMs) to seek health advice, much like searching for diagnoses on Google. We evaluate clinical accuracy of GPT-3·5 and GPT-4 for suggesting initial diagnosis, examination steps and treatment of 110 medical cases across diverse clinical disciplines. Moreover, two model configurations of the Llama 2 open source LLMs are assessed in a sub-study. For benchmarking the diagnostic task, we conduct a naïve Google search for comparison. Overall, GPT-4 performed best with superior performances over GPT-3·5 considering diagnosis and examination and superior performance over Google for diagnosis. Except for treatment, better performance on frequent vs rare diseases is evident for all three approaches. The sub-study indicates slightly lower performances for Llama models. In conclusion, the commercial LLMs show growing potential for medical question answering in two successive major releases. However, some weaknesses underscore the need for robust and regulated AI models in health care. Open source LLMs can be a viable option to address specific needs regarding data privacy and transparency of training.

Internet searching and stock price informativeness: Evidence from Google withdrawal in China.

We analyze whether and how internet searching impacts stock price informativeness. Using the 2010 Google withdrawal in China as a quasi-natural experiment, we establish a causal effect between internet searching and stock price informativeness using a difference-in-difference framework. We find that firms with higher Google search volume experience a 10% decrease in stock price informativeness after the Google withdrawal. The negative effect of the Google withdrawal on stock price informativeness is pronounced in firms with more retail investors, larger state-ownership, and poor analysts' earnings forecasts. Our results suggest that retail investors can benefit from internet searching to collect and process firm-specific information more efficiently.

CRISPR-powered quantitative keyword search engine in DNA data storage.

Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

BACKGROUND: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. OBJECTIVE: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. METHODS: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. RESULTS: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). CONCLUSIONS: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.