scholarly journals Google Trends for pain search terms in the world’s most populated areas before and after the first recorded COVID-19 case: an infodemiological study (Preprint)

10.2196/27214 ◽  
2021 ◽  
Author(s):  
Istvan Szilard Szilagyi ◽  
Torsten Ulrich ◽  
Kordula Lang-Illievich ◽  
Christoph Klivinyi ◽  
Gregor Alexander Schittek ◽  
...  
Keyword(s):  
Google Trends ◽  
Search Terms ◽  
Before And After

scholarly journals Increase in Google Trends Regarding Telogen Effluvium Due To COVID-19

2021 ◽  
Vol 5 (2) ◽  
pp. 137-139
Author(s):  
Jasmine Garg ◽  
Abigail Cline ◽  
Frederick Pereira
Keyword(s):  
United States ◽  
Hair Loss ◽  
Public Interest ◽  
The United States ◽  
Google Trends ◽  
Therapeutic Interventions ◽  
The Public ◽  
Search Terms ◽  
The Future ◽  
Before And After

Objective: The purpose of this study was to assess the public interest in the United States of telogen effluvium before and after the COVID-19 pandemic in order to investigate the best therapeutic interventions for dermatologists in the future. Methods: We performed Google TrendsTM search for “COVID hair loss”, “telogen effluvium” and “hair loss” between 5/1/20 and 8/16/20. Conclusion: All three terms have increased in popularity for search terms since mid-March and were the most prevalent in the states that experienced the earliest increase in number of coronavirus cases.

scholarly journals Internet Searches for Terms Related to Child Maltreatment During COVID-19: Infodemiology Approach (Preprint)

2021 ◽  
Author(s):  
Madelon M E Riem ◽  
Pietro De Carli ◽  
Jing Guo ◽  
Marian J Bakermans-Kranenburg ◽  
Marinus H van IJzendoorn ◽  
...  
Keyword(s):  
Google Trends ◽  
World Health ◽  
Strong Increase ◽  
Online Information ◽  
Search Terms ◽  
Before And After ◽  
Conflict Tactics Scales ◽  
Internet Searches ◽  
Physical Maltreatment ◽  
Conflict Tactics

UNSTRUCTURED We examined internet searches indicative of abusive parental behaviors before and after the World Health Organization’s declaration of COVID-19 as a pandemic (March 11, 2020) and subsequent lockdown measures in many countries worldwide. Using Google Trends, we inferred search trends between December 28, 2018, and December 27, 2020, for queries consisting of “mother,” “father,” or “parents” combined with each of the 11 maltreatment-related verbs used in the Conflict Tactics Scales, Parent-Child version. Raw search counts from the Google Trends data were estimated using Comscore. Of all 33 search terms, 28 terms showed increases in counts after the lockdowns began. These findings indicate a strong increase in internet searches relating to occurrence, causes, or consequences of emotional and physical maltreatment since the lockdowns began and call for the use of maltreatment-related queries to direct parents or children to online information and support.

scholarly journals Google Trends for Pain Search Terms in the World’s Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study (Preprint)

2021 ◽  
Author(s):  
Istvan-Szilard Szilagyi ◽  
Torsten Ullrich ◽  
Kordula Lang-Illievich ◽  
Christoph Klivinyi ◽  
Gregor Alexander Schittek ◽  
...  
Keyword(s):  
Chest Pain ◽  
Public Interest ◽  
The United States ◽  
Google Trends ◽  
Search Activity ◽  
Web Based ◽  
Pain Location ◽  
Search Terms ◽  
Before And After ◽  
Exploratory Data

BACKGROUND Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. OBJECTIVE This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. METHODS Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann–Whitney <i>U</i> tests. RESULTS Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, <i>P</i>&lt;.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. CONCLUSIONS As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.

scholarly journals Infodemiology of systemic lupus erythematous using Google Trends

Lupus ◽  
2017 ◽  
Vol 26 (8) ◽  
pp. 886-889 ◽  
Author(s):  
M Radin ◽  
S Sciascia
Keyword(s):  
Big Data ◽  
Northern Hemisphere ◽  
Lupus Erythematosus ◽  
Kendall Test ◽  
Google Trends ◽  
Data Monitoring ◽  
Search Term ◽  
Systemic Lupus ◽  
Search Terms ◽  
Google Search

Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the Internet and search engines to look for terms related to their disease and its possible causes, symptoms and treatments. ‘Infodemiology’ and ‘infoveillance’ are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach. Methods We analysed the large amount of data generated by Google Trends, considering ‘lupus’, ‘relapse’ and ‘fatigue’ in a 10-year web-based research. Google Trends automatically normalized data for the overall number of searches, and presented them as relative search volumes, in order to compare variations of different search terms across regions and periods. The Menn–Kendall test was used to evaluate the overall seasonal trend of each search term and possible correlation between search terms. Results We observed a seasonality for Google search volumes for lupus-related terms. In the Northern hemisphere, relative search volumes for ‘lupus’ were correlated with ‘relapse’ (τ = 0.85; p = 0.019) and with fatigue (τ = 0.82; p = 0.003), whereas in the Southern hemisphere we observed a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.85; p = 0.018). Similarly, a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.70; p < 0.001) was seen also in the Northern hemisphere. Conclusion Despite the intrinsic limitations of this approach, Internet-acquired data might represent a real-time surveillance tool and an alert for healthcare systems in order to plan the most appropriate resources in specific moments with higher disease burden.

Predicting Public Interest in Nonsurgical Cosmetic Procedures Using Google Trends

2019 ◽  
Vol 40 (11) ◽  
pp. 1253-1262 ◽  
Author(s):  
Jonathan D Tijerina ◽  
Shane D Morrison ◽  
Ian T Nolan ◽  
Matthew J Parham ◽  
Rahim Nazerali
Keyword(s):  
Hyaluronic Acid ◽  
Public Interest ◽  
The United States ◽  
Google Trends ◽  
Search Term ◽  
Data Set ◽  
Fat Transfer ◽  
Laser Hair Removal ◽  
Search Terms ◽  
Search Volume

Abstract Background Google Trends (GT) provides cost-free, customizable analyses of search traffic for specified terms entered into Google’s search engine. GT may inform plastic surgery marketing decisions and resource allocation. Objectives The aim of this study was to determine GT’s utility in tracking and predicting public interest in nonsurgical cosmetic procedures and to examine trends over time of public interest in nonsurgical procedures. Methods GT search volume for terms in 6 ASPS and ASAPS nonsurgical procedure categories (Botox injections, chemical peel, laser hair removal, laser skin resurfacing, microdermabrasion and soft tissue fillers [subcategories: collagen, fat, and hyaluronic acid]) were compared with ASPS and ASAPS case volumes for available dates between January 2004 and March 2019 with the use of univariate linear regression, taking P &lt; 0.01 as the cutoff for significance. Results Total search volume varied by search term within the United States and internationally. Significant positive correlations were demonstrated for 17 GT terms in all 6 ASPS and ASAPS categories: “Botox®,” “collagen injections,” “collagen lip injections” with both databases; and “chemical skin peel,” “skin peel,” “acne scar treatment,” “CO2 laser treatment,” “dermabrasion,” “collagen injections,” “collagen lip injections,” “fat transfer,” “hyaluronic acid fillers,” “hyaluronic acid injection,” “hyaluronic acid injections,” “Juvederm®,” and “fat transfer” with just 1 database. Many search terms were not significant, emphasizing the need for careful selection of search terms. Conclusions Our analysis further elaborates on recent characterization of GT as a powerful and intuitive data set for plastic surgeons, with the potential to accurately gauge global and national interest in topics and procedures related to nonsurgical cosmetic procedures.

scholarly journals Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends

10.2196/20588 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e20588
Author(s):  
Amy Kristen Johnson ◽  
Runa Bhaumik ◽  
Irina Tabidze ◽  
Supriya D Mehta
Keyword(s):  
Public Health ◽  
Correlation Analysis ◽  
Prediction Accuracy ◽  
Cross Correlation ◽  
Secondary Syphilis ◽  
Google Trends ◽  
Sexually Transmitted ◽  
Search Terms ◽  
Cross Correlation Analysis ◽  
Case Data

Background Sexually transmitted infections (STIs) pose a significant public health challenge in the United States. Traditional surveillance systems are adversely affected by data quality issues, underreporting of cases, and reporting delays, resulting in missed prevention opportunities to respond to trends in disease prevalence. Search engine data can potentially facilitate an efficient and economical enhancement to surveillance reporting systems established for STIs. Objective We aimed to develop and train a predictive model using reported STI case data from Chicago, Illinois, and to investigate the model’s predictive capacity, timeliness, and ability to target interventions to subpopulations using Google Trends data. Methods Deidentified STI case data for chlamydia, gonorrhea, and primary and secondary syphilis from 2011-2017 were obtained from the Chicago Department of Public Health. The data set included race/ethnicity, age, and birth sex. Google Correlate was used to identify the top 100 correlated search terms with “STD symptoms,” and an autocrawler was established using Google Health Application Programming Interface to collect the search volume for each term. Elastic net regression was used to evaluate prediction accuracy, and cross-correlation analysis was used to identify timeliness of prediction. Subgroup elastic net regression analysis was performed for race, sex, and age. Results For gonorrhea and chlamydia, actual and predicted STI values correlated moderately in 2011 (chlamydia: r=0.65; gonorrhea: r=0.72) but correlated highly (chlamydia: r=0.90; gonorrhea: r=0.94) from 2012 to 2017. However, for primary and secondary syphilis, the high correlation was observed only for 2012 (r=0.79), 2013 (r=0.77), 2016 (0.80), and 2017 (r=0.84), with 2011, 2014, and 2015 showing moderate correlations (r=0.55-0.70). Model performance was the most accurate (highest correlation and lowest mean absolute error) for gonorrhea. Subgroup analyses improved model fit across disease and year. Regression models using search terms selected from the cross-correlation analysis improved the prediction accuracy and timeliness across diseases and years. Conclusions Integrating nowcasting with Google Trends in surveillance activities can potentially enhance the prediction and timeliness of outbreak detection and response as well as target interventions to subpopulations. Future studies should prospectively examine the utility of Google Trends applied to STI surveillance and response.

scholarly journals Exploring online search behavior for COVID-19 preventive measures: The Philippine case

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249810
Author(s):  
Adrian Galido ◽  
Jerina Jean Ecleo ◽  
Atina Husnayain ◽  
Emily Chia-Yu Su
Keyword(s):  
Information Needs ◽  
Preventive Measures ◽  
Face Mask ◽  
Disease Outbreak ◽  
Hand Washing ◽  
Google Trends ◽  
Social Distancing ◽  
The Public ◽  
Hand Sanitizer ◽  
Search Terms

Public health agencies have suggested nonpharmaceutical interventions to curb the spread of the COVID-19 infections. The study intended to explore the information-seeking behavior and information needs on preventive measures for COVID-19 in the Philippine context. The search interests and related queries for COVID-19 terms and each of the preventive measures for the period from December 31, 2019 to April 6, 2020 were generated from Google Trends. The search terms employed for COVID-19 were coronavirus, ncov, covid-19, covid19 and “covid 19.” The search terms of the preventive measures considered for this study included “community quarantine”, “cough etiquette”, “face mask” or facemask, “hand sanitizer”, handwashing or “hand washing” and “social distancing.” Spearman’s correlation was employed between the new daily COVID-19 cases, COVID-19 terms and the different preventive measures. The relative search volume for the coronavirus disease showed an increase up to the pronouncement of the country’s first case of COVID-19. An uptrend was also evident after the country’s first local transmission was confirmed. A strong positive correlation (rs = .788, p < .001) was observed between the new daily cases and search interests for COVID-19. The search interests for the different measures and the new daily cases were also positively correlated. Similarly, the search interests for the different measures and the COVID-19 terms were all positively correlated. The search interests for “face mask” or facemask, “hand sanitizer” and handwashing or “hand washing” were more correlated with the search interest for COVID-19 than with the number of new daily COVID-19 cases. The search interests for “cough etiquette”, “social distancing” and “community quarantine” were more correlated with the number of new daily COVID-19 cases than with the search interest for COVID-19. The public sought for additional details such as type, directions for proper use, and where to purchase as well as do-it-yourself alternatives for personal protective items. Personal protective or community measures were expected to be accompanied with definitions and guidelines as well as be available in translated versions. Google Trends could be a viable option to monitor and address the information needs of the public during a disease outbreak. Capturing and analyzing the search interests of the public could support the design and timely delivery of appropriate information essential to drive preventive measures during a disease outbreak.

scholarly journals Estimating the Outpatient Burden of Venous Thromboembolism in the United States: An Analysis of Google Trends Data from 2004 to 2015

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4453-4453 ◽  
Author(s):  
Adeel M Khan ◽  
Alok A. Khorana
Keyword(s):  
United States ◽  
Venous Thromboembolism ◽  
Varicose Veins ◽  
Blood Clot ◽  
Seasonal Pattern ◽  
The United States ◽  
Google Trends ◽  
Internet Search ◽  
Advisory Committees ◽  
Search Terms

Abstract Background: Analysis of internet search traffic has provided a new proxy measure into the trends and patterns of patients' diseases and their information-seeking behaviors. In recent years, Google Trends has become a data resource of interest given its status as the largest internet search provider in the world with publicly-viewable, passively-collected, and expansive data on any searchable term or combination of terms. For instance, search terms related to influenza (e.g. fever) predicted influenza spread faster than standard surveillance as shown by Ginsberg et al in Nature 2009. The true outpatient burden and incidence of venous thromboembolism (VTE) has long been debated. Extant VTE data are limited to cases that present to medical attention, thus missing any cases that do not come to an emergency department or clinic. We hypothesized that Google Trends analysis offers potential insight into the general populations' blood clot burden and awareness. This study aimed to explore general trends of VTE-related terms and seasonal variation in searches. Methods: Google Trends was utilized to obtain relative search engine traffic values (defined as search volume indices, SVIs) for terms related to DVT in the United States from summer 2004 - winter 2015. Terms related to LEG SWELLING, CALF PAIN, VARICOSE VEINS, and LEG CLOT were used and combined with Boolean operators to combine similar terms. A separate search occurred for BLOOD CLOT and related terms to investigate awareness of VTEs. Analysis was performed in R (V3.1.1) in accordance with previously published Google Trends investigations. Results: The average relative volume of searches was highest for VARICOSE VEINS and lowest for LEG SWELLING by approximately 3.2 fold. A seasonal pattern was seen with summer months (May-Aug) having higher SVIs than winter months (Nov-Feb) for all terms in the 11 year study period except for BLOOD CLOT. Using a Wilcoxon signed rank test, mean SVI difference comparing summer to winter for LEG SWELLING showed W = 66 (p = 0.004), for CALF PAIN W = 66 (p = 0.003), for VARICOSE VEINS W = 67 (p = 0.004), and for LEG CLOT W = 65 (p = 0.005). For BLOOD CLOT, a gradual increase in SVIs was seen and characterized by a Mann-Kendall test as having a significant positive trend, S = 898, p = 0.024. Conclusions: Search terms related to VTE in the United States show a strong seasonal pattern with greater search activity in summer months compared to winters months. These data suggest a higher incidence and burden of VTE in the summer. This challenges previous notions of a weakly higher incidence of VTE in winter months, calculated as a relative risk of 1.143 by Dentali et al in 2011. The gradual increase in relative search traffic for BLOOD CLOT terms reflects a likely rising awareness and/or true rise in the incidence of VTEs in the United States from 2004-2015. Further studies should investigate whether internet search traffic correlates directly with total yearly DVT incidence rates. Keywords: Population, venous thromboembolism, incidence Figure 1. Figure 1. Figure 2. Figure 2. Disclosures Khorana: Leo Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy, Honoraria; sanofi: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Boehringer-Ingelheim: Consultancy, Honoraria.

Assessing neurosurgical trends in literature and online: a Google Trends, Google Books and PubMed study (Preprint)

2019 ◽  
Author(s):  
Tomasz Szmuda ◽  
Shan Ali ◽  
Paweł Słoniewski
Keyword(s):  
Google Trends ◽  
Historical Trends ◽  
Scientific Publications ◽  
The Public ◽  
Search Terms ◽  
Printed Books ◽  
Google Books ◽  
Major Increase ◽  
Medicine Today ◽  
Over Time

BACKGROUND Google Books Ngram Viewer (Ngram) is an open online database of 5 million printed books where the frequency of words occurring in books can be analyzed over time. Google Trends is a tool that allows one to gauge popularity of search terms and topics over time. These tools have not yet been used together to assess the historical and current the trends in neurosurgery. OBJECTIVE To assess the neurosurgical trends in literature and online in the past and currently. METHODS Ngram, Google Trends and PubMed data were used to analyze the neurosurgical trends from 2004–2008. Next, Google Trends and PubMed data was obtained from 2018 to compare the data. The following keywords were searched on all three platforms: brain tumor, sciatica, neurosurgery, hydrocephalus and concussion. RESULTS Each platform had a characteristic interest in each topic. Online Google searches were most concerned with sciatica (62/100 worldwide), the scientific community with neurosurgery (7661 average yearly publications) and book authors wrote most about concussions (0.00013% worldwide Google One Million books). Sciatica held both the least scientific publications (129 average yearly publications) and one of the least mentions in printed books (0.000030% worldwide Google One Million books). The online and PubMed interest levels stayed the same from 2004 to 2018. However, concussion—which held one of the lowest online search interests from 2004 to 2008—had a major increase in 2018 online searches. CONCLUSIONS Ngram, Google Trends and PubMed data together provide valuable insights into the health interests among physicians and the public. It is crucial for neurosurgeons to be aware of historical trends as they offer vital insight on the driving factors in medicine today. Physicians can use this understanding to better align public and scientific concerns for the future, provide better patient education and raise awareness on issues that might be overlooked by the public.

Sign in / Sign up

Export Citation Format

Share Document