TY - JOUR
T1 - Google trends for pain search terms in the world's most populated regions before and after the first recorded COVID-19 case
T2 - Infodemiological study
AU - Szilagyi, Istvan Szilard
AU - Ullrich, Torsten
AU - Lang-Illievich, Kordula
AU - Klivinyi, Christoph
AU - Schittek, Gregor Alexander
AU - Simonis, Holger
AU - Bornemann-Cimenti, Helmar
N1 - Publisher Copyright:
© Istvan-Szilard Szilagyi, Torsten Ullrich, Kordula Lang-Illievich, Christoph Klivinyi, Gregor Alexander Schittek, Holger Simonis, Helmar Bornemann-Cimenti. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.04.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
PY - 2021/4/1
Y1 - 2021/4/1
N2 - 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 U 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, P<.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.
AB - 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 U 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, P<.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.
KW - COVID-19
KW - Data mining
KW - Google trends
KW - Incidence
KW - Interest
KW - Internet
KW - Pain
KW - Research
KW - Trend
UR - http://www.scopus.com/inward/record.url?scp=85105364391&partnerID=8YFLogxK
U2 - 10.2196/27214
DO - 10.2196/27214
M3 - Article
C2 - 33844638
AN - SCOPUS:85105364391
SN - 1439-4456
VL - 23
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
IS - 4
M1 - e27214
ER -