Tendencias temporales de los patrones de búsqueda sobre Servicios de Atención de Salud a Domicilio antes y después del COVID-19  <span class="so-article-trans-title" dir="auto"> Translated title: Temporal trends in Home Care Services search patterns before and after COVID-19 </span>

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Abstract

Resumen Objetivo: Analizar las tendencias temporales de los patrones de búsqueda de información, sobre los Servicios de Atención de Salud a Domicilio a través de Google Trends antes y después de la aparición del COVID-19. Método: Estudio ecológico y correlacional. Los datos se obtuvieron de la consulta directa en la herramienta «Google Trends». Término de búsqueda: “Servicio de asistencia sanitaria domiciliaria”. Fecha de consulta 01-09-2021. Resultados: El máximo de búsquedas se alcanzó en enero de 2020, coincidiendo con el inicio de la pandemia del COVID-19. Durante el período pre-COVID se observó una estacionalidad en el interés de la población (ADF: - 0.49; *p > 0.05) que desapareció con la irrupción del COVID-19 (ADF: -8.55; p < 0.05). La comparación de las medianas mostró diferencias estadísticamente significativas antes y después del COVID (KW: 31.15; *** p-valor < 0.001). Conclusiones: Se ha demostrado que la aparición del COVID-19 ha supuesto un hito significativo respecto al interés de la población general sobre los Servicios de Atención de Salud a Domicilio.

Translated abstract

Abstract Objetive: To analyze the temporal trends of the information search patterns on Home Care Services through Google Trends before and after the appearance of COVID-19. Method: Ecological and correlational study. The data were obtained from direct queries in the «Google Trends» tool. Search term: “Home Care Services.” Date of consultation 01-09-2021. Results: The maximum number of searches was reached in January 2020, coinciding with the start of the COVID-19 pandemic. During the pre-COVID period, a seasonality was observed in the interest of the population (ADF: - 0.49; * p> 0.05) that disappeared with the emergence of COVID-19 (ADF: -8.55; p < 0.05). The comparison of the medians showed statistically significant differences before and after COVID (KW: 31.15; *** p-value < 0.001). Conclusions: It has been shown that the appearance of COVID-19 has been a significant milestone regarding the general population’s interest in Home Care Services.

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Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Amaryllis Mavragani, Gabriela Ochoa, Konstantinos P Tsagarakis (2018)

Background In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

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Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings

Gianfranco Cervellin, Ivan Comelli, Giuseppe Lippi (2017)

Internet-derived information has been recently recognized as a valuable tool for epidemiological investigation. Google Trends, a Google Inc. portal, generates data on geographical and temporal patterns according to specified keywords. The aim of this study was to compare the reliability of Google Trends in different clinical settings, for both common diseases with lower media coverage, and for less common diseases attracting major media coverage. We carried out a search in Google Trends using the keywords “renal colic”, “epistaxis”, and “mushroom poisoning”, selected on the basis of available and reliable epidemiological data. Besides this search, we carried out a second search for three clinical conditions (i.e., “meningitis”, “Legionella Pneumophila pneumonia”, and “Ebola fever”), which recently received major focus by the Italian media. In our analysis, no correlation was found between data captured from Google Trends and epidemiology of renal colics, epistaxis and mushroom poisoning. Only when searching for the term “mushroom” alone the Google Trends search generated a seasonal pattern which almost overlaps with the epidemiological profile, but this was probably mostly due to searches for harvesting and cooking rather than to for poisoning. The Google Trends data also failed to reflect the geographical and temporary patterns of disease for meningitis, Legionella Pneumophila pneumonia and Ebola fever. The results of our study confirm that Google Trends has modest reliability for defining the epidemiology of relatively common diseases with minor media coverage, or relatively rare diseases with higher audience. Overall, Google Trends seems to be more influenced by the media clamor than by true epidemiological burden.

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More Diseases Tracked by Using Google Trends

Camille Pelat, Clement Turbelin, Avner Bar-Hen … (2009)

To the Editor: The idea that populations provide data on their influenza status through information-seeking behavior on the Web has been explored in the United States in recent years ( 1 , 2 ). Two reports showed that queries to the Internet search engines Yahoo and Google could be informative for influenza surveillance ( 2 , 3 ). Ginsberg et al. scanned the Google database and found that the sum of the results of 45 queries that most correlated with influenza incidences provided the best predictor of influenza trends ( 3 ). On the basis of trends of Google queries, these authors put their results into practice by creating a Web page dedicated to influenza surveillance. However, they did not develop the same approach for other diseases. To date, no studies have been published about the relationship of search engine query data with other diseases or in languages other than English. We compared search trends based on a list of Google queries related to 3 infectious diseases (influenza-like illness, gastroenteritis, and chickenpox) with clinical surveillance data from the French Sentinel Network ( 4 ). Queries were constructed through team brainstorming. Each participant listed queries likely to be used for searching information about these diseases on the Web. The query time series from January 2004 through February 2009 for France were downloaded from Google Insights for Search, 1 of the 2 websites with Google Trends that enables downloading search trends from the Google database ( 5 ). Correlations with weekly incidence rates (no. cases/100,000 inhabitants) of the 3 diseases provided by the Sentinel Network were calculated for different lag periods (Pearson coefficient ρ). The highest correlation with influenza-like illness was obtained with the query grippe –aviaire –vaccin, the French words for influenza, avian, and vaccine respectively (ρ = 0.82, p 1 of the terms. The second highest correlation was obtained when the keyword gastro (ρ = 0.88, p<0.001) (Appendix Figure, panel B) was used. The highest correlation with chickenpox was obtained with the French word for chickenpox (varicelle) (ρ = 0.78, p<0.001) (Appendix Figure, panel C). A time lag of 0 weeks gave the highest correlations between the best queries for influenza-like illness and acute diarrhea and the incidences of these diseases; the peak of the time series of Google queries occurred at the same time as that of the disease incidences. The best query for chickenpox had a 1-week lag, i.e., was 1 week behind the incidence time series. In conclusion, for each of 3 infectious diseases, 1 well-chosen query was sufficient to provide time series of searches highly correlated with incidence. We have shown the utility of an Internet search engine query data for surveillance of acute diarrhea and chickenpox in a non–English-speaking country. Thus, the ability of Internet search-engine query data to predict influenza in the United States presented by Ginsberg et al. ( 3 ) appears to have a broader application for surveillance of other infectious diseases in other countries. Supplementary Material Appendix Figure Time series of search queries plotted along the incidence of 3 diseases (influenza-like illness, gastroenteritis, and chickenpox), 2004-2008. Black lines show trends of search fractions containing the French words for influenza (A), gastroenteritis (B), and chickenpox (C). Red lines show incidence rates for the 3 corresponding diseases (influenza-like illness, acute diarrhea, and chickenpox). Search fractions are scaled between 0 and 100 by Google Insights for Search's internal processes ( 5 ). Incidence rates are expressed in no. cases for 100,000 inhabitants, as provided by the Sentinel Network ( 4 ).

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Journal

Journal ID (publisher-id): had

Title: Hospital a Domicilio

Abbreviated Title: Hosp. domic.

Publisher: Centro Internacional Virtual de Investigación en Nutrición (CIVIN) (Alicante, Alicante, Spain )

ISSN (Electronic): 2530-5115

Publication date (Print and electronic): December 2021

Volume: 5

Issue: 4

Pages: 187-195

Affiliations

[3] Elche Valencia orgnameUniversidad Miguel Hernández de Elche Spain

[1] Sant Joan d'Alacant Alicante orgnameUniversidad Miguel Hernández orgdiv1Departamento de Salud Pública e Historia de la Ciencia Spain

[2] Alicante orgnameCentro Internacional Virtual de Investigación en Nutrición (CIVIN) España

Article

Publisher ID: S2530-51152021000400187 Publisher ID: S2530-5115(21)00500400187

DOI: 10.22585/hospdomic.v5i4.148

SO-VID: 50925a13-1c16-47d6-ad75-ee05c6476ef3

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

History

Date accepted : 12 October 2021

Date received : 29 September 2021

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Figures: 0, Tables: 0, Equations: 0, References: 15, Pages: 9

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Tendencias temporales de los patrones de búsqueda sobre Servicios de Atención de Salud a Domicilio antes y después del COVID-19 Translated title: Temporal trends in Home Care Services search patterns before and after COVID-19

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Most cited references 15

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings

More Diseases Tracked by Using Google Trends

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