Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends : Infodemiology Study

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Sousa-Pinto , B , Halonen , J , Anto , A , Jormanainen , V , Czarlewski , W , Bedbrook , A , Papadopoulos , N G , Freitas , A , Haahtela , T , Anto , J M , Fonseca , J A & Bousquet , J 2021 , ' Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends : Infodemiology Study ' , Journal of Medical Internet Research , vol. 23 , no. 7 , 27044 . https://doi.org/10.2196/27044

Title: Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends : Infodemiology Study
Author: Sousa-Pinto, Bernardo; Halonen, Jaana; Anto, Aram; Jormanainen, Vesa; Czarlewski, Wienczyslawa; Bedbrook, Anna; Papadopoulos, Nikolaos G.; Freitas, Alberto; Haahtela, Tari; Anto, Josep M.; Fonseca, Joao Almeida; Bousquet, Jean
Contributor organization: HUS Inflammation Center
Department of Dermatology, Allergology and Venereology
University of Helsinki
Date: 2021-07-06
Language: eng
Number of pages: 15
Belongs to series: Journal of Medical Internet Research
ISSN: 1438-8871
DOI: https://doi.org/10.2196/27044
URI: http://hdl.handle.net/10138/339548
Abstract: Background: In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. Objective: This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (rho=0.82-0.84), and Brazil (rho=0.77-0.83) and moderate correlations with those occurring in Norway (rho=0.32-0.35) and Finland (rho=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. Conclusions: Common cold-related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them.
Subject: asthma
common cold
Google Trends
hospitalizations
time series analysis
mobile phone
EXACERBATIONS
INFECTIONS
3121 General medicine, internal medicine and other clinical medicine
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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