Portal de investigação
Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study
Data de publicação:
Data Ahead of Print:
Autores da FMUP
Participantes de fora da FMUP
- Halonen, JI
- Anto, A
- Jormanainen, V
- Czarlewski, W
- Bedbrook, A
- Papadopoulos, NG
- Haahtela, T
- Anto, JM
- Bousquet, J
Unidades de investigação
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.
©Bernardo Sousa-Pinto, Jaana I Halonen, Aram Antó, Vesa Jormanainen, Wienczyslawa Czarlewski, Anna Bedbrook, Nikolaos G Papadopoulos, Alberto Freitas, Tari Haahtela, Josep M Antó, João Almeida Fonseca, Jean Bousquet. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.07.2021.
Dados da publicação
- ISSN/ISSNe:
- 1439-4456, 1438-8871
- Tipo:
- Article
- Páginas:
- -
- DOI:
- 10.2196/27044
- Link para outro recurso:
- www.scopus.com
Journal of Medical Internet Research JMIR Publications Inc.
Citações Recebidas na Web of Science: 13
Citações Recebidas na Scopus: 17
Documentos
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Filiações
Keywords
- asthma; common cold; Google Trends; hospitalizations; time series analysis; mobile phone
Campos de estudo
Financiamento
Proyectos asociados
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Use of secondary data, health technology assessment methods and economic modelling applied to penicillin allergy
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Estudo Clínico Académico . 2020
A machine learning-based approach to support the assessment of clinical coded data quality in the context of Diagnosis-Related Groups classification systems
Investigador Principal: José Alberto da Silva Freitas
Estudo Clínico Académico . 2020
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Estudo Clínico Académico . 2020
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Citar a publicação
Sousa B,Halonen JI,Anto A,Jormanainen V,Czarlewski W,Bedbrook A,Papadopoulos NG,Freitas A,Haahtela T,Anto JM,Fonseca J,Bousquet J. Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study. J. Med. Internet Res. 2021. 23. (7):e27044. IF:7,076. (1).
