Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Wednesday, July 01, 2020 at 7:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Wednesday, July 01, 2020 at 7:00 PM.
Estimation of asthma symptom onset using Internet search queries: A lag-time series analysis
Yulin Hswen;
Amanda Zhang;
Bruno Ventelou
ABSTRACT
Background:
Asthma affects over 330 million people worldwide. Timing of the asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms.
Objective:
This study evaluates the utility of the Internet search query data for the identification the onset of asthma symptoms.
Methods:
Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks prior to hospital admission to 4 weeks after hospital admission.
Results:
Google search volume for asthma had the highest correlation at 2 weeks before hospital admission.
Conclusions:
Our findings demonstration Internet search queries can earlier predict asthma events and may be a better use for classifying the measurement of timing of symptom onset.
Citation
Please cite as:
Hswen Y, Zhang A, Ventelou B
Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis