Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Mar 6, 2020
Date Accepted: Mar 11, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Estimation of asthma symptom onset using Internet search queries: A lag-time series analysis
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
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Copyright
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