Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis.
JMIR Public Health Surveill
; 7(5): e18593, 2021 05 10.
Article
in English
| MEDLINE | ID: covidwho-1256222
ABSTRACT
BACKGROUND:
Asthma affects over 330 million people worldwide. Timing of an 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 of 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 before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks -1 and -2 as exogenous variables were conducted to validate our correlation results.RESULTS:
Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04).CONCLUSIONS:
Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Asthma
/
Search Engine
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
JMIR Public Health Surveill
Year:
2021
Document Type:
Article
Affiliation country:
18593
Similar
MEDLINE
...
LILACS
LIS