ArticleJournal of medical Internet research2021
Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- ARIA-EAACI 2025: Person-Centred, Digitally Enabled and Artificial Intelligence-Assisted Change Management in Airway Diseases: An OECD Best Practice for Integrated Care for Chronic Diseases.Clinical and translational allergy · 2026Article
- Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations.Journal of medical Internet research · 2025Article
- Food allergy: What are people looking for? An infodemiology study.Clinical and translational allergy · 2023Article
- Impact of asthma in Europe: A comparison of web search data in 21 European countries.The World Allergy Organization journal · 2023Article
- The association between air pollutants and hospitalizations for asthma: an evidence from Chengdu, China.Annals of translational medicine · 2023Article
- Digitally-enabled, patient-centred care in rhinitis and asthma multimorbidity: The ARIA-MASK-airClinical and translational allergy · 2023Article
- The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data.Journal of medical Internet research · 2022Article
- Infodemiology of RSV in Italy (2017-2022): An Alternative Option for the Surveillance of Incident Cases in Pediatric Age?Children (Basel, Switzerland) · 2022Article
- Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.NPJ digital medicine · 2022Article
- Heightened religiosity proactively and reactively responds to the COVID-19 pandemic across the globe: Novel insights from the parasite-stress theory of sociality and the behavioral immune system theory.International journal of intercultural relations : IJIR · 2022Article
- Usage patterns of oral H1-antihistamines in 10 European countries: A study using MASK-air® and Google Trends real-world data.The World Allergy Organization journal · 2022Article
- United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study.JMIR public health and surveillance · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 6 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations.
objectiveThis study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations.
methodsWe 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.
resultsIn 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 (ρ=0.82-0.84), and Brazil (ρ=0.77-0.83) and moderate correlations with those occurring in Norway (ρ=0.32-0.35) and Finland (ρ=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.
conclusionsCommon cold-related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.