ArticleJMIR public health and surveillance2020
Google Searches and Suicide Rates in Spain, 2004-2013: Correlation Study.
Article in JMIR public health and surveillance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Temporal Association Between ChatGPT-Generated Diarrhea Synonyms in Internet Search Queries and Emergency Department Visits for Diarrhea-Related Symptoms in South Korea: Exploratory Study.Journal of medical Internet research · 2025Article
- Promises and Pitfalls of Internet Search Data in Mental Health: Critical Review.JMIR mental health · 2025Review
- Cross-Correlation Analysis of Monthly Google Search Volume and Suicide in Taiwan, 2012-2022.Depression and anxiety · 2025Article
- Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis.Journal of medical Internet research · 2024Observational
- Seasonal Trends in Suicide Attempts-Keywords Related Searches: A Google Trends Analysis.Healthcare (Basel, Switzerland) · 2024Article
- Estimating national and state-level suicide deaths using a novel online symptom search data source.Journal of affective disorders · 2023Article
- Korean adolescent suicide and search volume for "self-injury" on internet search engines.Frontiers in psychiatry · 2023Article
- Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model.JMIR public health and surveillance · 2021Article
- COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study.Journal of medical Internet research · 2020Article
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3 authors.
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Abstract
backgroundDifferent studies have suggested that web search data are useful in forecasting several phenomena from the field of economics to epidemiology or health issues.
objectiveThis study aimed to (1) evaluate the correlation between suicide rates released by the Spanish National Statistics Institute (INE) and internet search trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide and an analysis of these results using a linear regression model and (2) study the differential association between male and female suicide rates published by the INE and internet searches of these 57 terms.
methodsThe study period was from 2004 to 2013. In this study, suicide data were collected from (1) Spain's INE and (2) local internet search data from GT, both from January 2004 to December 2013. We investigated and validated 57 suicide-related terms already tested in scientific studies before 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of a GT search through a cross-correlation analysis and by linear regression of the suicide incidence data with the GT data.
resultsSuicide rates in Spain in the study period were positively associated (r<-0.2) for the general population with the search volume for 7 terms and negatively for 1 from the 57 terms used in previous studies. Suicide rates for men were found to be significantly different than those of women. The search term, "allergy," demonstrated a lead effect for new suicide cases (r=0.513; P=.001). The next significant correlating terms for those 57 studied were "antidepressant," "alcohol abstinence," "relationship breakup" (r=0.295, P=.001; r=0.295, P=.001; and r=0.268, P=.002, respectively). Significantly different results were obtained for men and women. Search terms that correlate with suicide rates of women are consistent with previous studies, showing that the incidence of depression is higher in women than in men, and showing different gender searching patterns.
conclusionsA better understanding of internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big data sources.
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