Evidence map›Paper›PMID 34873449›Full record

ArticleDigital health

Increasing utility of Google Trends in monitoring cardiovascular disease.

Conor Senecal, Madeline Mahowald, Lilach Lerman, Francisco Lopes-Jimenez, Amir Lerman

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Conor SenecalDepartment of Cardiovascular Diseases, Mayo Clinic and College of Medicine, USA.ORCID https://orcid.org/0000-0002-5000-6254
Madeline MahowaldDepartment of Cardiovascular Diseases, Mayo Clinic and College of Medicine, USA.
Lilach LermanDepartment of Internal Medicine, Division of Nephrology Mayo Clinic and College of Medicine, USA.
Francisco Lopes-JimenezDepartment of Cardiovascular Diseases, Mayo Clinic and College of Medicine, USA.
Amir LermanDepartment of Cardiovascular Diseases, Mayo Clinic and College of Medicine, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCardiovascular disease is the most common cause of morbidity and mortality in the United States. Patients are increasingly using internet search to find health-related information, including searches for cardiovascular diseases and risk factors. We sought to evaluate the change in the state by state correlation of cardiovascular disease and risk factors with Google Trends search volumes.

methodsData on cardiovascular disease hospitalizations and risk factor prevalence were obtained from the publically available Centers for Disease Control and Prevention website from 2006 to 2018. Google Trends data were obtained for matching conditions and time periods. Simple linear regression was performed to evaluate for an increase in correlation over time.

resultsHospitalizations for six separate cardiovascular disease conditions showed moderate to strong correlation with online search data in the last period studied (heart failure (0.58,

conclusionThe prevalence of and hospitalizations for cardiovascular conditions in the United States strongly correlate with online search volumes in the United States when analyzed by state. This relationship has progressively strengthened or been strong and stable over recent years for these conditions. Google Trends represents an increasingly valuable tool for evaluating the burden of cardiovascular disease and risk factors in the United States.

Indexed as

Cardiovascular diseaseGoogle Trends

Identifiers

PMID34873449
PMCPMC8642777

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Registered trials

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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.