ArticleClinical rheumatology2019
Potential uses of an infodemiology approach for health-care services for rheumatology.
Article in Clinical rheumatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study.BMC health services research · 2023Article
- The comparison of curcuminoid formulations or its combination with conventional therapies versus conventional therapies alone for knee osteoarthritis.Clinical rheumatology · 2022Article
- Public interest in musculoskeletal symptoms and disorders during the COVID-19 pandemic : Infodemiology study.Zeitschrift fur Rheumatologie · 2022Article
- Population-level interest in anti-rheumatic drugs in the COVID-19 era: insights from Google Trends.Clinical rheumatology · 2021Article
- Assessment of the Effect of the Go Red for Women Campaign on Search Engine Queries for Cardiovascular Disease in Women.Cardiology research · 2020Article
- Big data and data processing in rheumatology: bioethical perspectives.Clinical rheumatology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis is a demand-based infodemiology study using the Google Trends and AdWords tools to illustrate infodemiology's potential use in rheumatology. The study investigates three questions in North American countries: (1) What terms associated with "rheumatology" and "arthritis" do people search for on Google? (2) What is the search volume for disease-modifying antirheumatic drugs (DMARDs)? and (3) What is the search volume for the term "arthritis" compared with for "hepatitis C" and "breast cancer"?
methodsWe conducted independent searches by country and search term for 2015-2017. Seventeen DMARDs were searched for 2015 through May 2018, with the turmeric remedy included for comparison. Data were exported to Excel for further analysis, adjusted by country population, and expressed as searches per 100,000 inhabitants (SpTh).
resultsThere were approximately 550 associated terms for "arthritis" in each country, and 5679 SpTh for DMARDs across the three countries. Searches for turmeric numbered slightly lower than for all DMARDs together in Canada and the USA, but were 70% higher in Mexico. Turmeric was also searched four times more than the most-searched biological DMARD in Canada and the USA, and 60 times more in Mexico. Arthritis was more commonly searched for in Canada than hepatitis C and breast cancer, but hepatitis C was highest in the USA and breast cancer in Mexico. Monthly trends did not show expected peaks associated with arthritis awareness campaigns.
conclusionInfodemiology provides preliminary information that could help in generating hypotheses, assessing health-care interventions, or even in providing patient-centered care.
Indexed as
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30448932What 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.