Sweetening the deal: an infodemiological study of worldwide interest in semaglutide using Google Trends extended for health application programming interface
Bibliographic record
Abstract
BACKGROUND: Off-label use of semaglutide for non-diabetic weight loss (which regulators have linked to social media promotion) created worldwide supply shortages. We evaluated worldwide semaglutide interest measured by online search behavior to gauge social media and conventional print media reporting's effect on search interest. METHODS: Using Google Trends Extended for Health (GTEH) multiple sampling, we retrieved regional online interest (ROI) for all countries and extracted timelines and top search queries for January 2021-August 2023 for countries with median ROI ≥ 20 using the "semaglutide" topic. We obtained semaglutide media reporting from the ProQuest database. We estimated the effect of media and within-country semaglutide interest on between-country interest with Granger causality analysis. We determined changepoints for trends within each country with joinpoint regression. We determined prominent themes in search queries for each country with natural language processing thematic analysis. RESULTS: Twenty-seven countries were included. Most countries showed an increase in semaglutide interest over time, with Canada and the USA showing the largest sustained interest. Most of the search interest arose from 2022 onwards. Granger's analysis showed that media coverage could only partially explain interest, and interest in some countries partially preceded interest in others, with the UK and Germany showing strong relationships between news reports and lagged search interest. Joinpoint analysis identified up to four significant within-country changepoints. Most countries showed significant positive weekly trends in 2021-2022, although uptrends in search interest varied considerably between countries. One episode of the Dr. Oz show (TV media event) coincided with strong peaks in numerous countries. Natural language processing of top search queries showed some agreement between countries and country-specific themes. Weight loss was a major theme in most countries, while a diabetes theme was generally absent or weak. Some countries (Australia, Chile, South Africa, UK) had themes for buying Ozempic from (named) local retailers, and Germany had a theme related to buying Ozempic without a script. CONCLUSIONS: GTEH data provided insights into global search interest in semaglutide and regional variation. Studies focusing on specific countries which include social media data can elucidate specific drivers behind the surge in off-label use of semaglutide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".