The Celebrity Effect: How Social Media Changed Ozempic Utilization by Medicaid Patients
Bibliographic record
Abstract
Background: Recently, glucagon-like peptide-1 receptor agonists (GLP-1 agonists), a drug class used to treat Type 2 Diabetes, has gained popularity on social media for cosmetic weight loss. Celebrity endorsement of Ozempic, brand name of GLP-1 agonist semaglutide, has increased public demand and caused supply shortages. However, effects on Medicaid patients, who use Ozempic for diabetes, have yet to be investigated. Methods: We sourced publicly available drug utilization datasets from Medicaid.gov. Nationwide Medicaid reimbursement data for Ozempic, Wegovy (another brand name of semaglutide), and Jardiance (different diabetes medication drug class) were extracted for 2021, 2022, and 2023. Rates of change per quarter per calendar year were calculated, and two-tailed student’s paired T-tests were conducted. Results: Social media promotions for Ozempic largely began 2022Q4 (Oct 1-Dec 31). Medicaid Ozempic utilization prior to 2022Q4 were significantly different from Jardiance regarding reimbursed units, number of prescriptions, total amount, and Medicaid amount (p0.05), potentially correlating with a difference in publicity for weight loss use compared to Ozempic. Conclusion: This study strongly suggests that social media has impacted Ozempic usage spanning different socioeconomic classes nationwide. Plastic surgeons with aesthetic services should be conscientious of the downstream effects of prescribing cosmetic weight loss drugs and manage patient expectations accordingly as social media continues to drive public demand.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".