Abstract 14413: Contemporary Prevalence, Comorbidity Burden, and Treatment of Overweight and Obesity: Insights From the Multicenter Mass General Brigham Healthcare System
Bibliographic record
Abstract
Background: Real-world evidence is critical to identify treatment gaps and inform healthcare service design, but contemporary anti-obesity medication (AOM) use patterns are sparsely reported. Aims: To describe the prevalence of obesity/overweight, evidence-based obesity-related conditions (ORCs), and AOM use among eligible patients, with a focus on cardiovascular disease (CVD). Methods: In this cross-sectional analysis of the multicenter Mass General Brigham healthcare system spanning 2018-2022, we identified all adult patients eligible for AOM (BMI 27-29.9 kg/m 2 with ≥1 ORC or BMI ≥30 kg/m 2 ). The prevalence of ORCs was ascertained using administrative codes and available EHR data. Prescription of FDA-approved AOM by BMI category, number of ORCs, number of key CVD risk factors, and the presence of prior MACE were also evaluated. Results: Of 2,469,473 individuals who met inclusion criteria, 1,111,396 (44%) were eligible for AOM (mean age, 54 years; 57% female). Of these, 31%, 41%, 17%, and 11% had a BMI (kg/m 2 ) of 27-29.9, 30-34.9, 35-39.9, and ≥40, respectively. Prior metabolic/bariatric surgery was seen in 1%. Musculoskeletal disorders (54%), dyslipidemia/hyperlipidemia (36%), and hypertension (35%) were the most common ORCs, with ≥2 ORCs observed in 62%. Prescription of any FDA-approved AOM was observed in only 1.4% of all eligible patients. Liraglutide 3.0 mg (46% of all AOM) was the most prescribed AOM. AOM prescriptions increased modestly with higher BMI, ORC burden, and number of CVD risk factors ( Figure ). Among those with prior MACE (9%), 1.2% (1.0% if without T2DM) were prescribed FDA-approved AOM. Conclusions: Although more than 1 in 3 contemporary patients in a large healthcare system are eligible by guidelines and FDA labeling, AOM prescription remains exceedingly low, even among high-risk persons with severe obesity and established CVD. Novel care delivery pathways are needed to accelerate closure of these considerable implementation gaps.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".