MON-826 Impact Of Demographic And Social Determinants On Access To SGLT2 Inhibitors And GLP-1 Receptor Agonists: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Abstract Disclosure: N. Shah: None. J. Edjimbi: None. A. Grimshaw: None. C. Gunderson: None. S. Gupta: None. Sodium–glucose cotransporter2 inhibitors (SGLT2i) and glucagonlike peptide1 receptor agonists (GLP1RA) are established treatments for type 2 diabetes mellitus (DM), obesity, and cardiovascular disease; however, significant disparities in their prescription persist. To date, no systematic review or metaanalysis has evaluated the impact of demographics and social determinants of health (SDOH) on utilization patterns for these agents. We hypothesized that among adults with DM, those from racial minority groups, of female sex, in the lowest income strata, or with public insurance would have lower odds of receiving SGLT2i and GLP1RA compared with white, male, higherincome, and commercially insured counterparts. To test this, we conducted a comprehensive search of the Cochrane Library, Google Scholar, Ovid Embase, Ovid Medline, Scopus, and Web of Science Core Collection for observational studies reporting adjusted odds ratios (aORs) for prescribing across these demographic and SDOH characteristics. Our search yielded over 8,100 records, of which 21 studies (study years 2016-2025; regions: US, United Kingdom, Netherlands, South Korea, Canada, Australia) directly compared SGLT2i and GLP1RA prescribing across these demographic and social-determinant characteristics. We extracted studyspecific aORs and 95% confidence intervals (CIs) for each drug class and pooled them separately using DerSimonian–Laird randomeffects models. Heterogeneity was assessed with I² statistics. Among 7,340,058 patients in 21 studies, 47.2% were female, 55.2% white, mean/median age 60–74.9 years, 28.8% lowest-income, and 45.4% publicly insured. For SGLT2i, pooled analyses showed lower odds among black patients (aOR 0.71, 95% CI 0.51–0.99; I² = 99.8%); female patients (0.89, 0.82–0.95; I² = 99.2%); low-income patients (0.96, 0.92–1.00; I² = 96.7%); and patients with Medicare (0.68, 0.60–0.78; I² = 52.7%) or Medicare Advantage (0.41, 0.30–0.57; I² = 99.8%). For GLP-1RA, lower odds were seen in black patients (0.73, 0.57–0.94; I² = 99.3%), Hispanic patients (0.81, 0.69–0.96; I² = 98.6%), and Asian patients (0.49, 0.41–0.58; I² = 96.7%); low-income patients (0.86, 0.81–0.92; I² = 96.4%); and patients with Medicaid (0.70, 0.55–0.89; I² = 73.4%), Medicare (0.73, 0.55–0.96; I² = 92.0%), or Medicare Advantage (0.49, 0.36–0.68; I² = 99.6%), whereas female patients had higher odds (1.33, 1.23–1.43; I² = 96.1%). These results reveal prescribing disparities across race, sex, income, and insurance status, with minority groups, lowincome, and publicly insured patients with DM less likely to receive SGLT2i or GLP1RA. Further research should elucidate the mechanisms driving these differences and evaluate interventions to promote equitable use of these therapies. Presentation: Monday, July 14, 2025
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".