Abstract
Purpose:
To describe patterns of glucagon-like peptide-1 receptor agonist (GLP1-RA) medication awareness, use, and access in a US adult cohort.
Methods:
Using self-reported December 2023 data from a nationwide cohort (N=4,555), we examined predictors of awareness, use (≤3 years), and inability to access GLP1-RA medications. Poisson regression with robust standard errors estimated age/sex-adjusted prevalence ratios. Analyses were conducted overall and among participants meeting FDA clinical-eligibility criteria (BMI ≥30 kg/m2 or ≥27 kg/m2 + ≥1 comorbidity; N=1,883).
Results:
Awareness was 81.6%; use was 8.3% overall, 13.4% among clinically-eligible participants, and 4.0% among ineligible participants. Overall, use was highest at ages 30–39 and 50–64 and among Hispanic and Black adults, parents, smokers, and those with healthcare barriers. Among clinically-eligible participants, use was lower among men (aPR=0.76, 95% CI: 0.60-0.96) and those with healthcare access barriers (aPR=0.47 [0.35-0.63]), and higher among those with income ≥$100,000 versus <$49,000 (aPR=1.49 [1.12-1.98]). Among those who attempted treatment, 49.9% were unsuccessful; inability was more common for those with healthcare barriers (aPR=1.42 [1.14-1.77]).
Conclusions:
GLP1-RA awareness is high, but predictors of uptake and access differ by eligibility. Sociodemographic factors predicted uptake in the full cohort; income, sex, and healthcare barriers were significant predictors among clinically-eligible adults.
Keywords: Glucagon-Like Peptide 1 Receptor Agonists, Socioeconomic Factors, Cohort Studies, Risk factors, United States, and Health Knowledge, Attitudes, Practice
1. Introduction
Glucagon-like-peptide-1 receptor agonist (GLP1-RA) medications, marketed under brands such as Ozempic, Wegovy, Mounjaro, Zepbound, Saxenda, and others, have demonstrated substantial health benefits, including considerable body weight reduction and reduced risk cardiovascular disease and mortality.(1–3) While initially indicated for type 2 diabetes and commonly prescribed off-label for obesity treatment,(4) the Food and Drug Administration (FDA) approved active ingredients semaglutide in 2021 and tirzepatide in 2023 for the use of chronic weight management.(5,6) These approvals, combined with extensive media attention, contributed to sharp increases in use.(7–9) Among overweight or obese individuals without diabetes, use rose by 700% between 2019 and 2023, from ~21,000 to ~174,000.(10) In the last quarter of 2022, U.S. providers wrote over 9 million prescriptions, reflecting a 300% increase over three years.(11)
Since FDA approval of GLP1-RAs for weight loss, evidence on sociodemographic patterns of awareness, use, and access remains limited.(12) Characterizing these patterns can help clarify which populations are more or less likely to use and benefit from these therapies and inform efforts to ensure they are equitably accessible. Nationally representative surveys provide initial snapshots. A 2024 survey (n=1,479) reported 82% awareness and 6% current use, with higher awareness among Black vs White adults and higher use among older and higher-income adults.(13) A 2025 survey (n=8,793) found the highest use among women aged 50-64, with women aged 30-49 more likely than men in their age group to report use.(12)
Population-wide analyses are informative for understanding how GLP1-RAs are diffusing across the U.S. population and which groups are more likely to be aware of or report use. However, interpretation is limited by the fact that most U.S. adults do not meet clinical criteria for weight loss treatment. As a result, observed associations may reflect differences in underlying eligibility rather than disparities in access or uptake among individuals who meet clinical criteria for treatment; GLP1-RA agonists are approved for chronic weight management in adults with a body mass index (BMI) ≥30 kg/m2 or ≥27 kg/m2 with at least one weight-related comorbidity.(14) Understanding awareness, uptake and access specifically among clinically-eligible individuals is vital given their greater medical need for weight reduction to prevent or improve adverse health outcomes.(15)
Moreover, eligibility does not ensure access. High out-of-pocket costs, inconsistent insurance coverage, and supply shortages have been documented as barriers to GLP1-RA use.(4,16) These challenges are especially consequential for clinically-eligible individuals who face structural barriers to healthcare and require sustained therapy.(17,18) To date, research has not examined barriers to healthcare in relation to GLP1-RA awareness, access, and use, nor explicitly contrasted population-wide patterns with those observed among clinically-eligible adults.
This study uses self-reported data from a diverse nationwide cohort of U.S adults to describe sociodemographic and healthcare-related patterns of GLP1-RA awareness, use, and access, both overall and among participants meeting FDA clinical-eligibility criteria for weight loss treatment. We additionally examine predictors of inability to access GLP1-RAs among individuals who attempted to obtain them. These analyses are descriptive and intended to characterize population-level patterns rather than to estimate causal effects. Analyses of the full cohort provide context and comparability with prior population-based studies, and examine a broader set of sociodemographic and healthcare-related factors than previously assessed. Analyses restricted to clinically-eligible adults allow more direct interpretation of uptake and access among those with clinical need. By delineating these population-level patterns, this work is intended to inform future etiologic and qualitative research aimed at elucidating underlying mechanisms and supporting the development of interventions and policies to reduce avoidable inequities.
2. Materials and Methods
2.1. Data Source
The CHASING COVID Cohort is a community-based, prospective cohort study of adults aged 18 years and older residing in the United States or U.S. territories. While primarily designed to examine incidence and determinants of SARS-CoV-2 infection,(19) the cohort also collected longitudinal data on a wide range of health-related behaviors and outcomes. From March 2020 through December 2023, enrolled participants completed online assessments approximately every three months. Recruitment and follow-up procedures have been previously described.(20) The cohort is geographically and sociodemographically diverse, enhancing its relevance for examining population-level health patterns.
This study was approved by the Institutional Review Board at the City University of New York Graduate School for Public Health and Health Policy, and participants could voluntarily discontinue participation at any time.
2.2. Analytic Sample
In the December 2023 wave of data collection, questions related to GLP1-RA medication awareness, use, and access were included in the assessment questionnaire.(21) Of the 5,798 participants enrolled in the CHASING COVID Cohort, 4,555 completed this assessment, and were included in the analytic sample. Baseline characteristics of participants included in the analytic sample were compared with those of the full cohort to assess potential differences related to survey completion (Supplementary Table S1).
2.3. Study Measures
All study measures were based on participant self-report. Definitions of measures used, including timing of assessment, are detailed in Supplementary Table S2.
2.3.1. Clinical Eligibility for a GLP1-RA Prescription
GLP1-RA medications are approved for multiple indications, including glycemic control in type 2 diabetes and chronic weight management. Because this study focuses on patterns of awareness, use, and access related to weight management, clinical eligibility was defined based on FDA-approved criteria for chronic weight management only. It was defined using baseline height and weight with body mass index (BMI) calculated from continuous values and rounded to the nearest whole number. Participants met clinical eligibility criteria if they had: (1) BMI ≥30 kg/m2, or (2) BMI ≥27 kg/m2 plus a diagnosis of type 2 diabetes, high blood pressure, dyslipidemia, angina, coronary heart disease, myocardial infarction, asthma, or depression. These conditions were selected based on those identified by the Centers for Disease Control as consequences of obesity, and examples of weight-related comorbidities provided by the manufacturer of semaglutide.(22,23) For chronic conditions, any diagnosis history was considered sufficient. For asthma and depression, where ever-diagnosis may not indicate current symptoms, participants were required to report current asthma or to have endorsed moderate/severe symptoms on at least one Patient Health Questionnaire-8 during follow up. Comorbidity distributions are shown in Supplementary Table S3.
2.3.2. Awareness, Use and Inability to Access GLP1-RA Medications
To assess awareness of prescription weight loss medications participants were asked how much they have heard about a new class of drugs being used for weight loss (e.g. Ozempic, Wegovy, Mounjaro).” Response options included: a lot, some, a little, or nothing at all. Participants who selected nothing at all were classified as unaware, while all others were categorized as aware.
For GLP1-RA use, participants were subsequently asked when they last used a prescription medication for weight loss. Participants reporting use within the past 3 years were classified as GLP1-RA users. We chose this cutoff to align with the approval of semaglutide for chronic weight management on June 4, 2021- approximately 2.5 years prior to the study assessment.(24)
To assess inability to access, participants were asked whether, in the past 12 months, they had tried to get access to a weight loss drug. Response options were: Yes, I tried but was unable to get a prescription; Yes, I was prescribed but unable to get a weight-loss drug; and No. Participants who endorsed either “Yes” option were classified as having tried but been unable to obtain the medication. For this analysis, the denominator included all participants who had attempted to obtain a prescription weight-loss medication in the prior 12 months, defined as either (1) reporting current use or use within the past year on the medication-use question, or (2) reporting inability to obtain on the access question.
2.3.3. Sociodemographics
Age, sex, race/ethnicity, education, annual household income, children in the household, residential area type, geographic region and cigarette smoking status were included as sociodemographic predictors for analysis. Sex at birth was used to enable male-female comparisons given its relevance for biological factors such as weight distribution and potential motivations for weight loss treatment. Participants who reported smoking on some days or every day were classified as cigarette smokers. Residential area type was derived from participant-reported ZIP code of residence, which was mapped to the National Center for Education Statistics Locale classifications using the ZCTA locale file.(25) Participants were assigned to one of four categories: Rural, Suburban, Urban, or Town. Because few participants were classified as residing in a Town (n=7), Suburban and Town were collapsed into a single category. Geographic region was determined from state of residence, collapsed into the four U.S. Census Bureau regions: Midwest, Northeast, South, and West.(26)
2.3.4. Healthcare Access
Barriers to healthcare included lacking health insurance, being unable to see a doctor in the past year due to cost-related barriers, and not having a primary care provider. These factors were analyzed both individually and as a composite binary indicator coded “yes” if any barrier was reported. This measure has been used previously to identify individuals with limited healthcare access.(27) Healthcare barriers were identified using assessments conducted during the year preceding the December 2023 assessment.
2.4. Statistical Analysis
We used Poisson regression with robust standard errors to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) for associations between clinical eligibility, sociodemographic, and healthcare-related factors and GLP1-RA awareness, use, and inability to access. For each characteristic, we calculated crude PRs, followed by age and sex-adjusted models. Analyses were conducted both in the full cohort and among participants meeting FDA clinical-eligibility criteria.
Sensitivity analyses were conducted to assess whether observed associations among clinically-eligible participants may have been driven by individuals with type 2 diabetes, for whom GLP1-RA prescribing and insurance coverage patterns may differ. Specifically, we repeated analyses restricted to clinically-eligible participants without a diagnosis of type 2 diabetes, thereby focusing only on eligibility related to overweight or obesity and weight-related comorbidities.
All analyses were conducted in SAS version 9.4 (SAS Institute, Cary, NC).
3. Results
Baseline sociodemographic and health characteristics of participants included in the analytic sample (N = 4,555) were similar to those of the full CHASING COVID Cohort (N = 5,798) (Supplementary Table S1).
3.1. Estimates and predictors of awareness
In the full cohort (N=4,555), 81.6% reported awareness of GLP1-RA medications. Awareness was slightly higher among those with clinical eligibility (84.2% versus 79.7%; aPR=1.04, 95% CI: 1.01-1.07) (Table 1). Awareness was higher among older adults and those with higher income and lower among participants with lower educational attainment, racial/ethnic minorities, smokers and those reporting any healthcare access barriers (Table 1). Specifically, participants aged 50–64 and 65+ had greater awareness compared with those aged 18–29 (aPR=1.14, 95% CI: 1.09–1.19 for both groups) as well as among those with a household income ≥$100,000 versus <$49,000 (aPR=1.20, 95% CI: 1.16–1.24). Awareness was lower among participants with a high school education or less (aPR=0.72, 95% CI: 0.67–0.77) or some college (aPR=0.89, 95% CI: 0.86–0.92) compared with college graduates as well as among Hispanic (aPR=0.88, 95% CI: 0.84–0.92) and Black non-Hispanic participants (aPR=0.85, 95% CI: 0.80–0.90) compared with White non-Hispanic participants. Associations were consistent across crude and age/sex-adjusted models. Similar patterns were observed among clinically-eligible participants, including in analyses excluding those with type 2 diabetes (Table 3 and Supplementary Table S4).
Table 1.
Prevalence and characteristics of CHASING COVID Cohort participants by awareness and use of GLP1-RA medications, December 2023 (N=4,555)
| Characteristic | Denominator | Aware of GLP1-RAs | Used GLP1-RA in previous 3 years⸷ | ||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| N (col %) | N (row %) | Crude PR (95% CI) | Age- and sex-adjusted PR* (95% CI) | N (row %) | Crude PR (95% CI) | Age- and sex-adjusted PR* (95% CI) | |
| Total | 4555 (100) | 3715 (81.6) | - | - | 380 (8.3) | - | - |
| Meets GLP1-RA indication for weight lossƛ§§ | |||||||
| Yes | 1883 (42.5) | 1586 (84.2) | 1.06 (1.03, 1.09) | 1.04 (1.01, 1.07) | 253 (13.4 | 3.39 (2.71, 4.23) | 3.31 (2.65, 4.13) |
| No | 2549 (57.5) | 2032 (79.7) | Ref | Ref | 101 (4.0) | Ref | Ref |
| Age | |||||||
| 18-29 | 998 (21.9) | 767 (76.9) | Ref | Ref | 66 (6.6) | Ref | Ref |
| 30-39 | 1314 (28.9) | 1046 (79.6) | 1.04 (0.99, 1.08) | 1.04 (0.99, 1.09) | 129 (9.8) | 1.48 (1.12, 1.97) | 1.51 (1.13, 2.01) |
| 40-49 | 844 (18.5) | 680 (80.6) | 1.05 (1.00, 1.10) | 1.05 (1.00, 1.10) | 71 (8.4) | 1.27 (0.92, 1.76) | 1.29 (0.93, 1.79) |
| 50-64 | 606 (13.3) | 530 (87.5) | 1.14 (1.09, 1.19) | 1.14 (1.09, 1.19) | 57 (9.4) | 1.42 (1.01, 2.00) | 1.45 (1.03, 2.04) |
| 65+ | 793 (17.4) | 692 (87.3) | 1.14 (1.09, 1.19) | 1.14 (1.09, 1.19) | 57 (7.2) | 1.09 (0.77, 1.53) | 1.09 (0.77, 1.53) |
| Sex§ | |||||||
| Male | 2026 (44.5) | 1651 (81.5) | 1.00 (0.97, 1.03) | 0.99 (0.97, 1.02) | 161 (8.0) | 0.92 (0.75, 1.11) | 0.87 (0.71, 1.05) |
| Female | 2522 (55.5) | 2058 (81.6) | Ref | Ref | 219 (8.7) | Ref | Ref |
| Race/ethnicity | |||||||
| Hispanic | 759 (16.7) | 564 (74.3) | 0.86 (0.82, 0.90) | 0.88 (0.84, 0.92) | 77 (10.1) | 1.30 (1.01, 1.66) | 1.30 (1.01, 1.66) |
| White, non-Hispanic | 2838 (62.3) | 2449 (86.3) | Ref | Ref | 222 (7.8) | Ref | Ref |
| Black, non-Hispanic | 494 (10.9) | 355 (71.9) | 0.83 (0.79, 0.88) | 0.85 (0.80, 0.90) | 51 (10.3) | 1.32 (0.99, 1.76) | 1.31 (0.98, 1.74) |
| Asian/Pacific Islander, non-Hispanic | 327 (7.2) | 245 (74.9) | 0.87 (0.81, 0.93) | 0.89 (0.84, 0.95) | 19 (5.8) | 0.74 (0.47, 1.17) | 0.79 (0.50, 1.25) |
| Other, non-Hispanic | 137 (3.0) | 102 (74.5) | 0.86 (0.78, 0.95) | 0.86 (0.78, 0.95) | 11 (8.0) | 1.03 (0.57, 1.83) | 1.00 (0.56, 1.78) |
| Education | |||||||
| High school or less | 519 (11.4) | 320 (61.7) | 0.71 (0.66, 0.76) | 0.72 (0.67, 0.77) | 47 (9.1) | 1.14 (0.84, 1.54) | 1.20 (0.89, 1.61) |
| Some college | 1191 (26.2) | 918 (77.1) | 0.89 (0.86, 0.92) | 0.89 (0.86, 0.92) | 107 (9.0) | 1.13 (0.91, 1.41) | 1.17 (0.94, 1.46) |
| College graduate | 2845 (62.5) | 2477 (87.1) | Ref | Ref | 226 (7.9) | Ref | Ref |
| Annual household income | |||||||
| <$49,000 | 1781 (39.1) | 1315 (73.8) | Ref | Ref | 145 (8.1) | Ref | Ref |
| $50,000-$99,999 | 1481 (32.5) | 1242 (83.9) | 1.14 (1.10, 1.18) | 1.13 (1.09, 1.17) | 114 (7.7) | 0.95 (0.75, 1.20) | 0.94 (0.74, 1.19) |
| $100,000+ | 1293 (28.4) | 1158 (89.6) | 1.21 (1.17, 1.25) | 1.20 (1.16, 1.24) | 121 (9.4) | 1.15 (0.91, 1.45) | 1.11 (0.88, 1.39) |
| Children in the household | |||||||
| Yes | 1409 (30.9) | 1068 (75.8) | 0.90 (0.87, 0.93) | 0.90 (0.87 0.93) | 156 (11.1) | 1.56 (1.28, 1.89) | 1.46 (1.19, 1.79) |
| No | 3146 (69.1) | 2647 (84.1) | Ref | Ref | 224 (7.1) | Ref | Ref |
| Residential area type§ | |||||||
| Suburban | 1196 (26.3) | 966 (80.8) | 0.97 (0.94, 1.00) | 0.96 (0.93, 1.00) | 103 (8.6) | 1.10 (0.86, 1.39) | 1.11 (0.87, 1.40) |
| Rural | 1369 (30.1) | 1092 (79.8) | 0.96 (0.93, 0.99) | 0.94 (0.91, 0.97) | 120 (8.8) | 1.12 (0.89, 1.40) | 1.10 (0.88, 1.39) |
| Urban | 1986 (43.6) | 1654 (83.3) | Ref | Ref | 156 (7.9) | Ref | Ref |
| Geographic region§ | |||||||
| Midwest | 815 (17.9) | 662 (81.2) | 0.95 (0.92, 0.99) | 0.95 (0.91, 0.99) | 68 (8.3) | 1.10 (0.81, 1.49) | 1.09 (0.80, 1.47) |
| Northeast | 1160 (25.5) | 988 (85.2) | Ref | Ref | 88 (7.6) | Ref | Ref |
| South | 1474 (32.4) | 1167 (79.2) | 0.93 (0.90, 0.96) | 0.93 (0.90, 0.96) | 143 (9.7) | 1.28 (0.99, 1.65) | 1.26 (0.98, 1.62) |
| West | 1097 (24.1) | 893 (81.4) | 0.96 (0.92, 0.99) | 0.96 (0.92, 0.99) | 80 (7.3) | 0.96 (0.72, 1.29) | 0.95 (0.71, 1.28) |
| Cigarette smoking status§ | |||||||
| Smoker | 972 (21.4) | 696 (71.6) | 0.85 (0.81, 0.89) | 0.85 (0.82, 0.89) | 119 (12.2) | 1.68 (1.37, 2.06) | 1.60 (1.30, 1.97) |
| Non-Smoker | 3581 (78.7) | 3018 (84.3) | Ref | Ref | 261 (7.3) | Ref | Ref |
| Any healthcare access barriers in past 12 months°§§§ | |||||||
| Yes | 1610 (37.1) | 1227 (76.2) | 0.90 (0.87, 0.93) | 0.92 (0.89, 0.95) | 121 (7.5) | 0.86 (0.69, 1.06) | 0.84 (0.68, 1.04) |
| No | 2736 (63.0) | 2323 (84.9) | Ref | Ref | 240 (8.8) | Ref | Ref |
| Healthcare Barriers by Type | |||||||
| No health insurance§§§ | |||||||
| Yes | 641 (14.1) | 434 (67.7) | 0.81 (0.76, 0.85) | 0.82 (0.78, 0.87) | 54 (8.4) | 1.01 (0.77, 1.33) | 1.01 (0.77, 1.33) |
| No | 3914 (85.9) | 3281 (83.8) | Ref | Ref | 326 (8.3) | Ref | Ref |
| Cost barriers§§§ | |||||||
| Yes | 491 (11.3) | 383 (78.0) | 0.95 (0.90, 1.00) | 0.97 (0.92, 1.02) | 71 (14.5) | 1.92 (1.51, 2.44) | 1.92 (1.51, 2.45) |
| No | 3848 (88.7) | 3166 (82.3) | Ref | Ref | 290 (7.5) | Ref | Ref |
| No primary care provider§§§ | |||||||
| Yes | 1064 (24.5) | 793 (74.5) | 0.89 (0.85, 0.92) | 0.91 (0.87, 0.94) | 43 (4.0) | 0.42 (0.31, 0.57) | 0.40 (0.30, 0.55) |
| No | 3276 (75.5) | 2757 (84.2) | Ref | Ref | 318 (9.7) | Ref | Ref |
<1% missing,
1-<3% missing,
3-5% missing
Barriers to healthcare: Either did not have a primary care provider, did not see doctor due to cost, did not see a doctor due to immigration, or did not have insurance in past 12 months.
BMI ≥30 or BMI ≥27 with weight-related condition
Defined as use of prescription weight loss medication within the past three years
Age was modeled as a continuous variable with linear and quadratic terms. Model fit assessed using QIC favored the quadratic specification (QIC = 48,305 vs. 48,327 for linear-only age).
Table 3.
Characteristics of CHASING COVID Cohort participants who attempted to access weight loss medications, by inability to obtain, December 2023 (N=525)
| Characteristic | Denominator | Unable to Obtain GLP1-RA | ||
|---|---|---|---|---|
|
| ||||
| N (col %) | N (row %) | Crude PR (95% CI) | Age- and sex-adjusted PR* (95% CI) | |
| Total | 525 (100) | 276 (52.6) | - | - |
| Meets GLP1-RA indication for weight lossƛ§§ | ||||
| Yes | 355 (73.1) | 177 (49.9) | 0.95 (0.78, 1.15) | 1.00 (0.83, 1.22) |
| No | 131 (27.0) | 69 (52.7) | Ref | Ref |
| Age | ||||
| 18-29 | 97 (18.5) | 63 (65.0) | Ref | Ref |
| 30-39 | 163 (31.1) | 75 (46.0) | 0.71 (0.57, 0.88) | 0.70 (0.56, 0.87) |
| 40-49 | 120 (22.9) | 75 (62.5) | 0.96 (0.79, 1.18) | 0.96 (0.79, 1.18) |
| 50-64 | 76 (14.5) | 31 (40.8) | 0.63 (0.46, 0.85) | 0.63 (0.46, 0.86) |
| 65+ | 69 (13.1) | 32 (46.4) | 0.71 (0.53, 0.96) | 0.73 (0.54, 0.98) |
| Sex§ | ||||
| Male | 235 (44.8) | 137 (58.3) | 1.22 (1.03, 1.43) | 1.20 (1.03, 1.41) |
| Female | 290 (55.2) | 139 (47.9) | Ref | Ref |
| Race/ethnicity Hispanic | 111 (21.1) | 62 (55.9) | 1.10 (0.90, 1.34) | 1.00 (0.81, 1.23) |
| White, non-Hispanic | 302 (57.5) | 154 (51.0) | Ref | Ref |
| Black, non-Hispanic | 72 (13.7) | 39 (54.2) | 1.06 (0.84, 1.35) | 1.01 (0.79, 1.30) |
| Asian/Pacific Islander, non-Hispanic | 26 (5.0) | 12 (46.2) | 0.91 (0.59, 1.39) | 0.88 (0.57, 1.35) |
| Other, non-Hispanic | 14 (2.7) | 9 (64.3) | 1.26 (0.84, 1.89) | 1.25 (0.84, 1.87) |
| Education | ||||
| High school or less | 70 (13.3) | 47 (67.1) | 1.36 (1.11, 1.65) | 1.29 (1.05, 1.60) |
| Some college | 144 (27.4) | 75 (52.1) | 1.05 (0.87, 1.28) | 1.06 (0.87, 1.28) |
| College graduate | 311 (59.2) | 154 (49.5) | Ref | Ref |
| Annual household income | ||||
| <$49,000 | 200 (38.1) | 103 (51.5) | Ref | Ref |
| $50,000-$99,999 | 156 (29.7) | 81 (51.9) | 1.01 (0.82, 1.23) | 0.99 (0.81, 1.22) |
| $100,000+ | 169 (32.2) | 92 (54.4) | 1.06 (0.87, 1.28) | 1.08 (0.88, 1.31) |
| Children in the household | ||||
| Yes | 210 (40.0) | 111 (52.9) | 1.01 (0.86, 1.19) | 1.02 (0.86, 1.21) |
| No | 315 (60.0) | 165 (52.4) | Ref | Ref |
| Residential area type§ | ||||
| Suburban | 135 (25.8) | 72 (53.3) | 1.00 (0.82, 1.22) | 1.03 (0.84, 1.25) |
| Rural | 157 (30.0) | 80 (51.0) | 0.95 (0.78, 1.16) | 1.00 (0.82, 1.21) |
| Urban | 232 (44.3) | 124 (53.5) | Ref | Ref |
| Geographic region§ | ||||
| Midwest | 98 (18.7) | 52 (52.5) | 1.16 (0.88, 1.53) | 1.18 (0.90, 1.55) |
| Northeast | 116 (22.1) | 56 (45.5) | Ref | Ref |
| South | 194 (37.0) | 96 (52.5) | 1.20 (0.94, 1.52) | 1.21 (0.95, 1.53) |
| West | 116 (22.1) | 71 (60.2) | 1.33 (1.03, 1.71) | 1.31 (1.02, 1.68) |
| Cigarette smoking status§ | ||||
| Smoker | 165 (31.4) | 98 (59.4) | 1.20 (1.02, 1.42) | 1.13 (0.95, 1.34) |
| Non-Smoker | 360 (68.6) | 178 (49.4) | Ref | Ref |
| Any healthcare access barriers in past 12months °§§§ | ||||
| Yes | 184 (36.9) | 117 (63.6) | 1.42 (1.21, 1.67) | 1.37 (1.15, 1.65) |
| No | 315 (63.1) | 141 (44.8) | Ref | Ref |
| Healthcare Barriers by Type | ||||
| No health insurance§§§ | ||||
| Yes | 76 (14.5) | 47 (61.8) | 1.21 (0.99, 1.48) | 1.15 (0.94, 1.41) |
| No | 449 (85.5) | 229 (51.0) | Ref | Ref |
| Cost barriers§§§ | ||||
| Yes | 109 (21.8) | 75 (68.8) | 1.47 (1.25, 1.73) | 1.42 (1.19, 1.69) |
| No | 391 (78.2) | 183 (46.8) | Ref | Ref |
| No primary care provider§§§ | ||||
| Yes | 62 (12.4) | 34 (54.8) | 1.07 (0.84, 1.37) | 1.00 (0.79, 1.28) |
| No | 438 (87.6) | 224 (51.1) | Ref | Ref |
<1% missing,
1-<3% missing,
3-5% missing
Barriers to healthcare: Either did not have a primary care provider, did not see doctor due to cost, did not see a doctor due to immigration, or did not have insurance in past 12 months.
BMI ≥30 or BMI ≥27 with weight-related condition
Defined as use of prescription weight loss medication within the past three years
Age was modeled as a continuous variable with linear and quadratic terms. Model fit assessed using QIC favored the quadratic specification (QIC = 48,305 vs. 48,327 for linear-only age).
3.2. Estimates and predictors of use
Overall, 8.3% (95% CI, 7.5%–9.1%) of participants reported GLP1-RA use; among clinically-eligible participants, prevalence was 13.4% (95% CI, 11.9%–15.0%), and 4.0% (95% CI, 3.2%–4.7%). In the full cohort, use was correlated with age, race/ethnicity, children, smoking, and healthcare barriers (Table 1). Specifically, compared with those aged 18–29, use was higher among participants aged 30–39 (aPR=1.51, 95% CI: 1.13–2.01) and 50–64 (aPR=1.45, 95% CI: 1.03–2.04); compared to White non-Hispanic participants use was higher among Hispanic participants (aPR=1.30, 95% CI: 1.01–1.66), and Black, non-Hispanic participants (aPR=1.31, 95% CI: 0.98–1.74). Use was also greater among those with children (aPR=1.46, 95% CI: 1.19–1.79), cigarette smokers (aPR=1.60, 95% CI: 1.30–1.97), and those with cost-related barriers to care (aPR=1.92, 95% CI: 1.51–2.45). Those without a primary care provider had lower use (aPR=0.40, 95% CI: 0.30–0.55).
Among clinically-eligible participants, males had a lower prevalence of use compared with females (aPR=0.76, 95% CI: 0.60–0.96) (Table 2). Income showed a positive association, with participants in the highest vs. lowest income group more likely to report use (aPR=1.49, 95% CI: 1.12–1.98). Healthcare access barriers were more prominent predictors compared to the full cohort: participants with any barrier (aPR=0.47, 95% CI: 0.35–0.63), no health insurance (aPR=0.55, 95% CI: 0.35–0.84), or no primary care provider (aPR=0.22, 95% CI: 0.13–0.37) were less likely to report use. Unlike in the full cohort, cost-related barriers, age, race/ethnicity, children in the household, and cigarette smoking were not significantly associated with use. In sensitivity analyses restricted to clinically-eligible participants without type 2 diabetes, education also emerged as a significant predictor of use, while other associations remained similar (Supplementary Table S4).
Table 2.
Prevalence and characteristics of clinically-eligible CHASING COVID Cohort participants by awareness and use of GLP1-RA medications, December 2023 (N=1,883)
| Characteristic | Denominator | Aware of GLP1-RAs | Used GLP1-RA in previous 3 years⸷ | ||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| N (Col %) | N (Row %) | Crude PR (95% CI) | Age- and sex-adjusted PR* (95% CI) | N (Row %) | Crude PR (95% CI) | Age- and sex-adjusted PR* (95% CI) | |
| Total | 1883 (100) | 1586 (84.2) | - | - | 253 (13.4) | - | - |
| Age | |||||||
| 18-29 | 287 (15.2) | 225 (78.4) | Ref | Ref | 34 (11.9) | Ref | Ref |
| 30-39 | 508 (27.0) | 424 (83.5) | 1.06 (0.99, 1.14) | 1.07 (0.99, 1.15) | 80 (15.8) | 1.33 (0.91, 1.93) | 1.38 (0.95, 2.02) |
| 40-49 | 408 (21.7) | 334 (81.9) | 1.04 (0.98, 1.13) | 1.04 (0.97, 1.13) | 51 (12.5) | 1.06 (0.70, 1.58) | 1.10 (0.73, 1.66) |
| 50-64 | 304 (16.1) | 273 (89.8) | 1.15 (1.07, 1.23) | 1.15 (1.07, 1.23) | 40 (13.2) | 1.11 (0.72, 1.70) | 1.16 (0.76, 1.78) |
| 65+ | 376 (20.0) | 330 (87.8) | 1.12 (1.04, 1.20) | 1.12 (1.04, 1.20) | 48 (12.8) | 1.08 (0.71, 1.63) | 1.11 (0.73, 1.67) |
| Sex§ | |||||||
| Male | 822 (43.7) | 693 (84.3) | 1.00 (0.96, 1.04) | 0.99 (0.96, 1.03) | 96 (11.7) | 0.78 (0.62, 1.00) | 0.78 (0.62, 1.00) |
| Female | 1059 (56.3) | 891 (84.1) | Ref | Ref | 157 (14.8) | Ref | Ref |
| Race/ethnicity* | |||||||
| Hispanic | 332 (17.6) | 260 (78.3) | 0.89 (0.84, 0.95) | 0.90 (0.85, 0.96) | 45 (13.6) | 0.99 (0.73, 1.35) | 0.97 (0.71, 1.33) |
| White, non-Hispanic | 1176 (62.5) | 1035 (88.0) | Ref | Ref | 161 (13.7) | Ref | Ref |
| Black, non-Hispanic | 243 (12.9) | 188 (77.4) | 0.88 (0.82, 0.94) | 0.89 (0.83, 0.95) | 26 (10.7) | 0.78 (0.53, 1.16) | 0.75 (0.51, 1.11) |
| Asian/Pacific Islander, non-Hispanic | 64 (3.4) | 55 (85.9) | 0.98 (0.88, 1.08) | 1.00 (0.90, 1.10) | 11 (17.2) | 1.26 (0.72, 2.19) | 1.23 (0.70, 2.18) |
| Other, non-Hispanic | 68 (3.6) | 48 (70.6) | 0.80 (0.69, 0.94) | 0.80 (0.68, 0.93) | 10 (14.7) | 1.07 (0.60, 1.94) | 1.05 (0.58, 1.89) |
| Education | |||||||
| High school or less | 255 (13.5) | 174 (68.2) | 0.75 (0.69, 0.82) | 0.75 (0.69, 0.82) | 29 (11.4) | 0.76 (0.52, 1.11) | 0.76 (0.52, 1.11) |
| Some college | 570 (30.3) | 453 (79.5) | 0.88 (0.84, 0.92) | 0.87 (0.83, 0.92) | 70 (12.3) | 0.83 (0.64, 1.08) | 0.83 (0.64, 1.08) |
| College graduate | 1058 (56.2) | 959 (90.6) | Ref | Ref | 154 (14.6) | Ref | Ref |
| Annual household income | |||||||
| <$49,000 | 862 (45.8) | 675 (78.3) | Ref | Ref | 100 (11.6) | Ref | Ref |
| $50,000-$99,999 | 604 (32.1) | 527 (87.3) | 1.11 (1.06, 1.17) | 1.11 (1.06, 1.16) | 83 (13.7) | 1.18 (0.90, 1.55) | 1.21 (0.92, 1.59) |
| $100,000+ | 417 (22.1) | 384 (92.1) | 1.18 (1.12, 1.23) | 1.17 (1.11, 1.22) | 70 (16.8) | 1.45 (1.09, 1.92) | 1.49 (1.12, 1.98) |
| Children in the household | |||||||
| Yes | 573 (30.4) | 443 (77.3) | 0.89 (0.84, 0.93) | 0.88 (0.84, 0.93) | 84 (14.7) | 1.14 (0.89, 1.45) | 1.01 (0.79, 1.30) |
| No | 1310 (69.6) | 1143 (87.3) | Ref | Ref | 169 (12.9) | Ref | Ref |
| Residential area type§ | |||||||
| Suburban | 513 (27.3) | 433 (84.4) | 0.99 (0.94, 1.04) | 0.99 (0.94, 1.03) | 73 (14.2) | 1.04 (0.79, 1.38) | 1.02 (0.77, 1.36) |
| Rural | 649 (34.5) | 538 (82.9) | 0.97 (0.93, 1.02) | 0.96 (0.92, 1.01) | 82 (12.6) | 0.93 (0.71, 1.22) | 0.90 (0.68, 1.18) |
| Urban | 719 (38.2) | 613 (85.3) | Ref | Ref | 98 (13.6) | Ref | Ref |
| Geographic region§ | |||||||
| Midwest | 407 (21.6) | 347 (85.3) | 0.96 (0.91, 1.01) | 0.96 (0.91, 1.01) | 48 (11.8) | 0.87 (0.60, 1.25) | 0.87 (0.61, 1.25) |
| Northeast | 391 (20.8) | 347 (88.8) | Ref | Ref | 53 (13.6) | Ref | Ref |
| South | 686 (36.5) | 555 (80.9) | 0.91 (0.87, 0.96) | 0.91 (0.87, 0.96) | 99 (14.4) | 1.06 (0.78, 1.45) | 1.06 (0.78, 1.44) |
| West | 397 (21.1) | 336 (84.6) | 0.95 (0.90, 1.01) | 0.95 (0.90, 1.01) | 52 (13.1) | 0.97 (0.68, 1.38) | 0.97 (0.68, 1.39) |
| Cigarette smoking status§ | |||||||
| Smoker | 421 (22.4) | 308 (73.2) | 0.84 (0.79, 0.89) | 0.84 (0.79, 0.89) | 55 (13.1) | 0.96 (0.73, 1.27) | 0.90 (0.68, 1.20) |
| Non-Smoker | 1461 (77.6) | 1278 (87.5) | Ref | Ref | 198 (13.6) | Ref | Ref |
| Any healthcare access barriers in past 12 months °§§§ | |||||||
| Yes | 596 (33.3) | 466 (78.2) | 0.89 (0.85, 0.93) | 0.90 (0.85, 0.94) | 49 (8.2) | 0.51 (0.38, 0.69) | 0.47 (0.35, 0.63) |
| No | 1192 (66.7) | 1049 (88.0) | Ref | Ref | 192 (16.1) | Ref | Ref |
| Healthcare Barriers by Type | |||||||
| No health insurance§§§ | |||||||
| Yes | 248 (13.2) | 178 (71.8) | 0.83 (0.77, 0.90) | 0.85 (0.78, 0.92) | 20 (8.1) | 0.57 (0.37, 0.88) | 0.55 (0.35, 0.84) |
| No | 1635 (86.8) | 1408 (86.1) | Ref | Ref | 233 (14.3) | Ref | Ref |
| Cost barriers§§§ | |||||||
| Yes | 214 (12.0) | 163 (76.2) | 0.89 (0.82, 0.96) | 0.90 (0.83, 0.97) | 30 (14.0) | 1.04 (0.73, 1.48) | 0.98 (0.69, 1.40) |
| No | 1575 (88.0) | 1353 (85.9) | Ref | Ref | 212 (13.5) | Ref | Ref |
| No primary care provider§§§ | |||||||
| Yes | 367 (20.5) | 287 (78.2) | 0.90 (0.85, 0.96) | 0.92 (0.87, 0.97) | 14 (3.8) | 0.24 (0.14, 0.40) | 0.22 (0.13, 0.37) |
| No | 1423 (79.5) | 1230 (86.4) | Ref | Ref | 228 (16.0) | Ref | Ref |
<1% missing,
1-<3% missing,
3-5% missing
Barriers to healthcare: Either did not have a primary care provider, did not see doctor due to cost, did not see a doctor due to immigration, or did not have insurance in past 12 months.
BMI ≥30 or BMI ≥27 with weight-related condition
Defined as use of prescription weight loss medication within the past three years
Age was modeled as a continuous variable with linear and quadratic terms. Model fit assessed using QIC favored the quadratic specification (QIC = 48,305 vs. 48,327 for linear-only age).
3.3. Predictors of inability to access
Among participants attempting to obtain medications in the past year (N=525), 52.6% (95% CI: 48.3%-56.9%) reported being unable to access them (n=276) (Table 3). Of the N=276, 64.9% (95% CI: 59.2%-70.5%) reported being unable to get a prescription and 39.9% (95% CI: 34.0%-45.7%) reported being prescribed but unable to obtain the medication; among eligible participants, these shares were 59.6% (95% CI: 52.2%-67.1%) and 43.3% (95% CI: 35.8%-50.8%), respectively (Supplementary Table S3). In the full cohort, inability to access was more common among men compared with women (aPR=1.20, 95% CI: 1.03–1.41), participants with high school education or less versus college graduates (aPR=1.29, 95% CI: 1.05–1.60), and those in the West compared with the Northeast (aPR=1.31, 95% CI: 1.02–1.68). Participants with healthcare access barrier(s) (aPR=1.37, 95% CI: 1.15–1.65), particularly cost-related barriers were also more likely to report inability to access medications (aPR=1.42, 95% CI: 1.19–1.69). In contrast, compared with those aged 18–29, inability to access medications was less common in those aged 30–39 (aPR=0.70, 95% CI: 0.56–0.87), 50–64 (aPR=0.63, 95% CI: 0.46–0.86), and 65+ (aPR=0.73, 95% CI: 0.54–0.98). Associations were consistent across crude and age/sex-adjusted models.
In the subgroup of clinically-eligible participants who attempted to access medications (N=355), 49.9% were unable to obtain them. Healthcare barriers were the only significant predictors of inability to obtain medications (Table 4). Participants with any barrier had a higher likelihood of being unable to access treatment (aPR=1.42, 95% CI: 1.14–1.77), including those without health insurance (aPR=1.47, 95% CI: 1.15–1.88) and with cost-related barriers (PR=1.34, 95% CI: 1.04–1.73). Lack of a primary care provider was associated with an elevated risk of being unable to access medications, with a borderline association (PR=1.33, 95% CI: 0.99–1.78). Crude and adjusted estimates were similar. In sensitivity analyses restricted to clinically eligible participants without type 2 diabetes, lower educational attainment was additionally associated with greater inability to obtain medications, while associations with healthcare access barriers remained consistent (high school or less vs. college graduate: aPR = 1.41, 95% CI: 1.03–1.92) (Supplementary Table S5).
Table 4.
Characteristics of clinically-eligible CHASING COVID Cohort participants who attempted to access weight loss medications, by inability to obtain GLP1-RA, December 2023 (N=355)
| Characteristic | Total | Unable to Obtain GLP1-RA | ||
|---|---|---|---|---|
|
| ||||
| N (%) | N (row %) | Crude PR (95% CI) | Age- and sex-adjusted PR* (95% CI) | |
| Total | 355 (100) | 177 (49.9) | - | - |
| Age | ||||
| 18-29 | 48 (13.5) | 28 (58.3) | Ref | Ref |
| 30-39 | 107 (30.1) | 48 (44.9) | 0.77 (0.56, 1.06) | 0.76 (0.55, 1.05) |
| 40-49 | 86 (24.2) | 50 (58.1) | 1.00 (0.74, 1.34) | 0.99 (0.73, 1.34) |
| 50-64 | 54 (15.2) | 23 (42.6) | 0.73 (0.49, 1.08) | 0.72 (0.49, 1.07) |
| 65+ | 60 (16.9) | 28 (46.7) | 0.80 (0.56, 1.15) | 0.80 (0.55, 1.14) |
| Sex§ | ||||
| Male | 143 (40.3) | 72 (50.4) | 1.02 (0.82, 1.26) | 1.03 (0.83, 1.27) |
| Female | 212 (59.7) | 105 (49.5) | Ref | Ref |
| Race/ethnicity | ||||
| Hispanic | 66 (18.6) | 34 (51.5) | 1.09 (0.83, 1.43) | 1.05 (0.80, 1.39) |
| White, non-Hispanic | 217 (61.1) | 103 (47.5) | Ref | Ref |
| Black, non-Hispanic | 45 (12.7) | 26 (57.8) | 1.22 (0.91, 1.62) | 1.19 (0.89, 1.59) |
| Asian/Pacific Islander, non-Hispanic | 15 (4.2) | 7 (46.7) | 0.98 (0.56, 1.72) | 0.96 (0.54, 1.70) |
| Other, non-Hispanic | 12 (3.4) | 7 (58.3) | 1.23 (0.75, 2.02) | 1.23 (0.75, 2.02) |
| Education | ||||
| High school or less | 43 (12.1) | 26 (60.5) | 1.29 (0.97, 1.71) | 1.26 (0.94, 1.69) |
| Some college | 99 (27.9) | 51 (51.5) | 1.10 (0.86, 1.39) | 1.09 (0.86, 1.40) |
| College graduate | 213 (60.0) | 100 (47.0) | Ref | Ref |
| Annual household income | ||||
| <$49,000 | 143 (40.3) | 76 (53.2) | Ref | Ref |
| $50,000-$99,999 | 114 (32.1) | 55 (48.3) | 0.91 (0.71, 1.16) | 0.92 (0.72, 1.18) |
| $100,000+ | 98 (27.6) | 46 (46.9) | 0.88 (0.68, 1.15) | 0.91 (0.69, 1.19) |
| Children in the household | ||||
| Yes | 111 (31.3) | 53 (47.8) | 0.94 (0.75, 1.18) | 0.92 (0.72, 1.18) |
| No | 244 (68.7) | 124 (50.8) | Ref | Ref |
| Residential area type§ | ||||
| Suburban | 97 (27.3) | 47 (48.5) | 0.96 (0.74, 1.25) | 0.97 (0.75, 1.26) |
| Rural | 109 (30.7) | 55 (50.5) | 1.00 (0.78, 1.28) | 1.02 (0.79, 1.31) |
| Urban | 149 (42.0) | 75 (50.3) | Ref | Ref |
| Geographic region§ | ||||
| Midwest | 68 (19.2) | 33 (48.5) | 1.10 (0.77, 1.56) | 1.12 (0.79, 1.59) |
| Northeast | 77 (21.8) | 34 (44.2) | Ref | Ref |
| South | 132 (37.3) | 68 (51.5) | 1.17 (0.86, 1.58) | 1.17 (0.86, 1.58) |
| West | 77 (21.8) | 42 (54.6) | 1.24 (0.89, 1.71) | 1.23 (0.89, 1.71) |
| Cigarette smoking status§ | ||||
| Smoker | 83 (23.4) | 44 (53.0) | 1.08 (0.86, 1.37) | 1.07 (0.85, 1.36) |
| Non-Smoker | 272 (76.6) | 133 (48.9) | Ref | Ref |
| Any healthcare access barriers in past 12 months°§§§ | ||||
| Yes | 88 (25.9) | 56 (63.6) | 1.43 (1.16, 1.77) | 1.42 (1.14, 1.77) |
| No | 252 (74.1) | 112 (44.4) | Ref | Ref |
| Healthcare Barriers by Type | ||||
| No health insurance§§§ | ||||
| Yes | 38 (10.7) | 27 (71.1) | 1.50 (1.19, 1.90) | 1.47 (1.15, 1.88) |
| No | 317 (89.3) | 150 (47.3) | Ref | Ref |
| Cost barriers§§§ | ||||
| Yes | 49 (14.4) | 31 (63.3) | 1.35 (1.05, 1.72) | 1.34 (1.04, 1.73) |
| No | 292 (85.6) | 137 (46.9) | Ref | Ref |
| No primary care provider§§§ | ||||
| Yes | 29 (8.5) | 19 (65.5) | 1.37 (1.03, 1.83) | 1.33 (0.99, 1.78) |
| No | 312 (91.5) | 149 (47.8) | Ref | Ref |
<1% missing,
1-<3% missing,
3-5% missing
Barriers to healthcare: Either did not have a primary care provider, did not see doctor due to cost, did not see a doctor due to immigration, or did not have insurance in past 12 months.
BMI ≥30 or BMI ≥27 with weight-related condition
Defined as use of prescription weight loss medication within the past three years
Age was modeled as a continuous variable with linear and quadratic terms. Model fit assessed using QIC favored the quadratic specification (QIC = 48,305 vs. 48,327 for linear-only age).
4. Discussion
In this diverse nationwide cohort, we found high levels of awareness of GLP1-RA medications for weight management, with more than four out of five participants reporting familiarity, consistent with recent nationally representative estimates.(13) Awareness was higher among older adults, those with higher incomes, and White, non-Hispanic participants, and lower among those with lower education and barriers to healthcare. These patterns suggest early inequities in the diffusion of knowledge about GLP1-RAs, indicating that information about emerging obesity treatments may be more readily reaching socioeconomically advantaged groups who may be more able to explore and access treatment.(17)
GLP1-RA use was 8.3% overall and higher among those clinically-eligible (13.4%) than among those classified as ineligible (4.0%). This suggests an appreciable level of off-label use, though estimates should be interpreted cautiously given potential misclassification from unmeasured or undiagnosed comorbidities. These findings warrant further investigation into the prevalence, drivers and consequences of off-label use. In the full cohort, use differed by various socioeconomic factors, and broadly mirrored those from previous national estimates.(12,13) When restricted to clinically-eligible participants, however, only income, sex and healthcare access were associated with use. Because sociodemographics shape BMI and related conditions that determine eligibility, conditioning on eligibility removes this pathway and yields smaller residual differences. The predictive factors observed in the clinically-eligible, including income, sex, and healthcare access barriers, are consistent with inequities in translating clinical need to treatment. These findings align with a 2025 retrospective cohort study among commercially insured individuals with obesity but without diabetes in which sex and insurance type were the strongest factors predicting GLP1-RA initiation,(28) and highlight opportunities to prevent reproducing long-standing socioeconomic inequities seen with other weight loss therapies.(17) The emergence of education as a significant predictor of use and access in sensitivity analyses of clinically-eligible participants without type 2 diabetes, where lower educational attainment was associated with lower use and greater difficulty accessing GLP1-RAs, suggests that disparities become more apparent when treatment is pursued for weight management alone, where access relies more heavily on patient navigation and advocacy rather than established disease-management pathways.
Among clinically-eligible participants who attempted to access therapy, half were unable to obtain it, raising concerns that a substantial proportion of individuals most likely to benefit were unable to receive treatment. The majority of access failures among the clinically-eligible were attributable to being unable to secure a prescription (59.6%), highlighting provider-level barriers. Healthcare access barriers- including cost barriers, no health insurance and no primary care provider- were significantly associated with inability to access GLP1-RAs. Lower educational attainment was also associated with greater inability to access medications, consistent with literature linking education and health literacy to successful navigation of healthcare systems.(29,30) While no significant differences in access were observed by race/ethnicity, income, or residential setting, limited sample size (n=355) may have reduced power to detect subgroup effects, and further research is warranted to clarify these relationships.
Cost-related barriers were associated with patterns of use and reported inability to access treatment. In the full cohort, they were paradoxically associated with higher likelihood of use, likely reflecting the nature of this measure, which captures whether participants had forgone healthcare to financial concerns and may act as a proxy for underlying need. In the clinically-eligible subgroup, cost-related barriers were not predictive of use. However, when analyses were restricted only to individuals who attempted to obtain GLP1-RA medications in the past year, cost barriers were associated with greater inability to access them- both in the full cohort and the clinically-eligible subgroup, suggesting that financial constraints may contribute to difficulty obtaining treatment even among individuals who meet clinical criteria; further research is needed to clarify the mechanisms underlying this association.
A gender gap was also evident in the clinically-eligible subgroup: men were 22% more likely than women to report being unable to access GLP1-RAs and 24% less likely to report use. This mirrors prior evidence of gender disparities in obesity counseling and treatment, particularly in bariatric surgery, where women disproportionately receive treatment despite comparable eligibility among men.(17,31) Prior work suggests that provider bias, differential counseling, and gendered norms around body image and healthcare-seeking behavior contribute to these disparities.(17,32–34) Our findings extend this literature to GLP1-RAs, highlighting that inequities in obesity care may be shaped by similar dynamics.
This study has several limitations. All measures were self-reported, which may introduce recall and social desirability bias. Notably, BMI was not clinically assessed which may have led to misclassification of clinical eligibility. Additionally, medical conditions used to determine eligibility relied on provider diagnoses. Prior literature has shown that undiagnosed conditions such as hypertension and diabetes are more common among socially disadvantaged groups, which may result in underestimation of eligibility in these populations.(35) As a result, subgroup analyses may underestimate disparities in uptake among clinically-eligible individuals, suggesting that sociodemographic predictors could play an even greater role than observed. GLP1-RA use and reported inability to access treatment could not be independently validated using prescribing, authorization, or dispensing records, although both represent distinct experiences that participants may reasonably be expected to recall. Subgroup analyses were further constrained by reduced sample sizes. Although our cohort was large and diverse, it was not nationally representative and was recruited in the context of the COVID-19 pandemic, limiting generalizability. However, several observed patterns in awareness and use were consistent with estimates from recent nationally representative surveys, providing some reassurance regarding the external validity of key findings. Consistent with exploratory analyses, we emphasize that these findings are intended to generate hypotheses and highlight areas for further investigation, with confirmation requiring studies designed to test predefined hypotheses. Finally, GLP1-RA utilization and insurance coverage have continued to evolve since data collection.(36–38) Continued research will be important to assess how patterns of use and access change as the treatment landscape develops.
Overall, this study shows that while awareness of GLP1-RAs is widespread, use and access vary by financial resources, healthcare access, and gender, beyond differences in clinical eligibility alone. The observed patterns suggest that cost, insurance coverage, and interactions with the healthcare system may influence whether eligibility translates into realized access, and highlight the need for future research to identify the mechanisms underlying inequities in weight-loss treatment.
Supplementary Material
Acknowledgements
We thank the participants of the Communities, Households, and SARS-CoV-2 Epidemiology COVID Cohort Study for their contribution to the advancement of science.
Funding Sources
This work was supported by the National Institute of Allergy and Infectious Diseases (NIAID), award number UH3AI133675 (MPIs: D Nash and C Grov), NIMH award RF1MH132360 (MPIs: D Nash and A Parcesepe), National Institute of Child Health and Human Development grant P2C HD050924 (Carolina Population Center), Pfizer Inc., the CUNY Institute for Implementation Science in Population Health (cunyisph.org), and the COVID-19 Grant Program of the CUNY Graduate School of Public Health and Health Policy. The funders played no role in the production of this manuscript or necessarily endorse the findings.
Footnotes
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CRediT Author Statement
Jenna Sanborn: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing-Original Draft. Sasha Fleary: Writing-Review & Editing. Denis Nash: Writing-Review & Editing, Funding acquisition. Kate Penrose: Writing-Review & Editing. Angela M Parcesepe: Writing-Review & Editing, Funding acquisition. Rachael Piltch-Loeb: Writing-Review and Editing. Josefina Nuñez: Writing- Review and Editing. Yanhan Shen: Writing- Review and Editing. McKaylee Robertson: Methodology, Writing-Review & Editing, Supervision
Declaration of generative AI and AI-assisted technologies in the manuscript preparation process
During the preparation of this work, the author(s) used ChatGPT to assist with text editing. The authors reviewed and revised the content as needed and take full responsibility for the content of the published article.
Declaration of Competing Interest
The City University of New York (CUNY) School of Public Health received research support from Pfizer during the conduct of this study which was paid directly to CUNY, with Dr. Nash serving as the Principal Investigator.
Data availability statement
The datasets generated and/or analyzed during the current study are available in the repository, Zenodo: DOI: 10.5281/zenodo.6127734. Some data elements are not publicly available due to funder requirements, but are available from the authors upon reasonable request and with permission from Denis Nash.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are available in the repository, Zenodo: DOI: 10.5281/zenodo.6127734. Some data elements are not publicly available due to funder requirements, but are available from the authors upon reasonable request and with permission from Denis Nash.