Trends and Disparities in Newer GLP1 Receptor Agonist Initiation among Real-World Adult Patients Eligible for Obesity Treatment

Trends and Disparities in Newer GLP1 Receptor Agonist Initiation among Real-World Adult Patients Eligible for Obesity Treatment | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Trends and Disparities in Newer GLP1 Receptor Agonist Initiation among Real-World Adult Patients Eligible for Obesity Treatment Rotana M. Radwan , Yao An Lee , Pareeta Kotecha , Davene R. Wright , Inmaculada Hernandez , Ronald Ramon , William T. Donahoo , Yong Chen , John M. Allen , Jiang Bian , Jingchuan Guo doi: https://doi.org/10.1101/2025.01.20.25320839 Rotana M. Radwan 1 Department of Pharmaceutical Outcomes and Policy, University of Florida , Gainesville, FL, USA . , , , PharmD, MS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rradwan{at}ufl.edu yaoanlee{at}ufl.edu pkotecha{at}ufl.edu guoj1{at}ufl.edu Yao An Lee 1 Department of Pharmaceutical Outcomes and Policy, University of Florida , Gainesville, FL, USA . , , , MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rradwan{at}ufl.edu yaoanlee{at}ufl.edu pkotecha{at}ufl.edu guoj1{at}ufl.edu Pareeta Kotecha 1 Department of Pharmaceutical Outcomes and Policy, University of Florida , Gainesville, FL, USA . , , , PharmD, MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rradwan{at}ufl.edu yaoanlee{at}ufl.edu pkotecha{at}ufl.edu guoj1{at}ufl.edu Davene R. Wright 2 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , Boston, MA, USA . PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Davene_Wright{at}hphci.harvard.edu Inmaculada Hernandez 3 Division of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California , La Jolla, CA, USA . PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: inhernandez{at}health.ucsd.edu Ronald Ramon 4 University of Florida College of Pharmacy , Gainesville, FL, USA . BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rramon1{at}ufl.edu William T. Donahoo 5 Division of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida , Gainesville, FL, USA . MD, FTOS Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: wdonahoo{at}ufl.edu Yong Chen 6 Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, PA, USA . PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ychen123{at}upenn.edu John M. Allen 7 Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy , Orlando, FL, USA . Pharm.D., BCPS, BCCCP, FCCM, FCCP Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: john.allen{at}cop.ufl.edu Jiang Bian 8 Department of Health Outcomes and Biomedical Informatics, University of Florida , Gainesville, FL, USA . PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: bianjiang{at}ufl.edu Jingchuan Guo 1 Department of Pharmaceutical Outcomes and Policy, University of Florida , Gainesville, FL, USA . , , , MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: guoj1{at}ufl.edu rradwan{at}ufl.edu yaoanlee{at}ufl.edu pkotecha{at}ufl.edu guoj1{at}ufl.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Aims To characterize trends in the initiation of newer anti-obesity medications (AOMs) and determine factors associated with their use among obese/overweight populations. Materials and methods This retrospective study utilized electronic health record data from OneFlorida+ (2015–2024). Adults eligible for AOMs were included, defined as having a BMI ≥30 kg/m² or a BMI of 27–29.9 kg/m² with at least one obesity-related comorbidity. The primary outcome was the initiation of newer AOMs, specifically glucagon-like peptide-1 receptor agonists (GLP-1 RAs) including liraglutide, semaglutide, and tirzepatide. Trends across years were examined, and a multivariable logistic regression identified sociodemographic, clinical, and healthcare utilization factors associated with AOM initiation. Results Of 319,949 adults, 1.8% initiated newer AOMs. Semaglutide accounted for 77.9% of initiations, tirzepatide 19.7%, and liraglutide 17.8%. Initiation trends showed liraglutide uptake peaked at 5% in 2018 but declined afterward, while semaglutide and tirzepatide uptake increased exponentially since 2022. Odds of initiation were lower for Black (aOR (95% CI): 0.87 [0.80– 0.94]) and Hispanic (0.84 [0.78–0.91]) groups vs. Whites, and for Medicaid (0.69 [0.63–0.76]) and uninsured (0.81 [0.74–0.87]) patients vs. privately insured. Higher odds were associated with being female, middle-aged, having more outpatient visits, and visiting endocrinologists. Conclusions The initiation of newer AOMs among overweight and obese populations remains low, but uptake has increased exponentially since 2022. Our findings reveal significant disparities in obesity care, highlighting the importance of addressing inequities in AOM access to improve obesity outcomes. Background Obesity is a multifactorial condition resulting from a complex interaction of environmental, genetic, and behavioral factors ( 1 , 2 ). Obesity contributes to the global burden of disease and disability by increasing the risk of heart disease, diabetes, hypertension, osteoarthritis, and certain cancers, among other conditions ( 1 ). In the United States (US), 41.9% of adults have obesity, and projections show that one in every two adults will have obesity by 2030 ( 3 ). According to clinical guidelines, individuals with a body mass index (BMI) ≥30 kg/m² or BMI ≥27 kg/m² with at least one obesity-associated comorbidity can be offered pharmacotherapy in addition to lifestyle interventions to help achieve their weight-loss goals ( 4 , 5 ). Historically, anti-obesity medications (AOM) such as orlistat (ALLI/Xenical), phentermine/topiramate (Qsymia), bupropion/naltrexone (Contrave), and lorcaserin (Belviq) have demonstrated an average body weight loss of ≤10% after one year, but with serious safety concerns such as cardiotoxicity, neuropsychiatric disorders, and cancer ( 6 ). Recently approved by the US Food and Drug Administration (FDA), glucagon-like peptide-1 receptor agonists (GLP-1 RA), including liraglutide (Saxenda), semaglutide (Wegovy), and tirzepatide (Zepbound), have shown greater effectiveness, health benefits, and safety profiles (7; 8). For example, semaglutide demonstrated up to 15% mean weight loss ( 8 ), sustained over at least two years ( 9 ), and tirzepatide, showed up to 22% mean weight loss after 72 weeks, along with improved cardiometabolic risk factors, such as blood pressure, insulin levels, and lipid levels ( 10 ). Despite the high prevalence of obesity, the use of AOMs remains relatively low in the US. Between 2015 to 2023, only 8.0% of adults had AOM prescriptions, and just 4.4% filled their prescriptions. Among those who filled prescriptions, 39% received naltrexone/bupropion, 29% phentermine-topiramate, 19% semaglutide, 11% liraglutide, and 1.2% orlistat ( 11 ). The initiation of AOMs in clinical practice requires balancing the benefits of treatment with the risks of adverse effects and costs ( 12 ). The disproportionate burden of obesity on underserved populations, combined with the lack of coverage under public health plans, raise significant concerns about exacerbating health disparities in AOM access ( 12 ). Findings from Gasoyan et al., using data from 2015 to 2023, underscore these concerns by revealing significant disparities in AOM access at both the prescribing and prescription-filling stages, influenced by patient characteristics and insurance type ( 11 ). Specifically, non-Hispanic Whites had higher odds of being prescribed an AOM compared to non-Hispanic Blacks, Hispanics, and other racial or ethnic groups, and individuals with private insurance were more likely to receive both AOM prescriptions and have them filled ( 11 ). Limited data exists on the initiation of newer AOMs (e.g., liraglutide, semaglutide, and tirzepatide). Understanding which patient populations are more likely to initiate these treatments may improve obesity management by prioritizing high-risk groups ( 12 ). This knowledge also informs health policies regarding insurance coverage and access to obesity specialists, which are vital for population-level obesity management ( 12 ). Thus, this study aimed to assess trends in the initiation of newer AOMs and to characterize the patient populations starting these treatments among overweight and obese adults in the US. Materials and methods Study design, database, and sample This retrospective cohort study utilized data from the OneFlorida+ Data Trust, a centralized patient data repository that includes longitudinal electronic health records (EHRs) ( 13 ). The study included adults aged 18 years or older who were eligible for AOMs, defined as a BMI of 30 kg/m² or higher, or a BMI between 27 and 29.9 kg/m² with at least one weight-related comorbidity between January 1, 2015, and January 31, 2024. The cohort entry date (index date) was the first date a patient was identified as being eligible for AOMs within the study period. The baseline period was the 12 months preceding the index date, and the follow-up period extended for 12 months after the index date. We included patients who had at least one encounter during the baseline period and follow-up period, respectively. Patients were excluded if they were pregnant, had an active malignant neoplasm, or had used any AOM during the baseline period. This study was approved as exempt by the University of Florida Institutional Review Board. Study Outcome The primary outcome was the initiation of newer AOMs (yes or no) during the follow-up year, including liraglutide, semaglutide, and tirzepatide. The secondary outcome was the initiation of any AOMs, including both newer and traditional AOMs (i.e., orlistat, phentermine/topiramate, bupropion/naltrexone, and lorcaserin). Covariates Baseline covariates were collected within the 1-year period prior to the index date. Demographic and socioeconomic factors included: age (18-40, 40-65, and ≥65), sex (female, male), race or ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic, and other), insurance coverage (Medicare, Medicaid, private insurance, uninsured, and other), and area deprivation index (ADI). The ADI is a validated measure of neighborhood adversity based on 17 variables related to education, employment, housing, and income from US Census and American Community Survey data ( 14 , 15 ). The ADI reflects the average socioeconomic characteristics of a population within a neighborhood. State-level ADIs are available in deciles from 1 to 10, ranking from least disadvantaged ( 1 ) to most disadvantaged ( 10 ) ( 14 , 15 ). For this study, ADI was grouped into three decile categories: 1-2 (low ADI), 3-8 (median ADI), and 9-10 (high ADI). Clinical factors included obesity status (overweight [BMI 27-29 kg/m 2 ] and obese [BMI >=30 kg/m 2 ]), smoking status (current, previous, and never smoker), and Charlson comorbidity index score (0, 1, and 2+) ( 16 ). Comorbidities and medication use were coded as indicator variables for the following: coronary artery disease, heart failure, type 2 diabetes (T2D), arthritis, chronic obstructive pulmonary disease, anxiety, depression, antihypertensives, statins, metformin, other glucose-lowering drugs, opioids, and antidepressants except for bupropion. Additional factors included provider specialty (cardiology, endocrinology, primary care, and specialized nurse practitioner/physician assistant), and healthcare utilization in the baseline year, including the number of outpatient visits (0, 1-2, and 3+), hospitalizations (0, 1+), and emergency room visits (0, 1+). Statistical analysis Sample characteristics were described using categorical data, presented as frequencies and proportions for the overall eligible cohort and subgroups, including newer AOM initiators, traditional AOM initiators, and non-AOM initiators. The proportion of initiating newer AOM for each drug was calculated by calendar year, and trends were plotted. Additionally, the incidence proportion of initiating newer AOMs, both overall and by individual drugs, was calculated across five-digit ZIP code regions in Florida. Geographic variation in newer AOM initiation was visualized through maps covering the entire study period (January 1, 2015, to January 31, 2024) and specifically highlighting the years 2022 and 2023. To identify factors associated with the initiation of newer AOMs (yes/no) or any AOMs (yes/no) among the eligible cohort, two multivariable logistic regressions were conducted. The dependent variables were the initiation of newer AOMs and the initiation of any AOMs, respectively. Independent variables included age, sex, race and ethnicity, insurance coverage, ADI, comorbidities, medication use, provider specialty, and healthcare utilization data, such as outpatient visits, emergency visits, and hospitalizations. Subgroup analyses were conducted to identify factors associated with the initiation of newer AOMs among individuals with and without T2D, as well as among those who initiated treatment in 2023 and earlier years. Additionally, the likelihood of tirzepatide initiation was assessed specifically among non-diabetic patients receiving either semaglutide or tirzepatide in the 2023 cohort. Adjusted odds ratios (aOR) and 95% confidence intervals (CIs) were reported. All statistical tests were two-tailed, with a significance level of p < 0.05. Analyses were performed using Python 3 (Python Software Foundation, US). Results A cohort of 319,949 adults eligible for AOMs between January 1, 2015, and January 31, 2024, was identified from OneFlorida+. Of these, 2.0% initiated any AOM, with 0.2% initiating traditional AOMs and 1.8% initiating newer AOMs. Semaglutide accounted for the highest proportion of newer AOM initiations (77.9%), followed by tirzepatide (19.7%) and liraglutide (17.8%). Most individuals initiating newer AOMs were obese (74.9%), female (70.6%), non-Hispanic White (19.1%), aged 40–65 years (60.1%), and privately insured (61.2%). Patient characteristics at baseline are presented in Table 1 . View this table: View inline View popup Table 1. Characteristics of 2015-2023 OneFlorida+ data trust patients at baseline (n=319949) Figure 1 illustrates the proportion of newer AOMs initiated by calendar year from 2015 to 2023. Liraglutide initiation remained relatively stable, starting at 1.5% in 2015, peaking at 5.1% in 2018, and gradually declining to 3.8% by 2023. Semaglutide showed a sharp increase beginning in 2018, rising from 1.4% to 69.1% by 2023. Tirzepatide, introduced in 2022, rapidly grew from 6.5% in its first year to 25.2% in 2023. Download figure Open in new tab Figure 1. Trends of newer AOM initiation by calendar year Figures 2a–2d illustrate the proportion of newer AOM initiation in 2022 and 2023 by ZIP code areas in Florida. Darker colors indicate higher uptake; gray represents missing data. Overall, higher AOM uptake was seen in Central, North, and Southeast Florida, with lower uptake in the Southwest, Northwest, and Panhandle. Semaglutide had higher uptake in Central and North Florida and parts of Southeast Florida. Liraglutide showed higher uptake in scattered Central and Southeast counties, with lower uptake in the Panhandle and North Florida. Tirzepatide uptake was higher in select Central and Southeast counties and lower in the Panhandle and Southwest. Supplementary Figure S1 presents ZIP code-level AOM uptake for all study years. Download figure Open in new tab Figure 2. Incidence proportion of initiating overall newer AOMs and each individual drug among the eligible cohort in year 2022 and 2023, stratified by five-digit ZIP codes in Florida. (A) Overall newer AOM initiation, (B) semaglutide initiation, (C) liraglutide initiation, and (D) tirzepatide initiation. Factors associated with newer AOM initiation among the eligible cohort Sociodemographic factors—including age, gender, race or ethnicity, and insurance status—were significantly associated with newer AOM initiation ( Table 2 ). Higher odds were found among females compared to males (aOR 1.60 [1.51, 1.70]) and individuals aged 40–65 years compared to 18–40 years (1.21 [1.13, 1.29]). Lower odds were found in individuals aged ≥65 years compared to 18–40 years (0.38 [0.34, 0.43]), non-Hispanic Black (0.87 [0.80, 0.94]), Hispanic (0.84 [0.78, 0.91]), and other race or ethnic groups (0.89 [0.83, 0.95]) compared to the non-Hispanic White group. Those with Medicaid (0.69 [0.63, 0.76]), Medicare (0.86 [0.78, 0.95]), other insurance (0.90 [0.83, 0.98]), or no insurance (0.81 [0.74, 0.87]) had lower odds compared to private insurance. View this table: View inline View popup Table 2. Factors associated with initiating newer AOMs (n= 5835) among all eligible cohort (n= 319949) Among clinical variables, higher odds of initiating newer AOMs were observed among individuals with obesity (1.66, [1.55, 1.77]) compared to those who were overweight, as well as those with T2D (2.19, [1.99, 2.40]) or anxiety (1.13, [1.01, 1.26]). Similarly, higher odds were associated with the use of metformin (2.94, [2.60, 3.31]), other glucose-lowering agents (2.22, [1.93, 2.56]), and antihypertensives (1.39, [1.25, 1.55]). In contrast, lower odds were observed among individuals with coronary artery disease (0.82, [0.68, 0.99]), heart failure (0.55, [0.39, 0.77]), or arthritis (0.49, [0.33, 0.74]). Current smokers (0.37, [0.24, 0.58]) and former smokers (0.81, [0.67, 0.97]) had lower odds compared to never smokers. Lower odds were also seen among opioid users (0.83, [0.77, 0.89]) and those with a Charlson Comorbidity Index score of 1 (0.84, [0.73, 0.95]) compared to 0. Regarding healthcare utilization, individuals with 1-2 (1.31, [1.20, 1.44]) or ≥3 outpatient visits (1.26, [1.15, 1.38]) had higher odds of AOM initiation compared to none. Consulting an endocrinologist (1.92, [1.67, 2.20]) or a specialized nurse practitioner/physician assistant (1.60, [1.50, 1.71]) was also associated with higher odds compared to primary care providers. Lower odds were associated to ≥1 emergency room visits (0.48, [0.45, 0.51]) or hospitalizations (0.54, [0.48, 0.61]) compared to none. Factors associated with newer AOM initiation stratified by diabetes status In individuals with and without T2D, higher odds of initiating newer AOMs were observed among those with obesity, anxiety, antihypertensive or metformin use, visits to an endocrinologist or specialized nurse/physician assistant, and ≥1 outpatient visits. Lower odds were noted among individuals aged 65 or older, current smokers, and those with hospitalizations or emergency room visits (Tables S2 and S3). Among non-diabetic individuals, higher odds were seen in females, those aged 40–65 years, and antidepressant users. Lower odds were associated with former smoking, heart failure, coronary artery disease, arthritis, opioid use, minority racial or ethnic groups, and public or no insurance (Table S2). For diabetic individuals, lower odds were observed among those aged 65 or older, Medicaid insurance, heart failure, and current smoking, (Table S3). Factors associated with newer AOM initiation stratified by year of initiation Findings stratified by initiation in 2023 versus prior years were similar. Odds were higher among those with obesity, females, middle age, users of metformin, antihypertensives, or antidepressants, those who visited an endocrinologist or specialized nurse/physician assistant, and ≥1 outpatient visits. Lower odds were observed among minority populations, those with public or no insurance, individuals aged 65 or older, opioid users, current smokers, those with a Charlson Comorbidity Index score of 1, and those with ≥1 hospitalizations or emergency room visits (Tables S4 and S5). In 2023, additional factors associated with lower odds included anxiety, the use of other glucose-lowering medications, coronary artery disease, and former smoking (Table S5). Factors associated with tirzepatide initiation Among non-diabetic patients receiving either semaglutide or tirzepatide in the 2023 cohort, patients using statins were less likely to initiate tirzepatide (0.54, [0.33, 0.89]). Similarly, non-Hispanic Black (0.65, [0.49, 0.87]) and Hispanic patients (0.75, [0.57, 0.98]) had lower odds of initiation of tirzepatide compared to non-Hispanic White patients. Healthcare utilization also played a role, with patients who had one or more emergency room visits (0.76, [0.60, 0.96]) or 1–2 outpatient visits (0.68, [0.51, 0.92]) being less likely to initiate tirzepatide compared to those with no visits (Table S6) Discussion In a cohort of real-world populations eligible for AOM treatment, we observed that only 2.0% and 1.8% initiated any or a newer AOM between January 1, 2015, and January 31, 2024. We identified significant disparities in initiating newer AOMs. For example, racial and ethnic minority groups were less likely to initiate a newer AOM; uninsured people or public insurance enrollees were less likely to use a newer AOM compared to private insurance enrollees. We also found that health care use, e.g., patients having regular outpatient visits and seeing an endocrinologist, were more likely to be prescribed newer AOMs. Other factors influencing initiation included age, sex, smoking status, comorbidities and medication use. Greater odds of initiation were associated with being female, middle-aged, having T2D, and using glucose-lowering drugs, or antihypertensive medications. Our results align with previous research indicating disparities in the use of AOMs across racial and ethnic groups in the US ( 11 , 17 ). Gasoyan et al. found significant disparities in access to AOMs, both in terms of prescribing and filling prescriptions. Specifically, Black, Hispanic, and other racial or ethnic groups (compared to Whites) had lower odds of receiving a newer prescription for AOMs, and Hispanics had lower odds of having the prescription filled ( 11 ). Similarly, a cross-sectional analysis of the 2015-2020 National Health and Nutrition Examination Survey reported that many Americans eligible for semaglutide for obesity were likely unable to afford it, with Black and Hispanic adults having less access than White individuals; this difference was attributable to differences in insurance status, having a usual source of care, family income, and education ( 17 ). Our subgroup analyses, which included patients without diabetes and cohorts from both 2023 and earlier years, further corroborated these findings, showing that belonging to a minority racial or ethnic group was consistently associated with lower odds of receiving AOMs. Both racial and weight-related biases are documented to influence healthcare interactions ( 18 – 20 ). These biases can impact how patients communicate with healthcare providers and their willingness to seek treatment for obesity, potentially leading to underutilization of effective treatments like newer AOM ( 18 – 20 ). More research is needed to understand intrinsic (e.g., perceptions of clinicians or patients), and extrinsic factors (e.g., structural or external barriers) that contribute to racial and ethnic disparities in the treatment of obesity. Insurance coverage is a barrier to initiating AOMs, as evidenced by our findings and those of Gasyon et al. ( 11 ). Individuals covered by Medicaid, Medicare, other types of insurance, or no insurance have lower odds of initiating AOMs compared to those with private insurance. Unfortunately, for an obesity indication, most state Medicaid programs and Medicare Part D plans do not cover AOMs, including Florida (21; 22). Among those with private insurance, coverage for AOMs varies by carrier and often involves strict preauthorization ( 23 ). In contrast, access to GLP-1 RAs to treat T2D has better coverage. Our subgroup analyses showed that for individuals with T2D, only Medicaid enrollees had significantly lower odds of initiating AOMs. For those without diabetes, significant disparities exist, with all public insurance enrollees (Medicaid and Medicare) having lower odds of initiating AOMs. When Medicare Part D was established, traditional AOMs were excluded from coverage due to unfavorable safety profiles ( 6 ). However, newer AOMs have demonstrated significant weight reduction with improved safety profiles (7; 8). In response, promising changes are underway. More recently, in 2024, the Biden-Harris Administration proposed expanding Medicare and Medicaid coverage for AOMs to improve accessibility and affordability. This expansion is estimated to reduce Medicare enrollees’ out-of-pocket costs by up to 95% and benefit 4 million Medicaid enrollees ( 24 ). Provider specialty was significantly associated with initiating newer AOMs. Our findings showed that patients who visited an endocrinologist or a specialized nurse practitioner/physician assistant were more likely to initiate AOMs compared to those who visited a primary care provider. Similarly, Thomas et al. found that endocrinologists had the highest prevalence of prescribing newer AOMs, reflecting their expertise in this area ( 27 ). However, projections from 2003 and 2014 reveal that the endocrinology workforce is insufficient to meet current and future demand to significantly impact the obesity epidemic ( 28 ). There is also substantial US nationwide variations in geographic accessibility to endocrinologists at both county and state levels, as well as disparities in access to an endocrinologist within a reasonable driving distance, by urban/rural status and by age ( 29 ). The unequal geographic distribution, including shortages, of the endocrine workforce in the US, highlight areas with limited access to these specialists ( 29 ), which may continue to negatively impact AOM use for those who need them. A potential solution is the use of telehealth to improve access to endocrinologists at a lower cost and with reduced travel time ( 30 ). Additionally, promoting the growth of the endocrinology profession by creating systematic and institutional incentives for endocrinology trainees, such as funding opportunities or loan repayment, could help address the workforce shortage ( 31 ). The association between clinical characteristics and newer AOM initiation varies across studies. In our study, individuals with T2D and those using metformin, glucose-lowering, or anti-hypertensive medications were more likely to initiate newer AOMs. Conversely, individuals with coronary artery disease, heart failure, arthritis, or higher Charlson comorbidity scores were less likely. Another study found that higher Charlson comorbidity scores, myocardial infarction, or heart failure lowered the odds of both receiving and filling an AOM prescription. However, patients with T2D had lower odds of receiving an AOM prescription but higher odds of filling one ( 11 ). The authors proposed that patients with diabetes might be prescribed liraglutide or semaglutide primarily for glucose control with added weight loss benefits ( 11 ). A retrospective study investigating obesity treatment modalities reported that weight reduction procedures or surgeries were more common among individuals with severe obesity and higher comorbidity burdens, while AOM use was associated with cardiovascular risk factors ( 32 ). Unlike our study, which focuses exclusively on newer AOMs, these studies included traditional AOMs in their analyses. The difference in safety profiles and the larger body of evidence supporting surgical outcomes compared to traditional AOMs may have influenced clinical preferences ( 33 – 35 ). Our findings emphasize the need for increased awareness of newer AOMs’ cardiometabolic benefits, which could shift prescribing patterns over time. Further research is essential to update treatment guidelines for obesity, particularly for newer AOMs. Our findings carry significant public health and clinical implications, offering real-world data on newer AOM uptake, including trends over time and geographic variation across Florida. We identified lower initiation rates among racial and ethnic minorities, the uninsured, and those with limited healthcare access, revealing systemic barriers to obesity treatment. These findings support designing targeted interventions and policies to improve AOM equity and ultimately clinical outcomes. We propose strategies to increase AOM uptake. These include advocating for policy reforms to expand insurance coverage and ease prior authorization requirements. To improve access to obesity specialists, we recommend fostering the growth of the endocrinology field through targeted incentives for trainees and expanding telehealth services. Finally, previous research revealed clinician and patient concerns about AOM side effects and limited awareness of their benefits ( 36 – 38 ). Prioritizing outreach efforts is crucial to educate both groups about newer AOMs, including improving understanding of obesity science, updating obesity management guidelines, and recognizing obesity as a chronic disease. Several limitations of this study should be noted. First, data on patients’ income or educational level were not available and, therefore, could not be accounted for. However, the ADI was used as a close proxy, but because it is multifaceted, it may not fully capture the individual-level socioeconomic differences that could impact health behaviors and access to care, potentially leading to residual confounding in our analysis. Second, the use of secondary data from the OneFlorida+ database limited our ability to capture certain latent variables, such as patient or clinician perceptions and beliefs, that could have influenced the initiation of newer AOMs. Third, while a strength of our study is its focus on newer AOMs, including tirzepatide, it was difficult to directly compare our findings with previous studies, which primarily included traditional AOMs. When newer AOMs were included, they typically involved semaglutide, liraglutide, or both, but none included tirzepatide. Finally, because our focus was on investigating the initiation of newer AOMs, patients with any AOM use at baseline were excluded. As a result, we were unable to assess whether prior use of traditional AOMs influenced the decision to initiate a newer AOM. In conclusion, the initiation of newer AOMs remains low among overweight and obese adults in the US but has notably increased since 2022. Our findings underscore significant disparities in obesity care, with racial and ethnic minority groups and individuals without private insurance being less likely to access these treatments. Addressing inequities in AOM access is essential to mitigate disparities in obesity outcomes. Conflict of interest None. Funding and assistance NIH/NIDDK R01DK133465 Author contributions and guarantor statement R. M. R, Y.A.L, and J.G. were involved in the conception, design, and conduct of the study and the analysis and interpretation of the results. R.M.R. wrote the first draft of the manuscript, and all authors reviewed and approved the final version of the manuscript. J. G. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Data Availability All data produced in the present study are available upon reasonable request to the authors. Footnotes Funding and Assistance: The project is supported by NIH/NIDDK R01DK133465. Conflict of Interest: None. References 1. ↵ Apovian CM . Obesity: definition, comorbidities, causes, and burden . Am J Manag Care 2016 ; 22 : s176 – 185 OpenUrl PubMed 2. ↵ Engin A . The Definition and Prevalence of Obesity and Metabolic Syndrome . Adv Exp Med Biol 2017 ; 960 : 1 – 17 OpenUrl CrossRef PubMed 3. ↵ Ward ZJ , Bleich SN , Cradock AL , Barrett JL , Giles CM , Flax C , Long MW , Gortmaker SL. Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity . N Engl J Med 2019 ; 381 : 2440 – 2450 OpenUrl CrossRef PubMed 4. ↵ Jensen MD , Ryan DH , Apovian CM , Ard JD , Comuzzie AG , Donato KA , Hu FB , Hubbard VS , Jakicic JM , Kushner RF , Loria CM , Millen BE , Nonas CA , Pi-Sunyer FX , Stevens J , Stevens VJ , Wadden TA , Wolfe BM , Yanovski SZ , Jordan HS , Kendall KA , Lux LJ , Mentor-Marcel R , Morgan LC , Trisolini MG , Wnek J , Anderson JL , Halperin JL , Albert NM , Bozkurt B , Brindis RG , Curtis LH , DeMets D , Hochman JS , Kovacs RJ , Ohman EM , Pressler SJ , Sellke FW , Shen WK , Smith SC , Jr. , Tomaselli GF , American College of Cardiology/American Heart Association Task Force on Practice G, Obesity S . 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society . Circulation 2014 ; 129 : S102 – 138 OpenUrl FREE Full Text 5. ↵ Garvey WT , Mechanick JI , Brett EM , Garber AJ , Hurley DL , Jastreboff AM , Nadolsky K , Pessah-Pollack R , Plodkowski R , Reviewers of the AACEOCPG. American Association of Clinical Endocrinologists and American College of Endocrinology Comprehensive Clinical Practice Guidelines for Medical Care of Patients with Obesity . Endocr Pract 2016 ; 22 Suppl 3 : 1 – 203 OpenUrl CrossRef 6. ↵ Onakpoya IJ , Heneghan CJ , Aronson JK . Post-marketing withdrawal of anti-obesity medicinal products because of adverse drug reactions: a systematic review . BMC Med 2016 ; 14 : 191 OpenUrl CrossRef PubMed 7. Jastreboff AM , Kushner RF . New Frontiers in Obesity Treatment: GLP-1 and Nascent Nutrient-Stimulated Hormone-Based Therapeutics . Annu Rev Med 2023 ; 74 : 125 – 139 OpenUrl CrossRef PubMed 8. ↵ Wilding JPH , Batterham RL , Calanna S , Davies M , Van Gaal LF , Lingvay I , McGowan BM , Rosenstock J , Tran MTD , Wadden TA , Wharton S , Yokote K , Zeuthen N , Kushner RF , Group SS . Once-Weekly Semaglutide in Adults with Overweight or Obesity . N Engl J Med 2021 ; 384 : 989 – 1002 OpenUrl CrossRef PubMed 9. ↵ Garvey WT , Batterham RL , Bhatta M , Buscemi S , Christensen LN , Frias JP , Jodar E , Kandler K , Rigas G , Wadden TA , Wharton S , Group SS . Two-year effects of semaglutide in adults with overweight or obesity: the STEP 5 trial . Nat Med 2022 ; 28 : 2083 – 2091 OpenUrl CrossRef PubMed 10. ↵ Jastreboff AM , Aronne LJ , Ahmad NN , Wharton S , Connery L , Alves B , Kiyosue A , Zhang S , Liu B , Bunck MC , Stefanski A , Investigators S -. Tirzepatide Once Weekly for the Treatment of Obesity . N Engl J Med 2022 ; 387 : 205 – 216 OpenUrl CrossRef PubMed 11. ↵ Gasoyan H , Pfoh ER , Schulte R , Sullivan E , Le P , Rothberg MB . Association of patient characteristics and insurance type with anti-obesity medications prescribing and fills . Diabetes Obes Metab 2024 ; 26 : 1687 – 1696 OpenUrl PubMed 12. ↵ Ogden CL , Fakhouri TH , Carroll MD , Hales CM , Fryar CD , Li X , Freedman DS. Prevalence of Obesity Among Adults, by Household Income and Education - United States, 2011-2014 . MMWR Morb Mortal Wkly Rep 2017 ; 66 : 1369 – 1373 OpenUrl CrossRef PubMed 13. ↵ Hogan WR , Shenkman EA , Robinson T , Carasquillo O , Robinson PS , Essner RZ , Bian J , Lipori G , Harle C , Magoc T , Manini L , Mendoza T , White S , Loiacono A , Hall J , Nelson D . The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope . J Am Med Inform Assoc 2022 ; 29 : 686 – 693 OpenUrl CrossRef PubMed 14. ↵ Neighborhood Atlas [ article online ], 2024 . Available from https://www.neighborhoodatlas.medicine.wisc.edu/ . 15. ↵ Kind AJH , Buckingham WR . Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas . N Engl J Med 2018 ; 378 : 2456 – 2458 OpenUrl CrossRef PubMed 16. ↵ Austin SR , Wong YN , Uzzo RG , Beck JR , Egleston BL . Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work . Med Care 2015 ; 53 : e65 – 72 OpenUrl CrossRef PubMed 17. ↵ Lu Y , Liu Y , Krumholz HM . Racial and Ethnic Disparities in Financial Barriers Among Overweight and Obese Adults Eligible for Semaglutide in the United States . J Am Heart Assoc 2022 ; 11 : e025545 OpenUrl CrossRef PubMed 18. ↵ Ryan L , Coyne R , Heary C , Birney S , Crotty M , Dunne R , Conlan O , Walsh JC . Weight stigma experienced by patients with obesity in healthcare settings: A qualitative evidence synthesis . Obes Rev 2023 ; 24 : e13606 OpenUrl PubMed 19. Sarwer DB , Gasoyan H , Bauerle Bass S , Spitzer JC , Soans R , Rubin DJ . Role of weight bias and patient-physician communication in the underutilization of bariatric surgery . Surg Obes Relat Dis 2021 ; 17 : 1926 – 1932 OpenUrl CrossRef PubMed 20. ↵ Blair IV , Steiner JF , Fairclough DL , Hanratty R , Price DW , Hirsh HK , Wright LA , Bronsert M , Karimkhani E , Magid DJ , Havranek EP . Clinicians’ implicit ethnic/racial bias and perceptions of care among Black and Latino patients . Ann Fam Med 2013 ; 11 : 43 – 52 OpenUrl Abstract / FREE Full Text 21. Hernandez I , Wright DR , Guo J , Shrank WH . Medicare Part D Coverage of Anti-obesity Medications: a Call for Forward-Looking Policy Reform . J Gen Intern Med 2024 ; 39 : 306 – 308 OpenUrl PubMed 22. Alliance SO. Medicaid Obesity Treatment Coverage 2024 . 2024 23. ↵ Gasoyan H , Sarwer DB . Addressing insurance-related barriers to novel antiobesity medications: Lessons to be learned from bariatric surgery . Obesity (Silver Spring ) 2022 ; 30 : 2338 – 2339 OpenUrl PubMed 24. ↵ House TW. FACT SHEET: Biden-Harris Administration Takes Latest Step to Lower Prescription Drug Costs by Proposing Expanded Coverage of Anti-Obesity Medications for Americans with Medicare and Medicaid . 2024 25. Hwang CS , Kesselheim AS , Rome BN . Medicare Should Cover Weight Loss Drugs as Long as the Prices are Affordable . J Law Med Ethics 2024 ; 52 : 188 – 190 OpenUrl PubMed 26. A Call for New Research in the Area of Obesity [ article online ], 2023 . Available from https://www.cbo.gov/publication/59590 . 27. ↵ Thomas CE , Mauer EA , Shukla AP , Rathi S , Aronne LJ . Low adoption of weight loss medications: A comparison of prescribing patterns of antiobesity pharmacotherapies and SGLT2s . Obesity (Silver Spring ) 2016 ; 24 : 1955 – 1961 OpenUrl CrossRef PubMed 28. ↵ Vigersky RA , Fish L , Hogan P , Stewart A , Kutler S , Ladenson PW , McDermott M , Hupart KH . The clinical endocrinology workforce: current status and future projections of supply and demand . J Clin Endocrinol Metab 2014 ; 99 : 3112 – 3121 OpenUrl CrossRef PubMed 29. ↵ Lu H , Holt JB , Cheng YJ , Zhang X , Onufrak S , Croft JB. Population-based geographic access to endocrinologists in the United States, 2012 . BMC Health Serv Res 2015 ; 15 : 541 OpenUrl CrossRef PubMed 30. ↵ Anderson D , Porto A , Koppel J , Macri G , Wright M . Impact of Endocrinology eConsults on Access to Endocrinology Care for Medicaid Patients . Telemed J E Health 2020 ; 26 : 1383 – 1390 OpenUrl PubMed 31. ↵ Romeo GR , Hirsch IB , Lash RW , Gabbay RA . Trends in the Endocrinology Fellowship Recruitment: Reasons for Concern and Possible Interventions . J Clin Endocrinol Metab 2020 ; 105 : 1701 – 1706 OpenUrl PubMed 32. ↵ Kan H , Swindle JP , Bae J , Dunn JP , Buysman EK , Gronroos NN , Bengtson L , Chinthammit C , Ford J , Ahmad N . Weight management treatment modalities in patients with overweight or obesity: A retrospective cohort study of administrative claims data . Obes Pillars 2023 ; 7 : 100072 OpenUrl PubMed 33. ↵ Wilding JPH , Jacob S . Cardiovascular outcome trials in obesity: A review . Obes Rev 2021 ; 22 : e13112 OpenUrl PubMed 34. Jain P , Hejjaji V , Thomas MB , Garcia RA , Kennedy KF , Goyal A , Sperling L , Das SR , Hafida S , Enriquez JR , Arnold SV . Use of primary bariatric surgery among patients with obesity and diabetes . Insights from the Diabetes Collaborative Registry. Int J Obes (Lond ) 2022 ; 46 : 2163 – 2167 OpenUrl PubMed 35. ↵ Schauer PR , Bhatt DL , Kirwan JP , Wolski K , Aminian A , Brethauer SA , Navaneethan SD , Singh RP , Pothier CE , Nissen SE , Kashyap SR , Investigators S . Bariatric Surgery versus Intensive Medical Therapy for Diabetes - 5-Year Outcomes . N Engl J Med 2017 ; 376 : 641 – 651 OpenUrl CrossRef PubMed 36. ↵ Oshman L , Othman A , Furst W , Heisler M , Kraftson A , Zouani Y , Hershey C , Cho TC , Guetterman T , Piatt G , Griauzde DH . Primary care providers’ perceived barriers to obesity treatment and opportunities for improvement: A mixed methods study . PLoS One 2023 ; 18 : e0284474 OpenUrl CrossRef PubMed 37. Huq S , Todkar S , Lahiri SW . Patient Perspectives on Obesity Management: Need for Greater Discussion of Bmi and Weight-Loss Options Beyond Diet and Exercise, Especially in Patients with Diabetes . Endocr Pract 2020 ; 26 : 471 – 483 OpenUrl PubMed 38. ↵ Fujioka K , Harris SR . Barriers and Solutions for Prescribing Obesity Pharmacotherapy . Endocrinol Metab Clin North Am 2020 ; 49 : 303 – 314 OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted January 20, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Trends and Disparities in Newer GLP1 Receptor Agonist Initiation among Real-World Adult Patients Eligible for Obesity Treatment Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Trends and Disparities in Newer GLP1 Receptor Agonist Initiation among Real-World Adult Patients Eligible for Obesity Treatment Rotana M. Radwan , Yao An Lee , Pareeta Kotecha , Davene R. Wright , Inmaculada Hernandez , Ronald Ramon , William T. Donahoo , Yong Chen , John M. Allen , Jiang Bian , Jingchuan Guo medRxiv 2025.01.20.25320839; doi: https://doi.org/10.1101/2025.01.20.25320839 Share This Article: Copy Citation Tools Trends and Disparities in Newer GLP1 Receptor Agonist Initiation among Real-World Adult Patients Eligible for Obesity Treatment Rotana M. Radwan , Yao An Lee , Pareeta Kotecha , Davene R. Wright , Inmaculada Hernandez , Ronald Ramon , William T. Donahoo , Yong Chen , John M. Allen , Jiang Bian , Jingchuan Guo medRxiv 2025.01.20.25320839; doi: https://doi.org/10.1101/2025.01.20.25320839 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Endocrinology (including Diabetes Mellitus and Metabolic Disease) Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffdef0baf8858f4',t:'MTc3OTQ3NTA2Mw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();