Three Sides to the Story: Adherence Trajectories During the First Year of SGLT2 Inhibitor Therapy Among Medicare Beneficiaries
Open accessAbstract
OBJECTIVE: We aimed to understand the factors associated with sodium–glucose cotransporter 2 inhibitor (SGLT2i) adherence and longitudinal adherence trajectories in older adults with type 2 diabetes. RESEARCH DESIGN AND METHODS: Using Medicare claims data (April 2013–December 2017), we identified 83,675 new SGLT2i users ≥66 years old with type 2 diabetes. We measured SGLT2i adherence as the proportion of days covered (PDC) during the first year of SGLT2i therapy. We used linear regression to assess the association between baseline covariates and PDC. Then we used group-based trajectory modeling to identify distinct longitudinal SGLT2i adherence groups and used a multivariable logistic regression model to examine the association between baseline covariates and membership in these adherence groups. RESULTS: Unadjusted mean PDC was 63%. Previous adherence to statins had the strongest positive association with PDC (regression coefficient 6.00% [95% CI 5.50, 6.50]), whereas female sex (−5.51% [−6.02, −5.00]), and Black race/ethnicity (−5.06% [−6.03, −4.09]) had the strongest negative association. We identified three adherence trajectory groups: low (23% of patients, mean PDC 17%), moderate (32%, mean PDC 50%), and high (45%, mean PDC 96%) adherence. More patients in the high adherence group were previously adherent to statins (odds ratio 1.43 [95% CI 1.39, 1.48]), and more women (1.28 [1.23, 1.32]) and Black patients (1.31 [1.23, 1.40]) were in the low adherence group. CONCLUSIONS: In a large population of older patients with type 2 diabetes, 45% were highly adherent during the first year of SGLT2i treatment. Female sex and Black race/ethnicity were most strongly associated with low adherence.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
High out-of-pocket drug costs are identified as a factor that limits initiation of and adherence to prescribed glucose lowering therapies.
“High out-of-pocket (OOP) drug costs are identified as a factor that limit initiation of and adherence to prescribed glucose lowering therapies.8,9,10,11,12”
From Evaluation of Out-of-Pocket Costs and Treatment Intensification With an SGLT2 Inhibitor or GLP-1 RA in Patients With Type 2 Diabetes and Cardiovascular Disease (2023) · cites this paper as [10]
In Covariates Associated With SGLT2i Adherence (page unavailable), the paper reports that a higher SGLT2 inhibitor co-pay was positively associated with adherence, contrary to the claimed limiting effect of higher costs for this glucose-lowering drug. The study included patients who had already initiated therapy, so it does not establish an effect of costs on initiation. (LLM, conf=0.83)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 42 · Cited by 22
Research works printed but not linked: 31
- Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. 2017;377:644-657
- Heerspink HJ, Johnsson E, Gause-Nilsson I, Cain VA, Sj€ ostr€ om CD. Dapagliflozin reduces albu- minuria in patients with diabetes and hyper- tension receiving renin-angiotensin blockers. Diabetes Obes Metab 2016;18:590-597
- McMurray JJV, Solomon SD, Inzucchi SE, et al.; DAPA-HF Trial Committees and Investigators. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med 2019; 381:1995-2008 6.
- Luo J, Feldman R, Rothenberger SD, Hernandez I, Gellad WF. Coverage, formulary restrictions, and out-of-pocket costs for sodium-glucose cotran- sporter 2 inhibitors and glucagon-like peptide 1 receptor agonists in the Medicare Part D program. JAMA Netw Open 2020;3:e2020969
- McCoy RG, Van Houten HK, Deng Y, et al. Comparison of diabetes medications used by adults with commercial insurance vs Medicare Advantage, 2016 to 2019. JAMA Netw Open 2021;4:e2035792
- Bell KF, Cappell K, Liang M, Kong AM. Comparing medication adherence and persis- tence among patients with type 2 diabetes using sodium-glucose cotransporter 2 inhibitors or sulfonylureas. Am Health Drug Benefits 2017; 10:165-174
- Ofori-Asenso R, Liew D, Lalic S, et al. Adherence, persistence, and switching among people prescribed sodium glucose co-transporter 2 inhibitors: a nationwide retrospective cohort study. Adv Ther 2019;36:3265-3278
- Franklin JM, Shrank WH, Pakes J, et al. Group-based trajectory models: a new approach to classifying and predicting long-term medi- cation adherence. Med Care 2013;51:789-796
- Franklin JM, Krumme AA, Tong AY, et al. Association between trajectories of statin adhe- rence and subsequent cardiovascular events. Pharmacoepidemiol Drug Saf 2015;24:1105-1113
- Franklin JM, Krumme AA, Shrank WH, Matlin OS, Brennan TA, Choudhry NK. Predicting adhe- rence trajectory using initial patterns of medi- cation filling. Am J Manag Care 2015;21: e537-e544
- Franklin JM, Gopalakrishnan C, Krumme AA, et al. The relative benefits of claims and electronic health record data for predicting medication adherence trajectory. Am Heart J 2018;197:153-162
- Kumamaru H, Lee MP, Choudhry NK, et al. Using previous medication adherence to predict future adherence. J Manag Care Spec Pharm 2018;24:1146-1155
- Mody R, Grabner M, Yu M, et al. Real-world effectiveness, adherence and persistence among patients with type 2 diabetes mellitus initiating dulaglutide treatment. Curr Med Res Opin 2018;34:995-1003
- Nguyen H, Dufour R, Caldwell-Tarr A. Glucagon-like peptide-1 receptor agonist (GLP- 1RA) therapy adherence for patients with type 2 diabetes in a Medicare population. Adv Ther 2017;34:658-673
- Hernandez I, He M, Chen N, Brooks MM, Saba S, Gellad WF. Trajectories of oral anti- coagulation adherence among Medicare bene- ficiaries newly diagnosed with atrial fibrillation. J Am Heart Assoc 2019;8:e011427
- Lauffenburger JC, Choudhry NK. A call for a systems-thinking approach to medication adhe- rence: stop blaming the patient. JAMA Intern Med 2018;178:950-951
- Kim DH, Schneeweiss S, Glynn RJ, Lipsitz LA, Rockwood K, Avorn J. Measuring frailty in Medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci 2018;73:980-987
- Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol 2011;64: 749-759
- Weiss T, Iglay K, Carr RD, Mishra AP, Yang L, Rajpathak S. Real-world adherence and discontinuation of GLP-1 receptor agonist (GLP- 1RA) therapy in type 2 diabetes (T2D) patients in the U.S. (Abstract). Diabetes 2019;68(Suppl. 1):984-P 23. Alhazami M, Pontinha VM, Patterson JA, Holdford DA. Medication adherence trajectories: a systematic literature review. J Manag Care Spec Pharm 2020;26:1138-1152
- Lauffenburger JC, Franklin JM, Krumme AA, et al. Predicting adherence to chronic disease medications in patients with long-term initial medication fills using indicators of clinical events and health behaviors. J Manag Care Spec Pharm 2018;24:469-477
- Choudhry NK, Avorn J, Glynn RJ, et al.; Post- Myocardial Infarction Free Rx Event and Economic Evaluation (MI FREEE) Trial. Full coverage for preventive medications after myo- cardial infarction. N Engl J Med 2011;365: 2088-2097
- Choudhry NK, Bykov K, Shrank WH, et al. Eliminating medication copayments reduces disparities in cardiovascular care. Health Aff (Millwood) 2014;33:863-870
- Liang J, Xu X, Bennett JM, Ye W, Quiñones AR. Ethnicity and changing functional health in middle and late life: a person-centered approach. J Gerontol B Psychol Sci Soc Sci 2010;65:470-481
- Vadhariya A, Fleming ML, Johnson ML, et al. Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults. Am Health Drug Benefits 2019; 12:202-211
- Ajrouche A, Estellat C, De Rycke Y, Tubach F. Trajectories of adherence to low-dose aspirin treatment among the French population. J Cardiovasc Pharmacol Ther 2020;25:37-46
- Zongo A, Simpson S, Johnson JA, Eurich DT. Change in trajectories of adherence to lipid- lowering drugs following non-fatal acute coronary syndrome or stroke. J Am Heart Assoc 2019;8:e013857 31. Hargrove JL, Pate V, Casteel CH, et al. Antihypertensive adherence trajectories among older adults in the first year after initiation of therapy. Am J Hypertens 2017;30:1015-1023
- Lauffenburger JC, Robinson JG, Oramasionwu C, Fang G. Racial/ethnic and gender gaps in the use of and adherence to evidence-based preventive therapies among elderly Medicare Part D bene- ficiaries after acute myocardial infarction. Cir- culation 2014;129:754-763
- Chan DC, Shrank WH, Cutler D, et al. Patient, physician, and payment predictors of statin adherence. Med Care 2010;48:196-202
- Rasmussen JN, Gislason GH, Rasmussen S, et al. Use of statins and beta-blockers after acute myocardial infarction according to income and education. J Epidemiol Community Health 2007; 61:1091-1097
- Aarnio E, Martikainen J, Winn AN, Huupponen R, Vahtera J, Korhonen MJ. Socioeconomic inequalities in statin adherence under universal coverage: does sex matter? Circ Cardiovasc Qual Outcomes 2016;9:704-713
- Krumme AA, Glynn RJ, Schneeweiss S, et al. Medication synchronization programs improve adherence to cardiovascular medications and health care use. Health Aff (Millwood) 2018; 37:125-133
- Authors
- Hawley, C. E., Lauffenburger, J. C., Paik, J. M., Wexler, D. J., Kim, S. C., & Patorno, E.
- Affiliations
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Corresponding author
- Patorno, Elisabetta
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2022-01-18
- Volume
- 45
- Issue
- 3
- Pages
- 604-613
- ISSN
- 0149-5992 (ISSN-L)
- PMC
- PMC8918201
- OpenAlex
- W4206404428
- Cited by
- 22
- References
- 42
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 6
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (K08AG055670)
- National Heart, Lung, and Blood Institute (K01HL141538)
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- SGLT2 inhibitors, medication adherence, type 2 diabetes, older adults, adherence trajectories, statin adherence, sex differences, racial disparities, Black patients, Medicare beneficiaries
- MeSH terms
- Sodium-Glucose Transporter 2 Inhibitors, Aged, Diabetes Mellitus, Type 2, Female, Medicare, Humans, Retrospective Studies, United States, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Medication Adherence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review