Using a Budget Impact Model Framework to Evaluate Antidiabetic Formulary Changes and Utilization Management Tools

Incidence and Predictors of Primary Nonadherence to Sodium Glucose Co-transporter 2 Inhibitors and Glucagon-Like Peptide 1 Agonists in a Large Integrated Healthcare System

BACKGROUND

Newer glucose-lowering drugs, including sodium glucose co-transporter 2 inhibitors (SGLT2i) and GLP-1 agonists, have a key role in the pharmacologic management of type 2 diabetes. No studies have measured primary nonadherence for these two drug classes, defined as when a medication is prescribed for a patient but ultimately not dispensed to them.

OBJECTIVE

To describe the incidence and predictors of primary nonadherence to SGLT2i (canagliflozin, empagliflozin) or GLP-1 agonists (dulaglutide, liraglutide, semaglutide) using a dataset that links electronic prescribing with health insurance claims.

DESIGN AND PARTICIPANTS

A retrospective cohort design using data of adult patients from a large health system who had at least one prescription order for a SGLT2i or GLP-1 agonist between 2012 and 2019. We used mixed-effects multivariable logistic regression to determine associations between sociodemographic, clinical, and provider variables and primary nonadherence.

MAIN MEASURES

Primary medication nonadherence, defined as no dispensed claim within 30 days of an electronic prescription order for any drug within each medication class.

KEY RESULTS

The cohort included 5146 patients newly prescribed a SGLT2i or GLP-1 agonist. The overall incidence of 30-day primary medication nonadherence was 31.8% (1637/5146). This incidence rate was 29.8% (n = 726) and 33.6% (n = 911) among those initiating a GLP-1 agonist and SGLT2i, respectively. Age ≥ 65 (aOR 1.37 (95% CI 1.09 to 1.72)), Black race vs White (aOR 1.29 (95% CI 1.02 to 1.62)), diabetic nephropathy (aOR 1.31 (95% CI 1.02 to 1.68)), and hyperlipidemia (aOR 1.18 (95% CI 1.01 to 1.39)) were associated with a higher odds of primary nonadherence. Female sex (aOR 0.86 (95% CI 0.75 to 0.99)), peripheral artery disease (aOR 0.73 (95% CI 0.56 to 0.94)), and having the index prescription ordered by an endocrinologist vs a primary care provider (aOR 0.76 (95% CI 0.61 to 0.95)) were associated with lower odds of primary nonadherence.

CONCLUSIONS

One third of patients prescribed SGLT2i or GLP-1 agonists in this sample did not fill their prescription within 30 days. Black race, male sex, older age, having greater baseline comorbidities, and having a primary care provider vs endocrinologist prescribe the index drug were associated with higher odds of primary nonadherence. Interventions targeting medication adherence for these newer drugs must consider primary nonadherence as a barrier to optimal clinical care.

Modeling the Budgetary Impact of Payer Utilization Management Strategies: An Adapted Framework Based on Lessons Learned

Historically, budget impact models have been used to demonstrate how a new drug or technology entering the market could influence payer budgets, with the hope of influencing coverage of the drug or technology. This article proposes the adaptation of a budget impact model framework to answer the payer question "What is the budget impact of our approaches to managing the utilization of drugs and technologies?" Throughout this article, we also describe lessons learned while collaborating with a U.S. payer to evaluate the budgetary impact of specific antidiabetic drug utilization management approaches. DISCLOSURES: The study discussed in this commentary was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Lugo has nothing to disclose. Mullins reports grants and other fees from AHRQ, during the conduct of the study; grants and personal fees from Bayer and Pfizer; and personal fees from Boehringer-Ingelheim, Janssen/J&J, Regeneron, and Sanofi, outside the submitted work. Parts of the study were presented as a poster presentation at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. The study was also published as Hung's PhD dissertation.

Validating a Budget Impact Model Using Payer Insight and Claims Data: A Framework and Case Study

BACKGROUND

There is a paucity of studies validating budget impact models. The lack of such studies may contribute to the underuse of budget impact models by payers in formulary decision making.

OBJECTIVE

To assess the face validity, internal verification, and predictive validity of a previously published model that assessed the budgetary impact of antidiabetic formulary changes.

METHODS

4 experts with diverse backgrounds were selected and asked questions regarding the face validity of the structure/conceptual model, input data, and results from the budget impact model. To assess internal verification, structured "walk-throughs," unit tests, extreme condition tests, traces, replication tests, and double programming techniques were used. The predictive validity of the model was evaluated by comparing the predicted and realized budget using mean absolute scaled error. "Realized" budgetary impact of the formulary changes was calculated by taking the difference between realized budget in the year after the formulary changes and the budget had there been no formulary changes (i.e., the counterfactual). The counterfactual budget was modeled using the best fit autoregressive integrated moving average model.

RESULTS

When assessing the face validity of the model, the 4 experts brought up issues such as how to incorporate other health insurance, recent policy changes, cost inflation, and potential impacts on insulin use. The 6 internal verification techniques caught mistakes in equations, missing data, and misclassified data. The realized budget was found to be lower than the predicted budget, with 13% error and an absolute scaled error of 2.60. After removing the model assumption that past utilization trends would continue, the model's predictive accuracy improved (the absolute scaled error dropped below 1 to 0.48). The "realized" budgetary impact was found to be greater than the predicted budgetary impact, largely because of lower-than-expected utilization.

CONCLUSIONS

The budget impact model overpredicted utilization in the year after the formulary changes. Discoveries through the validation process improved the accuracy and transparency of the model.

DISCLOSURES

This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. AHRQ had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer Ingelheim, Janssen/J&J, Regeneron, and Sanofi-Aventis, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.