Interventions Addressing Cost-Related Medication Nonadherence in Diabetes: A Scoping Review

ABSTRACT

One in 6 patients with diabetes in the US reports rationing or abandoning their medications to save costs. Our objective was to describe the breadth, approach, and impact of interventions that sought to address cost-related nonadherence among patients with diabetes in 2003-2023. Studies were eligible if they were published in English, pertained to diabetes, described interventions or policies that reduced or eliminated diabetes medication costs, and evaluated medication adherence as a primary or secondary outcome. We identified studies using MEDLINE, Embase, and Scopus. Two independent reviewers assessed each article’s abstract and full text in 2 phases; 29 articles met inclusion criteria. Sixteen interventions reduced diabetes-related co-payments: Seven found improvements in adherence, 6 found no improvement, and 3 did not evaluate changes over time. Eight interventions eliminated all or some diabetes-related costs: Five found improvements in adherence, 2 found no improvement, and 1 did not evaluate changes over time. Interventions that combined cost-reduction or cost-elimination strategies with wellness and disease management programs tended to lead to improved short- and long-term adherence. Six articles evaluated statewide or federal policies (eg, insulin co-payment caps), with varying effects on adherence. Interventions that eliminate diabetes-related costs and provide additional diabetes management assistance may improve access and adherence to medications. Additional work is needed to evaluate the impact of these interventions on long-term health and utilization outcomes.

Am J Manag Care. 2025;31(Spec. No. 15):SP1108-SP1117

The out-of-pocket costs associated with managing diabetes are high and rising. The average patient with insulin-dependent diabetes spent $4600 annually on medical appointments, supplies, medications, and hospitalizations in 2021.¹ In 2013-2017, 41% of families of patients with diabetes reported financial hardship related to medical expenses, and 16% could not pay their medical bills.² When patients with diabetes cannot afford their medical care, many try to save money by rationing medications.^2,3 In the US, 17% of nonelderly adults and 7% of older adults with diabetes report skipping doses or delaying filling prescriptions to save costs, a phenomenon known as cost-related nonadherence (CRNA).^3-6

Interventions to reduce or eliminate the out-of-pocket costs of diabetes medications could reduce CRNA and improve access to diabetes care. However, it is not clear which entities should lead these interventions and which approaches (eg, cost reduction or cost elimination) are effective. Private entities (eg, insurers) can make major changes to formularies, such as eliminating all diabetes-related costs for all enrollees and/or providing complementary disease management programs (DMPs), but can include only limited population subgroups (eg, employed individuals).^7,8 State and government entities, in contrast, can target larger and more diverse populations (eg, an entire state) but may be more limited by legislation and regulations in the types of interventions they can implement (eg, insulin cost reductions, but not complete elimination).⁹

Given the high and rising costs of diabetes care,¹⁰ a clear synthesis of existing evidence is essential to inform the next generation of policy and program development. In this scoping review, we describe the interventions that insurers, integrated health systems, and government entities have implemented to reduce CRNA between 2003 and 2023. We pay special attention to who implemented the intervention (insurers/health systems vs state/federal government entities), the approaches taken (cost reduction vs cost elimination), any special population subgroups (eg, low-income patients), and impacts on adherence. Our findings will inform the types of interventions that should be pursued moving forward and the entities that should be called on to lead those interventions.

METHODS

Data Sources and Searches

This review followed the scoping review guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the Institute of Medicine.¹¹ A medical librarian composed MEDLINE, Embase, and Scopus searches (eAppendix Table 1 [eAppendix available at ajmc.com]). The search was peer-reviewed by a second medical librarian using a modified Peer Review of Electronic Search Strategies (PRESS) checklist.¹² We also searched for trials at ClinicalTrials.gov that included the following terms: (cost) AND (adher* OR nonadher* OR non-adher* OR compliance OR noncompliant OR noncompliance). All search results were compiled in EndNote 20 and imported into Covidence for deduplication, screening, and extraction. This review was registered in Open Science Framework.

Study Selection

Studies were eligible for inclusion if they (1) included patients with diabetes (with or without other comorbidities) and presented results for patients with diabetes; (2) described an intervention that reduced or eliminated diabetes-related medication out-of-pocket costs; (3) included medication adherence as a primary or secondary outcome; (4) were published in English; (5) used data from 2003 or later; (6) were located in the US; and (7) fell into 1 of the following categories: retrospective and prospective observational studies, pragmatic clinical trials, randomized clinical trials, and quality improvement studies. Examples of eligible interventions include insurer formulary modifications and statewide policies that place caps on medication out-of-pocket costs. We limited our search to the years 2003 and later because prescription drug coverage became available for Medicare enrollees in 2003 with the establishment of Medicare Part D.¹³

All other studies were excluded. Studies conducted in Veterans Administration Medical Centers were also excluded due to differences in costs and payment structures.

Screening Phase

Duplicate studies were automatically removed using Covidence software. Titles and abstracts that met search criteria were screened per the eligibility criteria above by 2 authors (D.A.S., L.C.). A third author (C.E.S.) acted as a tiebreaker for any disagreements.

Eligibility Phase

Any abstracts that met inclusion criteria were read in full by 2 reviewers (D.A.S., L.C.). Any articles that upon further review did not meet criteria were discarded. Conflicts were reviewed and resolved in the presence of a third reviewer (C.E.S.). Interrater reliability was high, with a Cohen κ statistic of 0.80.

Data Extraction

A data extraction form was created in Microsoft Excel using relevant methodological guidance.¹⁴ Two authors (D.A.S., L.C.) extracted data from each article, including publishing year, intervention implementer, intervention description, participant details, adherence measure, data source, sample size, data years, baseline characteristics, adherence outcomes, and clinical/economic outcomes.

Data Synthesis and Analysis

We did not conduct systematic evaluations of the methodological quality of the included studies, including risk of bias assessment,¹⁵ because our objective was to offer a comprehensive overview of the breadth of interventions that have addressed CRNA, rather than to perform a formal quality assessment, because the most appropriate intervention may depend on the implementer and target population. Additionally, adherence interventions use a variety of follow-up times and methods for measuring adherence, making comparisons among studies difficult.

Two reviewers (D.A.S., L.C.) first determined whether interventions were implemented by insurers or health systems vs state or government entities. We then determined whether interventions reduced or eliminated cost sharing. Cost-reduction interventions were defined as interventions that lowered, but did not eliminate, out-of-pocket costs for diabetes medications (eg, placing target medications on lower-cost formulary tiers). Cost-elimination interventions were defined as removing all out-of-pocket costs for some or all diabetes medications.

A narrative evaluation of all included studies was conducted according to established scoping review protocols.¹⁴

RESULTS

Search Results

The electronic search resulted in a total of 7014 abstracts (Figure). Automatic removal of duplicates resulted in 4005 records. Following abstract screening, 99 articles were selected for full-text review. Seventy full texts were excluded due to ineligible interventions (n = 5), ineligible study designs (n = 56), and insufficient adherence data (n = 9). No ongoing trials listed at ClinicalTrials.gov met inclusion criteria. Twenty-nine articles met all inclusion criteria.

Study Characteristics

Most interventions were conducted by insurance plans or integrated health systems (n = 24) and a smaller number were implemented by state or federal government entities (n = 5) (Table 1^12,13). Almost all follow-up periods were 6 months or more (n = 24). Among interventions conducted by insurers and health systems, 8 were cost-reduction interventions with follow-up lengths of 1 to 3 years (Table 2^7-9,16-41),^16-23 8 were combined cost-reduction and cost-elimination strategies with follow-up of up to 45 months,^7,8,24-29 and 8 were cost-elimination interventions with follow-up of up to 24 months.^30-37 All 5 interventions conducted by government entities were cost-reduction interventions with follow-up of up to 12 months.^9,38-41 Intervention details are described in eAppendixTable 2, and a synthesis of results is in Table 2.

Most studies used either medication possession ratio (MPR; n = 8) or proportion of days covered (PDC; n = 20) as a measure of adherence, with a minority using self-report (n = 2). PDC and MPR measure adherence in 2 different ways. PDC is calculated as the number of days covered by prescription fills, divided by the number of days in the observation period.⁴² It accounts for early medication fills and corrects for oversupply of medication. MPR is calculated as the sum of days’ supply dispensed to the patient divided by the number of days in the observation period.⁴³ It does not correct for early fills or oversupply, so it may overestimate adherence. Adherence is typically deemed good if PDC or MPR is at least 0.8.^16,24 No studies reported adherence outcomes according to the Ascertaining Barriers to Compliance taxonomy of adherence, which defines 3 phases of adherence and potential nonadherence: medication initiation, implementation (ie, continuation), and discontinuation.⁴⁴

Adherence Outcomes

Insurer and health system cost-reduction interventions. We discuss the combined interventions and the cost reduction alone interventions together because patients in both groups continued to incur nonzero diabetes-related medication costs. Of 16 studies, 3 used $10 retail co-payments for diabetes medications with variable mail-order co-payments, 5 used tiered co-payment structures, and 7 used tiered co-payment structures plus a complementary disease management program (DMP) (Table 2).

Eight studies followed patients for up to 12 months after the intervention. One of these studies did not conduct a pre-post analysis or include a comparator group.²⁷ Data from 7 showed modest improvements in adherence, ranging from a 4– to 10–percentage point (PP) increase in PDC or MPR at 12 months.^{7,8,18,21-23,25,28} For example, 1 health plan set the co-payment for most diabetes medications and supplies at $10 per month for a subset of 71 enrollees. The 5037 nonintervention participants continued paying $10 for generic drugs, 30% coinsurance for preferred brand drugs, and 50% coinsurance for nonpreferred brand drugs.²³ At 12 months, the odds of having PDC of at least 0.80 in the intervention group vs the nonintervention group was 1.56 (95% CI, 1.04-2.34; P = .03).²³ Another study evaluated multiple employer-based plans that used a variety of cost-reduction and care-enhancement strategies. At 12 months, PDC was significantly higher among patients enrolled in plans that provided generous benefits, established targeted interventions for patients at high risk of CRNA, provided access to wellness programs, and/or encouraged patients to use mail-order prescriptions.⁸

In 6 of the 8 studies that followed patients for longer than 12 months, the data showed either no change or a significant decline in adherence over time.^{16,19-21,24,29} One employer-sponsored insurance-based intervention reduced coinsurance rates for diabetes, asthma, and hypertension medications from 10% to 20% of the medication price to 7.5% to 10% of the medication price. At year 1, the intervention was associated with a significant decline in adherence compared with propensity-matched controls (–0.21 PP; P < .01; adherence measure not defined), but at years 2 and 3, adherence was similar among groups.¹⁶

Only 2 cost-reduction interventions led to persistent improvements in adherence over follow-up periods greater than 1 year.^17,26 They both included a cost-reduction intervention and a concomitant DMP. Gibson and colleagues described 2 interventions: one that only reduced costs and a second that both reduced costs and provided a DMP that included patient education, coaching, and additional monitoring.¹⁷ After 3 years of follow-up, MPR in the combined intervention arm was 6.5 PP higher than in a comparator group that received only the disease management intervention (P < .01).

Insurer and health system cost-elimination interventions. These interventions eliminated costs for all diabetes medications and supplies (n = 4) or just preferred diabetes medications (n = 4). Four interventions included a complementary DMP (Table 2). Five interventions led to improvements in medication adherence after 6 months to 2 years of follow-up.^30-37 Patients enrolled in one employee health benefit program had an average PDC of 0.82 at 18 months after their diabetes medication costs were eliminated, compared with 0.73 for patients enrolled in other payment plans (P < .001).³⁰ Another optional employee health benefit program that enrolled patients in a DMP and waived co-payments for preferred formulary diabetes medications/supplies found that PDC in the subgroup of patients with lower baseline adherence (ie, PDC < 1) was 8 PP higher 6 months after the intervention than before the intervention (P < .05).³⁵ Another study found declines in MPR in both intervention and control groups after 2 years of follow-up, with a greater decline among controls (19 PP) than among the intervention group (10 PP; P = .009).³³ Two studies found no difference in adherence after intervention implementation.^34,36

Only 2 studies evaluated adherence outcomes by income category. Huang and colleagues found no difference in adherence rates among patients in lower vs higher income brackets at 2 years, whereas Cong and colleagues found that patients in the lowest income category had the greatest improvement in PDC at 10 months compared with controls in a difference-in-difference analysis (3.6 PP; P = .035).^29,31

State and federal government interventions. All government interventions that met inclusion criteria reduced costs of medications and/or other health services (n = 4) or health-related costs such as food assistance (n = 1).^9,38-41 Zeng and colleagues evaluated how medication adherence among Medicare enrollees changed after the Affordable Care Act (ACA), which reduced the Medicare Part D coverage gap limit coinsurance from 100% of the price of any medication to 50% of the price of branded medications and 7% of the price of generic medications.⁴¹ Post ACA, PDC for patients with diabetes who were 65 years or older increased by 3 to 6 PP in the coverage gap at 12 months (P < .001 to .019).⁴¹ Likewise, a cross-sectional analysis by Yala and colleagues found that individuals qualifying for the Medicare Part D low-income subsidy, which covers roughly 95% of out-of-pocket cost sharing for resource-limited enrollees, exhibited adherence rates 5.1 PP higher than those eligible but not enrolled.³⁸ Li and colleagues found that Utah’s HB 207, which capped co-payments on insulin at $30 for a 30-day supply starting in January 2021, was not associated with any changes in adherence at 6 months despite a 67-PP decline in out-of-pocket spending.⁹ Other policy interventions included the Supplemental Nutrition Assistance Program (SNAP)⁴⁰ and Medicaid expansion.³⁹

Clinical and Economic Outcomes

Reporting on clinical and economic outcomes was inconsistent across studies (see eAppendix Table 3 for details). Among the 4 studies that assessed hemoglobin A_1c (HbA_1c) outcomes, one found no change in HbA_1c ,³² whereas 3 found significant declines in HbA_1c at 1 to 2 years of up to 1.5% among patients with baseline HbA_1c levels of at least 9%.^33,34,37 The Utah policy that reduced insulin co-pays was associated with an increase in HbA_1c values from 8.0% in the preintervention period to 8.5% in the postintervention period (P = .03), but only 90 of 244 participants had HbA_1c data available for analysis.⁹

Five studies assessed emergency department utilization outcomes: 2 found reduced utilization and 3 found no change (eAppendix Table 3).^{20,23,24,27,32} Six studies found no change in hospitalizations long-term.^{19,20,23,24,27,32} One study found an increase in primary care visits¹⁷ whereas another found no change.²⁴

Most studies describing the impact of cost-elimination programs on per-member per-year costs and spending by insurers led to reduced costs.^{16,17,24,30,31,33,36} Both cost-reduction and cost-elimination programs were associated with increased pharmacy spending by health plans.^{18,20,26,34,35,37}

DISCUSSION

In this scoping review, we found that co-payment elimination and reduction interventions had variable impacts on CRNA. Although adherence often improved in the first year after implementation, benefits decreased and even disappeared completely after longer follow-up periods. The interventions that were successful over the long term tended to involve government-led cost-reduction interventions and employer-sponsored interventions that combined co-payment reduction or elimination with wellness support programs and/or DMPs. It is notable that cost-reduction interventions were associated with persistent improvements in adherence, suggesting that eliminating all costs may not be necessary for patient access to improve. This finding also suggests that value-based insurance design may be a successful approach to CRNA for both private and public payers.

The insurer-led and health system–led interventions that included wellness programs and DMPs may have fared better because they took a multipronged approach to addressing nonadherence. The reasons for poor adherence among patients with chronic conditions are often complex and may involve more than just high out-of-pocket costs, including poor health literacy, concerns about adverse effects, drug-drug interactions, and poor access to pharmacies and specialists.⁴⁵ About one-third of adults with diabetes have multimorbidity,⁴⁶ and these supplemental programs may have improved patients’ ability to manage their comorbidities. Future interventions that include wellness programs or DMPs should evaluate not only diabetes-related but also comorbidity-related adherence and health outcomes.

Our findings point to several gaps in prior insurer- and health system–led CRNA interventions and suggest potential directions for a future comprehensive CRNA intervention. First, these interventions should work to ensure that adherence outcomes are sustainable over the long term by including educational programs and/or DMPs. Second, given known differences in medication access and adherence by geographic region and sociodemographics,^47,48 future studies should be powered to evaluate for differences across distinct subgroups. Few of the studies in our review did this. Third, few studies evaluated the impact of cost-reduction and co-payment–elimination interventions on clinical^9,32-34,37 or utilization outcomes.^{19,20,23,24,32} Finally, future studies should describe the resource and personnel costs of running these interventions, to help assess sustainability.

The government-led interventions included in this review tended to be successful at reducing CRNA.^9,38-41 By reducing their spending on nondiabetes medications, food, and other expenses, patients likely had more disposable income to spend on their diabetes care. Increasing access to programs that reduce general financial barriers to care may be an effective strategy for reducing diabetes-related CRNA. Patients who were receiving SNAP benefits, for example, were less likely to report CRNA than matched SNAP nonparticipants.⁴⁰ In 2019, 18% of SNAP-eligible individuals were not enrolled in the program.⁴⁹ One avenue for addressing CRNA could include ensuring that eligible patients are enrolled in programs such as The Emergency Food Assistance Program and the Women, Infants, and Children supplement program. Exploring existing programs as a solution for CRNA offers the advantage of capitalizing on established resources, potentially enabling cost-effective interventions that can be readily implemented.

Limitations

Our study has several limitations. First, interventions targeted at reducing CRNA use a variety of terminologies. We may therefore have missed articles that did not include any of our search terms. Although many articles were excluded during screening, this is typical of scoping reviews employing intentionally broad search criteria to maximize capture of relevant literature; however, generalizability may still be limited given the relatively small number of studies that ultimately met inclusion criteria. Second, the included studies did not adhere to a standard method for reporting adherence, so we could not directly compare interventions against each other. No studies evaluated the impact of interventions on each stage of adherence as described in the taxonomy of adherence either.⁴⁴ This is an important gap in the literature because prior research has found that the relationship between patient medication out-of-pocket costs and adherence varies by adherence phase.⁵⁰ Similarly, inconsistent reporting of clinical and economic outcomes limits cross-study comparisons (eAppendix Table 3). Standardized outcome measures and reporting frameworks are needed. Future interventions should use consistent adherence measurements and terminology so they can be compared against each other. Lastly, our review may overestimate the number of studies finding an association between medication cost reduction/elimination interventions and adherence, due to publication bias.

CONCLUSIONS

This scoping review highlights the intricacies involved in addressing CRNA among patients taking medications for diabetes. Future interventions should leverage the characteristics we found to be associated with the greatest reductions in CRNA: inclusion of DMPs, use or expansion of existing resources and policies, and targeting of individuals at highest risk of CRNA. Standardizing nomenclature and adherence measurement methods is also imperative to enhance comparability, fostering a more comprehensive understanding of the effectiveness of interventions in reducing CRNA.

Author Affiliations: School of Medicine (DAS, LC, SK) and Duke-Margolis Institute of Health Policy (CES), Duke University, Durham, NC; Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System (LLZ), Durham, NC; Department of Population Health Sciences (LLZ, CES) and Department of Medicine (CES), Duke University Medical Center, Durham, NC.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DAS, LC, SK, LLZ, CES); acquisition of data (DAS, LC, SK); analysis and interpretation of data (DAS, LC, CES); drafting of the manuscript (DAS, LC, LLZ); critical revision of the manuscript for important intellectual content (DAS, LC, LLZ, CES); administrative, technical, or logistic support (SK); and supervision (LLZ, CES).

Send Correspondence to: Caroline E. Sloan, MD, MPH, Duke University, 710 Main St, Durham NC 27701. Email: caroline.sloan@duke.edu.

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