The Association Between Patient Out-of-Pocket Medical Costs and Medication Adherence

ABSTRACT

Objectives: Interventions to reduce cost-related medication nonadherence have focused on reducing out-of-pocket (OOP) prescription drug costs. However, little is known about the impact of OOP medical costs on adherence. This study examines the relationship between patient all-cause OOP medical costs and medication adherence.

Study Design: A retrospective study was conducted using July 2017 to December 2018 Medicare Advantage data from the Optum Clinformatics Data Mart deidentified database. Inclusion criteria were adapted from the specifications of 3 medication adherence measures used in the Medicare Part D Star Ratings program: diabetes, renin-angiotensin system antagonists (RASAs), and statins.

Methods: Multivariable logistic regression models were used to assess the relationship between OOP medical costs and medication adherence across 4 mutually exclusive cohorts—diabetes only, RASA only, statins only, and diabetes-RASA-statins—after adjusting for covariates.

Results: A total of 47,091, 443,831, 510,394, and 284,137 individuals were included in the diabetes-only, RASA-only, statins-only, and diabetes-RASA-statins cohorts, respectively. OOP medical costs were associated with a significant decrease in medication adherence across all cohorts. A $1000 increase in patient all-cause OOP medical costs reduced the odds of adherence by 7.3% (OR, 0.927; 95% CI, 0.919-0.935; P < .001) for statins, 7.4% (OR, 0.926; 95% CI, 0.903-0.949; P < .001) for diabetes medications, and 11.6% (OR, 0.884; 95% CI, 0.876-0.892; P < .001) for RASAs. The negative impact of OOP medical costs on adherence was greatest in the diabetes-RASA-statins cohort.

Conclusions: Interventions aimed at improving medication adherence should focus on patient OOP prescription drug costs but should also consider the potential impact on adherence of total patient OOP costs incurred for all medical services.

Am J Manag Care. 2026;32(9):510-514

doi:10.37765/ajmc.2026.90006

Takeaway Points

Medication nonadherence remains a persistent problem among Medicare Advantage beneficiaries even when prescription drug costs are subsidized, underscoring the need to look beyond pharmacy costs alone when designing adherence interventions.

• High out-of-pocket (OOP) prescription costs are a well-established barrier to medication adherence, but far less is known about the impact of high OOP medical costs on medication adherence.
• This study found a negative association between high OOP medical costs and medication adherence across all therapies examined.
• Interventions aimed at improving medication adherence should focus not only on subsidies for prescription drugs but also on subsidies and support for patients’ nondrug and medical costs (eg, hospitalization and emergency department visits).

Over the past 2 decades, prescription drug costs have risen substantially, increasing patients’ share of these costs.^1-3 High patient out-of-pocket (OOP) cost is one of the primary drivers of medication nonadherence, and studies have found that cost-related nonadherence (CRN)—taking less medication than prescribed as a result of cost concerns—is associated with higher use of health care resources, higher health care costs, and higher mortality rates.^4-7 The risk factors associated with CRN, including lower household income and a higher number of chronic conditions, make older adult patients more susceptible to it.^8,9

Many efforts have been made to improve medication adherence among older adults, driven in part by 3 medication adherence quality measures developed and stewarded by the Pharmacy Quality Alliance and adapted by CMS for Medicare Part D Star Ratings, an incentive-based, ratings-driven program that provides bonus payments to health plans.¹⁰ Three triple-weighted adherence measures—Medication Adherence for Diabetes Medications,¹¹ Medication Adherence for Hypertension (renin-angiotensin system antagonists [RASAs]),¹² and Medication Adherence for Cholesterol (statins)¹³—disproportionately influence a plan’s overall star rating.¹⁴ As such, health plans have implemented several mechanisms to improve medication adherence among Medicare beneficiaries, including member and prescriber education, rewards programs, and drug subsidies, among others.¹⁵

These interventions have mostly been effective, with adherence among Medicare enrollees significantly higher compared with Medicaid or commercial enrollees.^16,17 However, even in the Medicare population, CRN remains a significant issue. Among US adults 65 years or older, 20% report CRN.¹⁸ More specifically, Van Alsten and Harris found that 16% of Medicare beneficiaries with diabetes, 13% with hypertension, and 15% with cardiovascular disease reported CRN.¹⁹ Understandably, efforts to combat high prescription drug costs—and subsequently reduce CRN—have generally focused on OOP prescription drug costs. For example, the federal government created the low-income subsidy (LIS), or Extra Help program, to provide subsidies for Medicare Part D prescription drug program costs, including premiums, deductibles, and coinsurance.²⁰ The Inflation Reduction Act (IRA) of 2022 further strengthened this program by expanding the eligibility for full LIS benefits to 150% of the federal poverty level. This change effectively eliminated partial LIS benefits, which provided lower subsidies for the prescription drug program.²¹ 

In addition to interventions, most of the literature on medication adherence and cost has focused on prescription drug OOP costs, and this is certainly important because studies have consistently found that high OOP prescription drug costs are associated with lower adherence.^22-24 However, CRN persists among older adults despite the LIS program and other drug subsidies and rebate programs implemented by payers and providers. For this reason, it is important to consider other cost barriers that may affect CRN, as individuals are likely to decide whether to fill prescriptions based on their overall financial resources.

Similar to high drug OOP costs, high medical expenses may act as a barrier to medication adherence, particularly for patients with limited financial resources. Nationally representative data found that 4 in 10 adults with health care debt have cut pills in half, skipped doses of medication, or avoided filling a prescription compared with 12% of adults without medical debt.²⁵ This suggests that medical expenditures may function as a competing financial demand that reduces a patient’s ability to afford and adhere to prescriptions. Therefore, this study explored the relationship between patient all-cause OOP medical costs and medication adherence to the 3 adherence measures used in the Medicare Part D Star Ratings program.

METHODS

Study Design and Data Source

This retrospective study used July 2017 to December 2018 administrative claims data from the Optum Clinformatics Data Mart (CDM) database. The CDM contains deidentified, longitudinal health information, including medical records, prescription claims, laboratory results, and enrollment information, for commercial and Medicare Advantage beneficiaries. Because the CDM data are deidentified and do not constitute human participants research, institutional review board approval was not required for this study.

Study Population

Specifications for the 3 adherence measures in the Part D Star Ratings program were adapted to identify CDM cohorts of Medicare Advantage beneficiaries eligible for inclusion in the study.^11-13 Individuals were included if they were 18 years or older on the index prescription start date (IPSD; ie, first prescription fill in 2018 for a medication included in the adherence measures) and had at least 2 prescription claims in 2018 for a medication included in the adherence measures. Individuals in hospice care, with end-stage renal disease, or with claims for insulin products (for the diabetes medication adherence measure) or for sacubitril-valsartan (Entresto; for the hypertension medication adherence measure) were excluded. Individuals were required to be continuously enrolled in medical and pharmacy insurance coverage and not to have a recorded date of death during the study period. Four mutually exclusive cohorts were created for analysis: (1) diabetes only, (2) RASA only, (3) statins only, and (4) diabetes-RASA-statins (enrollees included in all 3 adherence measures).

Study Variables

Medication adherence, the outcome of interest, was assessed using the proportion of days covered (PDC) metric and measured from the IPSD to the end of the study period. PDC was calculated as a binary variable, with PDC of 80% or higher classified as adherent and PDC less than 80% as nonadherent. Patient all-cause OOP medical costs were the key independent variable, measured as the sum of all inpatient and outpatient deductibles, co-pays, and coinsurance in 2018, excluding OOP prescription drug costs. Covariates adjusted for in the models included age, sex, race/ethnicity, geographic region, plan type, LIS/dual-eligibility (DE) status, treatment-naive status (measured July-December 2017 in the 6 months prior to the IPSD), comorbidity burden (assessed using the Deyo-Charlson Comorbidity Index), use of 90-day fills, and mail-order prescriptions during the study period. Plan type was categorized by network design (ie, health maintenance organization, preferred provider organization, and other). Patients were identified as LIS/DE if they were dually eligible for Medicare and Medicaid or eligible for the Medicare LIS program for at least 1 month during the study period.

Statistical Analyses

All analyses were conducted using SAS 9.4 (SAS Institute Inc). All hypothesis tests were 2-sided with an a priori significance level of .05. Baseline descriptive statistics are provided for the variables previously identified for each cohort, as well as medication burden. Categorical variables are reported as counts (percentages), and continuous variables are reported as mean (SD).

Multivariable logistic regression models were used to examine the association between patients’ all-cause OOP medical costs and medication adherence, after adjusting for the previously noted covariates. We calculated adjusted ORs with 95% CIs. OOP medical costs were rescaled to thousands of dollars to improve the interpretation of model results. Separate regression models were built for the diabetes-only, RASA-only, and statins-only cohorts. In addition, for the diabetes-RASA-statins cohort, separate logistic regression models were built to predict adherence to each therapy (ie, diabetes medications, RASAs, and statins).

RESULTS

After applying all study inclusion and exclusion criteria, a total of 47,091 individuals were included in the diabetes-only cohort, 443,831 in the RASA-only cohort, 510,394 in the statins-only cohort, and 284,137 in the diabetes-RASA-statins cohort. Across all cohorts, a majority of the study population was female (50.1% to 60.7%) and non-Hispanic White (56.3% to 70.9%), with a mean age of 73.4 years. Mean patient all-cause OOP medical costs were $857 for the diabetes-only cohort, $747 for the RASA-only cohort, $835 for the statins-only cohort, and $759 for the diabetes-RASA-statins cohort. In addition, LIS/DE status was highest among the diabetes-only cohort (25.0%) and lowest among the statins-only cohort (18.9%). Additional descriptive statistics are provided in Table 1 for the single-measure cohorts and in the eAppendix (available at ajmc.com) for the diabetes-RASA-statins cohort.

Results of the multivariable logistic regression models are provided in Table 2. Across all models, a negative association was observed between patient OOP medical costs and adherence, with higher costs associated with lower odds of adherence. For the single-measure cohorts, a $1000 increase in patient all-cause OOP medical costs was associated with lower odds of adherence by 7.4% (OR, 0.926; 95% CI, 0.903-0.949; P < .001) for diabetes medications, 11.6% (OR, 0.884; 95% CI, 0.876-0.892; P < .001) for RASAs, and 7.3% (OR, 0.927; 95% CI, 0.919-0.935; P < .001) for statins. The negative impact was even larger in the diabetes-RASA-statins cohort (Table 2).

DISCUSSION

This study examined the underexplored relationship between patient OOP medical costs and medication adherence and found a negative association. Our results showed that for every $1000 increase in patient OOP medical costs, the odds of adherence decreased by 7.3% to 11.6% among the single-measure cohorts, with even lower odds of adherence for the more complex patients in the diabetes-RASA-statins cohort. These results highlight the complex decision-making process that drives patient medication refill behavior—and, subsequently, adherence—and the importance of a more comprehensive approach to addressing CRN, rather than focusing solely on subsidies and policies for OOP prescription costs.

A study by Wang et al published in 2021 examined total OOP costs and financial toxicity and found medication costs and health insurance premiums to be the 2 largest drivers of OOP costs among individuals with heart failure.²⁶ In addition, a study by the Robert Wood Johnson Foundation and Urban Institute found hospital bills to be a significant driver for medical debt for approximately 73% of patients.²⁷ Unsurprisingly, OOP costs disproportionately affect individuals with low incomes.^26,27 Prescription costs typically require immediate payment at the point of sale and can substantially impactperceptions of drug affordability.²⁸ Although policy makers have sought to address prescription costs by expanding the LIS program, capping OOP prescription drug costs for Medicare beneficiaries, and allowing Medicare beneficiaries to pay for OOP prescription costs in monthly installments—a process known as smoothing—under the IRA, these interventions are limited to Part D pharmacy costs.²¹ Because OOP medical costs are typically billed after services are rendered and paid over time, interventions similar to smoothing are operationally challenging to implement for medical costs. The distinct policy approaches needed to address medical costs incurred during inpatient stays, emergency department visits, and outpatient services have received little attention in the context of medication adherence.

Even when prescription drug OOP costs are subsidized—such as through programs implemented under the IRA—the financial burden of OOP medical costs may simply highlight other barriers to medication access, such as travel costs or ongoing office visit costs required to optimize a medication regimen. Results of this study show that policy makers and payers seeking to improve medication adherence and, subsequently, health outcomes should consider establishing mechanisms that provide financial assistance for medical OOP costs in addition to prescription costs. Structures in place to provide subsidies for the LIS program could serve as a vehicle for payers and policy makers to provide similar subsidies for Medicare beneficiaries for hospital and outpatient services. In addition, Medicare could consider a cap on OOP medical costs for traditional fee-for-service Medicare, similar to limits in prescription drug plans and the Medicare Advantage program, while potentially extending smoothing beyond prescription costs.

Health care providers and clinicians can also play an important role by constantly engaging with patients on health care costs and their ability to afford health care services.²⁹ A study by Patel et al found that patients who engaged with clinicians to identify lower-cost medications had lower odds of CRN.⁹ Moreover, patients have expressed a strong interest in discussing estimated medication costs with prescribers during visits.¹⁸ Thus, as part of routine patient care, clinicians should actively screen and identify patients potentially at risk for CRN and provide resources to address this issue. Additionally, health plans are uniquely positioned to facilitate these conversations through mandated comprehensive medication reviews (CMRs) under the Medicare Part D Star Ratings program. These regular touchpoints provide an opportunity to screen for CRN while fulfilling the CMR’s goals of identifying medication-related problems and improving health outcomes. Further, documenting the risk for CRN as part of the billing record using Z codes can aid health plan interventions and future research on CRN.³⁰ Finally, providers and clinicians, as part of care coordination, should ensure that patients not only fill prescriptions from hospital and office visits but also have the resources needed to fill other prescriptions as needed. It is also important to recognize, screen, and address social and structural determinants of health, such as health literacy, food insecurity, and pharmacy deserts, as well as patient beliefs that may lead to CRN among individuals with chronic conditions.^9,31

Limitations

This study has several limitations. In administrative claims–based analyses of medication adherence, the filling of a medication does not imply its use as prescribed. Second, decisions about whether to fill a prescription are made at the individual dispensing level, so aggregating data at the annual level may miss nuances that could be observed closer to the time a prescription needs to be dispensed. Third, the data source and variable operationalization limit the ability to incorporate temporality into the study design. Although a design that measures OOP medical costs in time period 1, followed by adherence measurement in time period 2, can be implemented, using aggregated claims data to measure key variables may not be sufficient to fully address the impact of OOP medical spending on medication adherence. To address these 2 limitations, future studies should explore patient OOP costs at the monthly, daily, or per-visit level when evaluating the subsequent impact on medication adherence and prescription-fill behavior (eg, delays).³² Longitudinal designs using data collection methods such as ecological momentary assessment or daily diaries may be needed to capture aspects of this complex decision-making process. Another limitation is that the analysis did not account for the Part D coverage gap for non-LIS/DE beneficiaries. Further studies should stratify beneficiaries by Part D benefit phase to better understand how the Part D coverage gap influences the relationship between medical costs and adherence. Finally, this study focused on Medicare Advantage beneficiaries. As such, these results may not be generalizable to traditional Medicare enrollees or other populations. Future studies should explore whether the impact of OOP medical costs on adherence is larger among the traditional Medicare population.

CONCLUSIONS

High patient OOP medical costs are associated with lower odds of medication adherence among Medicare Advantage enrollees. Interventions focused on improving medication adherence should address prescription drug costs and also consider the broader patient cost burden, incorporating OOP medical subsidies and payment-reduction programs into the Medicare program. n

Author Affiliations: Pharmacy Quality Alliance (MAP), Alexandria, VA; University of Mississippi School of Pharmacy (IN, SR, JPB), University, MS; Merck & Co, Inc (PJC, TK, HB), Rahway, NJ.

Source of Funding: This study was funded by Merck Sharp & Dohme, LLC, a subsidiary of Merck & Co, Inc, in Rahway, NJ. The study was designed and analyzed and the manuscript was prepared in collaboration with the funder.

Author Disclosures: Dr Ramachandran reports being a consultant with and receiving grants from the Pharmacy Quality Alliance. Dr Campbell, Dr Karmakar, and Dr Black are full-time employees at Merck & Co, Inc. Dr Campbell and Dr Black own stock in Merck & Co, Inc. Dr Bentley reports receiving consulting fees from Merck & Co, Inc, as part of the project.

Authorship Information: Concept and design (PJC, TK, HB, JPB); acquisition of data (TK, HB); analysis and interpretation of data (MAP, IN, SR, PJC, JPB); drafting of the manuscript (MAP, IN, SR, PJC, TK, HB, JPB); critical revision of the manuscript for important intellectual content (IN, SR, PJC, TK, HB, JPB); statistical analysis (MAP, IN, SR, PJC, JPB); provision of patients or study materials (HB); obtaining funding (PJC); administrative, technical, or logistic support (JPB); and supervision (PJC, JPB).

Address Correspondence to: Megha A. Parikh, PhD, Pharmacy Quality Alliance, 5911 Kingstowne Village Pkwy #130, Alexandria, VA 22315. Email: megha.parikh@gmail.com.

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