Abstract
BACKGROUND:
Insurers increasingly use copay maximizer programs to control costs. Although these programs shield patients from out-of-pocket (OOP) exposure for drugs, the impact on OOP costs for other health care services is unknown.
OBJECTIVE:
To examine the impact of copay maximizer programs on overall patient liability for all health care services.
METHODS:
This retrospective analysis of pharmacy and medical claims from the IQVIA PharMetrics Plus database included patients who were required to have 3 or more prescriptions (for autoimmune, multiple sclerosis, or oral oncolytic drugs) in a calendar year between 2018 and 2022 and have been continuously enrolled in a commercial plan during that year. An algorithm was applied to identify patients with presumed exposure to a copay maximizer program within each calendar year. Patients with presumed exposure to a maximizer program in a given year and no exposure to a maximizer program in prior years were eligible for the maximizer cohort. Patients without presumed exposure to a maximizer program were eligible for the nonmaximizer cohort. Eligible patients were matched 1:1 for the study cohorts. The outcome of interest was the effect of copay maximizer programs on patient liability for other health care services (via a difference-in-difference [DiD]) approach using a generalized linear mixed-effects model).
RESULTS:
In total, 5,976 patients were included in the analysis. Assuming no change in total costs from baseline to follow-up, copay maximizer programs were associated with increased patient liability for other health care services. When patient liabilities for the maximizer drug in the baseline period were $125, there was no effect on patient liabilities for other health care services (DiD [95% CI] = 0.98 [0.71-1.37]), whereas at $4,000, there was a 51% increase in patient liabilities for other health care services (1.51 [1.17-1.95]). In scenario analyses for which total costs changed from baseline to follow-up, results were similar to the base case. In the patient subgroup with no other health care patient liability at baseline ($0), a greater proportion of those who participated in a copay maximizer program had some (>$0) patient liability for other health care services in the follow-up period, compared with patients who did not participate (94.3% vs 63.2%).
CONCLUSIONS:
Our results indicated that copay maximizer programs are associated with an increase in patient liability for other health care services, especially for patients who relied heavily on the maximizer drug to meet deductible requirements or OOP maximums. These findings should be factored into decisions and policies on implementing and regulating these programs.
Plain language summary
Drug companies and other organizations help patients to afford medications by covering their out-of-pocket (OOP) costs. However, some health insurances use copay maximizer programs, which do not allow this support to count toward a patient’s OOP costs. This study found that copay maximizer programs may increase what patients owe for their other health care services. Additionally, copay maximizer programs increase the number of patients who owe money and did not previously owe anything for their health care.
Implications for managed care pharmacy
This study offers supporting evidence on the impact that copay maximizer programs may have on patient liability beyond that of the drug subject to the maximizer program. The potential increase in patient liability with copay maximizer programs is an important consideration for patients and plan sponsors when evaluating the use of copay maximizers as part of their plans, as well as for policymakers considering the legislation regulating these programs.
Cost sharing is a common tool used by health insurers to manage the use and cost of health care services by requiring patients to pay a portion of the cost.1,2 Examples of cost sharing include copays (a flat fee), coinsurance (a proportion of the cost of the service), and deductibles (a fixed amount that patients need to pay before copays or coinsurance applies).1 The use of coinsurance and deductibles by insurers has increased over time, thereby increasing the share of the cost for patients.3 Although cost sharing may encourage patients to thoroughly consider whether a health care service is needed, the associated increased financial burden may make cost sharing unaffordable, limiting patient access to necessary health care.
Patients for whom cost sharing for prescription medicines poses an affordability challenge may turn to copay assistance programs to alleviate their financial burden. These programs are often offered through manufacturer-sponsored programs or nonprofit foundations and help to offset the out-of-pocket (OOP) costs due to cost sharing. Although copay assistance programs may improve medication adherence and reduce the risk of early discontinuation or prescription abandonment,4–6 they are not without controversy, with some arguing that such programs encourage patients to use drugs that cost more.7–9 In response to the use of copay assistance programs from manufacturers, commercial insurers have implemented new programs, such as copay accumulators and maximizers, which aim to shift the cost of medicines back to patients and/or manufacturers when copay assistance is used.10 Insurer copay accumulator programs do not allow the value of a copay card to count against patients’ deductibles and OOP maximums, and patients remain responsible for meeting their entire deductible and OOP maximums after the copay assistance has been exhausted.10 Although research on copay accumulator programs has suggested that these lead to great OOP costs and poor treatment adherence/persistence, little is known about the impact of copay maximizer programs.11,12 In contrast to copay accumulator programs, copay maximizer programs categorize certain drugs as nonessential health benefits, allowing insurers to remove them from the OOP maximums required by the Affordable Care Act (ACA). Insurers will typically set a patient’s cost-sharing obligations for the drug under a copay maximizer program to match that of the value of the copay assistance program used. However, the value of the copay assistance is excluded from counting toward a patient’s deductible and maximum OOP for other health care services.13,14 The way copay maximizer programs have been operationalized has changed over time, with early iterations of copay maximizer programs setting a patient’s liability to the maximum value of the copay assistance program applied evenly throughout the year (ie, if a copay assistance program’s maximum benefit was $24,000, a patient’s liability would be set to $2,000 per month). As manufacturers have pushed back on the use of copay maximizer programs by altering the terms of their copay assistance programs, maximizer programs have become increasingly sophisticated in setting patient liabilities.15 This includes front-loading patient liabilities to mimic a traditional deductible benefit design.
Copay maximizer programs have been largely focused on key specialty medicine areas, such as autoimmune, multiple sclerosis, and oncology drugs. Since 2019, the use of programs has grown approximately 3-fold, accounting for 11%-18% of patients using copay assistance in 2023.16 One key feature of copay maximizer programs is that patients face low or no OOP costs for the drug used under the copay maximizer program because liabilities are set to match the value of the copay assistance programs used.14 However, because of the copay maximizer program drug no longer counting against their maximum OOP limit, the impact of copay maximizer programs on patients’ OOP costs for all of their other health care services is unknown. Therefore, the objective of this analysis was to assess the impact of copay maximizer programs on patient liability for other health care services (ie, patient liability for health care services other than the maximizer drug) for patients enrolled in commercial health plans.
Methods
STUDY DESIGN
This was a retrospective analysis of pharmacy and medical claims from the IQVIA PharMetrics Plus database, which contains claims for more than 210 million unique enrollees and is nationally representative of the commercially insured US population under the age of 65 years.17 Patients were required to have commercial insurance and at least 3 prescriptions for the same drug among a select list of autoimmune, multiple sclerosis, or oral oncolytic drugs (Supplementary Table 1 (266.7KB, pdf) , available in online article) in a given calendar year between 2018 and 2022. These 3 therapeutic areas were chosen because of copay adjustment programs primarily targeting high-cost specialty medicines.18 Patients must also have been continuously enrolled in a commercial plan throughout a calendar year to be considered on a drug for that year. Cohort attrition can be found in Supplementary Table 2 (266.7KB, pdf) .
Patients were presumed as participating or not participating in a copay maximizer program in any given calendar year if the drug was or was not subject to a maximizer (maximizer drug) during that year. In general, drugs subject to a copay maximizer program were identified based on the following:
-
1.
Having at least 3 claims for a given drug in a calendar year with a minimum patient liability of $1,000 and of approximately the same value (±$50) OR
-
2.
Having at least 2 claims for a given drug in a calendar year with a patient liability (rounded to the nearest dollar) equal to the monthly value of the corresponding copay card program.
Additional criteria were applied to some drugs based on the specifics of their respective copay assistance programs (Supplementary Table 3 (266.7KB, pdf) ).
We identified incident patients presumed as participating in a copay maximizer program (maximizer cohort) via lack of exposure to a copay maximizer program in the calendar year immediately preceding (baseline period) the earliest year of exposure to a copay maximizer program (follow-up period) during the study period. These patients were matched 1:1 to patients with the same maximizer drug during the baseline and follow-up periods, the same state of residence (limited to 50 US states), the same payer type (self-insured vs fully insured), and the same plan type (such as preferred provider organization or health maintenance organization) who did not participate in a copay maximizer program (nonmaximizer cohort) (Figure 1). Note that for both cohorts, patients were receiving a drug during both the baseline and follow-up periods and were thus subject to the continuous enrollment requirement for 2 consecutive calendar years. To focus the study on the impact of copay maximizer programs, patients with a total liability for the maximizer drug exceeding the ACA limit during the baseline period were identified as potential copay accumulator patients and excluded from the analysis.19 Patients in the nonmaximizer cohort were also excluded if their total liability for the maximizer drug exceeded the ACA limit during the follow-up period. Additionally, patients in both the maximizer and nonmaximizer cohorts must have never had a total patient liability exceeding the ACA maximum during the baseline period (for all claims).
FIGURE 1.
Study Concept and Design
STATISTICAL METHODS
The outcome of interest was patient liability for other health care services, that is, total patient liability for all health care services besides utilization of the maximizer drug, during the follow-up period. To assess the effect of copay maximizer programs on this outcome, a difference-in-difference (DiD) approach was applied using a generalized linear mixed-effects model. The DiD approach enables the estimation of the effect of the copay maximizer program while controlling for potential biases due to differences in baseline characteristics by patients serving as their own controls for baseline vs follow-up periods (Figure 1). To account for potential differences in baseline costs and changes in utilization between baseline and follow-up, covariates including baseline total costs (allowed amounts), baseline patient liability of the maximizer drug, and changes in both between baseline and follow-up periods were added to the model. This allowed for assessment of the DiD effect under the assumption of no change (base case) or change (scenario analyses) in total costs from baseline to follow-up. Additionally, a subgroup analysis was performed to describe the likelihood of any nondrug patient liability among patients with no patient liability for other health care services in the baseline period. All costs were inflation-adjusted to 2022 US dollars using the medical care component of the consumer price index.
Results
BASELINE CHARACTERISTICS
In total, 5,976 patients were included in the analysis (2,988 patients per group). The mean (SD) age in both cohorts was 48 (14) years and the proportion of female patients was similar between the maximizer and nonmaximizer cohorts (56.0% and 54.9%, respectively) (Supplementary Table 4 (266.7KB, pdf) ). Mean (SD) baseline calendar year patient liability for the maximizer drug was higher in the maximizer than in the nonmaximizer cohort ($1,331 [$1,613] vs $1,015 [$1,289]; P < 0.01). Baseline patient liability for other health care services (excluding the maximizer drug) was not significantly different between the respective cohorts ($1,076 [$1,210] vs $1,016 [$1,076]; P = 0.69). Baseline total costs (allowed amounts) were statistically higher in the nonmaximizer than in the maximizer cohort ($73,155 [$41,148] vs $69,490 [$42,498]; P < 0.01).
IMPACT OF THE COPAY MAXIMIZER PROGRAM ON TOTAL PATIENT LIABILITY FOR OTHER HEALTH CARE SERVICES
Mean (SD) follow-up calendar year patient liability for the maximizer drug was $12,428 ($7,428) in the maximizer cohort and $1,462 ($1,731) in the nonmaximizer cohort (Supplementary Table 5 (266.7KB, pdf) ). Mean follow-up calendar year patient liability for other health care services (excluding the maximizer drug) was $1,529 ($3,496) in the maximizer cohort and $1,068 ($1,297) in the nonmaximizer cohort. Assuming no change in total costs from baseline to follow-up, copay maximizer programs were associated with increased patient liability on other health care services. As patient liability for the maximizer drug in the baseline period increased, the effect of copay maximizer programs on patient liabilities for other health care services also increased. When patient liability for the maximizer drug in the baseline period was $125 (DiD [95% CI] = 0.98 [0.71-1.37]), there was no effect on liabilities for other health care services, whereas at $4,000 (DiD [95% CI] = 1.51 [1.17-1.95]), there was a 51% DiD increase in patient liabilities for other health care services (Figure 2). In absolute US dollar terms, the 51% increase represented an increase in patient liability between $75 and $493, depending on the assumed patient liability for other health care services in the baseline period (Figure 3).
FIGURE 2.
Effect of Copay Maximizer Program Assuming No Change in Total Costs From Baseline to Follow-Up (Base Case)
*Statistically significant at P < 0.05.
a Patient liability for all health care services except for the maximizer drug.
FIGURE 3.
Effect of Copay Maximizer Program in Absolute US Dollar Change for Varying Levels of Baseline Patient Liability for Other Health Care Services
*Statistically significant at P < 0.05.
aPatient liability for all health care services except for the maximizer drug; assumes no change in total costs from baseline to follow-up (base case).
SCENARIO ANALYSES: INCREASING AND DECREASING TOTAL COSTS FROM BASELINE TO FOLLOW-UP
Given that health care utilization may vary from year to year, scenario analyses were performed for which total health care costs increased or decreased from baseline to follow-up. In scenario analyses for which total costs changed from baseline to follow-up, results were generally similar to the base case, with the maximizer effect increasing with larger patient liabilities for the maximizer drug in the baseline period (Figure 4). Assuming a 25% or 50% increase in total costs from baseline to follow-up, significant DiD increases in patient liabilities for other health care services were observed in patient liabilities for the maximizer drug of $500 or more. Up to an 82% DiD increase was observed in patient liabilities for other health care services (a scenario of a 50% increase in total costs and $4,000 maximizer patient liability at baseline, DiD [95% CI] = 1.82 [1.27-2.61]). In the scenario for which health care costs decreased by 10%, significant DiD increases in patient liabilities for other health care services were observed when patient liabilities for the maximizer drug were $250 or more.
FIGURE 4.
Scenario Analyses Assuming Changes in Total Costs From Baseline to Follow-Up
*Statistically significant at P < 0.05.
aPatient liability for all health care services except for the maximizer drug.
SUBGROUP ANALYSIS: PATIENTS WITH NO OTHER HEALTH CARE SERVICE PATIENT LIABILITY AT BASELINE
In the subgroup of patients with no other health care service patient liability at baseline ($0; n = 293), a greater proportion of the patients participating in a copay maximizer program (n = 157) vs those who did not (n = 136) had some (ie>$0) patient liability for other health care services in the follow-up period (94.3% vs 63.2%). The difference between these patients was more pronounced with higher patient liabilities for the maximizer drug in the baseline period (<$1,000, 25.9% point difference; >$1,000, 36.8% point difference) (Figure 5).
FIGURE 5.
Proportion of Patients Who Had Any Patient Liability for Other Health Care Services During Follow-Up After Having No Liability During Baseline
aPatient liability for all health care services except for the maximizer drug.
Discussion
In this retrospective analysis, we found that copay maximizer programs were associated with an increase in total patient liability. To our knowledge, this is the first study examining whether maximizers affect patient costs for health care services beyond that of the drug under a copay maximizer program. These results have important implications for patients and for policymakers debating the regulation of copay maximizer programs.
Recent analyses have suggested that copay maximizer programs are growing in prevalence. For example, among multiple sclerosis and oncology drugs, the estimated prevalence was 5%-6% in 2019 to 18% in 2023 among commercially insured patients.16 One reason for the increased use of maximizers could be the perception that patients are generally shielded from OOP cost exposure.20 Although this may be true for OOP costs associated with the maximizer drug, our results suggest that patients’ OOP costs for other health care services may rise. This is likely due to the copay maximizer programs disallowing patients’ copay cards to count against the maximum OOP limits and/or deductibles of their plans. Therefore, whereas in a noncopay maximizer scenario patients are using the value of copay cards for the maximizer drug to reduce their OOP exposure, under a copay maximizer program patients are facing more OOP exposure from cost sharing of other health care services (to some extent replacing the amounts by which the copay cards reduced their exposure).
We also observed that the effect of the copay maximizer program on patient liability increased with larger baseline maximizer-drug patient liabilities. This trend indicates that for those patients with larger maximizer-drug OOP exposures (eg, due to higher deducible plans or higher formulary tier placement), copay maximizer programs could be particularly impactful on their OOP exposure. In absolute terms, we estimated that the copay maximizer programs could increase a patient’s overall OOP exposure by up to $500. Although the impact of this increase was beyond the scope of this study, existing literature may provide insights into potential downstream effects. For example, Doshi et al estimated that an increase in OOP costs for anticancer medicines from $50-$100 to $100-$500 would increase the prescription abandonment rate from 16% to 36%.21 This may have implications for patients taking more than 1 medicine owing to comorbid conditions. Additionally, the impact of this increase may disproportionately affect patients who are not White, who are more likely to be exposed to copay maximizer programs than White patients.22 Further research is needed to understand the additional financial burden that copay maximizer programs may have on patients and any exacerbation of disparities.
Given that patients’ diseases and conditions may change over time, we sought to understand how our results may vary because of changing health care use from year to year. We found that regardless of changes in health care costs from –10% to +50%, our results were similar, suggesting that the effect of copay maximizer programs on increasing patient liability for other health care services may persist regardless of variations in health care utilization. Additionally, we observed that copay maximizer programs may increase the proportion of patients exposed to cost sharing for other health care services for which they may not have previously had any cost-sharing exposure to. This could be an unexpected financial burden to patients who may not have anticipated this additional financial responsibility. Patients’ awareness of maximizer programs may be limited23; thus, further education of the potential impacts of being enrolled in a maximizer program is needed to assist patients in making informed health plan choices and in financially planning for the potential impacts of being enrolled in a maximizer program.
LIMITATIONS
There are several limitations to consider for this study. First, although the database used could ascertain patient liability, we were unable to detect actual payments made by patients. Although patient liability likely tracks similarly to that of actual patient OOP costs, if a patient used financial assistance for their other health care services, our estimates may be overestimates. That being said, we have no indication that the use of financial assistance for other health care services may have differed between the two cohorts; hence, this may not have had a substantial impact on the relative effects observed. Second, copay maximizer programs are challenging to identify, so an algorithm was used to identify these, allowing potential misclassification. In particular, in the nonmaximizer cohort, repeated similar large values due to coinsurance could potentially be misclassified as maximizer patients. That being said, this scenario may be most likely for those who did not have any deductible (since patient liability would differ in the deductible and coinsurance phases), and it is uncommon for patients to have nondeductible plans (estimated at 12% to 18% of employees during the period of this study).24 Furthermore, the large estimates of patient liability in the maximizer cohort in the follow-up period, well above the ACA limits of any given year, combined with the fact that patients were required to have total patient liabilities under the ACA limit in the baseline period, provide confidence in our cohort selection methodology (Supplementary Table 4 (266.7KB, pdf) ). Last, the database we used lacked information on specific benefit designs. Additional information on plan characteristics, such as maximum OOP limits, would provide additional variables to control for, as well as assist in interpreting results. Future research is warranted in examining and accounting for these factors.
Conclusions
Given that patient liability for the maximizer drug is not allowed to count against a patient’s deductible or OOP maximums, copay maximizer programs are associated with an increase in patient liability for other health care services. This is especially the case for patients who relied heavily on the maximizer drug to meet their deductible requirements or OOP maximums. Additionally, maximizer programs increase the number of patients who newly have patient liability. These findings should be factored into decisions and policies focused on implementing and regulating these programs.
Disclosures
This study was funded by Genentech, Inc. Genentech, Inc., was involved in conducting the study. Drs Sheinson, Patel, and Wong are employees and shareholders of Genentech, Inc.
Acknowledgments
The authors acknowledge Rebecca Spencer Martín, MSci, and Rebecca Hornby, PhD, of Oxford PharmaGenesis, Oxford, UK, for providing medical writing support, with funding from Genentech, Inc.
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