Key Points
Question
What are the funding sources for out-of-pocket cost offsets such as drug coupons or vouchers; which drugs have a high proportion of offsets; what is the percentage of out-of-pocket costs covered by offsets; and what are the characteristics of individuals who use offsets?
Findings
This cohort study found that the source of approximately half of offsets arose from pharmacies or pharmacy benefit managers, and half from pharmaceutical manufacturers. Offsets were concentrated among a small percentage of medications, and use did not vary substantially across counties of varying socioeconomic characteristics.
Meaning
This study suggests that offsets were associated with a significant reduction in out-of-pocket costs but were concentrated among a small set of drugs and not targeted to areas with relatively more vulnerable residents.
Abstract
Importance
Despite ongoing debate regarding the high prices that patients pay for prescription drugs, to our knowledge, little is known regarding the use of coupons, vouchers, and other types of copayment “offsets” that reduce patients’ out-of-pocket drug spending. Although offsets reduce patients’ immediate cost burden, they may encourage the use of higher-cost products and diminish health insurers’ ability to optimize pharmaceutical value.
Objective
To examine the drugs most commonly covered by offsets, the percentage of out-of-pocket costs covered by offsets, and the characteristics of patients using offsets for retail pharmacy transactions in the United States in 2017 through 2019.
Design, Setting, and Participants
A retrospective cohort analysis was conducted of a 5% nationally random sample of anonymized pharmacy claims from IQVIA’s Formulary Impact Analyzer, which captures more than 60% of all US pharmacy transactions. This analysis focused on 631 249 individuals who used at least 1 offset between October 1, 2017, and September 30, 2019.
Main Outcomes and Measures
Offset source, types of drugs covered by offsets, offset dollar value and percentage of out-of-pocket payment covered, and county characteristics of offset recipients.
Results
The 631 249 individuals in the study (361 855 female participants [57.3%]; mean [SD] age, 45.7 [18.6] years) had approximately 33 million prescription fills, of which 12.8% had an offset used. Of these, 50.2% originated from a pharmaceutical manufacturer, 47.2% originated from a pharmacy or pharmacy benefit manager (PBM), and 2.6% originated from a state assistance program. A total of 80.0% of manufacturer-sponsored offsets were concentrated among 6.2% of unique products, and 79.9% of pharmacy-PBM offsets were concentrated among 4.9% of unique products. Most manufacturer offsets (88.2%) were for branded products, while most pharmacy-PBM offsets were for generic products (90.5%). The median manufacturer offset was $51.00, covering 87.1% of out-of-pocket costs; the median pharmacy-PBM offset was $16.30, covering 39.3% of out-of-pocket costs. There was no meaningful association between offset magnitude and county-level income, health insurance coverage, or race/ethnicity.
Conclusions and Relevance
In this analysis of patient-level pharmacy claims from 2017 to 2019, approximately half of all offsets involved pharmacy-PBM contractual arrangements, and half were offered by manufacturers. All offsets were associated with a significant reduction in patients’ out-of-pocket costs, were highly concentrated among a few drugs, and were generally not more generous among individuals in counties with lower income or larger Black or uninsured populations.
This cohort study examines the drugs most commonly covered by out-of-pocket cost offsets such as drug coupons or vouchers, percentage of out-of-pocket costs covered by offsets, and characteristics of patients using offsets for retail pharmacy transactions in the United States in 2017 through 2019.
Introduction
Millions of US citizens pay out-of-pocket prescription drug costs, with up to one-third reporting cost-related nonadherence during the previous year.1,2,3 The increasing cost of prescription drugs has prompted health plans to increase the use of benefit designs, such as cost sharing and utilization management, to direct enrollees to lower-priced products, with the goal of curtailing drug spending. Pharmaceutical manufacturers have developed strategies to maintain market share in response to these benefit-design tools, including offering patients prescription drug coupons, vouchers, and other types of copayment “offsets.”
Copayment offsets provide out-of-pocket cost discounts or free product at the point of sale (eg, pharmacy). These programs are distinct from foundation-sponsored patient assistance programs, which are administered separately from manufacturers and typically provide free drugs or reimburse copayments after transactions have been adjudicated. Offset programs are also distinct from free samples distributed through clinicians’ offices. In one analysis, among the top 200 best-selling drugs, 90% of brand-name products and no generic products had a coupon available in 2014; among brand-name drugs with a coupon, almost half had a generic competitor.4 Such manufacturer offsets have been the source of both praise and criticism, with evidence suggesting that they may improve adherence while increasing the use of low-value products, interfering with formulary design and potentially interfering with physician decision-making.5,6,7,8,9
To our knowledge, relatively little is known regarding how often, and in what context, offsets are used, and even less work has examined offsets not offered by manufacturers. We conducted a retrospective cohort analysis of anonymized pharmacy claims to characterize copayment offset use in the United States from 2017 to 2019. In addition to describing the sources of offsets, we quantified the dollar value of offsets and their distribution across specific brand-name and generic products. Finally, given the high burden of out-of-pocket costs on vulnerable individuals, we explored the degree to which offset use was concentrated among individuals residing in counties with relatively more vulnerable residents.
Methods
Data Source
The IQVIA Formulary Impact Analyzer (FIA) was the primary data source. This data set consists of anonymized, individual-level pharmacy claims representing transactions at retail pharmacies in the United States.10 The data are collected weekly from approximately 55 000 pharmacies and represent more than 95% of chain pharmacies and approximately two-thirds of independent pharmacies in the country.11 The data include information from retail, specialty, mail-order, and long-term-care pharmacies and transactions covered by all payers, including commercial insurers, public insurers (eg, Medicare Part D), and cash pay. For each transaction, the database includes payer and plan information for both the primary and secondary sources of payment, as well as the amount of any given payment and whether it was used to decrease, or offset, an individual’s out-of-pocket costs. We supplemented the data from the FIA with information on county characteristics from the 2019 Health Resources & Services Administration Area Health Resources Files and price information on drug wholesale acquisition cost from Wolters Kluwer Medi-Span. Our analysis was exempt from a Johns Hopkins Bloomberg School of Public Health institutional review board approval because it did not constitute human participants research.
Cohort Derivation
Our cohort was derived from an initial sample of 248 337 577 unique individuals with a paid pharmacy claim with secondary payer information available during a 2-year period from October 1, 2017, through September 30, 2019. Of these, the FIA identified 26 774 102 individuals with at least 1 offset use, defined as either (1) use of a drug coupon, voucher, or other type of discount program as the primary payer for a transaction or (2) an adjudicated claim with an insurer as the primary payer and a secondary payer that provided a copayment offset. The FIA data exclude posttransaction foundation-sponsored patient assistance programs because these programs are not applied at the point of sale.
Our analytic sample consisted of a nationally random 5% sample of these 26 774 102 individuals. From the 5% random sample, we excluded 104 345 individuals with claims for only nonprescription drugs or without any payer information, which yielded a final sample of 1 234 360 individuals with at least 1 offset use during the study period. We included individuals with Medicare or Medicaid coverage or with no insurance. Use of manufacturer-sponsored and pharmacy or pharmacy benefit manager (PBM)–sponsored offsets is prohibited among publicly insured beneficiaries; however, these patients may use offsets in situations in which a coupon or discount card gives them a lower out-of-pocket cost than their public insurance or if they use a secondary source of commercial insurance to cover that transaction.12
The 1 234 360 individuals in our sample had 53 457 872 transactions during the study period (Figure). To identify transactions for which an offset was used, we excluded transactions for which the primary payer was Medicare or Medicaid, those with an unspecified type of transaction, and those with incomplete information regarding offset magnitude. Our final sample included 631 249 individuals and 33 043 352 transactions that were potentially associated with offsets.
Figure. Transaction-Level Sample Selection.
Data from IQVIA Formulary Impact Analyzer (FIA), 2017-2019. PBM indicates pharmacy benefit manager.
Statistical Analysis
We characterized each offset from 1 of 3 mutually exclusive sources: (1) pharmaceutical manufacturers; (2) pharmacy-PBM contractual arrangements, such as GoodRx discount cards, which are specific to a drug and participating network of pharmacies; or (3) state initiatives, such as Pennsylvania’s Programs of All-Inclusive Care for the Elderly.13 We used descriptive statistics to characterize product type (ie, brand-name vs generic) and to identify the mean preoffset out-of-pocket cost, the mean percentage reduction in out-of-pocket costs due to offset, and the mean postoffset out-of-pocket cost of products with each type of offset. For this analysis, we limited our sample to the approximately 95% of transactions with complete information for these cost variables.
We examined the characteristics of individuals who used offsets (hereafter referred to as offset users) in terms of the characteristics of the county in which the patient filled prescriptions. We assigned each individual a county of residence based on the pharmacy zip codes where most of their prescription transactions occurred. Based on this algorithm, 92% of individuals in the data were assigned a single county; we randomly assigned the remaining 8% of individuals to 1 of the 2 or more counties in which they filled prescriptions during the study period. We described the percentage of offset users filling prescriptions in different types of counties and examined the mean offset magnitude across counties with varying income, health insurance, and racial/ethnic characteristics to assess whether recipients in counties where need is likely to be higher received more generous offsets.
Results
Offset Sources, Prevalence, and Association With Out-of-Pocket Costs
Among the 1 234 360 individuals included in our sample, 631 249 (51.1%) received an offset from a manufacturer, pharmacy-PBM, or state assistance program with an identifiable source and magnitude. These 631 249 individuals accounted for a total of 33 043 352 transactions during the study period, of which 12.8% were accompanied by an offset (Figure). Of these offsets, 50.2% originated from manufacturers, 47.2% from a pharmacy-PBM, and 2.6% from a state assistance program.
Approximately one-third (35.4%) of manufacturer offsets fully covered patients’ out-of-pocket costs compared with 0.8% of pharmacy-PBM offsets (Table 1). Manufacturer offsets were largely for brand-name products (88.2%), while pharmacy-PBM offsets were primarily for generic drugs (90.5%). List price and associated cost per claim reflected these differences. Furthermore, manufacturer offsets were associated with greater coverage of out-of-pocket costs (median, 87.1%; interquartile range [IQR], 63.8%-100% [median, $51.00; IQR, $30.00-$110.00]) compared with pharmacy-PBM offsets (median, 39.3%; IQR, 14.9%-62.5% [median, $16.30; IQR, $5.19-$45.80]).
Table 1. Characteristics of Offsets by Type and Sourcea.
| Characteristic | All | Source of offset | ||
|---|---|---|---|---|
| Manufacturer | Pharmacy-PBM | State | ||
| Total No. of transactions | 4 223 114 | 2 119 122 | 1 993 311 | 110 681 |
| Unique drugs with offset, No. | 3709 | 2661 | 3175 | 1278 |
| Type of offset, No. (%) | ||||
| Partial | 3 417 628 (80.9) | 1 368 143 (64.6) | 1 976 973 (99.2) | 72 512 (65.5) |
| Full (zero out-of-pocket costs) | 805 486 (19.1) | 750 979 (35.4) | 16 338 (0.8) | 38 169 (34.5) |
| Transactions by product type, No. (%) | ||||
| Brand-name | 2 070 839 (49.0) | 1 868 212 (88.2) | 168 364 (8.5) | 34 263 (30.9) |
| Generic | 2 128 456 (50.4) | 248 146 (11.7) | 1 804 170 (90.5) | 76 140 (68.8) |
| Branded-genericb | 22 872 (0.5) | 2635 (0.1) | 19 971 (1.0) | 266 (0.2) |
| Days’ supply per transaction, median (IQR) | 30 (28-30) | 30 (30-30) | 30 (15-30) | 30 (30-30) |
| Cost per claim, median (IQR), $ | 390.52 (270.63-619.87) | 397.80 (302.28-622.79) | 85.20 (41.10-205.70) | 46.10 (11.25-388.35) |
| Out-of-pocket cost before offset, median (IQR), $ | 65.00 (40.00-136.29) | 70.00 (40.00-144.48) | 48.30 (23.80-115.87) | 8.17 (2.91-40.00) |
| Offset amount, median (IQR), $ | 50.00 (25.00-102.65) | 51.00 (30.00-110.00) | 16.30 (5.19-45.80) | 3.70 (0.78-30.00) |
| Out-of-pocket cost after offset, median (IQR), $ | 10.00 (0.00-25.00) | 10.00 (0.00-25.00) | 23.79 (12.59-56.23) | 1.01 (0.00-5.00) |
| Reduction in out-of-pocket cost, median (IQR), % | 85.4 (60.0-100.0) | 87.1 (63.8-100.0) | 39.3 (14.9-62.5) | 52.2 (50.0-100.0) |
Abbreviations: IQR, interquartile range; PBM, pharmacy benefit manager.
Data from IQVIA Formulary Impact Analyzer, 2017-2019. Sample included 631 249 individuals with at least 1 offset use; see Methods for more detail.
A product that is a copy or new dosage of an existing off-patent product, made by a new company and given a trade name.
Characteristics of Offset Users
Among offset users, 9.9% were younger than 19 years, the median age was 48 years (range, 32-60 years), and 57.3% were female (Table 2). The distribution of age and sex was similar across manufacturer and pharmacy-PBM offsets, but state-sponsored offset users were older, and a greater proportion was male. State-sponsored offset users also had close to double the total number of transactions (ie, prescription fills) during the 24-month study period compared with other offset users.
Table 2. Characteristics of Individuals Using Offsetsa.
| Characteristic | All (N = 631 249) | Source of offset | ||
|---|---|---|---|---|
| Manufacturer (n=447 356) | Pharmacy-PBM (n=278 532) | State (n=5103) | ||
| Age, No. (%), y | ||||
| 0-18 | 62 400 (9.9) | 50 689 (11.3) | 16 861 (6.1) | 67 (1.3) |
| 19-45 | 215 321 (34.1) | 150 525 (34.7) | 97 108 (34.9) | 1331 (26.1) |
| 46-65 | 258 253 (40.9) | 189 878 (42.2) | 113 390 (40.7) | 1619 (31.7) |
| ≥66 | 95 251 (15.1) | 57 264 (12.8) | 51 173 (18.4) | 2086 (40.9) |
| Median (range) | 48 (32-60) | 48 (31-59) | 50 (35-61) | 57 (42-70) |
| Female, No. (%) | 361 855 (57.3) | 255 687 (57.0) | 163 835 (58.4) | 2460 (47.9) |
| No. of transactions per individual, median (IQR) | 35 (13-73) | 35 (13-72) | 42 (18-82) | 67 (30-119) |
| No. of transactions with offset per individual, median (IQR) | 3 (1-7) | 3 (1-7) | 4 (2-10) | 14 (6-31) |
| No. of unique drugs per individual, median (IQR) | 11 (6-17) | 10 (5-17) | 12 (7-20) | 15 (8-23) |
| No. of unique drugs with offset per individual, median (IQR) | 1 (1-2) | 1 (1-2) | 2 (1.4) | 4 (2.8) |
| County characteristics of offset users | ||||
| No. residing in counties predominantly | 599 827 | 426 067 | 267 967 | 4659 |
| Black, No. (%) | 18 224 (3.0) | 11 485 (2.7) | 9461 (3.5) | 124 (2.7) |
| Latinx, No. (%) | 20 031 (3.3) | 14 997 (3.5) | 7940 (3.0) | 163 (3.5) |
| White, No. (%) | 561 572 (93.6) | 399 585 (93.8) | 250 566 (93.1) | 4372 (93.8) |
| No. residing in counties, uninsured | 628 693 | 447 503 | 279 445 | 5131 |
| ≥25%, No. (%) | 4804 (0.8) | 3896 (0.9) | 1415 (0.5) | 65 (1.3) |
| <25%, No. (%) | 623 889 (99.2) | 443 607 (99.1) | 278 030 (99.5) | 5066 (98.7) |
| County household income, median (IQR), $ | 55 622 (48 727-68 560) | 56 333 (49 253-65 037) | 54 370 (48 219-62 532) | 54 895 (48 033-62 532) |
Abbreviations: IQR, interquartile range; PBM, pharmacy benefit manager.
Data from IQVIA Formulary Impact Analyzer, 2017-2019. Sample included individuals with at least 1 offset use; see Methods for more detail.
There was no meaningful variation in manufacturer or pharmacy-PBM offset magnitude across counties with varying income (median household income), health insurance (percentage of the uninsured population), or race/ethnicity (percentage of the Black population) characteristics (eFigures 1-4 in the Supplement). Among state-sponsored offsets, we observed a positive association between offset magnitude and percentage of the population that was Black (eFigure 2 in the Supplement).
Characteristics of Products for Which Offsets Were Commonly Used
Offsets were most common for brand-name drugs for diabetes, pulmonary disease, and cardiovascular disease (Table 3). The proportion of transactions with an offset ranged from 21.1% for Lantus Solostar to 67.7% for Truvada (mean proportion of transactions, 38%). There was also substantial variation in the percentage of transactions with a full offset (ie, zero out-of-pocket costs after the offset), ranging from 3.2% for Suboxone to 98.8% for Truvada. Higher-priced drugs were somewhat more likely to be covered by full offsets.
Table 3. Characteristics of Brand-Name Product Transactions Where Offsets Were Most Commonly Useda.
| Characteristic | Therapeutic area (N = 10 153 926) | All transactions | Offset transactions requiring OOPC, mean (median) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Transactions, No. | Full offsets, No. (%) | Mean product cost per transaction (median), $b | OOPC before offset, $ (N = 850 935) | Offset, $ | OOPC covered by offset, % | ||||
| Total | Offset | ||||||||
| Vyvanse (lisdexamfetamine) | Psychotropic | 339 956 | 91 735 | 10 421 (11.4) | 273.64 (270.63) | 93.20 (60.00) | 38.60 (35.00) | 46.9 (49.8) | |
| Suboxone (buprenorphine-naloxone) | Narcotic | 195 454 | 79 300 | 2525 (3.2) | 209.81 (124.16) | 79.44 (50.00) | 46.86 (45.00) | 73.5 (83.3) | |
| Eliquis (apixaban) | Hematology | 262 200 | 70 573 | 3748 (5.3) | 482.28 (388.35) | 153.33 (87.59) | 139.15 (75.00) | 83.6 (87.3) | |
| Xarelto (rivaroxaban) | Hematology | 193 039 | 69 848 | 21 458 (30.7) | 493.15 (388.02) | 133.47 (75.00) | 109.01 (60.00) | 78.7 (80.0) | |
| Symbicort (budesonide-formoterol) | Pulmonary | 206 209 | 69 027 | 51 597 (74.7) | 343.37 (308.68) | 187.41 (111.00) | 122.08 (80.00) | 56.0 (60.2) | |
| Truvada (emtricitabine-tenofovir) | Antiviral | 79 330 | 53 736 | 53 065 (98.8) | 1738.55 (1567.62) | 1202.40 (1555.49) | 713.70 (454.19) | 60.5 (65.9) | |
| Jardiance (empagliflozin) | Diabetes | 137 078 | 50 035 | 44 630 (89.2) | 564.18 (430.51) | 463.36 (468.53) | 290.80 (250.00) | 64.9 (54.3) | |
| Invokana (canagliflozin) | Diabetes | 91 832 | 47 533 | 43 262 (91.0) | 559.06 (426.60) | 413.50 (420.00) | 250.77 (200.00) | 64.9 (71.4) | |
| Farxiga (dapagliflozin) | Diabetes | 102 073 | 39 373 | 31 824 (80.8) | 564.62 (430.52) | 551.27 (487.98) | 445.95 (378.00) | 80.3 (78.4) | |
| Lantus Solostar (insulin glargine) | Diabetes | 170 802 | 36 066 | 32 214 (89.3) | 477.95 (372.76) | 295.07 (131.30) | 171.48 (74.43) | 56.3 (63.8) | |
| All brand-name offset products | NA | 10 153 926 | 2 070 839 | 805 486 (11.4) | 630.04 (335.48) | 158.61 (75.00) | 106.73 (53.50) | 66.7 (71.4) | |
Abbreviations: OOPC, out-of-pocket contribution; NA, not applicable.
Data from IQVIA Formulary Impact Analyzer, 2017-2019; see Methods for more detail. Table sorted by number of offset transactions.
Product cost per transaction is measured by National Drug Code–level cost per claim derived from wholesale acquisition cost per unit of each product from Wolters Kluwer Medi-Span data. Each transaction’s product National Drug Code and quantity in corresponding units were used to compute the cost per claim.
Among drugs with a partial offset (ie, requiring an out-of-pocket contribution after offset), the median reduction in out-of-pocket cost was 71.4% (IQR, 50%-86%). Standardization of cost data to a 30-day supply yielded substantively similar findings. Among generic drugs most commonly associated with offsets, the percentage of transactions with full offsets ranged from 0.2% for phentermine to 8.9% for metformin (Table 4). Among transactions with a partial offset, the median reduction in out-of-pocket cost was 50.0% (IQR, 27.1%-59.4%) for most products.
Table 4. Characteristics of Generic Product Transactions Where Offsets Were Most Commonly Useda.
| Characteristic | Therapeutic area | Transactions, No. | Full offsets, No. (%) | Offset transactions requiring OOPC, mean (median) | |||
|---|---|---|---|---|---|---|---|
| Total | Offset | OOPC before offset, $ (N = 85 532) | Offset, $ | OOPC covered by offset, % | |||
| Hydrocodone and acetaminophen | Pain | 837 148 | 56 334 | 3758 (6.7) | 31.71 (18.72) | 17.67 (5.83) | 47.0 (50.2) |
| Lisinopril | Hypertension | 1 302 004 | 52 610 | 3145 (6.0) | 9.89 (5.99) | 3.47 (0.80) | 35.7 (50.0) |
| Levothyroxine | Hormone | 1 090 444 | 46 858 | 1006 (2.1) | 17.57 (11.99) | 5.47 (1.70) | 35.2 (43.9) |
| Atorvastatin | Cardiovascular | 1 335 601 | 45 146 | 2868 (6.4) | 29.11 (11.77) | 17.50 (3.96) | 48.4 (50.2) |
| Sildenafil | Erectile dysfunction | 155 981 | 43 627 | 200 (0.5) | 342.81 (172.52) | 260.72 (100.57) | 65.1 (76.2) |
| Phentermine | Anorectic | 141 959 | 41 801 | 99 (0.2) | 29.87 (29.99) | 15.85 (14.99) | 46.9 (50.9) |
| Gabapentin | Anticonvulsant | 861 540 | 41 436 | 2889 (7.0) | 27.88 (11.66) | 15.67 (3.38) | 47.1 (50.1) |
| Amlodipine | Hypertension | 1 009 162 | 40 812 | 2730 (6.7) | 14.75 (7.51) | 7.08 (1.63) | 42.2 (50.0) |
| Alprazolam | Psychotropic | 500 943 | 31 596 | 1272 (4.0) | 13.30 (7.27) | 6.88 (1.71) | 45.4 (50.1) |
| Metformin | Diabetes | 982 766 | 30 681 | 2716 (8.9) | 11.01 (6.00) | 4.75 (1.00) | 37.7 (50.0) |
| All generic offset products | NA | 43 004 684 | 2 128 456 | 156 677 (7.4) | 62.38 (20.92) | 33.50 (6.50) | 45.3 (50.0) |
Abbreviations: OOPC, out-of-pocket contribution; NA, not applicable.
Data from IQVIA Formulary Impact Analyzer, 2017-2019; see Methods for more detail. Table sorted by number of offset transactions.
Concentration of Offsets Across Products
Both manufacturer and pharmacy-PBM offsets were highly concentrated. A total of 80.0% of manufacturer-sponsored offsets were concentrated among 6.2% of unique products (164 of 2661 products), and 79.9% of pharmacy-PBM offsets were concentrated among 4.9% of unique products (156 of 3175 products). State-sponsored offsets were less concentrated, with 79.9% of offsets concentrated among 14.6% of products.
Discussion
Our work builds on existing evidence in 3 ways.5,6,7,14,15,16 First, we expanded the scope of study beyond manufacturer coupons. In our sample, almost half the offsets arose from pharmacy-PBM contracts and half from pharmaceutical manufacturers. Second, we found that manufacturer offsets largely covered brand-name products, while pharmacy-PBM offsets largely covered generic products. These offset types also differed in coverage, with manufacturer offsets covering a higher percentage of out-of-pocket costs than pharmacy-PBM offsets. Third, offsets of both types were concentrated among a small percentage of products.
Finally, for most offsets (ie, manufacturer and pharmacy-PBM offsets), there were no meaningful associations between the county income, insurance, and race/ethnicity and the magnitude of offsets used. This finding is consistent with evidence that individuals with low income and who are uninsured are less likely than individuals with high income and who are insured to receive free samples, that independent pharmacies more prevalent in communities of lower socioeconomic status may be less likely to participate in drug discount programs than chain or big-box pharmacies, and that medication access and adherence are often lower among Black patients after controlling for income.17,18,19 Overall, these results suggest that offsets are not targeted to counties in which populations may be more in need of assistance.
Our findings are relevant to current drug-pricing policy debates.7,20 Some have proposed banning manufacturer offsets owing to the concern that they increase pharmaceutical spending and decrease value. Our findings suggest that a manufacturer-focused ban would leave nearly half of all offsets—those occurring through pharmacy-PBM arrangements—untouched. Other proposals include limiting offset use to specific populations (eg, uninsured individuals) or situations (eg, multisource brand-name products) or requiring that offsets be allowed only for use across an entire therapeutic class or a broader set of products rather than 1 drug, to lessen the incentive to use a costlier option when alternatives are present.7 Our finding that offsets are concentrated within a small percentage of products suggests that a more targeted approach to managing any adverse consequences of offsets may be appropriate.
Our results also imply that policy efforts will likely vary by offset type. Most manufacturer offsets are for brand-name drugs and therefore may encourage the use of more expensive brand-name drugs rather than available generic drugs, inceasing costs overall. In contrast, pharmacy-PBM offsets are largely directed toward reducing copayments on generic drugs. Therefore, these offsets have different associations with spending and market distortion. In both cases, however, offsets may threaten insurer tools to manage spending as well as patient access to medications because neither manufacturer nor pharmacy-PBM offsets appear to target more vulnerable populations. Future work is needed to better compare and contrast the associations of manufacturer vs pharmacy-PBM offsets with medication use, costs, and outcomes.
Policies to address the potential market-distorting effects of manufacturer offsets must also consider the possible unintended consequences of such actions, given that these programs lower out-of-pocket costs and thereby increase adherence.8,9 Our results underscore the substantial reduction in out-of-pocket costs that offsets provide and highlight the abrupt change in out-of-pocket costs that may accrue for patients whose access to, and use of, offsets varies across prescription fills.
Limitations
Our analysis has some limitations. First, our cohort was based on a sampling strategy that required the use of at least 1 offset during a 24-month period, and our findings should be interpreted accordingly. Comparison of offset users to never users is an important area for future work, as is work that examines how offset use varies based on individuals’ insurance design, such as the use of high-deductible health plans. Second, we did not assess the association of offsets with health care use or outcomes, nor were our analyses intended to assess the value of such programs. Third, to examine the distribution of offsets, we used county characteristics, which is an imperfect proxy for individual characteristics.21 We assigned county based on pharmacy zip code where the plurality of an individual’s prescriptions was filled (individual residential zip code was unavailable); this method may be subject to misclassification if a patient fills the majority of prescriptions in a zip code that is different from their residence (eg, near their physician’s office). Fourth, our focus on county income, insurance, and race/ethnicity was motivated by existing work on disparities in access to care; however, there are other characteristics that would lend insight into differential access to and associations of offset programs among vulnerable populations.
Conclusions
In this retrospective cohort analysis of pharmacy claims, nearly half of offsets used between 2017 and 2019 arose from pharmacy-PBM arrangements rather than pharmaceutical manufacturers. Offsets were concentrated among a small number of products, and their use was similar across counties of varying income, insurance, and race/ethnicity.
eFigure 1. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Population Black, Population Uninsured, and Median Income
eFigure 2. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Population Black
eFigure 3. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Population Uninsured
eFigure 4. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Median Household Income
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eFigure 1. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Population Black, Population Uninsured, and Median Income
eFigure 2. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Population Black
eFigure 3. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Population Uninsured
eFigure 4. Distribution of Percent Reduction of Out-of-Pocket Costs per Transaction With Offset (Magnitude of Offset) Across Counties by Decile of Median Household Income
