Abstract
Background:
Use of HIV preexposure prophylaxis (PrEP) has increased nationwide, but the magnitude and distribution of PrEP medication costs across the health care system are unknown.
Objective:
To estimate out-of-pocket (OOP) and third-party payments using a large pharmacy database.
Design:
Retrospective cohort study.
Setting:
Prescriptions for tenofovir disoproxil fumarate with emtricitabine (TDF-FTC) for PrEP in the United States in the IQVIA Longitudinal Prescriptions database, which covers more than 90% of retail pharmacy prescriptions.
Measurements:
Third-party, OOP, and total payments were compared by third-party payer, classified as commercial, Medicaid, Medicare, manufacturer assistance program, or other. Missing payment data were imputed using a generalized linear model to estimate overall PrEP medication payments.
Results:
Annual PrEP prescriptions increased from 73 739 to 1 100 684 during 2014 to 2018. Over that period, the average total payment for 30 TDF-FTC tablets increased from $1350 to $1638 (5.0% compound annual growth rate) and the average OOP payment increased from $54 to $94 (14.9% compound annual growth rate). Of the $1638 in total payments per 30 TDF- FTC tablets in 2018, OOP payments accounted for $94 (5.7%) and third-party payments for $1544 (94.3%). Out-of-pocket payments per 30 tablets were lower among Medicaid recipients ($3) than among those with Medicare ($80) or commercial insurance ($107). Payments for PrEP medication in the IQVIA database in 2018 totaled $2.08 billion; $1.68 billion (80.7%) originated from prescriptions for persons with commercial insurance, $200 million (9.6%) for those with Medicaid, $48 million (2.3%) for those with Medicare, and $127 million (6.1%) for those with manufacturer assistance.
Limitation:
The IQVIA database does not capture every prescription nationwide.
Conclusion:
Third-party and OOP payments per 30 TDF-FTC tablets increased annually. The $2.08 billion in PrEP medication payments in 2018 is an underestimation of national costs. High costs to the health care system may hinder PrEP expansion.
Primary Funding Source:
Centers for Disease Control and Prevention.
Protecting persons at risk for HIV with preexposure prophylaxis (PrEP) is a foundational pillar of the U.S. strategy on Ending the HIV Epidemic (1). Extensive clinical trial data demonstrate that PrEP with daily oral tenofovir disoproxil fumarate with emtricitabine (TDF-FTC) reduces risk for HIV among persons at risk from sexual or injection practices (2-5). In October 2019, the U.S. Food and Drug Administration also approved tenofovir alafenamide with emtricitabine (TAF-FTC) for use as PrEP for men and transgender women (6). Both medications are manufactured by Gilead Sciences. Despite the high effectiveness of PrEP, uptake remains suboptimal, especially among populations most affected by HIV (7, 8). The cost of the medication and the barriers patients face in getting coverage for PrEP may contribute to its uneven uptake by women, persons living in the South, and Black and Hispanic persons (9-12).
In 2018, the average wholesale price of a 30-day supply of TDF-FTC was $2011 (340B price, $1024) (13). Patients using PrEP typically have much lower out-of-pocket (OOP) costs for medication because insurance companies, public insurance, or medication assistance programs pay most of the cost (14). Copay assistance programs and coupons that cover patient OOP costs can further insulate patients from the cost of the medication (15). Gilead Sciences, the manufacturer of TDF-FTC, has both a copay assistance program that covers OOP costs for commercially insured patients and a medical assistance program that pays for the medication for uninsured patients who make less than 500% of the federal poverty level. The cost of PrEP among Medicare Part D recipients was recently described (16), but the overall cost of TDF-FTC for PrEP by different payers has not been previously quantified.
The cost of TDF-FTC is estimated to be the largest driver of the cost of providing PrEP care (17). Economic modeling of PrEP cost-effectiveness in the United States has resulted in estimates that vary widely from cost saving to $160 000 per quality-adjusted life-year, owing in part to the variability in the cost of TDF-FTC for PrEP used in the models (18-22). Further, the cost of TDF-FTC has increased annually above the rate of inflation, so the $10 000 annual cost of TDF-FTC used by several cost-effectiveness models may not reflect the actual cost to the health care system (13, 18, 19, 21). This study therefore sought to describe both third-party and OOP payments for TDF-FTC for PrEP using a national pharmacy database.
Methods
Data Source
The Centers for Disease Control and Prevention conducts PrEP surveillance by estimating the number of persons prescribed PrEP annually using the IQVIA Real World Data Longitudinal Prescriptions database (7). The IQVIA database captures prescriptions from all types of payers and represents approximately 92% of all prescriptions dispensed from retail pharmacies and 60% to 86% of those dispended from mail-order outlets in the United States. It does not capture prescriptions from closed health care systems, such as Kaiser Permanente, or federal health systems, such as the Veterans Health Administration.
Prescriptions in the IQVIA database are linked with medical claims and demographics databases using a deidentified patient number, allowing for the measurement of multiple prescriptions for a single patient over multiple years. The medical claims database contains International Classification of Diseases, Clinical Modification, codes. A previously validated algorithm using these codes was applied to TDF-FTC prescriptions among persons aged 16 years or older in the IQVIA database (23). The algorithm identified person-level use of other antiretroviral medications; use of TDF-FTC for 28 days or less; and medical claims with International Classification of Diseases, Clinical Modification, codes for HIV or hepatitis B treatment. It used these data to exclude prescriptions used for HIV treatment, HIV postexposure prophylaxis, or hepatitis B treatment. The remaining prescriptions were interpreted to represent PrEP prescriptions.
The OOP and third-party payments were recorded for each TDF-FTC prescription, and the sum was considered to be the total payment for TDF-FTC for PrEP. We used the term “payments,” as opposed to “costs,” to describe these financial transactions because discounts or bulk reimbursement mechanisms may be applied separately from the pharmacy transaction and lower the cost. The overall payment for TDF-FTC for PrEP in a given year was the sum of the total payments for TDF-FTC for all PrEP prescriptions.
Prescription Selection
Prescriptions for TDF-FTC in each year between 2014 and 2018 that were not excluded by the PrEP algorithm were included in the study. Prescriptions that were ordered, not picked up, and returned to inventory were excluded in this analysis because they did not generate a payment transaction. The total payment for the prescription was divided by the number of TDF-FTC tablets prescribed to calculate the total payment per tablet. Fewer than 1% of the prescriptions had a total payment per tablet greater than $100 (150% of the average wholesale price), usually because the third-party payment had been duplicated into the OOP payment entry. These payments were assumed to be data errors, and their payment data were considered missing.
The third-party payer for each prescription was classified as commercial, Medicaid (including the Children’s Health Insurance Program), Medicare, Gilead Sciences, cash payment (no third-party payer), or other. The “Gilead Sciences” category included their copayment assistance program that pays for commercial insurance copays and their medication assistance program that covers the drug for uninsured patients. The “other” category included patients who used coupons or had another federal or state third-party payer.
Statistical Analysis
The total numbers of TDF-FTC prescriptions, tablets, and payments were counted for the years 2014 through 2018. Compound annual percentage growth calculates the annualized rate of change between the base year and final year amounts, and this formula was used to assess annual growth in drug payments for 2014 to 2018 (24). Compound annual percentage growth rates were calculated for OOP, third-party, and total payments. For prescriptions with complete payment data, the mean OOP, third-party, and total payments per 30 tablets of TDF-FTC were stratified by age, sex, U.S. Census geographic region, and third-party payer type for 2018. Analysis of payments by race/ethnicity was not possible because of the limited availability of these data in the IQVIA database.
Because of data reliability issues from a single vendor, IQVIA changed 30% of the third-party payments in its database to null to preserve the validity of the remaining payment data for the years 2016 to 2018 (Appendix Table 1 [available at Annals.org] shows missingness of the data set). Payment data missingness was similar across third-party payer type but was slightly higher for prescriptions supported by Gilead Sciences and lower for Medicaid prescriptions (Appendix Table 2, available at Annals.org). To generate estimates of TDF-FTC payments across the health care system, the total payments for prescriptions with missing payment data were multiply imputed using a generalized linear model with 50 imputations; assuming a monotonic missing-at-random pattern; using an identity link function; and regressing year, patient age and sex, region, third-party payer type, total prescription days, pharmacy type, and patient OOP payment (25). Prescriptions with a total payment greater than $100 per pill that were assumed to be data errors were also imputed in the regression. The postimputation data were then used to calculate the overall payments represented in the IQVIA database by age, sex, geographic region, and third-party payer type for the years 2014 to 2018. In sensitivity analyses, overall payment for each year was estimated with a crude approach in which low ($1400) and high ($2000) payments per 30 pills were assigned to all missing prescriptions, as well as with a simple imputation approach that used the same identity link function and missing-at-random assumption. All statistical analyses were done using SAS statistical software (SAS Institute).
Role of the Funding Source
No funding external to the Centers for Disease Control and Prevention was provided for this study.
Results
The PrEP algorithm identified 2 833 945 TDF-FTC prescriptions between 2014 and 2018 as PrEP prescriptions. Among these, 184 045 were ordered but not picked up and were therefore excluded. The analysis ultimately included 2 649 900 paid prescriptions representing 90 994 854 TDF-FTC tablets. The number of persons dispensed PrEP increased from 20 315 in 2014 to 204 720 in 2018, and the number of PrEP prescriptions in the IQVIA database increased from 73 739 in 2014 to 1 100 684 in 2018 (Table 1). Over that period, mean total payments for TDF-FTC per 30 tablets increased from $1350 to $1638, representing a 5.0% compound annual growth rate. Average OOP payments per 30 TDF-FTC tablets increased from $54 in 2014 to $94 in 2018 (14.9% compound annual growth rate), and average third-party payments per 30 tablets increased from $1296 in 2014 to $1544 in 2018 (4.5% compound annual growth rate).
Table 1.
Number of PrEP Prescriptions, Tablets, and Payments Represented in the IQVIA Database, 2014-2018
| Variable | 2014 | 2015 | 2016 | 2017 | 2018 |
|---|---|---|---|---|---|
| All dispensed prescriptions, n | |||||
| Patients receiving PrEP | 20 315 | 51 544 | 94 277 | 141 080 | 204 720 |
| Prescriptions | 73 739 | 232 003 | 487 725 | 755 749 | 1 100 684 |
| TDF-FTC tablets | 2 534 309 | 7 965 375 | 16 616 618 | 25 890 065 | 37 988 487 |
| Prescriptions with payment data | |||||
| Patients receiving PrEP, n (%) | 20 046 (98.7) | 50 779 (98.5) | 68 460 (72.6) | 100 775 (71.4) | 145 001 (70.8) |
| Prescriptions, n (%) | 73 197 (99.3) | 229 410 (98.9) | 335 775 (68.8) | 519 956 (68.8) | 759 409 (69.0) |
| TDF-FTC tablets, n (%) | 2 517 808 (99.3) | 7 886 528 (99.0) | 11 805 896 (71.0) | 18 319 372 (70.8) | 26 736 956 (70.4) |
| Overall annual TDF-FTC payments, $ | 113 239 331 | 373 870 150 | 562 251 605 | 942 316 930 | 1 460 054 998 |
| Mean total payments per 30 tablets (SD), $ | 1350 (301) | 1422 (309) | 1428 (325) | 1543 (236) | 1638 (255) |
| Mean OOP payments per 30 tablets (SD), $ | 54 (186) | 62 (205) | 80 (249) | 84 (246) | 94 (270) |
| Mean third-party payments per 30 tablets (SD), $ | 1296 (352) | 1360 (367) | 1348 (395) | 1459 (340) | 1544 (370) |
OOP = out-of-pocket; PrEP = preexposure prophylaxis; TDF-FTC= tenofovir disoproxil fumarate with emtricitabine.
For prescriptions with complete payment data in 2018, mean OOP payments were lower for women ($72 per 30 tablets) than men ($95 per 30 tablets) and lower for adolescents ($37 per 30 tablets) than persons aged 65 years or older ($117 per 30 tablets) (Table 2). Mean OOP payments also differed by geographic region: The Northeast ($82 per 30 tablets) and West ($79 per 30 tablets) had lower OOP payments than the Midwest ($121 per 30 tablets) and South ($111 per 30 tablets). Finally, mean OOP payments differed by third-party payer, with Medicaid having the lowest ($3 per 30 tablets) and private insurance the highest ($107 per 30 tablets). Some of the variation in OOP PrEP payments by age and sex was related to underlying differences in third-party payer type, but regional differences in OOP payments persisted among persons with commercial insurance or Medicare (Appendix Table 3, available at Annals.org). Third-party and total payments did not differ by a large magnitude and were relatively consistent across age, sex, region, and third-party payer type for persons covered by commercial insurance, Medicaid, and Medicare.
Table 2.
Mean OOP, Third-Party, and Total PrEP Medication Payments With Complete Payment Data, by Age, Sex, Region, and Payer Type, 2018
| Variable | Prescriptions, n | Mean OOP Payment per 30 Tablets (SD), $ |
Mean Third-Party Payment per 30 Tablets (SD), $ |
Mean Total Payment per 30 Tablets (SD), $ |
|---|---|---|---|---|
| All prescriptions | 759 409 | 94 (271) | 1544 (370) | 1638 (255) |
| Age | ||||
| 16-17 y | 711 | 37 (187) | 1596 (247) | 1632 (187) |
| 18-24 y | 69 132 | 94 (277) | 1531 (371) | 1625 (252) |
| 25-34 y | 292 770 | 99 (281) | 1532 (373) | 1631 (250) |
| 35-44 y | 192 584 | 92 (265) | 1551 (360) | 1643 (250) |
| 45-54 y | 131 441 | 89 (260) | 1558 (365) | 1647 (260) |
| 55-64 y | 62 144 | 86 (256) | 1565 (373) | 1651 (272) |
| ≥65 y | 10 627 | 117 (263) | 1513 (451) | 1631 (341) |
| Sex | ||||
| Male | 723 483 | 95 (270) | 1543 (370) | 1639 (256) |
| Female | 35 653 | 72 (275) | 1561 (356) | 1633 (247) |
| Unknown | 273 | 303 (667) | 1352 (692) | 1656 (161) |
| Region | ||||
| Northeast | 225 846 | 82 (255) | 1570 (327) | 1651 (204) |
| Midwest | 94 861 | 121 (316) | 1521 (418) | 1642 (286) |
| South | 218 768 | 111 (286) | 1540 (360) | 1651 (217) |
| West | 212 130 | 79 (244) | 1530 (395) | 1609 (317) |
| Unknown | 7804 | 84 (304) | 1562 (430) | 1646 (242) |
| Third-party payer | ||||
| Commercial | 594 871 | 107 (272) | 1549 (334) | 1655 (181) |
| Medicaid/CHIP | 91 305 | 3 (45) | 1583 (289) | 1586 (285) |
| Medicare | 18 510 | 80 (201) | 1583 (320) | 1662 (184) |
| Gilead Sciences | ||||
| Medication assistance | 32 976 | 0 (27) | 1720 (46) | 1721 (37) |
| Copay assistance | 6595 | 31 (177) | 1272 (849) | 1303 (849) |
| Cash payment | 4619 | 1902 (268) | 0 | 1902 (268) |
| Other | 10 073 | 384 (749) | 387 (635) | 771 (808) |
| Unknown | 460 | 189 (544) | 760 (701) | 949 (715) |
CHIP = Children's Health Insurance Program; OOP =out-of-pocket; PrEP = preexposure prophylaxis.
Payment data were available for 99% of prescriptions from 2014 to 2015, compared with 69% of prescriptions from 2016 to 2018. Using multiply imputed payment data, we found that overall PrEP medication payments approximately doubled each year from 2014 to 2018, increasing from $114 million in 2014 to $2.08 billion in 2018 (Table 3). In 2018, persons aged 25 to 44 years accounted for $1.16 billion (55.8%) of overall PrEP medication payments, whereas men accounted for $1.99 billion (95.8%). Payments were largest in the South ($606 million [29.2%]), followed by the West ($569 million [27.4%]), Northeast ($543 million [26.2%]), and Midwest ($337 million [16.2%]). Finally, overall PrEP medication payments were $1.68 billion (80.7%) for persons with commercial insurance, $200 million (9.6%) for those with Medicaid, $48 million (2.3%) for those with Medicare, and $127 million (6.1%) for those covered by Gilead Sciences programs.
Table 3.
Overall PrEP Medication Payments, by Age, Sex, Region, and Payer Type, Using Multiply Imputed Payment Data, 2014-2018
| Variable | Payments (SD), $ (thousand) |
||||
|---|---|---|---|---|---|
| 2014 | 2015 | 2016 | 2017 | 2018 | |
| Overall imputed payment* | 113 979 (5) | 377 829 (9) | 795 085 (92) | 1 333 358 (127) | 2 076 568 (162) |
| Age | |||||
| 16-17 y | 172 (0.4) | 253 (0.3) | 556 (2) | 882 (3) | 1573 (3) |
| 18-24 y | 6707 (2) | 26 086 (4) | 59 737 (18) | 108 761 (37) | 176 336 (39) |
| 25-34y | 35 813 (3) | 136 551 (5) | 296 012 (57) | 506 405 (82) | 800 761 (115) |
| 35-44 y | 32 502 (3) | 103 808 (5) | 213 941 (41) | 349 755 (60) | 537 072 (77) |
| 45-54 y | 24 735 (3) | 78 649 (4) | 157 462 (32) | 249 492 (52) | 361 191 (61) |
| 55-64 y | 10 508 (2) | 26 580 (2) | 56 604 (21) | 100 598 (30) | 170 698 (43) |
| ≥65 y | 3542 (1) | 5900 (1) | 10 773 (10) | 17 465 (13) | 28 937 (23) |
| Sex | |||||
| Male | 103 759 (4) | 359 437 (9) | 762 240 (89) | 1 279 937 (129) | 1 990 135 (162) |
| Female | 10 124 (2) | 18 314 (3) | 32 542 (16) | 53 076 (19) | 85 904 (24) |
| Unknown | 95 (2) | 77 (0.3) | 303 (0.3) | 344 (0.6) | 529 (0.5) |
| Region | |||||
| Northeast | 26 990 (2) | 95 859 (5) | 207 115 (45) | 356 537 (41) | 543 392 (52) |
| Midwest | 20 916 (2) | 67 900 (5) | 128 250 (44) | 215 218 (69) | 337 212 (87) |
| South | 27 260 (4) | 91 936 (6) | 207 385 (41) | 353 868 (66) | 606 258 (83) |
| West | 36 327 (3) | 118 017 (5) | 230 259 (48) | 373 304 (71) | 569 283 (77) |
| Unknown | 2486 (0.9) | 4116 (1) | 22 076 (3) | 34 433 (4) | 20 423 (4) |
| Third-party payer | |||||
| Commercial | 75 690 (3) | 316 837 (9) | 666 517 (92) | 1 093 379 (122) | 1 676 166 (144) |
| Medicaid/CHIP | 9904 (2) | 35 436 (1) | 74 320 (27) | 130 707 (28) | 199 722 (41) |
| Medicare | 4468 (0.7) | 8885 (0.7) | 17 131 (15) | 29 251 (19) | 47 653 (23) |
| Gilead Sciences | |||||
| Medication assistance | 3076 (0.3) | 2444 (0.2) | 14 536 (12) | 49 475 (22) | 104 348 (37) |
| Copay assistance | 0 | 0 | 3751 (8) | 14 754 (14) | 22 619 (29) |
| Cash payment | 3762 (3) | 10 637 (4) | 7726 (8) | 9556 (7) | 14 814 (12) |
| Other | 623 (0.7) | 3094 (1) | 9504 (10) | 5432 (7) | 9472 (9) |
| Unknown | 16 455 (2) | 495 (0.5) | 1600 (2) | 804 (2) | 1775 (7) |
CHIP = Children's Health Insurance Program; PrEP = preexposure prophylaxis.
Includes the imputed payments from all dispensed prescriptions.
In sensitivity analyses, the results using values derived from crude imputations and single imputation were similar to those using multiple imputation to estimate missing payment values (Appendix Table 4, available at Annals.org).
Discussion
In this study, the overall cost of PrEP medication to the health care system was estimated to be $2.08 billion in 2018. By contrast, the Centers for Disease Control and Prevention estimates that only 18.1% of persons with an indication for PrEP were covered during this same year (8). Further, approximately half of persons receiving PrEP do not persist throughout the entire year (14). Increasing PrEP coverage to 50% of the population at risk for HIV with a PrEP indication is part of the federal Ending the HIV Epidemic initiative (1, 8). Therefore, reaching and sustaining this coverage goal and ensuring persistence in use of PrEP will entail even higher health care expenditures.
From 2014 to 2018, OOP payments for TDF-FTC for PrEP increased faster than third-party payments. Although the OOP payment relative to the total payment was similar in 2014 ($54 of $1350 [4.0%]) and 2018 ($94 of $1638 [5.7%]), the absolute increase from $54 to $94 for 30 tablets of TDF-FTC is substantial at the patient level. The U.S. Preventive Services Task Force recently gave a grade A recommendation to offer PrEP to persons at risk for HIV, which may reduce patient OOP costs for this preventive service (26). However, our study shows that third-party payers already bear the largest burden of PrEP medication costs, so lowering the OOP costs alone will likely shift the cost to the third-party payer and not substantially reduce the overall cost. In late 2020, TDF-FTC is set to become generic, and the introduction of a generic PrEP option may reduce the overall cost of PrEP to the health care system (27). Unfortunately, generic antiretroviral drugs typically retain about 80% to 90% of their brand-name cost, so the savings may be limited (13).
With the recent approval of TAF-FTC for PrEP in men and transgender women, providers will have the option of prescribing generic TDF-FTC or brand-name TAF-FTC (6, 27); TAF-FTC has a more favorable profile of renal and bone adverse effects and was noninferior to TDF-FTC for PrEP in the DISCOVER trial (28). However, several clinical studies (29, 30) have shown that the frequency of severe renal and bone adverse effects with TDF-FTC is low. Further, TAF-FTC may be associated with small increases in weight, cholesterol levels, and blood glucose levels compared with TDF-FTC (28, 31, 32). A recent cost-effectiveness model concluded that the incremental safety benefit of TAF-FTC over TDF-FTC was worth no more than an additional $370 per person per year (33). Given the equal efficacy and overall low rates of adverse effects with both drugs, providers and health care systems may take cost into account when prescribing TDF-FTC or TAF-FTC for PrEP.
Like previous studies of persons using PrEP, this study shows lower use among youth, women, persons living in the South, and uninsured patients relative to the estimated number of persons at risk for HIV in these demographics (7, 34). In addition to cost barriers, disparities exist in knowledge about PrEP, awareness of HIV risk, access to health care, and persistence in use of PrEP among key populations (35). Although reducing cost addresses 1 barrier to PrEP use, additional efforts to reduce disparities along the PrEP continuum are still needed to increase use nationally.
This study has limitations. First, although the IQVIA database captures the majority (>90%) of commercial and retail pharmacy prescriptions, it does not capture prescriptions from government systems, such as the Veterans Health Administration, or closed health care systems, such as Kaiser Permanente, resulting in an underestimation of national payments for PrEP medication. However, 1 study reported that 691 persons used PrEP in 2016 to 2017, suggesting that PrEP use at the Veterans Health Administration likely represents a small fraction of overall use (36). Second, the missingness of third-party payment data during 2016 through 2018 may skew the imputed overall payments higher or lower if the data are not missing at random. Third, the database does not include data on transgender identity or HIV risk factors and provides limited race/ethnicity data, preventing stratification of payments and imputation of overall costs by these variables. The database similarly does not capture if PrEP was taken daily or on demand–that is, relative to the time of sexual encounters, which can result in fewer tablets used compared with daily use (37). Finally, the IQVIA database collects payment data only for the primary third-party payer and cannot account for complex multipayer transactions for the few patients with multiple third-party payers. For instance, commercially insured patients whose uncovered portion of prescription costs was paid by Gilead Sciences copayment assistance may not be recorded as receiving this assistance. This may result in an underestimation of payments from Gilead copayment assistance programs and an overestimation of OOP costs for commercially insured persons, but it would not affect the estimate of overall PrEP payments. Further, certain government programs (such as Medicaid or the 340B Drug Pricing Program, which allows certain health care organizations providing care to medically underserved populations to purchase medications in bulk at a discount) may have discount or bulk reimbursement mechanisms outside each pharmacy transaction. These limitations together likely led to an underestimate of national PrEP costs.
Preexposure prophylaxis is a powerful tool to prevent HIV transmission, but its cost to the health care system must be fully understood. Although the cost per person may decrease with the debut of generic TDF-FTC, the overall health care cost of PrEP will likely increase as more persons gain access to and continue to use PrEP. The high cost of PrEP does not diminish its central role in the Ending the HIV Epidemic initiative. Rather, it should promote action around ways to lower PrEP costs to the health care system to prevent coverage denials, eliminate prior authorization requirements, and increase access.
Appendix
Appendix Table 1.
Missingness of PrEP Prescription Payments and Patient Age, Sex, Region, and Payer Type, 2016-2018
| Year | Total Prescriptions, n |
Total Payment | OOP Payment |
Sex | Region | Third-Party Payer Type |
|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | n | % | ||
| 2014 | 73 739 | 542 | 0.73 | 88 | 0.12 | 68 | 0.09 | 920 | 1.25 | 243 | 0.33 |
| 2015 | 232 003 | 2593 | 1.11 | 30 | 0.01 | 56 | 0.02 | 1608 | 0.69 | 281 | 0.12 |
| 2016 | 487725 | 151 950 | 31.15 | 55 | 0.01 | 181 | 0.04 | 9238 | 1.89 | 950 | 0.19 |
| 2017 | 755 749 | 235 793 | 31.16 | 559 | 0.07 | 187 | 0.02 | 10 204 | 1.35 | 1267 | 0.17 |
| 2018 | 1 100 684 | 341 275 | 30.91 | 401 | 0.04 | 280 | 0.03 | 8247 | 0.75 | 2728 | 0.25 |
| Total | 2 649 900 | 732 153 | 27.63 | 1133 | 0.04 | 772 | 0.03 | 30 217 | 1.14 | 5469 | 0.21 |
OOP = out-of-pocket; PrEP = preexposure prophylaxis.
Appendix Table 2.
PrEP Prescriptions Missing Payment Data in the IQVIA Database, by Payer Type, 2016-2018
| Payer Type | Total Prescriptions 2016–2018, n |
Missing |
|
|---|---|---|---|
| n | % | ||
| Commercial | 1 856 276 | 587 249 | 31.6 |
| Medicaid/CHIP | 249 695 | 58 871 | 23.6 |
| Medicare | 54 444 | 16 671 | 30.6 |
| Gilead Sciences | |||
| Medication assistance | 28 335 | 10 802 | 38.1 |
| Copay assistance | 100 475 | 45 750 | 45.5 |
| Cash payment | 15 135 | 3909 | 25.8 |
| Other | 36 496 | 4730 | 13.0 |
| Unknown | 3302 | 1036 | 31.4 |
| Total | 2 344 158 | 729 018 | 31.1 |
CHIP = Children's Health Insurance Program; PrEP = preexposure prophylaxis.
APPendix Table 3.
Demographic Differences in Mean OOP PrEP Payments, by Third-Party Payer Type, 2018
| Variable | Mean Commercial OOP Payment (SD), $ |
Mean Medicaid OOP Payment (SD), $ |
Mean Medicare OOP Payment (SD), $ |
|---|---|---|---|
| All | 107 (272) | 3 (45) | 80 (200) |
| Age | |||
| 16-17 y | 77 (224) | 0 (0) | - |
| 18-24 y | 118 (290) | 2 (31) | 49 (163) |
| 25-34 y | 114 (284) | 3 (35) | 39 (166) |
| 35-44 y | 103 (264) | 4 (62) | 43 (179) |
| 45-54 y | 98 (259) | 3 (39) | 46 (154) |
| 55-64 y | 94 (254) | 5 (67) | 67 (200) |
| ≥65 y | 86 (239) | 4 (31) | 148 (236) |
| Sex | |||
| Male | 107 (272) | 3 (43) | 86 (204) |
| Female | 100 (274) | 3 (56) | 34 (165) |
| Region | |||
| Northeast | 94 (258) | 5 (53) | 54 (159) |
| Midwest | 130 (309) | 3 (60) | 95 (231) |
| South | 120 (287) | 3 (41) | 99 (223) |
| West | 94 (246) | 1 (21) | 76 (187) |
OOP = out-of-pocket; PrEP = preexposure prophylaxis.
Appendix Table 4.
Sensitivity Analysis of Overall PrEP Payments Using Crude, Single, and Multiple Imputation From Prescriptions With Complete Payment Data, 2014-2018
| Variable | 2014 | 2015 | 2016 | 2017 | 2018 |
|---|---|---|---|---|---|
| IQVIA base data set | |||||
| IQVIA overall sample payments, $ | 113 239 331 | 373 870 150 | 562 251 605 | 942 316 930 | 1 460 054 998 |
| IQVIAsample completeness, % | 99.3 | 98.9 | 68.8 | 68.8 | 69.0 |
| Crude imputation, $ | |||||
| Using $1400 forall missing total payments | 114 009 378 | 377 549 677 | 786 751 965 | 1 295 615 937 | 1 985 126 445 |
| Using $2000 forall missing total payments | 114 339 398 | 379 126 617 | 882 966 405 | 1 447 029 797 | 2 210 157 065 |
| Single imputation, $* | |||||
| Overall payments | 113 858 036 | 377 720 405 | 796 480 451 | 1 337 044 949 | 2 084 113 989 |
| Multiple imputation, 50 times, $* | |||||
| Mean overall payment | 113 978 754 | 377 828 569 | 795 085 311 | 1 333 358 057 | 2 076 567 589 |
| SD | 4977 | 8879 | 91 936 | 126 549 | 161 857 |
| Median overall payment | 113 978 380 | 377 827 446 | 795 080 980 | 1 333 361 145 | 2 076 541 565 |
| Minimum overall payment | 113 965 462 | 377 814 539 | 794 927 253 | 1 333 034 461 | 2 076 154 738 |
| Maximum overall payment | 113 990 274 | 377 852 681 | 795 286 344 | 1 333 660 128 | 2 076 903 514 |
PrEP = preexposure prophylaxis.
Imputation model predictor variables: out-of-pocket payment amount, tablets dispensed, third-party payer type, pharmacy type, geographic region, patient sex, and year.
Footnotes
Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Financial Support: No funding external to the Centers for Disease Control and Prevention was provided for this study.
Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-0786.
Reproducible Research Statement: Study protocol and data set: Not available. Statistical code: Available from Dr. Zhu (nje7@cdc.gov).
References
- 1.Fauci AS, Redfield RR, Sigounas G, et al. Ending the HIV epidemic: a plan for the United States. JAMA. 2019;321:844–845. [PMID: 30730529] doi: 10.1001/jama.2019.1343 [DOI] [PubMed] [Google Scholar]
- 2.Baeten JM, Donnell D, Ndase P, et al. ; Partners PrEP Study Team. Antiretroviral prophylaxis for HIV prevention in heterosexual men and women. N Engl J Med. 2012;367:399–410. [PMID: 22784037] doi: 10.1056/NEJMoa1108524 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Choopanya K, Martin M, Suntharasamai P, et al. ; Bangkok Tenofovir Study Group. Antiretroviral prophylaxis for HIV infection in injecting drug users in Bangkok, Thailand (the Bangkok Tenofovir Study): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet. 2013;381:2083–90. [PMID: 23769234] doi: 10.1016/S0140-6736(13)61127-7 [DOI] [PubMed] [Google Scholar]
- 4.Grant RM, Lama JR, Anderson PL, et al. ; iPrEx Study Team. Preexposure chemoprophylaxis for HIV prevention in men who have sex with men. N Engl J Med. 2010;363:2587–99. [PMID: 21091279] doi: 10.1056/NEJMoa1011205 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Thigpen MC, Kebaabetswe PM, Paxton LA, et al. ; TDF2 Study Group. Antiretroviral preexposure prophylaxis for heterosexual HIV transmission in Botswana. N Engl J Med. 2012;367:423–34. [PMID: 22784038] doi: 10.1056/NEJMoa1110711 [DOI] [PubMed] [Google Scholar]
- 6.Supplement Approval: DESCOVY® (emtricitabine and tenofovir alafenamide) tablets, for oral use. U.S. Food and Drug Administration; 2019. [Google Scholar]
- 7.Huang YA, Zhu W, Smith DK, et al. HIV preexposure prophylaxis, by race and ethnicity – United States, 2014–2016. MMWR Morb Mortal Wkly Rep. 2018;67:1147–1150. [PMID: 30335734] doi: 10.15585/mmwr.mm6741a3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Harris NS, Johnson AS, Huang YA, et al. Vital signs: status of human immunodeficiency virus testing, viral suppression, and HIV pre-exposure prophylaxis – United States, 2013–2018. MMWR Morb Mortal Wkly Rep. 2019;68:1117–1123. [PMID: 31805031] doi: 10.15585/mmwr.mm6848e1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Marks SJ, Merchant RC, Clark MA, et al. Potential healthcare insurance and provider barriers to pre-exposure prophylaxis utilization among young men who have sex with men. AIDS Patient Care STDS. 2017;31:470–478. [PMID: 29087744] doi: 10.1089/apc.2017.0171 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Patel RR, Mena L, Nunn A, et al. Impact of insurance coverage on utilization of pre-exposure prophylaxis for HIV prevention. PLoS One. 2017;12:e0178737. [PMID: 28558067] doi: 10.1371/journal.pone.0178737 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Rolle CP, Rosenberg ES, Luisi N, et al. Willingness to use pre-exposure prophylaxis among Black and White men who have sex with men in Atlanta, Georgia. Int J STD AIDS. 2017;28:849–857. [PMID: 28632468] doi: 10.1177/0956462416675095 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Whitfield THF, John SA, Rendina HJ, et al. Why I quit pre-exposure prophylaxis (PrEP)? A mixed-method study exploring reasons for PrEP discontinuation and potential re-initiation among gay and bisexual men. AIDS Behav. 2018;22:3566–3575. [PMID: 29404756] doi: 10.1007/s10461-018-2045-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents Living With HIV. Department of Health and Human Services; 2019. Accessed at https://aidsinfo.nih.gov/guidelines/html/1/adult-and-adolescent-arv/459/cost-considerations-and-antiretroviral-therapy on 20 December 2019. [Google Scholar]
- 14.Coy KC, Hazen RJ, Kirkham HS, et al. Persistence on HIV preexposure prophylaxis medication over a 2-year period among a national sample of 7148 PrEP users, United States, 2015 to 2017. J Int AIDS Soc. 2019;22:e25252. [PMID: 30775846] doi: 10.1002/jia2.25252 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Dafny L, Ody C, Schmitt M. When discounts raise costs: the effect of copay coupons on generic utilization. Am Econ J Econ Policy. 2017;9:91–123. doi: 10.1257/pol.20150588 [DOI] [Google Scholar]
- 16.Tseng CW, Dudley RA, Chen R, et al. Medicare part D and cost-sharing for antiretroviral therapy and preexposure prophylaxis. JAMA Netw Open. 2020;3:e202739. [PMID: 32286656] doi: 10.1001/jamanetworkopen.2020.2739 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Smith DK, Van Handel M, Huggins R. Estimated coverage to address financial barriers to HIV preexposure prophylaxis among persons with indications for its use, United States, 2015. J Acquir Immune Defic Syndr. 2017;76:465–472. [PMID: 28834798] doi: 10.1097/QAI.0000000000001532 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Juusola JL, Brandeau ML, Owens DK, et al. The cost-effectiveness of preexposure prophylaxis for HIV prevention in the United States in men who have sex with men. Ann Intern Med. 2012;156:541–50. [PMID: 22508731] doi: 10.7326/0003-4819-156-8-201204170-00001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Bernard CL, Brandeau ML, Humphreys K, et al. Cost-effectiveness of HIV preexposure prophylaxis for people who inject drugs in the United States. Ann Intern Med. 2016;165:10–19. [PMID: 27110953] doi: 10.7326/M15-2634 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cambiano V, Miners A, Phillips A. What do we know about the cost-effectiveness of HIV preexposure prophylaxis, and is it affordable? Curr Opin HIV AIDS. 2016;11:56–66. [PMID: 26569182] doi: 10.1097/COH.0000000000000217 [DOI] [PubMed] [Google Scholar]
- 21.Drabo EF, Hay JW, Vardavas R, et al. A cost-effectiveness analysis of preexposure prophylaxis for the prevention of HIV among Los Angeles County men who have sex with men. Clin Infect Dis. 2016; 63:1495–1504. [PMID: 27558571] [DOI] [PubMed] [Google Scholar]
- 22.McKenney J, Chen A, Hoover KW, et al. Optimal costs of HIV pre-exposure prophylaxis for men who have sex with men. PLoS One. 2017;12:e0178170. [PMID: 28570572] doi: 10.1371/journal.pone.0178170 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Furukawa NW, Smith DK, Gonzalez CJ, et al. Evaluation of algorithms used for PrEP surveillance using a reference population from New York City, July 2016-June 2018. Public Health Rep. 2020;135: 202–210. [PMID: 32027559] doi: 10.1177/0033354920904085 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Brewer DJ, Picus LO, eds. Encyclopedia of Education Economics and Finance. Sage; 2014. [Google Scholar]
- 25.Galbraith S Applied Missing Data Analysis by Craig K Enders. Aust N Z J Stat. 2012;54:251. doi: 10.1111/j.1467-842X.2012.00656.x [DOI] [Google Scholar]
- 26.Owens DK, Davidson KW, Krist AH, et al. ; US Preventive Services Task Force. Preexposure prophylaxis for the prevention of HIV infection: US Preventive Services Task Force recommendation statement. JAMA. 2019;321:2203–2213. [PMID: 31184747] doi: 10.1001/jama.2019.6390 [DOI] [PubMed] [Google Scholar]
- 27.Gilead Sciences. Quarterly Report. U.S. Securities and Exchange Commission; 2019. Accessed at https://sec.report/Document/0000882095-19-000027 on 20 December 2019. [Google Scholar]
- 28.Hare CB, Coll J, Ruane P, et al. The phase 3 DISCOVER study: daily F/TAF or F/TDF for HIV preexposure prophylaxis [Abstract]. In: Conference on Retroviruses and Opportunistic Infections Abstract eBook, Seattle, Washington, 4–7 March 2019 International Antiviral Society-USA; 2019:41. Abstract no. 104LB. [Google Scholar]
- 29.Pilkington V, Hill A, Hughes S, et al. How safe is TDF/FTC as PrEP? A systematic review and meta-analysis of the risk of adverse events in 13 randomised trials of PrEP [Editorial]. J Virus Erad. 2018; 4:215–224. [PMID: 30515300] [PMC free article] [PubMed] [Google Scholar]
- 30.Preexposure Prophylaxis for the Prevention of HIV Infection in the United States – 2017 Update: A Clinical Practice Guideline. U.S. Public Health Service; 2017. [Google Scholar]
- 31.Eron JJ, Orkin C, Cunningham D, et al. ; EMERALD study group. Week 96 efficacy and safety results of the phase 3, randomized EMERALD trial to evaluate switching from boosted-protease inhibi-tors plus emtricitabine/tenofovir disoproxil fumarate regimens to the once daily, single-tablet regimen of darunavir/cobicistat/emtricitabine/tenofovir alafenamide (D/C/F/TAF) in treatment-experienced, virologically-suppressed adults living with HIV-1. Antiviral Res. 2019; 170:104543. [PMID: 31279073] doi: 10.1016/j.antiviral.2019.104543 [DOI] [PubMed] [Google Scholar]
- 32.Hill A, Waters L, Pozniak A. Are new antiretroviral treatments increasing the risks of clinical obesity? [Editorial]. J Virus Erad. 2019; 5:41–43. [PMID: 30800425] [PMC free article] [PubMed] [Google Scholar]
- 33.Walensky RP, Horn T, McCann NC, et al. Comparative pricing of branded tenofovir alafenamide-emtricitabine relative to generic tenofovir disoproxil fumarate-emtricitabine for HIV preexposure prophylaxis: a cost-effectiveness analysis. Ann Intern Med. 2020;172: 583–590. [PMID: 32150602] doi: 10.7326/M19-3478 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Sullivan PS, Giler RM, Mouhanna F, et al. Trends in the use of oral emtricitabine/tenofovir disoproxil fumarate for pre-exposure prophylaxis against HIV infection, United States, 2012–2017. Ann Epidemiol. 2018;28:833–840. [PMID: 30037634] doi: 10.1016/j.annepidem.2018.06.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Nunn AS, Brinkley-Rubinstein L, Oldenburg CE, et al. Defining the HIV pre-exposure prophylaxis care continuum. AIDS. 2017;31:731–734. [PMID: 28060019] doi: 10.1097/QAD.0000000000001385 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Chartier M, Gylys-Cowell I, Van Epps P, et al. Accessibility and uptake of pre-exposure prophylaxis for HIV prevention in the Veterans Health Administration. Fed Pract. 2018;35:S42–S48. [PMID: 30766393] [PMC free article] [PubMed] [Google Scholar]
- 37.Molina JM, Capitant C, Spire B, et al. ; ANRS IPERGAY Study Group. On-demand preexposure prophylaxis in men at high risk for HIV-1 infection. N Engl J Med. 2015;373:2237–46. [PMID: 26624850] doi: 10.1056/NEJMoa1506273 [DOI] [PubMed] [Google Scholar]