J. Otis Smith, EdD

Value-based insurance designs are being widely used. We undertook this study to examine whether a financial incentive that lowered co-payments for blood pressure medications below $0 improved blood pressure control among patients with poorly controlled hypertension.

Participants from 3 Pennsylvania hospitals (n = 337) were randomly assigned to: a) be paid $8 per medication per month for filling blood pressure prescriptions, b) a computerized behavioral intervention (CBI), c) both payment and CBI, or d) usual care. The primary outcome was change in blood pressure between baseline and 12 months post enrollment. We also measured adherence using the medication possession ratio in a subset of participants.

There were no significant interactions between the incentive and the CBI interventions. There were no significant changes in medication possession ratio in the treatment group. Blood pressure decreased among all participants, but to a similar degree between the financial incentive and control groups. Systolic blood pressure (SBP) dropped 13.7 mm Hg for the incentive group versus 10.0 mm Hg for the control group (difference = —3.7; 95% CI, –9.0 to 1.6; 

 = .17). The proportion of patients with blood pressure under control 12 months post enrollment was 35.6% of the incentive group versus 27.7% of the control group (odds ratio, 1.4; 95% CI, 0.8-2.5; 

 = .19). Diabetics in the incentive group had an average drop in SBP of 12.7 mm Hg between baseline and 12 months compared with 4.0 mm Hg in the control group (

 = .02). Patients in the incentive group without diabetes experienced average SBP reductions of 15.0 mm Hg, compared with 16.3 mm Hg for control group nondiabetics (

Among patients with poorly controlled blood pressure, financial incentives—as implemented in this trial&mdash;did not improve blood pressure control or adherence except among patients with diabetes.

Improving medication adherence through small rewards that lower co-payments below $0 may be effective in subgroups of the population, but may not improve outcomes overall. Consideration should be given to further testing of this approach in carefully selected high-risk populations in which increased adherence could have significant economic and health benefits.

Insurers are widely adopting value-based insurance design (V-BID), which is based on the premise that reducing costs will significantly increase the use of beneficial and cost-effective services. These approaches are seen as a way of trying to address widespread problems with adherence to medication for chronic diseases, as there is strong evidence that medication adherence for such chronic diseases as hypertension and hypercholesterolemia is low,

 limiting the potential for medications with high efficacy in randomized controlled trials to improve the health of the population. V-BID designs are also being used more broadly to increase the use of high-value services and reduce the use of low-value services through differential co-payments tied to the value of services. However, although observational studies have consistently shown that increases in co-payments are associated with both decreases in utilization of medications and worse outcomes,

 the impact of decreasing co-payments seen in observational studies has been more modest

; the underlying psychology of how people process changes in payments as losses compared with gains suggests that increases and decreases in co-payments may not be equivalent.

Nearly two-thirds of Americans with hypertension have poor control,

 which puts them at risk for substantial morbidity and mortality; poor adherence is an important factor in poorly controlled hypertension. Taking the logic behind V-BID initiatives that lower financial burden might improve adherence and patient outcomes one step further; we examined whether providing incentives of $8 per medication per month (a type of “negative co-payment”) for all antihypertensive medications would significantly improve blood pressure control among patients with poorly controlled blood pressure at 3 medical centers in Pennsylvania.

Study participants were drawn from patients at 3 hospitals in Pennsylvania: the Philadelphia Veterans Affairs Medical Center (PVAMC), the Veterans Administration Pittsburgh Health Care System (VAPitt), and the PinnacleHealth clinic in Harrisburg, with recruitment occurring between March 2005 and July 2007. (

 shows the study flow.) Potential participation was elicited by sending letters to patients who met eligibility criteria based on electronic or manual screening of records. Eligible patients were aged ≥21 years, with 1 or more active prescriptions for an antihypertensive medication, and systolic blood pressure (SBP) of at least 140 mm Hg (130 mm Hg in patients with diabetes), and with eligibility to receive medications without co-payments (due to low income or disability). Exclusion criteria included participation in another experimental study, markedly shortened life expectancy (due to diagnosis of metastatic cancer, end-stage renal disease on dialysis, New York Heart Association class IV congestive heart failure, or dementia), or atrial fibrillation (because of concerns with accuracy of blood pressure measurement). We enrolled 337 of the 1253 potentially eligible participants in the study (see Figure 1); approximately 20% of screened potentially eligible patients were excluded due to ineligibility.

The protocol was approved by the Institutional Review Boards of the PVAMC, VAPitt, PinnacleHealth, and the University of Pennsylvania, and all participants provided written informed consent prior to randomization. The study was registered at clinicaltrials.gov as Collaboration to Reduce Disparities in Hypertension, ID #NCT00133068. Participants were randomized to receive either: a) a financial incentive of $8 per medication prescription filled per month, b) a computerized behavioral intervention (CBI) provided at enrollment and at the 6-month follow-up visit, c) both the financial incentives and the CBI, or d) usual care (with study follow-up visits every 3 months). After an initial visit, participants were requested to return for follow-up blood pressure readings and surveys at 3, 6, 9, and 12 months; financial rewards were paid at each follow-up visit after confirmation that each prescription was filled using either the VA’s computer records, a prescription bottle, or a receipt. The CBI was designed to help study participants learn the basics about blood pressure control and its impact. Participants watched a video testimonial from a patient who had had multiple strokes due to medication nonadherence, and who felt he had learned the importance of medication adherence from that experience. This helped participants improve self-efficacy by reviewing techniques that could be used to enhance adherence.

Randomization was carried out using a random number generator and via permuted block randomization with a block size of 4. Randomization was stratified by site, income (<100%, 100%-200%, 200%-300%, and >300% of the federal poverty level [FPL]), and baseline blood pressure (SBP <160 mmHg or SBP ≥160 mm Hg). Randomization was performed after signed written consent forms were received. Allocation assignments were concealed, with staff unable to access randomization assignment for each subject until all eligibility criteria were entered in an electronic tracking system and consent forms were completed. Neither staff nor study participants could be blinded due to the nature of the intervention; investigators and analysts, however, remained blinded to intervention assignments until unblinding occurred, in coordination with the Data Safety Monitoring Board, once follow-ups were nearly complete.

The primary outcome variable was change in blood pressure from enrollment to 12 months post enrollment. Secondary outcome variables included change in blood pressure 6 months post enrollment, the percentage of patients with blood pressure in control at 6 and 12 months post enrollment, self-reported medication adherence, and prescription refill data from the VA electronic medical record system. Blood pressure control was defined as SBP below 140 mm Hg and diastolic blood pressure (DBP) below 90 mm Hg for patients without diabetes; for participants with diabetes, blood pressure control was defined as SBP below 130 mm Hg and DBP below 85 mm Hg.

Measurement of blood pressure was done following a standardized protocol using an automated blood pressure cuff (Omron HEM-90R), ensuring that the correct cuff size was used.

 Participants were instructed to relax for 5 minutes before their blood pressure was taken; then the patient’s arm (the dominant arm unless the patient expressed a preference to use the other arm) was supported on a chair or desk and the blood pressures were measured while the patient was sitting. Three measurements were taken, 2 minutes apart, and averaged. The blood pressure measurements were not revealed to the study participants. Although the study nurses could not be blinded to the randomization, the use of an automated blood pressure cuff and a standardized protocol protected against systematic differences among groups in the way blood pressure was measured.

Medication adherence was measured using self-reporting based on the Hill-Bone Scale,

 with supplemental assessment using electronic prescription fill records where available. For these records, we calculated medication possession ratios (MPRs; number of days a patient had a filled prescription divided by 360 days) and gap ratios of 30, 60, and 90 days (percentage of patients who had gaps in filled prescriptions of at least 30, 60, or 90 days).

Baseline blood pressure levels were assessed with other factors including height, weight, and creatinine level. We also collected information on income, baseline health status, health history, medication use, age, gender, and self-reported race or ethnicity. We used information on income and family size to calculate income as a percent of the FPL.

To evaluate the similarity of the treatment groups with respect to baseline covariates, we compared groups using Student’s 

 test for categorical variables, with Fisher’s Exact tests used for analyses with 5 or fewer subjects per cell. The trial was designed to test the effects of the 2 interventions individually; we did not assume an interaction, but powered the study adequately for individual comparisons of the 3 active arms against control. The primary analysis of the financial incentive began with a formal statistical test of interaction between the incentive and CBI; there was no evidence of this, so we collapsed across the CBI arms to assess the effect of the financial incentive alone.

The primary unadjusted analyses tested the mean differences in the degree of change in SBP and DBP between the incentive and control groups from baseline to 12 months post enrollment using Student’s 

 test. We similarly calculated differences in change in blood pressure from baseline to 6 months post enrollment. Some participants had missing blood pressure values at 6 and 12 months; we compared baseline data between participants with and without follow-up values. Missing values for 6-month and 12-month SBP and DBP readings were handled using the Markov Chain Monte Carlo multiple imputation method, utilizing 10 imputations after verification that this yielded relative efficiencies of 95% or higher; we also checked that imputed values were within the range of observed values.

 Separate imputation regression models were implemented for SBP and DBP. The primary analyses were conducted on each of the 10 imputed data sets and the results combined using the standard approach to yield a single result. Unadjusted odds ratios (ORs) for achieving in-control blood pressure were estimated via logistic regression using the imputed data in the same manner. We compared the results using multiply imputed data with models using a baseline observation carried-forward approach and with a last observation carried-forward approach.

Regression coefficients and their 95% confidence intervals (CIs) were estimated from an unadjusted linear regression model that incorporated only a factor indicating receipt of incentives versus control; these were compared with regression coefficients estimated from a model adjusted for the stratification variables (site, high SBP, and income), in all cases using the imputed data. In addition to prespecified subgroup analyses on race and income, we examined changes in SBP and DBP in a priori subgroups defined by study site, initial SBP (≥160 mm Hg vs <160 mm Hg), presence of diabetes, and education level (high school or lower, some college or college degree, beyond college). Homogeneity of the association between treatment groups and blood pressure change across subgroups was tested by assessing the significance of appropriate interaction terms included in the linear regression models described above.

The trial was powered to ensure that clinically meaningful differences in SBP of 10 mm Hg and in DBP of 5 mm Hg

 could be detected in any of the contrasts discussed above, assuming an interaction between the CBI and incentive interventions. We used an alpha of 0.05 and standard deviations of change in SBP and DBP of 20 mm Hg and 10 mm Hg, respectively (based on the upper limit of standard deviation directly measured in clinical trials).

 Based on these estimates, we projected we would need 63 subjects per arm to detect the clinically meaningful difference in BPs discussed above. To accommodate an estimated 20% loss to follow-up, recruitment goals for each arm were increased to 79 subjects, for a total of 316 subjects. The trial was not explicitly powered to detect effects on adherence; post hoc calculations indicate approximately 80% power to detect a difference of 8 percentage points in the MPR.

Study characteristics were generally balanced across the arms of the study (

); exceptions are noted below. The average age was 61 years, with approximately 81% males, 5% Hispanic, and 61% black; there were significantly more blacks in the control group (

 = .01). About 45% had incomes below 100% of the FPL, 26% at 100% to 200% FPL, 12% at 200% to 300% FPL, and the remainder above 300% FPL. Baseline SBP and DBP readings averaged 154 and 84 mm Hg, respectively.

Follow-up rates were slightly higher among incentive arm participants at 12 months, but the difference in follow-up rates was not significantly different (84% in incentive arms, 76% in nonincentive arms; 

 = .10) (Figure 1). Baseline SBP, number of antihypertensive medications, or number of medications overall did not differ between those who were lost to follow-up and those participants for whom we had data at 12 months.

Mean changes in SBP were —9.8 mm Hg (95% CI, –15.0 to –4.5) in the control group, –12.6 (95% CI, –18.0 to –7.1) in the co-payment reduction group, –10.2 (95% CI, –15.4 to –5.1) in the CBI group, and –14.8 (95% CI, –19.9 to –9.7) in the combined co-payment/CBI group. There were no significant interactions between incentive payments and receipt of the computerized behavioral intervention with respect to 12-month outcomes (

 = .76). Therefore, the primary and all subsequent analyses are collapsed across CBI status to focus on the impact of $8 incentives on blood pressure and outcomes (hereafter comparison of “incentive” vs “control”).

Baseline and follow-up MPR values were available for 79 control group participants and 69 incentive group participants. There was no relative difference in the change in MPR between baseline and 12 months (—0.35% and 3.08% in the control and incentive groups, respectively

 = .36). Changes, as measured by gaps in the MPR of 30 days (OR, 0.7; 95% CI, 0.3-1.4; 

 = .30), 60 days (OR, 1.5; 95% CI, 0.6-3.6

= .39), or 90 days (OR, 1.7; 95% CI, 0.6-5.0; 

 = .32) indicated no differences between the control and incentive groups.

We found no significant difference in blood pressure reduction between the incentive and control groups (

). The incentive group lowered their SBP by 13.7 mm Hg on average versus 10.0 mm Hg for the control group (

 .17). The drop in DBP was 6.8 mm Hg for the incentive group compared with 4.1 mm Hg for the control group (

 = .07). At the end of 12 months, 35.6% of the incentive group had their blood pressure in control versus 27.7% for the control group (OR, 1.4; 95% CI, 0.8-2.5; 

 = .19). Results of sensitivity analyses in which we assumed that the blood pressure in all patients lost to follow-up was equal to their last measured value or their baseline blood pressure (as opposed to the imputed blood pressure value) were qualitatively similar. Adjusted estimates of changes in SBP indicated no significant differences between incentive and control groups in the degree of change in blood pressure (

The degree of change in SBP between the incentive and control groups was compared among several a priori subgroups of the populations in the study, including those with and without diabetes, those with a baseline SBP above or below 160 mm Hg, and subgroups determined by race, income, and education. The subgroup of patients with diabetes showed a significant difference between the incentive and control groups (

 for interaction = .04). Specifically, patients with diabetes in the incentive group had an average drop in SBP of 12.7 mm Hg between baseline and 12-month follow-up, while patients with diabetes in the control group had an average SBP reduction of only 4.0 mm Hg (

 for the difference = .02). This difference was driven primarily by continued improvement in blood pressure between months 6 and 12 in the incentive group compared with a lack of such improvement in the control group (

). Patients without diabetes experienced an average SBP reduction of 15.0 mm Hg compared with an average reduction of 16.3 mm Hg for individuals without diabetes in the control group (

 value for the difference = .71.) None of the other subgroups experienced any significant differences.

In the first randomized controlled trial to provide financial incentives for antihypertensive medication prescription filling among patients with poorly controlled hypertension, we found no overall improvement in blood pressure control. We did see a relative improvement in blood pressure among patients with diabetes, but not among other subgroups.

These findings are important to ongoing discussions about V-BID, and specifically efforts to improve patient outcomes through reducing financial burdens for high-value prescription medications.

 Recent efforts to reduce the degree of patient cost sharing based on the value of prescriptions have garnered extensive interest among payers and employers.

Whereas increases in prescription co-payments have been associated with decreases in medication adherence and worse outcomes in numerous studies,

 only 2 clinical trials have examined the question of to what degree decreasing co-payments improves outcomes. The most definitive randomized trial on the impact of cost sharing on healthcare utilization—the RAND Health Insurance Experiment (HIE)&mdash;found sizable effects of patient cost sharing on use and expenditures, but more modest effects on health status. This was also performed more than 25 years ago when less effective medications were available; in addition, it excluded the elderly, more than 80% of the subjects were Caucasian, and it included a population with relatively few comorbidities

—a population for whom prescription drug coverage is most likely to be cost-effective. Of note, among low-income persons with high blood pressure, free care resulted in significant improvements in blood pressure. However, the RAND HIE randomly assigned roughly 2000 families to 1 of 14 experimental health plans that varied in their cost-sharing arrangements for all medical goods and services. The Post-Myocardial Free Rx Event and Economic Evaluation Trial (MI-FREEE) demonstrated that making medications free post acute myocardial infarction increased MPRs by about 5 to 6 percentage points (control group average MPR, 38.9%), which was associated with reductions in the rate of total major vascular events or revascularization without any increase in total healthcare spending.

Several observational studies have now indicated that lowering co-payments in a V-BID framework is associated with increases in MPR of about 3 to 5 percentage points on a base of about 60% to 80%.

 While such effects are statistically significant in large populations, these studies did not measure clinical outcomes and it seems unlikely that changes of this magnitude in MPR would have large clinical effects within a population. The findings of this study suggest that small reductions in co-payment below $0 do not significantly improve blood pressure or MPRs.

There are a number of reasons why financial incentives may have less of an impact on health than increases in co-payments. First, increases in co-payments affect utilization primarily among adherent patients, whereas financial incentives are targeted at affecting utilization among nonadherent patients in whom a change in cost of a given magnitude (ie, from $0 to —$8) is likely to have less impact. Second, increases in co-payments are likely processed as a loss by patients and behavioral economists have demonstrated that losses are felt much more strongly than equivalent gains.

 Third, incentives may be a bit like “the dog that didn’t bark.” For a nonadherent patient who doesn’t come to the pharmacy or fill prescriptions, communications about a financial incentive may be largely ignored. Fourth, we limited eligibility for the study to individuals who paid no co-payments at baseline; such individuals may have been less attuned to co-payments in relation to prescriptions since, at baseline, they would have had less reason to pay attention to co-payments.

Because studies have indicated that unbundling rewards from other payments make the rewards more effective

 and due to the fact that the PinnacleHealth system patients filled prescriptions at a large number of pharmacies in the Harrisburg area in which we could not control point-of-service pricing, we provided post hoc rebates rather than incentives at point of service. The lack of an overall effect on blood pressure or adherence could be due to this design feature of the trial. This had the disadvantage of introducing time delays in feedback after the incented behavior occurred. Other limitations include the incentive magnitude, as it may have been too small to induce changes in behavior, though previous work suggested that in low-income populations, co-payment increases as low as $0.50 to $1 per prescription (approximately $2-$3 adjusting for inflation) can reduce drug utilization.

 The study was conducted primarily among veterans at 2 VA hospitals, and thus had primarily male participants, although there are no obvious reasons to believe that the interventions would be less effective in men than women.

Financial incentives for blood pressure medications had little impact on blood pressure control except, perhaps, among patients with diabetes. Because this was an isolated finding in 1 subgroup, we conclude this intervention did not systematically improve patient outcomes. Further initiatives should examine the comparative effectiveness of different ways of delivering such incentives, the relationship between the magnitude of incentives and effectiveness, and the impact on different populations.

Other coauthors (included here rather than the byline due to space constraints) include: Jingsan Zhu, MBA; Yuanyuan Tao, MS; Wei Yang, PhD; John H. Holmes, PhD; Dominick L. Frosch, PhD; Katrina Armstrong, MD, MSCE; Shiriki Kumanyika, PhD; Kjell Enge, PhD; Raymond R. Townsend, MD; and Nirmal Joshi, MD. 

 Center for Health Equity Research & Promotion, Philadelphia Veterans Affairs Medical Center (KGV, JAL, SAI), PA; Center for Health Incentives and Behavioral Economics, Leonard Davis Institute, Penn CMU Roybal P30 Center in Behavioral Economics and Health (KGV, ABT, JAD, SEK), Philadelphia, PA; Department of Medicine (KGV, JAL, JAD, SEK, SAI) and Center for Therapeutic Effectiveness Research (SEK), Perelman School of Medicine of the University of Pennsylvania, Philadelphia; Department of Health Care Management, The Wharton School (KGV), and Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and Epidemiology (ABT, DA, JJ, SEK), University of Pennsylvania, Philadelphia; Center for Health Equity Research & Promotion, Pittsburgh Veterans Affairs Medical Center (SAI), PA; University of Pittsburgh School of Medicine (SAI), PA; Department of Psychology, Cheyney University (JOS), Cheyney, PA; Department of Psychology, Dickinson College (MH-L), Carlisle, PA.

 The work in this paper was primarily supported by a grant from the Commonwealth of Pennsylvania, titled Collaboration to Reduce Disparities in Hypertension, grant number ME-02-382. Supplemental support was received from Pfizer, Inc. The sponsors/funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Drs Volpp and Kimmel had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

 Dr Volpp has received research funding from the Aetna Foundation, Aramark, Discovery (South Africa), Horizon Blue Cross and Blue Shield, Humana, McKinsey, Merck, Hawaii Medical Services Association, Weight Watchers (all unrelated to the topic of this paper), research funding and consulting income from CVS Caremark, and is a principal at the behavioral economics consulting firm VAL Health. Dr Kimmel has done consulting for several pharmaceutical companies, including Pfizer, all unrelated to the topic of this paper. Dr Troxel has received consulting income from VAL Health. Other than what is listed above, there are no known financial conflicts of interest among any of the authors including, but not limited to, employment/affiliation, all grants or funding, honoraria, paid consultancies, expert testimony, and patents filed, received, or pending. Dr Doshi is a consultant for Alkermes, Boehringer Ingelheim, Forest Labs, Ironwood Pharmaceuticals, Merk, and Shire. She has grants pending from Humana and PhRMA, and has previously received grants from Humana, Pfizer, PhRMA, Merck, National Pharmaceutical Council, Amgen; her spouse holds stock in Merck and Pfizer. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (KGV, MH-L, SEK, JOS, JAD); acquisition of data (MH-L, SEK, JOS); analysis and interpretation of data (ABT, MH-L, SEK, JOS, DA, JAD); drafting of the manuscript (KGV, DA, JAD); critical revision of the manuscript for important intellectual content (KGV, ABT, SEK, JAD); statistical analysis (ABT, DA); obtaining funding (MH-L, SEK); and supervision (SEK, JOS).

 Kevin G. Volpp, MD, PhD, Leonard Davis Institute, Center for Health Incentives and Behavioral Economics, Perelman School of Medicine and the Wharton School of the University of Pennsylvania, 1120 Blockley Hall, 423 Guardian Dr, Philadelphia PA 19104-6021. E-mail: volpp70@wharton.upenn.edu.

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Articles

This study extends value-based insurance design concepts in testing the impact on blood pressure control of rewards that provided negative co-payments for blood pressure medication.

A Randomized Controlled Trial of Negative Co-Payments: The CHORD Trial

Efforts to improve adherence by reducing co-payments through value-based insurance design are become more prevalent despite limited evidence of improved health outcomes. The objective of this study was to determine whether eliminating patient co-payments for blood pressure medications improves blood pressure control.

The Collaboration to Reduce Disparities in Hypertension (CHORD) was a randomized controlled trial with 12 months’ follow-up conducted among patients from the Philadelphia and Pittsburgh Veterans Administration Medical Centers. We enrolled 479 patients with poorly controlled systolic blood pressure. Participants were randomly assigned to: a) receive reductions in co-payments from $8 to $0 per medication per month for each antihypertensive prescription filled, b) a computerized behavioral intervention (CBI), c) both co-pay reduction and CBI, or d) usual care. Our main outcome measure was change in systolic blood pressure from enrollment to 12 months post enrollment. We also measured adherence using the medication possession ratio in a subset of participants.

There were no significant interactions between the co-payment interventions and the CBI interventions. There was no relative difference in the change in medication possession ratio between baseline and 12 months (0.05% and —0.90% in control and incentive groups, respectively; 

 = .74) or in continuous medication gaps of 30, 60, or 90 days. Blood pressure decreased among all participants, but to a similar degree between the financial incentive and control groups. Systolic pressure within the incentive group dropped 13.2 mm Hg versus 15.2 mm Hg for the control group (difference = 2.0; 95% CI, —2.3 to 6.3; 

 = .36). The proportion of patients with blood pressure under control at 12 months was 29.5% in the incentive group versus 33.9 in the control group (odds ratio, 0.8; 95% CI, 0.5-1.3; 

Among patients with poorly controlled blood pressure, financial incentives—as implemented in this trial&mdash;that reduced patient cost sharing for blood pressure medications did not improve medication adherence or blood pressure control.

Small rewards that lowered co-payments to $0 did not improve blood pressure control significantly more than in control-group subjects who simply had blood pressure measured. This may be because of the way in which this program was administered—there were delays in providing rebates for co-payments following prescription filling. The exact nature of the program implementation may have a significant impact on program effectiveness and should be carefully considered in value-based insurance design program design and assessment of impact.

Hypertension—especially within socioeconomically disadvantaged communities&mdash;remains a leading cause of cardiovascular morbidity and mortality in the United States, affecting nearly 50 million Americans.

 Although there are effective medications to treat hypertension, nearly two-thirds of Americans with the condition have poorly controlled blood pressure.

 Lack of adherence to antihypertensive medications is considered a critically important factor in blood pressure management. Medication adherence for chronic diseases such as hypertension and hypercholesterolemia is extremely low,

 limiting the potential for highly efficacious medications to improve population health.

Value-based insurance design (V-BID)—an approach based on the premise that reductions in co-payments will significantly increase the use of beneficial and cost-effective services&mdash;is being widely adopted.

 As part of the Affordable Care Act, elimination of cost sharing for preventive services is being mandated to increase utilization of such services. Although observational studies have shown that increases in co-payments are associated with both decreased medication use and worsened health outcomes,

 and studies have generally focused exclusively on medication use rather than measured health outcomes. The underlying psychology of how people process changes in payments as losses compared with gains suggests that increases and decreases in co-payments may not be equivalent.

Only 2 randomized trials examining the relationship between co-payments and health have been published. The first, the RAND Health Insurance Experiment (HIE), was conducted in the 1970s and varied cost sharing for all services, not just those for medications. The second, the recently published Post-Myocardial Infarction Free Rx Event and Economic Evaluation (MI FREEE) study, was a test of co-payment reduction following discharge for myocardial infarction (MI) in an insurer-based intervention.

To examine whether reducing co-payments from $8 to $0 per medication per month for all antihypertensive medications significantly improves blood pressure control, we conducted a clinic-based, randomized controlled trial of elimination of co-payments among patients with poorly controlled blood pressure at 2 medical centers in Pennsylvania.

Study participants were drawn from patients at 2 ambulatory clinics in Pennsylvania: the Philadelphia Veterans Affairs Medical Center (PVAMC) and the VA Pittsburgh Healthcare System (VAPitt), with recruitment occurring between March 2005 and July 2007. Eligible patients were 21 years or older, with 1 or more active prescriptions for an antihypertensive medication, systolic blood pressure (SBP) of at least 140 mm Hg (130 mm Hg in patients with diabetes), and those who paid a co-payment for their medications. Exclusion criteria included: participation in another experimental study; markedly shortened life expectancy (due to diagnosis of metastatic cancer, end-stage renal disease on dialysis, New York Heart Association class IV congestive heart failure, or dementia), or atrial fibrillation (because of concerns with accuracy of blood pressure measurement). A total of 479 participants were enrolled (details on enrollment provided in 

The protocol was approved by the Institutional Review Boards of the PVAMC, VAPitt, and the University of Pennsylvania; all participants provided written informed consent prior to randomization. The study was registered at clinicaltrials.gov as ID # NCT00133068. Participants were randomized to receive 1 of the following: a) a financial incentive equal to their co-payments for all antihypertensive medications that effectively lowered co-payments from $8 to $0 per medication per month (note: on January 1, 2006, 9 months after initiation of enrollment, co-payments were raised from $7 to $8 per month per Veterans Health Administration Directive 2005-052 and incentives were adjusted accordingly); b) a computerized behavioral intervention (CBI) that was provided immediately following enrollment and repeated at the 6-month follow-up visit; c) both the financial incentive and the CBI; or d) usual care, which consisted only of their existing medical care.

The CBI was administered at baseline and at 6 months and was designed to help study participants learn the basics about blood pressure control and its impact. Participants watched a video testimonial from a patient who had had multiple strokes due to medication nonadherence and who felt that he had learned the importance of medication adherence from that experience. This helped to improve self-efficacy by reviewing techniques that could be used to enhance adherence. After an initial visit, all participants were requested to return for follow-up blood pressure readings and to complete surveys at 3, 6, 9, and 12 months. The financial reimbursements were paid as soon as study staff received confirmation of prescription fills using either the Veterans Administration’s (VA’s) computer records, prescription bottles, or receipts.

Randomization was carried out using a random number generator and via permuted block randomization with a block size of 4. Randomization was stratified by site, income (<100%, 100%-200%, 200%-300%, and >300% of the federal poverty line [FPL]), and baseline blood pressure (SBP <160 mm Hg or SBP ≥160 mm Hg). Allocation assignments were concealed, with staff unable to access the randomization assignment for each subject until all eligibility criteria were entered into an electronic tracking system and consent forms were completed. Neither staff nor study participants could be blinded due to the nature of the intervention; investigators and analysts, however, remained blinded to intervention assignments until unblinding occurred, in coordination with the Data Safety Monitoring Board, once follow-ups were complete.

The primary outcome variable was change in SBP and diastolic blood pressure (DBP) from enrollment to 12 months post enrollment. Secondary outcome variables included change in SBP and DBP 6 months post enrollment, the percentage of patients with well-controlled blood pressure at 6 and 12 months post enrollment, self-reported medication adherence, and prescription refill data from the VA electronic medical record system. Blood pressure control was defined as SBP below 140 mm Hg and DBP below 90 mm Hg for patients without diabetes, and as SBP below 130 mm Hg and DBP below 85 mm Hg for patients with diabetes.

 Participants were instructed to relax while seated for 5 minutes before their blood pressure was taken; the patient’s arm was supported on a chair or desk and blood pressures were measured 3 times, each reading 2 minutes apart, and averaged, but not revealed to the study participants. Although the study nurses could not be blinded to the randomization due to the need to administer the intervention, the use of an automated blood pressure cuff and a standardized protocol protected against differences in blood pressure measurement.

Medication adherence was measured using self-report based on the Hill-Bone Scale,

 with supplemental assessment using electronic prescription fill records where available. For patients with these records, we calculated the proportion of days covered (number of days with antihypertensive drug supply on hand divided by the number of days in the observation period).

 In addition, continuous medication gap measures were calculated as at least 1 continuous episode with no antihypertensive medication for a minimum of 30, 60, or 90 days.

Other factors assessed at baseline included height, weight, serum creatinine level, income, health status, health history, medication use, age, gender, and self-reported race or ethnicity. We used information on income and family size to calculate income as a percent of the FPL.

 test for categorical variables, with Fisher’s Exact tests used for analyses with 5 or fewer subjects per cell.

Because of the factorial design, we first assessed whether receipt of CBI affected the impact of incentive payments. The trial was designed to test the effects of the 2 interventions individually; we did not assume an interaction but powered the study adequately for individual comparisons of the 3 active arms against control. The primary analysis of the financial incentive began with a formal statistical test of interaction between the incentive and CBI; there was no evidence of this, so we collapsed across the CBI arms to assess the effect of the financial incentive alone.

 test. We similarly calculated differences in change in SBP and DBP from baseline to 6 months post enrollment. Some participants had missing blood pressure values at 6 and 12 months; we compared baseline data between participants with and without follow-up values. Missing values for 6-month and 12-month SBP and DBP readings were handled using the Markov Chain Monte Carlo multiple imputation method, utilizing 10 imputations after verification that this yielded relative efficiencies of 95% or higher; we also checked that imputed values were within the range of observed values.

 Separate imputation regression models were implemented for SBP and DBP. Unadjusted odds ratios (ORs) for achieving in-control blood pressure were estimated via logistic regression using the imputed data in the same manner. We compared the results using multiply imputed data with models using a baseline observation carried-forward approach and with a last observation carried-forward approach.

We estimated regression coefficients and their 95% confidence intervals (CIs) from an unadjusted linear regression model that incorporated only a factor indicating receipt of incentives versus control. We then compared these with regression coefficients estimated from a model adjusted for the stratification variables (site, high SBP, and income) in all cases using the imputed data.

In addition to prespecified subgroup analyses on race and income, we examined changes in SBP and DBP in subgroups defined by study site, initial SBP (≥160 mm Hg vs <160 mm Hg), presence of diabetes, and education level (high school/GED or lower, some college or college degree, beyond college). Homogeneity of the association between treatment groups and blood pressure change across subgroups was tested by assessing the significance of appropriate interaction terms included in the linear regression models described above.

The trial was powered to ensure that clinically meaningful differences in SBP of 10 mm Hg and of DBP of 5 mm Hg

 could be detected in any of the contrasts discussed above, assuming an interaction between the CBI and incentive interventions. We used an alpha of 0.01 to account for multiple comparisons and standard deviations of change in SBP and DBP of 20 and 10, respectively (based on the upper limit of standard deviation directly measured in clinical trials).

 Based on these estimates, we estimated that we would need 93 subjects per arm to detect the clinically meaningful difference in blood pressures discussed above. Recruitment goals for each arm for this study were increased to 116 subjects to accommodate an estimated 20% loss to follow-up, for a target of 464 subjects.

Study sample characteristics were generally balanced across the arms of the study (

). Average age was 69, with a predominantly male population (97%) that was about 56% white and 42% black. Fifteen percent of the study population had incomes <100% of the FPL, 32% between 100% and 200% of the FPL, and the rest fairly evenly divided between the 200%-to-300% FPL and >300% FPL ranges. The baseline mean SBP and DBP were 158 and 81 mm Hg, respectively.

Follow-up rates at 12 months were slightly higher among incentive arm participants, with rates of 82.4% and 71.6% for incentive arm and control arm participants, respectively (

 <.01). Baseline SBP, number of antihypertensive medications, and number of medications overall did not differ between those who were lost to follow-up and those participants for whom we had data at 12 months.

Baseline and follow-up medication possession ratio (MPR) values were available for 143 control group participants and 154 incentive group participants. There was no relative difference in the change in MPR between baseline and 12 months (0.05% and —0.90% in the control and incentive groups, respectively; 

 = .74). Changes, as measured by continuous medication gaps of 30 days (OR, 1.0; 95% CI, 0.6-1.7; 

= .98), 60 days (OR, 1.2; 95% CI, 0.6-2.6, 

 = .61), or 90 days (OR, 0.6; 95% CI, 0.3-1.5; 

= .27) indicated no detectable differences between the control and incentive groups.

Mean changes in SBP were —12.9 mm Hg (95% CI, –17.4 to –8.4) in the control group, –12.6 (95% CI, –17.0 to –8.3) in the co-payment-reduction group, –17.4 (95% CI, –21.7 to –13.0) in the CBI group, and –13.7 (95% CI, –18.0 to –9.4) in the combined co-payment/CBI group. There were no significant interactions between incentive payments and receipt of the CBI with respect to 12-month outcomes (

 = .45). Therefore, the primary and all subsequent analyses are combined by CBI status to focus on the impact of co-payment reduction on blood pressure and adherence (hereafter comparison of “incentive” vs “control”).

We found no significant difference in SBP and DBP reduction between the incentive and control groups (

SBP decreased an average of 13.2 mm Hg from baseline to 12 months post enrollment in the incentive group compared with an average of 15.2 mm Hg in the control group (

 = .36). At the end of 12 months, there were no differences in the proportion of participants with blood pressure in control (29.5% of the incentive group vs 33.9% for the control group (OR, 0.8; 95% CI, 0.5-1.3

 = .36). Results of sensitivity analyses in which we assumed that the blood pressure in all patients lost to follow-up was equal to their last measured value or their baseline blood pressure (as opposed to the imputed blood pressure value) were qualitatively similar.

The pattern of changes in blood pressure from baseline to 6 months was similar, with no significant differences observed between the incentive and control group participants (

). Adjusted estimates of changes in SBP indicated no significant differences between incentive and control groups in the degree of change in blood pressure (

The degree of change in SBP between the incentive and control groups was compared among several subgroups of the populations in the study, including those with MPRs at baseline above and below 80%, those with and without diabetes, those with a baseline SBP above or below 160 mm Hg, and subgroups determined by race, income, and education. There were no statistically significant differences in change in blood pressure between the incentive and control groups 12 months post enrollment in any of the subgroups tested.

In a sample of 479 primary care patients with uncontrolled hypertension receiving care at 2 Pennsylvania VA medical centers, we found that a financial incentive that essentially eliminated co-payments for antihypertensive medications had no effect on blood pressure control. To our knowledge, this is the first clinic-based experiment to test the impact of reduced prescription cost sharing while holding constant the level of coverage for other services. The lack of an impact on blood pressure or medication adherence highlights the importance of incentive design in impacting program effectiveness. Previous studies have typically shown small but significant improvements in medication adherence with reduced co-payments.

 In this case, providing the monetary incentive as a retrospective reimbursement, rather than at the time of purchase of the medications, likely compromised effectiveness. The negative results here highlight the importance of how program design and implementation matter substantially in terms of impact when it comes to incentives (like other programs).

Our findings inform ongoing discussions about value-based insurance design and the national effort to improve patient outcomes through reduction in co-payments for high-value prescription medications.

 Strategies to modify the degree of patient cost sharing based on the value of the prescriptions provided have garnered extensive publicity and are of great interest nationally.

 Reducing the price of cost-effective treatments or preventive services encourages utilization of highly cost-effective preventive services that could potentially save money by reducing the rate of adverse events from poorly controlled health.

 Although increases in prescription co-payments have been associated with decreases in medication adherence and increases in poor health outcomes in numerous studies,

 previously published studies on co-payment reduction had not examined change in health outcomes until the MI FREEE study, which indicated reductions in the rate of total major vascular events or revascularization without any increase in total healthcare spending from a 6-percentage-point increase in cardiovascular medication adherence.

The RAND HIE—the most definitive study conducted to date on the relationship between patient cost-sharing and expenditures&mdash;found sizable effects of patient cost sharing on healthcare utilization, but more modest effects on health status. Of note, however, among low-income persons with high blood pressure, free care resulted in significant improvements in blood pressure. That study, however, was performed more than 25 years ago when fewer effective medications were available; it also excluded the elderly, was composed of participants who were largely (more than 80%) Caucasian, and included a population with relatively few comorbidities.

 Most importantly, the HIE did not isolate the effect of prescription drug cost sharing.

While we did not observe significant improvements in either medication adherence or blood pressure, observational studies of V-BID programs that have lowered co-payments have generally found that adherence, as measured by MPR, increases by an average of about 3 to 5 percentage points on a baseline MPR of 60% to 80%. This means that for every employee who was completely nonadherent (MPR = 0%) who became fully adherent (MPR ≥80%) there would be 20 to 25 employees who would now receive co-payment reductions but whose adherence did not change.

Reductions in co-payments may have less of an impact on patient outcomes than increases in co-payments, for several reasons. First, increases in co-payments affect utilization among adherent patients, whereas decreases in co-payments are targeted at affecting utilization among nonadherent patients in whom a change in co-payment of a given magnitude is likely to have less impact. Second, increases in co-payments are likely processed as a loss by patients, and behavioral economists have demonstrated that losses are felt much more strongly than equivalent gains.

 Third, co-payment reductions may be a bit like the “dog that didn’t bark.” For a nonadherent patient who doesn’t come to the pharmacy or fill prescriptions, communications about a reduction in co-payments may be largely ignored.

There are several limitations to consider. First, because prescriptions for chronic medications within the VA system are typically filled by mail and billed at the end of the month, as opposed to immediately upon filling, our use of post hoc rebates could be partly responsible for the absence of an effect—people tend to have strong present-biased preferences,

 making rewards provided in the future less valuable. We had incorporated this method of providing incentives since studies have indicated that unbundling rewards from other payments make the rewards more effective.

 Second, patients may be nonadherent for many reasons and the magnitude of the reduction in co-payments may have been too small to induce changes in behavior, though previous work has found co-payment increases as low as $0.50 to $1 per prescription (approximately $2-$3 adjusting for inflation) in a low-income population can reduce drug utilization.

 Third, our study sample was predominantly male veterans from 2 VA hospitals, so the findings may not generalize to women or patients who receive care in the private sector. However, there are no obvious reasons to believe that intervention effectiveness would be different in women. Finally, there may have been contemporaneous efforts to improve blood pressure control within VA facilities through quality improvement initiatives, and the effectiveness of these efforts may have limited our ability to measure a differential impact on blood pressure of co-payment reduction.

We found that incentives that effectively eliminated co-payments for high-value medications to treat blood pressure had no impact on blood pressure control. Reducing barriers to high-value care in and of itself makes sense; future studies should examine whether other incentive designs are more successful in improving blood pressure control.

Other coauthors (listed here rather than the byline due to space constraints) include Wei Yang, PhD; John H. Holmes, PhD; Dominick L. Frosch, PhD; Katrina Armstrong, MD, MSCE; Shiriki Kumanyika, PhD; Kjell Enge, PhD; and Raymond R. Townsend, MD. 

 Center for Health Equity Research & Promotion, Philadelphia Veterans Affairs Medical Center (KGV, JAL), PA; Center for Health Incentives and Behavioral Economics, Leonard Davis Institute, Penn CMU Roybal P30 Center in Behavioral Economics and Health (KGV, ABT, JAD, SEK), Philadelphia, PA; Department of Medicine (KGV, JAL, JAD, SEK) and Center for Therapeutic Effectiveness Research (SEK), Perelman School of Medicine of the University of Pennsylvania, Philadelphia; Department of Health Care Management, The Wharton School (KGV), and Center for Clinical Epidemiology and Biostatistics and Department of Biostatistics and Epidemiology (ABT, DA, JJ, SEK), University of Pennsylvania, Philadelphia; Center for Health Equity Research & Promotion, Pittsburgh Veterans Affairs Medical Center (SAI), PA; University of Pittsburgh School of Medicine (SAI), PA; Department of Psychology, Cheyney University (JOS), Cheyney, PA; Department of Psychology, Dickinson College (MH-L), Carlisle, PA.

 The work in this paper was primarily supported by a grant from the Commonwealth of Pennsylvania, titled Collaboration to Reduce Disparities in Hypertension, grant number ME-02-382. Supplemental support was received from Pfizer, Inc. The sponsors/funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

 Trial registration: Clinicaltrials.gov ID # NCT00133068. Drs Volpp and Kimmel had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Volpp is a principal at the behavioral economics consulting firm VAL Health, and has received funding from the Aetna Foundation, Aramark, Discovery (South Africa), Horizon Blue Cross and Blue Shield, Humana, McKinsey, Merck, the Hawaii Medical Services Agency, and Weight Watchers (all unrelated to the topic of this paper); and research funding and consulting income from CVS Caremark. Dr Kimmel has received funding from the Aetna Foundation and from several pharmaceutical companies and has done consulting for several pharmaceutical companies, including Pfizer, all unrelated to the topic of this paper. Dr Troxel has received consulting income from VAL Health. Other than what is listed above, there are no known financial conflicts of interest among any of the authors including, but not limited to, employment/affiliation, all grants or funding, honoraria, paid consultancies, expert testimony, and patents filed, received, or pending. Dr Doshi is a consultant for Alkermes, Boehringer Ingelheim, Forest Labs, Ironwood Pharmaceuticals, Merk, and Shire. She has grants pending from Humana and PhRMA, and has previously received grants from Humana, Pfizer, PhRMA, Merck, National Pharmaceutical Council, Amgen; her spouse holds stock in Merck and Pfizer. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (KGV, MH-L, SK, JOS); acquisition of data (MH-L, SK, JOS); analysis and interpretation of data (ABT, MH-L, SK, JOS, DA); drafting of the manuscript (KGV, MH-L, DA); critical revision of the manuscript for important intellectual content (ABT, SK); statistical analysis (ABT, DA); obtaining funding (MH-L, SK); and supervision (SK, JOS).

This study tests the impact on blood pressure control of a reward that lowered co-payments for blood pressure medication to $0.

J. Otis Smith, EdD

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