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The American Journal of Managed Care August 2015
Differential Impact of Mental Health Multimorbidity on Healthcare Costs in Diabetes
Leonard E. Egede, MD, MS; Mulugeta Gebregziabher, PhD; Yumin Zhao, PhD; Clara E. Dismuke, PhD; Rebekah J. Walker, PhD; Kelly J. Hunt, PhD, MSPH; and R. Neal Axon, MD, MSCR
Clinical Efficacy: A Cost Containment Weapon for the 21st Century
Lonny Reisman, MD, Chief Executive Officer, HealthReveal
Opportunity Costs of Ambulatory Medical Care in the United States
Kristin N. Ray, MD, MS; Amalavoyal V. Chari, PhD; John Engberg, PhD; Marnie Bertolet, PhD; and Ateev Mehrotra, MD, MPH
A Comparison of Relative Resource Use and Quality in Medicare Advantage Health Plans Versus Traditional Medicare
Bruce E. Landon, MD, MBA, MSc; Alan M. Zaslavsky, PhD; Robert Saunders, PhD; L. Gregory Pawlson, MD, MPH; Joseph P. Newhouse, PhD; and John Z. Ayanian, MD, MPP
Medicare Shared Savings Program: Public Reporting and Shared Savings Distributions
John Schulz, BA; Matthew DeCamp, MD, PhD; and Scott A. Berkowitz, MD, MBA
Global Payment Contract Attitudes and Comprehension Among Internal Medicine Physicians
Joshua Allen-Dicker, MD, MPH; Shoshana J. Herzig, MD, MPH; and Russell Kerbel, MD, MBA
Addressing the Primary Care Workforce Crisis
Zirui Song, MD, PhD; Vineet Chopra, MD, MSc; and Laurence F. McMahon, Jr, MD, MPH
The Association Among Medical Home Readiness, Quality, and Care of Vulnerable Patients
Lena M. Chen, MD, MS; Joseph W. Sakshaug, PhD; David C. Miller, MD, MPH; Ann-Marie Rosland, MD, MS; and John Hollingsworth, MD, MS
Trends in Public Perceptions of Electronic Health Records During Early Years of Meaningful Use
Jessica S. Ancker, MPH, PhD; Samantha Brenner, MD; Joshua E. Richardson, PhD, MLIS, MS; Michael Silver, MS; and Rainu Kaushal, MD, MPH
Feasibility of Integrating Standardized Patient-Reported Outcomes in Orthopedic Care
James D. Slover, MD, MS; Raj J. Karia, MPH; Chelsie Hauer, MPH; Zachary Gelber, DDS; Philip A. Band, PhD; and Jove Graham, PhD
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A Randomized Controlled Trial of Co-Payment Elimination: The CHORD Trial
Kevin G. Volpp, MD, PhD; Andrea B. Troxel, ScD; Judith A. Long, MD; Said A. Ibrahim, MD, MPH; Dina Appleby, MS; J. Otis Smith, EdD; Jane Jaskowiak, BSN, RN; Marie Helweg-Larsen, PhD; Jalpa A. Doshi, PhD; and Stephen E. Kimmel, MD, MSCE

A Randomized Controlled Trial of Co-Payment Elimination: The CHORD Trial

Kevin G. Volpp, MD, PhD; Andrea B. Troxel, ScD; Judith A. Long, MD; Said A. Ibrahim, MD, MPH; Dina Appleby, MS; J. Otis Smith, EdD; Jane Jaskowiak, BSN, RN; Marie Helweg-Larsen, PhD; Jalpa A. Doshi, PhD; and Stephen E. Kimmel, MD, MSCE
This study tests the impact on blood pressure control of a reward that lowered co-payments for blood pressure medication to $0.

Objectives: 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.

Study Design: Randomized controlled trial.
Methods: 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.
Results: 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; P = .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; P = .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; P = .36).
Conclusions: Among patients with poorly controlled blood pressure, financial incentives—as implemented in this trial—that reduced patient cost sharing for blood pressure medications did not improve medication adherence or blood pressure control.
Am J Manag Care. 2015;21(8):e455-e464
Take-Away Points
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—remains a leading cause of cardiovascular morbidity and mortality in the United States, affecting nearly 50 million Americans.1 Although there are effective medications to treat hypertension, nearly two-thirds of Americans with the condition have poorly controlled blood pressure.2 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,3-7 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—is being widely adopted.8,9 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,10-18 the impact of decreasing co-payments seen in observational studies has been more modest19-25 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.26

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.27

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 Population

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 Figure 1).

Study Protocol

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 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 Procedures

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.

Outcome Assessments

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.

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.28 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,29 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).30-33 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.34

Study Covariates

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.

Statistical Analysis

To evaluate the similarity of the treatment groups with respect to baseline covariates, we compared groups using Student’s t test for continuous variables and χ2 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.

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 t 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.35 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.

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