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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
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
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A Randomized Controlled Trial of Negative Co-Payments: 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; Jalpa A. Doshi, PhD; Jane Jaskowiak, BSN, RN; Marie Helweg-Larsen, PhD; and Stephen E. Kimmel, MD, MSCE

A Randomized Controlled Trial of Negative Co-Payments: 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; Jalpa A. Doshi, PhD; Jane Jaskowiak, BSN, RN; Marie Helweg-Larsen, PhD; and Stephen E. Kimmel, MD, MSCE
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.
ABSTRACT
 
Objectives: 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.
 
Study Design: Randomized controlled trial.
 
Methods: 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.
 
Results: 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; P = .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; P = .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 (P = .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 (P = .71).
 
Conclusions: Among patients with poorly controlled blood pressure, financial incentives—as implemented in this trial—did not improve blood pressure control or adherence except among patients with diabetes.
 
Am J Manag Care. 2015;21(9):e465-e473
Take-Away Points
 
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,1-5 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,6-14 the impact of decreasing co-payments seen in observational studies has been more modest15-22; 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.23

Nearly two-thirds of Americans with hypertension have poor control,24 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.

 
METHODS
Study Population

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. (Figure 1 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.

Study Protocol

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 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 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.

Outcome Assessments

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.25 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,26 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).27

Covariates

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.

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. 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 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.28 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.

 
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