Increasing Copayments and Adherence to Diabetes, Hypertension, and Hyperlipidemic Medications

A copayment increase from $2 to $7 adversely affected veterans' adherence to statins, antihypertensives, and oral hypoglycemic agents.

Objective: To examine the impact of a medication copayment increase on adherence to diabetes, hypertension, and hyperlipidemic medications.

Study Design: Retrospective pre—post observational study.

Methods: This study compared medication adherence at 4 Veterans Affairs medical centers between veterans who were exempt from copayments and propensity-matched veterans who were not exempt. The diabetes sample included 1069 exempt veterans and 1069 nonexempt veterans, the hypertension sample included 3545 exempt veterans and 3545 nonexempt veterans, and the sample of veterans taking statins included 2029 exempt veterans and 2029 nonexempt veterans. The main outcome measure was medication adherence 12 months before and 23 months after the copayment increase. Adherence differences were assessed in a difference-in-difference approach by using generalized estimating equations that controlled for time, copayment exemption, an interaction between time and copayment exemption, and patient demographics, site, and other factors.

Results: Adherence to all medications increased in the short term for all veterans, but then declined in the longer term (February-December 2003). The change in adherence between the preperiod and the postperiod was significantly different for exempt and nonexempt veterans in all 3 cohorts, and nonadherence increased over time for veterans required to pay copayments. The impact of the copayment increase was particularly adverse for veterans with diabetes who were required to pay copayments.

Conclusion: A $5 copayment increase (from $2 to $7) adversely impacted medication adherence for veterans subject to copayments taking oral hypoglycemic agents, antihypertensive medications, or statins.

(Am J Manag Care. 2010;16(1):e20-e34)

Adherence to diabetes, hypertension, and hyperlipidemic medications among veterans exempt from copayments and propensity-matched nonexempt veterans was examined at 4 Veterans Affairs medical centers.

  • A $5 copayment increase (from $2 to $7) adversely impacted medication adherence for veterans subject to copayments.
  • Copayment increases need to be considered carefully by the Department of Veterans Affairs to ensure that veterans who have greater comorbidity and lower incomes than the general US population do not forgo needed medications.

Diabetes and hypertension are among the most common chronic conditions in the United States, with prevalence rates of 9.6% and 29.7% for individuals age 20 years and over in 1999-2001.1,2 Between 20% and 40% of patients with these conditions receive no medication management.3-6 Pharmacologic therapy is a mainstay in the management of both conditions, but only 50% to 70% of patients receiving medication management are adherent to medications.7,8

Improving medication adherence for individuals with diabetes or hypertension has been challenging as health plans and employers increase medication copayments,9-16 lower limits on the number or total reimbursement of covered medications,17,18 and introduce tiered benefits.19-23

Between 2000 and 2005, average copayments for commercially insured individuals increased from $7 to $10 for generic medication, $13 to $22 for preferred medications, and $17 to $35 for nonpreferred medications.24 Cost-related nonadherence has increased with higher copayments.

The Department of Veterans Affairs (VA) copayment policy mirrored these market trends by increasing medication copayments from $2 to $7 for a 30-day fill on February 4, 2002. In 1999-2000, 19.6% of veterans had diagnosed diabetes25 and 36.8% had diagnosed hypertension.26 Lipid-lowering medications are indicated for nearly all of these veterans. This study examined the impact of the VA medication copayment increase on adherence to diabetes, hypertension, and hyperlipidemic medications by veterans with diabetes or hypertension during a 35-month period (February 2001-December 2003).

This analysis contributes to the extensive literature on the effect of copayments on medication adherence by assessing several therapeutic classes across several conditions, allowing us to examine whether copayment increases have a differential impact across conditions. In addition, we assessed a copayment increase in a population with higher comorbidity and lower income than those in many prior studies, which is critical to clarify whether copayment impacts vary by income.27

A recent review article of interventions to impact medication adherence found that many evaluations of formulary and cost-sharing changes lacked control groups or pre—post comparisons, which limited their internal validity.28 We included a colocated control group that controlled for site effects not included in other studies,11-13,15,16 compared adherence before and after the copayment increase,12,13,15,29 and reduced potential bias from the nonequivalent control group with propensity score matching and covariate adjustment that could have confounded copayment effects in prior studies.30-32 This longitudinal comparison provides information on additional decrements in adherence that might occur with further copayment increases in the VA or private insurers. Identifying patients with chronic conditions who might be especially adversely impacted by copayment increases also could suggest targets for interventions to offset the adherence impacts of increased copayments.

METHODS

VA Copayment Increases

The systemwide increase in the VA medication copayment33 from $2 to $7 in February 2002 created a natural experiment to examine changes in medication adherence for veterans with diabetes or hypertension. Prior to this medication copayment increase, the VA implemented $15 copayments for primary care, $50 copayments for specialty care, and $10 per diem copayments for inpatient care effective December 6, 2001.34,35 On January 1, 2006, the medication copayment was increased again to $8 for a 30-day fill.35

Design and Study Populations

We used a retrospective, pre—post cohort design with a nonequivalent, colocated control group at 4 large tertiary Veterans Affairs medical centers (VAMCs). We identified 60,017 veterans with diabetes (n = 23,182) or hypertension (n = 51,503) who were diagnosed and prescribed a medication for either of these conditions in 2000. Veterans were included in the analysis if they (1) were alive during the entire study period, (2) had a majority of their primary care visits at 1 of the 4 VAMCs, (3) had complete information on level of military service–connected disability to determine exemption from drug copayments, (4) were not hospitalized when the copayment increase went into effect or for more than 1 year during the study period, (5) had at least 1 fill in a relevant drug class during the quarter prior to the copayment change, and (6) had at least 1 fill during the second, third, and fourth quarters prior to the copayment change.19-23 We excluded subjects on non-NPH insulin therapy that would preclude taking oral hypoglycemic agents. We did not exclude patients on NPH, because NPH insulin may be added to oral regimens in a stepped approach. The application of these criteria resulted in analytic samples of 7852 veterans with hypertension, 4407 veterans with diabetes, and 4217 veterans with diabetes or hypertension who were taking statins.

A veteran’s obligation to pay medication (and healthcare) copayments is determined by priority group assignment, based on military service-connected disability for each diagnosed condition, and on income. In 2002, veterans were exempt from medication copayments if (1) their annual income was less than $9556 if single and $12,516 if married; (2) their diabetes, hypertension, or hyperlipidemia was a service-connected disability; or (3) their diabetes, hypertension, or hyperlipidemia was not a service-connected disability, but they exceeded the $840 copayment cap in a given year. Priority group 1 veterans are exempt from all healthcare and medication copayments because the VA has determined that 50% or more of their overall disability is due to their military service, whereas priority group 7 and 8 veterans are required to pay all healthcare and medication copayments because they have no military service—related disability and have income and/or net worth above the VA national income threshold.We excluded veterans in priority groups 2 to 6 from the study because we were unable to determine whether they were required to pay medication copayments.

Unadjusted comparisons of exempt and nonexempt veterans demonstrated significant differences in every observed characteristic. To reduce potential bias from imbalance in observed covariates between exempt and nonexempt veterans and to improve equivalence of the control groups, we conducted 1-to-1 nearest-neighbor propensity score matching with replacement.36-38 After running 3 logistic regressions to generate propensity scores and matching exempt and nonexempt veterans, 762 exempt veterans with hypertension, 317 exempt veterans with diabetes, and 159 exempt veterans taking statins were excluded because there were no nonexempt veterans with similar propensity scores.

Our final hypertension matched sample included 3545 exempt veterans and 3545 nonexempt veterans. Our final diabetes matched sample included 1069 exempt veterans and 1069 nonexempt veterans. Our final matched sample of veterans with diabetes or hyperlipidemia taking statins included 2029 exempt veterans and 2029 nonexempt veterans. The unit of analysis was person-month with each veteran having up to 35 repeated measures. Human Subjects committees for all coinvestigators’ facilities (Ann Arbor, MI, Durham, NC, Hines, IL, Little Rock, AR, and Seattle, WA, VAMCs) reviewed and approved this study.

Data Sources

We used 4 VA datasets for 2001-2003. All medications dispensed from the VA are recorded in the national Pharmacy Benefits Management database; data elements include drug name, date dispensed, number of days of medication supplied, and dosage.39 The VA inpatient and outpatient care files provided information on veteran demographic characteristics and diagnoses for every inpatient hospitalization and outpatient visit in the national VA system. Benefit Identification and Record Locator System death record data identified which veterans died during the study period. Finally, the Diagnostic Cost Group Hierarchical Cost Category (DCG/HCC) version 6.0 score was used to adjust for overall comorbidity; this measure has been shown to predict veterans’ total costs40,41 and risk of hospitalization or death.42

Prescription Drug Use and Assessment of Medication Adherence

We calculated monthly medication adherence using the validated ReComp algorithm, a modification of a widely used method that is correlated with a variety of clinical outcomes.43-45 This algorithm estimates the proportion of days covered for a given measurement interval using the date dispensed and the number of days supplied with each fill. Subjects were considered adherent if they had medications available for at least 80% of each month, which is a conventional threshold that was used to maintain congruence with prior studies.46-49

For adherence to oral hypoglycemic agents (OHAs) (sulfonylureas, metformin, thiazolidinediones) and antihypertensive medications (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, beta-blockers, and alpha-1 antagonists), we calculated refill adherence separately for each class of medications. We averaged scores to produce a monthly composite OHA adherence score among the diabetes cohort and a monthly composite antihypertensive adherence score among the hypertension cohort. Adherence in veterans with diabetes and/or hypertension taking HMG-CoA reductase inhibitors (statins) was calculated by combining all statin drugs in a single adherence measurement.

Covariates

There were 3 explanatory variables of interest in the medication adherence analysis using the propensity-matched cohorts: (1) an indicator of whether a veteran was required to make copayments; (2) time indicators for the 12-month preperiod before the copayment increase (February 2001-January 2002), the 12 months (short-term postperiod) just after the copayment increase (February 2002-January 2003), and the subsequent 11-month longer-term postperiod (February 2003-December 2003); and (3) an interaction of the copayment exemption and time indicators to enable a difference-in-difference analysis. The postperiod was subdivided to examine whether adherence differed in the short term and longer term.

All models also were adjusted for age, sex, race, marital status, patient comorbidity measured as DCG/HCC score, whether a veteran was hospitalized in prior or current months, presence of a depression diagnosis at baseline, presence of comorbid diabetes (if hypertension cohort) or hypertension (if diabetes cohort), and the number of all other medications that the patient was prescribed during the preperiod. To adjust for the impact of the December 2001 outpatient visit copayment increases, we adjusted for the number of primary care, specialty care, and mental health visits 90 to 180 days prior to the current month. It is possible that the increase in healthcare copayments would decrease outpatient visits and decrease prescription renewals; the lagged visit counts attempted to control for these cross-price effects.50 Models that included a pre—post indicator for initiation of the healthcare copayment generated similar results (results not presented).

Analysis

Logistic regressions were estimated to identify whether a veteran was exempt or nonexempt from copayments and to generate predicted probabilities of being nonexempt, which served as the propensity scores for matching exempt and nonexempt veterans in the diabetes, hypertension, and statin cohorts. The propensity score model for the diabetes cohort included 19 main effects related to demographics, comorbidity, and medication burden and 18 interactions, because this specification reduced covariate imbalance within propensity score quintiles better than other specifications. Similar iterative specification tests were conducted for the logistic regressions on the hypertension cohort with a final specification including 11 main effects and 18 interactions, and the statin cohort with a final specification including 20 main effects and 28 interaction terms. Detailed results are given in Appendices A through C.

Bivariate statistics (t tests, χ2 test) were estimated to compare patient characteristics between matched exempt and nonexempt veterans, and to compare medication adherence at baseline (February 2001) and the last month of the study period (December 2003). To examine pre—post differences between propensity-matched exempt and nonexempt veterans in medication refill adherence over 35 months (12 preperiod and 23 postperiod months), we used generalized estimating equations assuming a binomial distribution, logit link, and independent working covariance structure with person-month as the unit of analysis. The working covariance structure was specified to obtain unbiased parameter estimates for the timevarying covariates.51,52 Detailed results are given in Appendices C through F.

To estimate first differences, predictions from these generalized estimating equations were obtained to identify the proportion of exempt and nonexempt veterans in each cohort who were adherent in the preperiod, the immediate postperiod, and the longer-term postperiod. Confidence intervals (CIs) for these proportions were estimated through 1000 bootstrap iterations. All analyses used Stata software (StataCorp, College Station, TX). All reported P values are 2-sided, and significance was lowered to P = .01 to account for multiple comparisons.

RESULTS

Descriptive Statistics

Propensity matching eliminated imbalance in several covariates (proportion of veterans with diabetes, hypertension, or both; marital status, sex, race, baseline hospitalization rates, and number of diabetes medications) across all 3 cohorts, but did not eliminate imbalance in site composition (Table). Nonexempt veterans with diabetes were slightly older than exempt veterans with diabetes (P <.001) and were taking more hypertension medications (P <.001) but fewer medications of all kinds (P <.0001). Nonexempt veterans with diabetes had slightly fewer primary care and specialty care visits on average than exempt veterans (both P <.001).

Nonexempt veterans with hypertension were older than exempt veterans with hypertension (P <.0001), had slightly higher DCG/HCC risk scores (P <.01), and fewer specialty care visits (P <.0001). Nonexempt veterans with diabetes and/or hypertension taking statins were older than exempt veterans taking statins (P <.0001), were taking fewer medications overall (P <.001), had slightly fewer primary care, specialty care, and mental health visits on average (all P <.0001), and slightly fewer mental health visits (P <.001).

Changes in Medication Adherence

The unadjusted proportion of nonexempt and exempt veterans with diabetes who were adherent to their OHAs at baseline (February 2001) was similar, but the proportion adherent in the last month of the study period (December 2003) was significantly lower among nonexempt veterans with diabetes (60% vs 69%; P <.0001). A greater proportion of nonexempt veterans with hypertension were adherent to their antihypertensive medication at baseline (80% vs 76%; P <.0001), but there were no differences in adherence in December 2003. Unadjusted adherence rates were similar between nonexempt and exempt veterans taking statins.

After covariate adjustment, OHA adherence among exempt veterans with diabetes increased 4.1 percentage points in the year after the copayment increase (February 2002-January 2003) compared with the preperiod, while adherence remained constant for nonexempt veterans subject to copayments (first difference −3.8%; 95% CI = −3.7%, −3.9%). In the longer-term postperiod (February-December 2003), OHA adherence declined for both diabetes cohorts compared with the preperiod but significantly more so for nonexempt veterans (−10.3% vs −0.9%; P <.001) (first difference −9.6%; 95% CI = −9.5%, −9.8%). See Figure 1.

Adherence to antihypertensive medications increased for exempt and nonexempt veterans in the year after the copayment increase (4.1% vs 5.9%; first difference −1.8%; 95% CI = −1.8%, −1.9%) compared with the preperiod, but decreased thereafter for both groups compared with the preperiod. The decline in adherence to antihypertensive medications was greater for nonexempt veterans (−5.4% vs −2.3%; first difference −3.2%; 95% CI = −3.1%, −3.3%). See Figure 2.

Adherence to statins increased for exempt and nonexempt veterans in the year after the copayment increase compared with the preperiod, but more so for exempt veterans (3.5% vs 6.6%; first difference −3.0%; 95% CI = −2.9%, −3.1%). Statin adherence continued to increase (1.2%) for exempt veterans in the longer-term postperiod (February-December 2003) compared with the preperiod, but decreased for nonexempt veterans (−1.9%; first difference −3.1%; 95% CI = −3.0%, −3.2%). See Figure 3.

DISCUSSION

Adherence to OHAs, antihypertensive medications, and statins by veterans with diabetes or hypertension increased in the year after a $5 medication copayment increase (from $2 to $7), but subsequently declined. The change in adherence between the preperiod and postperiod (as indicated by the first differences) was significantly different for exempt and nonexempt veterans in all 3 cohorts, which indicates that the medication copayment increase had adverse effects on medication adherence, as has been found in similar studies. The longer-term impact of this policy change was particularly adverse for veterans with diabetes who were required to make copayments, because their adherence to OHAs 13 to 23 months after the copayment increase was 10.3% lower than their preperiod adherence and 9% lower than the adherence of comparable veterans who were exempt.

The initial increase in adherence may have been due to VA physicians encouraging all of their patients (regardless of copay status) to stockpile medications in anticipation of the copayment increase, because VA physicians tend not to be aware of a veteran’s copayment exemption status and to treat all veterans in their panel similarly. This initial increase and subsequent decline also could have been due to a lagged effect of the copayment increase on adherence, because veterans may initially refill medications until their budgets get stretched and cost-related nonadherence ensues.

Cost-related nonadherence may have been reinforced by the cross-price effect50,53 of the increased primary care copayment (from $0 to $15) and specialty care copayment (from $15 to $50) that occurred 2 months prior to the medication copayment increase. Increased healthcare copayments could impact adherence rates in 2 ways. The out-of-pocket costs of outpatient visits could have reduced visit rates at which prescriptions would be initiated, modified, or renewed. (In the postperiod, we observed significantly lower rates of primary care visits for veterans in all 3 cohorts [P <.0001].) In addition, veterans with fixed incomes who were keeping their outpatient appointments might sacrifice their medications to pay for these visits.29,54 It also is possible that the initial adherence increase and subsequent decline could have been an artifact of our inclusion criteria. In a sensitivity analysis, we included veterans with 2 or more fills in the 12 months prior to the copayment increase (instead of 1 fill in the 3 months prior and another fill 4-9 months prior), but the difference-in-difference results were unchanged because trends were similar for exempt and nonexempt veterans.

The decline in adherence to OHAs was significantly greater for veterans required to pay copayments than for exempt veterans, but the change in adherence to antihypertensives and statins was more modest. It may simply be that patients value their cardiovascular medications more than their diabetes medications, as has been shown in prior studies.13,55 A study of commercially insured populations by Goldman et al13 found that patients with diabetes had a greater reduction in days supply in response to a doubling of copayments than patients with hypertension or hyperlipidemia (25% vs 10%), which is consistent with our results. However, a study of nonelderly, disability-eligible Medicare beneficiaries by Soumerai et al56 reported similar rates of cost-related nonadherence for beneficiaries with diabetes and beneficiaries with hypertension. Finally, our statin findings are consistent with a recent study of veterans from 1 VAMC taking lipid-lowering medications,32 despite several study design differences that make comparison difficult, including that study’s significantly larger sample size, less equivalent control group, lack of propensity score matching, and more limited covariate adjustment.

We attempted to improve upon prior studies of cost-sharing and adherence by including a colocated control group11-13,15,16 instead of a nonequivalent, geographically distinct control group,10,19-23,57 by reducing potential bias through propensity score matching, and by contrasting pre—post changes of treatment and control groups.12,13,15,29 We also examined medication adherence across a number of conditions to illustrate a range of clinical impacts of the copayment increase, which provides further evidence that the adherence decline that we observed in the diabetes sample was due to the copayment increase and not other factors.

However, several limitations remain. We were unable to track veterans’ use of non-VA medications. It is possible that veterans with Medicaid or private insurance who became nonadherent to VA-acquired medications were simply obtaining them elsewhere, but this omission is likely to be minimal because VA copayments were lower than prevailing rates in commercial insurance and Medicare Part D plans at the time, and Walmart prescription drug programs were not available until 2006. The generalizability of our results is somewhat limited because our sample was drawn from 4 large VAMCs. However, we chose geographically dispersed VAMCs to reduce small area variation biases. Despite the propensity score matching, there remained a few differences in observed factors between exempt and nonexempt veterans, although we significantly reduced the extent of covariate imbalance and controlled for remaining imbalances directly in the adherence regression. However, the longitudinal natural experiment enabled us to control for fixed person-specific effects and time trends to minimize unobserved confounding, whereas propensity score matching and covariate adjustment reduced the likelihood of observed or unobserved confounding. These results appear to be robust.

It appears that a $5 copayment increase from $2 to $7 was sufficiently large to adversely impact medication adherence among veterans who have greater comorbidity and lower incomes than the general US population. The VA has since increased medication copayments to $8. Future copayment increases need to be considered carefully. If implemented, copayment increases should be matched with nonfinancial interventions to offset cost-related nonadherence. The VA may want to consider linking copayments with the clinical value of medications, because copayment reductions for high-value medications have been shown to reduce nonadherence in commercial populations.58 Given that many veterans taking diabetes, hypertension, and hyperlipidemic medications are elderly or near elderly and are on fixed incomes, such copayment reductions may generate a sizable response if adherence changes mirror the decline observed in this analysis in response to a $5 copayment increase.

Funding Source: This work was supported by the Office of Research and Development, Health Services Research and Development Service, Department of Veterans Affairs, project number IIR 03-200. The views expressed are those of the authors and do not reflect the views of the Department of Veterans Affairs.

Author Disclosure: Dr Maciejewski reports serving as a consultant for Takeda Pharmaceuticals and reports owning stock in Amgen. The other authors (CLB, MP, DKB, FEC, JCF, SLK, KTS, NDS, and C-FL) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MLM, CLB, FEC, DKB, JCF, KTS, NDS, C-FL); acquisition of data (MLM, MP, DKB, FEC, SLK, KTS, NDS, C-FL); analysis and interpretation of data (MLM, CLB, MP, DKB, JCF, SLK, C-FL); drafting of the manuscript (MLM, CLB, MP, JCF, NDS, C-FL); critical revision of the manuscript for important intellectual content (MLM, CLB, DKB, JCF, SLK, KTS, NDS, C-FL); statistical analysis (MLM, CLB, MP, DKB, C-FL); provision of study materials or patients (FEC, C-FL); obtaining funding (MLM, CLB, C-FL); administrative, technical, or logistic support (MLM, SLK, NDS, C-FL); and supervision (MLM, C-FL).

Address correspondence to: Matthew L. Maciejewski, PhD, Center for Health Services Research in Primary Care (152), Durham VA Medical Center, 508 Fulton St, Durham, NC 27705. E-mail: matthew.maciejewski@va.gov.

1. Ioannou GN, Bryson CL, Boyko EJ. Prevalence and trends of insulin resistance, impaired fasting glucose, and diabetes. J Diabetes Complications. 2007;21(6):363-370.

2. National Center for Health Statistics. Health, United States, 2006 with Chartbook on Trends in the Health of Americans. Hyattsville, MD; 2006. www.cdc.gov/nchs/data/hus/hus06.pdf. Accessed December 2, 2009.

3. Psaty BM, Manolio TA, Smith NL, et al; Cardiovascular Health Study. Time trends in high blood pressure control and the use of antihypertensive medications in older adults: the Cardiovascular Health Study. Arch Intern Med. 2002;162(20):2325-2332.

4. Hajjar I, Kotchen TA. Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988-2000. JAMA. 2003;290(2):199-206.

5. Gu Q, Paulose-Ram R, Dillon C, Burt V. Antihypertensive medication use among US adults with hypertension. Circulation. 2006;113(2):213-221.

6. Beaton SJ, Nag SS, Gunter MJ, Gleeson JM, Sajjan SS, Alexander CM. Adequacy of glycemic, lipid, and blood pressure management for patients with diabetes in a managed care setting [published correction appears in Diabetes Care. 2004;27(7):1855]. Diabetes Care. 2004;27(3):694-698.

7. Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27(5):1218-1224.

8. Psaty BM, Koepsell TD, Yanez ND, et al. Temporal patterns of antihypertensive medication use among older adults, 1989 through 1992. An effect of the major clinical trials on clinical practice? JAMA. 1995;273(18):1436-1438.

9. Smith DG. The effects of copayments and generic substitution on the use and costs of prescription drugs. Inquiry. 1993;30(2):189-198.

10. Motheral BR, Henderson R. The effect of a copay increase on pharmaceutical utilization, expenditures, and treatment continuation. Am J Manag Care. 1999;5(11):1383-1394.

11. Tamblyn R, Laprise R, Hanley JA, et al. Adverse events associated with prescription drug cost-sharing among poor and elderly persons. JAMA. 2001;285(4):421-429.

12. Joyce GF, Escarce JJ, Solomon MD, Goldman DP. Employer drug benefit plans and spending on prescription drugs [published correction appears in JAMA. 2002;288(19):2409]. JAMA. 2002;288(14):1733-1739.

13. Goldman DP, Joyce GF, Escarce JJ, et al. Pharmacy benefits and the use of drugs by the chronically ill. JAMA. 2004;291(19):2344-2350.

14. Gibson TB, McLaughlin CG, Smith DG. A copayment increase for prescription drugs: the long-term and short-term effects on use and expenditures. Inquiry. 2005;42(3):293-310.

15. Schultz JS, O’Donnell JC, McDonough KL, Sasane R, Meyer J. Determinants of compliance with statin therapy and low-density lipoprotein cholesterol goal attainment in a managed care population. Am J Manag Care. 2005;11(5):306-312.

16. Taira DA, Wong KS, Frech-Tamas F, Chung RS. Copayment level and compliance with antihypertensive medication: analysis and policy implications for managed care. Am J Manag Care. 2006;12(11):678-683.

17. Soumerai SB, McLaughlin TJ, Ross-Degnan D, Casteris CS, Bollini P. Effects of a limit on Medicaid drug-reimbursement on the use of psychotropic agents and acute mental health services by patients with schizophrenia. N Engl J Med. 1994;331(10):650-655.

18. Hsu J, Price M, Huang J, Brand R, et al. Unintended consequences of caps on Medicare drug benefits. N Engl J Med. 2006;354(22):2349-2359.

19. Motheral B, Fairman KA. Effect of a three-tier prescription copay on pharmaceutical and other medication utilization. Med Care. 2001;39(12):1293-1304.

20. Huskamp HA, Deverka PA, Epstein AM, Epstein RS, McGuigan KA, Frank RG. The effect of incentive-based formularies on prescription-drug utilization and spending. N Engl J Med. 2003;349(23):2224-2232.

21. Fairman KA, Motheral BR, Henderson RR. Retrospective, long-term follow-up study of the effect of a three-tier prescription drug copayment system on pharmaceutical and other medical utilization and costs. Clin Ther. 2003;25(12):3147-3161.

22. Kamal-Bahl S, Briesacher B. How do incentive-based formularies influence drug selection and spending for hypertension? Health Aff (Millwood). 2004;23(1):227-236.

23. Landsman PB, Yu W, Liu X, Teutsch SM, Berger ML. Impact of 3-tier pharmacy benefit design and increased consumer cost-sharing on drug utilization. Am J Manag Care. 2005;11(10):621-628.

24. Kaiser Family Foundation. Prescription Drug Trends. June 2006. Fact sheet 3057-05. http://www.kff.org/rxdrugs/upload/3057-05.pdf. Accessed February 20, 2007.

25. Miller DR, Safford MM, Pogach LM. Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data. Diabetes Care. 2004;27(suppl 2):B10-B21.

26. Yu W, Ravelo A, Wagner TH, et al. Prevalence and costs of chronic conditions in the VA health care system. Med Care Res Rev. 2003;60(3 suppl):146S-167S.

27. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. JAMA. 2007;298(1):61-69.

28. Lu CY, Ross-Degnan D, Soumerai SB, Pearson SA. Interventions designed to improve the quality and efficiency of medication use in managed care: a critical review of the literature—2001-2007. BMC Health Serv Res. 2008;8:75.

29. Piette JD, Wagner TH, Potter MB, Schillinger D. Health insurance status, cost-related medication underuse, and outcomes among diabetes patients in three systems of care. Med Care. 2004;42(2):102-109.

30. Stroupe KT, Smith BM, Lee TA, et al. Effect of increased copayments on pharmacy use in the Department of Veterans Affairs. Med Care. 2007;45(11):1090-1097.

31. Zeber JE, Grazier KL, Valenstein M, Blow FC, Lantz PM. Effect of a medication copayment increase in veterans with schizophrenia. Am J Manag Care. 2007;13(6 pt 2):335-346.

32. Doshi JA, Zhu J, Lee BY, Kimmel SE, Volpp KG. Impact of a prescription copayment increase on lipid-lowering medication adherence in veterans. Circulation. 2009;119(3):390-397.

33. Veterans Health Administration. VHA Directive 2001-081: Implementation of Medication Copayment Changes. December 18, 2001.

34. Veterans Health Administration. VHA Directive 2001-072: Implementation of Copayments for Inpatient Hospital Care and Outpatient Medicare Care. November 28, 2001.

35. Veterans Health Administration. VHA Directive 2005-052: Implementation of Medication Co-Payment Changes. November 15, 2005.

36. Baser O. Too much ado about propensity score models? Comparing methods of propensity score matching. Value Health. 2006;9(6):377-385.

37. Rubin DB, Thomas N. Combining propensity score matching with additional adjustments for prognostic covariates. J Am Stat Assoc. 2000;95:573-585.

38. Jones AS, Richmond DW. Causal effects of alcoholism on earnings: estimates from the NLSY. Health Econ. 2006;15(8):849-871.

39. Sales MM, Cunningham FE, Glassman PA, Valentino MA, Good CB. Pharmacy benefits management in the Veterans Health Administration: 1995 to 2003. Am J Manag Care. 2005;11(2):104-112.

40. Sales AE, Liu CF, Sloan KL, et al. Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population. Med Care. 2003;41(6):753-760.

41. Maciejewski ML, Liu CF, Derleth A, McDonell MB, Anderson SM, Fihn SD. The performance of administrative and self-reported measures for risk adjustment of Veterans Affairs expenditures. Health Serv Res. 2005;40(3):887-904.

42. Petersen LA, Pietz K, Woodard LD, Byrne M. Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes. Med Care. 2005;43(1):61-67.

43. Bryson CL, Au DH, Young B, McDonell MB, Fihn SD. A refill adherence algorithm for multiple short intervals to estimate refill compliance (ReComp). Med Care. 2007;45(6):497-504.

44. Steiner JF, Koepsell TD, Fihn SD, Inui TS. A general method of compliance assessment using centralized pharmacy records. Description and validation. Med Care. 1988;26(8):814-823.

45. Steiner JF, Prochazka AV. The assessment of refill compliance using pharmacy records: methods, validity, and applications. J Clin Epidemiol. 1997;50(1):105-116.

46. Benner JS, Glynn RJ, Mogun H, Neumann PJ, Weinstein MC, Avorn J. Long-term persistence in use of statin therapy in elderly patients. JAMA. 2002;288(4):455-461.

47. Rudd P. Compliance with antihypertensive therapy: a shifting paradigm. Cardiol Rev. 1994;2(4):230-240.

48. Insull W. The problem of compliance to cholesterol altering therapy. J Intern Med. 1997;241(4):317-325.

49. Andrade SE, Kahler KH, Frech F, Chan KA. Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf. 2006;15(8):565-574.

50. Goldman DP, Joyce GF, Karaca-Mandic P. Varying pharmacy benefits with clinical status: the case of cholesterol-lowering therapy. Am J Manag Care. 2006;12(1):21-28.

51. Pepe MS, Sullivan M, Anderson GL. A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data. Communications in Statistics: Simulation and Computation. 1994;23:939-951.

52. Pepe MS, Couper D. Modeling partly conditional means with longitudinal data. J Am Statistical Assoc. 1997;92(439):991-998.

53. Cecil WT, Barnes J, Shea T, Coulter SL. Relationship of the use and costs of physician office visits and prescription drugs to travel distance and increases in member cost share. J Manag Care Pharm. 2006;12(8):665-676.

54. Heisler M, Wagner TH, Piette JD. Patient strategies to cope with high prescription medication costs: who is cutting back on necessities, increasing debt, or underusing medications? J Behav Med. 2005;28(1):43-51.

55. Inui TS, Carter WB, Pecoraro RE, Pearlman RA, Dohan JJ. Variations in patient compliance with common long-term drugs. Med Care. 1980;18(10):986-993.

56. Soumerai SB, Pierre-Jacques M, Zhang F, et al. Cost-related medication nonadherence among elderly and disabled medicare beneficiaries: a national survey 1 year before the Medicare drug benefit. Arch Intern Med. 2006;166(17):1829-1835.

57. Gibson TB, Mark TL, McGuigan KA, Axelsen K, Wang S. The effects of prescription drug copayments on statin adherence. Am J Manag Care. 2006;12(9):509-517.

58. Chernew ME, Shah MR, Wegh A, et al. Impact of decreasing copayments on medication adherence within a disease management environment. Health Aff (Millwood). 2008;27(1):103-112.