Felicia Forma, BSc

To quantify how adherence mismeasurement affects the estimated impact of adherence on inpatient costs among patients with serious mental illness (SMI).

Proportion of days covered (PDC) is a common claims-based measure of medication adherence. Because PDC does not measure medication ingestion, however, it may inaccurately measure adherence. We derived a formula to correct the bias that occurs in adherence-utilization studies resulting from errors in claims-based measures of adherence. 

We conducted a literature review to identify the correlation between gold-standard and claims-based adherence measures. We derived a bias-correction methodology to address claims-based medication adherence measurement error. We then applied this methodology to a case study of patients with SMI who initiated atypical antipsychotics in 2 large claims databases.

 Our literature review identified 6 studies of interest. The 4 most relevant ones measured correlations between 0.38 and 0.91. Our preferred estimate implies that the effect of adherence on inpatient spending estimated from claims data would understate the true effect by a factor of 5.3, if there were no other sources of bias. Although our procedure corrects for measurement error, such error also may amplify or mitigate other potential biases. For instance, if adherent patients are healthier than nonadherent ones, measurement error makes the resulting bias worse. On the other hand, if adherent patients are sicker, measurement error mitigates the other bias. 

Measurement error due to claims-based adherence measures is worth addressing, alongside other more widely emphasized sources of bias in inference. 

Using pharmacy claims data on patients with serious mental illness (SMI), this study demonstrates that increased medication adherence correlates with lower inpatient costs. A bias-correction formula was used to show that measurement error in claims-based adherence measures results in the effects of adherence being underestimated by a factor of 5.3. 

		Medication nonadherence is a burdensome problem in SMI, substantially contributing to healthcare costs. 

		Few studies address errors in adherence measurement, which can introduce bias when estimating the impact of adherence on inpatient costs. 

		Using a reliable estimate of the correlation between true adherence and claims-based adherence can identify the effects of measurement error on the estimated relationship between adherence and cost.

Nonadherence to medication is a prevalent and burdensome problem among patients with serious mental illness (SMI).

 Estimates of adherence to antipsychotics among patients with schizophrenia-spectrum disorders, for instance, range from 47% to 95%.

 The consequences of poor adherence include suboptimal health outcomes and higher avoidable healthcare costs.

 In patients with schizophrenia, medication nonadherence impedes recovery,

 and extends the length of in-hospital stays.

 Overall, hospitalizations due to medication nonadherence have been estimated to cost more than $100 billion annually in the United States,

 and hospitalization costs due to antipsychotic nonadherence specifically have been estimated at $1.5 billion annually.

CMS includes medication adherence as a rating measure when determining healthcare quality.

 A common method to indirectly assess adherence is the proportion of days covered (PDC),

 which typically uses prescription claims data and is calculated as the proportion of days in the measurement period, usually 1 year, for which the patient has medication on hand. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) has determined that PDC is one of the preferred methods to calculate medication adherence.

 PDC also is used to measure adherence to antipsychotic medications as part of the Healthcare Effectiveness Data and Information Set quality measures.

Although PDC is widely used, it suffers from 2 key shortcomings. First, PDC underestimates adherence when patients pay cash for medication or use other coverage options that fail to result in a recorded insurance claim. Second, PDC overstates adherence when patients purchase but do not take a given medication. New technologies, such as electronic pillboxes, smart caps, or ingestible sensors, may provide more accurate adherence measurements, but currently, payers and providers rarely use these technologies to monitor adherence. 

Neither the magnitude nor the direction of the bias associated with PDC adherence estimates are widely discussed or incorporated into adherence analyses. This study aimed to quantify how adherence mismeasurement affects the estimated impact of adherence on inpatient costs among patients with SMI. 

Our methodology relies on a 3-step approach to estimate the potential impact of measurement error on inpatient spending. We mathematically derived a formula for a bias-correction factor. As this factor depends principally on the link between true and measured adherence, we next conducted a review of the literature to identify studies that measured the relationship between a “gold standard” measure of adherence (eg, Medication Event Monitoring System [MEMS] caps, electronic pill counts) and adherence measured in claims data. Finally, we applied the bias-correction factor to a case study of patients with SMI who initiated therapy with an atypical antipsychotic. 

Consider the case where a researcher wants to measure the relationship between patient adherence and inpatient spending. One commonly used approach is an ordinary least squares (OLS) regression, such as the following: 

 represents inpatient spending for patient 

 represents patient adherence to atypical antipsychotics, and the vector W

' contains other patient covariates of interest. The coefficient on adherence, 

Mismeasurement in PDC biases the estimated effect of adherence on inpatient spending (ie, 

 Ì‚) toward 0. However, the true effect of adherence on inpatient costs can be derived if the relationship between measured adherence in claims data and true adherence is known. As shown in eAppendix 1 (

), one can correct for measurement error bias using the following formulation:

  is the true effect of adherence on inpatient spending after adjusting for adherence mismeasurement, 

 Ì‚is the OLS estimator from the regression in equation 1, and (1-

) is the correction factor for the mismeasurement in 

 is the R-squared value of linear regression of measured adherence (

 is the square of the correlation between measured and true adherence. 

The true effect of adherence is feasible to estimate, but problematic. First, one must assume that there cannot be any unobserved patient characteristics that affect both medication adherence and inpatient spending. For instance, patients with less severe forms of SMI may be more likely to be adherent to their medication and have lower inpatient costs.

 To address this, we derived the bias that would remain in the case where medication adherence was an endogenous variable in eAppendix 1. The formula we derived fully addresses the bias due to measurement error, even when adherence is endogenous, so that researchers will recover the same parameter that would have been estimated if they had had access to correctly measured adherence. To be clear, our approach does not simultaneously address the endogeneity itself; rather, it represents a full solution to the problem of measurement error in adherence due to the use of claims data. 

Second, one needs a reliable estimate of the correlation between true medication adherence and claims-based adherence. The following section describes our review of the literature that was used to identify this parameter. 

A targeted literature review was conducted in Google Scholar and PubMed to identify estimates of the relationship between gold-standard adherence measures and claims-based adherence measures. The search combined free text and medical subject headings (MeSHs; in PubMed only) that describe various measures of adherence (MEMS caps, direct observation, PDC, medication possession ration, self-report, prescription claims, lab tests, pill count, and physician estimate) and the search term “adherence accuracy.” Searches were conducted without disease specification, with the term “schizophrenia,” and with the term “serious mental illness.” After reviewing the results of this initial search, the search was conducted again with other central nervous system diseases, specifically multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease. A maximum of 50 titles were screened in each search or until titles were no longer generally relevant to the research questions. 

Abstracts were screened from relevant titles, which were defined as papers that discussed or compared multiple measures of medication adherence. Titles were not relevant if they indicated an intervention to improve adherence, factors influencing adherence, or outcomes associated with adherence or nonadherence, without language suggesting relevance. Titles that were non-English, nonhuman, and did not focus on schizophrenia or another SMI were also not investigated. Full texts were assessed if their abstracts included adherence measures collected using different methodologies and these data were explicitly compared in the results or conclusions sections. Abstracts and full texts were not identified or screened more than once if they appeared as results from multiple searches or were duplicated in databases. Additional citations were identified through previous literature searches, forward reference searches of each manuscript, and the references used in each manuscript. A complete description of the search terms used, number of full texts, abstracts, and articles reviewed is contained in eAppendix 2. 

We used the Truven Health Analytics MarketScan (MarketScan) Commercial Claims and Encounters Database and the Medicaid Multi-State Database from October 1, 2007 through December 31, 2013 to identify patients with SMI. The commercial database included medical and pharmacy claims for individuals and their dependents who were covered by employer-sponsored private health insurance. The Medicaid database included medical and pharmacy claims of Medicaid beneficiaries from 11 deidentified but geographically dispersed states. We limited the sample to individuals aged 18 or older who had at least 1 inpatient or 2 outpatient claims with a diagnosis code for schizophrenia (

International Classification of Diseases, Ninth Revision, Clinical Modification

] diagnosis code: 295.x), bipolar disorder (

 diagnosis code: 296.0x—296.1x, 296.4x–296.8x), or major depressive disorder (

 diagnosis code: 296.2x—296.3x, 311.x). 

To measure adherence and healthcare utilization among patients initiating therapy, patients were required to have a new prescription for an antipsychotic and to be continuously enrolled for 6 or more months before and 12 or more months after the date they filled the new prescription. We required that a patient have an SMI diagnosis and no antipsychotic prescriptions during the 6-month “clean” period before the medication initiation date. Both atypical (eg, aripiprazole, asenapine, iloperidone, lurasidone, olanzapine, paliperidone, quetiapine, risperidone, ziprasidone) and typical (eg, chlorpromazine, fluphenazine haloperidol, perphenazine) oral antipsychotics were included in the analysis. Patients using clozapine were excluded from the sample, as clozapine is typically used only for patients who do not respond to at least 2 other antipsychotic medications.

 Also excluded were patients missing data on age or patients who received antipsychotics via mail order.

We applied an OLS regression to measure the effect of medication adherence on inpatient cost measured over the 365 days following the initiation of the antipsychotic medication regimen. Costs included payments made by primary payers (ie, commercial insurers or Medicaid), patient out-of-pocket payments, and other third-party payments. All costs were inflated to 2015 US dollars using the Consumer Price Index.

 The primary independent variable of interest was a patient’s PDC, which was calculated as the total days covered by all antipsychotics supplied across all claims in the 365 days after the initial medication fill divided by 365. As this study focused on nonadherence rather than overuse, PDC values were capped at 100%. 

To account for compositional differences between the populations of adherent and nonadherent patients, we included patient demographics and health status as explanatory variables in our regression analysis. The Charlson Comorbidity Index

 was used to measure patients’ overall health status.

 Mental health status was measured based on the presence of comorbid SMI conditions (ie, schizophrenia, bipolar disorder, or major depressive disorder), a diagnosis of alcoholism (

: 265.2, 291.1-291.3, 291.5-291.9, 303.0, 303.9, 305.0, 357.5, 425.5, 535.3, 571.0-571.3, 980.x, V11.3), or drug dependence or abuse (

 We also included an indicator for whether the patient was commercially insured or covered by Medicaid and a measure of inpatient spending levels during the 6 months before antipsychotic initiation, as a measure of disease severity. In addition to our baseline specification, we included interaction terms of PDC with indicator terms for specific SMI diagnoses (ie, schizophrenia, bipolar disorder, and major depressive disorder) to estimate the effect of adherence for patients with a specific type of SMI.

All statistical analyses were performed using Stata-MP version 13.0 (StataCorp LP; College Station, Texas).

Estimates of the correlation between gold-standard medication adherence and other adherence measures varied widely in the literature. As shown in 

, only 2 studies measured the correlation between electronic monitoring systems and prescription refill records, with correlation values of 0.32 and 0.48, respectively.

 Two additional studies calculated the correlation between electronic monitoring systems and non-claims measures.

 When we broadened our definition of gold standard to include pill counts, we found 2 additional studies.

 Ignoring the relationship between adherence and patient characteristics, these figures indicate that the effect of adherence on inpatient spending could be underestimated by a factor ranging from 1.2 to 9.8, due to measurement error alone.

In our empirical case study, 145,235 patients in the commercially insured population and 86,321 patients in the Medicaid population had an SMI and initiated an antipsychotic medication regimen (

). Descriptive statistics for patient characteristics are shown in 

. Patient age was concentrated between 28 and 55 years, and 65.3% of patients in our sample were women. Of these patients, 17.7% had an alcohol dependence problem and 9.8% suffered from drug abuse. Among patients with an SMI, 16.4%, 47.8%, and 75.6% had schizophrenia, bipolar disorder, or major depressive disorder, respectively. The average patient had a PDC of 48.0%. 

Results from the regression analysis indicated that patients with higher adherence had lower levels of inpatient spending (

). In our baseline approach (model 1), a 10-percentage-point increase in PDC was correlated with a change in annual inpatient cost of —$41 (95% confidence interval [CI], –$65 to –$16; 

= .001) per patient with an SMI. When allowing for PDC to interact with each of the 3 SMIs of interest (model 2), schizophrenia patients with a 10-percentage-point higher PDC had a higher inpatient cost of $86 (95% CI, —$152 to –$20; 

= .011). The corresponding amounts for major depressive disorder and bipolar disorder were $67 (95% CI, —$106 to –$27; 

When we corrected for adherence mismeasurement using the bias-correction formula above and our preferred estimate for the correlation between claims-based and true adherence, the impact of medication on inpatient spending increased by a factor of 5.3. This estimate is calculated from the correlation from Hansen et al

 between the PDC and patient covariates: 

When we applied this bias-correction factor to our case study, patients with a 10-percentage-point greater PDC had $217 (95% CI, —$347 to –$87) higher inpatient cost per patient (

). Even if we applied the most conservative adjustment factor from Bruce et al,

 that would still inflate the relationship by 20%:

Articles

This study demonstrates that common pharmacy claims-based measures underestimate the effect of actual adherence on inpatient costs among patients with serious mental illness.

The Cost of Adherence Mismeasurement in Serious Mental Illness: A Claims-Based Analysis

As the nation transforms its healthcare system—with or without the Affordable Care Act (ACA)&mdash;it must face the challenge of how to maintain, or even improve, the quality of care. This requires the system to be more nuanced; to encourage use of those healthcare services that produce greater health and discourage the use of those that produce less. Implementation of this simple idea requires first identifying the clinical benefit associated with different services.

Unfortunately, we cannot always identify when services improve health, as health benefit is dependent on the specific clinical scenario and patient population. In diabetes care, this is often borne out by the existence of complex comorbidities and complications. Understanding the clinical nuances of when and to whom services render the greatest benefit requires more research. The type of research that addresses this issue is commonly labeled comparative effectiveness research (CER)—although CER by other names has been around for years. Recognizing the need for stronger evidence-based practice, the ACA invested in greater CER by establishing the Patient-Centered Outcomes Research Institute (PCORI) which will commission independent CER.

Starting in 2011 and moving forward, PCORI will act as a funding source for independent research institutions to conduct CERrelated research and will synthesize these findings and make them available to the public. 

CER is especially important in diabetes care because of the pervasiveness and multifaceted nature of the disease. Diabetes is one of the nation’s most prevalent chronic conditions, with over 8% of the US population living with the disease.

 In part due to its chronic nature and because of several widely accepted high-value treatments, the management of diabetes has become a widespread benchmark of quality. For example, most standard quality measurement systems, including the Healthcare Effectiveness Data and Information Set and most pay-for-performance systems, include a series of diabetes-related measures. These include clinical services (eg, eye and foot exams) as well as drug management for hyperglycemia, hypertension, and hypercholesterolemia. Due in part to the effectiveness of treatment and disease management as compared with non-treatment, the US Preventive ServicesTask Force recommended diabetes screening as a high-value preventive service and in turn the ACA has mandated payers to provide diabetes screening without patient cost-sharing. While there is broad consensus that several treatments are clinically effective, there is still great opportunity for CER to elucidate the optimal combination of treatments for each patient population.

There is also an opportunity to gain much more value for the money we spend on the treatment of diabetes.The American Diabetes Association estimated around $116 billion in medical expenditures associated with treating diabetes in 2007, and perhaps as much as $58 billion in reduced worker productivity.

 Pharmaceuticals represent about 12% of total healthcare expenditure, but could potentially have a sizable spillover effect on inpatient and emergency department visits, as well as substantially reduce the risk of costly complications such as cardiovascular complications, amputation, and end-stage renal disease.

 In addition, despite the proven value of early and aggressive treatment, diabetes-related medications generally share the same problematic adherence patterns seen in prescription use. While the drivers of adherence are many and complex, benefit design (and, specifically, member cost-sharing levels) has been tied to significant changes in patient behavior regardless of the clinical value of the medication.

 Thus payers, and especially pharmacy benefit managers (PBMs) who play a pivotal role in managing formularies, have an opportunity to use CER to improve the value of spending on patients with diabetes.

The promise of CER will be realized only if patients, providers, insurers, and other stakeholders act on the findings. This can be done in several ways including supply side initiatives (Value-Based Purchasing [VBP]) and demand side initiatives (Value Based Insurance Design [VBID]). In the case of pharmaceuticals, VBID entails using CER to guide formulary placement. Specifically, the alignment of clinical knowledge and financial incentives can promote an efficient delivery system. The status quo generally has failed to align quality improvement and cost containment initiatives. In fact, in some instances, these actually compete with each other, contributing directly to inefficiency.

In most situations, formulary placement and patient copayment amounts are based on the cost of a drug within its sub-class of therapeutically similar alternatives (eg, dipeptidyl peptidase-4 [DPP-4] X vs DPP-4 Y), not the value of the drug relative to treatments for other disease areas (eg, a DPP-4 vs an acne therapy), relative to alternative therapies for the same disease (eg, a DPP-4 X vs a thiazolidinedione [TZD] or a glucagon-like peptide-1), or relative to varying uses of the product (eg, a DPP-4 as first-line or fourth-line therapy). As a result, patients face the same out-of-pocket costs for all drugs on a given tier regardless of the relative therapeutic value provided.

A more thoughtful approach would be to use CER to guide and target formulary placement more efficiently and effectively. The idea of using CER to guide formulary placement is an important component of the principles of VBID. The basic VBID premise is to align patients’ out-of-pocket costs with the value (defined as benefit relative to cost) of health services. This approach to designing benefit plans recognizes that different health services have different levels of value. By reducing barriers to high-value treatments (through lower costs to patients) and discouraging low-value treatments (through higher costs to patients), these plans can achieve improved health outcomes at any level of healthcare expenditure. This can be incorporated at 3 levels of sophistication—across disease areas (eg, diabetes vs acne), within disease areas (eg, DPP-4s vs TZDs), and within drug (eg, DPP-4 use first vs fourth line, or for patient x vs patient y, etc). The broadest application, and the easiest to implement and communicate, would be the entire class of diabetes medications placed on a tier that incentivizes (or avoids dis-incentivizing) adherence. A more refined approach would reserve preferred tier placement for diabetes treatment sub-classes. Finally, and both most coherent and most difficult, individual medications or services could be tiered according to specific needs or actions of a patient (ie, according to indication, place in therapy, program participation, etc). However, such an alignment of incentives is only possible in the setting of improved clinical evidence. Driven by CER, VBID represents a clinically sensitive, fiscally responsible approach that advocates keeping patient out-of-pocket payments low on high-value services and raising them on services of no or marginal clinical value. Such an alignment of clinical and economic incentives facilitates patients making appropriate choices both because evidence suggests that formulary positioning can influence patient  decisions on what product to use (ie, formulary positioning can drive positive change to more high-value use and not just lower-cost use), and to mitigate against the unintended consequence of increasingly higher cost sharing. When faced with higher costs, patients often make poor clinical decisions, which in fact could, in some cases, lead to greater overall costs.

 By using incentives to encourage the use of high-value services and discourage the use of low-value ones, VBID has the potential to achieve marked increases in the efficiency of the healthcare system. More health can be achieved at any level of spending. In the case of diabetes, VBID has already been implemented in several large corporations. Recognizing the clinical and potentially cost-saving value of diabetes medication, both Pitney Bowes and Marriott International, Inc, implemented a VBID program that reduced the copayments for diabetes treatment and a handful of other chronic conditions.

 The University of Michigan took a related approach that focused solely on diabetes treatment, lowering copayments and coinsurance for diabetes-related office visits and medication.

 These initiatives all identified the clinical and potential economic benefit of incentives-based disease management among diabetics and lowered out-of-pocket expenses across the board. This broad-stroke approach is a first, blunt step to aligning copayments with value. Refining the approach by adjusting formulariesbetween classes of diabetes-related therapies to incentivize high-value use may prove effective at guiding patients to more efficient treatments. Going yet another step, there is potential for incorporating clinical nuance that differentiates the value of individual medications based on the profile or behavior of a specific patient.

Regardless of sophistication in application—across disease, within disease, across drug (the current formulary focus), or within drug&mdash;more coherent formulary development will also need to address the consequences of some CER-influenced decisions. From one extreme to the other&mdash;whether all diabetes products are placed in a low tier because they are categorically considered to be of high value or whether only specific high-value uses of diabetes products are in a low tier&mdash;removing the primary role of formularies today (ie, tier differentiation based on cost for products with similar profiles) will greatly increase healthcare costs. This does not imply that the application of greater CER-facilitated sophisticated tier differentiation will come at the  expense of cost management. For example, a low-cost diabetes product with high CER defined health benefit could be placed in a low copay tier, while a high-cost product with a similar CER health benefit could be placed in a high copay tier despite high clinical utility. In these scenarios, however, it is crucial to recognize that incentives must balance the role of cost-sharing in unit cost control and behavior change with the potential to discourage the high value treatment. Patients facing higher copays for drugs they are currently using may opt to take no medications instead of switching drugs. Payers and PBMs will need to evaluate the value of cost containment across products, with the potential trade-offs in high value product use&mdash;particularly as cost-sharing rises over time.

In incorporating CER into formularies a few basic principles should be considered. First, CER should take a broad perspective. Notably, many CER studies will not compare one drug against another, but instead will compare one broad treatment strategy (eg, drug treatment) against another broad strategy (eg, surgery). In fact the Institute of Medicine CER priority list generally focused on these broad strategies as opposed to drug versus drug studies.

Formulary placement should not be based only on drug versus drug information. For example, the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial demonstrated that outcomes from stents were no better than those with medical management.

Yet, use of drugs did not change with these findings, suggesting the need for improvement in the transition from research to implementation.

Second, CER should incorporate all outcomes, not just those directly related to the service. This includes the possible reduction in use of other services associated with greater medication adherence or disease management. While CER may not include economic outcomes (existing rules prohibit PCORI funded CER from examining cost), assessment of value, and thus formulary placement, does require some attention to cost. Previous work has demonstrated that the spending offset associated with better drug adherence may be significant. For instance, a Medicare policy change in California in 2002 that raised office visit and drug copayments by $5 to $20 was associated with a 6% greater likelihood of being hospitalized.

 This greater expenditure on inpatient hospitalization offset around 20% of the savings associated with increased patient cost sharing. Similar research on the impact of Medicare Part D shows that increased medication coverage was associated with fewer hospitalizations,

 and that for groups with the greatest change in benefit generosity, the increased expenditure on drugs was totally offset by a reduction in medical expenditure.

 While these studies provide useful analysis for several types of medical expenditure, offsets should be even broader to include non-medical components such as productivity in the workplace. Better chronic care management may reduce disability and absenteeism. Currently, data limitations often prevent such analysis. Advocating greater investment in building such data is crucial to the next stages of CER and cost containment.

A third principle to guide CER-related formulary placement is recognition that the goal of the healthcare system is not to save money, but to improve health. Similarly, CER-guided formulary placement should aim to encourage high-value uses, which will often increase drug and even total expenditures. That said, the system must be fiscally sustainable, which is a far cry from today’s reality. CER can help achieve financial sustainability by allowing targeting of populations that will benefit from specific products and by identifying low-value services. Strategies that finance favorable formulary placement of high value by spreading the cost across other services can improve the efficiency of the system.

Formulary design traditionally has focused on saving money through cost-sharing tiers that differentiate medications with therapeutically similar effects. Over time, the number of tiers and associated cost-sharing amounts have increased, and there is reason for concern that these growing costs may increasingly impact patient adherence and adversely affect health outcomes. Historically, the decisions have been made largely on the basis of the cost of medications across a range of therapeutically similar alternatives, without much consideration of the clinical benefit achieved in one disease area versus another, one disease sub-class versus another, or even in one patient type versus another. Without a strong investment in CER that enables greater sophistication in formulary structure, patients are more likely to face “across the board” increases in cost sharing. That trend makes it increasingly likely that the unintended consequences of discouraging appropriate management of chronic disease will grow. In the case of diabetes—partially due to the fact that, in many cases, the management of hyperglycemia, hypertension, and hypercholesterolemia require more than 1 medication&mdash;suboptimal management (ie, lack of adherence due to cost-related issues) may lead to expensive complications. CER can provide the knowledge base necessary to add clinical nuance to formularies&mdash;possibly by distinguishing across disease states (eg, diabetes versus acne), within disease drug classes (eg, insulin vs DPP-4s), and within drug uses (eg, Januvia first versus fourth line)&mdash;in addition todistinguishing across a class of similar products (eg, generic vs brand versus non-preferred brand), which has been the main focus of formulary management to date. Cost-containment efforts that rely on an improved evidence base are probably preferable to current efforts to drive all practice toward those of the lowest cost. Thus, formularies of the future can use findings from CER to better target, not limit, care.

 From Department of Health Care Policy, Harvard Medical School (MEC, RM), Boston, MA; Stanford University School of Medicine (DKO), Stanford, CA; Philip R. Lee Institute for Health Policy Studies (WA), University of California San Francisco, San Francisco, CA; Jaeb Center for Health Research (RB), Tampa, FL; RxAnte Inc (JB), McLean, VA; Health Intelligence Partners (JEB), Chicago, IL; Evidence Based Medicine, sanofi (FF), Bridgewater, NJ; University of Michigan (AMF), Ann Arbor, MI; University of Southern California (DG), Los Angeles, CA; McKenna Long and Aldridge (RK), Washington DC; University of Southern California (DL), Los Angeles, CA; Joe Stahl Consulting LLC (JS), Minneapolis, MN; USC Keck School of Medicine(APH), USC Westside Center for Diabetes Los Angeles, CA.

part of his Stanford University duties or responsibilities. Dr Fendrick reports being a consultant for Abbott; Active-Health Management/Aetna; AstraZeneca; BlueCross BlueShield Association; Centers for Medicare & Medicaid Services; Genentech; GlaxoSmithKline; Health Alliance Plan; Aon Hewitt; Highmark BlueCross BlueShield; Integrated Benefits Institue; MedImpact HeathCare Systems, Inc; Merck; National Business Coalition on Health; National Pharmacutical Council; Pfizer; POZEN, Inc; Regence BlueCross BlueShield of Oregon; sanofi-aventis Pharmaceuticals; Thomson Reuters; TriZetto; and zanzors. He also reports that he is a speaker for Merck and Pfizer; and receives research support from Abbott; AstraZeneca; Eli Lilly; Genentech; GlaxoSmithKline; Merck; Novartis; Pfizer; and sanofi-aventis Pharmaceuticals.Drs Goldman and Lakdawalla are partners at Precision Health Economics, which received financial compensation from sanofi to support this work. Dr Goldman reports consulting income from sanofi, Novartis, Eli Lilly, Bristol Myers-Squibb, and Pfizer. Dr Peters Harmel reports board membership with ADA, and has received consultancies from Amylin, Lilly, Abbott, Takeda, sanofi, Roche, and Janssen. She also has grants pending with sanofi and has received lecture fees from Amylin, Lilly, and Novo Nordisk. Ms Forma reports 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 (MEC, RM, WA, RB, JB, JEB, FF, DG, APH, RK, DL, JS); acquisition of data (DG, APH); analysis and interpretation of data (WA, AMF, DG, APH); drafting of the manuscript (MEC, DKO, JB, AMF, DG, RK, RM, JS, APH); critical revision of the manuscript for important intellectual content (MEC, DKO, WA, RB, JB, JEB, AMF, FF, DG, RK, RM, DL, JS, APH); obtaining funding (FF); and administrative, technical, or logistic support (JEB, RM). 

 Michael E. Chernew, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Ste 207, Boston, MA 02115. E-mail: chernew@hcp.med.harvard.edu.

 Chernew reports receiving consultancies from Peer Health Exchange. Drs Chernew, Aubry, Beck, Benner, Berger, Owens, Peters Harmel, Ms Killion, Mr McKellar, and Mr Stahl report receipt of payment from sanofi for involvement in the preparation of this manuscript. Dr Owens’ contribution to this publication was as a paid consultant to sanofi-US and was not

1. US Congress. Patient Protection and Affordable Care Act. http://www.gpo.gov/fdsys/pkg/PLAW-111publ148/pdf/PLAW-111publ148.pdf. Published March 23, 2010. Accessed May 2011.

2. Centers for Disease Control and Prevention. 2011 National Diabetes Fact Sheet, 2012.

3. American Diabetes Association. Economic costs of diabetes in the US in 2007. Diabetes Care.2008;31(3):596-615.

4. Selvin E, Marinopoulos S, Berkenblit G, et al. Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus. Ann Intern Med. 2004;141(6):421-431.

5. Chandra, A, Gruber J, McKnight R. Patient cost-sharing and hospitalization offsets in the elderly. Amer Econ Rev. 2010;100(1):193-213.

6. Cramer J. A systematic review of adherence with medication for diabetes. Diabetes Care. 2004; 27(5):1218-1224.

7. Chernew ME, Rosen AB, Fendrick AM. Rising out-of-pocket costs in disease management programs. Am J Manag Care. 2006;12(3):150-154.

8. 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.

9. 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.

10. 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.

12. Spaulding A, Fendrick AM, Herman WH, et al. A controlled trial of value-based insurance design the MHealthy: Focus on Diabetes (FOD) trial. Implement Sci. 2009;4:19.

13. Chernew M, Fendrick M. The business case for comparative effectiveness research. Appendix 7 in Learning What Works Best: The Nation’s Need for Evidence on Comparative Effectiveness in Health Care. IOM Roundtable on Evidence-Based Medicine. Institute of Medicine, March 2007.

14. Weintraub WS, Spertus JA, Kolm P, et al. Effect of PCI on quality of life in patients with stable coronary disease. N Engl J Med. 2008;359(7):677-687.

15. Afendulis CC, He Y, Zaslavsky AM, Chernew ME. The impact of Medicare Part D on hospitalization rates. Health Serv Res. 2011;46(4):1022-1038.

16. Zhang Y, Donohue JM, Lave JR, O’Donnell G, Newhouse JP. The effect of Medicare Part D on drug and medical spending. N Engl J Med. 2009;361(1):52-61.

11. Choudhry NK, Fischer MA, Avorn J, et al. At Pitney Bowes, value-based insurance design cut copayments and increased drug adherence. Health Aff (Millwood). 2010;29(11):1995-2001.

Comparative Effectiveness Research and Formulary Placement: The Case of Diabetes

Formularies of the future should use evidence founded on findings from comparative effectiveness research (CER) to better target, not limit, diabetes care.

 In the case of diabetes, partially due to the fact that many cases require more than 1 medication, suboptimal management (ie, lack of adherence due to cost-related issues) may lead to expensive complications.

 CER can provide the knowledge base for patient-specific formulary design, rather than class-specific design.

As the nation transforms its healthcare system— with or without the Affordable Care Act (ACA)&mdash; it must face the challenge of how to maintain, or even improve, the quality of care. This requires the system to be more nuanced; to encourage use of those healthcare services that produce greater health and discourage the use of those that produce less. Implementation of this simple idea requires first identifying the clinical benefit associated with different services.

 Starting in 2011 and moving forward, PCORI will act as a funding source for independent research institutions to conduct CER-related research and will synthesize these findings and make them available to the public.

 In part due to its chronic nature and because of several widely accepted high-value treatments, the management of diabetes has become a widespread benchmark of quality. For example, most standard quality measurement systems, including the Healthcare Effectiveness Data and Information Set and most pay-for-performance systems, include a series of diabetes-related measures. These include clinical services (ie, eye and foot exams) as well as drug management for hyperglycemia, hypertension, and hypercholesterolemia. Due in part to the effectiveness of treatment and disease management as compared with non-treatment, the US Preventive Services Task Force recommended diabetes screening as a high-value preventive service and in turn the ACA has mandated payers to provide diabetes screening without patient cost-sharing. While there is broad consensus that several treatments are clinically effective, there is still great opportunity for CER to elucidate the optimal combination of treatments for each patient population.

There is also an opportunity to gain much more value for the money we spend on the treatment of diabetes. The American Diabetes Association estimated around $116 billion in medical expenditures associated with treating diabetes in 2007, and perhaps as much as $58 billion in reduced worker productivity.

 Pharmaceuticals represent about 12% of total health expenditure, but could potentially have a sizable spillover effect on inpatient and emergency department visits, as well as substantially reduce the risk of costly complications such as cardiovascular complications, amputation, and endstage renal disease.

In addition, despite the proven value of early and aggressive treatment, diabetes-related medications generally share the same problematic adherence patterns seen in prescription use. While the drivers of adherence are many and complex, benefit design (and, specifically, member cost-sharing levels) has been tied to significant changes in patient behavior regardless of the clinical value of the medication.

Thus payers, and especially pharmacy benefit managers (PBMs) who play a pivotal role in managing formularies, have an opportunity to use CER to improve the value of spending on patients with diabetes.

The promise of CER will be realized only if patients, providers, insurers, and other stakeholders act on the findings. This can be done in several ways including supply side initiatives (Value Based Purchasing [VBP]) and demand side initiatives (Value Based Insurance Design [VBID]). In the case of pharmaceuticals, VBID entails using CER to guide formulary placement. Specifically, the alignment of clinical knowledge and financial incentives can promote an efficient delivery system. The status quo generally has failed to align quality improvement and cost containment initiatives. In fact, in some instances, these actually compete with each other, contributing directly to inefficiency.

 In most situations, formulary placement and patient copayment amounts are based on the cost of a drug within its sub-class of therapeutically similar alternatives (eg, dipeptidyl peptidase-4 [DPP-4] X vs DPP-4 Y), not the value of the drug relative to treatments for other disease areas (eg, a DPP-4 vs an acne therapy), relative to alternative therapies for the same disease (eg, a DPP-4 X vs a thiazolidinedione [TZD] or a glucagon-like peptide-1), or relative to varying uses of the product (eg, a DPP-4 as firstline or fourth-line therapy). As a result, patients face the same out-of-pocket costs for all drugs on a given tier regardless of the relative therapeutic value provided.

A more thoughtful approach would be to use CER to guide and target formulary placement more efficiently and effectively. The idea of using CER to guide formulary placement is an important component of the principles of VBID. The basic VBID premise is to align patients’ out-of-pocket costs with the value (defined as benefit relative to cost) of health services. This approach to designing benefit plans recognizes that different health services have different levels of value. By reducing barriers to high-value treatments (through lower costs to patients) and discouraging lowvalue treatments (through higher costs to patients), these plans can achieve improved health outcomes at any level of healthcare expenditure. This can be incorporated at 3 levels of sophistication—across disease areas (eg, diabetes vs acne), within disease areas (eg, DPP-4s vs TZDs), and within drug (eg, DPP-4 use first vs fourth line, or for patient x vs patient y, etc). The broadest application, and the easiest to implement and communicate, would be the entire class of diabetes medications placed on a tier that incentivizes (or avoids dis-incentivizing) adherence. A more refined approach would reserve preferred tier placement for diabetes treatment sub-classes. Finally, and both most coherent and most difficult, individual medications or services could be tiered according to specific needs or actions of a patient (ie, according to indication, place in therapy, program participation, etc). However, such an alignment of incentives is only possible in the setting of improved clinical evidence. Driven by CER, VBID represents a 

 approach that advocates keeping patient out-of-pocket payments low on high-value services and raising them on services of no or marginal clinical value.

Such an alignment of clinical and economic incentives facilitates patients making appropriate choices both because evidence suggests that formulary positioning can influence patient decisions on what product to use (ie, formulary positioning can drive positive change to more high-value use and not just lower-cost use), and to mitigate against the unintended consequence of increasingly higher cost sharing. When faced with higher costs, patients often make poor clinical decisions, which in fact could, in some cases, lead to greater overall costs.

 By using incentives to encourage the use of high-value services and discourage the use of low-valueones, VBID has the potential to achieve marked increases in the efficiency of the healthcare system. More health can be achieved at any level of spending.

In the case of diabetes, VBID has already been implemented in several large corporations. Recognizing the clinical and potentially cost-saving value of diabetes medication, both Pitney Bowes and Marriott International, Inc, implemented a VBID program that reduced the copayments for diabetes treatment and a handful of other chronic conditions.

The University of Michigan took a related approach that focused solely on diabetes treatment, lowering copayments and coinsurance for diabetes-related office visits and medication.

 These initiatives all identified the clinical and potential economic benefit of incentives disease management among diabetics and lowered out-of-pocket expenses across the board. This broad-stroke approach is a first, blunt step to aligning copayments with value. Refining the approach by adjusting formularies between classes of diabetesrelated therapies to incentivize high-value use may prove effective at guiding patients to more efficient treatments. Going yet another step, there is potential for incorporating clinical nuance that differentiates the value of individual medications based on the profile or behavior of a specific patient.

Regardless of sophistication in application—across disease, within disease, across drug (the current formulary focus), or within drug&mdash;more coherent formulary development will also need to address the consequences of some CER-influenced decisions. From one extreme to the other&mdash;whether all diabetes products are placed in a low tier because they are categorically considered to be of high value or whether only specific high-value uses of diabetes products are in a low tier&mdash;removing the primary role of formularies today (ie, tier differentiation based on cost for products with similar profiles) will greatly increase healthcare costs. This does not imply that the application of greater CER-facilitated sophisticated tier differentiation will come at the expense of cost management. For example, a low-cost diabetes product with high CERdefined health benefit could be placed in a low copay tier, while a high-cost product with a similar CER health benefit could be placed in a high copay tier despite high clinical utility. In these scenarios, however, it is crucial to recognize that incentives must balance the role of cost-sharing in unit cost control and behavior change with the potential to discourage the high-value treatment. Patients facing higher copays for drugs they are currently using may opt to take no medications instead of switching drugs. Payers and PBMs will need to evaluate the value of cost containment across products, with the potential trade-offs in high value product use&mdash;particularly as cost sharing rises over time.

 Yet, use of drugs did not change with these findings, suggesting the need for improvement in the transition from research to implementation. Second, CER should incorporate all outcomes, not just those directly related to the service. This includes the possible reduction in use of other services associated with greater medication adherence or disease management. While CER may not include economic outcomes (existing rules prohibit PCORI-funded CER from examining cost), assessment of value, and thus formulary placement, does require some attention to cost. Previous work has demonstrated that the spending offset associated with better drug adherence may be significant. For instance, a Medicare policy change in California in 2002 that raised office visit and drug copayments by $5 to $20 was associated with a 6% greater likelihood of being hospitalized.

Formulary design traditionally has focused on saving money through cost-sharing tiers that differentiate medications with therapeutically similar effects. Over time, the number of tiers and associated cost-sharing amounts have increased, and there is reason for concern that these growing costs may increasingly impact patient adherence and adversely affect health outcomes. Historically, the decisions have been made largely on the basis of the cost of medications across a range of therapeutically similar alternatives, without much consideration of the clinical benefit achieved in one disease area versus another, one disease sub-class versus another, or even in one patient type versus another. Without a strong investment in CER that enables greater sophistication in formulary structure, patients are more likely to face “across the board” increases in cost sharing. That trend makes it increasingly likely that the unintended consequences of discouraging appropriate management of chronic disease will grow. In the case of diabetes—partially due to the fact that, in many cases, the management of hyperglycemia, hypertension, and hypercholesterolemia require more than 1 medication&mdash;suboptimal management (ie, lack of adherence due to cost-related issues) may lead to expensive complications. CER can provide the knowledge base necessary to add clinical nuance to formularies&mdash;possibly by distinguishing across disease states (eg, insulin vs acne), within disease drug classes (eg, insulin vs DPP-4s), and within drug uses (eg, Januvia first vs fourth line)&mdash;in addition to distinguishing across a class of similar products (eg, generic vs brand vs non-preferred brand), which has been the main focus of formulary management to date. Cost-containment efforts that rely on an improved evidence base are probably preferable to current efforts to drive all practice toward those of the lowest cost. Thus, formularies of the future can use findings from CER is to better target, not limit, care.

From Department of Health Care Policy, Harvard Medical School (MEC, RM), Boston, MA; Stanford University School of Medicine (DKO), Stanford, CA; Philip R. Lee Institute for Health Policy Studies (WA), University of California San Francisco, San Francisco, CA; Jaeb Center for Health Research (RB), Tampa, FL; RxAnte Inc (JB), McLean, VA; Health Intelligence Partners (JEB), Chicago, IL; Evidence Based Medicine, sanofi (FF), Bridgewater, NJ; University of Michigan (AMF), Ann Arbor, MI; University of Southern California (DG), Los Angeles, CA; McKenna Long and Aldridge (RK), Washington DC; University of Southern California (DL), Los Angeles, CA; Joe Stahl Consulting LLC (JS), Minneapolis, MN; USC Keck School of Medicine (AP), USC Westside Center for Diabetes Los Angeles, CA.

 Dr Chernew reports receiving consultancies from Peer Health Exchange. Dr Chernew, Dr Aubry, Dr Beck, Dr Benner, Dr Berger, Dr Peters, Ms Killion, Mr McKellar, Dr Owens, and Mr Stahl report receipt of payment from sanofi for involvement in the preparation of this manuscript. Dr Owens’ contribution to this publication was as a paid consultant to sanofi-US and was not part of his Stanford University duties or responsibilities. Dr Fendrick reports being a consultant for Abbott; ActiveHealth Management/Aetna; AstraZeneca; BlueCross BlueShield Association; Center for Medicare & Medicaid Services; Genentech; GlaxoSmithKline; Health Alliance Plan; Aon Hewitt; Highmark BlueCross BlueShield; Integrated Benefits Institue; MedImpact HeathCare Systems, Inc; Merck; National Business Coalition on Health; National Pharmacutical Council; Pfizer; POZEN, Inc; Regence BlueCross BlueShield of Oregon; sanofi-aventis Pharmaceuticals; Thomson Reuters; TriZetto; and zanzors. He also reports that he is a speaker for Merck and Pfizer; and receives research support from Abbott; AstraZeneca; Eli Lilly; Genentech; GlaxoSmithKline; Merck; Novartis; Pfizer; and sanofiaventis Pharmaceuticals. Drs Goldman and Lakdawalla are partners at Precision Health Economics, which received financial compensation from sanofi to support this work. Dr Goldman reports consulting income from sanofi, Novartis, Eli Lilly, Bristol Myers-Squibb, and Pfizer. Dr Peters reports board membership with ADA, and has received consultancies from Amylin, Lilly, Abbott, Takeda, sanofi, Roche, and Janssen. She also has grants pending with sanofi and has received lecture fees from Amylin, Lilly, and Novo Nordisk. Ms Forma reports 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 (MEC, RM, WA, RB, JB, JEB, FF, DG, AP, RK, DL, JS); acquisition of data (DG, AP); analysis and interpretation of data (WA, AMF, DG, AP); drafting of the manuscript (MEC, DKO, JB, AMF, DG, RK, RM, JS, AP); critical revision of the manuscript for important intellectual content (MEC, DKO, WA, RB, JB, JEB, AMF, FF, DG, RK, RM, DL, JS, AP); obtaining funding (FF); and administrative, technical, or logistic support (JEB, RM).

Michael E. Chernew, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Ste 207, Boston, MA 02115. E-mail: chernew@hcp.med.harvard.edu.1. US Congress. Patient Protection and Affordable Care Act. http://www.gpo.gov/fdsys/pkg/PLAW-111publ148/pdf/PLAW-111publ148.pdf. Published March 23, 2010. Accessed May 2011.

3. American Diabetes Association. Economic costs of diabetes in the US in 2007. Diabetes Care. 2008;31(3):596-615.

6. Cramer J. A systematic review of adherence with medication for diabetes. Diabetes Care. 2004;27(5):1218-1224.

7. Chernew ME, Rosen AB, Fendrick AM. Rising out-of-pocket costs in disease management programs. Am J Manag Care. 2006;12(3): 150-154.

11. Choudhry NK, Fischer MA, Avorn J, et al. At Pitney Bowes, valuebased insurance design cut copayments and increased drug adherence. Health Aff (Millwood). 2010;29(11):1995-2001.

12. Spaulding A, Fendrick AM, Herman WH, et al. A controlled trial of value-based insurance design-the MHealthy: Focus on Diabetes (FOD) trial. Implement Sci. 2009;4:19.

13. Chernew M, Fendrick M. The business case for comparative effectiveness research. Appendix 7 in Learning What Works Best: The Nation’s Need for Evidence on Comparative Effectiveness in Health Care. IOM Roundtable on Evidence-Based Medicine. Institute of Medicine,  March 2007.

14. Weintraub WS, Spertus JA, Kolm P, et al. Effect of PCI on quality of life in patients with stable coronary disease. N Engl J Med. 2008;359(7): 677-687.

15. Afendulis CC, He Y, Zaslavsky AM, Chernew ME. The impact of Medicare Part D on hospitalization rates. Health Serv Res. 2011;46(4): 1022-1038.

Formularies of the future should use evidence-produced CER to better target, not limit, diabetes care.

 To assess the relationship between cost sharing and adherence to antidiabetic medications in patients with type 2 diabetes and to examine the relationship between medication adherence and outcomes, including complication rates, medical service utilization, and workplace productivity measures.

A retrospective, cross-sectional study analyzing the healthcare experience of patients with type 2 diabetes on oral antidiabetic medication (OAD) with or without insulin (n = 96,734) and patients on OAD only (n = 55,356) with employer-sponsored insurance in the 2003-2006 MarketScan Database.

Using a 2-stage residual inclusion model, the first stage estimated the effects of cost sharing on adherence to antidiabetic medications in an 18-month time frame (January 2003 through June 2004). Adherence was determined from the percentage of days covered. The second stage estimated the effects of adherence on complication rates (eg, retinopathy, neuropathy, peripheral vascular disease), medical service utilization rates, and measures of productivity (absence days and short-term disability days) in the subsequent 2 years (July 2004 through June 2006).

A $10 increase in the patient cost-sharing index resulted in a 5.4% reduction in adherence to antidiabetic medications for patients on OAD only and a 6.2% reduction in adherence for patients on OAD with or without insulin. Adherence was associated with lower rates of complications (eg, amputation/ulcers, retinopathy) and also was associated with fewer emergency department visits and short-term disability days.

Medical plans, employers, and policy makers should consider implementing interventions targeted to improve antidiabetic medication adherence, which may translate to better outcomes.

	This retrospective, cross-sectional study investigated the relationships between cost sharing and adherence to antidiabetic medication, and among adherence, diabetes-related complications, and utilization measures.

		Higher levels of patient cost sharing for antidiabetes medications were associated with lower levels of adherence (percentage of days covered) to antidiabetic medications. For example, among patients on oral antidiabetic medications only, those whose cost-sharing amount was $10 higher had adherence rates that were 5.4% lower.

		Antidiabetic medication adherence was associated with lower rates of many diabetes-related complications (eg, amputation/ulcer and retinopathy), and with fewer inpatient visits, emergency department visits, and short-term disability days.

		Medical plans, employers, and policy makers should consider ways to promote adherence to medications, including reduced patient cost sharing.

	Approximately 23.6 million individuals in the United States have diabetes, a serious metabolic disorder that affects all systems of the body, particularly the neurologic and cardiovascular systems.

 Proper medication management among patients with diabetes is a key component of preventing diabetes-related complications.

	Inadequate medication adherence among patients with chronic illness has been associated with increased healthcare utilization, costs, and risk for adverse health outcomes.

 Encinosa and colleagues studied adherence among privately insured patients with diabetes on oral antidiabetic medication (OAD), finding that improved adherence was associated with decreased hospitalization and emergency department (ED) costs.

 Similarly, Sokol and colleagues found that a high level of medication adherence among privately insured patients with diabetes was associated with decreased condition-specific medical spending.

 Regarding health outcomes, Ho and colleagues found that nonadherence to OAD among privately insured patients with diabetes was associated with an increase in glycosylated hemoglobin (A1C) levels, all-cause hospitalizations, and all-cause mortality.

 Such evidence underscores the value of medication adherence to patient health.

	These findings also suggest that important differences may exist between patients who are adherent and those who are nonadherent, and these potential selection effects may affect the relationship between adherence and outcomes. Thus, rigorous methods must be adopted to account for possible endogeneity in the relationship between adherence and outcomes.

	Barriers to medication adherence such as high patient cost sharing for prescription drugs inhibit patient adherence.

 Few studies have linked the effects of cost sharing to adherence, and subsequently, to outcomes. Hunt and colleagues measured the relationships between cost sharing and adherence and between adherence and glycemic control among patients with diabetes, finding a positive relationship between cost sharing and A1C level.

 However, the ultimate effects of cost sharing on diabetes-related complications, through adherence, are not yet well known.

	This study contributes to the literature in several ways. First, we investigated the relationships between cost sharing and adherence to antidiabetic medication. Second, we addressed the relationship among adherence, diabetes-related complications, and utilization measures using rigorous methods. We assumeda more comprehensive view of the impact of medication adherence, also incorporating measures of productivity for employees with diabetes. We used 2-stage residual inclusion models to estimate these  relationships and produce consistent estimates.

	This study is based upon the Thomson Reuters MarketScan Database, 2002-2006, which represents the healthcare experience of more than 21 million enrollees with  employer-sponsored benefits annually. Inpatient medical, outpatient medical, and outpatient pharmacy claims, absenteeism data (dates of absence from work due to illness), and claims for short-term disability benefits were linked to enrollment information to create the analytic data set.

	A retrospective, cross-sectional study was conducted among patients aged 18 years or older with diabetes who used OAD (sulfonylureas, meglitinides, biguanides,  thiazolidinediones, or alpha-glucosidase inhibitors) and were continuously enrolled from January 1, 2002, through June 30, 2006. Patients were included if they filled at least 2  prescriptions for an antidiabetic agent from January 1 through June 30, 2003, and had a diagnosis of diabetes (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] code 250.xx) as indicated by 1 ED visit, 1 inpatient admission, or 2 outpatient visits at least 30 days apart. Patients also were required to have the first  observed fill for an OAD occurring in 2002 or the first quarter of 2003. Patients with a diagnosis of type 1 diabetes or gestational diabetes (

 code 648.8x) were excluded. The final samples consisted of 96,734 patients with type 2 diabetes on OAD with or without insulin in the full sample and 55,356 patients who filled an OAD prescription only (no insulin). We created 2 additional subsets of patients who were employees with available absenteeism data (n = 1829; n = 1040 OAD only) and short-term disability data (n =   3027; n = 1753 OAD only).

	Adherence was measured as the percentage of days covered (PDC), expressed as the percentage of days with an antidiabetic medication on hand during the 18-month time period (January 2003 through June 2004).

 The PDC was first calculated for each therapeutic class (ie, sulfonylureas, meglitinides, biguanides, thiazolidinediones, alpha-glucosidase  inhibitors, insulin) based on prescription drug claims, using the fill date and the intended days supply of medication from each claim. Information from prescriptions filled in the fourth quarter of 2002 provided prescription utilization information for early 2003. If a patient refilled the same medication before the end of the days supply from the previous prescription fill, then the days supply for the new prescription was appended to the end of the previous fill. If a patient switched medication within a therapeutic class, the remainder of the previous prescription was discarded and coverage commenced with the supply of the new medication. Days of hospitalization were counted as adherent days (to at least 1 antidiabetes medication), as some of the factors that influence patient medication adherence may change during days of hospitalization. For insulin prescriptions, where the days supply may not indicate the full extent of coverage, we multiplied the days supply of each fill by 1.5.

 We produced results using various algorithms for insulin adherence and adherence to OAD alone for those patients who used insulin with no material change in findings.

	After calculating the number of days that medication was on hand in each medication class, we compiled the information from all therapeutic classes into a single measure and measured adherence based on whether a patient had any antidiabetes medication on hand on each day. Consistent with previous studies, patients were classified as adherent to antidiabetic medications if the PDC equaled or exceeded 80%,

 a threshold at which clinical benefits are most likely to occur.

	Complications were recorded using dummy variables that indicated the complication occurred on a claim from July 2004 through June 2006. Codes for each complication are noted in 

. Utilization events for July 2004 through June 2006 included physician office visits, ED visits, and inpatient admissions, coded as the number of events in the 24-month  period. Patients with productivity data available (with days of absence and short-term disability reported) were included in the productivity analysis, even if zero days were reported.

	Patient cost sharing for antidiabetic medications was measured, using a cost-sharing index created for each employer/plan combination. The index was based on the average  cost-sharing amount (ie, copayment, coinsurance) per prescription (standardized to a 30-day supply) for brand and generic drugs in each antidiabetic medication class. The index  aggregated the brand and generic copayments using weights, developed from the overall proportion of utilization of brand and generic drugs within each medication class.

 If we were to examine cost sharing for the medications filled by each patient, this method might introduce selection bias, as more adherent consumers might be selecting medications (eg, generics) on the basis of cost-sharing amounts. Thus, aggregating cost sharing into levels for each employer/plan combination reduced the effects of selection bias related to actual, individual-level cost sharing and related choices. The coinsurance flag indicated which plans used coinsurance (a percentage of the total payment) for   prescription drug cost sharing versus a flat fee copayment. Any mail-order use in the past year, which can be associated with higher levels of adherence,

 also was indicated. As outpatient physician visit cost sharing has been associated with reductions in the use of prescription drugs,

 the per-visit amount was included in the models.

	Other explanatory variables associated with medication adherence were included in the models. Patient-level sociodemographic characteristics included sex, age, census region,  urban residence, and median income in the patient’s area of residence (by ZIP code) from the US Census files. An indicator for employee status (vs spouse/dependent) was  included, along with plan type (eg, health maintenance organization, preferred provider organization). An indicator for a visit to a related specialist (ie, cardiologist, endocrinologist) in the past year accounted for disease severity and possible differences in practice patterns.

	To account for differences in health status, the following explanatory variables were incorporated into the models as well. Because newly diagnosed patients may have different utilization patterns than patients with existing disease,

 a flag indicated whether a patient was newly diagnosed with type 2 diabetes in 2003. Scores on the Charlson Comorbidity Index, a numeric scale reflecting the risk of death or serious disability in the next year based on the presence of a diagnosis for 1 of 19 conditions (eg, diabetes, heart disease,  cancer) in the 12-month preindex period, were included.

 The Charlson Comorbidity Index is associated with cost and other utilization measures such as length of stay and readmissions.

 The total patient out-of-pocket burden for chronic conditions (medical and drug) in the preindex year also may reflect lower health status. For prescription drug claims, chronic out-of-pocket spending was classified per claim by using the National Drug Code and Redbook for drug claims (claims with a drug type of “maintenance”) and for  medical claims by using the chronic conditions listed in the Charlson Comorbidity Index (as identified by a nosologist).

	We analyzed the relationships among patient cost sharing for antidiabetic medication, adherence, and diabetes-related events or outcomes by first estimating the relationship  between patient cost sharing for antidiabetic medication and adherence to antidiabetic medications (controlling for covariates) during an 18-month time frame (January 2003 through June 2004). We then estimated the relationship between adherence in the initial time frame and outcomes, controlling for covariates in the subsequent 2 years, July 2004 through June 2006.

Higher cost-sharing levels reduced adherence to antidiabetic medications in patients with type 2 diabetes.

Felicia Forma, BSc

Articles

The Cost of Adherence Mismeasurement in Serious Mental Illness: A Claims-Based Analysis

Comparative Effectiveness Research and Formulary Placement: The Case of Diabetes

Comparative Effectiveness Research and Formulary Placement: The Case of Diabetes

Cost Sharing, Adherence, and Health Outcomes in Patients With Diabetes