An estimated 23.6 million people in the United States,

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1 Impact of Treatment Complexity on Adherence and Glycemic Control: An Analysis of Oral Antidiabetic Agents Michael Pollack, MS, Benjamin Chastek, MS, Setareh A. Williams, PhD, and Jane Moran, MD Abstract Objective: To evaluate the impact of treatment complexity on adherence and subsequent glycemic control among patients with type 2 diabetes mellitus. Methods: This was a retrospective evaluation of patients enrolled in a national health plan during the period 2000 to Patients diagnosed with diabetes, naive to oral antidiabetic agents (OADs), and not using insulin were included. A treatment complexity score was assigned based on treatment characteristics. First-year adherence was calculated as a medication possession ratio (MPR) weighted by duration of OAD treatment. HbA1c values were obtained for those with available laboratory data. Multivariate analyses were conducted, controlling for patient demographics, baseline HbA1c, and comorbidities. Results: A total of 94,860 patients were identified, 16,198 with HbA1c values. Mean age was 52.6 years; 78% initiated monotherapy, 20% initiated dual therapy, and the remaining 2% were on 3 or more therapies. Mean treatment complexity score was 3.33 (range, 0 14), with 29%, 57%, and 14% in low-, medium-, and high-complexity categories. Mean 1-year adherence was 58%, 51%, and 43% for low-, medium-, and high-complexity treatments, respectively. Probability of adherence (MPR 80%) was significantly lower for medium- and highcomplexity versus low-complexity regimens in all multivariate analyses (P < 0.05). More patients with low-complexity regimens were at or below the HbA1c goal (< 7%) during the baseline and followup periods. The probability of reaching and maintaining goal was significantly higher for adherent patients (P < 0.01). Conclusion: Treatment complexity has negative effects on adherence and glycemic control. Appropriate choice of therapy based on each individual s need may overcome these limitations. An estimated 23.6 million people in the United States, or 8% of the population, have diabetes [1]. Type 2 diabetes mellitus accounts for 90% to 95% of all cases of diagnosed diabetes in adults, and its prevalence is currently at epidemic proportions in developed and developing countries [2]. In 2007, the total cost of diabetes in the United States was estimated to be $174 billion, with direct medical costs accounting for $116 billion and the remaining $58 billion attributed to lost productivity. While treatment of diabetes-related complications is a major cost driver of care, the excess general medical costs deriving from frequent outpatient visits, emergency room visits, frequent hospital admissions, and longer hospital stays also contribute significantly to the overall cost [3]. Lifestyle modification, supplemented with pharmacotherapy and frequent glucose monitoring, is required to slow the progression of diabetes and delay the onset of related complications [4,5]. Such intense management requires education, motivation, and continuing support from health care professionals [6 8], often resulting in comprehensive yet complex treatment algorithms. These regimens can prove challenging to implement [9] and sustain over the long term [10], contributing to the current unmet need in diabetes treatment [11]. A recent review of treatment adherence in patients with type 2 diabetes illustrated that many patients took less than their prescribed amount of oral antidiabetic drugs (OADs) and the overall adherence rates decreased over time [12]. Several factors may influence adherence, including the patient s understanding of the regimen and its benefits, potential side effects, costs, and treatment complexity [13]. The complexity of a patient s medication regimen, in turn, is influenced by several attributes, including the number of medications, the dosage frequency and dosage form, as well as special administration instructions. Consequences of nonadherence or limited adherence may From AstraZeneca, LP, Wilmington, DE (Mr. Pollack and Dr. Williams), i3 Innovus, Eden Prarie, MN (Mr. Chastek), and i3 Research, Cary, NC (Dr. Moran). Vol. 17, No. 6 June 2010 JCOM 257

2 diabetes treatment include a lack of glycemic control and subsequently higher associated medical costs [14 19]. A 10% increase in adherence has been associated with a 0.1% decrease in HbA1c level [18]. Moreover, a retrospective study of more than 1500 patients with type 2 diabetes followed for 1 year after initial diagnosis demonstrated an independent association between glycemic control and both medication adherence and maintenance of scheduled appointments [20]. While some studies have evaluated the association between treatment complexity and adherence and others analyzed the association between adherence and health outcomes, the interrelations between these parameters have not, to date, been examined thoroughly in a population of managed care patients. The purpose of this study was to examine the relationship of diabetes regimen complexity on medication adherence and subsequent achievement of therapeutic goals. Methods This was a retrospective analysis of health care claims data for patients enrolled in a large geographically diverse managed care health plan in the United States. The claims database contained eligibility information, pharmacy claims, and medical claims for the approximately 28 million patients enrolled in the plan during the study period. All personal identifiers were removed prior to data analyses, and data were accessed in a manner that was compliant with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) [21]. As this was a de-identified retrospective analysis, institutional review board approval was not needed. Study Sample Commercial health plan members were included in this study if they had a primary diagnosis of diabetes and at least 1 filled prescription for an OAD between 1 January 2001 and 31 December The date of the first filled prescription was considered the study index date. Patients who were naive to OAD therapy were required to be continuously enrolled with a medical and pharmacy benefit for 12 months prior to (baseline period) and 12 months following the index date (follow-up period). Patients younger than 18 years and those who received an OAD or insulin during the baseline period were excluded from the study. Diabetes was defined using diagnosis codes from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). The use of OADs was identified through prescriptions filled at either a retail pharmacy or through mail order and included metformin, sulfonylureas, thiazolidinediones (TZDs), alpha-glucosidase inhibitors (AGIs), meglitinide derivatives, and dipeptidyl peptidase-4 inhibitors (DPP-4). OAD prescriptions that were filled within 30 days of the index date and subsequently refilled more than 30 days following the index date constituted the index regimen. Patients who did not have a qualifying index regimen (eg, had only 1 fill) were excluded from the study as this could have indicated lack of tolerability of the prescribed regimen and not necessarily lack of willingness to take the OAD. Treatment Complexity Patients were assigned a complexity score based on an algorithm developed by George and colleagues [22]. The Medication Regimen Complexity Index (MRCI) is a 65-item instrument that has been used in adherence studies [23,24] and is a reliable, valid tool for quantifying the complexity of medication regimens [25]. The instrument consists of 3 sections related to route of drug administration, dosing frequency, and additional directions (eg, time of day or with food). The sum of the scores of each of the 3 sections contributes to a complexity index. This study used only those items in the MRCI that were applicable to OAD treatment. Complexity scores were obtained for each of the OADs that the patient used during the 1-year follow-up period. Medication complexity scores were then weighted and combined based on the proportion of the follow-up period that the patient was considered to be using each specific medication. Patients were defined as using a medication from the day of the first fill for the medication until the earlier of either the end of the follow-up or a medication switch, since the latter would place patients in a different therapeutic category. A switch was defined as no subsequent refill for the duration of the follow-up subsequent to the initiation of any new OAD. Medication complexity scores were calculated from details available in the claims data and based on the directions for use identified in the product insert information [26]. Since all OADs are in capsule or tablet form, 1 point was given for administration of each OAD. For each drug that was identified for each patient, points related to dosing frequency were assigned in the following manner: 1 point was assigned if 1 tablet was used per day, 2 points if 2 tablets were used per day, and 3 points if 3 or more tablets were used per day. The average number of tablets used per day was calculated from the claims data for each of the medication fills, based on the observed ratio of quantity of tablets to days supplied. Points associated with additional directions or special characteristics were assigned as follows: 1 point for tablet splitting, the need to be taken with a meal, or for use at a specific time of day. Tablet splitting was identified by examining the ratio of quantity of medication supplied to days supplied for each of the fills in the claims. If the ratio was less than 0.80, the patient was assumed to be splitting 258 JCOM June 2010 Vol. 17, No. 6

3 tablets. This method did not recognize those individuals taking half of a tablet twice a day; therefore, complexity may have been underestimated in this population. Sulfonylureas, metformin, meglitinides, and AGIs each had instructions to be taken with a meal as identified from product insert information. The continuous measure for complexity was converted into a categorical measure after examination of the empirical data and distribution of scores. Patients with 2 or fewer points were considered to have a medication regimen of low complexity; those with 3 or 4 points were considered to have a medium complexity regimen; and those with 5 or more points were considered to have a high complexity regimen. HbA1c Levels HbA1c results were identified during the baseline and follow-up period. Given the time frame required for medications to affect HbA1c level, results identified during the first 30 days of the follow-up period were considered to be part of the baseline [27], and results more than 30 days after the index date were attributed to the follow-up. Treatment Adherence and Persistence The medication possession ratio (MPR), a measure of compliance, was calculated for each patient in the study population by first identifying the MPR for each OAD that the patient used. MPR was calculated from the claims data as the sum of days supplied for all fills divided by the number of days between the first fill and the earlier of either the first fill for a switch medication or the end of the study period [28,29]. MPR measures for each OAD were then weighted by the percentage of time that the medication was used to create a composite weighted value. The value of the MPR ranged from 0 to 1.0, with any value greater than 1.0 rounded to 1.0. Consistent with other studies [30,31], subjects were considered adherent if they had a MPR 0.8 (80%). Similarly, for outcomes examining the association between adherence and HbA1c values, a weighted adherence value was calculated for the 6 months leading up to the best observed follow-up HbA1c. Weighted adherence was calculated for all OADs that were used in the months before the HbA1c test. For patients with 1 to 5 months of followup before the best HbA1c value, weighted adherence was calculated for the period between the study index date and the testing date. Persistence was calculated as the number of days of continuous use of the OADs in the index regimen from the index date until discontinuation of at least one of the medications in the index regimen, addition of a new medication, switching to a new OAD, or the end of the 12-month followup, whichever occurred first. A gap in therapy of at least 60 days was considered a discontinuation. A 60-day gap was chosen because it represents twice the day-supply of a standard 1-month fill. Patient Characteristics and Socioeconomic Status Information on age, gender, geographic location, and benefit type were available for all patients. The Charlson Comorbidity Index (CCI), which includes a list of 19 chronic conditions, was used to evaluate disease burden [32]. Additional information that is not considered in CCI but could impact adherence included number of non-oad medications, diabetes-related comorbidities and complications, pre-index health utilization and costs, and conditions impacting mental health status (eg, psychiatric). Socioeconomic status data including race, income, and net worth were also available for a subset of the population. Statistical Analysis All study variables, including baseline and outcome measures, were analyzed descriptively. For continuous measures, P values represent an overall F test of the means. For categorical variables, a chi-square test was performed to assess statistical significance. Multivariate analyses were performed examining the relationship between treatment complexity and adherence and between adherence and HbA1c. For each regression, 3 models were created to ensure that the results were robust and not sensitive to changes. These included models using (1) all patients, (2) the subset of patients with available socioeconomic status data, and (3) a reduced model with all patients and complexity/adherence as the only independent variable. Logistic regression examined the association between regimen complexity and adherence (MPR 80%). Categorical regimen complexity was the primary independent variable, and the model was adjusted for potential confounders. Odds ratios are provided for all covariates. Logistic regression was implemented to evaluate the association between being adherent and attaining or remaining at an HbA1c goal level below 7.0%. Being adherent in the 6 months prior to the best HbA1c measurement was the primary independent variable, and the analysis adjusted for other factors including age, gender, geographic region, and comorbid illnesses. This analysis was performed using patients with both a baseline and follow-up HbA1c result. All statistical analyses were conducted using SAS version 9.2, and STATA version Results Patient Population A total of 651,366 patients were observed as having both a diabetes diagnosis and a fill for an OAD during the identification period. Less than 1% of the sample was Vol. 17, No. 6 June 2010 JCOM 259

4 diabetes treatment 1,091,388 diagnosed with diabetes (1/1/ /31/2006) 651,366 fill for OAD (1/1/ /31/2006) 144,436 continuous health plan eligibility (12 mo before and after the study index date) 94,860 Final study cohort SES data n = 43,080 HBA1c values n = 16,198 Removed 1728 age < 18 yr 135 gender information not available Removed 11,047 insulin use 26,479 history of OAD use 12,050 lack of study regimen Figure 1. Study sample selection criteria. OAD = oral antidiabetic drug; SES = socioeconomic status. removed because of age requirement or unknown gender. A total of 144,436 patients were continuously enrolled for the 12 months prior to and after the index date: 11,047 were removed for baseline use of insulin; 26,479 were removed for baseline use of an OAD; and 12,050 were removed for not having a qualifying index regimen. In total, 94,860 patients with a diagnosis of diabetes and newly initiated OAD therapy were included in the study. Approximately 50% of the sample had both race/ethnicity and net worth data available, and 16,198 patients had both baseline and followup HbA1c results (Figure 1). The mean age of the study population was 52.8 years; 55.9% were men (Table 1). Nearly half (47.3%) of the study population was from the southern and mid-atlantic regions of the United States. The mean number of non-oad medications taken at some point during baseline by the study population was 8.4 (standard deviation [SD], 5.6). Among diabetes-related comorbidities, hyperlipidemia (69.7%) and hypertension (64.5%) were the most frequent across all complexity groups, with over 45% and over 70% of patients receiving prescription medications for these 2 conditions, respectively. Race and ethnicity data were available for approximately half of the study population. Among those with this information, 77% (36,644) were Caucasian, 6% (2837) were African-American, and 4.3% (2053) were Hispanic. Income data were missing for 66.7% of the study population, and net worth data were missing for 25.5%. Among those with socioeconomic status data, at least 50% had a yearly income greater than $65,000 and a net worth of at least $100,000. Treatment The majority of patients initiated monotherapy (74,616, 78%); of these, 45,578 (48%) initiated metformin monotherapy, and 16,126 (17%) initiated sulfonylurea monotherapy. Approximately 1 in 5 patients initiated dual therapy, most commonly a sulfonylurea plus metformin (11%), and fewer than 2% initiated triple therapy. At the beginning of the study period, 42.9% of patients had an index regimen that was considered to be low complexity, 47.8% medium complexity, and 9.3% high complexity. At the end of the 1-year follow-up period, 28.7% of patients had low-, 56.9% had medium-, and 14.4% had highcomplexity regimens. Patients were persistent with their initial OAD regimen for 7 to 8 months, on average. Approximately 48% were persistent with their index regimen through the 1-year follow-up period, and the rate was highest among those with low-complexity (54%), compared with medium- (47%) and high-complexity regimens (33%) (P < 0.01). Adherence Mean MPR was highest among patients with low-complexity regimens (75.0%) compared with medium- (72.6%) and highcomplexity regimens (69.1%) (P < 0.01). Moreover, the percentage of diabetes patients who were adherent (MPR 80%) decreased as treatment complexity increased. Among those with low-complexity regimens, 57.7% were adherent, compared with 51.3% of those adherent to medium-complexity and 42.7% to high-complexity regimens (P < 0.01, Table 1). Multivariate analysis indicated that the probability of being adherent decreased as the complexity of the patient s regimen increased (Table 2). In the full model, patients with medium-complexity regimens had 16% lower probability and those with high-complexity regimens had 37% lower probability of being adherent, compared with low-complexity regimens (P < 0.01 for both comparisons). Odds ratios were comparable for all 3 versions of the model. Compared with low-complexity regimens, the probability of being adherent for 260 JCOM June 2010 Vol. 17, No. 6

5 Table 1. Continuous Medication Possession Ratio and Rate of Being Adherent by 1 Year Regimen Complexity n Continuous MPR Mean (SD) Median Adherent (MPR 80%), n (%) Low 27, (28) ,701 (57.7) Medium 53, (27) ,668 (51.3) High 13, (27) ,5853 (42.7) MPR = medication possession ratio; SD = standard deviation. medium-complexity regimens ranged from 15% to 23% lower, and the probability of being adherent for high-complexity regimens ranged from 35% to 45% lower, on average. Increased age, male gender, increased number of concomitant non-oad medications used, and diagnoses of hyperlipidemia and hypertension were each significantly associated with increased probability of being adherent (P < 0.01). Increased baseline CCI, a diagnosis of depression, and alcohol or drug abuse were each associated with lower probability of being adherent. When socioeconomic status data were included in the model, Caucasian patients had higher probability of being adherent compared with non- Caucasians. Patients with increased net worth had higher probability of being adherent. Compared with those with net worth less than $50,000, the probability of adherence was 15% higher among those with net worth between $50,000 and $100,000, and 40% higher among those with net worth over $100,000 (Table 2). HbA1c Levels Laboratory results were available for the subset of patients who had their tests processed at one of the large laboratory processing facilities. A total of 23,464 patients had at least 1 baseline HbA1c value. However, the majority of patients (72.4%) did not have an HbA1c result during the follow-up period, 13.9% had only 1 result, 8.4% had 2 results, and 4% had 3 results. A total of 16,198 (17.1%) patients were identified as having both a baseline and follow-up HbA1c result required for analysis. Over the course of the 1-year follow-up period, some patients were observed as having multiple HbA1c results. To account for multiple tests, this study identified the best (lowest), worst (highest), and last HbA1c result. For the purposes of the multivariate analyses, the best result was chosen. Similar results were observed when using the worst and last values, and thus, these results are not presented. Among those with both baseline and follow-up HbA1c results, patients with low-complexity regimens had the lowest mean baseline HbA1c value (7.67%), compared with those Table 2. Logistic Regression for Being Adherent (MPR 80%) Reduced Model* (n = 94,860) Odds Ratio Full Model (n = 94,860) Model with SES Data (n = 43,080) Complexity (control: low) Medium High Age Male Baseline CCI Number of non-oad medications Hyperlipidemia Hypertension Depression Alcohol Schizophrenia Caucasian 1.41 Net worth (control: < $50,000) $50,000 $100, > $100, MPR = medication possession ratio; CCI = Charlson Comorbidity Index; OAD = oral antidiabetic drug; SES = socioeconomic status. *In this model, complexity is the only independent variable. P < P < who had medium- or high-complexity regimens (8.46% and 9.80%, respectively; P < 0.01). Complexity of therapy was associated with patients ability to reach the American Diabetes Association (ADA) recommended HbA1c goal of < 7.0%: 44.4% with low-, 30.4% with medium-, and 15.3% with high-complexity regimens reached the recommended goal during the baseline period (P < 0.01). The mean best (lowest) observed HbA1c result in the follow-up period was < 7.0% for all complexity groupings. The mean best HbA1c result was 6.5%, 6.6%, and 6.8% for those in the low-, medium-, and high-complexity groups, respectively (P < 0.01). The time to reach the best HbA1c result was approximately 6 months for each treatment group, and the percentage of patients whose best result was below the goal level was: 78.0% (low complexity), 74.1% (medium complexity), and 66.6% (high complexity) (P < 0.01). Additional trends in HbA1c by level of adherence and complexity are shown in Figure 2. Vol. 17, No. 6 June 2010 JCOM 261

6 diabetes treatment 8.0 Mean best HbA1c in follow-up, % High complexity Medium complexity Low complexity H: M: L: n = 110 n = 466 n = n = 203 n = 637 n = n = 249 n = 788 n = n = 362 n = 1454 n = n = 388 n = 1043 n = n = 453 n = 1196 n = n = 439 n = 1320 n = n = 417 n = 1390 n = n = 612 n = 3263 n = 2072 Mean OAD adherence, % Figure 2. Mean HbA1c by adherence to oral antidiabetic drug (OAD) therapy within complexity level. No. of patients within each complexity level shown for each adherence range. Multivariate analysis indicated that patients who were adherent to OAD therapy had 78% higher probability, on average, of achieving target HbA1c levels, compared with patients who were not adherent (Table 3). Odds ratios were similar when the model was reduced to include only the measure of adherence (64% greater probability) and when socioeconomic data were included (82% greater probability) (P < 0.01). Other significant factors (P < 0.05) included baseline HbA1c level, age, gender, geographic region, baseline CCI, time from index date to best HbA1c value, the number of non-oads used, cancer, and cognitive impairment. When socioeconomic data were included, Caucasians had significantly greater probability of achieving target HbA1c levels, compared with patients of other races/ethnicities (P < 0.05) (Table 3). Additional regression analyses that examined the association between adherence and alternate follow-up outcomes, including follow-up HbA1c values, absolute changes in follow-up HbA1c, and percentage change in HbA1c, consistently demonstrated similar and significant (P < 0.05) relationships with adherence and are not presented. These regressions controlled for the same confounding variables as the main analysis as well as baseline HbA1c where appropriate. Discussion The current study evaluated adherence with OAD therapy over a 1-year follow-up period in a newly diagnosed type 2 diabetes mellitus patient population. As seen in previous studies, fewer than half of patients were persistent with their OAD regimen throughout the follow-up [12,13]. OAD treatment complexity was a significant predictor of adherence even after adjusting for patient demographic and clinical characteristics. Complexity of OAD regimen also impacted glycemic control as assessed by HbA1c lab values. As indicated by the association between timing of HbA1c testing and treatment complexity, it is possible that patients with higher baseline HbA1c received more complex regimens and initiated those regimens sooner. The association between increased regimen complexity and decreased adherence indicates that treatment complexity can add to disease burden in patients with diabetes. After adjusting for confounding factors, patients with highcomplexity treatment regimens were 43% less likely to be adherent than patients with low-complexity regimens. Nonadherence is a barrier to achieving and maintaining recommended glycemic goals. Descriptive and multivariate analyses show a significant association between adherence 262 JCOM June 2010 Vol. 17, No. 6

7 Table 3. Logistic Regressions of Reaching HbA1c Goal Reduced Model* (n = 16,198) Odds Ratio Full Model (n = 16,198) Model with SES Data (n = 5949) MPR 80% before best HbA1c Latest baseline HbA1c result Age as of index date Male Region (control: Midwest) Northeast South West Baseline CCI score Time to lowest HbA1c result reading in 1-year follow-up Number of unique nondiabetic medications in 1-year follow-up Neoplasm in baseline period Cognitive impairment in baseline period Hyperlipidemia Hypertension Caucasian 1.20 Net worth (control: < $50,000) $50,000 $100, > $100, MPR = medication possession ratio; CCI = Charlson Comorbidity Index. *In this model, adherence is the only independent variable. P < P < to OAD therapy and improvement in HbA1c. After controlling for patient characteristics, patients who were adherent had 78% greater probability of achieving or maintaining treatment goals. Mean HbA1c values decrease as adherence increases, and this change is most notable among patients with high-complexity regimens, as such patients also have the highest baseline HbA1c values and therefore the greatest room for improvement. Poor adherence to medical regimens is common and a significant barrier to obtaining desired treatment effects. This study highlights the importance of adherence by linking it to improvement in outcomes as described by the National Quality Forum [33]. For patients with diabetes, adherence often means initial increases in medical costs before improved outcomes are attained. Strategies aimed at simplifying treatment regimens for patients with diabetes could aid in promoting adherence and improving treatment outcomes. The current study was the first to evaluate the association between medication complexity and adherence, including the subsequent HbA1c goal attainment, in a group of newly diagnosed type 2 diabetes mellitus patients in a managed care setting. The large sample size and the availability of numerous demographic and clinical variables allowed for an evaluation of this relationship in fully adjusted models. Despite these advantages, there are several limitations associated with the use of administrative claims data, as claims data may be limited by coding errors, rule-out coding, and undercoding. Pharmacy claims can provide evidence of prescriptions filled but cannot supply information regarding whether a patient actually took the medication, used the medication as directed, or received free samples. Prior to establishing weights for OAD use, OAD claims were carefully reviewed to minimize error in medication weight assignment. Patients 65 years and older may represent a cohort of elderly patients with higher socioeconomic status, which may affect access to medication and, subsequently, adherence. Moreover, among those patients with socioeconomic data, the mean level of income and household net worth may be higher than those observed for the general U.S. population. Generalizability of the results is therefore limited to use of an OAD in a managed care setting. The current study was limited to a select group of patients who had a qualifying index regimen, which eliminates patients who had only 1 prescription fill. The study objective was to evaluate patients currently treated with an OAD. It is likely that those who do not fill a second prescription may stop treatment due to their physician s recommendation or the occurrence of side effects and are not necessarily nonadherent. This requirement also increased the likelihood that patients who had less than 6 months of follow-up before their HbA1c test had consistent therapy before that test. Medication use during an inpatient stay was not available and therefore could not be assessed. Modifications to therapeutic regimens that may have occurred among hospitalized patients may have inadvertently rendered them nonadherent. Lastly, various factors may have served to temporarily increase adherence between 2000 and 2007, including disease management programs, health education initiatives, and changes in physician and other health care staffing. Unfortunately, this information was not available through claims data, and its impact on adherence and outcomes could not be assessed. Vol. 17, No. 6 June 2010 JCOM 263

8 diabetes treatment Conclusion Over the 1-year follow-up period, the percentage of patients considered adherent (MPR 80%) was low. Nonadherence to OAD therapy can be a significant barrier to attaining glycemic control. Comprehensive treatment plans that can reduce treatment complexity should be considered. The development of strategic interventions to improve adherence will be critical to achieving optimum outcomes in diabetes. Acknowledgments: Elisabeth Stahl and Klas Bergenheim, AstraZeneca Health Economics and Outcomes Research, and Hongjun Kan, Bristol Myers Squibb Global Economics and Outcomes Research, provided valuable comments for this article. Corresponding author: Setareh A. Williams, PhD, AstraZeneca LP, 1800 Concord Pike, PO Box (Room D3C-114), Wilmington, DE , setareh.williams@astrazeneca.com. Funding/support: This manuscript was supported by funding from AstraZeneca. Financial disclosures: Mr. Chastek and Ms. Moran are employees and stock holders of UnitedHealth Group. 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9 of adherence in pharmacy administrative databases: a proposal for standard definitions and preferred measures. Ann Pharmacother 2006;40: Benner JS, Glynn RJ, Mogun H, et al. Long-term persistence in use of statin therapy in elderly patients. JAMA 2002;288: Avorn J, Monette J, Lacour A, et al. Persistence of use of lipidlowering medications: a cross-national study. JAMA 1998;279: Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40: National Quality Forum. National Quality Forum endorses measures to improve medication safety and quality. Accessed 29 Apr 2010 at Press_Releases/2009/National_Quality_Forum_Endorses_ Measures_to_Improve_Medication Safety_and_Quality.aspx. Vol. 17, No. 6 June 2010 JCOM 265