To evaluate the effects of switching from multiple daily injection (MDI) therapy to insulin pump therapy, also called continuous subcutaneous insulin infusion (CSII), on antidiabetic drug and healthcare resource utilization.

This study was a retrospective analysis of administrative claims data from a large geographically diverse health plan in the United States from January 1, 2005, through April 30, 2008.

 Changes in antidiabetic drug use, antidiabetic drug switching and augmentation, and healthcare utilization during the baseline period and after CSII initiation were assessed using paired t test.

 There were 3649 possible subjects, of whom 943 met the criteria for analysis. The mean number of antidiabetic drugs used decreased by 46% after CSII initiation, and the mean reduction in antidiabetic drug utilization was 0.67; both were statistically significant. More than one-third of subjects who were taking antidiabetic drugs before CSII initiation discontinued oral therapy after CSII initiation. The number of subjects using multiple antidiabetic drugs significantly decreased after CSII initiation by 58%, and rates of switching or augmenting significantly decreased from 42% at baseline to 25% after CSII initiation.The rates of emergency department visits and inpatient admissions significantly decreased, and the rate of ambulatory visits significantly increased.

 CSII was associated with significant decreases in antidiabetic drug and healthcare resource utilization, contributing to stability of care. The evidence from this study indicates that CSII should be considered as an option for patients with type 2 diabetes mellitus who are using MDI and are experiencing a high degree of antidiabetic drug and healthcare resource utilization.

A retrospective analysis of a large sample of managed care enrollees with type 2 diabetes mellitus was performed to examine the effect on antidiabetic drug and healthcare resource utilization of switching from multiple daily injections to insulin pump therapy, also called continuous subcutaneous insulin infusion.

After initiation of continuous subcutaneous insulin infusion, antidiabetic drug utilization was reduced by 0.67 drug (a change of 46%), and more than one-third of subjects discontinued antidiabetic drugs altogether. 

Inpatient admissions and emergency department visits decreased significantly. Ambulatory visits increased significantly. 

Continuous subcutaneous insulin infusion should be considered an alternative to continued use of multiple daily injections after the latter therapy has failed.

	A major challenge of managing type 2 diabetes mellitus (T2D) is the escalation of therapies in the response to treatment regimen failure. Indeed, this treatment escalation is now the standard of care for this disease. Antidiabetic drugs are added to therapy regimens if hyperglycemia and elevated glycosylated hemoglobin (A1C) values persist after the first stage of lifestyle and pharmacologic interventions.1 Intensive insulin therapy is often not added until multiple antidiabetic drugs fail to maintain euglycemia. However, even with the addition of insulin, many patients continue using multiple antidiabetic drugs. Moreover, as the disease progresses, patients with T2D have increased healthcare resource utilization, including emergency department (ED) visits and inpatient admissions for diabetesrelated complications and comorbidities.

 Insulin pump therapy, also called continuous subcutaneous insulin infusion (CSII), is not usually initiated in most T2D treatment regimens until multiple daily injection (MDI) therapy has failed. A few studies

 have examined whether switching from antidiabetic drugs or conventional insulin therapy to CSII offers a clinical benefit over switching to MDI. Such studies have focused on change in A1C level  and have had mixed results. These studies did not examine whether CSII reduces utilization of antidiabetic drugs or other healthcare resources.

	A plausible area for investigation drawn from the sparse literature about CSII in T2D is whether switching to this therapy after MDI has failed can reduce the number of antidiabetic drugs used and the amount of healthcare resource utilization needed to manage the disease. The present study investigates antidiabetic drug and healthcare resource utilization among a large real-world population of patients with T2D who switched from MDI to CSII.

	This study was a claims analysis using medical, pharmacy, enrollment, and laboratory data to assess the effects of switching from MDI to CSII. Subjects were enrollees in a large geographically diverse US managed care health plan with documentation of T2D. No identifiable protected health information was extracted or accessed during the course of the study. Pursuant to the Health Insurance Portability and Accountability Act of 1996,

 the use of deidenti-fied data does not require institutional review board approval or waiver of authorization. Clinical outcomes (change in A1C levels and A1C targets) of this population are published separately.

	Medical claims or encounter data were collected from all available points of service such as inpatient and outpatient hospital records, ED visits, physician’s office encounters, and surgery center data, including specialty, preventive, and office-based treatments. Medical claims and coding conformed to insurance industry standards. Included in the database were pharmacy claims representing all prescriptions filled at outpatient pharmacies and submitted to the insurance plan for reimbursement. A 12-month period before CSII initiation was used to examine baseline demographic and clinical characteristics. The follow-up period was a minimum of 6 months and a maximum of 40 months. Baseline and follow-up periods were measured from the date of CSII initiation. Data from January 1, 1998, through April 30, 2008, were included in the study.

	Patients were included if they initiated CSII during the subject identification period (January 1, 2005, through October 31, 2007) and had evidence of T2D. Evidence of T2D was determined by the presence of 

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes for T2D (a fifth digit of 0 or 2) in the medical record or by the use of any oral antidiabetic drug or exenatide in the claims history. Exclusion criteria included age younger than 18 years, missing information on sex, evidence of CSII use during the baseline period, lack of continuous enrollment in the health plan for a 12-month baseline period or less than a 6-month follow-up period, and type 1 diabetes mellitus—related 

codes without evidence of oral antidiabetic drug or exenatide use.

	Claims were analyzed at the baseline period and after CSII initiation for changes in antidiabetic drug (defined as any oral antidiabetic drug or exenatide) and healthcare resource  utilization. Change in antidiabetic drug utilization was calculated as the number of antidiabetic drugs used after CSII initiation minus the number of antidiabetic drugs used during the baseline period. Healthcare resource utilization was calculated as the rate of ED visits, inpatient admissions, and ambulatory visits per subject per month (PSPM) during the baseline period and after CSII initiation. The number of ambulatory visits represented all visits for outpatient services that generated costs for care and not just physician office visits for the care and management of diabetes.

	All study variables were analyzed descriptively. Numbers and percentages were provided for dichotomous and polychotomous variables. Means, medians, standard deviations,and percentiles were calculated for continuous variables. Univariate analysis using paired t test was performed on the changes in antidiabetic drug and healthcare resource utilization. 

 <.05 was considered statistically significant.

	Of 3649 enrollees identified as having T2D and using CSII, 943 met criteria for inclusion in the study (

). Their mean age was 48 years, and they were followed up for a mean of 17 months (range, 6-39 months) (

). Commonly reported comorbid conditions included lipid metabolism disorders, hypertension, and diseases of the heart, thyroid, and eye.

	The mean (SD) number of antidiabetic drugs used per subject decreased from 1.45 (1.16) during the baseline period to 0.78 (1.01) after CSII initiation (a change of 46%, 

<.001), which represented a mean (SD) reduction in antidiabetic drug utilization of 0.67 (1.26) (

 < .001). Among 4 commonly used antidiabetic drugs (exenatide, metformin, sulfonylurea, and thiazolidine-dione), the reductions in utilization after CSII initiation ranged from 27% to 59% (

	There was a statistically significant increase in the number of subjects taking zero antidiabetic drugs and a statistically significant decrease in the number of subjects taking 2 or 3 or more antidiabetic drugs (

). The number of subjects using multiple antidiabetic drugs decreased from 380 (40%) during the baseline period to 161 (17%) after CSII  initiation, a statistically significant reduction of 58% (

 <.001). More than onethird of subjects (270 of 755 [36%]) taking antidiabetic drugs during the baseline period discontinued antidiabetic drugs after CSII initiation.

	Significantly fewer subjects added antidiabetic drugs to their therapy regimen after CSII initiation (236 subjects [25%]) than during the baseline period (396 subjects [42%]) (

 <.001). More subjects discontinued and switched antidiabetic drugs after CSII initiation than during the baseline period, but the difference was not statistically significant (62 [7%] to 82 [9%] for discontinuation [

 = .09]; 21 [2%] to 12 [1%] for switching [

	From the baseline period to after CSII initiation, there was a statistically significant decrease in ED visits and inpatient admissions (0.03 PSPM [

<.001], respectively) and a statistically significant increase in ambulatory visits (0.21 PSPM [

 <.001]). When the change in PSPM rate was projected over 4 years, the resultant healthcare resource utilization was 1.4 fewer ED visits and 1 fewer inpatient admission by using CSII rather than MDI. When the change in PSPM rate was projected over 1 year, the result was 0.4 less ED visit and 0.2 less inpatient admission (

	This large real-world population experienced significant and meaningful benefits by switching from MDI to CSII. We found statistically significant reductions in antidiabetic drug utilization, ED visits, and inpatient admissions. The results from our study suggest that CSII may allow patients with T2D to reduce their antidiabetic drug use. The number of  subjects using multiple antidiabetic drugs decreased after CSII initiation, while the number of subjects taking zero antidiabetic drugs increased; these differences were statistically significant. When projected over 4 years of CSII use, the statistically significant reductions in healthcare resource utilization resulted in approximately 1 fewer inpatient admission and 1.4 fewer ED visits. Reduced ED visits and inpatient admissions may result from better glycemic control, although the study design restricted our making firm conclusions. Over time, we would expect that reductions in the rates of inpatient admissions and ED visits could decrease even more. As expected, there was a statistically significant increase in ambulatory visits from the baseline period to after CSII initiation. This increase likely resulted from additional training and therapy customization required to use an insulin pump. Indeed, the decreased antidiabetic drug utilization, ED visits, and inpatient admissions support the conclusion that the increase in ambulatory visits did not result from disease progression. Positive results in other areas of the subjects’ claims histories indicate that expertise reduced the need for concomitant medication and the incidence ofconditions  requiring urgent care.

	We note several limitations to our study. We could not capture all aspects of each subject’s medical history because the data were collected for payment and not for research. Such data help us to understand healthcare resource utilization but may be biased by coding errors, undercoding, and coding for the purpose of ruling out something other than the actual  disease. Also, the design of our analysis did not allow us to control for treatment decisions. Inclusion in the study was determined by CSII initiation, thus negating the possibility of a control group. The 1-year and 4-year projections of healthcare resource utilization did not take disease progression into account, and ambulatory visits could not be  projected owing to the many training visits immediately after CSII initiation. Finally, the high PSPM rate of ambulatory visits was influenced by the method of data extraction, which included all visits for outpatient services that generate costs for care. For example, the rate includes laboratory services at a freestanding laboratory and visits for diabetes education.

	We did not include a control group but chose instead to analyze utilization before and after initiation of CSII. Both pre—post and treatment–control group designs are used in the assessment of observational data. The treatment–control group design, while perhaps controlling for temporal changes in diabetes management, suffers from inherent selection  bias issues. That is, subjects who receive an insulin pump for management of their diabetes are likely in a different stage of disease compared with those who do not. We determined that the disadvantages of this type of selection effect outweighed the benefits of this design. The pre–post design (similar to a repeated-measures design in its handling  of the data) offers the advantage of each subject serving as his or her own control, although it is not without limitations. Moreover, the limitation of not having a control group is somewhat mitigated by the use of real-world data, thus showing a possible treatment effect without a clinical study effect.

	In conclusion, continuous subcutaneous insulin infusion was associated with significant decreases in antidiabetic drug and healthcare resource utilization, contributing to stability of  care. The evidence from this study indicates that CSII should be considered as an option for patients with T2D who are using MDI and are experiencing a high degree of antidiabetic drug utilization and healthcare resource utilization.

From Medtronic, Inc (PML, TP, JL, SWL), Northridge, CA; i3 Innovus (AAR, NS, YF), Eden Prairie, MN.

 Mr Lynch, Mr Peoples, Ms Levinson, and Dr Lee report being employees and stockholders of Medtronic, Inc. Dr Riedel, Mr Samant, and Mr Fan are employees of i3 Innovus, and their company received payment from Medtronic, Inc to complete this analysis.  Authorship Information: Concept and design (PML, AAR, JL, SWL); acquisition of data (PML, AAR, YF); analysis and interpretation of data (PML, AAR, NS, YF, TP, JL, SWL); drafting of the manuscript (PML, AAR, NS, TP, SWL); critical revision of the manuscript for important intellectual content (PML, AAR, TP, JL); statistical analysis (PML, AAR, NS, YF); provision of study materials or patients (SWL); obtaining funding (PML); administrative, technical, or logistic support (TP); and supervision (PML, JL, SWL).

 Peter M. Lynch, MPH, Medtronic, Inc, 18000 Devonshire St, Northridge, CA 91325. E-mail:  peter.m.lynch@medtronic.com.

1. Nathan DM, Buse JB, David son MB, et al. Management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement from the American Diabetes Association and the European Association for the Study of Diabetes [published correction appears in: Diabetes Care. 2006;29(11):2816-2818]. Diabetes Care. 2006;29(8):1963-1972.

Articles

CLINICAL

A retrospective claims analysis of managed care enrollees with type 2 diabetes mellitus showed that insulin pump therapy reduced antidiabetic drug and healthcare resource use.

Resource Utilization With Insulin Pump Therapy for Type 2 Diabetes Mellitus

	Recent estimates suggest that medication nonadherence may result in annual costs to the United States healthcare system in excess of $300 billion, as well as contributing to adverse clinical outcomes including hospitalization and death.

 Specifically for patients with coronary artery disease, nonadherence has been shown to be associated with an increased risk for acute myocardial infarction or heart failure as well as cardiovascular mortality; similar increased risks of hospitalization and mortality exist for nonadherent diabetic patients.

 Developing effective interventions to improve patients’ medication adherence remains an active field of research—an endeavor that aims to enhance not only compliance but also health outcomes. The systematic review of interventions to improve medication adherence in this issue of The American Journal of Managed Care sheds light on both effective and ineffective means by which patients have been encouraged to adhere to regimens prescribed  to them by medical professionals.

	The current study examined methods and results from 51 randomized clinical trials published since 1975 involving  interventions aimed at improving adherence to cardiovascular medications. Most of the included studies were in-person interventions—those involving direct, personal message delivery by a trained layperson or medical professional, either over the phone or at the site of care. Other modes of intervention delivery included mailed, faxed, or hand-distributed messages and electronic systems. The inclusion criteria allowed for multiple measures of adherence across all studies. Results indicated that electronic interventions and in-person delivery either by a pharmacist or at hospital discharge were the most successful at improving medication adherence. In addition, nonautomated phone calls did not result in significant adherence improvement and, by and large, were less likely to lead to improvement compared with in-person modes of message delivery.

	Cutrona and colleagues found that several modes of intervention may prove successful in improving the adherence rates of patients for whom cardiovascular treatment regimens have been prescribed. Assessing a diverse set of interventions across a wide time horizon, these researchers provide an informative overview of formal methods (both person-dependent and person-independent) by which improving adherence has been addressed.

	The outcomes reported in the study, however, lacked a connection to clinically meaningful outcomes such as disease control or mortality analysis. The results focused primarily on medication adherence. Analysis assessing the health outcomes associated with successful adherence interventions would have added weight to the argument for initiating such methods of message delivery in real-world settings. Similarly, reporting costs (if available) would be helpful to translate the results of individual interventions into economically meaningful results relevant to potential payers and other financial stakeholders.

	A recent assessment of adherence-improving interventions involving cardiovascular medications determined that a favorable cost-effectiveness ratio could be achieved when considering the lifetime benefits of improved medication adherence.

 Further assessment or modeling of the current findings could lead to similar interpretations or, at the very least, consequential economic evidence for or against particular interventions.

	The suggestions made by this study mirror those reported in the literature on medication adherence interventions. Support for pharmacist-led interventions is well represented, suggesting that methods used by pharmacists lead to improved rates of cardiovascular medication adherence.

Programs centered at community pharmacies, however, fail to capture patients who either never fill their prescription or who opt to use a mail-order pharmacy. Similarly, interventions done at the time of hospital discharge fail to reach patients who have not been hospitalized. Means by which interventions can spur proper adherence from the point of initial prescribing, irrespective of the prescribing or dispensing location, deserve further investigation.

	However, the study’s finding that medication adherence can be improved by messages delivered electronically is encouraging, as innovative approaches to electronic message delivery continue to evolve. Although the current study focused on electronic monitoring and automated calling, other electronic systems are emerging and show promise. Low-cost systems including  short message service (SMS) text and smartphone applications have been introduced to the market for both medication use monitoring and patient reminders, with results from several studies pending.

 Innovative approaches such as these have been previously suggested by other researchers, after study results indicated that current interventions to increase medication adherence have only shown modest success.

 Evidence exists, however, that SMS-delivered interventions can lead to positive

	The content of the intervention also has been discussed as an important factor in the potential success of efforts to improve adherence. Tailoring messages to patients through motivational interviewing has been suggested as a technique that interventionists can consider when crafting approaches to improve patient-centricity.

 Used in combination, tailored electronic messages may present an opportunity for potentially low-cost interventions that could aid in improving patient adherence to medication.

	The current study is a reminder to the medical community that medication nonadherence remains a pressing issue. Furthermore, it sheds light on a related issue: current interventions to improve adherence lack a clear, uniform approach by which patient compliance can be universally improved. Several options reviewed in their study, however, show promise (eg, electronic systems, pharmacist-led interventions). Future research should focus on how these approaches may be combined with tailored messages in order to produce successful results that are clinically meaningful and economically attractive.

From the Department of Clinical, Social, and Administrative Sciences (JG, SRE), University of Michigan, Ann Arbor.

The authors report no external funding for this work.

 The authors (JG, SRE) report 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 (JG, SRE); acquisition of data (JG); analysis and interpretation of data (JG, SRE); drafting of the manuscript (JG, SRE); critical revision of the manuscript for important intellectual content (JG, SRE); administrative, technical, or logistic support (SRE); and supervision (SRE).

 Steven R. Erickson, PharmD, Department of Clinical, Social, and Administrative Sciences, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065. E-mail: serick@med.umich.edu.

1. Balkrishnan R. The importance of medication adherence in improving chronic-disease related outcomes: what we know and what we need to further know. Med Care. 2005;43(6):517-520.

2. Ho PM, Magid DJ, Shetterly SM, et al. Medication nonadherence is associated with a broad range of adverse outcomes in patients with coronary artery disease. Am Heart J. 2008;155(4):772-779.

3. Ho PM, Rumsfeld JS, Masoudi FA, et al. Effect of medication nonadherence on hospitalization and mortality among patients with diabetes mellitus. Arch Intern Med. 2006;166(17):1836-1841.

4. Cutrona SL, Choudhry NK, Fischer MA, et al. Modes of delivery for interventions to improve cardiovascular medication adherence. Am J Manag Care. 2010;16(11):939-952.

5. Chapman RH, Kowal SL, Cherry SB, et al. The Modeled Lifetime Cost-Effectiveness of Published Adherence-Improving Interventions for Antihypertensives and Lipid-Lowering Medications. Value Health. 2010 June 7 [Epub ahead of print].

6. Robinson JD, Segal R, Lopez LM, Doty RE. Impact of a pharmaceutical care intervention on blood pressure control in a chain pharmacy practice. Ann Pharmacother. 2010;44(1):88-96.

7. Lee JK, Grace KA, Taylor AJ. Effect of a pharmacy care program on medication adherence and persistence, blood pressure, and lowdensity lipoprotein cholesterol: a randomized controlled trial. JAMA. 2006;296(21):2563-2571.

8. Murray MD, Young J, Hoke S, et al. Pharmacist intervention to improve medication adherence in heart failure: a randomized trial. Ann Intern Med. 2007;146(10):714-725.

9. Barclay E. Text messages could hasten tuberculosis drug compliance [published correction appears in Lancet. 2009;373(9664):632]. Lancet. 2009;373(9657):15-16.

10. Intelecare. Consumer products. 2010. http://www.intelecare.com/ products.php. Accessed September 9, 2010.

11. McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: scientific review [published correction appears in JAMA. 2003;289(24):3242]. JAMA. 2002;288(22):2868-2879.

12. Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009;36(2):165-173.

13. Possidente CJ, Bucci KK, McClain WJ. Motivational interviewing: a tool to improve medication adherence? Am J Health Syst Pharm. 2005;62(12):1311-1314.

EDITORIAL

Current interventions to improve medication adherence lack a clear, uniform approach by which patient compliance can be universally improved.

Medication Adherence: The Search for Interventions That Work

To evaluate the impact of Medicare Part D coverage gap (donut hole) on adherence to diabetes medications.

 Retrospective cohort analysis based on pharmacy claims data.

The sample included 12,881 Medicare Part D beneficiaries with diabetes who entered the coverage gap in 2008. Sample patients had 3 different levels of coverage in the donut hole: no coverage, generic drug coverage only, and both generic and brand-name drug coverage. Adherence was measured by the proportion of days covered. We used a difference-in-difference model to evaluate the effect of coverage gap on adherence.

 In the donut hole, the average copayment for diabetes medications increased substantially for beneficiaries with no coverage and beneficiaries with generic drug coverage only, whereas the average copayment for beneficiaries with both generic and brand-name medication coverage declined slightly. Compared with beneficiaries with full coverage of both generic and brand-name drugs, beneficiaries with no coverage (odds ratio[OR] = 0.617, 

 <.0001, 95% confidence interval [CI] = 0.523, 0.728) and beneficiaries with generic drug coverage only (OR = 0.702, 

 <.0001, 95% CI = 0.604, 0.816) were significantly less likely to be adherent after entering the donut hole. The difference between having generic coverage and no coverage was not significant (

 The coverage gap in the Medicare Part D program has a significant negative impact on medication adherence among beneficiaries with diabetes. Availability of brand-name drug coverage in the donut hole is critical to adherence to diabetes medications.

The Medicare Part D coverage gap has a significant negative impact on adherence to diabetes medications.

The average copayment of diabetes medications increased substantially for beneficiaries with no coverage or only generic coverage in the donut hole. 

Beneficiaries with no coverage or only generic coverage in the donut hole were significantly less likely to be adherent to diabetes medications in the donut hole. 

Offering only generic coverage in the donut hole has a limited impact on patients' adherence to diabetes medications.

	The Medicare Part D program, introduced on January 1, 2006, provides prescription drug coverage for Medicare beneficiaries. One unique feature of the Part D benefit design is the coverage gap (or donut hole). The defined standard benefit in 2008 started with a $275 deductible and a 25% copayment for drug spending between $275 and $2510. After the initial coverage period, beneficiaries entered a coverage gap, in which they paid 100% of the drug cost, until their true out-of-pocket drug spending reached the catastrophic limit of $4050 (or total drug spending of $5726.25). Under the catastrophic coverage, beneficiaries pay the greater of a 5% or a $2.25/$5.60 (generic/brand-name) copayment.

	The donut hole is a controversial component of the Part D design because a lack of benefit may negatively impact patients’ drug utilization and subsequent clinical outcomes. Studies have shown that beneficiaries of private drug benefit plans with annual benefit limits are more likely to discontinue their medications and have a higher rate of hospitalization compared with beneficiaries who have no benefit limits.

 Sun et al reported that the donut hole was associated with reductions in medications used for potentially disabling and life-threatening conditions.

 Another study by Zhang et al indicated that beneficiaries with no coverage in the donut hole decreased their use of monthly prescriptions by about 14%.

	In this study, we examine the effects of the Part D coverage gap on adherence to diabetes medications. We focused on diabetes because diabetes is a serious chronic medical condition with considerable morbidity and mortality. The prevalence rate of diabetes is high among senior citizens (23.1%), compared with 7.8% in the US population.

 In 2007, over half of the $174 billion economic cost of diabetes was attributed to people 65 years and older, and a large portion of these costs were borne by Medicare.

	Previous studies on the Part D coverage gap generally used the number of prescriptions filled as the outcome variable.

 It is difficult to link the number of prescriptions with important outcomes such as hospitalizations and glycosylated hemoglobin (A1C) levels to help decision making in Part D benefit design. Our research focused on adherence to  diabetes medications, a critical component in the management of diabetes. Aggressive glycemic management, as measured by A1C, can reduce long-term complications

 and medical costs associ-ated with diabetes treatment.

 Poor adherence to diabetes medications is a major contributor to unsatisfactory glycemic control

 and increases the risk of adverse health events associated with diabetes.

 Inadequate adherence to diabetes medications in the donut hole could have serious long-term consequences for both Medicare and Medicare beneficiaries.

	This study is a retrospective cohort analysis based on prescription drug claims data provided by MedImpact Healthcare Systems, Inc, a national pharmacy benefit management company. Compared with the national data, Part D beneficiaries in MedImpact had the same sex distribution but were less likely to be under age 65 years (

	The sample in this research included patients over age 65 years who were continuously enrolled in a Medicare Part D plan from January 1, 2007, to December 31, 2008, and had at least 2 diabetes medication claims in each year. This study required patients to use diabetes medications in 2007 to ensure that all patients had used diabetes medications prior to the study period, January 1, 2008, to December 31, 2008. Utilization data in 2007 were used to acquire comorbidity information. Patients who received low-income subsidies for their premium and copayment were excluded from the sample because of their unique benefit structure.

	A total of 35,426 patients were identified using these criteria. Among these patients, 37.10% reached the donut hole in 2008 but did not reach the catastrophic limit, 13.38% reached both the donut hole and the catastrophic limit, 49.44% never reached the donut hole, and the remaining 0.09% reached the catastrophic limit directly without entering the donut hole. We selected only patients who had reached the donut hole but not the catastrophic limit for this research.

 The final sample contained 12,881 patients from more than 30 different health plans.

	Part D patients can have different types of Part D plans, including the defined standard benefit (DSB) plan, the actuarially equivalent (AE) plan, the basic alternative (BA) plan, and the enhanced alternative (EA) plan. The AE and BA plans are equivalent to DSB plans in value and do not provide coverage in the donut hole. The EA plans charge a supplemental premium and provide supplemental benefits. Many EA plans cover either generic drugs only or both generic and brand-name drugs as supplemental benefits.

	In our analysis, we designated 3 groups of Part D beneficiaries based on their coverage in the donut hole. The first group consisted of beneficiaries with no donut hole coverage (“no coverage” group). This group included beneficiaries with DSB, AE, or BA plans and beneficiaries with EA plans that did not offer coverage in the donut hole. The second group comprised beneficiaries with only generic drug coverage in the donut hole (“generic coverage only” group) from EA plans. The third group included beneficiaries with both generic and brand-name drug coverage in the donut hole (“full coverage” group) from EA plans. All Part D plans identified in this research had $2510 and $5726.25 for the start and end of the donut hole.

	The outcome measure of interest was adherence to diabetes medications. In this analysis, adherence to diabetes medications was measured by the proportion of days covered (PDC), calculated as the number of days covered by at least 1 diabetes medication in a certain period of time, divided by the number of days in that period. To calculate a PDC, each day in that period was evaluated as covered or as not covered by a medication based on fill date and days of supply. If a new claim for the same diabetes medication occurred before the end date of the last claim, the starting date of the new claim was pushed forward to the end date of the last claim. We calculated 2 PDCs for each patient: one for the period before reaching the coverage gap (pregap PDC) and the other for the period after reaching the coverage gap (postgap PDC). Patients with a PDC of 80% or more were classified as adherent.

In addition to a dichotomous adherence variable, we used continuous PDC as the outcome variable to test the robustness of the results.

	Control variables include age, sex, whether patient used insulin in the prior 12 months, geographic region (Northeast, South, Midwest, and West), and comorbidities in the prior 12 months. Comorbidity indicators were constructed based on the RxRisk model of comorbid conditions.

 A series of dummy variables for each major comorbid condition were

	We used a difference-in-difference regression analysis to measure the effect of coverage gap on adherence to diabetes medications. The difference-in-difference method used patients with full coverage in the donut hole as the control group because these patients experienced little copayment change in the donut hole. The experimental group consisted of patients in the other 2 groups (no coverage and generic coverage only), who experienced substantial copayment increases in the donut hole.

	Two challenges in measuring the effect of the donut hole are that (1) patients may self-select different levels of coverage and (2) they may have different propensities for being adherent to diabetes medications. The difference-in-difference method addresses this issue by having repeated observations for the same patient in both the pregap period and postgap periods. A group fixed effect was used to control for the permanent differences across different groups, and a time fixed effect was used to control for the secular trend within the study period. The true donut hole effect is the difference in adherence to diabetes medications between the control and the experimental groups in the donut hole, adjusting for the difference between the control and the experimental groups before entering the donut hole.

presents the patients’ characteristics. The average age for the entire sample was 74.97 years, and 48.06% of patients in the sample were male. A typical patient spent around 104 days in the donut hole. The vast majority of patients in the sample lived in the Midwest (82.85%). The most significant comorbidities were hypertension (87.41%), hyperlipidemia (81.54%), and heart disease/hypertension (69.47%).

	About a third (33.47%) of patients used insulin in the prior 12 months. Among patients who used insulin in 2007, the percentages using short-acting, intermediate-acting, long-acting, and mixed insulin were 32.27%, 25.68%, 55.56%, and 27.67%, respectively. These categories are not mutually exclusive because a patient could have used more than 1 type of insulin in 2007.

	In the sample, 19.63% of the 12,881 beneficiaries had no coverage in the donut hole, 20.08% of beneficiaries were covered for generic drugs only, and 54.28% of beneficiaries had full coverage. Patients with no coverage in the donut hole were slightly older (75.40 years) than patients with generic coverage only (74.85 years) and patients with full coverage (74.87 years). Patients with generic coverage only had the longest stay in the donut hole (107.96 days), and patients with no coverage in the donut hole spent the shortest time in the donut hole (99.91 days); patients with full coverage fell in between (103.54 days).

 shows the adherence rates and average copayments for patients in the sample. The adherence rate before reaching the donut hole for the no-coverage group was 0.82, which was close to the adherence rates of 0.81 for the group with generic coverage only and 0.85 for the group with full coverage. After reaching the donut hole, the adherence rate of patients with no coverage dropped 8 percentage points to 0.74. Similarly, the adherence rate of patients with generic coverage dropped 6 percentage points to 0.75. In contrast, patients with full coverage maintained their adherence rate at 0.85 in the donut hole.

	The coverage gap had a substantial impact on copayment. The average copayment (per 30 days of supply, the same thereafter) of all diabetes medications before reaching the donut hole was $18.63 for patients with no coverage, $18.63 for patients with generic coverage only, and $16.48 for patients with full coverage. After reaching the donut hole, the average copayment increased to $69.20 (a 271% increase) for the group with no coverage and to $67.18 (a 261% increase) for the group with generic coverage only. Partially because of the increased use of 90 days of supply, the average copayment for patients with full drug coverage actually decreased slightly to $14.33 after reaching the donut hole.

	Table 3 breaks down the change in average copayment by generic and brand-name diabetes medications. For patients with no coverage, the average copayment for generic drugs almost doubled after reaching the donut hole ($12.13 vs $6.42). In contrast, the average copayment for generic drugs did not change for patients with generic coverage only ($6.64

	vs $6.52) and even decreased from $6.37 to $4.63 for patients with full coverage. The average copayment for brand-name drugs quadrupled after reaching the donut hole for patients with no coverage ($126.05 vs $31.79) and patients with generic coverage only ($133.34 vs $32.47).

 presents the changes in the market share by diabetes medication classes before and in the donut hole. Market share was defined as the sum of days of supply in each class of diabetes medications divided by the sum of days of supply for all diabetes medications. Overall, the market shares of different diabetes medications changed little before and in the donut hole. There was no evidence that patients with no brand-name drug coverage tried to increase the use of genericdrugs in response to increases in copayment for brand-name drugs in the donut hole. In fact, the market share of insulin and dipeptidyl peptidase-4 inhibitors, 2 classes of medications dominated by brand-name medications, increased in the donut hole for patients in all 3 groups.

 Difference-in-Difference Regression Analysis

POLICY

The coverage gap in the Medicare Part D program has a significant negative impact on adherence to diabetes medications.

Part D Coverage Gap and Adherence to Diabetes Medications

 To determine whether nurse practitioner (NP) comanagement can improve the quality of care for 5 chronic conditions in an academic geriatrics practice.

 From September 2006 to September 2007, 18 primary care geriatricians were divided into an intervention group that could refer patients to an NP for comanagement of dementia, depression, falls, heart failure, and/or urinary incontinence, or a control group that indicated which patients would have been referred to the NP for these conditions. The NP used structured visit notes to guide care delivery for the 5 conditions concordant with Assessing Care of Vulnerable Elders-3 (ACOVE-3) quality indicators. We reviewed charts to determine adherence to recommended processes of care.

 A total of 200 patients (108 intervention, 92 control) were eligible for at least 1 process of care recommended by ACOVE-3 for the 5 conditions. Patients' mean (SD) age was 85 years (7 years), 67% were women, and patients were eligible for a mean (SD) of 6.9 (4.4) processes of care. Intervention patients were eligible for more care processes than controls (7.8 vs 5.9 processes per patient; 

 = .002). Quality of care was higher for patients in the intervention group compared with the control group (54% vs 34% of care processes completed; 

 <.001). The adjusted absolute difference between intervention and control groups in care processes completed was 20% (95% confidence interval = 13%, 27%).

 NP comanagement of 5 chronic conditions was associated with higher quality of care, even in a practice of geriatricians.

	A nurse practitioner markedly improved the quality of care for dementia, depression, falls, heart failure, and urinary incontinence in an academic geriatrics practice.

		Adding a nurse practitioner focused on managing these 5 chronic conditions improved adherence to recommended geriatric care by 20% in a setting where all physician providers were geriatricians.

		Improved quality of care came at the expense of 2.6 additional visits to the nurse practitioner per patient during a 13-month period.

	The United States needs a more robust primary care system, yet fewer medical students are pursuing careers in primary care.

 This quandary, coupled with the need for interprofessional collaboration to care effectively for older adults,

 has renewed interest in care models that maintain the primary care physician (PCP) as the key decision maker with patients, but involve others in care implementation, including registered nurses with additional training (eg, nurse practitioners [NPs]), social workers, and physician assistants.

 Data suggest that NPs focused on a particular condition can provide at least comparable quality of care for that condition and sometimes achieve better patient  outcomes.

 However, informationis sparse regarding the effectiveness of NP comanagement of complex  older patients in outpatient settings. An NP/social worker team collaborating with PCPs and a geriatrician-led interdisciplinary team improved the quality of medical care for low-income seniors,

 and a trial of a registered nurse working with    teams of PCPs to improve care for older adults with multimorbidity has shown an early beneficial effect on quality.

 In addition, specialized nurses, NPs, or psychologists have successfully comanaged patients with depression or dementia in primary care.

	Available studies do not, however, address whether NP comanagement can improve quality in a primary care practice staffed by geriatricians. By virtue of their training, interest, and patient population served, geriatricians are likely to be expert in managing geriatric conditions. Yet many of the barriers to providing recommended care that affect all PCPs (eg, lack of time during the office visit, inability to overcome existing practice habits)

 also confront geriatricians. This study’s goal was to evaluate a quality improvement program that  compared usual primary care by academic geriatricians with care comanaged by an NP for 5 chronic conditions: dementia, depression, falls, heart failure, and urinary incontinence.

	The quality improvement project ran from July 1, 2006, to September 30, 2007. Subsequently, University of California, Los Angeles Institutional Review Board approval was obtained to examine de-identified data. 

 contains a description of the project setting.

	The project consisted of run-in (July 1, 2006 to August 30, 2006) and intervention (September 1, 2006 to September 30, 2007) periods. For these periods, an experienced, gerontology-certified NP (JKB), who was hired specifically for the comanagement role, was supported by a grant from the John A. Hartford Foundation. The NP was colocated with the geriatricians at the large outpatient clinic (see Table 1 for details on practice locations).

	During the run-in period, the NP customized condition-specific structured visit notes based on previous versions

 to address the Assessing Care of Vulnerable Elders-3 (ACOVE-3) quality indicators for the 5 chronic conditions

 and American College of Cardiology/American Heart Association heart failure guidelines.

 These note templates were designed to support Medicare documentation requirements and could be used for both initial and follow-up visits. The run-in period also allowed time for the NP to develop her practice style, familiarize herself with clinic operations, and build trust among the referring physicians while seeing patients together with her supervisor (BKK). The practice also developed condition-specific patient education handouts (based on published materials

) and referrals to local community resources, most of which were available to both intervention and control physicians.

	Before the intervention period, physicians were first paired by patient volume; then 1 physician from each pair was selected by coin toss to be able to refer to the NP. Intervention physicians could refer only their primary care patients, not other physicians’ patients whom they might have seen while covering. Control physicians could not refer patients to the NP but were requested to indicate which patients they would have referred.

	During the intervention period, the night before a physician’s scheduled clinic, practice staff clipped an additional sheet to each patient’s chart. Control physicians were asked to note which (if any) of the 5 conditions would have prompted referral of the patient for NP comanagement and the priority level for each “referred” condition (more than 1 condition could have the same priority level). Intervention physicians were asked to note whether they would like the patient to see the NP for any of the 5 conditions and the priority level for each referred condition. The clinic scheduler received completed forms and, in the case of intervention patients, arranged an appointment with the NP. The NP could order tests and treatments without approval from the patient’s geriatrician, but could obtain guidance if needed. The NP delegated certain tasks to clinic nurses, including orthostatic vital signs, urinalyses, bladder scans, and visual acuity measurement. Patient follow-up visits were scheduled by the NP as needed. After each visit, the NP e-mailed or faxed geriatricians a written assessment and plan; she handled more urgent issues via phone or pager.

	The project was introduced at a faculty meeting in September 2006, with follow-up by BKK (the clinical champion) to reinforce the importance of referrals. In February 2007, one pair of high-volume physicians switched roles, with the intervention physician switching to the control group and vice versa, because the physician originally assigned to the intervention group was not referring patients to the NP. This switch was made to increase the number of NP referrals.

	For participating intervention and control physicians who were internists, the practice successfully applied to the American Board of Internal Medicine to receive credit for the Practice Improvement Module of the Maintenance of Certification program on the basis of this project. After the project period, all geriatricians could make referrals to the NP.

	After project completion, 2 abstractors, who had worked as physicians outside the United States, were trained by a nurse researcher with extensive experience in chart abstraction (CPR). Abstractors reviewed both the paper and electronic medical record using structured forms. All intervention and a sample of control group records were reviewed (see 

 for details on control group selection).

	The chart abstraction focused on determining eligibility for, and completion of, care processes specified by relevant ACOVE-3 quality indicators for vulnerable elders, the population served by the practice (see 

for indicator list). The quality indicators specify a time window during which relevant care should be completed; if this window extended beyond the end of the project period for a particular indicator, the patient was considered ineligible for that indicator. The abstractors’ decision about completion of recommended   processes of care was based on the entire chart, regardless of who completed the care process or for which conditions the patient was referred to the NP. Thus, the care reviewed for patients in the intervention group was a compriorbination of both physician and NP care, and included project conditions that physicians had chosen not to refer to the NP. Abstractors were not blind to intervention versus control group status (because structured visit notes appeared only in intervention patients’ records) but were blind to which    conditions patients were referred for.

	Fifteen charts were reabstracted (8 for test-retest reliability and 7 for inter-rater reliability), demonstrating a pooled kappa22 of 0.64 for agreement on eligibility for care processes and (where agreement existed on eligibility) 0.31 for completion of care processes. Data from the paper abstraction forms were entered electronically (with more than 99% accuracy). Abstractors also reviewed encounter data in the electronic health record to determine the number of geriatrician and NP office visits in the intervention and control groups.

	Patient demographics were limited to information in the chart, which included age and sex. Patients were assigned to the intervention or control group based on the group status of their primary geriatrician. For the 2 physicians who switched intervention/control group status during the project, analysis was based on actual referrals (intervention or control) at the time of referral rather than intention to treat. Adherence to each recommended care process was scored dichotomously (process completed/not completed). The percent adherence was computed for the intervention and control groups, both in aggregate and stratified by condition. The primary analysis included all of the patient’s conditions. In a sensitivity analysis, we included only those conditions designated as “top priority” for referral to the NP on the referral sheets. Details on statistical analyses may be found in 

	Charts from 275 patients (139 intervention, 136 control) were reviewed. For 2 patients, no record was found of any geriatric outpatient care; these patients were excluded. Of the remaining 273 patients, 200 (73%, 108 intervention, 92 control) were eligible for at least 1 ACOVE-3 process of care; these patients were eligible for a total of 1384 care processes (mean of 6.9 processes per patient [SD 4.4 processes]). Patients’ mean age was 85 years (SD 7 years); 67% were women. Age and sex did not differ by intervention versus control group assignment (

 = .51, respectively). Intervention patients were eligible for more processes of care than controls (7.8 vs 5.9 processes per patient; 

	Quality of care was higher for the intervention group than for the control group (54% vs 34% of care processes completed;

 <.001). The adjusted difference in quality of care  between intervention and control groups remained significant (

<.001), and the bootstrapped quality of care difference between groups was 20% favoring the intervention group (95% confidence interval = 13%, 27%). Quality of care was higher in the intervention group for all 5 conditions, and the improvement was statistically significant for dementia, falls, and incontinence (

). When we restricted quality-of-care data to those conditions designated by physicians as top prior ity for referral to the NP (found in 148 patients, 74% of  the sample eligible for at least 1 care process), quality of care was 60% in the intervention group and 38% in controls (

 <.001). The intervention group had higher quality of care across all 5 conditions, and the improvement was statistically significant for dementia, falls, and heart failure (Table 2).

	Encounter data were available for 273 patients (99%, 138 intervention and 135 control). During the project period, intervention patients made a mean of 2.5 (SD 3.2) visits to the NP. There was no difference in the number of other visits to the practice by group (7.2 intervention, 6.7 control; 

 =.32). Among those patients (73%, 108 intervention and 92 control) who were eligible for at least 1 ACOVE-3 process of care, intervention patients made a mean of 2.6 (SD 3.3) visits to the NP. In this subsample, other visits to the practice did not differ (6.9 intervention, 6.7 control; 

	When compared with usual care by academic geriatricians, NP comanagement of 5 chronic conditions with geriatricians correlated with an absolute 20% increase in adherence to recommended care. Care for all 5 conditions was better, and this improvement was statistically significant for dementia, falls, and incontinence. Thus, NP comanagement appears to augment the quality of geriatric care even with geriatricians. Comanagement improved quality with a mean of 2.6 NP office visits during a 13-month period.

	These findings extend prior research. Among low-income seniors, one study found better quality of care for depression, urinary incontinence, and falls (among other conditions)  using an NP and social worker who provided home-based care management, together with a geriatrics interdisciplinary team.

 Our project differed in that we provided a less    intense intervention (the NP saw patients in the office and followed up with them by phone rather than making home visits), and our intervention did not target low-income patients. A test of primary care comanagement using specially trained registered nurses showed an early beneficial effect on quality; more definitive results will be reported in the future.

	Strikingly, the quality of care for chronic conditions received by control patients in this project was similar to that received by patients in previous studies who were cared for primarily by nongeriatricians, as measured by ACOVE-3 indicators.

 Differences from previous studies in the methods used to identify conditions and to abstract medical records, as well as the evolution of quality indicators over time, are potential explanations. Alternatively, the results may indicate that physicians face similar barriers to providing high technical quality of care for older adults, regardless of specialty.

	This analysis has limitations. First, intervention group patients were eligible for more quality indicators than controls, suggesting nonequivalence of the 2 groups. Because sicker patients may receive better quality of care

and because being eligible for more quality indicators may indicate a higher level of disease burden, results may be biased in favor  of the intervention group. However, this potential bias is unlikely to account for the entire intervention effect. Second, 1 pair of physicians switched intervention/control group status during the project. We believe that the physician unwilling to refer patients to the NP felt total responsibility for patients and was unwilling to delegate care for this reason. Because the switch occurred to increase referrals to the NP, data were analyzed accounting for the switch rather than as intention to treat. Third, about 25% of patients were not eligible for ACOVE-3 quality indicators. This finding reflects the restricted scope of the ACOVE-3 quality indicators and potentially sparse clinical documentation that limited verification of patients’ eligibility for quality indicators.

	Fourth, enhanced documentation by the NP in the intervention group may have facilitated abstractors noting completion of certain processes of care; however, prior work (not specifically including NPs) suggests that documentation is not the sole basis of poor quality of care.

 Fifth, reliability of chart reabstraction was only fair for completion/ noncompletion of recommended care processes. However, the resulting random misclassification would make estimates of between-group differences conservative. Finally, this project may be a best-case scenario because several of the study investigators, including the NP, also were clinicians in the improvement project and were motivated for the project to be properly implemented.

	This study has significant strengths, however. First, external validity was improved by the quality improvement design, because all patients were eligible for participation as long as they had 1 of the 5 chronic conditions. Second, the intervention was relatively simple and can be replicated with an experienced, motivated NP using materials in the public domain.

	In conclusion, a quality improvement project using an NP to manage 5 chronic conditions was associated with markedly higher technical quality of care for these conditions,  suggesting that deploying NPs as chronic care clinicians may successfully complement physician practice and enhance care.

	We acknowledge all the physicians and staff in the UCLA Division of Geriatrics who participated in this project. Weijuan Han, MSPH, provided programming support. Shakiba Mobaraki, MD, and Farzam Hariri, MD, MPH, did the chart abstraction. We thank the 2 anonymous peer reviewers for their comments.

 From Division of Geriatric Medicine and Gerontology (DAG, JKB, HEM, DBR) and Division of General Internal Medicine (NSW), University of California, Los Angeles; Health Services Research & Development Center of Excellence (DAG), Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA; and RAND (CPR), Santa Monica, CA.

WEB EXCLUSIVE

Nurse practitioner comanagment improved quality of care for 5 chronic conditions in an academic geriatrics practice.

Nurse Practitioner Comanagement for Patients in an Academic Geriatric Practice

To evaluate the cost-effectiveness of 70-gene MammaPrint signature (Agendia Inc, Huntington Beach, CA) vs Adjuvant! Online software (AS) (http://www.adjuvantonline.com) in patients 60 years or younger with early-stage breast cancer.

 Cost-effectiveness and cost-utility analyses from a US payer perspective.

 A Markov model with 3 health states was constructed. In the base case model, risk classification and patient outcomes were based on a 70-gene signature validation study. Efficacy of chemotherapy was derived from a published meta-analysis of clinical trials. An alternative model using data from AS and from the Surveillance, Epidemiology and End Results registry was built to examine the external validity of the base case model. The incremental benefits, costs, and cost-effectiveness of treatment guided by 70-gene signature were calculated.

 In the base case model, 70-gene signature reclassified 29% of patients and spared 10% of patients from chemotherapy. Compared with the AS strategy, the 70-gene signature strategy was associated with $1440 higher total cost per patient and with 0.14 additional life-year or 0.15 additional quality-adjusted life-year. Overall, the incremental cost-effectiveness ratios were approximately $10,000 per life-year or quality-adjusted life-year in the base case model and $700 in the alternative model. The model results were sensitive to estrogen receptor status, the proportion of patients classified as high risk vs low risk, and the overall survival in each risk group.

 A 70-gene signature is likely to be a cost-effective strategy to guide adjuvant chemotherapy treatment in younger patients with early-stage breast cancer.

	A 70-gene MammaPrint signature (Agendia Inc, Huntington Beach, CA) is commercially available and is being integrated into clinical practice.

Targeting chemotherapy with 70-gene MammaPrint signature in patients 60 years or younger with node-negative breast cancer is likely to be cost-effective.

December 2010

Resource Utilization With Insulin Pump Therapy for Type 2 Diabetes Mellitus

Medication Adherence: The Search for Interventions That Work

Part D Coverage Gap and Adherence to Diabetes Medications

Nurse Practitioner Comanagement for Patients in an Academic Geriatric Practice

Cost-Effectiveness of 70-Gene MammaPrint Signature in Node-Negative Breast Cancer

Prescribing by Physician Assistants and Nurse Practitioners

"Under the Radar": Nurse Practitioner Prescribers and Pharmaceutical Industry Promotions

Comparison of Healthcare Utilization Among Patients Treated With Alcoholism Medications

Modes of Delivery for Interventions to Improve Cardiovascular Medication Adherence

Step-Up Care Improves Impairment in Uncontrolled Asthma: An Administrative Data Study

Can Electronic Health Records Help Improve Patients' Understanding of Medications?