William H. Herman, MD, MPH

In this segment, William Herman, MD, MPH, analyzes Dr Huckfeldt's presentation on glycemic targeting from a health service and clinical research perspective. "There is no one-size-fits-all measure of diabetes quality or glycemic care," said Dr Herman. Dr Herman also discussed his viewpoint as a clinical endocrinologist diabetologist. "So patients who are remarkably likely to have targets I think the targets were appropriately modified based on risk of hypoglycemia," said Herman. "I do have general concern that maybe the targets are even too low based on the evidence."

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In this segment, William Herman, MD, MPH, analyzes Dr Huckfeldt's presentation on glycemic targeting from a health service and clinical research perspective.

Session 1: Discussant

We report the 10-year effectiveness and within-trial cost-effectiveness of the Diabetes Prevention Program (DPP) and its Outcomes Study (DPPOS) interventions among participants who were adherent to the interventions.

DPP was a 3-year randomized clinical trial followed by 7 years of open-label modified intervention follow-up.

Data on resource utilization, cost, and quality of life were collected prospectively. Economic analyses were performed from health system and societal perspectives. Lifestyle adherence was defined as achieving and maintaining a 5% reduction in initial body weight, and metformin adherence as taking metformin at 80% of study visits.

The relative risk reduction was 49.4% among adherent lifestyle participants and 20.8% among adherent metformin participants compared with placebo. Over 10 years, the cumulative, undiscounted, per capita direct medical costs of the interventions, as implemented during the DPP, were greater for adherent lifestyle participants ($4810) than adherent metformin participants ($2934) or placebo ($768). Over 10 years, the cumulative, per capita non—interventionrelated direct medical costs were $4250 greater for placebo participants compared with adherent lifestyle participants and $3251 greater compared with adherent metformin participants. The cumulative quality-adjusted life-years (QALYs) accrued over 10 years were greater for lifestyle (6.80) than metformin (6.74) or placebo (6.67). Without discounting, from a modified societal perspective (excluding participant time) and a full societal perspective (including participant time), lifestyle cost <$5000 per QALY-gained and metformin was cost saving compared with placebo.

Over 10 years, lifestyle intervention and metformin were cost-effective or cost saving compared with placebo. These analyses confirm that lifestyle and metformin represent a good value for money.

Over 10 years, among adherent participants, lifestyle intervention and metformin were effective and cost-effective for diabetes prevention compared with placebo.

Interventions to delay or prevent chronic diseases are often not cost saving in the short term, as intervention costs are incurred early and savings from complications averted accrue late in the natural history of disease.

In real-world clinical settings, lifestyle and metformin interventions are likely to be more effective and more cost-effective than they were during the randomized controlled clinical trial and its observational follow-up study.

Interventions for diabetes prevention represent a good value for money.

Previously, we reported the clinical and economic outcomes of an intent-to-treat analysis of the combined 10 years of the Diabetes Prevention Program/Diabetes Prevention Program Outcomes Study (DPP/DPPOS).

 In that analysis, participants were analyzed according to their randomized treatment group whether or not they adhered to their assigned treatment. In this paper, we report the results of an “on treatment” or “per protocol” analysis of DPP/ DPPOS. We assess outcomes by treatment group for participants who were adherent to their randomized assignments and compare the effectiveness and cost utility of the intensive lifestyle intervention (lifestyle) and the metformin intervention (metformin) with the placebo intervention (placebo).

Our previously published 10-year intent-to-treat analysis of DPP/ DPPOS used data from all randomized participants including those who did not adhere to their randomized treatments, and so likely underestimated both the effectiveness and the benefits of lifestyle and metformin. In real-world clinical practice, neither costs nor benefits are incurred by nonparticipants, but among participants, both intervention costs and benefits are likely to be greater. Our goal in this analysis of the DPP/ DPPOS is to extend our previous analyses to estimate the clinical effectiveness and cost-effectiveness of diabetes prevention among metformin participants who remained on treatment with metformin and lifestyle participants who succeeded in losing weight.

The DPP enrolled 3234 participants with impaired glucose tolerance and fasting hyperglycemia who were at least 25 years of age and had body mass index of /m

 The protocol and informed consent procedures were approved by all responsible institutional review boards. Participants signed written consent forms after discussion of all aspects of the study with study staff. For this analysis, we assigned costs related to finding the participants randomized to lifestyle and metformin but not the participants randomized to placebo. In estimating the costs of finding participants, we assumed that 10.4% of adults aged 45 to 74 years would be eligible to participate.

 We further assumed that potentially eligible subjects would be tested with a random capillary glucose level.

110 mg/dl, an oral glucose tolerance test (OGTT) would be performed

 and those with abnormal OGTTs would have a 15-minute visit with a physician to discuss the results. Based on the sensitivity, specificity, and reproducibility of the tests, we estimated that 12.8% of subjects screened would be eligible to participate in the DPP and that the average cost per eligible participant identified would be $173.

The goals for participants randomized to lifestyle were to achieve and maintain a weight reduction of at least 7% of initial body weight through diet and physical activity of moderate intensity, such as brisk walking, for at least 150 minutes per week.

 A 16-session core curriculum (given approximately weekly in individual participant sessions) and subsequent individual sessions (usually monthly) and group sessions with case managers were designed to reinforce the behavioral changes. The medication interventions (metformin and placebo) were initiated at a dose of 850 mg taken orally once a day. At 1 month, the dose was increased to 850 mg twice daily. Adherence was reinforced during individual quarterly visits with case managers. Standard lifestyle recommendations were provided to all groups through written information and an annual 20-to- 30-minute individual session that emphasized the importance of a healthy lifestyle. Mean follow-up at the end of DPP was 3.2 years. For the purposes of this analysis, we assumed that all subjects were enrolled in DPP for exactly 3 years.

At the end of the DPP, all participants, regardless of their random treatment assignment, were offered a group-implemented 16-session lifestyle intervention before their enrollment in the DPP Outcomes Study. During this bridge period, 40% of lifestyle, 58% of metformin, and 57% of placebo participants attended at least 1 session.

 The original lifestyle group was offered additional lifestyle support and was not encouraged to take metformin. The original metformin group was encouraged to continue metformin and to participate in the group lifestyle intervention. Those randomized to placebo stopped placebo and were encouraged to participate in the group lifestyle intervention. For the purposes of this analysis, we assumed that year 4 represented the DPP/DPPOS bridge.

All active participants were eligible for continued follow-up during the DPPOS maintenance phase and 2766 of 3150 (88%) enrolled.

 These included 910 participants originally randomized to lifestyle, 924 to metformin, and 932 to placebo. For the purposes of this analysis, we assumed that years 5 to 10 represented DPPOS maintenance.

During DPPOS maintenance, the group lifestyle intervention was implemented as the Healthy Lifestyle Program (HELP) for all participants. HELP reinforced the original weight loss and physical activity goals and focused on current topics in nutrition, physical activity, stress management, and diabetes prevention. Although all participants were invited to attend all HELP sessions, many chose to attend fewer.

DPP participants initially randomized to lifestyle were also eligible to receive 2 BOOST sessions per year to reinvigorate their self-management behaviors for weight loss. Those randomized to metformin and placebo were excluded from BOOST sessions. The sessions reinforced specific behavioral self-management activities (eg, self-monitoring of fat, calories, and/or physical activity, as well as weight checks) important for weight loss and physical activity adherence and/or maintenance. In addition, the sessions promoted home-based behavioral self-management of weight and physical activity through the use of motivational campaigns.

Only metformin participants were encouraged to take metformin. One percent of non-diabetic participants in lifestyle and 3% of non-diabetic participants in placebo took metformin for diabetes prevention at any time during DPPOS maintenance.

 Lifestyle and placebo participants who took metformin during DPPOS maintenance were not excluded from the analyses.

Interventions for Participants With Diabetes.

 Participants identified with glucose levels diagnostic of diabetes at their semi-annual visits were seen within 6 weeks for glucose testing to confirm the diagnosis. Participants with confirmed newly diagnosed diabetes received individual counseling focused on self-monitoring of blood glucose, were provided with meters and test strips and encouraged to monitor their glucose levels once daily, and, for purpose of analysis, were maintained in their randomized intervention groups. Treatment for diabetes and surveillance for complications and comorbidities were performed by the participants’ own healthcare providers. Medications used by DPP participants for management of diabetes were recorded every 6 months on a drug summary form.

 For these analyses, we wished to assess resource utilization, costs, and outcomes by treatment group among participants randomized to lifestyle and metformin who adhered to their randomized interventions. We assumed that all participants randomized to lifestyle and metformin were adherent during year 1, when the interventions were initially implemented. We defined adherent lifestyle participants as those without diabetes who achieved and maintained 5% weight loss at 

 50% of their semi-annual visits beginning at the end of year 1. We defined adherent metformin participants as those without diabetes who took 

 80% of their prescribed metformin based on pill counts at 

 80% of their semi-annual follow-up visits beginning at the end of year 1. We defined adherent placebo participants as all those randomized to the placebo group regardless of their adoption of lifestyle or metformin treatments during DPP, DPPOS Bridge, or DPPOS maintenance. Of the 1079 participants randomized to lifestyle, 587 (54%) were defined as adherent at the end of DPP year 1. Of 1073 participants randomized to metformin, 666 (62%) were defined as adherent at the end of DPP year 1. Participants in the lifestyle and metformin groups were defined as adherent until the time they became nonadherent or developed diabetes. For the most part, participants who were adherent at the end of DPP year 1 remained adherent for the duration of follow-up.

 We calculated the mean per capita direct medical costs associated with the DPP/DPPOS interventions for participants who remained adherent to their randomized treatment assignments over each of the 10 years after randomization. Direct medical costs of the interventions were estimated from the resources used and unit costs adjusted to 2010 US dollars. We have previously reported in detail how costs were calculated.

 To estimate the cost of the lifestyle intervention if it had been administered in a group format rather than individually, we recalculated the costs of lifestyle assuming that the core curriculum and monthly follow-up visits with the lifestyle case managers, which were conducted individually during the 3 years of the DPP, were conducted as group sessions with 10 participants. Studies have shown that group lifestyle intervention programs for obesity are at least as effective as individual programs.

 Although metformin was implemented with brand name metformin (Glucophage), we assumed that it was implemented with generically priced metformin throughout the 10 years of DPP/DPPOS.

We also estimated the per capita direct medical costs of care outside the study for adherent participants.

 The direct medical cost of care outside the study was estimated from case report forms and surveys administered every 6 months. Direct medical costs included the costs of hospital, emergency department, urgent care, and outpatient services, the cost of telephone calls to healthcare providers, and the cost of prescription medications.

Direct non-medical costs were assessed twice, once during DPP and once during DPPOS, and costs were annualized.

 In estimating the direct non-medical costs of the interventions for adherent participants, we considered the cost of food, food preparation items, exercise classes, gym memberships, personal trainers, and exercise equipment.

 We also considered the costs of transportation to study visits and to medical visits.

 The value of the time that participants spent shopping, cooking, exercising, and traveling to and attending appointments was also assessed.

 The costs of exercise were valued according to whether participants “disliked,” were “neutral,” or “liked” leisure time physical activity.

 Although direct non-medical costs are not usually paid by private insurers or government health programs, we included them in our cost calculations from a societal perspective.

We assessed outcomes for adherent participants as both incident diabetes and quality-adjusted life-years (QALYs).

 QALYs measure length of life adjusted for quality of life as assessed by the health utility score. By convention, health utility scores are placed on a continuum where perfect health is assigned a value of 1.0 and health judged equivalent to death is assigned a value of 0.0. We assessed health utilities annually using the Self-Administered Quality of Well-Being Index.

Mathematically, QALYs are calculated as the sum of the product of the number of years of life and the quality of life, measured in health utilities, in each of those years.

For the primary analysis, we followed the recommendations of the Panel on Cost-Effectiveness in Health in Medicine

 and took the perspective of a health system. Thus, we included only direct medical costs of the interventions and non-intervention—related medical care in our basecase analysis. We included direct nonmedical costs excluding participant time in a sensitivity analysis from a modified societal perspective and direct nonmedical costs including participant time in a sensitivity analysis from a full societal perspective. These sensitivity analyses assessed the impact of covering the cost of the interventions implemented by the study participants on society as a whole.

The analyses of lifestyle and metformin were conducted for participants who adhered to the lifestyle intervention (as assessed by weight loss) and who regularly took metformin (as assessed by pill counts). The analyses of placebo were conducted for all participants randomized to the placebo group. For the DPP group lifestyle analysis, we estimated what the costs of lifestyle would have been during the 3 years of DPP if the 16-session core curriculum and monthly follow-up visits with the case managers had been conducted as closed-group sessions with 10 participants. We assumed that outcomes for DPP group lifestyle would have been the same as observed for the lifestyle intervention as originally implemented. We excluded from the analyses the costs of the research component of the DPP/DPPOS. All costs were expressed as year 2010 US dollars. Analyses were performed with a 10-year time horizon. Data on resource utilization were aggregated using SAS (SAS Institute, Cary, North Carolina). The aggregated resource utilization data were then multiplied by the unit cost and by the probability that a participant was followed during the time period. The latter analyses and the tables and figures were generated using Excel (Microsoft Inc, Redmond, Washington). Initial analyses were performed without discounting. Subsequently, where noted, both costs and health outcomes were converted to net present value using a 3% discount rate.

At 10 years, the cumulative incidence of diabetes was 52.4% among participants originally randomized to the minimal intervention arm that included placebo medication and standard lifestyle recommendations (ie, the “placebo” group). The incidence of diabetes was 41.5% among metformin participants who regularly took metformin, and 26.5% among lifestyle participants who achieved and maintained a 5% reduction in initial body weight (

). Compared with placebo, the absolute risk reduction at 10 years was 25.9% with lifestyle and 10.9% with metformin. The relative risk reduction was 49.4% with lifestyle compared with placebo and 20.8% with metformin compared with placebo. Due largely to the reduced incidence of diabetes, quality of life, as assessed by health utility scores, was better among adherent lifestyle and  adherent metformin participants than placebo participants. At 10 years, the mean undiscounted cumulative QALYsaccrued were 6.80 for lifestyle, 6.74 for metformin, and 6.67 for placebo participants. Compared with placebo participants, adherent lifestyle participants accrued 0.13 more QALY (ie, years of perfect health) over 10 years and adherent metformin participants accrued 0.07 more QALY.

The annual undiscounted per capita direct medical costs of lifestyle, DPP group lifestyle, metformin, and placebo over 10 years for adherent participants are summarized in 

. The costs of lifestyle ($3801) are $1578, or over 70%, greater than the costs of offering lifestyle in a group format ($2223) in DPP years 1 to 3 (DPP group lifestyle) because of the difference in resource utilization between an individual- and group-implemented intervention. The per capita costs of lifestyle were substantially lower during DPPOS than during DPP because of the change from an individual- to a group-implemented intervention, less frequent intervention sessions, and lower session attendance. The costs of placebo were slightly higher during DPPOS than during DPP because placebo participants engaged in the group lifestyle intervention.

The cumulative undiscounted per participant cost of the lifestyle intervention ($4810) was substantially greater than the estimated cost of the DPP group lifestyle intervention ($3232), the metformin intervention ($2934), or the placebo intervention ($768) (

). Over 10 years, the cumulative undiscounted per capita incremental direct medical costs of the interventions were greater for adherent participants in lifestyle ($4042), group lifestyle ($2464), and metformin ($2166) compared with placebo.

The cumulative undiscounted per capita direct medical costs of non—intervention-related medical care by intervention group and year following randomization for adherent participants are shown in 

. These are the costs of medical care received outside the DPP/DPPOS. The cumulative direct medical costs of non—intervention-related medical care ($23,218 to $27,468 per person over 10 years) were substantially greater than the costs of the interventions ($768 to $4810 per person over 10 years). Among all groups, the costs of non–intervention-related medical care increased over time. Over 10 years, the cumulative, per capita non–intervention-related direct medical costs were $4250 greater for placebo participants compared with adherent lifestyle participants and $3251 greater for placebo participants compared with adherent metformin participants.

By year 10, cumulative undiscounted per participant total direct medical costs of the DPP/DPPOS interventions and medical care received outside the DPP/DPPOS were higher for placebo ($28,236) than for lifestyle ($28,027), DPP group lifestyle ($26,449), or metformin ($27,150) (

). Thus, when both intervention- and non—intervention-related medical costs were considered, all 3 interventions saved money relative to the placebo intervention.

Cumulative, 10-year, diet-, physical activity-, transportation-,and time-related costs were similar across treatment groups ($147,704 for lifestyle, $146,999 for metformin, and $147,043 for placebo). Although adherent lifestyle participants spent more time exercising, the adjusted value of the time they spent exercising was not greater than for either metformin or placebo because of their greater enjoyment of leisure time physical activity and the lower opportunity cost.

summarizes the differences in costs and QALYs and the incremental cost-effectiveness ratios of lifestyle, DPP group lifestyle, and metformin versus placebo and for lifestyle compared with metformin. From the health system perspective and without discounting, the total direct medical costs for the lifestyle, DPP group lifestyle, and metformin participants were less than for placebo participants and the interventions were more effective as assessed by QALYs gained. In other words, all 3 interventions were cost saving compared with placebo. With discounting and compared with metformin, lifestyle cost $2004 more but produced an additional 0.06 QALY over 10 years. From a health system perspective, with both costs and health outcomes discounted at 3% per year, the cost of lifestyle compared with placebo was $19,988 per QALY gained, the cost of DPP group lifestyle compared with placebo was $9688 per QALY gained, and the cost of metformin compared with placebo was $20,183 per QALY gained. The cost of lifestyle compared with metformin was $19,662 per QALY gained.

Effectiveness and Cost-Effectiveness of Diabetes Prevention Among Adherent Participants

 To determine if processes and outcomes of diabetes care improved between 2000 and 2006 in a managed care health plan with a comprehensive diabetes disease management program.

A total of 1650 randomly selected members with diabetes mellitus completed surveys in 2000, and 1256 randomly selected members with diabetes completed surveys in 2006. Survey and medical record data were analyzed using multivariable regression and predictive probabilities adjusted for age, education, and comorbidities. 

In 2006, patients were more likely to have proteinuria assessed (85% vs 74% in 2000), foot examinations performed (90% vs 86%), glycosylated hemoglobin levels measured (94% vs 87%), lipids measured (81% vs 70%), aspirin use recommended (67% vs 56%), and influenza immunizations administered (70% vs 63%). Glycosylated hemoglobin levels decreased by 0.60% (

<.001), systolic blood pressures by 3 mm Hg (

= .002), and low-density lipoprotein cholesterol levels by 18 mg/dL (

<.001). Those who were continuously enrolled in the health plan were significantly more likely to report having had dilated retinal examinations (

= .004), and lower low-density lipoprotein cholesterol levels (by 6 mg/dL, 

Implementation of a disease management program was associated with substantial improvements in processes and outcomes of diabetes care over 6 years. Although secular trend likely contributed somewhat, improvement in other measures was significantly associated with duration of enrollment in the health plan, making secular trend an unlikely explanation for all of our findings.

Processes and outcomes of diabetes care improved substantially over 6 years in a managed care health plan with a comprehensive diabetes disease management program.

It is likely that secular trend contributed to some of the improvement in processes and outcomes.

Improvements in processes and outcomes were significantly associated with duration of enrollment in the health plan.

Despite a strong evidence base for diabetes care,

 it remains suboptimal in routine clinical practice.

 Population-based care management strategies—including establishment of registries, dissemination of guidelines, self-management support, care management, tailored patient and provider feedback, enhanced clinical information systems, and expansion of benefits&mdash;have been shown to improve care for persons with chronic diseases.

 These approaches to care underlie the chronic care model and disease management.

 Despite optimism regarding the benefits of interventions based on these strategies, improved processes of care have not been consistently linked to improved intermediate outcomes.

Between 1986 and 2007, the University of Michigan implemented an independent practice association model managed care health plan, M-CARE. The plan contracted with the University of Michigan and 42 other independent practice associations across southeastern Michigan. Approximately 5% of primary care providers were employed by the University of Michigan, and 29% of patients with diabetes received care from university providers. M-CARE integrated several care management strategies into a health plan—directed diabetes disease management program. M-CARE established a diabetes registry, disseminated guidelines, incorporated nurse care management, expanded benefits for members, and implemented targeted reminders for patients and providers to improve processes of care (eye examinations, renal screening, foot examinations, glycosylated hemoglobin [A1C] and lipids testing, and influenza immunizations) and intermediate outcomes of care (aspirin use, smoking cessation, angiotensin-converting enzyme or angiotensin receptor blocker use, and A1C and low-density lipoprotein [LDL] cholesterol control). M-CARE provided members access to diabetes education and nutritional counseling with no copayments, gave discounts for commercial weight-loss programs, covered smoking cessation classes and pharmacologic aids to smoking cessation with no copayments, offered self-monitoring supplies and insulin with no copayments, and waived referral requirements and copayments for diabetic eye examinations.

The objective of this study was to assess the effect of M-CARE’s comprehensive diabetes disease management program. We determined if processes and outcomes of diabetes care improved between 2000 and 2006.

 It was performed at the University of Michigan site of Translating Research Into Action for Diabetes, a prospective observational study of diabetes care in managed care. The University of Michigan Institutional Review Board approved the study, and all participants provided informed consent.

In 2000 and again in 2006, patients with diabetes mellitus who were 18 years or older, community dwelling, enrolled in the health plan for at least 18 months, not pregnant, and had at least 1 claim for health services were sampled at random from the health plan and were surveyed and had their medical records reviewed. There were 3972 eligible patients with diabetes in 2000 and 3652 eligible patients with diabetes in 2006. A total of 1650 patients completed the survey by computer-assisted telephone interview or by mail in 2000, and 1256 patients completed the survey by mail in 2006. If we assume that patients unable to be contacted had the same rate of eligibility as those contacted and were counted in the denominator, survey response rate was 69% in 2000 and 54% in 2006. For the present analyses, we included patients who had both survey and medical record data (1349 patients in 2000 and 1050 patients in 2006).

In 2000 and 2006, processes of care were assessed over a 12-month period (except where noted) and included dilated retinal examinations, urine microalbumin or protein testing, foot examinations, A1C and lipids testing, aspirin use (measured over an 18-month period) or recommendation to take aspirin, and influenza immunization. We based performance of dilated retinal examinations, foot examinations, and aspirin use or recommendation to take aspirin on self-report or medical record documentation. Influenza immunization was based on self-report, and urine, A1C, and lipids testing was based on documentation in the medical record. Intermediate health outcomes were defined as the last recorded values for systolic and diastolic blood pressures and for A1C and LDL and total cholesterol levels.

We stratified the populations by age and performed bivariate analyses using t tests for continuous variables and 

 tests for categorical variables. We also performed multivariable regression analyses adjusted for age, education, and comorbidities. We used logistic regression models to derive the adjusted (conditional) predicted probabilities of each outcome.

 Conditional predicted probabilities may be interpreted as the percentage of persons receiving each process of care adjusted for age, education, and comorbidities. All analyses were performed using SAS (version 9.1.3, service pack 4; SAS Institute, Cary, NC).

Between 2000 and 2006, the managed care organization stopped offering Medicare managed care. As a result, the percentage of persons with diabetes 65 years or older decreased from 50% to 6%, and the 2006 population was significantly younger (mean [SD] age, 62 [14] years in 2000 vs 52 [10] years in 2006). There were statistically significant changes in the distribution of education, diabetes treatment, and comorbidities between 2000 and 2006 and a small increase in the mean body mass index (

The percentage of persons who received 6 of 7 processes of care improved between 2000 and 2006, as did intermediate health outcomes. Among patients aged 18 to 44 years, there was significant improvement in rates of lipids testing and aspirin use or recommendation to take aspirin, as well as in A1C and LDL and total cholesterol levels (Table 1). Although blood pressure did not improve, the mean systolic and diastolic blood pressures were at goal (<130 and <80 mm Hg, respectively) at both time points. Among patients aged 45 to 64 years, there was significant improvement in urine microalbumin or protein, A1C, and lipids testing, as well as in aspirin use or recommendation to take aspirin, influenza immunization, systolic blood pressure, and A1C and LDL and total cholesterol levels. Among patients 65 years or older, all processes of care except dilated retinal examinations and aspirin use or recommendation to take aspirin and all intermediate health outcomes improved significantly.

After adjusting for age, education, and comorbidities, patients in 2006 were more likely to have urine microalbumin or protein assessed, foot examinations performed, A1C tested, and lipids evaluated and to report aspirin use or recommendation to take aspirin (

). They were also more likely to receive influenza immunizations. Statistically significant improvements were seen in all intermediate health outcomes. The A1C level decreased by 0.60% (to convert A1C level to proportion of total hemoglobin, multiply by 0.01), systolic blood pressure by 3 mm Hg, diastolic blood pressure by 1 mm Hg, LDL cholesterol level by 18 mg/dL, and total cholesterol level by 24 mg/dL (to convert cholesterol level to millimoles per liter, multiply by 0.0259). The proportion of patients with A1C level less than 7%, blood pressure less than 130/80 mm Hg, and total cholesterol level less than 200 mg/dL improved from 6% in 2000 to 19% in 2006.

To further assess the effect of the health plan’s comprehensive diabetes disease management program on processes and outcomes of care, we stratified respondents by duration of enrollment in the health plan, a measure of disease management program exposure, and reassessed processes and outcomes of care. In the 2006 survey, 830 of 1256 respondents (66%) reported that they had been continuously enrolled in the health plan for 5 years or longer. After adjusting for age, education, and comorbidities, those who were continuously enrolled were significantly more likely to have had dilated retinal examinations (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.19-2.30, 

= .003) and influenza immunizations (OR, 1.51; 95% CI, 1.14-2.00, 

= .004) and to report aspirin use or recommendation to take aspirin (OR, 1.32; 95% CI, 1.00-1.75, 

= .049). They also had lower diastolic blood pressure (by 2 mm Hg, 

= .02) cholesterol levels than those who had been continuously enrolled for less than 5 years but longer than 18 months.

After 6 years of comprehensive diabetes disease management, 6 of 7 diabetes processes of care and all measured intermediate health outcomes improved significantly. In 2006, the mean systolic and diastolic blood pressures and LDL cholesterol levels for the population were at goal. Longer duration of enrollment in the health plan, a measure of exposure to care management, was associated with a greater likelihood of having dilated retinal examinations, influenza immunizations, lower diastolic blood pressure, and LDL cholesterol levels and of reporting aspirin use or recommendation to take aspirin.

Many of the components of the disease management program were initiated between 1995 and 1999, including establishment of a registry, dissemination of guidelines to providers and patients, enhanced clinical information systems, and care management. Improvements in the targeting and tailoring of patient and provider feedback and expansion of benefits occurred largely between 2001 and 2005. Therefore, our initial cross-sectional survey in 2000 assessed the effect of care enhanced by traditional elements of care management, and the follow-up survey in 2006 assessed the effect of further improvements in delivery system design, including waiving copayments and expanding benefits. Our results suggest that these population-based care management strategies, including innovative changes in insurance benefit design, may improve the processes and outcomes of diabetes care. Future studies should explore the independent contribution of traditional care management strategies and more innovations in delivery system design.

We acknowledge that the population changed dramatically from 2000 to 2006, driven by the fact that the managed care organization stopped offering Medicare managed care in 2002. In our multivariable models, we adjusted for the major population differences in age, education, and comorbidities. We also conducted a sensitivity analysis for persons younger than 65 years and found that this did not change the estimates or 

Because of the lack of a formal control group, we cannot say with certainty that the disease management program caused the observed changes. In addition, because the disease management program incorporated numerous interventions and was implemented over several years, we cannot quantify the effect of individual interventions on outcomes. It is likely that some of the observed improvements in processes and outcomes of care were due to secular trend. The finding that continuous health plan enrollment was associated with improvement in some processes and outcomes of care and the degree to which outcomes improved make secular trend an unlikely explanation for all of our findings.

This study was jointly funded by Program Announcement 04005 from the Centers for Disease Control and Prevention (Division of Diabetes Translation) and the National Institute of Diabetes and Digestive and Kidney Diseases. The conclusions of this study are those of the authors and do not necessarily represent the views of the funding organizations. Members of the Translating Research Into Action for Diabetes (TRIAD) Study Group contributed to this study. The authors gratefully acknowledge the contribution of our health plan partner.

: Department of Internal Medicine (LNM, VCH, WHH), University of Michigan, Ann Arbor, MI; M-CARE (EMN-K, GJK, KGS, WHH), Ann Arbor, MI; and Department of Epidemiology (WHH), University of Michigan, Ann Arbor, MI.

: This study was jointly funded by Program Announcement 04005 from the Centers for Disease Control and Prevention (Division of Diabetes Translation) and the National Institute of Diabetes and Digestive and Kidney Diseases.

: The authors (LNM, VCH, EMN-K, GJK, KGS, WHH) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. Authorship Information: Concept and design (EMN-K, GJK, KGS, WHH); acquisition of data (EMN-K, WHH); analysis and interpretation of data (LNM, VCH, WHH); drafting of the manuscript (LNM, VCH, GJK, KGS, WHH); critical revision of the manuscript for important intellectual content (LNM, VCH, WHH); statistical analysis (LNM); provision of study materials or patients (GJK, KGS, WHH); obtaining funding (WHH); administrative, technical, or logistic support (EMN-K, GJK, WHH); and supervision (EMN-K, WHH).

: William H. Herman, MD, MPH, Department of Internal Medicine, University of Michigan, 1500 E Medical Center Dr, 3920 Taubman Center, SPC 5354, Ann Arbor, MI 48109. E-mail: wherman@umich.edu.

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Effect of a Managed Care Disease Management Program on Diabetes Care

: To examine the association between physicians' reimbursement perceptions and outpatient test performance among patients with diabetes mellitus.

 Participants were physicians (n = 766) and their managed care patients with diabetes mellitus (n = 2758) enrolled in 6 plans in 2003. Procedures measured included electrocardiography, radiography or x-ray films, urine microalbumin levels, glycosylated hemoglobin levels, and Pap smears for women. Hierarchical logistic regression models were adjusted for health plan and physician-level clustering and for physician and patient covariates. To minimize confounding by unmeasured health plan variables, we adjusted for health plan as a fixed effect. Therefore, we estimated variation between physicians using only the variance within health plans.

Patients of physicians who reported reimbursement for  electrocardiography were more likely to undergo electrocardiography than patients of physicians who did not perceive reimbursement (unadjusted mean difference, 4.9%; 95% confidence interval, 1.1%-8.9%; and adjusted mean difference, 3.9%; 95% confidence interval, 0.2%-7.8%). For the other tests examined, no significant differences in procedure performance were found between patients of physicians who perceived reimbursement and patients of physicians who did not perceive reimbursement.

Reimbursement perception was associated with electrocardiography but not with other commonly performed outpatient procedures. Future research should investigate how associations change with perceived amount of reimbursement and their interactions with other influences on test-ordering behavior such as perceived appropriateness.

)In managed care, perceptions of reimbursement for particular outpatient procedures have inconsistent associations with test ordering among primary care physicians who care for patients with diabetes mellitus. Previous studies have documented an association between reimbursement perceptions and performance of electrocardiography but not for other common outpatient procedures.

Associations may exist for electrocardiography but not for recommended diabetes care measures such as urine microalbumin or glycosylated hemoglobin monitoring, screening measures such as Pap smears, or other diagnostic tests such as radiography.

To improve performance of certain measures, additional interventions may be necessary, including greater physician detailing, levels of reimbursement, and discussion of appropriateness.

Ideally, physician test ordering is determined by clinical factors such as  patient symptoms or disease screening recommendations. In reality, healthcare is delivered in a complex environment that exposes the physician to a wide range of nonclinical factors

 that may influence test-ordering behavior. These competing influences may be especially strong for primary care physicians, who diagnose and treat a wide array of diseases.

Patients, patient advocates, policy makers, and healthcare organizations attempt to manage these nonclinical influences to elicit their preferred version of test-ordering behavior. One of these influences is reimbursement. Reimbursement initiatives are predicated by the assumption that if physicians perceive financial rewards for ordering a particular test, this perception will affect their test-ordering behavior.

 Few studies have examined the association between primary care physicians’ reimbursement perceptions and performance of particular procedures. Epstein and colleagues

 compared test performance among fee-for-service patients with test performance among managed care enrollees. Their hypothesis was that reimbursement perceptions would be stronger in fee-for-service plans than in managed care plans and that fee-forservice patients would receive more tests. After adjustment for physician years in practice and for patient age, sex, duration of disease, and blood pressure levels, they found that tests perceived as more profitable (ie, electrocardiography) were performed more frequently by physicians in fee-for-service settings than in managed care plans. Tests perceived as less profitable, such as urinalyses and radiography, did not differ in frequency between managed care and fee for service. Therefore, perceived reimbursement seemed to have a role in test-ordering practices.

 have examined actual reimbursement as opposed to perceived reimbursement. These studies have focused on procedure performance in fee-for-service versus salaried or capitated systems

 and, more recently, on specific pay-for-performance initiatives.

 Although such studies examine performance of procedures in different financial systems, they have usually not queried physicians about their reimbursement perceptions. Reimbursement perceptions may better predict actual test-ordering behavior because physicians may have limited awareness of actual reimbursement; in a survey, 16% of physicians did not know the percentage of their compensation from salary.

 physicians were unaware of added reimbursement for vaccinations and cancer screening.

Since the studies by Epstein and colleagues,

 the healthcare environment has changed; the current healthcare market has higher managed care penetration,

 and physician groups may contract with fee-for-service and managed care plans. In addition, pay-for-performance programs may affect more than 80% of managed care enrollees.

 To our knowledge, the association between physicians’ reimbursement perceptions and test performance has not been examined in this environment. Such an examination would inform our understanding of the importance of reimbursement perceptions in the clinical decision-making process. Therefore, we tested the hypothesis that the patients of physicians who perceived reimbursement for a particular procedure were more likely to have received that procedure than patients of physicians who did not perceive reimbursement for the  procedure. We used detailed clinical data from Translating Research Into Action for Diabetes (TRIAD), a large cohort study of managed care enrollees with diabetes mellitus (DM) and their physicians enrolled in multiple health plans.

A detailed description of TRIAD has been previously published.

 In summary, 6 translational research centers collaborated with 10 health plans, including staff-model health maintenance organizations (HMOs), network association HMOs, point-of-service plans, and preferred provider organizations. Structural and health system factors were assessed using standardized in-person interviews of health plan and provider group medical directors and leadership personnel. These interviews assessed general organizational and financial characteristics, as well as DM-specific infrastructure and clinical activities. Patient data were obtained from surveys, medical record reviews, and administrative data. A patient survey, administered by computer-assisted telephone interview or in writing, assessed sociodemographic characteristics, recommended DM care services received, and general health status and quality-of-life measures. It also assessed access to care, patient satisfaction (Consumer Assessment of Health Plans Survey), DM education received, participation in self-care activities, disease management, and financial barriers. The medical record review in TRIAD included data abstracted from paper and electronic medical records during 18 months before the patient survey. Administrative data were derived from inpatient and outpatient claims and pharmacy and laboratory data provided by the health plans. These data were used to establish the initial sampling frame, measure health services utilization and costs (eg, hospitalization and ambulatory care visits), and tabulate quality indicators (eg, laboratory and diagnostic tests and drug prescriptions).

Eligible patients were 18 years or older, were community dwelling, were not pregnant, had DM for at least 1 year, spoke English or Spanish, were continuously enrolled in their health plan for at least 18 months, used at least 1 service during that time, and could provide informed consent. Patients’ ages and races/ethnicities varied widely across health plans.

The data herein were based on a survey of TRIAD primary care physicians (54% physician survey response rate) and their patients. Patients participated in the 2003 wave of data collection and were continuously enrolled during 12 months before the physician survey. We excluded 3 health plans for which we had only institutional claims and 1 plan that had only a single continuously enrolled patient. Physicians were enrolled in group-network or staff plans. The study included 766 physicians and their 2758 patients. When we compared patients who were continuously enrolled and their physicians versus patients who were not continuously enrolled and their physicians, their demographics were similar (data not shown).

The 12-month observation period for each study participant was immediately before the month during which the physicians filled out a survey and began anytime between August 2002 and January 2003. Patient data were collected from mailed surveys, computer-assisted telephone interviews, and medical record reviews. The interrater reliability (κ statistic) for the process-of-care variables at each of the 6 translational research centers ranged from 0.86 to 0.94.

Procedure performance was ascertained from health plan administrative data. For each patient, we recorded any claim in the 12-month review period for each of the following procedures: electrocardiography, radiography or x-ray films, urine microalbumin monitoring, glycosylated hemoglobin (A1C) monitoring, and Pap smears for women. We dichotomized the measures because few participants had any procedure performed more than once. The only exception was measurement of A1C level, which was multiply coded for 70% of patients. Among all participants, the median numbers of procedures performed were 0 except for measurement of A1C level. Among participants who had at least 1 claim for a particular procedure, the median numbers were as follows: 1 (interquartile range [IQR], 1-2) for electrocardiography, 2 (IQR, 1-3) for radiography, 2 (IQR, 1-3) for urine microalbumin monitoring, 3 (IQR, 2-4) for A1C monitoring, and 1 (IQR, 1-1) for Pap smears. Current Procedural Terminology codes used to define each procedure are given in the 

The primary independent variable was a set of dichotomous indicators for whether the physician perceived reimbursement for each procedure. The physician survey inquired, “Which of these services do you get paid to perform and/or interpret on a fee-for-service basis?” Therefore, the question assessed perception of reimbursement from several potential sources. The list of procedures included electrocardiography, radiography, urine microalbumin monitoring, A1C monitoring, and Pap smears. Other independent variables included physician sex, race/ethnicity, specialty, and years of practice, as well as patient age, sex, education, income, current smoking, body mass index, coverage by other insurance, DM treatment (diet controlled, oral agents only, oral agents and insulin, or insulin alone), and the Charlson comorbidity index.

Cross-sectional associations between physician perceptions of reimbursement for each procedure and patient claims for each procedure were tested in unadjusted and adjusted models. Because we defined our outcome as the presence or absence of a procedure code, we had no missing data for our dependent variable. Distributions for variables were examined, and missing values for covariates were imputed using IVEware version 2.0 (University of Michigan, Ann Arbor).

 IVEware uses sequential regression in which each covariate was predicted as a function of all other covariates. Five multiply-imputed data sets were created.

Hierarchical logistic regression models were used to account for the clustering of patients within physicians and health plans. Health plan effects were modeled as fixed and physician effects as random. An implication of this approach is that all health plan characteristics that do not vary across patients within the same health plan (eg, size, profit status, organizational type, referral management) are subsumed into these fixed effects and are implicitly controlled for in the model. All analyses were performed using SAS version 9.1.3 NLMIXED with full maximized likelihood estimation (SAS Institute, Inc, Cary, NC).

Results are presented as mean differences in marginal predicted probabilities. These represent the mean difference between the probability of having a claim for a particular procedure if fee-for-service reimbursement was perceived for that procedure and the probability of having a claim if fee-forservice reimbursement was not perceived for that procedure, holding all other factors constant at their original values.

We performed several sensitivity analyses. We sought to determine whether percentage compensation from salary confounded the association between perception of reimbursement and test ordering. The physician survey inquired, “As a primary care physician, what percent of your total compensation is based on salary as opposed to productivity or fee-for-service? Fill in the blank.” We included percentage compensation from salary as a main effect in a sensitivity analysis. This did not change the estimates (data not shown). For a subset of physicians (n = 144), surveys were fielded between September 2003 and April 2004, but a more specific date was not available. We conducted a sensitivity analysis in which these physicians were excluded. The estimates did not change appreciably (data not shown). For another subset of physicians (n = 206), there was a gap of at least 1 month between the last available administrative data for their patients and the date on which the physician filled out the survey. When we excluded these physicians from the analyses, the estimates were not noticeably affected (data not shown). Finally, we examined whether perceptions of reimbursement were stronger in non—staff-model plans; when we performed analyses stratifying by staff model versus non–staff model, the strata did not seem to be different (data not shown).

. When asked about reimbursement perceptions for specific procedures, physicians did not always respond to all of the items. For example, 733 physicians responded to the item inquiring about reimbursement perception for electrocardiography, with 47% of them reporting reimbursement perception for electrocardiography. However, only 659 physicians responded to the item about reimbursement perception for radiography. Therefore, Table 1 lists the denominator for each procedure and the percentage of physicians reporting reimbursement perception for that procedure.

 gives patient characteristics. On average, each physician who completed a survey had 3 patients included in the study. The percentage of patients who had at least 1 performance of a specific procedure ranged from 12% for Pap smears to 70% for A1C monitoring. Therefore, 559 patients had at least 1 electrocardiogram, 873 patients had at least 1 radiograph, 1319 patients had at least 1 urine microalbumin measurement, 1930 had at least 1 A1C measurement, and 328 women had at least 1 Pap smear during the study period.

 gives the mean differences in marginal predicted probabilities. These differences represent the mean difference between the probability of having a claim for a particular procedure if fee-for-service reimbursement was perceived for that procedure (unadjusted mean difference, 4.9%; 95% confidence interval [CI], 1.1%-8.9%; and adjusted mean difference, 3.9%, 95% CI, 0.2%-7.8%) and the probability of having a claim if fee-for-service reimbursement was not perceived for that procedure. In unadjusted comparisons, perception of reimbursement was associated with slightly more frequent performance of electrocardiography and A1C monitoring but not more frequent performance of radiography, urine microalbumin monitoring, and Pap smears. These patterns did not change with adjustment for other patient and physician factors. Only the difference for electrocardiography was statistically significant; perceived reimbursement was associated with a regression-adjusted predicted probability of 23.4% for electrocardiography, whereas lack of reimbursement was associated with a predicted probability of 18.7%. The significant difference of 4.7% represents a 25.0% increase compared with 18.7%.

The use of percentage compensation from salary as a main effect in a sensitivity analysis did not change the effect estimates, although the CIs widened, so the adjusted mean difference in electrocardiography performance was no longer statistically significant (4.4%; 95% CI, -0.6% to 9.5%). When we excluded physicians without an exact survey date from the analyses, the point estimates did not change significantly, although the CIs widened, so the mean difference in electrocardiography performance

was no longer statistically significant (4.8%; 95% CI, -0.03% to 9.6%).

Among a large, geographically diverse sample of managed care enrollees with DM and their physicians, we found inconsistent associations between physicians’ reimbursement perceptions and procedure performance. Reimbursement perception for electrocardiography was associated with more frequent test performance, but reimbursement perceptions for other tests were not associated with test performance. We found little change in these patterns after adjustment for physician characteristics and for patient covariates. Our findings lend support to previous evidence from the 1980s suggesting that reimbursement perception is test specific and that any associations with test performance are limited to electrocardiography. 

 Our findings are also in accord with previous work suggesting that report of compensation and performance of DM care measures (such as urine microalbumin and A1C monitoring) are not tightly linked.

Earlier DM health services research examining associations between reimbursement and test performance consists of the following: (1) previously cited comparisons of reimbursement perceptions for outpatient tests among fee-for-service and salaried physicians,

 (2) comparisons of DM quality of care in fee-for-service and salaried settings,

 and (3) structured interventions based on financial incentives.

 After adjustment for potential confounding characteristics of healthcare organizations, actual reimbursement does not seem to be strongly related to DM quality of care.

 Comparisons of fee-for-service and salaried organizations in terms of DM measures have shown that fee-for-service organizations may provide poorer quality of DM care, suggesting that fee-for-service reimbursement for these measures may not be sufficient to increase procedure rates. We found that the association between perception of reimbursement for electrocardiography and electrocardiography  performance did not persist after adjustment for percentage compensation from salary. This suggests that such other aspects of healthcare organization characterized by fee-for-service versus salaried payment delivery potentially

confound the association. Alternatively, if percentage compensation from salary is high, the importance of perceived reimbursement for a particular procedure should decrease; therefore, these factors are somewhat collinear. To date, structured interventions based on financial incentives, or pay-for-performance initiatives, have had minimal-to-moderate effects.

Our study examined physicians’ reimbursement perceptions, which may more accurately reflect physician decision making about test ordering than actual reimbursement. To our knowledge, no previous studies have examined implications of perceptions for patients with DM. In their examination of a  pay-for-performance initiative, Hillman and colleagues

 found that little association existed between physician incentives for vaccination and vaccination rates and that little association existed between physician incentives for cancer screening and cancer screening rates. They found that most physicians in the program were unaware of the initiatives; hence, the initiatives did not affect their practices.

The mean incentives to physicians in a particular group may not be the same as incentives faced by any individual physician. In addition, physicians respond to the incentives they perceive to be in effect, even if their perception is incorrect. Therefore, physicians’ reimbursement perceptions may more accurately reflect reimbursement effects than actual reimbursement. By asking physicians directly whether they perceived reimbursement, we measured this influence on test ordering closest to the source.

The lack of significant associations between perception of reimbursement and procedure ordering may relate to several reasons aside from perception of reimbursement effects. Performance of specific procedures (particularly A1C monitoring) was high, and in quality improvement studies, such high baseline performance can blunt the degree of improvement with any initiative and the power to detect significant differences.

 However, the other outcomes in our study were performed at least once in less than half of the participants and may have been less susceptible to such effects. By using fixed effects at the health plan level, we accounted for health plan confounders, but in doing so, we may have overadjusted and minimized perception effects because these are integrated with reimbursement delivery.

Our study has several limitations. We inquired about perceptions of reimbursement, but we did not inquire about the perceived amount of reimbursement. This may have biased our results to the null. Because we were interested in the broad category of reimbursement, we did not measure particular aspects of reimbursement such as perceived reimbursement for reading radiographs versus performing radiography versus downstream profits from ownership of radiography facilities. However, it is possible that specific subtypes of reimbursement are more closely associated with testing behavior. We did not inquire about each plan’s reimbursement policies, and it is possible that physicians tailor their test-ordering practices according to the patient’s health plan. Because physician groups often contract with several plans, we reasoned that it would be difficult for physicians to quantify the proportions of patients enrolled in a health plan and the compensation for particular procedures associated with each plan. If such tailoring occurs, it would also have biased our results toward the null. We inquired about reimbursement perceptions after the observation period, and it is possible that reimbursement schemes changed in the time between our survey and the period during which tests were performed. We measured all of the procedures ordered for a particular patient, but we only assessed perceptions of reimbursement for the primary care physician. Therefore, it is possible that physicians other than those surveyed ordered procedures, biasing our results to the null. Because few studies have examined perceptions of reimbursement, as opposed to reimbursement schemes, which may be examined on a group level, the importance of this issue is unknown. If ordering by other physicians with different reimbursement perceptions was a significant issue, A1C and urine microalbumin monitoring should have shown larger or more significant effects than electrocardiography, but in fact the pattern of results showed the opposite. Procedures likely to be ordered by the primary care physician include those related to chronic care (such as A1C and urine microalbumin monitoring) rather than those related to acute issues. Therefore, the association for these procedures and primary care physicians’ awareness might have been stronger, but no association was observed. Conversely, electrocardiography may be ordered for acute symptom-based reasons and, less commonly, for “baseline” purposes because patients were at high risk for cardiovascular disease given their DM. Therefore, the association should be weakened by lack of perception of reimbursement but was the only significant association observed. Finally, the association between perception of reimbursement and performance of procedures may differ between study  participants, who all had DM and were managed care enrollees, and patients who were not study participants.

We conclude that in managed care the perceptions of reimbursement for particular outpatient procedures have inconsistent associations with test ordering among primary care physicians who care for patients with DM. Associations may exist for electrocardiography but not for recommended DM care measures such as urine microalbumin or A1C monitoring, screening measures such as Pap smears, or other diagnostic tests such as radiography. Further research is needed to determine whether larger incentives combined with greater physician detailing have a greater effect on test ordering, how such associations vary as reimbursement levels change, and how perception of reimbursement interacts with other influences on test-ordering behavior such as appropriateness of tests and mechanisms of reimbursement.

From the Department of Medicine (CK, WHH, DB), the Department of Obstetrics & Gynecology (CK), and the Department of Epidemiology (WHH), University of Michigan, Ann Arbor; the Division of Diabetes Translation (EFT, RBG), Centers for Disease Control and Prevention, Atlanta, GA; the Department of Medicine (CMM, SLE), University of California, Los Angeles; and the Rollins School of Public Health (KMVN), Emory University, Atlanta, GA.

: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: TRIAD was supported by grant U58/CCU523525-03 from the Centers for Disease Control and Prevention (CDC). This study was jointly funded by Program Announcement 04005 from the CDC (Division of Diabetes Translation) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Dr Kim was supported by grant K23DK071552 from the NIDDK. This research utilized the Biostatistics and Measurement Cores of the Michigan Diabetes Research and Training Program funded by grant NIH 5P60 DK20572 from the NIDDK. Dr Mangione was partially supported by grant AG-02-004 from the University of California at Los Angeles Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging Research, National Institutes of Health/National Institute on Aging.

: Concept and design (CK, WHH, CMM, KMVN, SLE); acquisition of data (CK, EFT, WHH, CMM, RBG, SLE); analysis and interpretation of data (CK, EFT, CMM, KMVN, RBG, SLE); drafting of the manuscript (CK, CMM, KMVN, DB, SLE); critical revision of the manuscript for important intellectual content (CK, EFT, WHH, CMM, KMVN, RBG, DB, SLE); statistical analysis (CK, EFT, RBG, DB, SLE); obtaining funding (WHH, CMM); and administrative, technical, or logistic support (WHH, DB).

Catherine Kim, MD, MPH, Division of General Internal Medicine, University of Michigan, 300 N Ingalls Bldg, Rm 7C13, Ann Arbor, MI 48109-0429. E-mail: cathkim@umich.edu.

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8. Greenfield S, Rogers W, Mangotich M, Carney MF, Tarlov AR

Perception of reimbursement was associated with electrocardiography but not with other common outpatient procedures. Future research should investigate how associations change with perceived reimbursement amount.

William H. Herman, MD, MPH

Articles

Session 1: Discussant

Effectiveness and Cost-Effectiveness of Diabetes Prevention Among Adherent Participants

Effect of a Managed Care Disease Management Program on Diabetes Care

Physician Perception of Reimbursement for Outpatient Procedures Among Managed Care Patients With Diabetes Mellitus