 pose an important question for the disease management (DM) industry in their article entitled &#8220;Evidence for the Effect of Disease Management: Is $1 Billion a Year a Good Investment?&#8221; They document few high-quality large-scale studies on the subject that use rigorous research designs and note that the peerreviewed literature reveals consistent clinical improvements but variable economic outcomes in DM. Although DM seems to improve quality of care, the authors conclude that its effect on cost is uncertain. Given that DM has been promoted for 15 years with significant penetration into the US healthcare fabric,

 how is it possible that there is widespread adoption of something that seems to have so little justification in the medical literature? Moreover, might DM still be a wise investment, despite the current state of research evidence?

There are good reasons why the the peer-reviewed literature has been slow to validate DM. First, purchasers of DM programs do not require randomized controlled trial (RCT) studies to satisfy their financial expectations for DM; most are sufficiently convinced of the economic benefits of these programs that they are unwilling to exclude any part of their population from the interventions to form a control group for an RCT study design.

Second, buyers of DM services are not in the business of proving new science but in managing a health plan population or their workforce. They base daily decisions on business intuition and common sense, appreciating also that DM outcomes evaluation is an evolving discipline. More credible evidence for the economic effect of DM is accumulating over time, thanks in large part to efforts by the DMMA: The Care Continuum Alliance to standardize evaluation methods for DM programs when RCTs are not feasible.

Third, even when buyers are aware of literature that demonstrates inconclusive results for multiple programs, they still are willing to believe that their individual program results are valid once they pass review by their actuaries, consultants, vendors, or chief financial officer.

 Disease management is a diverse set of interventions applied to a heterogeneous population and cannot be regarded as having a fixed &#8220;dose-response curve&#8221; like a new drug.

 Just because DM results, in aggregate, do not make the compelling case that savings are invariably achieved does not mean that a specific DM program cannot achieve substantial savings.

 and their colleagues have demonstrated that some DM programs do indeed save money.

Are those adopting DM programs guilty of making leaps of faith in the absence of indisputable scientific support for DM? The answer is yes only if a leap of faith is defined as any decision made in the absence of compelling RCT evidence, which does not exist for most of what physicians do in everyday practice.

 Scientists recognize a hierarchy of evidence, starting with case studies at the low end and extending to RCTs and meta-analyses at the top end, with case-control and other observational designs along the way.

 Many researchers have identified problematic evidence from RCTs and meta-analyses. Tunis et al

 called for greater numbers of &#8220;practical clinical trials&#8221; to better meet the needs of decision makers with regard to healthcare delivery and policy. Westfall and colleagues note that &#8220;What is efficacious in randomized clinical trials is not always effective in the real world of day-to-day practice.&#8221;

 &#8220;Practice-based research provides the laboratory that will help generate new knowledge and bridge the chasm between recommended care and improved health.&#8221;

 Horn and Gassaway conclude: &#8220;Although RCTs are important to confirm whether a new treatment causes an effect, they are unlikely to discover combinations of interventions or practices that are effective and efficient in routine care.&#8221;

 There is a sizable body of accumulated DM outcomes,

 despite the paucity of published RCT evidence.

Let us return to the question of whether the current excess of $1 billion spent annually for DM is a good investment. Let us suppose that it is eventually shown by replicated RCTs that, in aggregate, DM programs consistently improve clinical outcomes, quality of life, functional status, and worker productivity but do not invariably produce cost savings. Might it still be the case that DM is consistently cost-effective? If that were the case&#8212;and many DM experts believe that it is plausible&#8212; it would be noteworthy because little of what physicians do to patients is ever cost saving (albeit life saving). Medicare is not allowed to consider cost-effectiveness in approving new technology for reimbursement, and the US Food and Drug Administration must approve any new drug shown to be safe and effective regardless of cost or comparative effectiveness. Few would question whether health plans should conduct case management, whether hospitals should provide discharge planning, or whether physicians should educate patients about prevention and healthful lifestyles, but none of these accepted health interventions have been shown by replicated RCT evidence to be consistently cost saving or cost-effective, to my knowledge.

Perhaps Mattke et al consider their title question a rhetorical one. I regard it as a serious one that merits a comprehensive accounting of all the facts and circumstances, including (as well as beyond) peer-reviewed research. Health and DM programs have been in rapid evolution during the past decade, and&#8212;with the continued convergence of the electronic health record, the personal health record, and pay for performance, as well as the anticipated emergence of the patientcentered home&#8212;there are exciting opportunities ahead for further refinement of best practices in chronic care management for yet improved outcomes. There are sound reasons why DM outcomes satisfy buyers today, even if academics remain unconvinced. Taking all the data and circumstances as a whole, it is reasonable and responsible to conclude that we are wise to continue investments in DM, while accumulating more and better evidence about the total population effects. 

: Dr. Norman is an employee of Alere Medical, Inc, a health and disease management company. He also is a member of the board for DMMA: The Care Continuum Alliance.

: Gordon K. Norman, MD, MBA, Alere Medical, Inc, 4 Park Plaza, Irvine, CA 92614. E-mail: 

2. Matheson D, Psacharopoulos D, Wilkins A

; Boston Consulting Group. Realizing the promise of disease management: payer trends and opportunities in the United States. 2006. 

www.bcg.com/publications/files/Realizing_the_Promise_of_Disease_Management_

4. MacStravic S. Does disease management work yet? 

www.worldhealthcareblog.org/2007/04/13/does-disease-management-work-yet

. Does disease management save anybody money? December 9, 2007. 

www.worldhealthcareblog.org/category/diseasemanagement

. Economic effectiveness of disease management programs: a meta-analysis. 

. Disease management in the American market. 

12. Sidorov J, Shull R,Tomcavage J, Girolami S, Lawton N, Harris R

. Does diabetes disease management save money and improve outcomes? A report of simultaneous short-term savings and quality improvement associated with a health maintenance organization&#8211;sponsored disease management program among patients fulfilling health employer data and information set criteria. 

14. University of Sheffield School of Health and Related Research

. What proportion of healthcare is evidence based? resource guide. 

. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. 

. Practice-based evidence study design for comparative effectiveness research. 

: Dr Mattke reports conducting research and serving as a consultant on projects for various purchasers and vendors of disease management services. Drs Seid and Ma report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this letter.

: Soeren Mattke, MD, DSc, RAND Health, 1200 S Hayes St, Arlington, VA 22202. E-mail: 

Articles

LETTERS TO THE EDITORS

All Things Considered, the Answer Is a Resounding Yes

A potentially confusing clinical message was sent in the article by Goldberg et al.

 The study, which emphasized the need for reduction of LDL-C below 100 mg/dL, did not address the newer concept of lowering LDL-C below 70 mg/dL for optimal protection in high-risk patients. Accordingly, patients with coronary artery disease and diabetes mellitus should be treated for an LDL-C goal of 70 mg/dL or less. The concern, of course, is that clinicians will adhere to the less than 100-mg/dL LDL-C guideline instead of less than 70 mg/dL in the treatment of high-risk patients.

Regarding cholesterol treatment, the Pravastatin or Atorvastatin Evaluation and Infection Therapy [PROVE-IT] trial revealed that vascular events were reduced with aggressive lipid-lowering tactics (eg, atorvastatin at 80 mg/day vs pravastatin at 40 mg/day). The message conveyed was that lower is better in waging the war against LDL-C and coronary artery disease.

Additionally, the high-sensitivity C-reactive protein (hs-CRP) test has utility in the management of risk assessment as well as dosage adjustment of the statin. Attention should be directed to LDL-C and hs-CRP levels in the management of high-risk patients.

: Diane Binetti, 124 E Main St, Ste 106, Denville, NJ 07834. E-mail: 

We appreciate the thoughtful responses to our article by Drs Federman and Shanley.

Duke University and Durham VA Medical Center

: The author has no conflict of interest with any product discussed in this article.

: Kenneth C. Goldberg, MD, Division of Ambulatory Care, Box 11C, Durham VA Medical Center, 508 Fulton St, Durham, NC 27705. E-mail: 

Lowering LDL-C for Optimal Protection in High-risk Patients

Improving lipid control has tremendous public health implications, and I applaud the efforts aimed at provider education to overcome clinical inertia. The study by Goldberg et al

 reports that a low-cost educational intervention through staff meetings and electronic communications led to an intensification of therapy with simvastatin for patients with diabetes or coronary artery disease. The authors compare dose escalation rates during the preintervention period of January 1, 2003, until December 31, 2004, with a postintervention period of May 1, 2005, until July 31, 2005, and conclude that their low-cost intervention led to increased provider aggressiveness and more patients reaching their low-density lipoprotein cholesterol (LDL-C) target, which was an LDL-C of less than 100 mg/dL.

Events rarely transpire in a vacuum, and simultaneous occurrences could have served as important confounders that merit mention. Toward the end of their preintervention period in July 2004, a much heralded update by the National Cholesterol Education Program was published suggesting that, based on recent trials, healthcare professionals could consider LDL-C goals of less than 70 mg/dL in those at very high risk.

 It is quite possible that this widely cited document and mounting evidence of benefits of risk reduction for certain high-risk populations with reduced LDL-C targets were at least partially responsible for some of these investigators&#8217; findings of increased provider aggressiveness. While the authors do report that at least 1 highrisk subgroup (those with both coronary artery disease and diabetes) was less likely to have its simvastatin dose increased, the authors do not report preintervention and postintervention LDL-C levels, nor do they report results for others who might be targeted for LDL-C levels of less than 70 mg/dL (cardiovascular disease and either poorly controlled risk factors or the metabolic syndrome, as well as those with acute coronary syndromes).

Provider education is essential to keep practicing physicians abreast with scientific advances. However, we must acknowledge that knowledge is acquired in a wide variety of ways. While the investigators&#8217; initiative certainly might have been the major contributor to their findings, the possibility of outside confounders must be recognized.

: Daniel G. Federman, MD, VA Connecticut Health Care (11ACSL) System, 950 Campbell Ave, West Haven, CT 06516. E-mail: 

. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. 

Unrecognized Confounders in Overcoming Clinical Inertia Study

: To investigate whether the effects of regular diabetes health education or a holidayspecific pamphlet before the Chinese New Year holiday period could improve glycemic control during the winter holidays among patients with type 2 diabetes mellitus. 

: The study was conducted from October 2004 to December 2005 in Taipei Veterans General Hospital. Subjects were randomized to program 1 (receipt of regular diabetes education between October 20 and November 25, 2004, and then every 3-4 months) or to program 2 (receipt of a special reminder pamphlet during the holidays). The patients were seen and blood samples obtained on 4 occasions during the holidays and then every 4 months through December 2005.

: Ninety-three subjects completed the first 4 visits during the Chinese New Year holidays, and 89 subjects completed 12 months of the study. Fructosamine levels in program 1 increased more during the preholiday period than those in program 2 (mean [standard deviation] 7.4 [5.2] vs -5.3 [8.3] Âµmol/L, 

 = .03) during the preholiday period. Changes in fructosamine levels during the holiday and postholiday periods were similar in the 2 groups. At the end of the holidays, changes in glycosylated hemoglobin (A1C) levels were 0.34% (95% confidence interval, 0.03%-0.85%) in program 1 and 0.09% (95% confidence interval, -0.23% to 0.42%) in program 2. After the Chinese New Year holidays, the groups had similar changes in A1C levels, with a slight downward decline thereafter.

: A special educational reminder pamphlet for the holidays led to improvements in glycemic control during the Chinese New Year holidays.

Loss of glycemic control may be related to periods of increased energy intake, decreased energy expenditure, or both such as during holidays or particular seasons. For example, exposure to higher blood glucose levels during winter holidays every year is likely to increase the risk of diabetic complications.

Our main finding is that patients with type 2 diabetes mellitus who received holiday-specific educational pamphlets had better glycemic control during the Chinese New Year holidays than those who received diabetic managed care.

Distribution of a holiday-specific diabetes counseling pamphlet might be applied to settings such as Thanksgiving, Christmas Day, and other special holidays. 

Poor glycemic control in type 2 diabetes mellitus (DM) is a major risk factor for the development of diabetic complications, and good control of blood glucose levels leads to fewer complications.

 Although many patients with type 2 DM lose their glycemic control gradually during several years, this may also be related to periods of increased energy intake, decreased energy expenditure, or both such as during holiday periods or particular seasons.

 Previous data demonstrated a winter holiday effect on the glycemic control of patients who had type 2 DM, and this poor glycemic control might not be reversed during the summer and autumn months.

 Therefore, exposure to higher blood glucose levels during winter holidays every year is likely to increase the risk of diabetic complications.

The balance between dietary intake and energy consumption through daily physical activities is the most important factor in the glycemic control of patients with type 2 DM.

 Diabetes education is recognized as an essential element of good diabetes care and is important in managing DM to prevent, or at least to slow, the development of diabetes complications.

 documented the association of effective diabetes education with favorable outcomes in glycemic control. Diabetes care teams incorporating nurses, dietitians, health educators, and other nonphysician specialists have a key role in diabetes care and education.

 Patients who received periodic primary care sessions organized to meet the complex needs of patients with DM demonstrated improved diabetes care and had better outcomes.

 We hypothesized that patients who received holiday-specific diabetes education would have better glycemic control during the winter holidays. This study investigated the effects of diabetes health education before the Chinese New Year holiday period and subsequent decreased hyperglycemia during the winter holidays among patients with type 2 DM.

The study was conducted from October 2004 to December 2005 in Taipei Veterans General Hospital. One hundred two subjects with type 2 DM were recruited for the study after signing informed consent forms. Eligible participants were aged 50 to 70 years, had type 2 DM, and were treated with oral antidiabetic drugs. Their progress was followed up in our hospital for at least 6 months and for as long as they were willing to attend all study visits. Subjects were excluded if they had proliferative retinopathy or diabetic nephropathy with chronic kidney disease above stage 4, were taking insulin for glycemic control, or were pregnant.

Plasma glucose levels were measured using the glucose oxidase method via a glucose analyzer (model 2300; Yellow Springs Instruments, Yellow Springs, Ohio). The A1C level was measured using high-performance liquid chromatography instruments (HLC-723 GHB IIIs; Tosoh Corporation, Tokyo, Japan) with a reference range of 4.5% to 6.2%. The interassay coefficient of variance was less than 2.0% at mean A1C levels between 4.4% and 8.2%. The serum fructosamine level was measured using the colorimetric enzyme-based method (Hoffmann-La Roche, Basel, Switzerland) in an automated analyzer with a reference range of 205 to 285 &#956;mol/L. The interassay coefficients of variance were 4.5% at 206 &#956;mol/L, 5.7% at 483 &#956;mol/L, and 2.9% at 578 &#956;mol/L.

The subjects were weighed wearing clothes without shoes in the morning after breakfast to the nearest 0.1 kg using an electronic scale, and height was measured to the nearest 1 cm using a stadiometer. Blood pressure and pulse rate were obtained using an electronic sphygmomanometer in a sitting position after 10 minutes of rest.

Commercially available statistical software (SPSS for Windows, version 14.0; SPSS Inc, Chicago, Ill) was used for data analysis. Glycemic control data from the first 4 measurements were used to compute changes in fructosamine levels for the preholiday, holiday, and postholiday periods. Two-tailed 

 tests were used to analyze differences between groups at baseline and during follow-up. Paired 

 tests were used to determine differences in glycemic control among the 3 periods, and differences in blood pressure, pulse rate, and body weight were assessed between the baseline and postholiday periods. All analyses were based on the intent-to-treat principle. Data are given as mean (standard deviation) unless otherwise stated, and 

 <.05 was considered statistically significant.

Between September 1 and December 6, 2004, we randomly assigned 102 patients having type 2 DM without insulin therapy to each intervention program. Fifty-two subjects were randomly assigned to program 1 and 50 subjects to program 2. Subjects in the 2 groups were well matched with regard to baseline demographic characteristics (

). Ninety-three subjects (48 in program 1 and 45 in program 2) completed the first 4 visits during the Chinese New Year holiday period, and 89 subjects (46 in program 1 and 43 in program 2) completed 12 months of the study. The mean age of participants who dropped out was 63.8 (7.7) years in program 1 (

 = .69 compared with subjects who completed the study) and 65.1 (8.6) years in program 2 (

 = .49). The mean A1C level of participants who dropped out was 7.78% (1.13%) in program 1 (

 = .92). Those who dropped out did not differ in age or glycemic control from those who completed the study.

The absolute values of diabetic control at each visit during the Chinese New Year holiday period are given in 

. Systolic blood pressure was statistically significantly lower in program 1 at visit 3. Fasting plasma glucose and fructosamine levels were higher in program 1 at visits 2 and 3. There were no statistically significant differences in body weight or diastolic blood pressure between the 2 groups at the visits.

 shows the fructosamine levels during the holiday period. During the preholiday period, the fructosamine level was increased in program 1 and was decreased in program 2. The mean fructosamine level in program 1 increased more during the preholiday period than that in program 2 (7.4 [5.2] vs &#8722;5.3 [8.3] &#956;mol/L, 

 = .03). The changes in fructosamine levels during the holiday and postholiday periods were similar between the 2 groups. The mean change in A1C level during the holiday period was increased in both groups and was statistically significantly higher in program 1 (0.34% [0.21%] vs 0.09% [0.16%], 

The main finding of our study is that patients with type 2 DM who received holiday- specific educational pamphlets had better glycemic control during the Chinese New Year holidays than those who received diabetic managed care. Our data revealed that patients with type 2 DM assigned to receive educational pamphlets for the holidays (program 2) had favorable fasting blood glucose and fructosamine levels at visit 2 (preholiday visit) and at visit 3 (postholiday visit). The change in A1C level during the holiday period was less increased in program 2, reflecting good glycemic control during the Chinese New Year holidays.

Diabetes health education is the cornerstone of care for all individuals with DM who want to achieve successful health-related outcomes. Some diabetes management programs are associated with better processes of care but not with improvement of glycemic control.

evaluated the effectiveness of 11 categories of interventions designed to improve the outpatient care of patients with type 2 DM. They found that most strategies produced small-tomodest improvements in glycemic control. They also found that team changes and case management demonstrated more robust improvements, especially for interventions in which case managers could adjust medications without awaiting physician approval. Our regular diabetes education (program 1) provided general diabetes information and nutrition recommendations. However, our holiday reminder pamphlets provided specific information to deal with problems arising during the Chinese New Year holidays. For patients with DM, a serum fructosamine assay can better reflect the mean blood glucose level during the previous 3 to 6 weeks.

 In the present study, we used changes in fructosamine levels to assess glycemic control between visits. These results revealed better glycemic control in program 2 during the preholiday period, which was when subjects had just received their holiday reminder pamphlets. Therefore, this finding suggests that simple and easy-to-follow reminder pamphlets may facilitate effective glycemic control in patients with DM during the winter holidays.

Although health education can substantially improve glycemic control, the extent of treatment benefits may be limited by lack of treatment adherence. Some believe that good health education is essential but is not necessarily related to the final outcome in patients with DM.

 Documentation of clinical nutrition education is associated with favorable trends in glycemic control.

 However, a problem in diabetes care is poor translation of knowledge to lifestyle modification. Previous studies

 provide evidence that changes in lifestyle using nonpharmacologic intervention are effective in glycemic control in patients with type 2 DM. These studies attempted to determine whether an intervention could achieve better lifestyle changes and obtain improved outcomes. In our study, we did not address lifestyle changes but, rather, compared the outcomes between the 2 interventions. We hypothesized that patients who received regular diabetes health education would have better glycemic control during the winter holidays, but they did not. Our results seem compatible with daily clinical practice, as some subjects cannot follow recommendations about diet and exercise from healthcare providers.

A previous study indicated a negative influence of winter holidays on glycemic control in patients with type 2 DM.

 The present study was designed to test whether routine diabetic managed care could decrease elevated blood glucose levels during the Chinese New Year holidays. Our primary hypothesis was that patients who received diabetic management would have better glycemic control during the winter holidays. For ethical reasons, we gave the control group (program 2) a holiday- specific pamphlet. We believed that diabetic management could maintain glycemic control during the holidays among patients with type 2 DM. However, our data revealed that subjects with type 2 DM assigned to receive educational pamphlets for the holidays had favorable glycemic control during the Chinese New Year holidays. Therefore, it seems that distribution of a holidayspecific diabetes counseling pamphlet is a feasible patient-centered intervention that improves intermediate outcomes. This might be applied to settings such as Thanksgiving, Christmas Day, and other special holidays. Further study should comprise a larger trial of longer duration across some major holidays to measure selfefficacy and health outcomes.

Taken together, our findings demonstrate that patients receiving a special reminder pamphlet for the holidays maintained better glycemic control during the Chinese New Year holidays than patients receiving regular diabetes health education. Holiday-specific health education should have favorable effects on glycemic control. Therefore, we suggest that holiday reminder pamphlets be included in general diabetes education before some special events.

: From the Division of Endocrinology and Metabolism, Department of Medicine (H-SC, R-LC, H-DL), and Section of Biochemistry, Department of Pathology and Laboratory Medicine (T-SJ), Taipei Veterans General Hospital, and the National Yang-Ming University School of Medicine (H-SC, T-EW, T-SJ, H-DL), Taipei, Taiwan.

: This study was supported by grant VGH94-169 for medical research from Taipei Veterans General Hospital. 

: The authors (H-SC, T-EW, T-SJ, R-LC, H-DL) 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 (H-SC, T-SJ, H-DL); acquisition of data (H-SC, T-EW, R-LC, H-DL); analysis and interpretation of data (H-SC, T-EW, T-SJ, H-DL); drafting of the manuscript (H-SC); statistical analysis (T-EW); provision of study materials or patients (H-SC, R-LC); obtaining funding (H-DL); administrative, technical, or logistic support (RLC); and supervision (T-SJ, H-DL). 

 to: Hong-Da Lin, MD, Division of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, 201, Sec 2, Shih-Pai Rd, Taipei 112, Taiwan. E-mail: 

. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). 

. Seasonal variation in HbA1c in patients with type 2 diabetes. 

. Nutrition recommendations and interventions for diabetes (position statement). 

7. Tuomilehto J, LindsrÃ¶m J, Eriksson JG, et al

. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. 

9. Wilson C, Acton K, Brown T, Gilliland S

. Effect of clinical nutrition education and educator discipline on glycemic control outcomes in the Indian Health Service. 

11. Wagner EH, Grothaus LC, Sandhu N, et al

. Chronic care clinics for diabetes in primary care: a system-wide randomized trial.

13. Shojania KG, Ranji SR, McDonald KM, et al

. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. 

. Improving diabetes processes of care in managed care. 

. Pro-Active Call Center Treatment Support (PACCTS) to improve glucose control in type 2 diabetes: a randomized controlled trial. 

TRENDS FROM THE FIELD

Effects of Health Education on Glycemic Control During Holiday Time in Patients with Type 2 Diabetes Mellitus

: To evaluate whether attending diabetes group visits (GVs) leads to lower medical care charges for inadequately insured patients with type 2 diabetes mellitus (DM). 

: Data were abstracted from financial records for 186 patients with uncontrolled type 2 DM randomized to receive care in GVs or usual care for 12 months. Mann-Whitney tests for differences of means for outpatient visits (primary and specialty care), emergency department (ED) visits, and inpatient stays were performed. Separate charge models were developed for primary and specialty outpatient visits. Because GV adherence is potentially dependent on unobserved patient characteristics, treatment effect models of outpatient charges and specialty care visits were estimated using maximum likelihood methods.

: Mann-Whitney test results indicated that GV patients had reduced ED and total charges but more outpatient charges than usual care patients. Ordinary least squares estimations confirmed that GVs increased outpatient visit charges; however, controlling for endogeneity by estimating a treatment effect model of outpatient visit charges showed that GVs statistically significantly reduced outpatient charges (P <.001). Estimation of a separate treatment effect model of specialty care visits confirmed that GV effects on outpatient visit charges occurred via a reduction in specialty care visits.

: After controlling for endogeneity via estimation of a treatment effect model, GVs statistically significantly reduced outpatient visit charges. Estimation of a separate treatment effect model of specialty care visits indicated that GVs likely substitute for more expensive specialty care visits.

Various organizations are using group visits for their patients with chronic diseases. The literature to date has not shown this type of healthcare delivery model to lower the cost of care.

In this study, by estimating a treatment effect model of charges and specialty group visits, we found a statistically significant savings in outpatient charges due to reduction in the use of more expensive specialty visits among a group visit population with diabetes mellitus. 

This information should be taken into account when considering group visits for an organization.

During these times of tightly controlled resources, healthcare organizations endeavor to deliver efficient and effective care to patients with type 2 diabetes mellitus (DM) consistent with American Diabetes Association (ADA) standards of care.

 The group visit (GV) model, initially developed in a managed care setting to improve efficiency and throughput of patients by increasing access and decreasing backlogs of patients awaiting appointments, is promising. Previous studies

 in managed care have shown GVs to be less costly and at least as effective as usual care in terms of quality. However, a recent review of the literature did not find that GVs substantially reduced costs for individuals with DM.

Research on GVs remains in its infancy. Although more than a dozen articles

 describing GVs have been published, only 6 randomized controlled trials

have been reported on to date. None of the literature regarding GVs for DM has shown notable cost results. Herein, we evaluate whether GVs can lower healthcare utilization outpatient charges for patients with DM. We also investigate whether the lack of statistically significant findings in previous studies could have been due to potential endogeneity of the GV variable in cost models.

This study was conducted at the Adult Primary Care Center, Medical University of South Carolina, serving approximately 6000 inadequately insured patients (predominantly of minority races/ethnicities) in Charleston. Using previously described procedures,

 patients with uncontrolled type 2 DM, defined as having a glycosylated hemoglobin (A1C) level of greater than 8.0%, were identified and were invited to participate. Willing patients were enrolled in the study after signing institutional review board&#8211;approved consent documents.

Research assistants blinded to study assignment contacted patients by mail and telephone for baseline, 6-month, and 12-month data collection, assisting those who needed help. Although patients received modest compensation for transportation and for their time at each data collection point, no patients received compensation for medical care. Visit deposit fees for uninsured intervention patients were decreased such that their annual onsite costs were commensurate with those of uninsured control patients ($180 per year,representing $15 per month for intervention patients and $45 per quarter for ADA guideline&#8211;recommended quarterly visits for control patients).

Group visits were modeled after those of the Cooperative Health Care Clinic developed by Beck et al.

 Participating physicians and nurses received onsite training by a senior internist who previously conducted GV training, and all participating staff members received a 3-hour educational session from the coordinator/trainer for Cooperative Health Care Clinic providers and staff.

Patients randomized to the intervention condition met monthly for 12 months in groups of 14 to 17 patients in the same building as the clinic, with the visits functioning as the patients&#8217; primary source of medical care. Physicians provided care for medical needs not amenable to GVs (Papanicolaou smears and rectal examinations) and any care needed between scheduled GVs in one-on-one visits scheduled separately from the GVs.

As with the Cooperative Health Care Clinic model, the schedule provided for each visit to last 2 hours, with 10 to 15 minutes for &#8220;warm-up&#8221; and socialization, 30 to 45 minutes for presentation of a health-related topic (facilitated by the physician or another team member with special expertise), and 60 minutes for one-on-one consultations with the physician. Physicians performed key preventive measures (eg, pneumonia and influenza vaccinations and foot examinations) at the GVs; mammograms and Papanicolaou smears were performed separately from the GVs. Although study participants guided the GV educational topics, the content included core topics appropriate for patients with DM such as nutrition, exercise, and foot care.

Charges for patients attending GVs and those for patients receiving usual care were identical. Because the institution in our study charges a visit deposit fee for patients without insurance, and because group patients were to come monthly as opposed to quarterly for usual care patients (the minimum recommended by the ADA for patients with type 2 DM), the institution decreased the visit deposit fee for uninsured intervention patients such that it was comparable to what uninsured usual care patients would pay during 1 year. Therefore, neither group paid more in visit deposit fees than the other. Otherwise, charges for the patients were the same, and both groups had to reconcile their accounts accordingly; Medicare, Medicaid, and insured patients did not have to pay the visit deposit fees. All patient charges were based on the time that patients spent with the physician one-on-one, following evaluation and management guidelines. Other healthcare utilization and charges (outpatient visits, ED visits, and inpatient stays) were collected from billing records in the Medical University of South Carolina system.

Charges (portioned into outpatient visits, ED visits, and inpatient stays) were collected at the end of the study period and included all resources used in the clinic, associated ED, and hospital. Mann-Whitney tests were performed to analyze differences in the mean charges by service (outpatient visits, ED visits, and inpatient stays). If 2 groups tested have the same distribution, the Mann-Whitney test determines the differences in the means and the medians.

 Separate charge models were then estimated for outpatient visits; ED visits and inpatient stays contained too few nonzero observations to reliably estimate models. Outpatient visit models include controls for payer, Charlson score, and distance to provider, as well as a binary indicator of GV treatment. We included distance to provider in the charge models as binary indicators of whether patients lived within 10, 20, or 30 miles of the provider (with the omitted category being >30 miles) because patients living farther away from the provider may have been more likely to use outpatient services from other providers whose charges were not captured in our data. The Charlson score, a widely accepted measure of illness severity based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedure codes in claims data, should capture the effects of differences in the severity of illness among patients with DM.

While first estimating the outpatient charge model using ordinary least squares, we also hypothesized that, when unobserved characteristics of the patients correlate with both the intervention (GVs) and the outcome (healthcare costs), a potential problem known as endogeneity can bias ordinary least squares results. In many randomized control trials, treatment assignment, assessment, and termination and dosage are controlled for by the researcher (exogenous). This is necessary for statistical tests of between-group differences in patient outcomes to be unbiased.

 Patients in our study were randomized to treatment modality (GVs or usual care), but the intensity (dosage) of their treatment is determined largely by the patient, who may choose not to attend all of the GVs or not to stay for the entire duration of the GV. This choice was likely to be affected by the treatment arm to which the patient was randomized, creating endogeneity of the GV variable in the charge model. When endogeneity exists, it can be controlled for by estimating a treatment effect model based on Heckman control function.

 Endogeneity arises in this case because the treatment (GVs) is correlated with the error term in the outcome (charges) equation. The treatment effect model simultaneously estimates equations for the likelihood of treatment (participation in GVs) and the outcome of the treatment (charges for various health service categories). This simultaneous estimation allows the elimination of endogeneity, although with the trade-off of making the assumption that the error terms are jointly normally distributed. Maximum likelihood techniques were used to estimate the model using STATA 9.0 (StataCorp LP, College Station, Tex), which provides a command (

To control for potential endogeneity, we reestimated the outpatient charge model using the treatment effect model that simultaneously estimates the likelihood of GV participation and outpatient charges. We initially ran a probit model to determine which factors were likely to affect GV participation and should be included in the GV participation likelihood equation in the treatment effect model. We ran ordinary least squares for the outpatient charge model to do the same for the charge equation in the treatment effect model. We included drive time as a continuous measure in the GV participation part of the model and included payer, Charlson score, and binary indicators for distance to provider in the outpatient charge models. This is a logical specification because patients are likely to be sensitive to distance when choosing an outpatient provider. To further explore our findings relative to outpatient charges in the treatment effect model, we estimated a treatment effect model for specialty care visits.

Mann-Whitney test results show that GV patients had 34.7% higher outpatient expenditures, 49.1% lower ED expenditures, and 30.2% lower total expenditures compared with those of the control group (

). Our initial ordinary least squares with robust standard errors estimates of the unadjusted outpatient charge model showed a statistically significant effect of GV treatment in outpatient care, with a positive marginal effect of $699.52 (

). Based on these initial estimates, it seemed that GV treatment increased outpatient costs by $699.52 per patient per year. Although these findings were consistent with previous literature on GVs for patients with DM,

 we questioned the unbiasedness of the results because of the potential of unobserved patient characteristics to affect adherence to the intervention.

The results for the treatment effect model of outpatient charges are given in Table 2. Although we found a statistically significant and marginally positive effect on GVs in the outpatient cost model that did not correct for endogeneity, the treatment effect model showed a statistically significant marginally negative effect of GV treatment on outpatient charges of $3065.47. To understand how GV treatment reduced outpatient charges, we estimated a treatment effect model of specialty care visits; the results are summarized in 

. We found that GV treatment leads to a reduction of 4.15 specialty care visits.

In our study of GVs for inadequately insured patients with type 2 DM, we demonstrate that, after controlling for endogeneity of the GV variable, GV treatment statistically significantly lowers outpatient charges by decreasing specialty care visits. This may be because the longer duration of GVs compared with that of a typical primary care encounter gives the provider more time to address process-of-care indicators and screening guidelines than the mean 16.5-minute primary care encounter in the United States.

 Being seen on a monthly basis provides patients with more frequent contact with their physicians and gives them more opportunities to ask questions, while giving providers more opportunities to address process-of-care indicators in a systematic fashion. In addition, when providers care for patients in groups, they are delivering consistent messages to multiple patients at once rather than giving the same message multiple times.

The results we obtained were all under the control of the provider team. On the other hand, charge outcomes are much more under the control of the patients and depend on their following lifestyle guidelines and adhering to medication regimens. Therefore, it is necessary to control for unobserved patient characteristics in estimating the charge model. One of the strengths of this study is that we had 6 different provider teams conducting GVs for 12 months. Because our findings are not the result of a single provider, we can be certain that the effect of GV treatment is not provider dependent.

One possible weakness of our study design is that providers participated in both arms of the study; therefore, there may have been contamination in that providers may have adopted some of the GV strategies (eg, GV educational content and methods for fulfilling process-of-care indicators) for usual care patients. We did not randomize providers (which could have been an alternative study design to prevent contamination) because it was unrealistic and unlikely in an office practice that any provider would only deliver care in GVs. Furthermore, we considered it a fair comparison that the same providers treated patients in both arms. It is noteworthy that, after the 12-month formal study period, all of the GV providers and their patients have chosen to continue with GVs.

Another potential weakness of our study is that only local inpatient outpatient and ED charges were captured in our data. If patients sought providers outside of the system, these were not captured. However, we believe that by controlling for distance to provider in the utilization and charge models, this effect has been reduced. 

This cost study of GVs among inadequately insured patients with type 2 DM showed statistically significant reductions in outpatient charges after controlling for endogeneity of the GV variable in the charge model via a treatment effect model. Because the GV model of care is an intervention that depends on patient adherence, we hypothesized and found evidence of endogeneity of the GV variable. Therefore, we believe that future studies on GVs should consider the potential for endogeneity in estimating the effect of GV treatment on healthcare utilization and charges.

: From the Departments of Medicine (DEC, DB), Health Administration and Policy (CED, KNS), and Psychiatry and Behavioral Sciences (KMM), Medical University of South Carolina, Charleston. 

: The authors (DEC, CED, KMM, KNS, DB) 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 (DEC, CED, KMM); acquisition of data (DEC, KMM, KNM); analysis and interpretation of data (DEC, CED, KMM, KNS, DB); drafting of the manuscript (DEC, CED, KMM); critical revision of the manuscript (DEC, CED, KMM); statistical analysis (CED, KNS); and obtaining funding (DEC, KMM).

: Dawn E. Clancy, MD, MSCR, Maybank Internal Medicine, 3312 Maybank Hwy, Ste A, Johns Island, SC 29455. E-mail: 

. Standards of medical care in diabetes: 2007. 

. A 5-year randomized controlled study of learning, problem solving, and quality of life modifications in people with type 2 diabetes managed in group care. 

. Diabetes management in a health maintenance organization. 

7. Trento M, Passera P, Tomalino M, et al

. Group visits improve metabolic control in type 2 diabetes. 

9. Trento M, Passera P, Tomalina M, et al

. Lifestyle intervention by group care prevents deterioration of Type II diabetes: a 4-year randomized controlled trial. 

11. Clancy DE, Cope DW, Magruder KM, Salter K, Huang P, Fields AW

. Evaluating Group Visits in an Uninsured or Inadequately Insured Patient Population with Uncontrolled Type 2 Diabetes. 

. Effectiveness of a group outpatient model for chronically ill older health maintenance organization members: a 2-year randomized trial of the cooperative health care clinic. 

15. Davis KS, Magruder KM, Lin Y, Powell CK, Clancy DE

. Trainee Provider Perceptions of Group Visits. 

17. Clancy DE, Yeager DE, Huang P, Magruder KM

. Further evaluating the acceptability of group visits in an uninsured or inadequately insured patient population with uncontrolled type 2 diabetes. 

. Preventive Services Resource Links. Agency for healthcare research and quality, Rockville, MD. September 2003. 

21. Charlson ME, Pompei P, Ales KL, MacKenzie CR

. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. 

. The effect of private insurance on the health of older, working age adults: evidence from the Health and Retirement Study. 

POLICY

Among a diabetes group visit population, this study found substantial savings in outpatient charges due to reduction in the use of more expensive specialty visits.

Do Diabetes Group Visits Lead to Lower Medical Care Charges?

: To determine the effect of a clinical diagnosis of diabetes mellitus (DM) on healthcare utilization and health outcomes.

: A total of 197 United Kingdom family practices with 4974 subjects (mean age, 62.8 years; 52.2% men) with type 2 DM and 9948 matched nondiabetic control subjects. Healthcare utilization and the occurrence of complications were estimated from 2 years before to 2 years after the first clinical diagnosis of DM. 

: From 24 months before the DM diagnosis, primary care consultations were increased in prediagnosis cases compared with controls (relative rate [RR], 1.31; 95% confidence interval [CI], 1.27-1.35), as were emergency and hospital care consultations, hospital specialist referrals, and prescription drug items. At diagnosis of DM, utilization of all forms of healthcare was increased (RR, 4.27; 95% CI, 4.17-4.36 for primary care consultations; RR, 2.49; 95% CI, 2.46-2.52 for prescription drug items). In the quarter following diagnosis, healthcare utilization was increased for acute myocardial infarction (RR, 6.29; 95% CI, 2.69-14.73), cerebrovascular disease (RR, 5.14; 95% CI, 3.37-7.84), ischemic heart disease (RR, 3.65; 95% CI, 2.77-4.80), and peripheral nerve disorders (RR, 5.01; 95% CI, 2.81-8.95). First diagnoses of myocardial infarction, cerebrovascular disease, and peripheral nerve disorders were increased during the period from 6 months before to 6 months after diagnosis.

: Clinical diagnosis of DM is often the end of a process leading to established complications and is associated with greatly increased utilization of care. This adds to the justification of strategies for earlier detection of hyperglycemic states.

This study aimed to evaluate the ways in which a clinical diagnosis of DM is

associated with healthcare utilization and the occurrence of clinical complications

Family practices were selected if they continuously provided data from January 1, 2001, to December 31, 2005. There were 1 283 429 patients 100 years or younger who were continuously registered with 197 family practices during this time. From this cohort, medical diagnostic codes for DM and prescriptions for oral hypoglycemic drugs or insulin were used to identify cases with DM. The DM diagnosis date was defined as the earlier of the first DM medical code or the first DM prescription date. Cases with DM were selected if their diagnosis date was between January 1, 2003, and December 31, 2003 (5635 cases), and if they were aged between 30 and 89 years at diagnosis (5091 cases). Two controls were selected for each case (matching for age, sex, and family practice) from among subjects who had never been diagnosed as having DM or prescribed oral hypoglycemic drugs or insulin. Twenty-two cases were excluded because there were insufficient matched controls. Finally, we included only 4974 patients who had never been diagnosed as having type 1 DM.

Data were analyzed for 4974 subjects (mean age, 62.8 years [age range, 30-89 years]; 52.2% men), drawn from 197 family practices, who were diagnosed as having type 2 DM during 2003. There were 9948 control subjects, never diagnosed as having DM, who were matched for age, sex, and family practice.

 shows the utilization of healthcare before and after the diagnosis of DM compared with that of nondiabetic control subjects. In the quarter from 24 to 22 months before the diagnosis of DM, the RRs were 1.31 (95% CI, 1.27-1.35) for primary care consultations, 1.23 (95% CI, 1.08-1.39) for emergency and hospital care, 1.24 (95% CI, 1.14-1.35) for hospital specialist referrals, and 1.55 (95% CI, 1.52-1.57) for prescription drug items. The relative excess of medical care utilization continued through the prediagnosis period and then showed a marked increase, reaching a maximum in the quarter following diagnosis of DM. At this time, the RR for primary care consultations was 4.27 (95% CI, 4.17-4.36) times higher than that for controls; the RRs were 2.41 (95% CI, 2.18-2.66) times higher for emergency and hospital care, 2.97 (95% CI, 2.79-3.16) times higher for hospital specialist referrals, and 2.49 (95% CI, 2.46-2.52) times higher for prescription drug items. By 22 to 24 months after diagnosis, utilization of medical care had declined but remained higher than that in controls and higher than that in the prediagnosis period. Compared with controls, the RRs for cases were 1.64 (95% CI, 1.60-1.68) for primary care consultations, 1.42 (95% CI, 1.26-1.59) for emergency and hospital care, 1.50 (95% CI, 1.42-1.57) for hospital specialist referrals, and 2.10 (95% CI, 2.08-2.13) for prescription drug items. 

 gives the absolute rates and RRs of healthcare utilization for 3 broad periods before, during, and after the diagnosis of DM confirming these findings.

 shows the RRs of healthcare utilization events, combined across various types of care, for 4 different conditions. These analyses of healthcare utilization for specific groups of conditions showed that utilization of medical care was increased from 24 to 22 months before DM diagnosis for ischemic heart disease (RR, 2.13; 95% CI, 1.60-2.84) but not for acute myocardial infarction (RR, 1.33; 95% CI, 0.47- 3.75), cerebrovascular disease (RR, 1.00; 95% CI, 0.57-1.76), or peripheral nerve disorders (RR, 1.33; 95% CI, 0.68-2.62). There were statistically significant peaks in the utilization of care for these conditions at the time of diagnosis. In the quarter following diagnosis of DM, the RRs were 3.65 (95% CI, 2.77-4.80) for ischemic heart disease events, 6.29 (95% CI, 2.69-14.73) for acute myocardial infarction, 5.14 (95% CI, 3.37-7.84) for cerebrovascular disease, and 5.01 (95% CI, 2.81-8.95) for peripheral nerve disorders. However, utilization of care for these conditions declined to levels similar to those of controls by 22 to 24 months after diagnosis (RR, 1.05; 95% CI, 0.70-1.58 for ischemic heart disease; RR, 2.01; 95% CI, 0.41-9.96 for acute myocardial infarction; RR, 1.47; 95% CI, 0.81-2.65 for peripheral nerve disorders; and RR, 1.64; 95% CI, 0.93-2.87 for cerebrovascular disease). 

 summarizes how, in the period from 6 months before to 6 months after diagnosis, the absolute rates and RRs of new diagnoses of each condition were statistically significantly elevated. New ischemic heart disease and peripheral nerve disorder events were increased in the postdiagnosis period, but cerebrovascular disease events and acute myocardial infarctions were not.

We hypothesized that the clinical recognition of DM may be an event of limited significance in the course of a condition that is already present. Our results show that this is not the case. A clinical diagnosis of DM is associated with a profound increase in healthcare utilization and with increased occurrence of newly diagnosed ischemic heart disease, acute myocardial infarction, cerebrovascular disease, and peripheral nerve disorders. A clinical diagnosis of DM is a costly occurrence in terms of healthcare resource utilization and associated adverse health outcomes of DM. However, the nature of the interaction between health services and affected individuals is complex. The occurrence of illness may lead to contacts with health services at which DM may be diagnosed. Conversely, a diagnosis of DM may lead to the detection of comorbid conditions that were already present. In either case, there will be delivery of needed care for treatment of present conditions and prevention of future complications. Clinical practice recommendations for good-quality DM care require that utilization of care is increased to facilitate assessment for complications and intervention to control hyperglycemia, hypertension, and lipid disorders. Therefore, even when diabetic complications are not present, some increase in utilization of care around the time of DM diagnosis may be anticipated. Furthermore, there may be subgroups of patients in whom the occurrence of complications, or the increase in utilization of care associated with DM diagnosis, is more or less evident.

This was a population-based study among a large number of family practices drawn from across the United Kingdom. The large sample size yielded precise estimates. Cases and controls were matched for age, sex, and family practice, but unmeasured variables such as cigarette smoking, obesity, and individual-level socioeconomic status may have accounted for observed differences. Smoking,

 are associated with an increased frequency of DM and, in some studies,

January 2008

All Things Considered, the Answer Is a Resounding Yes

Lowering LDL-C for Optimal Protection in High-risk Patients

Unrecognized Confounders in Overcoming Clinical Inertia Study

Effects of Health Education on Glycemic Control During Holiday Time in Patients with Type 2 Diabetes Mellitus

Do Diabetes Group Visits Lead to Lower Medical Care Charges?

Diabetes Diagnosis, Resource Utilization, and Health Outcomes

Lower Severe Hypoglycemia Risk: Insulin Glargine Versus NPH Insulin in Type 2 Diabetes!

Diabetes Complications Severity Index and Risk of Mortality, Hospitalization, and Healthcare Utilization

How Managed Care Organizations Contribute to Improved Diabetes Outcomes