PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of hsresearchLink to Publisher's site
 
Health Serv Res. 2011 August; 46(4): 1158–1179.
PMCID: PMC3165182

Cost Implications to Health Care Payers of Improving Glucose Management among Adults with Type 2 Diabetes

Teryl K Nuckols, M.D., M.S.H.S., Elizabeth A McGlynn, Ph.D., John Adams, Ph.D., Julie Lai, M.P.H., Myong-Hyun Go, M.A., Joan Keesey, and Julia E Aledort, Ph.D.

Abstract

Objective

To assess the cost implications to payers of improving glucose management among adults with type 2 diabetes.

Data Source/Study Setting

Medical-record data from the Community Quality Index (CQI) study (1996–2002), pharmaceutical claims from four Massachusetts health plans (2004–2006), Medicare Fee Schedule (2009), published literature.

Study Design

Probability tree depicting glucose management over 1 year.

Data Collection/Extraction Methods

We determined how frequently CQI study subjects received recommended care processes and attained Health Care Effectiveness Data and Information Set (HEDIS) treatment goals, estimated utilization of visits and medications associated with recommended care, assigned costs based on utilization, and then modeled how hospitalization rates, costs, and goal attainment would change if all recommended care was provided.

Principal Findings

Relative to current care, improved glucose management would cost U.S.$327 (U.S.$192–711 in sensitivity analyses) more per person with diabetes annually, largely due to antihyperglycemic medications. Cost-effectiveness to payers, defined as incremental annual cost per patient newly attaining any one of three HEDIS goals, would be U.S.$1,128; including glycemic crises reduces this to U.S.$555–1,021.

Conclusions

The cost of improving glucose management appears modest relative to diabetes-related health care expenditures. The incremental cost per patient newly attaining HEDIS goals enables payers to consider costs as well as outcomes that are linked to future profitability.

Keywords: Quality of health care, cost and cost analysis, cost–benefit analysis, diabetes mellitus

Managing blood glucose well is fundamental to caring for people with type 2 diabetes mellitus. According to recent data from the United Kingdom Prospective Diabetes Study (UKPDS), people randomized to tighter control (mean hemoglobin A1c [HbA1c] 7.0 percent) for 10 years have a 15–33 percent lower risk of myocardial infarction and a 13–27 percent lower risk of death long term than those receiving conventional care (HbA1c 7.9 percent) (UKPDS Group 1998; Holman et al. 2008;). Yet in the 2003 Community Quality Index (CQI) study, the most comprehensive assessment of quality of care to date, U.S. adults received only 45 percent of the essential care processes recommended for diabetes. HbA1c testing, follow-up visits, and medication adjustments were substantially underused (McGlynn et al. 2003). Given 17.5 million people in the United States have been diagnosed with diabetes, rectifying this shortfall in quality may have substantial effects on health and health care expenditures nationally (American Diabetes Association [ADA] 2008a).

Although people with diabetes benefit from improved care, public and private health care payers are likely to be interested in quantifying the associated expenditures. Payers play major roles in ameliorating deficits in quality of care and can align financial incentives with improvement. For example, the National Committee on Quality Assurance's Health Care Effectiveness Data and Information Set (HEDIS) enables health plans to monitor and report the quality of the care their enrollees receive. Ninety percent of health plans participate in the HEDIS program and employers consider HEDIS scores in health care purchasing decisions (National Committee for Quality Assurance 2008).

Despite the role that payers play in improving care, costs and benefits of interest to payers have received limited attention. For type 2 diabetes, the cost-effectiveness of tight glucose control over the long term has been well established from the societal perspective (The Diabetes Control and Complications Trial Research Group 1996; Gray et al. 2000; Clarke et al. 2001, 2005; CDC Diabetes Cost-effectiveness Group 2002; Gray and Clarke 2008; Kahn et al. 2008). Several studies have estimated the financial effects of reducing HbA1c and diabetes-related complications over one or more decades (Wagner et al. 2001; Caro, Ward, and O'Brien 2002; Minshall et al. 2005; Huang et al. 2007; Kahn et al. 2008; Eggleston et al. 2009;). However, private payers are more likely to be concerned about short-term costs and benefits, such as the cost of enabling additional patients to achieve HEDIS goals, because enrollee turnover is substantial (Cunningham 2000). Reducing the average HbA1c to 7 percent nationwide would eliminate U.S.$65.4 billion (inflated to 2009) in medical expenditures for diabetes-related complications over 20 years—but only 20 percent of the savings would occur within 5 years (Minshall et al. 2005). Longer-term savings may be relevant to payers with low turnover rates, such as public payers, but much of the U.S. population has private insurance.

Consequently, this analysis sought to estimate the incremental per-patient cost and cost-effectiveness to payers of consistently providing the basic elements of glucose management to U.S. adults with established type 2 diabetes mellitus. We focused on process-of-care criteria because they identify specific care processes that are associated with improved outcomes, quality improvement efforts can improve adherence to the criteria, and the costs of adherence are readily quantifiable (Brook, McGlynn, and Cleary 1996). Taking the payer perspective influenced our time horizon (1 year) and measure of cost-effectiveness (cost per patient newly attaining HbA1c goals). We based our analysis on the CQI study because it represents the only nationally representative data on the quality of care processes for diabetes to date. Using that data, we identified care processes that are recommended for individual patients, assessed how frequently those processes were provided, and determined how often patients attained treatment goals. We then modeled how direct costs to payers and goal attainment would change if all recommended care processes were provided.

METHODS

To achieve our study objective, we compared the costs and proportions of diabetic individuals attaining treatment goals between a “status quo” scenario (glucose management in the CQI study) and an “improved care” scenario (100 percent provision of recommended care processes). The analysis involved the following steps: (1) selecting quality-of-care criteria; (2) developing a probability model; (3) applying the model to the CQI study population (status quo scenario); (4) estimating the effects of complete adherence to the process-of-care criteria on HbA1c outcomes (improved care scenario); (5) estimating utilization and costs under both scenarios; (6) calculating costs and cost-effectiveness ratios; and (7) performing sensitivity analyses. Costs are represented in 2009 U.S. dollars. Appendix SA2 contains additional detail.

Quality-of-Care Criteria

Recommended care processes, timeframes, and treatment goals were drawn from 2010 ADA standards (ADA 2010), 2010 HEDIS measures, and other sources (Table 1). We used multiple HbA1c goals because this is consistent with the HEDIS measures and reflects the fact that intensive blood glucose control may not be optimal for certain patients.

Table 1
Quality of Care Criteria Used in Model

Probability Model

The probability model depicted major considerations involved in managing blood glucose among patients with existing type 2 diabetes over a 1-year “modeling period,” including the recommended care processes and HbA1c outcomes from Table 1. Sequential branch points in the probability model included the following: (1) taking antihyperglycemic medications before the modeling period (yes or no), (2) receipt of an initial HbA1c test within the recommended interval (≤180 days, >180 days, no tests), (3) first HbA1c test result ≥8 percent (yes or no), (4) if that result was ≥8 percent, medication initiation or adjustment within the recommended interval (yes or no), (5) receipt of a follow-up HbA1c test within the recommended interval (≤180 days, >180 days, no tests), and (6) HbA1c outcome at the end of the modeling period (3 iterations: <7 percent versus ≥7 percent, <8 percent versus ≥8 percent, and ≤9 percent versus >9 percent). A cycle of recommended HbA1c testing, medication changes, and repeat testing takes up to 1 year.

Status Quo Scenario

Clinical parameters for this scenario (Table 2) were based on data from the CQI study. Random-digit-dial telephone surveys identified 6,712 adults from 12 metropolitan areas (round 1) and a national sample of 7,598 adults (round 2). In both rounds, trained nurses collected data from medical records in up to two 2-year waves per patient from 1996 to 2002 (McGlynn et al. 2003). Data from outpatient physician visits included laboratory tests, medication changes, and lifestyle counseling. For the current study, subjects included 821 adults with type 2 diabetes at the start of one of the 2-year waves. We obtained the first available 2-year wave of data for each subject and selected the first year of the wave as the “modeling period.” The RAND Human Subjects Protection Committee exempted this study.

Table 2
Model Parameters, Base Case Values, and Ranges Used in Sensitivity Analyses (SA)

For each individual subject, we based the receipt of HbA1c tests within 180 days on his/her average HbA1c testing frequency over the 2-year wave period, rather than on whether he/she actually received an HbA1c test within the first 180 days of the modeling period, for the following reason. When applying early versions of the probability model to the CQI data, we observed substantial inter- and intra-patient variability in HbA1c testing frequency. Failing to account for such variability would exaggerate costs for the status quo scenario because many patients whose testing interval is usually longer than 180 days would by chance have a test during the first 180 days of the modeling period.

We based HbA1c outcomes on the test results closest to the end of the modeling period; that is, the last test in the first year or first test in the second year of the 2-year wave period. For patients without a second test during the wave period, we used the result of their first test to impute their HbA1c outcome. For patients without any tests, we imputed HbA1c outcomes based on the first tests from the population tested at <180-day intervals.

Improved Care Scenario

We used the same probability model to represent the improved care scenario but made two additional assumptions. First, we assumed that adherence to the process-of-care criteria increased to 100 percent. Second, we assumed that individual subjects' HbA1c outcomes at the end of the modeling period improved as described in Table 2. Under these assumptions, the mean HbA1c for the study population declined by 0.4 percent with improved care (0.3–0.6 percent in sensitivity analyses). Quality improvement strategies typically reduce mean HbA1c by 0.4 percent, varying from 0.2 percent to 0.8 percent across strategies, and the UKPDS reduced it by 0.9 percent (Huang et al. 2007).

Utilization and Costs of Glucose Management

Physician Visits

We assumed that each HbA1c test and each medication initiation/adjustment involved one physician visit. Although in practice physicians handle some tests and medication changes by telephone or during visits for other conditions, we counted these as diabetes visits because compensation would be an incentive for providing the care. We assigned current procedural terminology (CPT) codes to visits based on the complexity of the care, and costs to CPT codes using the Medicare Physician Fee Schedule [database online] (2009).

Laboratory Tests

In the status quo scenario, we assumed that each subject's number of HbA1c tests equaled 365 divided by his/her average HbA1c testing frequency. In the improved care scenario, we assumed that all subjects had at least two tests. Cost per test was from the Medicare Clinical Laboratory Fee Schedule [database online] (2009).

Medications

The CQI data contained information on medication initiation and adjustment but not on utilization (such as filling prescriptions) or expenditures. We sought to base medication costs on actual population-based expenditures, rather than average wholesale prices or drug prices from retail pharmacies, because the latter two data sources ignore negotiated price discounts and patient nonadherence. We needed a dataset that included diagnosis codes so that we could identify people with diabetes, and drug names so that we could identify antihyperglycemic medications. Consequently, we obtained pharmacy claims data for 40,000 nonelderly adults with diabetes who were continuously enrolled in four health plans in Massachusetts from 2004 to 2006. We then calculated the average cost to payers of antihyperglycemic medications and associated supplies per patient per day. The resulting estimates are similar to those from studies contemporaneous to the CQI study (Brown et al. 1999; Minshall et al. 2005;). Sensitivity analyses considered a range based on expenditures and prices in prior studies.

In the status quo scenario, we assumed that people taking medications before the modeling period had 365 days of medication utilization, and people initiated on medications during the modeling period had 182 days. In the improved care scenario, we assumed 365 days for all patients treated with medications.

Hyper- and Hypoglycemic Events

Severe hyper- and hypoglycemia sometimes result in urgent physician visits, emergency department visits, and hospitalization. Because the CQI data did not include hospitalization data, we considered hyper- and hypoglycemic events in a supplemental analysis based on published literature (see Appendix).

Calculating Costs and Incremental Cost-Effectiveness Ratios (ICERs)

We determined the proportions of subjects attaining HbA1c goals for both the status quo and improved care scenarios. We then used the probability model to calculate costs for each scenario (sum of costs weighted by the probabilities of occurrence). Finally, for each HbA1c goal, we calculated ICERs, defined as incremental costs per individual newly attaining that goal ([costimproved care−coststatus quo]/[proportion at goalimproved care−proportion at goalstatus quo]). Analyses were performed in the R programming language.

Sensitivity Analyses

We used available literature to determine plausible ranges for model parameters and then performed a Monte Carlo sensitivity analysis. For each variable, we randomly sampled from within the range and assumed a uniform distribution of probabilities.

RESULTS

Summary of Adherence to Process-of-Care Criteria in Status Quo Scenario

Among diabetic patients in the CQI study, 17 percent had an average HbA1c testing frequency of ≤180 days, 55 percent received tests less frequently, and 28 percent received no tests. Among patients who received tests on time, 23 percent had an initial HbA1c result ≥8 percent and, of these individuals, 69 percent underwent medication initiation or adjustment when recommended. Among those who received HbA1c tests less frequently, 41 percent had initial HbA1c results ≥8 percent and, of these individuals, 26 percent had medications initiated or adjusted when recommended.

Per-Patient Costs, Proportion Achieving Goals, and ICERs

Table 3 shows the per-patient annual costs and percentages of patients achieving goals under both scenarios. Under the status quo scenario, the total cost of recommended care per patient annually was U.S.$503.38, which included U.S.$432.30 for medications, U.S.$58.21 for physician visits, and U.S.$12.87 for laboratory tests; hyper- and hypoglycemic events cost an additional U.S.$124.82–268.79. For the improved care scenario, the total cost of recommended care was U.S.$830.39, which included U.S.$682.91 for medications, U.S.$116.96 for physician visits, and U.S.$30.52 for laboratory tests; hyper- and hypoglycemic events cost an additional U.S.$94.01–102.80. The incremental cost (i.e., difference between the scenarios) was U.S.$327.01, varied from U.S.$191.50 to U.S.$710.80 in sensitivity analyses primarily due to medication costs, and declined to U.S.$161.02–296.10 (by U.S.$30.82–166.00) when hyper- and hypoglycemic events were included.

Table 3
Cost-Effectiveness of Providing All Basic Recommended Care Processes Pertaining to Blood Glucose Management

Per patient newly attaining HEDIS goals, the incremental cost was: HbA1c <7 percent, U.S.$2,336; HbA1c <8 percent, U.S.$3,270; HbA1c ≤9 percent, U.S.$5,450; any one of the three goals, U.S.$1,128 (Table 3). Because the basic recommended care processes do not vary across the three goals, costs remain the same. As the HbA1c goals rise, the ICERs increase because the proportions of patients newly attaining goals decline.

Table 4 shows results of sensitivity analyses. Including hyper- and hypo-glycemic events and assumptions about medication costs had the largest effects on results. The incremental cost of newly attaining any one of the HEDIS goals fell from U.S.$1,128 to U.S.$555–1,021 when hyper- and hypoglycemic events are included.

Table 4
Results of Sensitivity Analyses for Goal HbA1c <8%

DISCUSSION

Our analysis adds to the existing literature addressing the cost of improving quality of care for diabetes in two important ways. First, our work explicitly focuses on the cost implications of improved quality of care from the health care payer perspective. Second, the analysis uses a nationally representative sample to estimate the direct health care expenditures associated with improving glucose management, for which recent data have been limited.

Among adults with type 2 diabetes from the CQI study, we observed substantial underuse of HbA1c testing and medication adjustment. Ensuring that 100 percent of these essential care processes are provided would increase annual health care expenditures by U.S.$327 per person with diabetes, largely due to greater use of antihyperglycemic medications. HEDIS scores would improve for 29 percent of patients with diabetes, and payers would spend U.S.$1,128 on additional health care services per person with an improved score (U.S.$555–1,021 if hospitalizations for glycemic crises decline).

In addition to greater utilization of health care services, quality improvement programs and incentive payments to providers can also affect the costs and clinical outcomes associated with improving glucose management. Myriad quality improvement strategies have been attempted for diabetes, but the costs of implementation and oversight (as opposed to health services) have been reported in few studies (Shojania et al. 2006). Some disease-management and patient-education programs cost U.S.$481–1,500 and the UKPDS implementation program cost U.S.$321 per patient annually (all inflated to 2009 dollars) (Sidorov et al. 2002; Clarke et al. 2005; Keers et al. 2005; Rothman et al. 2006; Currency Converter for 164 Currencies [database online] 2008;). In one pay-for-performance program that paid physicians incentives equaling 1.5–7.5 percent of fees, monitoring of HbA1c tests increased substantially and hospitalization rates declined (Chen et al. 2010). However, the effectiveness of pay for performance remains unclear overall and it can have unintended consequences (McDonald and Roland 2009). Restructuring physician payment systems could align reimbursement with the resources involved in monitoring HbA1c testing and titrating medications; costs would depend on how these activities are valued. Summarizing the available information on the cost of improving glucose management, the total incremental cost of essential care processes, quality improvement programs, pay-for-performance incentives (U.S.$8–57 based on 1.5–7.5 percent of average diabetes-attributable outpatient physician costs), and hospitalizations for glycemic crises would be U.S.$490–1,853 annually per person (ADA 2008b). Average per-patient diabetes-attributable health care expenditures for those older than 45 are U.S.$5,450–10,400 (inflated to 2009) (ADA 2008b); therefore, providing 100 percent of essential care processes would represent a 3–6 percent increase and the total incremental cost would represent a 4.7–34 percent increase.

Two additional factors could also affect the costs and outcomes associated with glucose management: payers' pharmacy benefit management programs and patients' adherence to recommended care. Pharmacy benefit programs have the potential to influence which diabetes medications patients receive and also whether patients choose to fill prescriptions (Austvoll-Dahlgren et al. 2008). Because the data we used included many brand-name prescriptions, medication costs could be lower and adherence higher if more generics are used. Poor adherence to recommended testing and medication therapy by patients is common and associated with both worse HbA1c levels and higher health care expenditures (Balkrishnan et al. 2003; Cramer 2004; Karter et al. 2004; Rubin 2005; Sokol et al. 2005;). On the other hand, one study found that uncontrolled HbA1c levels are more commonly due to provider failures to intensify treatment than patient nonadherence (Schmittdiel et al. 2008). Our cost estimates accounted for patient nonadherence in two ways. First, we based medication costs on actual pharmacy expenditures rather than medication prices. Second, assumptions about the effect of improving care processes on HbA1c values were well within the range of documented effects of quality improvement interventions.

Although insurers often pay for the additional care processes that patients receive when quality is improved, researchers seldom examine the financial and health effects that are most relevant to payers. Indeed, the general public in the United States may be uncomfortable with the idea of payers conducting business-case analyses for any service, including quality improvement efforts. Yet payers often do consider cost-effectiveness when deciding which services to cover (Luce 2005). Because the interests of payers and society might sometimes be misaligned, it is important for public policy makers to examine short-term cost-effectiveness to payers as well as long-term cost-effectiveness to society. For glucose management, the potential differences between the payer and societal perspectives are particularly relevant to the Medicare program. A third of people with diabetes are age 65 or older and diabetes-attributable health care expenditures for this age group reached U.S.$65 billion in 2007 (ADA 2008a). Because diabetes is often diagnosed in late middle age, receiving poor versus adequate glucose management early on has the potential to affect Medicare expenditures a decade or more later. Consequently, private health care payors' policies regarding quality-of-care today can influence future Medicare expenditures.

Health care payers can apply our results in a manner analogous to how policy makers apply cost-effectiveness analyses performed from the societal perspective (Stokey and Zeckhauser 1978). First, a payer can determine whether the additional expenditures are within its budget. Next, the payer may consider whether improving glucose management is a good value. When assessing value, policy makers generally consider lifetime costs and health effects, and can, in theory, set a maximum acceptable cost per quality adjusted life year (e.g., U.S.$50,000). For payers, the incremental cost per patient newly attaining a HEDIS goal is a more useful metric of cost-effectiveness because it enables payers to consider costs as well as an outcome linked to future profitability. If a payer can predict the potential effect of raising its HEDIS scores on future enrollment and, in turn, the relationship between enrollment and profitability, it can determine whether the anticipated expenditures for improved glucose management may be balanced by an equal or greater rise in profits. Alternatively, a payer can judge subjectively whether a particular per person with an improved HEDIS score is a good value.

Other recent studies have examined the cost-effectiveness of improving quality of care for diabetes from the societal perspective. Using NHANES data for 1998–2004 and the Archimedes model, Kahn et al. (2008) found that the maximum feasible attainment of HEDIS goals for diabetes over 30 years would cost U.S.$56,666/QALY (inflated to 2009). Kahn and colleagues also estimated that increasing the attainment of HbA1c <7 percent from 42 percent to 60 percent of the 5.7 million U.S. adults with diabetes would prevent 652,000 myocardial infarctions and add 15 million years of life nationally (Saaddine et al. 2006; Kahn et al. 2008;). A second recent study evaluated a program that improved the management of blood glucose, cardiovascular risk factors, and microvascular complications at a national network of community health centers in 1998–2002. Using data from this program and the Eastman model, Huang et al. (2007) predicted that retinopathy, blindness, end-stage renal disease, and coronary artery disease would decline significantly and that the cost-effectiveness ratio would be U.S.$40,439/QALY (inflated to 2009) from the societal perspective.

Although we did not examine cost-effectiveness from the societal perspective, our analysis provides specific data on the direct cost of improving glucose management, which was lacking in these two prior publications. The study by Kahn and colleagues merely assumed that the additional care processes would cost U.S.$4,142 per patient annually (inflated to 2009), which substantially exceeds our estimate of U.S.$327. In the UKPDS, costs related to health care services were about U.S.$300–1,500 greater for intensive than conventional management (inflated to 2009 dollars) (Gray et al. 2000; Clarke et al. 2001, 2005; CDC Diabetes Cost-effectiveness Group 2002; Currency Converter for 164 Currencies [database online] 2008). Because our analyses and Kahn's are both from the same period and based on national samples, we used our estimate of the direct costs to recalculate Kahn's cost-effectiveness ratio, which then declined to U.S.$44,869/QALY.

Clearly, improving quality of care for diabetes represents an excellent value from the societal perspective. Payers may also believe it to be a good value from their perspective, given the small increase in annual costs involved and the incentives created by the HEDIS program. Indeed, payers' widespread use of disease management programs suggests that they already believe this.

Our data have several limitations. Since the CQI study began, more patients are on antihyperglycemics and mean HbA1c levels have declined (Saaddine et al. 2006; Alexander et al. 2008;). Sensitivity analyses modeled these trends by increasing, for the status quo scenario, the percentage of patients receiving recommended HbA1c tests and medication adjustments. Given our analysis included only 821 people, findings may not be representative; however, we did draw from a national sample and included other large studies in sensitivity analyses. Our estimate of medication expenditures was based on four health plans in one state, but it is within a wide range reported in prior studies. A strength of our analysis is that we used actual medication expenditures, rather than average wholesale prices, to account for negotiated price discounts and patient nonadherence. Lastly, we did not distinguish the perspectives of public and private payers.

In conclusion, ensuring that people with diabetes receive 100 percent of the essential care processes pertaining to glucose management will generate modest increases in total annual per-patient health care expenditures attributable to diabetes. Our results enable payers to consider short-term costs and outcomes that are important from their perspective.

Acknowledgments

Joint Acknowledgment/Disclosure Statement: Steven M. Asch, M.D., M.P.H.; Paul Shekelle, M.D., Ph.D.; David O. Meltzer, M.D., Ph.D.; and Gene Oppeneheim, M.D., provided assistance with the development of the probability trees. By contractual agreement with the funder, the National Pharmaceutical Council, RAND had complete control over the design and conduct of the study; the collection, management, analysis, and interpretation of the data; and the reporting of results. Teryl Nuckols is currently supported by a Mentored Clinical Scientist Career Development Award (K08) from the Agency for Health Care Research and Quality (grant number HS17954).

Disclosures: The funder, the National Pharmaceutical Council, reviewed the study design and draft manuscript. By contractual agreement, RAND controlled all aspects of the design, data sources, analysis, interpretation, and reporting of results.

Disclaimers: None.

SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article:

Appendix SA1: Author Matrix.

Appendix SA2: Cost Implications of Improving Blood Glucose Management among U.S. Adults.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

REFERENCES

  • Alexander GC, Sehgal NL, Moloney RM, Stafford RS. National Trends in Treatment of Type 2 Diabetes Mellitus, 1994–2007. Archives of Internal Medicine. 2008;168(19):2088–94. [PMC free article] [PubMed]
  • American Diabetes Association (ADA) Economic Costs of Diabetes in the U.S. in 2007. Diabetes Care. 2008a;31(3):596–615. [PubMed]
  • American Diabetes Association (ADA) Standards of Medical Care in Diabetes—2008. Diabetes Care. 2008b;31(suppl 1):S12–54. [PubMed]
  • American Diabetes Association (ADA) Standards of Medical Care in Diabetes—2010. Diabetes Care. 2010;33:S11–61. [PMC free article] [PubMed]
  • Aron D, Pogach L. Quality Indicators for Diabetes Mellitus in the Ambulatory Setting: Using the Delphi Method to Inform Performance Measurement Development. Quality & Safety in Health Care. 2008;17:315–7. [PubMed]
  • Austvoll-Dahlgren A, Aaserud M, Vist G, Ramsay C, Oxman AD, Sturm H, Kösters JP, Vernby A. Pharmaceutical Policies: Effects of Cap and Co-Payment on Rational Drug Use. Cochrane Database of Systemic Reviews. 2008;23(1):CD007017. [PubMed]
  • Balkrishnan R, Rajagopalan R, Camacho FT, Huston SA, Murray FT, Anderson RT. Predictors of Medication Adherence and Associated Health Care Costs in an Older Population with Type 2 Diabetes Mellitus: A Longitudinal Cohort Study. Clinical Therapeutics. 2003;25(11):2958–71. [PubMed]
  • Berlowitz DR, Ash AS, Glickman M, Friedman RH, Pogach LM, Nelson AL, Wong AT. Developing a Quality Measure for Clinical Inertia in Diabetes Care. Health Services Research. 2005;40(6, Part 1):1836–53. [PMC free article] [PubMed]
  • Bodmer M, Meier C, Krahenbuhl S, Jick SS, Meir C. Metformin, Sulfonylureas, or Other Antidiabetes Drugs and the Risk of Lactic Acidosis or Hypoglycemia: A Nested Case–Control Analysis. Diabetes Care. 2008;31(11):2086–91. [PMC free article] [PubMed]
  • Bolen S, Feldman L, Vassy J, Wilson L, Yeh HC, Marinopoulos S, Wiley C, Selvin E, Wilson R, Bass EB, Brancati FL. Systematic Review: Comparative Effectiveness and Safety of Oral Medications for Type 2 Diabetes Mellitus. Annals of Internal Medicine. 2007;147(6):386–99. [PubMed]
  • Brook RH, McGlynn EA, Cleary PD. Quality of Health Care. Part 2: Measuring Quality of Care. The New England Journal of Medicine. 1996;335(13):966–70. [PubMed]
  • Brown JB, Nichols GA, Glauber HS, Bakst AW. Type 2 Diabetes: Incremental Medical Care Costs during the First 8 Years after Diagnosis. Diabetes Care. 1999;22(7):1116–24. [PubMed]
  • Caro JJ, Ward AJ, O'Brien JA. Lifetime Costs of Complications Resulting from Type 2 Diabetes in the U.S. Diabetes Care. 2002;25(3):476–81. [PubMed]
  • CDC Diabetes Cost-effectiveness Group. Cost-Effectiveness of Intensive Glycemic Control, Intensified Hypertension Control, and Serum Cholesterol Level Reduction for Type 2 Diabetes. Journal of the American Medical Association. 2002;287(19):2542–51. [PubMed]
  • Chen JY, Tian H, Taira Juarez D, Hodges KA, Brand JC, Jr., Chung RS, Legorreta AP. The Effect of a PPO Pay-for-Performance Program on Patients with Diabetes. The American Journal of Managed Care. 2010;16(1):e11–9. [PubMed]
  • Clarke P, Gray A, Adler A, Stevens R, Raikou M, Cull C, Stratton I, Holman R. United Kingdom Prospective Diabetes Study (UKPDS) Group. Cost-Effectiveness Analysis of Intensive Blood-Glucose Control with Metformin in Overweight Patients with Type II Diabetes (UKPDS No. 51) Diabetologia. 2001;44(3):298–304. [PubMed]
  • Clarke PM, Gray AM, Briggs A, Stevens RJ, Matthews DR, Holman RR. UKPDS 72 United Kingdom Prospective Diabetes Study. Cost-Utility Analyses of Intensive Blood Glucose and Tight Blood Pressure Control in Type 2 Diabetes (UKPDS 72) Diabetologia. 2005;48(5):868–77. [PubMed]
  • Cramer JA. A Systematic Review of Adherence with Medications for Diabetes. Diabetes Care. 2004;27:1218–24. [PubMed]
  • Cunningham PJ. Health Plan Switching: Choice or Circumstance? Health Affairs. 2000;19(3):158–64. [PubMed]
  • Currency Converter for 164 Currencies [database online] 2008. [accessed on December 11, 2008]. Available at http://www.oanda.com/convert/classic.
  • The Diabetes Control and Complications Trial Research Group. The Absence of a Glycemic Threshold for the Development of Long-Term Complications: The Perspective of the Diabetes Control and Complications Trial. Diabetes. 1996;45(10):1289–98. [PubMed]
  • Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD, Zieve FJ, Marks J, Davis SN, Hayward R, Warren SR, Goldman S, McCarren M, Vitek ME, Henderson WG, Huang GD. Glucose Control and Vascular Complications in Veterans with Type 2 Diabetes. The New England Journal of Medicine. 2008;360:129–39. [PubMed]
  • Eggleston KN, Shah ND, Smith SA, Wagie AE, Williams AR, Grossman JH, Berndt ER, Long KH, Banerjee R, Newhouse JP. The Net Value of Health Care for Patients with Type 2 Diabetes, 1997 to 2005. Annals of Internal Medicine. 2009;151(6):386–93. [PubMed]
  • Gray A, Raikou M, McGuire A, Fenn P, Stevens R, Cull C, Stratton I, Adler A, Holman R, Turner R. Cost Effectiveness of an Intensive Blood Glucose Control Policy in Patients with Type 2 Diabetes: Economic Analysis Alongside Randomised Controlled Trial (UKPDS 41). United Kingdom Prospective Diabetes Study Group. British Medical Journal. 2000;320(7246):1373–8. [PMC free article] [PubMed]
  • Gray AM, Clarke P. The Economic Analyses of the UK Prospective Diabetes Study. Diabetic Medicine. 2008;25(suppl 2):47–51. [PubMed]
  • Hoerger TJ, Segel JE, Gregg EW, Saaddine JB. Is Glycemic Control Improving in U.S. Adults? Diabetes Care. 2008;31(1):81–6. [PubMed]
  • Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-Year Follow-Up of Intensive Glucose Control in Type 2 Diabetes. The New England Journal of Medicine. 2008;359(15):1577–89. [PubMed]
  • Huang ES, Zhang Q, Brown SE, Drum ML, Meltzer DO, Chin MH. The Cost-Effectiveness of Improving Diabetes Care in U.S. Federally Qualified Community Health Centers. Health Services Research. 2007;42(6, Part 1):2174–93. discussion 294–323. [PMC free article] [PubMed]
  • Kahn R, Robertson RM, Smith R, Eddy D. The Impact of Prevention on Reducing the Burden of Cardiovascular Disease. Diabetes Care. 2008;31(8):1686–96. [PMC free article] [PubMed]
  • Karter AJ, Parker MM, Moffet HH, Ahmed AT, Ferrara A, Liu JY, Selby JV. Missed Appointments and Poor Glycemic Control: An Opportunity to Identify High-Risk Diabetic Patients. Medical Care. 2004;42(2):110–5. [PubMed]
  • Keers JC, Groen H, Sluiter WJ, Bouma J, Links TP. Cost and Benefits of a Multidisciplinary Intensive Diabetes Education Programme. Journal of Evaluation in Clinical Practice. 2005;11(3):293–303. [PubMed]
  • Kelly TN, Bazzano LA, Fonseca VA, Thethi TK, Reynolds K, He J. Systematic Review: Glucose Control and Cardiovascular Disease in Type 2 Diabetes. Annals of Internal Medicine. 2009;151(6):394–403. [PubMed]
  • Kerr EA, Gerzoff RB, Krein SL, Selby JV, Piette JD, Curb JD, Herman WH, Marrero DG, Narayan KM, Safford MM, Thompson T, Mangione CM. Diabetes Care Quality in the Veterans Affairs Health Care System and Commercial Managed Care: The TRIAD Study. Annals of Internal Medicine. 2004;141(4):272–81. [PubMed]
  • Kim S. Burden of Hospitalizations Primarily Due to Uncontrolled Diabetes: Implications of Inadequate Primary Health Care in the United States. Diabetes Care. 2007;30(5):1281–2. [PubMed]
  • Luce BR. What will It Take to Make Cost-Effectiveness Analysis Acceptable in the United States? Medical Care. 2005;43(7, suppl):44–8. [PubMed]
  • Martirosyan L, Braspenning J, Denig P, de Grauw WJ, Bouma M, Storms F, Haaijer-Ruskamp FM. Prescribing Quality Indicators of Type 2 Diabetes Mellitus Ambulatory Care. Quality & Safety in Health Care. 2008;17(5):318–23. [PubMed]
  • McDonald R, Roland M. Pay for Performance in Primary Care in England and California: Comparison of Unintended Consequences. Annals of Family Medicine. 2009;7(2):121–7. [PubMed]
  • McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr EA. The Quality of Health Care Delivered to Adults in the United States. The New England Journal of Medicine. 2003;348(26):2635–45. [PubMed]
  • Medicare Clinical Laboratory Fee Schedule [database online] 2009. “Centers for Medicare and Medicaid Services” [accessed on May 7, 2009]. Available at http://www.cms.hhs.gov/ClinicalLabFeeSched/02_clinlab.asp#TopOfPage.
  • Medicare Physician Fee Schedule Look-Up [database online] 2009. “Centers for Medicare and Medicaid Services” [accessed on May 7, 2009]. Available at http://www.cms.hhs.gov/PFSlookup.
  • Minshall ME, Roze S, Palmer AJ, Valentine WJ, Foos V, Ray J, Graham C. Treating Diabetes to Accepted Standards of Care: A 10-year Projection of the Estimated Economic and Health Impact in Patients with Type 1 and Type 2 Diabetes Mellitus in the United States. 2005;27(6):940–50. [PubMed]
  • National Committee for Quality Assurance. 2008. “HEDIS 2009 Volume 2 Technical Update” [accessed on October 1, 2008]. Available at http://www.ncqa.org/tabid/784/Default.aspx.
  • Rodondi N, Peng T, Karter AJ, Bauer DC, Vittinghoff E, Tang S, Pettitt D, Kerr EA, Selby JV. Therapy Modifications in Response to Poorly Controlled Hypertension, Dyslipidemia, and Diabetes Mellitus. Annals of Internal Medicine. 2006;144(7):475–84. [PMC free article] [PubMed]
  • Rossi MC, Comaschi M, Ceriello A, Coscelli C, Cucinotta D, De Cosmo S, Di Blasi P, Giorda C, Otranto I, Pellegrini F, Pomili B, Valentini U, Vespasiani G, Nicolucci A. 2008. Correlation between Structure Characteristics, Process Indicators and Intermediate Outcomes in DM2: the QUASAR (Quality Assessment Score and Cardiovascular Outcomes in Italian Diabetic Patients) Study (Abstract). 68th Annual American Diabetes Association Annual Meeting, San Francisco, CA.
  • Rothman RL, So SA, Shin J, Malone RM, Bryant B, Dewalt DA, Pignone MP, Dittus RS. Labor Characteristics and Program Costs of a Successful Diabetes Disease Management Program. The American Journal of Managed Care. 2006;12(5):277–83. [PubMed]
  • Rubin RR. Adherence to Pharmacologic Therapy in Patients with Type 2 Diabetes Mellitus. The American Journal of Medicine. 2005;118(suppl 5A):27S–34. [PubMed]
  • Saaddine JB, Cadwell B, Gregg EW, Engelgau MM, Vinicor F, Imperatore G, Narayan KM. Improvements in Diabetes Processes of Care and Intermediate Outcomes: United States, 1988–2002. Annals of Internal Medicine. 2006;144(7):465–74. [PubMed]
  • Saaddine JB, Engelgau MM, Beckles GL, Gregg EW, Thompson TJ, Narayan KM. A Diabetes Report Card for the United States: Quality of Care in the 1990s. Annals of Internal Medicine. 2002;136(8):565–74. [PubMed]
  • Schmittdiel JA, Uratsu CS, Karter AJ, Heisler M, Subramanian U, Mangione CM, Selby JV. Why Don't Diabetes Patients Achieve Recommended Risk Factor Targets? Poor Adherence versus Lack of Treatment Intensification. Journal of General Internal Medicine. 2008;23(5):588–94. [PMC free article] [PubMed]
  • Shojania KG, Ranji SR, McDonald KM, Grimshaw JM, Sundaram V, Rushakoff RJ, Owens DK. Effects of Quality Improvement Strategies for Type 2 Diabetes on Glycemic Control: A Meta-Regression Analysis. Journal of the American Medical Association. 2006;296(4):427–40. [PubMed]
  • 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-Sponsored Disease Management Program among Patients Fulfilling Health Employer Data and Information Set Criteria. Diabetes Care. 2002;25(4):684–9. [PubMed]
  • Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of Medication Adherence on Hospitalization Risk and Healthcare Cost. Medical Care. 2005;43(6):521–30. [PubMed]
  • Stokey E, Zeckhauser R. A Primer for Policy Analysis. New York: WW Norton and Company Inc; 1978.
  • UK Prospective Diabetes Study (UKPDS) Group. Intensive Blood-Glucose Control with Sulphonylureas or Insulin Compared with Conventional Treatment and Risk of Complications in Patients with Type 2 Diabetes (UKPDS 33) Lancet. 1998;352(9131):837–53. [PubMed]
  • U.S. Department of Labor: Bureau of Labor Statistics. 2007. “Consumer price index, all urban consumers, 1997–2007.”.
  • Wagner EH, Sandhu N, Newton KM, McCulloch DK, Ramsey SD, Grothaus LC. Effect of Improved Glycemic Control on Health Care Costs and Utilization. Journal of the American Medical Association. 2001;285(2):182–9. [PubMed]
  • Wang J, Imai K, Engelgau MM, Geiss LS, Wen C, Zhang P. Secular Trends in Diabetes-Related Preventable Hospitalizations in the United States, 1998–2006. Diabetes Care. 2009;32(7):1213–7. [PMC free article] [PubMed]

Articles from Health Services Research are provided here courtesy of Health Research & Educational Trust