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Editorials |

Trying To Predict the Future for People with Diabetes: A Tough but Important Task FREE

Michael M. Engelgau, MD, MS
[+] Article and Author Information

From the National Center for Chronic Disease Prevention and Health Promotion Centers for Disease Control and Prevention, Atlanta, GA 30341.


Disclaimer: The findings and conclusions in this editorial are those of the author and do not necessarily represent the views of the funding agency or the Diabetes Prevention Program Research Group.

Grant Support: Dr. Engelgau is a federal employee of the Centers for Disease Control and Prevention.

Potential Financial Conflicts of Interest: None disclosed.

Requests for Single Reprints: Michael M. Engelgau, MD, MS, Division of Diabetes Translation, Mailstop K-10, 4770 Buford Highway NE, Atlanta, GA 30341; e-mail, mengelgau@cdc.gov.


Ann Intern Med. 2005;143(4):301-302. doi:10.7326/0003-4819-143-4-200508160-00011
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With growing health care costs, policymakers and health care providers need information on the cost-effectiveness of interventions. This issue contains an economic evaluation (1) of a prevention policy for diabetes—a condition that affects more than 18 million persons in the United States at a cost of approximately $132 billion annually.

To assist policymakers, researchers have developed models that simulate the progression of diabetes, expenditures on diabetes care, and effects of interventions. The outputs of these models include costs and health outcomes, such as length of life (often expressed as quality-adjusted life-years [QALYs]), a measure that considers the quality of life in each health state. Two principal types of diabetes models exist. Most researchers use a Markov model, which comprises disease states (for example, normal, impaired glucose tolerance [IGT], and diabetes) represented in a computer program. The computer simulates transitions from one disease state to another as chance events. A second novel type of model, named Archimedes (23), uses object-oriented computer programming and complex differential equations to simulate pathophysiologic processes (for example, hepatic glucose output after a meal) that change over time and can lead to disease. For a technical explanation of the Archimedes model, I refer to the accompanying editorial in this issue (4). My editorial focuses on the clinical and policy aspects of the Archimedes model.

The Archimedes model calculates the changes in costs and quality-adjusted length of life (in QALYs) from implementing the lifestyle intervention of the Diabetes Prevention Program (DPP) study (5) or prescribing metformin to a cohort with IGT like the enrollees in the DPP. Eddy and colleagues (1) took a societal perspective and used time horizons of 10, 20, and 30 years. Compared with no intervention, the cost per QALY of beginning the intensive lifestyle intervention or metformin for IGT was $62 600 and $35 400, respectively, over 30 years (1). The cost per QALY for an intensive lifestyle intervention started after the onset of diabetes was $24 500.

What do these results imply for health care policy? Acceptable cost-effectiveness depends on a society's willingness to pay. Interventions that cost less than $50 000 per QALY are reasonable to consider for rapid adoption. Thus, the Archimedes model results imply that we should not rush to adopt the DPP lifestyle intervention (6).

Another model, developed by the DPP Research Group (DPPRG), leads to a different conclusion. It used a Markov model, lifetime horizon, and societal perspective to determine the cost-effectiveness of the same interventions for IGT. The cost per QALY was $8800 for the lifestyle intervention and $29 900 for metformin, both less than the $50 000 threshold for rapid adoption (7). Eddy and colleagues critique the model in an online Appendix (1). Speaking as a coauthor of the DPPRG cost-effectiveness analysis, I believe that many of Eddy and colleagues' criticisms are based on inaccurate descriptions of the DPPRG model. However, my focus is to understand why the 2 models have such different policy implications.

Although these 2 analyses gave different values for cost-effectiveness, several clinically relevant results were similar. First, both analyses predicted that the lifestyle intervention would substantially reduce the proportion of patients at high risk who developed diabetes. Second, both analyses predicted that the lifestyle intervention would delay the onset of diabetes. The Archimedes model predicted that the time required for 50% of patients with IGT to develop diabetes would increase from 7 years to 14 years. The corresponding figures from the DPPRG study were 8 years and 18 years, respectively. Third, both analyses predicted that the lifestyle intervention will lead to fewer complications, longer life, and improved quality of life. Fourth, both analyses suggested that the cost of the lifestyle intervention exceeds the savings from lower rates of diabetes complications.

Why do the 2 studies differ in the cost per QALY of the lifestyle intervention and metformin therapy? This question is crucial because the different costs per QALY lead to different policy recommendations. To facilitate a comparison, I examined the results for the base-case cohorts (Archimedes model, no intervention; DPPRG analysis, standard lifestyle) for both models. The Archimedes' time horizon (30 years) is shorter than the DPPRG time horizon (the time from IGT diagnosis until death). This difference is important. A shorter time horizon will always be less favorable if some patients benefit only after decades of the intervention, as Eddy and colleagues show by comparing the 10-, 20-, and 30-year time horizon analyses. Because the DPPRG analysis followed the cohort longer but projected roughly the same life expectancy as the Archimedes model (approximately 24 years), one can infer that the Archimedes model projected less morbidity and mortality during its shorter 30-year time horizon. As the complication rate increases, the absolute benefit from an effective intervention increases.

Why are the complication rates higher in the DPPRG model? In the Archimedes model, the microvascular disease cumulative incidence rates were very low. The lifestyle intervention reduced the base-case cohort's 30-year cumulative incidence of kidney failure from 0.07% to 0.03% and the need for amputation from 0.03% to 0.02%. Comparable rates from the DPPRG were from 1.0% to 0.6% and from 1.9% to 1.3%, respectively. These differences are probably too large to be due solely to the shorter time horizon used in the Archimedes model.

What accounts for this difference in microvascular disease rates in the 2 studies? One reason is different assumptions for the rates of progression of glycemia. For the base case, the Archimedes analysis modeled the FPG level to increase at a rate of 0.1 to 0.2 mmol/L (2.0 to 3.0 mg/dL) per year from onset of diabetes until it reached 10.0 to 11.1 mmol/L (180.0 to 200.0 mg/dL), taking about 20 to 30 years. This is much longer than the empirical observation of roughly 10 years from onset until clinical diagnosis is made (810), which is the assumption used by the DPPRG. Slower rates of glycemic progression imply slower microvascular complication rates. This is consistent with results from a validation study reported by the Archimedes analysis that predicted a substantially lower cumulative incidence of retinopathy than that observed (15% in the Archimedes model vs. 30% in the comparison trial) (see Eddy and colleagues' Appendix Table 5 [1]).

Assumptions and modeling of cardiovascular outcomes will have a greater effect on life expectancy than microvascular outcome assumptions. Here, the difference between the Archimedes model and the DPPRG analysis was mostly due to the different time horizons. In blinded predictions of the results of the Collaborative Atorvastatin Diabetes Study (CARDS) (11), both models predicted the observed rates reasonably well.

In my editorial, I have tried to dissect the 2 models to show why the cost per QALY was so much higher in the Archimedes model despite the 2 models projecting several similar qualitative conclusions. Different assumptions about the rate of glycemic progression and a different time horizon (which allows a longer time in which events could occur) are probably the principal causes of the differences. The lower rate of complications in the Archimedes analysis means that the interventions will have a smaller effect on the outcomes of complications than in the DPPRG analysis. A smaller effect of the interventions, with roughly the same costs, would translate into lower cost-effectiveness in the Archimedes analysis.

The Archimedes model is a new, novel, and welcome addition to the diabetes care–modeling efforts. However, its inputs and assumptions need more refinement and transparency before we can understand it well enough to use it in setting national diabetes prevention policy. As my editorial shows, understanding why the Archimedes model differs from other models is an exacting and difficult task. However, whoever said that interpreting economic studies would be easy and straightforward? (1213). Trying to predicting the future is a tough job—but we should still try to do it and try to do it well.

Michael M. Engelgau, MD, MS

National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention

Atlanta, GA 30341

References

Eddy DM, Schlessinger L, Kahn R.  Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes. Ann Intern Med. 2005; 143:251-64.
 
Eddy DM, Schlessinger L.  Archimedes: a trial-validated model of diabetes. Diabetes Care. 2003; 26:3093-101. PubMed
CrossRef
 
Schlessinger L, Eddy DM.  Archimedes: a new model for simulating health care systems—the mathematical formulation. J Biomed Inform. 2002; 35:37-50. PubMed
 
Brandeau ML.  Modeling complex medical decision problems with the Archimedes model. Ann Intern Med. 2005; 143:303-4.
 
Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA. et al.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346:393-403. PubMed
 
Laupacis A, Feeny D, Detsky AS, Tugwell PX.  How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. CMAJ. 1992; 146:473-81. PubMed
 
Herman WH, Hoerger TJ, Brandle M, Hicks K, Sorensen S, Zhang P. et al.  The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med. 2005; 142:323-32. PubMed
 
Harris MI, Klein R, Welborn TA, Knuiman MW.  Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care. 1992; 15:815-9. PubMed
 
Thompson TJ, Engelgau MM, Hegazy M, Ali MA, Sous ES, Badran A. et al.  The onset of NIDDM and its relationship to clinical diagnosis in Egyptian adults. Diabet Med. 1996; 13:337-40. PubMed
 
Ferrannini E, Nannipieri M, Williams K, Gonzales C, Haffner SM, Stern MP.  Mode of onset of type 2 diabetes from normal or impaired glucose tolerance. Diabetes. 2004; 53:160-5. PubMed
 
Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HA, Livingstone SJ. et al.  Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004; 364:685-96. PubMed
 
Neumann PJ.  Why don't Americans use cost-effectiveness analysis? Am J Manag Care. 2004; 10:308-12. PubMed
 
Tunis SR.  Economic analysis in healthcare decisions [Editorial]. Am J Manag Care. 2004; 10:301-4. PubMed
 

Figures

Tables

References

Eddy DM, Schlessinger L, Kahn R.  Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes. Ann Intern Med. 2005; 143:251-64.
 
Eddy DM, Schlessinger L.  Archimedes: a trial-validated model of diabetes. Diabetes Care. 2003; 26:3093-101. PubMed
CrossRef
 
Schlessinger L, Eddy DM.  Archimedes: a new model for simulating health care systems—the mathematical formulation. J Biomed Inform. 2002; 35:37-50. PubMed
 
Brandeau ML.  Modeling complex medical decision problems with the Archimedes model. Ann Intern Med. 2005; 143:303-4.
 
Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA. et al.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346:393-403. PubMed
 
Laupacis A, Feeny D, Detsky AS, Tugwell PX.  How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. CMAJ. 1992; 146:473-81. PubMed
 
Herman WH, Hoerger TJ, Brandle M, Hicks K, Sorensen S, Zhang P. et al.  The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med. 2005; 142:323-32. PubMed
 
Harris MI, Klein R, Welborn TA, Knuiman MW.  Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care. 1992; 15:815-9. PubMed
 
Thompson TJ, Engelgau MM, Hegazy M, Ali MA, Sous ES, Badran A. et al.  The onset of NIDDM and its relationship to clinical diagnosis in Egyptian adults. Diabet Med. 1996; 13:337-40. PubMed
 
Ferrannini E, Nannipieri M, Williams K, Gonzales C, Haffner SM, Stern MP.  Mode of onset of type 2 diabetes from normal or impaired glucose tolerance. Diabetes. 2004; 53:160-5. PubMed
 
Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HA, Livingstone SJ. et al.  Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004; 364:685-96. PubMed
 
Neumann PJ.  Why don't Americans use cost-effectiveness analysis? Am J Manag Care. 2004; 10:308-12. PubMed
 
Tunis SR.  Economic analysis in healthcare decisions [Editorial]. Am J Manag Care. 2004; 10:301-4. PubMed
 

Letters

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Preventing diabetes in high risk people: a response from the authors
Posted on September 22, 2005
David Eddy
Kaiser Permanente
Conflict of Interest: None Declared

Response

In his editorial about our paper Engelgau asks several questions which we are happy to answer. His main concern is the progression of glycemia "“ specifically, the time required for diabetes to progress from onset to clinical diagnosis. In the Diabetes Prevention Program (DPP) "onset" is defined as FPG > 125 mg/dl or OGT > 199 mg/dl). "Clinical diagnosis" is the time people seek care in routine or usual practice, which in trials like the United Kingdom Prospective Diabetes Study (UKPDS) occurred at an average FPG of about 200 mg/dL (1). A previous analysis of diabetes prevention from Engelgau's group (2) assumed this duration to be exactly 10 years for all people with diabetes. In the Archimedes model, the rate of progression of glycemia varies across individuals, and on average takes considerably longer than 10 years. We agree that this is an important issue.

To support the assumption of a 10-year fixed progression time, Engelgau cites three studies (3 4 5). None of them actually addresses the progression of diabetes from onset to clinical diabetes, much less supports an assumption of a uniform or even average 10-year progression time. The first (3) used data on the prevalence of retinopathy as a function of time since clinical diagnosis. It assumed a linear relationship and estimated that the first case of retinopathy occurred 4-7 years before clinical diagnoses. It then referred to another study (6) that followed 30 people from onset of diabetes and found that the first case of retinopathy appeared 5 years after onset. The assumption of 10 years from onset to clinical diagnosis was derived by adding 4-7 years to 5 years.

There are two problems with this line of reasoning. First, the study calculated that the FIRST CASE of retinopathy appears 4"“7 years before clinical diagnosis IN A POPULATION. This has been misunderstood to mean that it appears 4"“7 years before clinical diagnosis IN EACH PERSON. Indeed the actual data in the paper show that (a) there was a very wide variation across persons in time of occurrence of retinopathy; (b) 4 to 7 years was the EARLIEST it can occur; (c) the average time of occurrence was actually 10 and 20 years AFTER clinical diabetes; and (d) even 20 years AFTER clinical diagnosis 20% to 40% of patients still had not developed retinopathy. The second problem is that in the follow-up of 30 people (6), 5 years was the SHORTEST time from onset to retinopathy. In all the other cases it was much longer than that, or not at all. Specifically over a minimum of ten years of follow-up only 8 of the 30 people developed retinopathy, with the eighth case appearing about 14 years after onset, and 22 out of 30 still not showing signs of retinopathy after the longest follow-up. The second study Engelgau cites to support a fixed 10-year assumption (4) was very similar in design to the first, used the 10-year follow-up study of 30 people (6) in the same way, reported very similar results, and has been misunderstood in the same way. Thus the main conclusions to be drawn from the first two papers are that people vary widely in the appearance of retinopathy, retinopathy is not a good indicator of either the onset or clinical diagnosis of diabetes, and the prevalence of retinopathy in a population tells us virtually nothing about the rate of progression of glycemia in individuals.

The third paper (5) does provide information on the progression of glycemia and contains some important results, but it does not address the progression of diabetes from onset to clinical diagnosis, and what it does describe supports a much slower rate of glycemic progression than assumed by Engelgau's group. Specifically, this study reported the progression of FPG from normal glucose tolerance (NGT) and impaired glucose tolerance (IGT) TO onset of diabetes; not the progression FROM onset TO clinical diabetes. It showed that IN PEOPLE WHO PROGRESS from NGT or IGT to diabetes, there was a sharp increase in FPG. But even if we focus on people who were closest to the onset of diabetes, those with IGT, (a) this was what happened before onset, not after it; (b) there was a very wide range in rates of progression across individuals (from an increase of about 15 mg/dL/year in 30% who progressed, to an actual regression from IGT to NGT in 43% of people); (c) the rapid increase seen in the subset of people who progressed does not apply to the entire group, 70% of whom did not progress; and (d) the AVERAGE rate of increase was about half the rate implied by a 10-year assumption. Thus this study contradicts an assumption that everyone progresses at the same rate, and fails to support even an average progression time from onset to clinical diagnosis of 10 years.

So how rapidly does glycemia progress from onset to clinical diagnosis? We based our analysis on the rates of progression actually seen in the placebo or conservative management groups of the clinical trials most pertinent to this analysis "“ the Diabetes Prevention Program (DPP) (7) and the UKPDS (8 9). Specifically, in the Archimedes model the progression of diabetes is different for each person, with the average rate of increase in FPG matching almost exactly the average rates seen in people before the onset of diabetes (as reported in the DPP), and after the onset of diabetes (as reported in the UKPDS). For details, see the online appendix to our paper (10), especially Figures 3 and 4.

Thus the progression of glycemia in our analysis does indeed differ from the 10-year fixed progression time assumed by Herman et al and Engelgau, but we stand by our representation of glycemic progression as matching the actual rates seen in the DPP and UKPDS, and we point out that any other assumption would be in conflict with the results of those trials. We agree that this difference in assumptions about glycemic progression is an important factor that helps explain the differences in our results. But we also stress this is not the only potentially important difference in our models. Many others that can help explain our different results are described the online appendix to our paper.

We are happy to address other questions raised by Engelgau. Concerning the prevalence of retinopathy estimated by the Archimedes model for people in the UK-based CARDS trial (11): it was not a prediction, but a calculation of the prevalence of retinopathy in a US population (based on NHANES) who meet the inclusion criteria for the CARDS trial. The difference between it (15%) and CARDS (30%) reflects differences in the UK and US populations. Conveniently, the existence of different retinopathy rates in different populations is well documented in the first paper cited by Engelgau (3); it reports a range of prevalence rates from 7% to 29% at time of clinical diagnosis in different populations. Concerning the calculation of CARDS results by Engelgau's group: their calculation was not blinded, but was conducted after the CARDS results had been publicly reported. Furthermore, the two models were not equally close to the real CARDS results; the truly blinded predictions of the Archimedes model were much closer. For the actual results see our online appendix.

Concerning the disclosure and review of the Archimedes model, we can assure readers that the Annals' review process was very rigorous, involving several rounds of questions and submission of the actual equations. Given the rigor of the Annals' process and that of other journals (12 13 14), the Archimedes model has certainly met the test of "peer review". For readers who want to study the inside of the model on their own, a Technical Report with the equations is available through our website (15). We acknowledge but do not apologize for the fact that the Archimedes model uses a higher level of mathematics than Markov models. As with all models, our model's "transparency" versus "opacity" will depend on the mathematical background of each reader. But in the end what matters is whether the model works, in the sense of accurately reproducing real events. Our extensive efforts to base our model on appropriate sources and validate it against a wide spectrum of major clinical trials relating to diabetes and its complications have been published (14) and are summarized in the online appendix and Technical Report to our paper.

David M Eddy MD PhD Leonard Schlessinger PhD Richard Kahn PhD

1. United Kingdom Prospective Diabetes Study Group. United Kingdom Prospective Diabetes Study (UKPDS 13) Relative Efficacy Of Randomly Allocation To Diet, Sulphonylurea, Insulin Or Metformin In Patients With Newly Diagnosed And Non-Insulin Dependent Diabetes Followed For 3 Years. BMJ 1995; 310;83-8

2. Herman WH, Hoerger TJ, Brandle M, Hicks K, Sorenson, S, Zhang P, Hamman RF, Ackerman RT, Englegau MM, Ratner RE, for the Diabetes Prevention Program Research Group. The Cost-Effectiveness of Lifestyle Modification or Metformin in Preventing Type 2 Diabetes in Adults with Impaired Glucose Tolerance, Annals of Internal Medicine. 2005;142:323-332.

3. Harris MI, Klein R, Welborn TA, Knuiman MW. Onset of NIDDM occurs at least 4-7 years before clinical diagnosis. Diabetes Care 1992;15:815- 819.

4. Thompson TJ, Engelgau MM, Hagazy M, Ali MM, Sous ES, Badran A, Herman WH. The onset of NIDDM and its relationship to clinical diagnosis in Egypotian adults. Diabetic Medicine, 1996; 13:337-340.

5. Ferrannini E, Nannipieri M, Williams K, Gonzales C, Haffner S, Stern M. Model of onset of type 2 diabetes from normal or impaired glucose tolerance. Diabetes, 2004;53:160-165.

6. Jarrett RJ. Duration of non-insulin dependent diabetes and development of retinopathy: analysis of possible risk factors. Diabetic Medicine 1986;3:261-263

7. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin New Engl J Med. 2002;356:393-402.

8. 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;352:837-852.

9. Colagiuri S, Cull CA, Holman RR; for the UKPDS Group. Are lower fasting plasma glucose levels at diagnosis of type 2 diabetes associated with improved outcomes? UKPDS Study 61. Diabetes Care. 2002;25:1410-1417.

10. Eddy DM, Schlessinger L, Kahn R, Clinical outcomes and cost- effectiveness of strategies for managing people at high risk for diabetes. Ann Int Med. 2005;143-251-264.

11. Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HA, Livingstone SJ, Thomason MJ, Mackness MI, Charlton-Menys V, Fuller JH; CARDS investigators. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicenter randomized placebo-controlled trial. Lancet. 2004;364:685-96.

12. Schlessinger L, Eddy DM. Archimedes: A new model for simulating health care systems: the mathematical formulation. J Biomedical Informatics. 2002;35:37-50

13. Eddy DM, Schlessinger L. Archimedes: a trial-validated model of diabetes, Diabetes Care. 2003;26:3093-3101

14. Eddy DM, Schlessinger L, Validation of the Archimedes diabetes model, Diabetes Care. 2003;26:3102-3110

15. www.archimedesmodel.com

Conflict of Interest:

None declared

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