### Commitment

Figure 4A and B shows the effect of committed care on the 30-year risk of a complication faced by a person today with diabetes and on the number of outcomes that would occur over 30 years, respectively. For a typical person today with diabetes, the risk of a heart attack would decline by 57%, and for the population of people with diabetes, 5.13 million fewer events would occur. Also, the death rate would decline by 16%, and 2.8 million person-years of life would be added. On average, each person with diabetes would live an additional 2.2 years (data not shown). Figure 4C shows that over 30 years this more realistic scenario would also save approximately $184 billion in costs associated with diabetes and CVD. Since the performance described in committed care would require more resources than currently used, we asked what would be the net cost (increase or decrease) of committed care if an additional$100/year were spent on office visits and an additional $300 were spent for each drug used to better control A1C, blood pressure, and lipids up to a maximum of$1,500 to control all three parameters. Figure 5 shows the net medical cost of diabetes and its complications per person per year allowing for these additional treatment costs. As the figure shows, lowering A1C to <7% by itself, or lowering A1C to <7% and lowering blood pressure to <130/80 mmHg, actually saves money. Achieving both of those goals and all the others in 80% of people with diabetes is cost neutral.

The effect of providing a polypill in addition to usual care is shown in Fig. 6. Over 30 years the polypill would reduce the number of MIs by 51%, ESRD by 21%, eye complications by 34%, and deaths by 10%. The polypill did cause in a modest increase in the number of amputations (from 510,820 to 516,592) and strokes (from 2,580,074 to 2,857,129) because it kept people alive for a longer time. Overall, if 80% of the diabetic population took a polypill without otherwise changing their current levels of care (including no change in the frequency of office visits, drugs taken, and tests performed) there would be ∼7.3 million fewer serious complications over the next 30 years.

We also calculated the financial impact of the polypill. Figure 7 shows that if the cost of the polypill were $500/year, our health care system would begin to save money by year 10. At$400/year the polypill becomes cost-saving by the fifth year. Of note, Kaiser Permanente in Southern California adopted the polypill concept at an annual cost of about $100 (personal communication), which suggests that this therapy could be very cost-saving very soon after implementation. In this analysis we show that over$300 million could be saved annually by curing diabetes in those affected by the disease today. Such savings suggest that investing far more in diabetes research to develop a cure should be a high priority for the Federal Government and private organizations. Surely the size of the investment should be commensurate with the risk that diabetes represents to our citizens. If not stopped, the diabetes epidemic has the likely potential to overwhelm our health care system and to undermine our economy.

In the absence of a cure, we show that improvements in diabetes care can also have a dramatic effect on reducing the rate of complications. Other studies have also documented the enormous burden of diabetes complications (2,12), and there is ample evidence that adherence to nationally recommended guidelines can be greatly beneficial (1317). Our analysis indicates that in an ideal scenario where all the goals of therapy are achieved in every person who has diabetes, we could expect a marked reduction in medical expenditures and a reduction in the complications related to diabetes. Achieving similar performance levels but in fewer people also offers a great return in lives saved and complications avoided as well as reduced medical expenditures (Fig. 4). In other words, the increased cost to bring 80% of those with diabetes to all treatment goals could be offset by the savings that result from the prevention of complications. Moreover, even if the cost of care increases modestly in order to have performance improve, millions of lives can be saved and serious complications prevented with no increase in current net medical costs (Fig. 5). If, as almost certainly will occur, prevention of life-threatening complications increases productivity and length of participation in the workforce, then the return on investment from optimal care of diabetes becomes even greater.

In other words, a reasonable increase in cost to bring 80% of those with diabetes to all the goals of therapy would be more than offset by the savings that result from the prevention of diabetes complications.

Finally, we explored the impact of a more simplified care delivery scheme. That is, by administering a “polypill” comprised of generic glucose, LDL cholesterol–and blood pressure–lowering drugs along with low-dose aspirin, given in addition to current usual care, we observed a dramatic reduction in costs and complications. A similar cocktail has been proposed by others (19) and was found to be cost-effective in reducing the burden of CVD (20). In the present study, administering a polypill not only holds promise of substantially reducing the medical burden of diabetes, but is also very likely to save money within a few short years.

There are many important caveats to our study. First, there is no way to confirm the accuracy of the results we obtained, or the results of any modeling study, when the predictions have not been confirmed empirically in clinical trials. Although we used a highly detailed, extensively validated mathematical model that simulates human physiology, a variety of diseases, and their treatments and health care systems, all to a very high degree, clinical research is needed to confirm our findings.

Second, we intentionally designed our analysis to derive estimates of benefit knowing that there are many variables that cannot be quantified or might be considered. We attempted to provide a framework for what the future holds and excluded a detailed sensitivity analysis that could encompass a wide-range of possibilities. For example, in our analysis of “cure” we do not know the mechanism, delivery, or cost of such a cure, nor do we know if a cure for diabetes is associated with any ill-toward side effect that may impose its own financial costs.

Third, in our analysis of the impact of “committed” care, achieving all the goals of therapy in a very high proportion of a population with diabetes is currently not routinely feasible, and even if it were now possible, the performance levels we assumed would be difficult to sustain over 30 years. There are, however, health care plans and practices that have achieved these performance levels in a smaller proportion of patients, which provides encouragement that high quality diabetes care can be provided. To actually achieve nationwide, the performance levels we studied may require a systematic structural change in care delivery that addresses the key features of chronic disease versus our current system, which is orientated far more toward the delivery of acute episodic care.

Fourth, we used the actual costs and protocols of a single, relatively efficient system (Kaiser Permanente, Southern California); these costs may be different in other settings. Visit frequency, tests performed, and medications used may also vary to achieve the desired results, and other settings may have a more expensive cost structure. Conversely, while we showed only the benefits of improved performance as they relate to diabetes and CVD, additional benefits would likely be seen. For example, smoking cessation impacts the incidence of lung cancer, weight loss affects the incidence of a wide variety of diseases, and aspirin may decrease the incidence of certain cancers. Also, we did not factor the myriad of indirect benefits that would accrue with improved diabetes care such as improved workplace productivity. The Archimedes model is primarily based on and validated against clinical trial data. The extent to which predictions of the model reflect those in diverse populations is not known.

Can any of the treatments we studied become reality? We believe that first and foremost America must invest heavily in diabetes research. Second, we must provide an environment to create and sustain heath care systems whose structure insures that every person with diabetes receives the best possible care. We must renew our commitment to people with diabetes, acknowledging that current performance is not acceptable and that we will improve. Finally, we must be willing to explore novel approaches to therapy, such as the polypill, which offers great promise of being an inexpensive yet very effective approach to achieving the results we want.

The treatment of diabetes is neither complex nor particularly difficult. A wide array of drugs and devices are available, and the goals of therapy are supported by a rich evidence base. But like all chronic diseases, diabetes requires the active involvement of the patient, a support system, and an engaged clinical team. It also requires regular follow-up visits, careful monitoring, and attention to a wide variety of risk factors and possible complications.

A world without diabetes and its complications is certainly possible, and the appropriate care for people with diabetes is within our grasp. Both, however, require unrelenting commitment and resolve (21). We can succeed.

Figure 1—

The impact of a cure for diabetes. A: Subsequent 30-year per-person risk of complications for a typical person with diabetes alive today. B: Subsequent 30-year per-person risk of complications for a person with pre-diabetes (FPG >100 mg/dl) alive today. C: Subsequent total number of events that will occur over 30 years in people alive with diabetes. D: Subsequent total number of events over 30 years in people alive with pre-diabetes.

Figure 1—

The impact of a cure for diabetes. A: Subsequent 30-year per-person risk of complications for a typical person with diabetes alive today. B: Subsequent 30-year per-person risk of complications for a person with pre-diabetes (FPG >100 mg/dl) alive today. C: Subsequent total number of events that will occur over 30 years in people alive with diabetes. D: Subsequent total number of events over 30 years in people alive with pre-diabetes.

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Figure 2—

The impact of a cure on the direct costs of diabetes. A: Subsequent 30-year total medical costs for people with diabetes. B: Subsequent 30-year total medical costs for people with pre-diabetes.

Figure 2—

The impact of a cure on the direct costs of diabetes. A: Subsequent 30-year total medical costs for people with diabetes. B: Subsequent 30-year total medical costs for people with pre-diabetes.

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Figure 3—

The impact of “ideal” (or optimal) care on people with diabetes. Optimal care is 100% compliance and performance for 100% of the population. A: 30-year per-person risk of complications. B: 30-year total number of complications. C: 30-year total medical costs.

Figure 3—

The impact of “ideal” (or optimal) care on people with diabetes. Optimal care is 100% compliance and performance for 100% of the population. A: 30-year per-person risk of complications. B: 30-year total number of complications. C: 30-year total medical costs.

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Figure 4—

The impact of “committed” on people with diabetes. Committed care is 100% compliance and performance on 80% of the population. A: 30-year per-person risk of complications. B: 30-year total number of complications. C: 30-year total medical costs.

Figure 4—

The impact of “committed” on people with diabetes. Committed care is 100% compliance and performance on 80% of the population. A: 30-year per-person risk of complications. B: 30-year total number of complications. C: 30-year total medical costs.

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Figure 5—

Net cost per person of a cascade of treatments on the medical cost of diabetes and its complications over a 30-year period in individuals receiving “committed care.” All costs assume an additional $100/year in office visits and$300/year in drug costs for each variable controlled, to a maximum of $1,500/person. Aspirin was assumed to be free. BP, blood pressure; TG, triglycerides. Figure 5— Net cost per person of a cascade of treatments on the medical cost of diabetes and its complications over a 30-year period in individuals receiving “committed care.” All costs assume an additional$100/year in office visits and $300/year in drug costs for each variable controlled, to a maximum of$1,500/person. Aspirin was assumed to be free. BP, blood pressure; TG, triglycerides.

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Figure 6—

Impact of a polypill taken by 80% of the population with diabetes who are receiving usual care. Data shown are the 30-year total number of events.

Figure 6—

Impact of a polypill taken by 80% of the population with diabetes who are receiving usual care. Data shown are the 30-year total number of events.

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Figure 7—

Net average annual savings per person from treating 80% of people with diabetes with a polypill.

Figure 7—

Net average annual savings per person from treating 80% of people with diabetes with a polypill.

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This study was supported in part by the Mayo Clinic and from a generous educational grant from Novo Nordisk.

R.A.R. is the Earl and Annette R. McDonough Professor of Medicine.

The authors thank Drs. Steve Smith and Nilay Shah for their helpful editorial suggestions.

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This article was adapted from the Presidential Address delivered by Robert Rizza, MD, at the 67th Annual Meeting and Scientific Sessions of the American Diabetes Association, Chicago, Illinois, 22–26 June 2006.

The views expressed in this article represent those of the authors and do not necessarily represent those of the American Diabetes Association.