Open Access

Health and economic impact of combining metformin with nateglinide to achieve glycemic control: Comparison of the lifetime costs of complications in the U.K

  • Alexandra J Ward1Email author,
  • Maribel Salas1,
  • J Jaime Caro1, 2 and
  • David Owens3
Cost Effectiveness and Resource Allocation20042:2

DOI: 10.1186/1478-7547-2-2

Received: 09 June 2003

Accepted: 15 April 2004

Published: 15 April 2004

Abstract

Background

To reduce the likelihood of complications in persons with type 2 diabetes, it is critical to control hyperglycaemia. Monotherapy with metformin or insulin secretagogues may fail to sustain control after an initial reduction in glycemic levels. Thus, combining metformin with other agents is frequently necessary. These analyses model the potential long-term economic and health impact of using combination therapy to improve glycemic control.

Methods

An existing model that simulates the long-term course of type 2 diabetes in relation to glycosylated haemoglobin (HbA1c) and post-prandial glucose (PPG) was used to compare the combination of nateglinide with metformin to monotherapy with metformin. Complication rates were estimated for major diabetes-related complications (macrovascular and microvascular) based on existing epidemiologic studies and clinical trial data. Utilities and costs were estimated using data collected in the United Kingdom Prospective Diabetes Study (UKPDS). Survival, life years gained (LYG), quality-adjusted life years (QALY), complication rates and associated costs were estimated. Costs were discounted at 6% and benefits at 1.5% per year.

Results

Combination therapy was predicted to reduce complication rates and associated costs compared with metformin. Survival increased by 0.39 (0.32 discounted) and QALY by 0.46 years (0.37 discounted) implying costs of £6,772 per discounted LYG and £5,609 per discounted QALY. Sensitivity analyses showed the results to be consistent over broad ranges.

Conclusion

Although drug treatment costs are increased by combination therapy, this cost is expected to be partially offset by a reduction in the costs of treating long-term diabetes complications.

Background

Type 2 diabetes is a prevalent disease with complications that cause substantial financial burden [1]. Improving glycemic control can influence the prognosis for patients with type 2 diabetes as it reduces the risk of developing microvascular complications (nephropathy, neuropathy and retinopathy) [2]. Recent guidelines from the National Institute of Clinical Excellence (NICE) recommend the initial use of diet and exercise and, when these fail to maintain glycemic control, metformin should be prescribed [3]. Monotherapy with any treatment, however, is often unable to sustain target HbA1c levels of 6.5–7.5% in the majority of patients. They are therefore expected to require additional therapy within six years [4].

Sulphonylureas have been frequently used in combination with metformin, but are not always appropriate choices as these may cause weight gain and increase the risk of hypoglycaemia [3]. The development of newer insulin secretagogues, such as nateglinide, provides physicians with an alternative to sulphonylureas when selecting the optimal combination of oral agents for an individual patient. Nateglinide (120 mg three times per day) is advantageous over other agents in that it helps to control postprandial glucose (PPG) levels, along with glycosylated hemoglobin, and also can be used in combination with metformin (500 mg three times per day) [5]. The use of combination therapy subsequent to the failure of monotherapy helps some patients to achieve the recommend levels of glycemic control. However, use of any combination is clearly also associated with an increased cost compared with metformin as monotherapy.

The purpose of this study was to estimate the potential long-term health and economic impact of adding nateglinide to metformin in order to improve glycemic control and thereby reduce complication rates. Together with the clinical data on the therapeutic efficacy of combination therapy, these economic analyses facilitate assessment of the long-term cost-effectiveness from the perspective of the health care system, of using this combination to achieve improved glycemic control.

Methods

Model framework

This model was developed to simulate the lifetime risk of developing diabetes-related complications rates (microvascular and macrovascular) in a cohort of patients diagnosed with type 2 diabetes [6, 7] (Figure 1). In this updated version of the model, both the level of HbA1c (glycosylated haemoglobin) and two-hour postprandial glucose (PPG) define the degree of glycemic control [8, 9]. Each year of remaining life is simulated for all the patients in the cohort and during each cycle, the patient is exposed to the risks of developing each type of complication. These risks are determined from the degree of glycemic control, as well as other known risk factors, such as duration of diabetes.
Figure 1

Schematic representation of model (Reprinted with permission from Can J Diabetes. 2003; 27(1): 33–41).

The microvascular complications (nephropathy, retinopathy, and neuropathy) have several stages through which each patient can progress. The most severe stages for the microvascular complications are end stage renal disease, blindness or amputations. The stages of a complication are assumed irreversible – only progression to more severe stages is possible. Complications such as hypoglycaemia and foot ulcer were assumed to resolve in the course of each cycle of one year. For the purpose of this model, macrovascular complications (stroke and myocardial infarction) were considered as finite events, rather than progressive conditions.

Each simulated patient had clinical characteristics that were determined by the input distributions specified. Using a Monte Carlo technique, each patient in the cohort was assigned gender, race and age. The assignment of cholesterol level, smoking status, body mass index and systolic blood pressure was then determined using the distributions and associations observed amongst patients with type 2 diabetes [1012].

For thirty annual cycles, the model checks each patient who has survived to that point, and updates the age, duration of disease and HbA1c level. Over each cycle, the estimated risks of developing a new complication or progressing to the next stage of an established one are assigned to each simulated patient in the cohort. During a pre-model period of seven years, the patients were allowed to accumulate complications but costs from managing these complications are not considered in the comparisons.

The model was assessed for face validity by clinical experts and health authorities. Previous analyses using the model have been evaluated by peer review [69]. Source data and other independently obtained results were used as comparisons to determine predictive validity [2, 13]. Model results for relative risk over 10 years for all-cause mortality and for microvascular disease and retinopathy at 12 years were consistent with UKPDS patients in intensive and conventional treatment groups.

Risk estimates

The risk of death in this updated model was linked to both PPG and HbA1c levels. Weibull functions were derived from the Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe (DECODE) study [14, 15] – and estimates were based on the patients' age, gender, systolic blood pressure, total cholesterol, body mass index, smoking status, and PPG level. As in the original model, the risk of death was also assessed from the age- and gender-dependent mortality for patients diagnosed with type 2 diabetes [16], with an adjustment if nephropathy develops [17, 18]. The higher of these three death risk estimates in each model cycle was applied.

The estimates for microvascular complications (nephropathy, retinopathy, and neuropathy) were determined from the available epidemiological studies [1921] and the risk gradients observed in the Diabetes Control and Complications Trial (DCCT) were assumed to apply to type 2 diabetes [22], an accepted assumption [2325] confirmed by the UKPDS [2]. The risks of each microvascular complication are estimated by adjusting each according to the patient's HbA1c level at a specific point in time (risk = 1 - e -λ-t, where λ = λbHr β, and Hr is the HbA1c value relative to a standard and β is a complication-specific coefficient) [16, 26]. The base hazard for a complication depends on factors such as duration of diabetes, race and for the retinopathy module, for example, also the probability of detection and treatment.

Evidence has recently been published that indicates PPG is an independent predictor of the occurrence of macrovascular complications, as well as of mortality [14, 27, 28]. In this updated model, the risk of stroke or myocardial infarction was estimated using Weibull functions derived from the DECODE study [15]. The risk equations derived from the DECODE study include established risk factors for macrovascular disease such as age, gender, systolic blood pressure, total cholesterol, body mass index, smoking status, as well as PPG level.

Costs

For each complication, the direct medical costs were estimated for the immediate impact of the event (costs arising in the year the event occurs) and the subsequent impact of the complication (costs accrued in years subsequent to the year of the event). Clarke et al combined resource use data collected from the UKPDS with cost estimates for these services, and published regression equations for estimating the cost of major complications [29]. The annual hospital in-patient costs, and non-hospital costs (general practioners, nurses, podiatrists, opticians, dieticians, hospital outpatient clinics) were estimated using these regression equations for the event year and subsequent years. As the inpatient costs were estimated for myocardial infarction, stroke, blindness, or an amputation. The inpatient costs of less severe stages of these complications were not included in these estimates the cost estimates are quite conservative. All complication costs are expressed in 1999 Great Britain Pounds (£1 GBP = $1.7 USD = €1.4 Euros). It should be noted that the cost of end stage renal disease was estimated based on data from 1996 [30]. We elected not to inflate this cost, however, as the applicability of general inflation rates to something as specialized as the management of end stage renal disease is fraught with inaccuracy and this was the most expensive complication (£21,456 per year).

The drug treatment cost estimates conservatively assumed full compliance with the treatment. The daily cost for metformin (1500 mg per day) was £0.07 [31], and £0.87 for the combination of nateglinide (360 mg/day = £0.80) with metformin (1500 mg per day) [31].

Analyses

The distributions of HbA1c and PPG at the beginning of the model period, as well as the effects of each treatment regimen were obtained from a clinical trial assessing the efficacy of combining nateglinide (360 mg/day) with metformin (1500 mg per day) compared with metformin alone [5] (Table 1). The mean HbA1c at baseline was 8.4%, at the trial end point the HbA1c was reduced with both metformin and for the combination (-0.8%, and -1.5% respectively), as was the PPG level (-0.9, and -2.3 respectively).
Table 1

Clinical characteristics of simulated cohort

Parameter

Value

Age (years)

 

   Mean

58

   Range

29–88

Gender (% Female)

38%

Race

 

   Caucasian

92%

   Afro-Caribbean

4%

   Asian

4%

Initial resulting HbA1c level (mean)

 

   Metformin monotherapy

7.6%

   Combination therapy

6.9%

HbA1c annual upward drift

0.15%

After processing each cohort of 10,000 patients over thirty years, the model provides estimates of the mean survival time, the frequency of each type of complication, and the mean accumulated complication and treatment costs per patient. Survival time is also weighted by the quality of life; the utility assigned depending on the complications present. The utilities assigned were as follows; amputation 0.50, stroke 0.62, blindness 0.71 and myocardial infarction 0.73 [32], end stage renal disease 0.59 [33]. The cost per life year gained (LYG) and cost per quality adjusted life year (QALY) was determined. Consistent with NICE recommendations, costs were discounted at 6% and benefits at 1.5% [34]. Sensitivity analyses were conducted on model parameters and uncertainty in the base case estimates was examined using the bootstrap technique with 250 model replications, and 1000 re-samples from the results of these simulations.

Results

Our analyses simulated a cohort of patients treated with metformin and estimated the mean survival time to be 13.5 years. Over their lifetime, microvascular complications were frequent – retinopathy was the most common affecting over a quarter of the patients, as well as foot ulcers and microalbuminuria (Table 2). The model predicted mean lifetime discounted costs per patient of about five thousand pounds (Table 3). Macrovascular disease was common (Table 2) and accounted for about 40% of the lifetime costs due to complications, with myocardial infarction being the slightly larger component of the macrovascular costs (63%). Amputation comprised one third of the cost estimate for management of microvascular complications.
Table 2

Frequency of microvascular and macrovascular complications by treatment

Complication

Metformin (/100 pt)

Combination (/100 pt)

Improvement

   

Absolute

Relative (%)

Nephropathy

    

Microalbuminuria

21.1

18.1

3.0

14.2

Gross proteinuria

18.8

13.4

5.4

28.7

End stage renal disease

5.9

4.4

1.5

25.4

Retinopathy

    

Background retinopathy

30.7

23.7

7.0

22.7

Macular edema:

    

Detected

25.4

20.6

4.7

18.7

Photocoagulated

24.3

19.9

4.5

18.4

Proliferative retinopathy:

    

detected

12.3

7.9

4.5

36.3

photocoagulated

12.1

7.7

4.4

36.3

Blindness

9.4

8.0

1.4

14.9

Neuropathy

    

Foot ulcer

21.1

16.3

4.8

22.7

Neuropathy

12.7

9.6

3.2

24.8

1st Lower-extremity amputation

9.0

7.5

1.5

16.5

2nd Lower-extremity amputation

5.1

4.3

0.7

14.6

Macrovascular Disease

    

Myocardial infarction

15.0

14.6

0.4

2.4

Stroke

13.7

13.4

0.3

1.9

Table 3

Health benefits and costs for metformin and the combination of metformin with nateglinide

 

Metformin

Combination

Difference

Cumulative cost (mean per patient)

   

   Complications

£3,548

£3,084

£-464

   Total

£5,093

£7,159

£2,066

Survival (mean, years)

   

   Life years (discounted)

13.5 (11.7)

13.9 (12.1)

0.39 (0.32)

   Quality Adjusted (discounted)

12.2 (10.7)

12.6 (11.0)

0.46 (0.37)

Cost-effectiveness

   

   Cost per LYG (discounted LYG)

  

£5,403 (6,772)

   Cost per QALY (discounted QALY)

  

£4,500 (5,609)

LYG = Life Year Gained QALY = Quality Adjusted Life Year

Base case

The improvement in glycemic control, in terms of both the HbA1c and the PPG, expected with the combination nateglinide with metformin is estimated to increase survival on average 0.39 years per patient (0.32 discounted years) or 0.46 (0.37 discounted) QALY (Table 3). Moreover, complications were expected to occur less frequently, or at least progress more slowly (Table 2).

Combination therapy is expected to reduce the frequency of complications and prolong survival, but also increase the average costs by an average of £2,066 per patient. To determine the impact of the nateglinide-metformin combination on the cost of managing complications, the difference in mean cost between metformin alone and the combination group was determined (Table 3). Thus, savings of £464 were estimated regarding the lifetime cost of managing complications. These arise mainly from a reduction in the costs of treating end stage renal disease (72%) and neuropathy (19%). The increase in the treatment costs due to combination therapy are therefore predicted to be partially offset by this reduction in the cost of managing complications, leaving an increment of £2,066 in the lifetime costs per patient (Table 3). This translates into a cost-effectiveness ratio of £6,772 (95%CI: £6,134 to 7,464) per additional discounted year of life, and £5,609 per discounted QALY.

Sensitivity analyses

The model inputs were varied to reflect different scenarios and Table 4 shows the impact on the estimates. The degree of upward drift of HbA1c and initial HbA1c were influential parameters. If a population with higher glycemic levels at baseline is modeled, a larger proportion of the cohort develops severe complications on metformin alone. Varying the discount rate had a major effect on the cost-effectiveness results.
Table 4

Sensitivity analysis

  

Change in Outcome

CER

Parameter

Net Cost

LYG

QALY

Cost/LYG

Cost/QALY

Base values

£2,066

0.32

0.37

£6,772

£5,609

Age (mean)

     

46.5 years

£2,531

0.34

0.45

£7,476

£5,589

82.5 years

£718

0.14

0.12

£5,303

£5,804

Cost of complications

     

+20%

£1,973

0.32

0.37

£6,213

£5,357

-20%

£2,159

0.32

0.37

£6,799

£5,861

Duration of disease before oral agent prescribed

     

5 years

£2,101

0.27

0.33

£7,680

£6,320

10 years

£1,971

0.31

0.35

£6,260

£5,553

Utilities

     

+20%

£2,066

0.32

0.36

£6,506

£5,807

-20%

£2,066

0.32

0.38

£6,506

£5,426

Race

     

100% Caucasian

£2,105

0.31

0.36

£6,686

£5,771

HbA1c level

     

HbA1c before prescription = 9.4%

£1,782

0.37

0.42

£4,784

£4,287

Metformin = 8.6%

     

Combination = 7.9%

     

HbA1c before prescription = 7.9%

£2,184

0.28

0.34

£7,904

£6,516

Metformin = 7.1%

     

Combination = 6.4%

     

HbA1c upward drift

     

Metformin = 1.5%; Combination = 0%

£1,478

0.54

0.65

£2,761

£2,272

Metformin = 0%; Combination = 0%

£2,307

0.28

0.31

£8,336

£7,338

HbA1c drift delay

     

Metformin = 0 years; Combination = 1 year

£1,987

0.35

0.41

£5,715

£4,870

Discount

     

Cost = 3%; Benefit = 3%

£2,420

0.26

0.30

£9,319

£8,058

Cost = 6%; Benefit = 6%

£2,066

0.18

0.21

£11,369

£9,888

Cost = 6%; Benefit = 0%

£2,066

0.39

0.46

£5,237

£4,500

Varying the efficacy of the combination of nateglinide and metformin on PPG values had a minor effect, a 50% reduction in efficacy led to a 3% increase in macrovascular disease related costs. Varying the impact of the combination of nateglinide and metformin treatment on HbA1c values had a larger impact on the total cost predicted. Decreasing the efficacy by 10%, or 25% led to total cost increases of 3%, and 9%, respectively. Also a 10% increase in efficacy led to a 4% decrease in costs.

Discussion

Improving glycemic control using combination therapy will inevitably increase drug treatment costs when compared with monotherapy. However, the reduction in HbA1c and PPG levels when treating patients with type 2 diabetes with a combination of nateglinide and metformin has the potential to translate into reduced complication rates. Long term therefore, combination treatment is likely to result in substantial offsets in overall costs. Thus, the additional glycemic control is achieved at a rate of £6,772 per year of additional life, an estimate generally considered cost-effective [35].

These results are consistent with the evidence emerging from the UK. Diabetes-related complications have been shown in several UK studies to require expensive medical interventions, frequently provided in a hospital inpatient setting [3639]. The UKPDS demonstrated that keeping glucose levels near normal decreased the incidence of microvascular complications over ten years [40]. In addition, cost-effectiveness analyses based on the UKPDS results indicate the costs of managing complications would be expected to be reduced, [41, 42] and, specifically, intensive blood glucose control with metformin is predicted to result in lower complications costs amongst overweight patients [42]. The DCCT results showed improved glycemic control can lower microvascular complication rates in patients with type 1 diabetes, and one key assumption of this model is that these rates also apply to type 2 diabetes. This assumption was demonstrated to be tenable by similar findings in the UKPDS [2, 3]. This model predicts comparable results to those of the UKPDS patients in the intensive and conventional treatment groups in terms of relative risk over ten years for microvascular disease or retinopathy at 12 years.

The economic implications of combination therapy depend to some extent on the characteristics of the cohort analyzed. For example, the sensitivity analyses illustrate that greater savings are predicted for patients diagnosed when they are young, with longer duration of disease and poorer glycemic control initially. These characteristics tend to identify patients at higher risk of developing complications later on.

Macrovascular disease is predicted to be the major component of the costs accounting for over one third of the costs accrued over a lifetime from managing diabetes related complications. This is of particular importance as these complications tend to arise earlier in the course of the disease than those that are microvascular in nature, and are the leading cause of death [43, 44]. Thus, from both the clinical and economic perspectives, it is important that in addition to glycemic control, any risk factors for cardiovascular disease that are known to be modifiable are managed such as smoking cessation, reducing obesity, high blood pressure and hypercholesterolaemia [3, 45].

The equations developed for predicting the risk of stroke and of myocardial infarction included the PPG level. These predictions are based on the results of the DECODE study that investigated the prevalence of macrovascular disease and mortality in Europe [14, 28, 46]. Thus, the assumption in the model that reducing PPG levels will reduce the risk of macrovascular disease remains to be proven conclusively[3, 47].

The long-term predictions were based on the efficacy of combining nateglinide with metformin demonstrated in clinical trials [5]. Even though these analyses were based on the efficacy observed in a randomized, controlled trial, it was necessary to make some assumptions about long-term glycemic control. Given the lack of specific data on the combination over longer timeframes, it was assumed that after the initial improvement in glycemic control, the HbA1c would begin to drift upward as it did with metformin and other hypo glycemic agents employed in the UKPDS [4, 48]. This is a conservative assumption as it is quite possible that with the combination there will be a slower, or at least delayed, upward drift.

The cost inputs for these economic analyses were limited to only the most severe stages of the complications. This was done in order to accord with the estimates' source, the UKPDS. The costs also did not include the less severe stages of the complications (such as gross proteinuria, foot ulcers or photocoagulation). Similarly, the macrovascular costs do not include the management of milder conditions such as angina or transient ischaemic attacks. Thus, the cost estimates are quite conservative implying that the savings are underestimated.

Conclusion

In conclusion, prescribing the combination of nateglinide and metformin for patients who are not maintaining good glycemic control on monotherapy alone should be cost-effective, as the combination is expected to reduce the rates of diabetes-related complications at an acceptable additional cost. Long-term data are needed to confirm these predictions.

Declarations

Authors’ Affiliations

(1)
Caro Research Institute
(2)
Division of General Internal Medicine, McGill University
(3)
Diabetes Research Unit, Llandough Hospital

References

  1. Bagust A, Hopkinson PK, Maslove L, Currie CJ: The projected health care burden of Type 2 diabetes in the UK from 2000 to 2060. Diabetic Medicine 2002, 19: 1–5. 10.1046/j.1464-5491.19.s4.2.xPubMedView ArticleGoogle Scholar
  2. UK Prospective Diabetes Study 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: 837–53. 10.1016/S0140-6736(98)07019-6View ArticleGoogle Scholar
  3. McIntosh A, Hutchinson A, Home PD, Brown F, Bruce A, Damerell A, Davis R, Field R, Frost G, Marshall S, Davis R, Roddick J, Tesfayes S, Withers H, Suckling R, Smith S, Griffin S, Kaltenthaler E, Peters J: Clinical guidelines and evidence review for Type 2 diabetes: blood glucose management. Sheffield: Sc HARR, University of Sheffield 2001.Google Scholar
  4. Turner R, Cull C, Frighi V, Holman RR: Glycemic control with diet, sulfonylurea, metformin, or insulin in patients with type 2 diabetes mellitus. Progressive requirement for multiple therapies (UKPDS 49). JAMA 1999, 281: 2005–12. 10.1001/jama.281.21.2005PubMedView ArticleGoogle Scholar
  5. Horton ES, Clinkingbeard C, Gatlin M, Foley J, Mallows S, Shen S: Nateglinide alone and in combination with metformin improves glycemic control by reducing mealtime glucose levels in type 2 diabetes. Diabetes Care 2000, 23: 1660–65.PubMedView ArticleGoogle Scholar
  6. Caro JJ, Klittich WS, Raggio G, Kavanagh P, O'Brien J, Shomphe LA, Flegel KM, Copley-Merriman C, Sigler C: Economic assessment of troglitazone as an adjunct to sulfonylurea therapy in the treatment of type 2 diabetes. Clin Ther 2000, 22: 116–27. 10.1016/S0149-2918(00)87983-7PubMedView ArticleGoogle Scholar
  7. Caro JJ, Ward A, O'Brien J: Lifetime Costs of Complications Resulting from Type 2 Diabetes in the U.S. Diabetes Care 2002, 25: 476–81.PubMedView ArticleGoogle Scholar
  8. Salas M, Ward A, Caro J: Health and economic effects of adding nateglinide to metformin to achieve dual control of glycosylated hemoglobin and postprandial glucose levels in a model of type 2 diabetes mellitus. Clin Ther 2002,24(10):1690–705. 10.1016/S0149-2918(02)80072-8PubMedView ArticleGoogle Scholar
  9. Caro JJ, Salas M, Ward AJ, Raggio G, O'Brien JA, Gruger J: Combination therapy for Type 2 Diabetes: What are the potential health and cost implications in Canada? Canadian Journal of Diabetes 2003,27(1):33–41.Google Scholar
  10. Cowie CC, Harris MI: Physical and metabolic characteristics of persons with diabetes. In Diabetes in America 2 Edition National Diabetes Data Group. Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, (NIH publ. No. 95–468 1995, 117–64.Google Scholar
  11. Fujimoto WY: Diabetes in Asian and Pacific Islander Americans. In Diabetes in America 2 Edition National Diabetes Data Group. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, NIH publ. no 95–468 1995, 661–82.Google Scholar
  12. Rewers M, Hamman RF: Risk factors for non-insulin-dependent diabetes. In:Diabetes in America 2 Edition National Diabetes Data Group, National Institutes of Health, NIH publication No 95–1468 1995, 179–220, 619.Google Scholar
  13. Eastman RC, Seibert CW, Harris , Gorden P: Implications of the Diabetes Control and Complications Trial. Diabetes Care 2001, 24: S28-S32.Google Scholar
  14. DECODE Study Group: Glucose tolerance and cardiovascular mortality. Comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med 2001, 161: 397–404. 10.1001/archinte.161.3.397View ArticleGoogle Scholar
  15. Glick H: The potential for CVD prevention by reducing postprandial hyperglycaemia. In Proceedings of the IDEG: Acapulco 2000.Google Scholar
  16. Eastman RC, Javitt JC, Herman WH, Dasbach EJ, Zbrozek AS, Dong F, Manninen D, Garfield SA, Copley-Merriman C, Maier W, Eastman JF, Kotsanos J, Cowie CC, Harris M: Model of complications of NIDDM. I. Model construction and assumptions. Diabetes Care 1997, 20: 725–34.PubMedView ArticleGoogle Scholar
  17. Mogensen CE: Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. N Engl J Med 1984, 310: 356–60.PubMedView ArticleGoogle Scholar
  18. Neil A, Hawkins M, Potok M, Thorogood M, Cohen D, Mann J: A prospective population-based study of microalbuminuria as a predictor of mortality in NIDDM. Diabetes Care 1993, 16: 996–1003.PubMedView ArticleGoogle Scholar
  19. Ballard DJ, Melton LJ, Dwyer MS, Trautmann JC, Chu CP, O'Fallon WM, Palumbo PJ: Risk factors for diabetic retinopathy: a population-based study in Rochester, Minnesota. Diabetes Care 1986, 9: 334–42.PubMedView ArticleGoogle Scholar
  20. Humphrey LL, Palumbo PJ, Butters MA, Hallett JW, Chu CP, O'Fallon M, Ballard DJ: The contribution of non-insulin dependent diabetes to lower extremity amputation in the community. Arch Intern Med 1994, 154: 885–92. 10.1001/archinte.154.8.885PubMedView ArticleGoogle Scholar
  21. Klein R, Klein BE, Moss SE, Davis MD, DeMets DL: The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Arch Ophthalmol 1989, 107: 244–49.PubMedView ArticleGoogle Scholar
  22. The Diabetes Control and Complications Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 1993, 329: 977–86. 10.1056/NEJM199309303291401View ArticleGoogle Scholar
  23. Nathan DM: Long-term complications of diabetes mellitus. N Engl J Med 1993, 328: 1676–85. 10.1056/NEJM199306103282306PubMedView ArticleGoogle Scholar
  24. American Diabetes Association: Implications of the Diabetes Control and Complications Trial. Diabetes Care 2001, 24: S2832.Google Scholar
  25. Pollet RJ, El-Kebbi IM: The applicability and implications of the DCCT to NIDDM. Diabetes Rev 1994, 2: 413–27.Google Scholar
  26. Eastman RC, Javitt JC, Herman WH, Dasbach EJ, Copley-Merriman C, Maier W, Dong F, Manninen D, Zbrozek AS, Kotsanos J, Garfield SA, Harris M: Model of complications of NIDDM. II. Analysis of the health benefits and cost-effectiveness of treating NIDDM with the goal of normoglycemia. Diabetes Care 1997, 20: 735–44.PubMedView ArticleGoogle Scholar
  27. Barrett-Connor E, Ferrara A: Isolated postchallenge hyperglycemia and the risk of fatal cardiovascular disease in older women and men: the Rancho Bernardo Study. Diabetes Care 1998, 21: 1236–39.PubMedView ArticleGoogle Scholar
  28. The DECODE Study Group on behalf of the European Diabetes EpidemiologyGroup: Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria. Lancet 1999, 354: 617–21. 10.1016/S0140-6736(98)12131-1View ArticleGoogle Scholar
  29. Clarke P, Gray A, Legood R, Briggs A, Holman R: The impact of diabetes-related complications on healthcare costs: results from the United Kingdom Prospective Diabetes Study (UKPD Study No. 65). Diabetic Medicine 2003, 20: 442–450. 10.1046/j.1464-5491.2003.00972.xPubMedView ArticleGoogle Scholar
  30. Lamping DL, Constantinovici N, Roderick P, Normand C, Henderson L, Harris S, Brown E, Gruen R, Victor C: Clinical outcomes, quality of life, and costs in the North Thames Dialysis Study of elderly people on dialysis: a prospective cohort study. Lancet 2000,4(356 (9241)):1543–50. 10.1016/S0140-6736(00)03123-8View ArticleGoogle Scholar
  31. Monthly Index of Medical Specialties Haymarket Publishing Services Ltd 2002.
  32. Clarke P, Gray A, Holman R: Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making 2002, 22: 340–49. 10.1177/027298902400448902PubMedView ArticleGoogle Scholar
  33. Lawrence WF, Grist TM, Brazy PC, Fryback DG: Magnetic resonance angiography in progressive renal failure: a technology assessment. Am J Kidney Dis 1995, 25: 701–709.PubMedView ArticleGoogle Scholar
  34. Guidance for manufacturers and sponsors (N0014) National Institute of Clinical Excellence 2001.
  35. Review of completed technology appraisals 2000/2001 Item 3 National Institute of Clinical Excellence Annual Public Meeting 18 July 2001.
  36. Alexander W, and South East Thames Diabetes Physicians Group: Diabetes care in a UK Health Region: Activity, facilities and costs. Diabet Med 1988, 5: 577–81.PubMedView ArticleGoogle Scholar
  37. Currie CJ, Williams DR, Peters JR: Patterns of in and out-patient activity for diabetes: a district survey. Diabet Med 1996, 13: 273–80.PubMedView ArticleGoogle Scholar
  38. Currie CJ, Morgan CLL, Peters JR: The epidemiology and cost of inpatient care for peripheral vascular disease, infection, neuropathy, and ulceration in diabetes. Diabetes Care 1998, 21: 42–8.PubMedView ArticleGoogle Scholar
  39. Morgan CL, Currie CJ, Hunt J, Evans JD, Rogers C, Stott N, Peters JR: Relative activity and referral patterns for diabetes and non-diabetes in general practice. Diabet Med 2000, 17: 230–5. 10.1046/j.1464-5491.2000.00208.xPubMedView ArticleGoogle Scholar
  40. UK prospective diabetes study (UKPDS) group: Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet 1998, 352: 854–65. 10.1016/S0140-6736(98)07037-8View ArticleGoogle Scholar
  41. Gray A, Raikou M, McGuire A, Fenn P, Stevens R, Cull C, Stratton I, Adler A, Holman R, Turner R, on behalf of the UKPDS study group: Cost effectiveness of an intensive blood glucose control policy in patients with type 2 diabetes: economic analysis alongside randomised controlled trial (UKPDS 41). BMJ 2000, 320: 1373–78. 10.1136/bmj.320.7246.1373PubMed CentralPubMedView ArticleGoogle Scholar
  42. Clarke P, Gray A, Adler A, Stevens R, Raikou M, Cull C, Stratton I, Holman R: 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. 10.1007/s001250051617PubMedView ArticleGoogle Scholar
  43. Walters DP, Gatling W, Houston C, Mullee MA, Julious SA, Hill RD: Mortality in diabetic subjects: an eleven year follow-up of a community based population. Diabet Med 1994, 11: 968–73.PubMedView ArticleGoogle Scholar
  44. Morrish NJ, Stevens LK, Head J, Fuller H, Jarrett , Keen H: A prospective study of mortality among middle-aged diabetic patients (the London cohort of the WHO Multinational Study of Vascular Disease in Diabetics) I: causes and death rates. Diabetologia 1990, 33: 538–41.PubMedView ArticleGoogle Scholar
  45. Turner RC, Millns H, Neil HA, Stratton IM, Manley SE, Matthews DR, Holman RR, for the United Kingdom Prospective Diabetes Study Group: Risk factors for coronary artery disease in non-insulin dependent diabetes mellitus: United Kingdom prospective diabetes study (UKPDS 23). BMJ 1998, 316: 823–8.PubMed CentralPubMedView ArticleGoogle Scholar
  46. The DECODE Study Group: Consequences of the new diagnostic criteria for diabetes in older men and women. Diabetes Care 1999, 22: 1667–71.View ArticleGoogle Scholar
  47. American Diabetes Association: Postprandial blood glucose. Diabetes Care 2001, 24: 775–8.View ArticleGoogle Scholar
  48. Turner R, Cull C, Holman R, United Kingdom Prospective Diabetes Study 17: A 9-year update of a randomized, controlled trial on the effect of improved metabolic control on complications in non-insulin dependent diabetes mellitus. Ann Intern Med 1996, 124: 136–45.PubMedView ArticleGoogle Scholar

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