Intervention-only programmes were in general more cost effective than programmes that also included a screening component .
The longer the period evaluated , the more cost-effective interventions appeared .
There was insufficient evidence to answer the question of ( 1 ) whether lifestyle programmes are more cost effective than metformin or ( 2 ) whether low-intensity lifestyle interventions are more cost effective than the more intensive lifestyle programmes that were tested in trials .
OBJECTIVE Explore the cost-effectiveness of lifestyle interventions and metformin in reducing subsequent incidence of type 2 diabetes  , both alone and in combination with a screening programme to identify high-risk individuals .
Clinical Outcomes and Cost-Effectiveness of Strategies for Managing People at High Risk for Diabetes
Context A previous Markov modelbased analysis estimated that use of the Diabetes Prevention Program diet and exercise intervention to forestall diabetes in high-risk people would be cost-effective from a societal perspective . Contribution Using a vali date d model design ed to be more complete and realistic than previous models , the authors estimated that the intervention would cost society about $ 62600 per quality -adjusted life-year saved . It would be cost-saving if the annual cost of the intervention decreased from $ 672 to $ 100 . Implication s This model suggests that the Diabetes Prevention Program intervention costs more per quality -adjusted life-year saved than previously estimated , and health plans and insurers may consider it too expensive to cover . The Editors Recent r and omized , controlled studies have shown that diabetes can be prevented or delayed in high-risk individuals by intensive lifestyle modification programs ( 1 , 2 ) or glucose-lowering drugs ( 2 - 4 ) . For example , in the Diabetes Prevention Program ( DPP ) , the relative reductions in the 2.8-year incidence of diabetes were 58 % in the lifestyle modification group and 31 % in the metformin group ( 2 ) . This raises hopes of substantially reducing the morbidity , mortality , and cost of this important disease . However , the trial was too short to observe the effects on microvascular or macrovascular outcomes , and the programs cost several hundred dollars a year ( 5 ) . These findings generate obvious questions : What are the long-term effects of trying to prevent diabetes in high-risk people ? Does lifestyle modification truly prevent or just postpone diabetes ? Is such a prevention program cost-effective ? What is the best strategy ? A previous analysis has suggested that lifestyle modification would be cost-effective over 75 years from a societal perspective ( 6 ) . We used a more thorough , clinical ly realistic , and independently vali date d model to estimate the short- and intermediate-term health and economic effects of different prevention programs for high-risk individuals and health plans , as well as for society . Methods We conducted the analysis by using the Archimedes model , which has been described elsewhere ( 7 - 9 ) . Briefly , it is a simulation model written at a relatively high level of anatomic , physiologic , clinical , and administrative detail . It uses object-oriented programming to create in the model objects that correspond to objects in reality , one-to-one . Among the hundreds of objects are  people , pancreases , cells , plasma glucose levels , coronary arteries , plaque , chest pain , emergency departments , electrocardiograms , aspirin , and angioplasties  . Helpful analogies might be a flight simulator ( in which the objects include the plane and its wings , airports , runways , buildings , and the wind ) , or the SimCity computer game . In the Archimedes model , each individual is simulated down to the level of hepatic glucose production , insulin resistance , -cell fatigue , and similar biological variables . The core of the model is a set of differential equations that represent the anatomy and physiology pertinent to diseases and their complications . Currently , the model includes diabetes , congestive heart failure , coronary artery disease , stroke , hypertension , and asthma in a single integrated model . The structure and equations of the model pertinent to diabetes and its complications are described elsewhere ( 8 , 9 ) . The Appendix and a technical report available through our Web site ( 10 ) describe additional aspects of the model and its validations that are pertinent to this analysis . Calculations are performed by using a distributed computing network . Clinical Events The model includes the biological variables and outcomes relevant to diabetes and its complications . Examples are basal hepatic glucose production ; insulin amount ; insulin resistance ; fasting plasma glucose ; hemoglobin A1c ( HbA1c ) ; 2-hour oral glucose tolerance ; high-density lipoprotein ( HDL ) cholesterol , low-density lipoprotein ( LDL ) cholesterol , and total cholesterol ; triglycerides ; systolic and diastolic blood pressures and their determinants ( for example , cardiac output , arterial compliance , peripheral resistance ) ; weight and body mass index ( BMI ) ; stenosis of coronary arteries ; retinopathy ( assessed by the Early Treatment of Diabetic Retinopathy scale ) ; urine protein ; creatinine ; peripheral neuropathy ; foot ulcers of varying degrees of severity ; and amputations . The use of differential equations preserves the continuous nature of biological variables as well as the interactions between them . Clinical outcomes are defined in terms of the underlying variables , as occurs in reality . For example , a person is said to have diabetes if his or her fasting plasma glucose level exceeds 6.9375 mmol/L ( 125 mg/dL ) or results on a 2-hour oral glucose tolerance test exceed 11.0445 mmol/L ( 199 mg/dL ) . This enables the model to incorporate different definitions and changes in definitions . The model is continuous : Biological variables are changing and interacting continuously , the natural histories and severity of conditions progress smoothly , any clinical event can occur at any time , and the timing of events is as condensed or drawn out as occurs in reality . The model also includes a detailed representation of the processes and logistics of clinical care and their related costs . Interventions , both to prevent diabetes and to manage it when it occurs , are modeled at the level of the underlying biology . Pertinent to this analysis is that in the model , diet and exercise reduce weight ( 2 ) ; reduce blood pressure ( 11 ) ; improve LDL cholesterol , HDL cholesterol , and total cholesterol levels ( 12 ) ; and decrease fasting plasma glucose levels ( 2 ) . The effects of metformin in the model are to reduce fasting plasma glucose and 2-hour oral glucose tolerance test results ( 2 ) ( by reducing basal hepatic glucose production ) , decrease LDL cholesterol and triglyceride levels ( 13 ) , and retard weight gain . Data used to build the model were derived from basic physiologic studies , surveys , epidemiologic studies , and clinical trials using methods described in the technical report ( 10 ) . Every variable in the model is estimated from at least 1 empirical source ; no variables are simply assumed . We identified specific sources by search ing MEDLINE from 1970 to 28 February 2005 and by consulting textbooks and clinical experts . Because the model includes scores of continuously valued , interacting variables , it does not have simplified states , transitions , or events at discrete time intervals that can be tabulated , as is commonly done for a Markov-type model . The equations themselves are in the technical report ( 10 ) . For nonmathematical readers , we have calculated annualized rates of change of representative biological variables and annualized rates of occurrence of representative clinical events , and compared them with rates for comparable events observed in epidemiologic studies and clinical trials . The Appendix reports those results . Costs The DPP measured the direct medical costs of delivering the lifestyle and metformin interventions  ( for example , personnel , health education material s , medications , and laboratory tests ) . Compared with the placebo group 's costs , costs in the lifestyle group were $ 1356 more per person in the first year , with approximately $ 672 in annual costs thereafter ; for the metformin group , costs were $ 977 in the first year and averaged $ 742 per year thereafter ( 5 ) . Following the completion of the DPP , metformin became generic . When this is considered , the cost of the metformin program is reduced to about $ 780 for 3 years , or about $ 260 a year . In the DPP study , costs apply to the year 2000 . To calculate the routine costs of providing health care to high-risk people before they develop diabetes , as well as to people with diabetes and its complications , the model includes a detailed mathematical representation of a health care system , including such elements as facilities , personnel , tests and treatments , protocol s , and provider behaviors . For the base-case analysis , we obtained itemized costs from Kaiser Permanente , a nonprofit , group- practice , integrated managed care organization that provides comprehensive care ( with no deductibles or copayments ) . The facilities , personnel , protocol s , and costs in the model are based on that organization 's records , at the level of detail at which actual accounts are kept ( for example , 37 different kinds of office visits ) . The model calculates costs by keeping track of the occurrence of every event that has cost implication s and adding them up . The costs assigned to any event or item were calculated by Kaiser Permanente 's cost-accounting department using micro-costing methods ( 14 ) , and they represent the real costs to the organization , not charges , reimbursements , or diagnosis-related groups . Because costs vary from setting to setting , the implication s of different cost structures are examined in the sensitivity analysis . Calculation of costs applies to the year 2000 . Indirect costs , such as lost time from work and decreased productivity , are included in the cost-effectiveness analysis through the Quality of Well-Being Index ( 14 ) . We calculated the effects of lifestyle and metformin interventions  on quality of life . For people who do not yet have diabetes , we used utility weights reported for the participants of the DPP study ( 15 ) . For people who have diabetes and its complications , we used the results of a published survey by Coffey and colleagues ( 16 ) . Both surveys used the Quality of Well-Being Index . The decrements in quality of life were assumed to be additive for people who have 2 or more complications , with a limit that quality of life could not be less than 0 . Use of an additive rule biases the calculation of cost/ quality -adjusted life-year ( QALY ) in favor of a prevention program , making the program appear more cost-effective than would occur if a multiplicative model were used . We discuss the
Intervention-only programmes were in general more cost effective than programmes that also included a screening component .
The longer the period evaluated , the more cost-effective interventions appeared .
There was insufficient evidence to answer the question of ( 1 ) whether lifestyle programmes are more cost effective than metformin or ( 2 ) whether low-intensity lifestyle interventions are more cost effective than the more intensive lifestyle programmes that were tested in trials .
OBJECTIVE Explore the cost-effectiveness of lifestyle interventions and metformin in reducing subsequent incidence of type 2 diabetes  , both alone and in combination with a screening programme to identify high-risk individuals .