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        Emerging Infectious Diseases Journal
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        Volume 7: No. 5, September 2010

        ORIGINAL RESEARCH
        Cost-Effectiveness Analysis of Efforts to Reduce Risk of Type 2 Diabetes and Cardiovascular Disease in Southwestern Pennsylvania, 2005-2007

        This model shows 6 possible health states: 1) no diabetes, risk-factor–negative; 2) risk-factor–positive, not enrolled in a modified Diabetes Prevention Program (mDPP); 3) risk-factor–positive, enrolled in an mDPP; 4) stable diabetes; 5) complicated diabetes; and 6) death. For each model cycle, patients either remain in the same health state (indicated with short curved arrows) or move (搕ransition? to another health state (indicated with straight arrows or long curved arrows). The following transitions are permitted. From health state 1, patients may remain in health state 1 or transition to health states 2, 3, 4, or 6. From health state 2, patients may remain in health state 2 or transition to health states 1, 4, or 6. From health state 3, patients may remain in health state 3 or transition to health states 1, 4, or 6. From health state 4, patients may remain in health state 4 or transition to health states 5 or 6. From health state 5, patients may remain in health state 5 or transition to health state 6.

        Figure 1. Model analyzing cost-effectiveness of a modified Diabetes Prevention Program, southwestern Pennsylvania, 2005-2007. Ovals indicate health states. Subjects may remain in a health state (short curved arrow) or may move to a different health state (straight arrow or long curved arrow) during each model cycle.

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        Figure 2 shows the 1-way sensitivity analysis for 8 model parameters. For each parameter, we summarize the parameter values (baseline value; range: minimum, maximum) and provide the corresponding cost-effectiveness ratios (CERs).

        For example, the first parameter listed is “Probability of reducing risk factors without an mDPP.” The baseline probability was 12.1%, but in sensitivity analyses, we varied this value from a low of 3.2% to a high of 25.9%. At the baseline value, the cost-effectiveness ratio was $3,420. If this probability decreases to 3.2%, then the cost-effectiveness ratio is $783 per QALY; if the probability increases to 25.9%, then the cost-effectiveness ratio is $18,580. We summarize this information as follows:

        • Probability of reducing risk factors without an mDPP: 12.1%; range, 3.2%-25.9% ($3,420; range, $783-$18,580)

        Analogous summaries for the remaining 7 parameters are

        • Probability of enrollment in an mDPP: 47.0%; range, 9.2%-86.7% ($3,420; range, $16,707-$1,911)
        • Probability of reducing risk factors with an mDPP: 16.2%; range, 4.2%-34.4% ($3,420; range, $13,087-$0)
        • Probability of screening risk-factor–positive: 31.0%; range, 7.2%-63.5% ($3,420; range, $14,046-$1,818)
        • Utility for risk-factor–positive patients with an mDPP: 0.75; range, 0.73-0.77 ($3,420; range, $13,178-$1,926)
        • Probability of diabetes for risk-factor–positive patients without an mDPP: 10.8%; range, 2.9%-23.3% ($3,420; range, 8,505-$0)
        • Probability of diabetes for risk-factor–positive patients with an mDPP: 4.8%; range, 1.3%-10.5% ($3,420; range, $7,085-$1,911)
        • Utility for risk-factor–positive patients without an mDPP: 0.73; range, 0.71-0.75 ($3,420; range, $2,280-$7,301)

        The parameters are listed based on the variation in CERs, with the parameter causing the most variation listed first.

        Model Parameter Parameter Value Cost-Effectiveness, $ per QALY
        Base case Min value Max value Base case Low CER High CER
        Probability of reducing risk factors without an mDPP 12.1% 3.2% 25.9% 3,420 783 18,580
        Probability of enrollment in an mDPP 47.0% 9.2% 86.7% 3,420 1,911 16,707
        Probability of reducing risk factors with an mDPP 16.2% 4.2% 34.4% 3,420 0 13,087
        Probability of screening risk-factor–positive 31.0% 7.2% 63.5% 3,420 1,818 14,046
        Utility for risk-factor–positive patients with an mDPP 0.75 0.73 0.77 3,420 1,926 13,178
        Probability of diabetes for risk-factor–positive patients without an mDPP 10.8% 2.9% 23.3% 3,420 0 8,505
        Probability of diabetes for risk-factor–positive patients with an mDPP 4.8% 1.3% 10.5% 3,420 1,911 7,085
        Utility for risk-factor–positive patients without an mDPP 0.73 0.71 0.75 3,420 2,280 7,301

        Figure 2. One-way sensitivity analyses assessing cost-effectiveness of a modified Diabetes Prevention Program (mDPP), southwestern Pennsylvania, 2005-2007. Horizontal bars depict the range of cost-effectiveness ratios for the values shown for each parameter. The vertical dotted line depicts the base case cost-effectiveness ratio. Variation of all other parameters not shown in the figure did not increase the cost-effectiveness ratio above $7,000 per QALY gained. Abbreviations: QALY, quality-adjusted life-year; Min, minimum; Max, maximum; CER, cost-effectiveness ratios; mDPP, modified Diabetes Prevention Program.

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        This acceptability curve depicts the likelihood of a modified Diabetes Prevention Program lifestyle intervention being favored for a given cost-effectiveness ceiling threshold (willingness to pay).

        Willingness to Pay, $ Probability of Cost-Effectiveness, %
        0 12
        5,000 50
        10,000 67
        15,000 75
        20,000 79
        25,000 82
        30,000 84
        35,000 85
        40,000 86
        45,000 87
        50,000 87
        55,000 87
        60,000 88
        65,000 88
        70,000 88
        75,000 88
        80,000 89
        85,000 89
        90,000 89
        95,000 89
        100,000 89

        Figure 3. Probabilistic (Monte Carlo) sensitivity analyses assessing cost-effectiveness of a modified Diabetes Prevention Program (mDPP), southwestern Pennsylvania, 2005-2007. The acceptability curve depicts the likelihood of an mDPP lifestyle intervention being favored for a given cost-effectiveness ceiling threshold (willingness to pay).

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        The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.


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