76 Review of economic models assessing the cost-effectiveness of lifestyle interventions
The cost per QALY was higher over shorter time horizons. From the societal perspective (i.e.
providing a national DPP lifestyle programme to all high risk people in the USA), the lifestyle intervention cost about US$62,000 per QALY compared with the baseline strategy. However, when the authors assessed the cost-effectiveness of implementing the intensive lifestyle intervention only when individuals actually developed diabetes, they predicted this strategy would result in a lower 30-year cost per QALY of US$24,500 compared with the control group.
Using this as the reference strategy, the incremental cost per QALY for the DPP strategy increased to US$202,000. But as people with IGT are at about 1.8 times the risk of heart disease, this approach would allow some to die – those who had MIs before being diagnosed as diabetic.
It is unclear whether or not this increased risk is captured by Eddy et al.’s model. Using this as the reference strategy, the incremental cost per QALY for the DPP strategy increased to US$202,000.
Thus, Eddy et al.’s243,244 findings are much less favourable than those of previous analyses that assessed the cost-effectiveness of the DPP interventions.
Sensitivity analysis
Two approaches have been used to assess the validity and robustness of the Archimedes Diabetes Model249 and its findings relating to the diabetes prevention interventions. First of all, the model has been validated by simulating 19 clinical trials, including the DPP,108 and comparing the results with those found in the actual trials. This provides a high degree of confidence in the validity of the model over short to medium time horizons. However, uncertainty remains over the ability of the model to accurately predict outcomes beyond the follow-up period of available clinical trials.
To further address uncertainty relating to the values of variables used in the model, the authors state that distributions were assigned to variables and individuals were created by simultaneously drawing a value for each variable from each distribution. To explore the uncertainty relating to the effectiveness of the DPP intervention, the authors incorporated a distribution for this parameter based on the 95% CI reported in the original trial. They observed that this source of uncertainty substantially increased the width of the CI for the 30-year chance of developing diabetes. However, the assumption of a linear increase in diabetes prevalence does not fit with the data from the GPRD (presented later) and it is more likely that most of those who will progress to diabetes will do so within 10 years, and so the Eddy model243,244 will underestimate person-years of diabetes. Based on their simulations, the authors present a probability distribution of the cost per QALY for the lifestyle intervention and conclude that it is extremely unlikely (< 0.1% chance) that the cost per QALY would fall below the threshold of US$50,000 for a 100,000-member health plan. The incremental cost per QALY was also found to be sensitive to the discount rate, and most notably, the cost of the lifestyle intervention. If, for example, the cost of delivering the DPP lifestyle intervention could be reduced to US$217 per person per year, without reducing its effectiveness, then the cost per QALY ratio would be much more favourable.
per QALY over a 5-year horizon from the perspective of a 100,000-member health plan. Possible explanations for these differences in findings are discussed below (Tables 6 and 7) and further recommendations are suggested.
The large discrepancy between the trial-based cost-effectiveness analysis254 and the 5-year ICER predicted by the Archimedes Diabetic Model248 is probably due to differences in the way the two studies estimate QALY gains associated with the lifestyle intervention. In the trial, patient-level utility scores and cost data were collected prospectively over a 3-year period, allowing mean costs and QALYs to be estimated for each arm of the trial. The authors of the trial report that the lifestyle intervention resulted in a mean QALY gain of 0.072 compared with placebo over the 3-year follow-up period.254 This gain may have been due to effects of the lifestyle intervention on general well-being (or weight loss) rather than its impact on the progression of diabetes. Such an effect may be consistent with the earlier reported finding that changes in QoL in people with IGT, as measured by SF-36, were independent of glycaemia control or changes in glycaemia control (see Chapter 1, Quality of life). However, it is unclear whether this QALY gain represents a real improvement in health-related QoL. This is because the authors did not present baseline scores for the individual arms of the trial and the differences between the arms do not appear to be statistically significant. It would be useful if future studies could clarify the impact that lifestyle interventions have on health-related QoL independently of their effect on the progression of diabetes, for example via a reduction in weight. In Eddy et al.’s model (2005),248 it appears that the lifestyle intervention affects QoL only through its impact on the progression of diabetes and its complications. As very few people progress to diabetic complications that significantly impact on QoL during the first 5 years of the simulation, the model predicts very low QALY gains over this period and high costs associated with implementing the intervention. This accounts for the very high 5-year ICER reported by Eddy et al. (2005).248 However, it is not clear whether Eddy et al. (2005)248 takes account of associated morbidity and mortality, most notably from CVD, before the development of diabetes. If not, the model may underestimate the beneficial effects of lifestyle interventions.
The above issue may also partly account for the different findings predicted by the different modelling studies. Herman et al. (2005)244 appear to have assigned a slightly higher utility weight to people with IGT receiving the lifestyle intervention compared with those in the control group.
It is difficult to establish from their report, but Eddy et al. (2005)248 may have assigned the same utility weights to everyone with IGT regardless of which intervention they were receiving. This could account for some of the difference in QALY gains observed between the two analyses.
TABLE 6 Cohort information used in the reviewed prevention models Authors Cohort demographic characteristics Source of cohort
information Cohort age
range (years) Number of patients in cohort Palmer et
al. 2004243 Mean age 50.6 years, mean body weight 92 kg, and mean BMI 34 kg/m2; 32% men and 45% from minority groups
Patients enrolled in the
DPP trial ≥ 25 Not reported
Herman et
al. 2005244 Mean age 50.6 years, mean body weight 92 kg, and mean BMI 34 kg/m2; 32% men and 45% from minority groups
Patients enrolled in the
DPP trial ≥ 25 Not reported
Eddy et al.
2005248 Individuals at high risk of developing diabetes as defined by the entry criteria for the DPP trial (IGT ≥ 25 years; BMI ≥ 24 kg/m2)
Published prevalence projections based on the NHANES III survey255
Not explicitly
reported 4000 individuals (4%) from a 100,000-member health plan 10,000 individuals for analysis from societal perspective NHANES, National Health and Nutritional Examination Survey.
78 Review of economic models assessing the cost-effectiveness of lifestyle interventions
TABLE 7Summarya of previously reviewed prevention models and model by Eddy et al. (2005)248 AuthorLifestyle interventions comparedModel typeComplications modelledBenefits modelledResults (ICERs)External validations Palmer et al. 20042431. Lifestyle intervention (as per DPP trial) 2. Metformin intervention (as per DPP trial) 3. Placebo (as per DPP trial) Markov model Not modelled explicitlyReduced mortality due to prevention of diabetes and future cost savings
€6381 per life-year (1 vs 3) UK health service perspectiveNo specific external validations reported Herman et al. 20052441. Lifestyle intervention (as per DPP trial) 2. Metformin intervention (as per DPP trial) 3. Placebo (as per DPP trial)
Markov model (cohort analysis)Retinopathy, nephropathy, neuropathy, CHD (angina and MI/ CA), stroke Reduced mortality due to prevention of diabetes, QALY gains from reduced diabetes complications, and future cost savings
US$1124 per QALY (1 vs 3) Single-payer perspectiveNo specific external validations reported Eddy et al. 20052481. DPP lifestyle intervention implemented immediately for people at risk of diabetes, until HbA1c levels ≥ 7%. Intensive diabetes treatment protocol thereafter 2. No intensive lifestyle intervention for people at risk of diabetes, followed by dietary advice and monitoring at onset of diabetes, and intensive diabetes treatment when HbA1c levels ≥ 7% 3. Implementation of DPP intervention and monitoring at onset of diabetes (FPG > 6.9 mmol/l); followed by intensive diabetes treatment when HbA1c levels ≥ 7% 4. Metformin intervention (as per DPP trial)
Simulation model using object-orientated programming (relies on a large number of differential equations to model the physiological mechanisms and interactions underlying the development and progression of diabetes and its complications in individual people) Retinopathy, nephropathy, neuropathy, MI and CHF, and stroke
Reduced mortality due to prevention of diabetes, QALY gains from reduced diabetes complications, and future cost savings
■US$143,000 per QALY (1 vs 2) ■US$24,500 per QALY (3 vs 2) ■US$202,000 per QALY (1 vs 3) All ICERs from a 100,000-member health plan perspective
Independently predicted the annualised rate of progression from pre-diabetes to diabetes as observed in the DPP trial Predicted rates of complication development, for individuals with clinically diagnosed diabetes, validated against various epidemiological and clinical studies344 Rate of disease progression (FPG) validated for people with pre- diabetes and clinical diabetes Uncertainty remains over validity of predicted rates of complication development in people with pre- diabetes and preclinical diabetes CHF, coronary heart failure. a Adapted from Waugh et al. (2007).72
The use of different time horizons may also partially explain the different cost per QALY ratios predicted by the two models. Eddy et al. (2005)248 used a 30-year time horizon, whereas Herman et al. (2005)244 assessed costs and outcomes over the lifetime of patients. If preventing or delaying the onset of diabetes prevents or delays complications far in the future then a 30-year time horizon may miss some of these events. However, Eddy et al. (2005)248 argue that it is cost- effectiveness over shorter time horizons that is most important because modelling outcomes and costs far into the future rely on the assumption that the programme will be in place for decades without change and that no new technologies will become available for the management of diabetes. Despite the difference in time horizons, it has been noted that the models project similar life expectancy.256 Therefore, it is likely that the difference in cost-per-QALY estimates is due to the Archimedes Diabetes Model249 reporting lower complication rates than the Herman et al. model (2005).244 As Engelgau (2005)256 points out in an editorial accompanying the publication of Eddy et al.’s (2005)248 analysis, the model by Herman et al.(2005)244 predicts higher cumulative incidences for all the major micro- and macrovascular complications, despite predicting similar survival times. To give an example, the lifestyle intervention reduced the cumulative 30-year incidence of retinopathy blindness in the at-risk population (those with IGT) from 0.03 to 0.016 in the model by Eddy et al. (2005).248 The lifetime risk reduction in the model by Herman et al.
(2005)244 is from 0.056 to 0.034. As indicated in Table 2, Eddy et al. (2005)248 have undertaken a series of external validations, showing that their model accurately predicts rates of complication development observed for people with clinical diabetes in epidemiological and clinical studies.
However, uncertainty still exists in relation to the rate of complication development beyond the follow-up period of existing clinical trials and the rate of complication development in people with pre-diabetes and preclinical diabetes.
The reason why Eddy et al. (2005)248 project lower complication rates than Herman et al. (2005)244 is very difficult to ascertain given the complexity of the models and the many differences between them. However, as Engelgau (2005)256 suggests, it probably has something to do with differences in the way glycaemia progression is modelled.
The speed of progression of hyperglycaemia from onset of diabetes to clinical diabetes and the progression of micro- and macrovascular complications during this period are subject to debate.
Herman et al. (2005)244 assume this progression takes 10 years. A much slower rate of progression predicted by Eddy et al.’s model (2005)248 is consistent with the lower complication rates reported.
The assumption of a linear progression257 has been challenged by Ferrannini et al. (2004),258 who postulate an initial slow progression followed by a rapid onset of clinical diabetes. More evidence is available relating to the progression of HbA1c level and the development of complications (and response to treatment) for people with clinically diagnosed diabetes. There has been debate between the authors as to which model provides the most accurate and reliable prediction of cost-effectiveness, but the debate seems to be inconclusive, with Eddy et al. (2005)248 sticking by their findings and Herman (2005)244 and Palmer et al. (2004)243 in agreement.
Despite disagreement in terms of the overall cost-effectiveness, the different models do agree on several qualitative points. First of all they agree that, if maintained, lifestyle changes and weight loss have a significant impact on the risk of developing diabetes and micro- and macrovascular complications. The cost-effectiveness estimates predicted by the different decision-analytic models are also sensitive to changes in the same parameters, particularly the cost of delivering interventions, likely adherence and, thus, maintenance of effectiveness.
Affordability is also a major concern relating to the implementation of resource-intensive lifestyle interventions, even if such interventions are shown to be cost-effective over a lifetime. To address this issue, Johnson et al. (2006)259 recently conducted a discrete choice experiment in the USA to estimate high-risk individuals’ willingness to pay for risk-reduction programmes. This was to
80 Review of economic models assessing the cost-effectiveness of lifestyle interventions
assess whether a potential cost-sharing scheme could be used to finance implementation. This study did find that individuals at high-risk were hypothetically willing to pay approximately 65% of the monthly cost of delivering a lifestyle intervention similar to that of the DPP trial.108 However, the study also found, not surprisingly, that individuals valued hypothetical programmes with large benefits (weight loss and risk reduction) and low sacrifices most highly. This finding suggests that a trade-off may exist between the intensity of the intervention (e.g. amount of exercise and dietary restriction) and likely engagement and adherence. The effectiveness of programmes that involve high levels of exercise and dietary restriction, which could in theory have large benefits in terms of reducing the risk of diabetes, might be undermined by poor uptake and adherence. On the other hand, if high uptake and adherence can be achieved by encouraging moderate lifestyle changes, then the overall benefits may be greater. This is an important point to consider when designing future intervention strategies. It may also be possible to consider flexible interventions that can be tailored to suit individual patients’ needs, rather than thinking of interventions in terms of one fits all.