The CDC-RTI Diabetes Cost-Effectiveness Model is a Markov simulation model of disease progression and cost-effectiveness for type 2 diabetes that follows patients from diagnosis to either death or age 95 years. The model simulates development of diabetes-related complications on three microvascular disease paths (nephropathy, neuropathy, and retinopathy) and two macrovascular disease paths (coronary heart disease [CHD] and stroke). Model outcomes include disease complications, deaths, costs, and quality-adjusted life-years (QALYs). In the model, progression between disease states is governed by transition probabilities that depend on risk factors and duration of diabetes. Interventions affect the transition probabilities and resulting complications. The model has been used to estimate the cost-effectiveness of interventions for patients with diagnosed diabetes or pre-diabetes (14
). Details about the model and its validation are presented elsewhere (14
Bariatric surgery is incorporated in the following ways. First, the model allows for diabetes remission and improvement, important results of bariatric surgery. We defined remission as normal glycemic levels following surgery without antidiabetes medications. This was incorporated in the model as no progression along the microvascular paths, no diabetes treatment costs, elimination of the diabetes indicator variable in the CHD and stroke equations, and elimination of the diabetes other-cause mortality multiplier. We created an “improved diabetes” state for people who reduced the use of antidiabetes medications but did not achieve full diabetes remission. The rates of diabetes remission and improvement following bariatric surgery procedures are shown in with values based on a meta-analysis (1
). The reduction in costs for improvement is based on two smaller studies (17
). The online appendix (available at http://care.diabetesjournals.org/cgi/content/full/dc10-0554/DC1
) provides additional details on sources and parameter derivation for the variables described in this section.
Key surgery-related model parameter values
Second, the model includes an annual probability of relapse from remission to diabetes. Because few studies examine the long-term effects of bariatric surgery, we focused on the SOS study, which followed patients for 10 years after bariatric surgery (3
). We used the diabetes remission rates reported at 2 and 10 years to calculate the probability of relapse in .
Third, the model accounts for perioperative mortality and the long-term effects of surgery on mortality. For perioperative mortality, we used separate rates for bypass and banding surgery (19
). The model calculates future changes in mortality based on surgery's effects on blood pressure, cholesterol, and the remission or improvement of diabetes. We used multiple literature sources to estimate the effect of surgery on blood pressure and cholesterol values. Remission or improvement in diabetes stops or slows progression of diabetes complications, which reduces mortality. For people in diabetes remission, we also lowered other-cause mortality to the baseline rate among people with no diabetes. These effects are listed in .
Fourth, the model includes the costs of bariatric surgery. First-year bypass and banding surgery costs are based on an analysis of Medstat claims by Eric A. Finkelstein et al. (2008, unpublished data). The analysis calculated the costs attributable to surgery, including the surgery costs and any complication costs in the first year. For costs in subsequent years, we included costs of follow-up care visits; nutritional supplements; long-term complications, such as revisional surgery, cholelithiasis, abdominoplasty, and nonoperative leaks; and band removal (for gastric banding). lists the complication costs by year after surgery.
Finally, in addition to changes in quality of life (QoL) following surgery that result from reductions in diabetes complications, the model includes changes in QoL directly associated with bariatric surgery. We included a change in QoL associated with bariatric surgery that was the product of the change in utility for a 1–BMI unit change in weight and the change in BMI associated with surgery.
To analyze the cost-effectiveness of bariatric surgery, we focused on the population with BMI ≥35 kg/m2 and diabetes. We defined the characteristics of this population by estimating the distribution of age, sex, race, hypertension status, cholesterol status, and smoking status as well as systolic blood pressure, total cholesterol, and HDL levels within the National Health and Nutrition Examination Survey for the subset of the obese population (BMI ≥30 kg/m2) with self-reported diabetes. Values for the population with BMI ≥35 kg/m2 were similar, so we used data from the full obese population with its larger sample size.
Within the severely obese diabetic population, we separately analyzed the newly diagnosed diabetic population and the established diabetic population. We distinguished between these two groups because studies have shown that surgery leads to significantly less weight loss and lower rates of diabetes remission in people with longer diabetes duration (17
). The primary differences between the populations are that the newly diagnosed diabetic population is younger (aged 35–74 years) than the established diabetic population (aged 45–74 years) to represent the 10-year difference in duration, and the diabetes remission rate is lower for the established diabetic population (18
). We adjusted diabetes duration to 10 years in the model to reflect changes in glycemic control and complications in the established diabetic population.
Using these two severely obese diabetic populations, we estimated the cost-effectiveness of gastric bypass and gastric banding surgery. The two surgeries differ in several factors, including diabetes remission rate, diabetes improvement rate, perioperative mortality rate, first-year and following-year costs, and effect on blood pressure, cholesterol, and QoL. includes the specific parameter values for each surgery type. For our baseline analyses for each type of surgery, we compared the surgery to usual diabetes care that included tight glycemic control similar to that provided in the UK Prospective Diabetes Study (20
). We assumed that patients who were not in diabetes remission would also receive tight glycemic control. In total, our baseline analyses included four model runs, with separate runs for each type of surgery and for each diabetic population (newly diagnosed and established).
We converted all costs to 2005 U.S. dollars using the medical-care component of the Consumer Price Index (21
). We discounted costs and QALYs by a 3% annual rate, and we estimated incremental cost-effectiveness ratios that were rounded to the nearest $1,000/QALY. We also report undiscounted remaining life-years.
We ran one-way sensitivity analyses to determine how key factors affected the cost-effectiveness ratios. When possible, we used end points of the published 95% (90% for surgery costs) CI of the model parameter to determine upper and lower values to input into the model. For most parameters where CIs were unavailable, we halved and doubled the baseline values. We varied the change in QoL per unit BMI change from 0 (i.e., surgery-related weight loss has no direct effect on QoL) to 0.017. We also analyzed the effect of surgery on the diabetic population with a BMI between 30 and 34 kg/m2
. We assumed a similar percentage change in excess weight loss (22
) as in our main analysis, which leads to a smaller change in BMI and QoL improvement.
To examine how conjoint parameter uncertainty affected the model results, we conducted probabilistic sensitivity analysis (PSA) on key parameters involved in estimating the cost-effectiveness ratios. Applying distributions for surgery costs, remission rates, BMI loss, and other input parameters (see online appendix), we drew 1,000 parameter combinations and ran the model separately for each combination for newly diagnosed patients undergoing bypass surgery. We repeated the process for newly diagnosed patients undergoing banding surgery. Due to run time constraints, we only looked at patients in the 45- to 54-year age-group, which had a cost-effectiveness ratio that was close to the cost-effectiveness ratio for the entire population.