Estimates of future total health care costs for diabetes must take into account two dynamic processes. First, the diabetes population is constantly changing over time. New people are diagnosed and added to the population; contemporaneously, other individuals with existing diabetes die and leave this subpopulation. With the balance of these two processes, the prevalence of diabetes in the total population changes on an annual basis. The pace of change differs over time depending on factors such as the rate of obesity and age of those at risk. For instance, the aging of the large baby boom generation will bring large numbers of new people into age categories that are at higher risk of developing the disease.
Second, costs associated with diabetes tend to follow a natural progression over time. Complications take time to develop and inflict damage to the eyes, kidneys, and circulatory and nervous systems. Therefore, robust projection models must include estimates of the expected natural history of the disease based on alternative levels of disease management.
In developing our forecasting model, we account for two types of cohorts—a prevalent and an incident cohort. The prevalent cohort is the population of individuals with diabetes in 2008. It reflects the distribution of different ages and different years with diabetes of the subpopulation in 2008. The second type of cohort is the incident cohort. This group represents the new people with diabetes entering the diagnosed population each year after the base year of 2008. The number of people with diabetes in any year is the sum of the population in the previous year (in 2008, it is the prevalent cohort) and the incident cohort, minus deaths from all causes in the previous year's population with diabetes.
To account for the costs of both cohorts, we tracked costs using two timelines: 1) the chronological timeline during which we will report our total cost estimates and 2) the age timeline for various heterogeneous subgroups within the prevalent and incident cohorts. For example, different patients may start with diabetes at different ages in the same calendar year. Other patients may start at the same age but in different calendar years.
We developed explicit models to address this dynamic nature of cost accumulation. presents the conceptual accounting of costs over time. This involves accounting for all health care costs incurred for the prevalent groups of people with diabetes, after the annual incident cohort for that year joins the prevalent cohort (illustrated by a dotted box in ). Empirically, we account for costs horizontally (as represented by arrows in ). That is, we take the prevalent cohort of patients in 2008 and lay out their lifetime cost profiles throughout the calendar time starting from 2008. Similarly, we take the incident cohort of patients in 2009 and lay out their lifetime cost profiles throughout the calendar time starting from 2009. We repeat this pattern for future incident cohorts of patients. We also account for heterogeneity in terms of patient characteristics for all cohorts.
Conceptual model of costs of diabetes with prevalent and future cohorts over time.
There are three components that are central to estimating this accumulation of costs: 1) defining the prevalent cohort and its heterogeneity, 2) the diabetes incidence model, and 3) the lifetime simulation model for diabetes progression.
Defining the prevalent cohort and its heterogeneity
We assume that the prevalent cohort of adult patients living with diabetes has the demographic and clinical characteristics of adult individuals reporting that they have diabetes in the National Health and Nutrition Examination Survey (NHANES) (2005–2006).
To create the prevalent cohort, we used self-reported disease to identify individuals with diabetes. We then estimated the U.S. population with diagnosed diabetes, undiagnosed diabetes, and no diabetes, categorized by sex, race/ethnicity, and age from 24 to 85 years. Because few clinical trial results include populations under 24 or over 85 years, this age range allows the model to estimate the effects of clinical trial results on the entire study population. Lifetime diabetes-related costs for the prevalent cohort are estimated using the lifetime simulation model for diabetes progression described below.
The diabetes incidence model
The main purpose of the incidence model is to account for new cases of undiagnosed and diagnosed diabetes in the population over time. Once new subjects are diagnosed, their lifetime costs are calculated using the cost estimates arising out of the lifetime model of diabetes progression.
Appendix Fig. S1A
(available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-0459/DC1
) displays the basic structure of the Markov model that traces the transition of the U.S. population across BMI categories over the age of the subjects. These transition probabilities determine the distribution of BMI categories at any point in time, which in turn affects the transition to diabetes. Online appendix Fig. S1B
displays the basic structure of the Markov model that tracks the movement of the population between four main states: 1
) no diabetes, 2
) undiagnosed diabetes, 3
) diagnosed diabetes, and 4
) death. It also displays the key transition probabilities driving the results of the model.
A fraction of the population without diabetes, conditional on their survival (death rate is denoted by d) to the next period, may progress to have diabetes. Annual progression rates are denoted by the parameter r. These people transition to become diagnosed or to remain undiagnosed with diabetes depending on whether they are screened. Annual screening rates are denoted by the parameter s. Similarly, depending on whether they are screened, those with currently undiagnosed diabetes transition to become diagnosed or remain undiagnosed. (Here we assume that the screening test is 100% sensitive and specific). As mentioned above, the group with diagnosed diabetes then is removed from this model and fed into the lifetime simulation model described below. The others continue.
Initial distribution of BMI categories are obtained from NHANES data (2005–2006). Yearly transitions across BMI categories are estimated using the 2004–2005 longitudinal data on the Panel 9 cohort from the Medical Expenditure Panel Survey. Estimates of d
are obtained from published U.S. Life Tables (2004). Estimates of s
are obtained from NHANES data (2005–2006). Finally, estimates of r
are obtained by fitting the Markov model to published incidence rates from the Centers for Disease Control and Prevention (using the National Health Interview Survey) (5
). All parameters are allowed to vary by sex, race, and ethnicity and smoothed over ages 24–85 years. Estimates of r
are separately smoothed for age-groups <45, 45–64, and >64 years due to substantial heterogeneity across these age ranges.
Age-specific annual hazard of progression to diabetes for people without diabetes for different sexes and BMI categories are calculated based on observed incidence of people with diagnosed diabetes and current screening rates. The progression hazards increase monotonically with age in all categories and are highest for the obese category followed by overweight and normal at all ages.
Lifetime simulation model of diabetes complications
Within a 1-year cycle, patients move from one disease state to another or stay in the current disease state until death or age 95 years.
Online appendix Fig. S2 displays the design of the model of diabetes complications. This figure presents the structure of the decision analytic model. Hypothetical patients move through the model from left to right for each cycle length (1 year). Based on initial patient clinical characteristics, patients are subject to the risk of various complications related to diabetes as well as mortality. Patients who survive a given year repeat the cycle until death.
Data on demographic characteristics (sex and race/ethnicity) as well as relevant clinical characteristics (blood pressure levels, cholesterol levels, GHb levels, and duration of diabetes) are obtained from NHANES and used as data inputs for the simulation models. For each clinical risk factor, we use age-, sex-, and race/ethnicity-specific distributions of these factors within the models.
The diabetes complication models in this analysis are derived from U.K. Prospective Diabetes Study (UKPDS) results (6
). Prediction models for all major diabetes-related complications have been developed by the UKPDS study group (7
). These models have been internally and externally validated with cardiovascular trial data (9
). The UKPDS model does not include glucose control as a predictor, making it unsuitable for evaluating the impact of improved diabetes care on end-stage renal disease. Instead, we modeled the development of microalbuminuria and proteinuria, which are linked to the intensity of glucose control (10
). We used prediction models for these intermediate complications using optimization procedures to fit observations from the UKPDS control arm to a functional form used in the original National Institutes of Health model (11
). For the transition between proteinuria to end-stage renal disease, we used probabilities from an observational study (12
For background mortality rates, we used race/ethnicity- and sex-specific background mortality rates reported in U.S. life table statistics from 1999 (13
). To calculate background mortality rates for individuals with diabetes, we subtracted cardiovascular mortality rates for the general population from the overall mortality rates found in life tables. We multiplied these rates by 2.75 as previously done to reflect higher background mortality rates for patients with diabetes (11
). When patients developed specific complications, such as coronary heart disease, stroke, end-stage renal disease, and amputation, we assumed that patients had higher mortality rates attributable to these complications (14
Within the model, we accounted for the effect of individual medications. The benefits of ACE inhibitors were based on the findings from the Heart Outcomes Prevention Evaluation (HOPE) Study (16
). Aspirin was assumed to reduce the probability of coronary heart disease but to increase the probability of gastrointestinal bleed (17
). We assumed that the joint effect of aspirin and an ACE inhibitor on cardiovascular effects was multiplicative. We did not assume that simply the processes of care such as foot examination or routine laboratory tests independently produced clinical benefits (18
Health service utilization and cost inputs
We assumed that the use of medications reflects the current distribution of use of insulin, oral agents, insulin plus oral agents, and diet therapy as observed in national studies of diabetes care (19
). Distribution of use of different oral glucose-lowering agents was assumed to be the observed distribution in national studies (20
). Use of ACE inhibitors and aspirin therapy was based on recent national reports of diabetes care (21
). Frequency of office visits and laboratory tests was assumed to be that observed in a recent national study (22
We estimated drug costs based on the average type and frequency of drug prescriptions, dosage of medications, and wholesale drug prices. Annual costs of microvascular and cardiovascular complications were obtained from recent studies in the literature (please see the online appendix Table for details).
For this analysis, we used the complication model to predict the average annual costs of living with diabetes by different ages, sexes, racial groups, and major durations of diabetes. A total of 10,000 Monte-Carlo iterations (each iteration representing a patient life) were used to generate average estimates. All costs are expressed in 2007 USD. In estimating costs for future years, we applied the cost growth assumptions used by the Congressional Budget Office.