The diabetes subsample consisted of 8,429 observations and contained similar fractions of women (50–52 percent), Hispanics (12–13 percent), and blacks (10–11 percent) as the subsample of people without diabetes (). However, people in the diabetes subsample were significantly older than people in the no-diabetes subsample (60 versus 35 years), were less likely to be uninsured—likely due to their age, and were more likely to have CVD, renal disease, and/or vision disorders.
Descriptive Statistics—Sample Weighted Means and 95% Confidence Intervals
Results from the two-part GLM models are shown by age group and by model stage in Appendix S3
. We used results from these age-group-specific models to calculate annual medical spending attributable to diabetes for the diabetes subsample. Individual level and aggregate results are shown in 2004 dollars in .
Estimated Aggregate and Per-Person Costs* by Age Group Using the Regression-Based Approach
Per-person spending attributable to diabetes is considerably higher for the oldest age group than for the 45–64 age group but only slightly higher than spending for the youngest age group. For the 65 and older age group, per-person diabetes-attributable medical spending is $4,690 per year. Predicted annual per-person spending is $3,720 for the 45–64 age group and $4,520 for the age group younger than 45. The relatively high costs for those younger than 45 years may reflect a duration effect if individuals with Type 1 diabetes are disproportionately represented in this age group.
We found that aggregate medical spending attributable to diabetes is $52.9 billion per year. Almost half of these expenditures are for people aged 65 and older, whereas 15 percent are for people younger than age 45.
We first estimated the annual aggregate cost of each condition identified as attributable to diabetes (). The results suggest that medical spending for diabetes alone (i.e., not including the cost of attributable conditions) was $21.9 billion per year in 2004 dollars. This estimate reflects the costs for events with diabetes as the only listed diagnosis and a portion of the costs for events with diabetes and one or more of its attributable conditions listed.
Estimated Attributable Fractions and Annual Medical Expenditures* for Eight Diabetes-Attributable Conditions
Estimated CVD spending was $85 billion per year; endocrine and metabolic disorders and neurological disorders had annual expenditures of $19.3 and $16.3 billion, respectively. Total annual costs for general medical conditions were $426.9 billion. Our estimates suggest that, on average, total medical spending per year for the categories of services included in MEPS was $607.9 billion in 2004 dollars.
also shows estimates of diabetes AFs. For all of the attributable conditions, the AFs are somewhat higher for those in the 45–64 years age group than for those in the youngest and oldest age groups. We also found that 1 percent or less of the prevalence of general medical conditions is attributable to diabetes.
We multiplied AFs by the total annual costs for each attributable condition to generate estimates of diabetes-attributable costs. These are shown by condition and age group in the last column of . Our estimates attribute $6.9 billion of CVD costs (8.2 percent) to diabetes. Of the $19.3 billion in annual costs for endocrine and metabolic disorders, $1.81 billion are attributed to diabetes (9.4 percent); of the $16.3 billion for neurological disorders, $1.37 billion are attributed to diabetes (8.4 percent). Although the estimated AFs for general medical conditions are low, $2.59 billion of general medical conditions costs are attributed to diabetes because of large annual costs for these conditions.
shows aggregate and per-person annual medical expenditures attributable to diabetes based on the AF approach. These results suggest that $37.1 billion in medical spending per year is attributable to diabetes. Per-person spending estimates vary by age, but are similar for people in the 45–64 and 65 and older age groups. For these groups, we found per-person diabetes-attributable spending of $3,020–$3,090. For the age group younger than 45 years, we estimated per-person annual expenditures of $2,500.
Estimated Aggregate and Per-Person Medical Expenditures* by Age Group and by Costing Approach
Comparison of RB and AF Model Results
Results from the RB approach are 43 percent higher than those from the AF approach (). The RB approach produced significantly higher spending estimates than the AF approach for age groups younger than 45 and older than 64 years. Per-person spending estimates were also higher from the RB approach—most notably for the youngest age group, for whom the RB estimates are $4,520 per person versus $2,500 from the AF approach.
Results from Alternative Specifications
To better understand why the RB and AF approaches produced such different cost estimates, we estimated several alternative specifications of both models. We first examined the impact of using different sets of control variables in the RB model. One specification included only age, sex, and race/ethnicity as independent variables, which led to an estimate of $54.7 billion, nearly 3.5 percent higher than our baseline estimate of $52.9 billion. Because this model does not fully control for observed differences between the diabetes and nondiabetes populations, it is not surprising that the resulting estimates are higher. For another specification, we included diabetes-attributable conditions, such as CVD, renal disease, and vision impairment, in addition to the other independent variables in our baseline model. As expected, this specification lowered the estimates for diabetes because it “overcontrolled” by attributing some costs to attributable conditions (e.g., CVD), that are legitimately attributable to diabetes. This specification resulted in an estimate of $49 billion—approximately 7 percent lower than our baseline RB estimate. Another specification included risk factors for diabetes—obesity (BMI>29) and hypertension—in addition to the variables from the previous model. Because obesity measures were available only for the 2000–2003 period, we estimated the percentage reduction in costs between this alternative specification and our baseline model re-estimated for this time period, which resulted in estimates that were 18 percent lower than baseline costs, or approximately $43.3 billion. However, it too likely overcontrols because some diabetes costs are assigned to obesity, hypertension, CVD, and other attributable conditions.
We also considered the effect of alternative specifications of the AF model. First, we analyzed the impact of using a broader range of ICD-9 codes to define CVD: 390–459, as used by the Centers for Disease Control and Prevention (CDC), and 390–459 and 745–747, as used by the American Heart Association (AHA). These broader definitions led to a decrease in estimated costs of 2 percent (to $36.3 billion) and 8 percent (to $34 billion) for the CDC and AHA definitions, respectively. The lower costs were a result of lower CVD AFs than in our baseline model, due to a weaker link between the added conditions and diabetes.
Second, we examined the impact of different methods of calculating total costs for conditions attributable to diabetes. Our baseline AF estimates assigned equal shares of costs to diabetes and any of the attributable conditions listed for an event. To generate an “upper bound,” we assigned all costs to diabetes for events that listed diabetes, even if other attributable conditions were also listed. For example, if there was an MI event that listed both CVD and diabetes, our baseline split the costs evenly between CVD and diabetes, whereas the upper bound approach assigned all costs to diabetes. This algorithm, which clearly over-attributes costs to diabetes, led to an 11 percent higher AF estimate of $41.3 billion.
Third, we examined the effect of using diabetes prevalence values from the National Health Interview Survey (NHIS). We used MEPS for baseline analyses to limit the possibility that differences in costs between RB and AF models were due to data differences. However, the NHIS is preferred for estimating diabetes prevalence (ADA 2003
; CDC 2007
). We used the NHIS to adjust the age-and condition-specific prevalence rates in equation (3)
upward, which resulted in slightly higher AFs for each condition (details in Appendix S4
). Estimated costs were $39.2 billion—5.7 percent higher than baseline AF results.
Finally, when the previous two approaches were combined, both applying the “upper bound” AF algorithm and adjusting diabetes AFs upward with NHIS prevalence data, the estimated cost was 16.7 percent higher than our baseline AF estimate, or $43.3 billion. This AF estimate, which appears to over-attribute costs to diabetes, was virtually equal to our lowest RB estimate of $43.3 billion. Because the upper bound AF estimate of $43.3 billion is constructed to over-attribute costs to diabetes, while the lower bound RB estimate of $43.3 billion likely underattributes costs to diabetes, we also examined the extent to which remaining cost differences are driven by higher treatment intensity for those with diabetes, which is captured in RB, but not AF, estimates.
As an example of this higher treatment intensity, we calculated the average length of hospital stay for CVD events by diabetes status. We found that, on average, people with diabetes spent 1.4 days longer in the hospital for CVD events than people without diabetes. This difference was statistically significant (p=.0031). For renal, endocrine/metabolic, PVD, and neurological disease events, people with diabetes had longer stays in the hospital than people without diabetes, although the differences were not statistically significant. These results suggest that the presence of diabetes complicates treatment for nondiabetes conditions and raises costs.