Estimating the Cost of Illness: The Case of Diabetes Gary Olin and Steven R. Machlin and Jeffrey Rhoades

Agency for Healthcare Research and Quality Working Paper No. 08001 January 2008

Suggested citation: Olin G, Machlin SR, Rhoades J. Estimating the Cost of Illness: The Case of

Diabetes. Agency for Healthcare Research and Quality Working Paper No. 08001, January 2008,

http://gold.ahrq.gov.

AHRQ Working Papers provide preliminary analysis of substantive, technical, and methodological

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intended or should be inferred.
 

Estimating the cost of illness: the case of diabetes 

Gary Olin, Steven Machlin, and Jeffrey Rhoades 

January 2008 

ABSTRACT 

We use the Medical Expenditure Panel Survey to estimate the direct medical costs of 

diabetes. Separate estimates of the cost of diabetes are developed from an attributable 

risk procedure and a two-part regression model. Costs include expenditures to treat 

diabetes, chronic complications of the disease, and co-morbidities requiring additional 

health care resources because the person is diabetic. The cost estimates range from $79.1 

billion based on attributable risk to $88.8 billion based on the two-part regression model, 

but the difference is not statistically significant. These estimates are derived from the 

actual health care use and expenditure patterns of the disease population. They indicate 

that 59 to 66 percent of health care spending by diabetics for physician and hospital care 

and prescription medicines should be attributed to the disease itself. 

Gary L. Olin, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Survey Operations 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

Gary.Olin@ahrq.hhs.gov 

Steven R. Machlin, MS 

Agency for Healthcare Research and Quality 

Division of Research and Survey Methods 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

Steven.Machlin@ahrq.hhs.gov 

Jeffrey Rhoades, Ph.D. 

Agency for Healthcare Research and Quality 

Division of Survey Operations 

Center for Financing, Access, and Cost Trends 

540 Gaither Road 

Rockville, MD 20850 

Jeffrey.Rhoades@ahrq.hhs.gov 

2
 
 

Estimating the cost of illness: the case of diabetes 

Introduction 

Health researchers and policymakers use cost of illness (COI) estimates as a 

measure of the economic burden of a disease on society. However, the estimates are not 

comparable in many cases, even for the same disease, because of differences in 

estimation techniques, data sources, and definitions of cost and disease (Kirschstein 

2000). The primary data sources for most COI studies have been surveys and reports of 

the federal government (Songer and Ettaro 1998). They include health expenditure data 

from the Centers for Medicare and Medicaid Services (CMS), cause of death data from 

the National Center for Health Statistics (NCHS), and health services use and cost data 

from CMS, NCHS, and the Agency for Healthcare Research and Quality (AHRQ). The 

Medical Expenditure Panel Survey (MEPS) sponsored by AHRQ and NCHS is the only 

one containing all of the information necessary to estimate the direct costs of a disease 

for a nationally representative sample of the U.S. civilian non-institutionalized 

population. 

In this paper we use the 2002 MEPS to illustrate two estimation methods 

commonly employed in COI studies. We have chosen diabetes as the example condition 

to illustrate the methods because it is a relatively prevalent and costly condition, and 

complications of the disease are well established (American Diabetes Association 1993). 

 The methods consist of an attributable risk procedure and a two-part regression model. 

We compare cost estimates from the two methods and assess the strengths and 

weaknesses of using MEPS as the sole data source for the estimates. We also compare 

3
 
the MEPS estimates to those from a report by the American Diabetic Association (ADA) 

for the same year (ADA 2003). The ADA report also uses a variant of the attributable 

risk procedure, but the cost estimates are based on data from several sources, including 

MEPS and other federal surveys. 

Data and Methods 

The MEPS is an annual survey of a nationally representative sample of the U.S. 

civilian non-institutionalized population conducted by the AHRQ and cosponsored by the 

NCHS. Descriptions of the survey and its methodology can be found in J. Cohen 1997, 

S. Cohen 2000, and S. Cohen 2003. We use the household component (HC) of the 2002 

MEPS in our study. The MEPS HC collects detail on demographic characteristics, health 

conditions, health status, use of medical care services, charges and payments, access to 

care, satisfaction with care, health insurance coverage, income, and employment. 

We use the expenditure and medical conditions data to estimate the direct cost of 

diabetes. The expenses represent amounts actually paid for health care services, and they 

are collected for each hospital stay, outpatient department visit, emergency room visit, 

office-based doctor visit, month of home health care, dentist visit, and prescription 

medicine purchase reported in the HC of the survey. Total expenses for an “event” are 

defined as the sum of direct payments by households, private insurance, Medicare, 

Medicaid, and other sources to providers of the care. Expenses for all of the event types 

except dental visits are used to estimate diabetes-attributable expenses. Dental care is 

excluded from the analysis because the survey respondents are not asked about the 

reason(s) for their visits to dentists. We also exclude “other medical expenses,” a catchall 

category containing annual expenses for vision products and durable medical equipment. 

4
 
Persons with diabetes are identified through the responses of survey respondents 

to questions in the household questionnaire about medical conditions and problems. In 

the priority conditions section, survey respondents are specifically asked whether they 

have ever been diagnosed with diabetes. In the conditions enumeration section, survey 

respondents are asked to list their injuries, physical conditions, and mental or emotional 

health conditions. In most of the medical events sections, survey respondents are asked 

about the reasons for their medical provider events and prescription medicine purchases. 

All of the health problems and medical conditions reported in the conditions enumeration 

and medical events sections of the questionnaire are recorded verbatim in the interviews 

and subsequently assigned a code by professional medical coders using the International 

Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Each 

hospital stay, ambulatory care visit, and prescription medicine purchase can have one or 

more ICD-9-CM codes associated with it. These self-reported conditions may not always 

conform to diagnoses by physicians, but an AHRQ staff study indicated agreement 

between household and provider reported conditions in the overwhelming majority of 

cases at the 3-digit ICD-9-CM level (Krauss and Kass 2000). 

 Our sample of persons with diabetes (2,164 respondents) contains the maximum 

number of people in the survey that could be identified as having diabetes. It includes the 

self-reported diabetics plus all other survey respondents with an event for the treatment of 

diabetes in 2002. This group represents approximately 15.5 million persons with 

diabetes, or 5.4 percent of the U.S. civilian non-institutionalized population in 2002 

(Table 1). One of the distinguishing characteristics of this group is it has a relatively high 

proportion of people with renal disease, ophthalmic disease, cardiovascular disease, 

5
 
endocrine/metabolic disease, peripheral vascular disease, neurological disease, and other 

chronic complications associated with diabetes. In 2002, for example, about threequarters of the diabetic population had at least of one these medical conditions compared 

with slightly more than one-fifth of the non-diabetic population. 

Not surprisingly, persons with diabetes have relatively high health care expenses. 

In 2002, their expenses for hospital stays, ambulatory care, home health care, and 

prescription medicines were $133.6 billion, or 18.3 percent of all health care spending for 

these types of services even though persons with diabetes comprised only 5.4 percent of 

the total population (Table 2). Nearly 90 percent of these expenses were incurred by the 

three-quarters of the diabetic population with at least one complication of diabetes. 

However, not all of the $133.6 billion in health care spending for persons with 

diabetes should be attributed to diabetes. In 2002, persons with diabetes had expenses of 

$15.9 billion for medical events where diabetes was the only reason given for the event 

(Table 3). All of these expenses are counted as part of the cost of diabetes. Another 

$11.1 billion was for medical events where diabetes was one of the reasons for the event. 

A large proportion of these expenses should be counted as part of the cost of diabetes. 

The remaining expenses ($106.5 billion) were for medical events where the reason(s) for 

the event was something other than the treatment of diabetes. Some proportion of these 

expenses should be counted as part of the cost of diabetes because they include care for 

conditions that would not have occurred or would be more easily treated in the absence of 

diabetes (ADA 1993). 

We use two methods—an attributable risk procedure and a two-part regression 

model—to estimate health care expenses attributable to diabetes. The expenses include 

6
 
 

the cost of treating chronic complications of the disease and co-morbidities requiring 

additional health care resources because the person is diabetic. Diabetics’ expenses for 

dental care, vision products, and durable medical equipment are excluded from the 

analysis because MEPS does not ask about medical conditions associated with these 

types of expenses. Further, we do not attempt to estimate indirect costs of diabetes such 

as lost workdays, permanent disability, and premature mortality. 

The Attributable Risk Procedure 

An attributable risk procedure can be used to estimate the costs for care associated 

with chronic complications of diabetes and for additional resources needed to treat a 

diabetic for the same co-morbidities as a non-diabetic. The procedure uses an etiologic 

fraction to estimate the relative contribution of a factor such as diabetes to another 

condition (Rothman 1986). For a sample of known persons with diabetes, the proportion 

of health care expenses for a condition that is attributable to diabetes is calculated based 

on the following equation: 

(1) APi = (RRi – 1)) / RRi, 

where APi is the proportion of health care for condition “i” that is attributable to diabetes 

and RRi is the relative risk of condition “i” for people with diabetes compared with 

people without diabetes. 

The attributable risk procedure assumes that other causes of conditions 

attributable to diabetes have equal impact on people with and without diabetes (Last 

1995). We estimate attributable fractions for eight categories of conditions including 

renal, ophthalmic, cardiovascular, cerebrovascular, peripheral vascular, neurological, 

other chronic complications, and all other care. The first seven categories are included in 

7
 
the analysis because they are chronic complications of diabetes—conditions that diabetics 

are at greater risk of having compared with non-diabetics. The last category—all other 

care—is included in the analysis because more resources are often needed to treat a 

diabetic for the same condition as a non-diabetic. In 1992, for example, the average 

hospital stay for a condition unrelated to be diabetes was estimated to be 2.8 days longer 

for diabetics than it was for non-diabetics (ADA 1993). 

The fraction of diabetes-attributable expenditures for each of the eight categories 

is derived from the odds of being in a specific category for people with diabetes 

compared with people without diabetes. To identify people in a specific category, we 

used the MEPS condition codes to create eight person-level flags for the 37,418 survey 

respondents in the 2002 MEPS HC. Each flag corresponds to one of the eight categories 

of conditions. A flag has a value of one if the condition in question was reported by the 

survey respondent in any section of the questionnaire collecting information on medical 

conditions and a value of zero otherwise. 

 Attributable fractions are estimated by age and race/ethnicity group for the eight 

categories of conditions. We stratified the sample by age and race/ethnicity because the 

prevalence of diabetes (and therefore a complication) increases with age and varies by 

race and ethnicity (CDC 2005). The age groups are 0-44, 45-65, and 65+, and the 

race/ethnicity categories are Hispanic of any race, white non-Hispanic, black nonHispanic black, and other non-Hispanic. The odds ratios used to determine the 

attributable fraction for each complication of diabetes and demographic group have been 

estimated using logistic regression equations specified as follows: 

Yi = 1 if the person was in category “i” and 0 otherwise. 

8
 
Xi = 1 if the person was diabetic and 0 otherwise. 

To determine which fraction to apply to expenditures for medical events by 

persons with diabetes, nine flags were created for each of their events based on the 

medical conditions associated with the events. The flags show whether the reason for the 

medical event was diabetes, one of the other eight categories of conditions, or a 

combination of conditions. The process of determining the share of expenses that should 

be attributed to diabetes is complicated, however, because the primary condition 

associated with an event is not identified in MEPS. To resolve this problem, we use the 

following rule to determine the share of expenses attributable to diabetes. If the sole 

condition code for an event was for diabetes, 100 percent of the expenses were attributed 

to diabetes. For other events with one condition code, expenses attributable to diabetes 

were determined by the attributable fraction for that condition and demographic group. 

For events with two or more condition codes, the share of expenses attributable to 

diabetes was calculated as a simple average of the attributable fractions for the conditions 

and demographic group associated with that event. 

The Two-Part Regression Model 

Our second method of estimating diabetes-attributable expenses is an application 

of the Rand Health Insurance Experiment two-part regression model. This model was 

proposed as a method of estimating health care expenditures characterized by a large 

number of zero values and a highly skewed distribution of expenditures among people 

with expenditures (Duan et al. 1983). Part one of the model is a logistic regression in 

which the dependent variable is set to 1 if the individual had expenses and to 0 otherwise. 

The independent variables include a dummy variable for diabetics (equal to 1 if the 

9
 
 

individual had diabetes and 0 otherwise) and indicator variables capturing the effect of 

the other control variables (age, race/ethnicity, and insurance coverage). Part two is an 

ordinary least-squares regression on the sub-sample of persons with expenses. The 

dependent variable is the natural logarithm of expenses, and the independent variables are 

the same ones used in part one. 

In contrast to the attributable risk procedure, the two-part regression model uses 

expenses for all sample persons rather than just those for persons with diabetes to 

estimate the cost of the disease (37,418 respondents in the 2002 MEPS). The model 

provides an estimate of the difference in expenses for diabetics relative to non-diabetics 

while controlling for other factors that may affect health care spending. This difference 

in expenses by the two groups represents the total cost of diabetes including expenses for 

complications of the disease and additional resources needed to treat other comorbidities. We control for age (<45, 45-64, 65 and over), race/ethnicity (Hispanic, 

white non-Hispanic, black non-Hispanic, all other), and insurance coverage for persons 

under age 65 (private coverage, only public coverage, uninsured) because they represent 

a basic set of factors associated with both the likelihood of a person having diabetes and 

the level of health care expenses. 

The estimate of diabetes-attributable expenses is developed from the two-part 

model as follows. First, we use the coefficients from the model to predict two expenses 

for each individual in the sample. The first predicted expense assumes the individual has 

diabetes. It is obtained by setting the diabetes indicator to one and multiplying the 

predicted probability of having any expenses from the first equation by the predicted 

average expenses from the second equation in the model. The second predicted expense 

10
 
 

assumes the individual does not have diabetes. It is obtained by setting the diabetes 

indicator to zero and multiplying the predicted probability of having any expenses from 

the first equation by the predicted average expenses from the second equation in the 

model. For both sets of predicted expenses, the log dollars are transformed to actual 

dollars using a smearing factor to correct for transformation bias (Duan 1983). Second, 

we estimate the predicted average per-person increase in medical expenses attributable to 

diabetes by taking the difference in predicted expenses for each person and computing 

the weighted average of the difference across the entire sample. Third, we estimate total 

expenses attributable to diabetes by summing the weights for all individuals with diabetes 

in the sample and then multiplying the total by the estimated per-person increase in 

expenses attributable to diabetes. 

Results 

Persons with diabetes incurred health care expenses of $133.6 billion in 2002 

(Table 4). The expenses include $15.9 billion for the treatment of diabetes, $11.1 billion 

for diabetes and other conditions, and $106.5 billion for conditions other than diabetes. 

Using the attributable risk procedure, we estimate that persons with diabetes had 

expenses of $79.1 billion for diabetes and associated problems in 2002. Approximately 

thirty percent of the diabetes-attributable expenses ($24.1 billion) were for medical 

events where diabetes alone ($15.9 billion) or diabetes and other conditions ($8.2 billion) 

were given as the reason for the event. The other diabetes-attributable expenses ($55.0 

billion) were for medical events where diabetes was not identified as a reason for the 

event (i.e., expenses for chronic complications of diabetes and other co-morbidities in 

general). These expenses represent about 59 percent of all health care expenses incurred 

11
 
 

by persons with diabetes. The other 41 percent of their expenses would have been 

incurred even if they did not have diabetes. 

The two-part model produces an estimate of $88.8 billion in diabetes-attributable 

expenses, or 66 percent of the total expenses of persons with diabetes. It has been 

derived by multiplying the weighted average difference in per person expenses of people 

with and without diabetes ($5,724) by the weighted estimate of persons with diabetes in 

the 2002 civilian noninstitutionalized population (approximately 15.5 million persons). 

The $88.8 billion represents the incremental cost of diabetes but, unlike estimates based 

on attributable risk, it cannot be disaggregated by type of condition being treated. 

However, the difference between the two estimates of total expenses attributable to 

diabetes is not statistically significant, and both estimates have small relative standard 

errors (5.4% for the attributable risk estimate and 7.9% for the two-part model estimate). 

We compare our estimates to those in the most recent report by the American 

Diabetic Association on the cost of diabetes (ADA 2003). The estimates in the ADA 

report are based on a prevalence-based attributable risk procedure designed for a mixed 

population of people with and without a disease (Rothman 1986, pp.38-39), and the cost 

and use data are from a variety of sources. Table 8 of that report (not shown) contains 

estimates of diabetes-attributable expenses for the entire U.S. population by medical 

condition and type of service in 2002. If nursing home and hospice expenses are 

excluded from the $91.9 billion in health care expenses attributable to diabetes, the 

remaining expenses of $77.4 billion are mostly those of diabetics in the civilian noninstitutionalized population. This estimate falls within the same range as ours, but the 

12
 
 

precision of the ADA estimate is unknown because standard errors are difficult or 

impossible to calculate for estimates derived from multiple sources of data. 

Discussion 

MEPS Strengths and Limitations 

MEPS is the only survey containing detailed information on medical conditions, 

health care use and expenses, and demographic and economic characteristics of a 

nationally representative sample of the U.S. civilian non-institutionalized population. 

The person-level information allows researchers to use the survey as a stand alone source 

to identify a disease population and estimate the proportion of the population’s expenses 

that are attributable to the disease. Assuming the survey respondents do not 

systematically under-report their health care, these estimates of disease-attributable 

expenses should be more accurate than those from administrative data and surveys of 

medical providers because they are based on the actual health care use and expenses of 

persons with the disease (Szava-Kovats and Johnson 1997). Moreover, the impact of 

sampling variability on the results can be assessed by calculating standard errors for the 

estimates. Because the MEPS has a complex survey design, we use SUDAAN 

(Profession software for SUrvey DAta ANalysis for Multi-stage Sample Designs) to 

compute the variance of weighted estimates of statistics derived directly from the survey 

data. For predictions of diabetes-attributable expenses, we use a balanced half-sample 

replication method to compute the variance of the predicted expenses (Korn and 

Graubard 1999). 

Another strength of MEPS is that estimates of disease-attributable expenses can 

be compared to the total expenses of the disease population to paint a more complete 

13
 
picture of the health care burden of people with the disease. In addition, although our 

illustrations focus on estimates of total expenses attributable to a disease, detailed 

estimates of the expenses can be made in many instances. Depending on the disease, it 

may be possible to estimate expenses by subpopulation, type of care or complication, or 

source of payment. All of this information can be used by health services researchers to 

highlight areas with the greatest potential for savings or by policymakers weighing 

decisions to fund research and other programs related to a disease. 

MEPS does have limitations. It excludes nursing home residents, a group with 

significant expenses for some diseases including diabetes. Furthermore, sample size may 

be an issue in estimating expenses of the civilian non-institutionalized population for less 

common diseases. Cost estimates will have relatively large standard errors if the sample 

contains few people with a disease or the disease population has few medical events. 

However, the annual files can be combined to get more precise estimates of the cost of a 

disease if sample size is a problem. 

Another consideration in using MEPS for COI work is that medical conditions are 

reported by the survey respondents. Household-reported conditions should produce 

better estimates of the prevalence and cost of a disease compared with diagnostic 

information from medical providers because identification of the disease population is 

not dependent on its seeking health care. Nonetheless, the information is householdreported and must be converted from verbatim text into ICD-9-CM codes before it can be 

used to estimate the prevalence and cost of a disease. This process of collecting and 

coding household information on medical conditions is subject to a variety of errors 

including lack of knowledge or recall problems regarding sample persons’ conditions, 

14
 
 

deliberate failure to report some conditions due to social stigma, inadequate information 

to code a condition, incomplete information on the number of conditions, and error due to 

proxy reporting (Johnson and Sanchez 1993). 

Comparison of Cost Methods 

Either method—the attributable risk procedure or the two-part model—can be 

used to estimate the cost of a disease such as diabetes. Both methods use a control group, 

although in very different ways, to estimate the cost of a disease. The attributable risk 

procedure uses odds ratios or estimates of relative risk to determine the fraction of 

expenses for a particular condition that should be attributed to a disease. Then the 

fraction is used to estimate expenses for the excess prevalence of the condition in the 

disease population. In contrast, the regression model is used to predict average expenses 

for people with and without a disease while controlling for other factors which can 

influence health care expenses. To estimate disease-attributable expenses, the difference 

in predicted average spending by the two groups is applied to the disease population. 

A general caveat in using attributable risk is that the fraction of expenses for a 

condition caused by a disease may be overstated if the influence of confounding factors is 

not accounted for in the procedure (Last 1995). For example, obesity may have a 

confounding effect on the proportion of services for cardiovascular disease that should be 

attributed to diabetes because it is associated with both diabetes and cardiovascular 

disease. In addition, estimates of the relative risk of a complication for people with and 

without a disease may be biased when based on household survey data. If diabetics, for 

instance, are monitored more closely by their physicians for complications of the disease, 

15
 
they are more likely than non-diabetics to be aware of the complications and report them 

to interviewers (Huse 1989). 

Caveats notwithstanding, one strength of the attributable risk procedure is that it 

yields information on the extent to which each complication of a disease contributes to 

medical expenses of people with the disease (not shown in this paper). This information 

is valuable because complications can add substantially to the total cost of a disease. 

However, the complications must fall within international classification of disease 

categories, and the sample must be large enough to provide reliable estimates of the 

relative risk of each complication for people with and without the disease. 

Another consideration in choosing to use the attributable risk procedure is that 

disease-specific attributable risks cannot always be estimated for every type of care. In 

MEPS, for example, medical conditions are not linked to dental care or purchases of 

vision products and durable medical equipment. This problem does not affect the twopart model because it relies on total expenses for diabetics and non-diabetics rather than 

on condition-specific expenses. However, we deliberately excluded dental care and other 

medical expenses from the two-part model estimates of the cost of diabetes in order to 

compare estimates from the two methods. 

Errors in the reporting of medical conditions can affect either cost method, but 

they may have more impact on the attributable risk procedure. It relies on householdreported conditions to identify people with and without specific conditions as well as all 

of the medical events with disease-attributable expenses. The two-part model, on the 

other hand, only uses the household-reported conditions to differentiate between people 

with and without a disease. Relatively complex statistical and modeling issues must be 

16
 
addressed, however, in a using a regression model to estimate the cost of disease. The 

two-part model used in this paper, for example, is a popular least squares method of 

modeling health care utilization and expenditures data characterized by a large number of 

zero values and a highly skewed distribution for the nonzero observations. Predictions 

from this model must be adjusted to remove the bias that results from retransforming log 

dollars to actual dollars—a straightforward process when the variance of the error term is 

constant across all observations (homoskedastic). The adjustment for retransformation 

bias is more complicated, though, when the error term is related to one or more of the 

explanatory variables in the model (heteroscedastic), and the two-part model does not 

necessarily produce estimates as precise as those from generalized linear models even 

when corrected for bias. As a consequence, the best choice of models can vary 

depending on the problem and data set (Manning and Mullahy 2001; Buntin and 

Zaslavsky 2005). 

Conclusion 

 We have used the 2002 MEPS to illustrate two popular methods of estimating the 

cost of disease. Our estimates of the cost of diabetes range from $79.1 billion based on 

attributable risk to $88.8 billion based on the two-part model. These estimates, which are 

not statistically different, indicate that a large proportion of the $133.6 billion in total 

health care spending by persons with diabetes should be attributed to the disease. 

Moreover, the two estimates are comparable to those published by the ADA ($77.4 

billion) , which used a prevalence-based attributable risk procedure and a variety of data 

sources in its report. 

17
 
MEPS sample size limitations and concerns about the accuracy of householdreported medical events and conditions may limit its use in estimating the cost of some 

diseases. Nonetheless, the survey seems well suited for analyses of highly prevalent 

diseases such as diabetes because it contains a wealth of detail on individuals and their 

health care use and expenses. The demographic and economic information can be used 

to create appropriate categories of people for cost estimation, and the detail on their use 

and expense patterns should produce cost estimates superior to those from administrative 

data sets and surveys of medical providers. In addition, disease-attributable expenses can 

be compared to total expenses of the disease population. Our point estimates of the cost 

of diabetes, for example, range from 59 to 66 percent of all expenses incurred by persons 

with diabetes. This information helps to paint a more complete picture of the impact of a 

disease on total expenses of the disease population, but it usually not presented in studies 

using other sources to estimate disease-attributable costs. 

The choice of estimation methods depends on the disease and the requirements of 

the analyst. Attributable risk provides more detail on expenses for disease-related 

conditions, but it also requires more information on the conditions of people with and 

without the disease. The two-part model requires less information on conditions 

affecting the disease population, but its estimates of the incremental cost of a disease can 

hide other information of interest to researchers and policymakers. Nonetheless, 

regardless of choice, we would argue that MEPS-based estimates of the cost of a disease 

can offer more insight about the economic burden of a disease than those from many 

other sources, including information on the impact of sampling variability on the cost 

18
 
estimates. The variance estimates from MEPS may be particularly important because 

they provide critical information on the precision and reliability of the cost estimates. 

19
 
References 

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American Diabetes Association. 2003. “Economic Costs of Diabetes in the U.S. in 

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Buntin, M. B. and A. M.. Zaslavsky. 2005. “Too much ado about two-part models and 

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Health Economics, 23(3): 525-542. 

Centers for Disease Control and Prevention. 2005. “National Diabetes Fact Sheet.” 

National Center for Chronic Disease Prevention and Health Promotion. [accessed on 

November 1, 2005]. Available at: http://www.cdc.gov/diabetes. 

Cohen, J. W. 1997. “Design and Methods of the Medical Expenditure Panel Survey 

Household Component.” Rockville (MD): Agency for Health Care Policy and Research. 

MEPS Methodology Report No. 1. AHCPR Pub. No. 97-0026. 

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Huse, D., G. Oster, G., A. Killen, M. Lacey, and G. Colditz. 1989. “The Economic 

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Johnson, A. and M. Sanchez. 1993. “Household and Medical Reports on Medical 

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Korn, E.and B. Graubard. 1999. Analysis of Health Surveys. New York: John Wiley & 

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Table 1. U.S. civilian non-institutionalized population, by diabetic status, 2002 

Diabetic Status No. of persons 

(in millions) 

Percent of total 

population 

Percent by diabetic 

and 

complication status 

All diabetics 15.5 5.4 100.0 

 No complications* 3.7 1.3 (24.1) 

 At least 1 complication* 11.8 4.1 (75.9) 

All non-diabetics 272.7 94.7 100.0 

 No complications* 213.2 74.0 (78.2) 

 At least 1 complication* 59.5 20.7 (21.8) 

All persons 288.1 100.0 

*Complications include renal disease, ophthalmic disease, cardiovascular disease, 

endocrine/metabolic disease, peripheral vascular disease, neurological disease, and other 

chronic complications. 

22
 
Table 2. Distribution of health care expenses* of the U.S. civilian noninstitutionalized population, by diabetic status, 2002 

Diabetic Status Median** Mean** Total (in millions) Percent of total 

All diabetics 3,937 8,612 133,561 18.3 

 No complications 1,528 4,303 (15,870) (2.2) 

 At least 1 complication 4,814 9,957 (117,691) (16.1) 

All non-diabetics 363 2,181 594,729 81.7 

 No complications 197 1,260 (268,574) (36.9) 

 At least 1 complication 2,099 5,483 (326,155) (44.8) 

All persons 421 2,527 728,290 100.0 

*Does not include expenses of diabetics for dental care, vision products, or durable 

medical equipment. 

** Includes a small number of persons with no expenses. 

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Table 3. Total health care expenses* of diabetics for diabetes and other conditions, 

2002 

Condition Diabetes 

only 

Diabetes and 

other conditions 

Other conditions 

Only 

All conditions 

Expense (in 

millions) 

$15,927 $11,126 $106,507 $133,561 

(Standard 

error) 

(1,202) (1,143) (6,315) (7,127) 

*Does not include expenses of diabetics for dental care, vision products, or durable 

medical equipment. 

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2002 

Table 4. Total and diabetes-attributable expenses* of diabetics by type of condition, 

Type of 

Condition 

Diabetes 

only 

Diabetes and 

other 

conditions 

Other 

conditions 

only 

Total 

expenses for 

diabetics 

Total expenses 

attributable to 

diabetes 

Total expenses of diabetics (in millions) 

Total $15,927 $11,126 $106,507 $133,561 --

(Standard error) 

(1,202) 

(1,143) (6,315) (7,127) 

Diabetes-attributable expenses (in millions) 

AR estimates $15,927 $8,211 $55,006 -- $79,144 

(Standard error) 

(1,089) 

(1,165) (5,208) (4,266) 

Two-part estimate -- -- -- -- $88,800 

(Standard error) (7,020) 

*Does not include expenses of diabetics for dental care, vision products, or durable 

medical equipment. 

25