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Keywords:

  • cardiovascular risk;
  • cost analysis;
  • diabetes;
  • risk factors

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Objective: Diabetes, hypertension, hyperlipidemia, and overweight/obesity often cluster together. The prevalence of these cardiometabolic risk factor clusters (CMRFCs) is increasing significantly for all sociodemographic groups, but little is known about their economic impact.

Research Methods and Procedures: The nationally representative Medical Expenditure Panel Survey was used (2000 and 2002). The current study estimated the national cost of CMRFCs independent of the cost of cardiovascular disease in the U.S., as well as the cost for all major payers and the marginal cost per individual using a Heckman selection model with Smearing retransformation. CMRFCs included BMI ≥ 25 and two of the following three: diabetes, hyperlipidemia, and/or hypertension. All amounts are expressed in 2005 $U.S.

Results: National medical expenditures attributable to CMRFCs in the U.S. totaled $80 billion, of which $27 billion was spent on prescription drugs. Private insurance paid the largest amount of the national bill ($28 billion), followed by Medicare ($11 billion), Medicaid ($6 billion), and the Veterans Administration ($4 billion), whereas individuals paid $28 billion out-of-pocket. For each individual with CMRFCs, $5477 in medical expenditures was attributable to CMRFCs, of which $1832 was for prescription drugs. On average, individuals with CMRFCs spent $1668 out-of-pocket, of which $830 was for prescription drugs.

Discussion: The results of this study show that CMRFCs result in significant medical cost in the U.S. independent of the cost of cardiovascular disease. Individuals, private insurers, Medicare, Medicaid, the Veterans Administration, and other payers all share this burden.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Diabetes, hypertension, hyperlipidemia, and overweight/obesity often cluster together in the same individual, resulting in a substantially increased risk of cardiovascular (CV)1 disease (1)(2) and mortality (3). The prevalence of these cardiometabolic risk factor clusters (CMRFCs) has increased significantly for both men and women and for all racial, ethnic, age, and education groups (particularly when considering diagnosed hypertension and hyperlipidemia) (2)(4). The aging of the population coupled with the explosive rise in cardiometabolic risk factors will contribute to increasing prevalence of CV disease over time (5). Currently, over one-fourth of the U.S. population (70 million individuals) live with CV disease, and it is the leading cause of death and morbidity, resulting in an estimated 40% of all deaths (6). Its economic impact is enormous; CV disease causes an estimated $242 billion in direct medical expenditures and $152 billion in lost productivity, resulting in a total estimated cost of $395 billion in the U.S. ($U.S. 2005) (2).

Because of the increasing prevalence of cardiometabolic risk factors and their higher likelihood of developing into CV disease, it is likely that the cost of CV disease will increase significantly in the future. However, CMRFCs result in significant medical expenditures in their own right, aside from their contribution to CV disease and before the overt development of CV disease. Although there are estimates of the cost of individual risk factors, there is a lack of data on the cost of CMRFCs independent of the cost of heart disease in the United States. The purpose of the current study was to assess the medical cost of CMRFCs independent of the cost of heart disease in the United States.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Data Source

The Medical Expenditure Panel Survey (MEPS) is cosponsored by the Agency for Healthcare Research and Quality and by the National Center for Health Statistics. The MEPS Household Component (HC), a nationally representative survey of the U.S. civilian non-institutionalized population, collects detailed information on demographic and socioeconomic characteristics, health conditions, smoking status, insurance status, and use and cost of medical care services (7). The MEPS HC collects data in each round on use and expenditures for office- and hospital-based care, home health care, dental services, vision aids, and prescribed medicines. The MEPS Medical Provider Component is a follow-back survey that collects data from a sample of medical providers and pharmacies that were used by sample persons; this survey supplements and validates information on medical care, pharmacy events, and expenditures (7). The sampling frame for the MEPS HC is drawn from respondents to the National Health Interview Survey (NHIS). NHIS provides a nationally representative sample of the U.S. civilian non-institutionalized population, with oversampling of Hispanics and blacks. The current research used the 2000 and 2002 MEPS public use data (7). The 2000 and 2002 data were used to provide a larger sample and because they each contain data on unique individuals that do not overlap (unlike MEPS 2001 data, which have panels that overlap with 2000 and 2002) but are equally nationally representative. The sample design of the MEPS-HC survey includes stratification, clustering, multiple stages of selection, and disproportionate sampling. MEPS sampling weights incorporate adjustment for the complex sample design and reflect survey non-response and population totals from the Current Population Survey.

Definition of CMRFCs

Although there is some controversy about whether common clusters of cardiometabolic risk factors reflect an underlying syndrome thought to be related to insulin resistance (commonly referred to as the metabolic syndrome), it is clear from decades of research on CV disease that diabetes, hypertension, hyperlipidemia, and overweight/obesity are prone to cluster together and result in an elevated risk of CV disease (1)(2). These common clusters of cardiometabolic risk factors were the focus of the current research. Self-reported information from the MEPS-HC survey was used for the assessment of BMI, medical conditions, and sociodemographic characteristics. Respondents were asked to estimate their current body weight and height, from which BMI was calculated (7). The following formula (from the Centers for Disease Control and Prevention, http:www.cdc.gov) was used to calculate the BMI for adults in MEPS based on reported height and weight: BMI = [weight in pounds/(height in inches)2] × 703. Full documentation is provided on page 97 of the MEPS H60 documentation file. BMI ≥ 25 was considered a cardiometabolic risk factor. In addition, respondents were asked whether they had ever been diagnosed as having diabetes (excluding gestational diabetes). MEPS mapped medical conditions to three-digit International Classification of Diseases (ICD-9) codes based on medical and pharmacy use and self-report. Then, 259 mutually exclusive clinical classification categories (CCCs) were mapped from ICD-9 codes to provide clinically homogenous groupings (7)(8). The ICD-9-to-CCC cross-walk is available at http:www.meps.ahrq.gov. The current research used CCC 053 “Disorders of Lipid Metabolism” and CCC 098 “Essential Hypertension” to identify individuals with hyperlipidemia and hypertension, respectively. Based on these risk factor definitions, individuals were considered to have CMRFCs if they had BMI ≥ 25 and any two of the following three risk factors: hypertension, hyperlipidemia, and/or diabetes, as defined in previous research (9). A dichotomous variable was created to indicate the existence of CMRFCs based on this criterion.

In addition, several comorbidity and sociodemographic characteristics were identified to control for confounding in the statistical analyses. The number of chronic conditions (NCC) was calculated by adding the total number of chronic ICD-9 codes reported for each individual, including CV disease-related conditions. From this, a measure of comorbidity burden was constructed by adding the total number of chronic conditions for each individual minus diabetes, hypertension, hyperlipidemia, and obesity. Age was categorized in the following categories: 18 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, 70 to 79, and ≥80. Education was categorized as less than high school degree, high school degree, other degree, bachelor's degree, and master's or Ph.D. Race was categorized as white, black, American Indian, or other. Ethnicity was categorized as Hispanic or non-Hispanic. Smoking status included current smoker and not current smoker. Insurance status included public insurance, private insurance, and no insurance.

Data Analysis

Medical cost data exhibit unique statistical properties that require the use of appropriate econometric techniques (10). For example, cost data are strongly right-skewed and have a significant percentage of zero-cost observations. To address these properties, Heckman selection models with logarithmic transformation and smearing retransformation (11) have been developed (12)(13). To estimate the impact of CMRFCs, a maximum likelihood Heckman selection model (14) was used with logarithmic transformation of expenditures and Smearing retransformation using the naïve (normal) assumption for residuals (11). The selection model included age, sex, race, ethnicity, education, health insurance type (public, private vs. none), chronic comorbidity (NCC), smoking status, CMRFCs (yes/no), underweight, and overweight with ≤one cardiometabolic risk factor. Underweight and overweight with ≤one cardiometabolic risk factor were included as dichotomous variables in the regression to ensure that the reference group was normal-weight. This was necessary to ensure that individuals with CMRFCs were compared with normal-weight individuals. (In contrast, if these two variables were excluded, the comparison would be between individuals with CMRFCs and all others without CMRFCs, which would include underweight individuals and overweight individuals with ≤one cardiometabolic risk factor.) The inclusion of NCC ensured that the regression equation controlled for the impact of chronic CV conditions as well as other chronic conditions that may have confounded the analysis. The expenditure equation regressed log cost on age, sex, race, ethnicity, health insurance type, chronic comorbidity (NCC), smoking status, CMRFCs (yes/no), underweight, and overweight with ≤1 cardiometabolic risk factor. From the expenditure equation, the marginal cost attributable to CMRFCs was estimated; then, using the smearing retransformation method, the average cost due to CMRFCs was calculated. To ensure that all expenditure data were expressed in a common year, 2000 and 2002 medical expenditure data were inflated to 2005 $U.S. using the Medical Care Services component of the Consumer Price Index (15). The total medical costs associated with CMRFCs in the U.S. were calculated using the following method: the average cost due to CMRFCs from the Heckman selection model described above was attributed to each individual with CMRFCs (the average cost was attributed to each individual in whom this dichotomous variable was equal to 1); then, the MEPS sample weights for each individual were used to sum the total cost for the U.S. population. Using the MEPS sample weights in this way ensured a nationally representative estimate of the total national cost. Using the rich information breaking down payer sources for medical expenditures in MEPS, the total expenditure estimates due to CMRFCs for different payers (such as Medicare, Medicaid, etc.) was calculated. The proportion of payments by payer was based on each payer's respective percentage of all payments for CMRFCs. This percentage was calculated based on each payer's contribution to the unadjusted total national expenditures for individuals with CMRFCs. The total annual national cost for each payer was then calculated by multiplying this percentage by the total national burden based on the adjusted marginal cost delineated above. All analyses incorporated MEPS sampling and variance adjustment weights to ensure nationally representative estimates.

In addition to the main approach, several sensitivity analyses were conducted to examine the impact of assumptions on the results. The purpose of the main analysis was to isolate the impact of CMRFCs, independent of their impact on CV disease and other chronic conditions. An additional sensitivity analysis was conducted without controlling for CV disease or other comorbid chronic disease. Another analysis was conducted without controlling for related CV chronic comorbidity. The unadjusted average and total national cost of CMRFCs were also compared. In addition, the impact of varying the BMI cut-off from 25 to 30 in the definition of CMRFCs was examined.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

The prevalence of CMRFCs and individual cardiometabolic risk factors by sex, age, race, education, and ethnicity are provided in Table 1. CMRFCs were more prevalent among male, older, black, and lower-educated adults. Individuals with CMRFCs had more chronic conditions and greater medical and pharmacy use compared with those without CMRFCs and compared with the national average (Table 2). In addition, they had greater unadjusted and adjusted mean healthcare expenditures and mean prescription drug expenditures overall and for all payer types when compared with the U.S. average and with those without CMRFCs (Table 3). For example, in the unadjusted descriptive statistics in Table 3, individuals with CMRFCs had mean medical expenditures of $9115 compared with $3064 for those without CMRFCs and $3444 for the U.S. average. Figure 1 displays the sources of healthcare expenditures for individuals with CMRFCs compared with those without CMRFCs and with the U.S. average. Individuals with CMRFCs had greater levels of expenditures overall, and these consisted primarily of higher spending on prescription drugs, hospitalizations, and office-based visits, whereas expenditures on dental and emergency room/urgent care appeared to be similar between individuals with and without CMRFCs. It appears that individuals with CMRFCs are more likely to have expenditures on pharmaceuticals and hospitalizations.

Table 1. . Prevalence of cardiometabolic risk factors and clusters* by selected characteristics (MEPS 2000 to 2002)
PopulationPrevalence of CV risk factor clusters* (%)Prevalence of BMI ≥ 25 (%)Prevalence of diabetes (%)Prevalence of hypertension (%)Prevalence of hyperlipidemia (%)
  • MEPS, Medical Expenditure Panel Survey; CV, cardiovascular; CMRFC, cardiometabolic risk factor cluster.

  • *

    CMRFCs = BMI ≥ 25 and two of the three following risk factors: diabetes, hypertension, and/or hyperlipidemia.

General MEPS, total N = 43,2216.2958.956.2920.188.62
Sex     
 Women5.7652.086.2921.127.74
 Men6.8666.256.2919.199.57
Age categories (yrs)     
 18 to 190.0031.360.660.440.06
 20 to 290.2548.170.872.210.36
 30 to 391.0059.921.966.492.26
 40 to 494.1164.974.1515.616.71
 50 to 599.8165.969.3929.7713.28
 60 to 6917.1467.4114.8343.7422.26
 70 to 7918.6160.8517.3051.8922.95
 80+12.6846.9614.3055.4715.80
Race     
 White6.2358.665.9019.809.15
 Black7.6468.789.1125.305.48
 Native American6.2670.008.6621.635.08
 Other race4.0537.635.9013.977.65
Education     
 No degree8.0859.449.4024.967.89
 High school degree6.4660.966.1020.598.68
 Other degree5.1360.514.9218.358.59
 Bachelor's degree4.5853.864.6814.998.25
 M.A. or Ph.D.5.2352.884.0518.1310.88
Ethnicity     
 Non-Hispanic6.5858.096.2321.209.22
 Hispanic4.0565.686.7912.173.95
Table 2. . Unadjusted mean annual number of chronic conditions, visits, and prescriptions with and without CMRFCs* in the U.S. for all adults
 U.S. average per personWith CV risk clusters* per personWithout CV risk clusters* per person
  • CMRFC, cardiometabolic risk factor cluster; CV, cardiovascular.

  • *

    CMRFCs = BMI ≥ 25 and two of the three following risk factors: diabetes, high blood pressure, and/or hyperlipidemia.

Number of chronic conditions1.615.291.84
Number of office-based visits5.0511.005.43
Number of emergency room visits0.180.330.18
Total number of prescriptions8.8039.3410.72
Table 3. . Mean healthcare expenditures with and without CMRFCs* in the U.S. for all adults†
 U.S. average Per person (unadjusted) ($US 2005)With CV risk clusters* per person (unadjusted) ($US 2005)With CV risk clusters* per person (adjusted) ($US 2005)Without CV risk clusters* per person (unadjusted) ($US 2005)
  • CMRFC, cardiometabolic risk factor cluster; CV, cardiovascular; VA, Veterans Administration; MEPS, Medical Expenditure Panel Survey.

  • *

    CMRFCs = BMI ≥ 25 and two of the three following risk factors: diabetes, high blood pressure, and/or hyperlipidemia.

  • MEPS 2000 to 2002 data adjusted for inflation to $US 2005 (15).

  • Based on the results from the multivariate regression.

Mean hospital expenditures106730141811937
Mean healthcare expenditures3444911554773064
Mean self-pay69816821668632
Mean expenditures: Medicare7502795985613
Mean expenditures: Medicaid298790352265
Mean expenditures: private insurance1384298719691276
Mean expenditures: VA9339825172
Mean prescription expenditures67625891832548
Mean prescription expenditures: self-pay2991126830244
Mean prescription expenditures: Medicare paid321709123
Mean prescription expenditures: Medicaid paid7531314359
Mean prescription expenditures: private insurance paid239802631201
Mean prescription expenditures: VA paid2214310014
image

Figure 1. : Sources of healthcare expenditures in the United States for all individuals and for those with and without CMRFCs. + unadjusted mean expenditures; * adjusted mean expenditures (from the results of the multivariate regression).

Download figure to PowerPoint

In the multivariate analysis, after controlling for sociodemographic characteristics and comorbidity, $5477 in additional medical expenditures was attributable to CMRFCs per individual, of which $1832 was for prescription drugs (Table 3). Although the majority was paid for by third party insurance, each individual spent $1668 out-of-pocket on medical expenses attributable to CMRFCs, of which $830 was spent on prescription drugs.

The national direct medical expenditures attributable to CMRFCs in the U.S. totaled $79.8 billion, of which $26.7 billion was spent on prescription drugs (Table 4). Among third party insurers, private insurance paid the largest amount of the national bill, followed by Medicare, Medicaid, and the Veterans Administration (VA); individuals paid out-of-pocket about the same as private insurance ($28 billion).

Table 4. . Total annual cost of CMRFCs* in the U.S. by insurance type ($US 2005)
Insurance payerTotal medical expenditures (including prescription expenditures; $US million)Total prescription expenditures ($US million)
  • CMRFC, cardiometabolic risk factor cluster; VA, Veterans Administration; MEPS, Medical Expenditure Panel Survey.

  • *

    Based on results from multivariate regression model.

  • CMRFCs = BMI ≥ 25 and two of the three following risk factors: diabetes, high blood pressure, and/or hyperlipidemia.

  • MEPS 2000 to 2002 expenditure data adjusted for inflation to $US 2005 (15).

Medicare10,8741324
Medicaid54832076
Private28,2729190
Self28,24812,083
VA38871449
Tricare810300
Other federal22952
Other state/local25324
Workers compensation3475
Other private803144
Other public762
Other sources46820
Total79,75026,669

It is interesting to note that being overweight with one or no cardiometabolic risk factor was not statistically significant and, hence, did not result in significantly increased cost compared with the cost for normal-weight individuals. Without controlling for CV disease or other comorbid chronic disease, sensitivity analyses resulted in a marginal and total national cost of CMRFCs of $6681 and $97.3 billion, respectively, and after controlling for chronic disease but not for chronic CV disease, the marginal and total national cost was $5752 and $83.8 billion, respectively. In addition, varying the BMI cut-off from 25 to 30 in the definition of CMRFCs resulted in a higher marginal cost of $5721, with a lower national prevalence of 3.3%, resulting in a total national cost of $45.2 billion.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

The results of this study show that CMRFCs result in significant medical cost in the U.S. independent of the cost of CV disease and that individuals, private insurers, Medicare, Medicaid, the VA, and other payers all share this burden. The purpose of this research was to examine the medical cost attributable to CMRFCs independently of assumptions about their etiology and independently of their contribution to overt CV disease. Previous research has estimated the cost of CV disease. In addition, prior research has estimated the cost of obesity. Our research adds to this body of literature by providing an estimate of the cost of CMRFCs as a distinct entity (other than the overlapping cost that may have been captured in previous cost studies of obesity and CV disease). Although it is clear that diabetes, hypertension, hyperlipidemia, and overweight/obesity are prone to cluster together, there is some controversy about whether these CMRFCs reflect an underlying syndrome thought to be related to insulin resistance (the metabolic syndrome) or simply that they tend to cluster together (1). Ezzati et al. (16) have quantified the significant detriment to global health caused by hypertension, hyperlipidemia, and high BMI, among other risk factors. It is clear that the prevalence of CMRFCs is increasing dramatically and represents a significant burden to public health in its own right. The current analysis examines the impact of these CMRFCs on medical expenditures irrespective of the underlying pathophysiology and independently of their contribution to overt CV disease.

Previous research has examined the economic cost of individual cardiometabolic risk factors. Hodgson et al. (17) estimated that the direct medical costs attributable to diabetes ranged from $34.3 to $63.7 billion in 1995 ($50 to $93 billion in 2005), depending on whether unrelated conditions, comorbidities, and/or chronic complications were included. In addition, Hogan et al. (18) extrapolated the cost of diabetes to include $23.2 billion for diabetes care, $24.6 billion for chronic complications attributable to diabetes, and $44.1 billion for excess prevalence of general medical conditions, totaling $91.8 billion in 2002 ($106 billion in 2005). Hodgson et al. (19) estimated that hypertension was responsible for $22.8 billion in direct medical expenditures in 1998 ($30.4 billion in 2005), whereas Druss et al. (20) estimated that 14.8 billion in the 1996 MEPS was attributable to treatment for hypertension ($21 billion in 2005). Finkelstein et al. (21), using the 1998 MEPS, estimated that the direct medical cost of overweight/obesity in the U.S. was $51.5 billion ($68.8 billion in 2005). In comparison, Druss et al. (20) estimated the direct medical costs associated with five common chronic conditions in the 1996 MEPS. They found that hypertension, mood disorders, asthma, diabetes, and heart disease were associated with direct medical expenditures for treatment of $14.8, $10.2, $5.7, $10.1, and $21.5 billion in 1996, respectively ($21, $14.5, $8, $14.3, and $30.5 in 2005). In addition, prior research has examined the economic impact of CV disease in the U.S. Although the source data are somewhat outdated, these estimates provide a reference point for comparison (2). It is estimated that CV disease caused an estimated $242 billion in direct medical expenditures in the U.S. in 2005 (2).

However, the estimates from the current research may not be comparable with the aforementioned studies because, although many are based on similar nationally representative data, the study designs are very different. For example, Druss et al. (20) used unadjusted means to derive estimates of treatment costs for five chronic conditions; these estimates may be more comparable with the unadjusted estimates presented in Table 3. Hodgson et al. (17)(19) attempted to specifically incorporate all attributable costs, such as unrelated conditions, comorbidities, and complications. Hogan et al. (18) used an attributable risk framework to model the expected costs of diabetes and its impact on other conditions based on rates and prices from different data sources. Finkelstein et al. (21) estimated the costs of overweight/obesity but did not explicitly control for comorbidity (directly related or unrelated to obesity). Although different in their methodological approach, these important and well-conducted studies document that individual cardiometabolic risk factors have a significant economic impact in the U.S. Their estimates also lend credibility to the economic toll of CMRFCs estimated in the current study.

Although there are estimates of the economic impact of individual CV risk factors, to the authors’ knowledge, there are no estimates of the combined impact of common CMRFCs. Particularly lacking is an assessment of the cost of CMRFCs independent of their impact on CV disease. The current research estimates the marginal impact of CMRFCs after controlling for CV disease and other comorbidities and sociodemographic characteristics. The results suggest that CMRFCs result in $80 billion in direct medical expenditures in the U.S. and underscore the economic impact of these common clusters of cardiometabolic risk factors.

The results of the current study also highlight two important trends: first, the enormous impact that CMRFCs have on federal and state budgets through Medicare, Medicaid, and the VA; second, the large proportion of medical expenditures attributable to prescription drugs. Because many of the most effective and promising treatments of individual cardiometabolic risk factors and CMRFCs are pharmaceutical, the impact on the Medicare budget will likely worsen dramatically as the prescription drug benefit is instituted. However, it should be noted that better access to and use of effective treatments (both primary and secondary) may offset or delay downstream medical costs of CV disease. The current study shows that a significant and increasing portion of pharmaceutical expenditures are paid out-of-pocket by the individual. Perhaps the Medicare drug benefit will result in a relatively larger increase in expenditures for Medicare and a leveling off of the proportion paid by individuals.

The current study likely represents a significant underestimate of the economic impact of CMRFCs in the U.S. To comprehensively quantify the costs attributable to CMRFCs, their impact on the development and cost of CV disease and the exacerbation of other comorbidities would need to be quantified and incorporated. There is strong clinical evidence of the link between CMRFCs examined in this research and the development of CV disease and the exacerbation of other comorbidities. For example, one method used to estimate the costs of CV disease and unrelated comorbidities attributable to CV risk factor clusters is to estimate the total national cost of CV disease and comorbid illness and then estimate the proportion of these costs due to cardiometabolic risk factors. This may be more controversial (and may be more likely to lead to an overestimate of the economic cost).

To provide a range of estimates, the current study conducted a sensitivity analysis using the same methods but without controlling for other comorbid conditions and using BMI ≥ 30 as the cut-off rather than BMI ≥ 25. The results of this sensitivity analysis suggest that the cost of CMRFCs may be as high as $97.3 billion ($U.S. 2005) in the U.S. if potential exacerbations in unrelated comorbidities are included. The individual cost would be highest with BMI ≥ 30 as the cut-off, but due to a lower prevalence, the national cost would be lowest ($45.2 billion). In addition, the present study does not estimate the cost of lost productivity due to morbidity and mortality attributable to CMRFCs, which is likely to be significant. Nonetheless, the figures presented in this research demonstrate the significant economic impact of CMRFCs in their own right.

Although higher than those estimated by Oldridge et al. (22), the diabetes, overweight/obesity, hypertension, and hyperlipidemia prevalence estimates presented here are consistent with other survey-based national-level estimates in the U.S. (23)(24)(25)(26) However, it is likely that the prevalence rates in this study are also underestimates of national prevalence. First, similar to the NHIS and the Behavioral Risk Factor Surveillance System, MEPS is based on self-report. There is evidence that self-reported conditions may be under-reported in general (27) and that blacks, whites, and Hispanics differ in reporting of disease labeling and levels of illness and disability (28)(29)(30). Second, previous studies have shown that overweight respondents tend to underestimate their weight and overestimate their height (31)(32). Third, unlike the National Health and Nutrition Examination Survey, MEPS does not contain information on undiagnosed diabetes, hypertension, or hyperlipidemia, contributing to the likelihood that the national estimates from the current analysis are biased downward. Recent estimates suggest that ∼35% of individuals with diabetes have not been diagnosed (33). Fourth, the NHIS/MEPS sample frame excludes institutionalized individuals. Fifth, more recent estimates show that the prevalence of cardiometabolic risk factors (treated and untreated) have increased since 2002 (34)(35). As a result, it is likely that the national prevalence of CMRFCs estimated in this analysis is an underestimate. Hence, the economic impact of CMRFCs is likely underestimated to a similar degree. Previous studies have found Hispanics to have a higher prevalence of diabetes, hyperlipidemia, and overweight/obesity (36)(37)(38)(39)(40). The current results are inconsistent with these findings, but there is evidence that blacks, whites, and Hispanics differ in self-reporting of disease labeling and levels of illness and disability (28)(29)(30). This fact may explain the inconsistent findings related to the prevalence of disease among Hispanics. Other sources that may contain more specific diagnosis and laboratory data (such as fasting glucose levels) do not contain the rich array of expenditure data available in MEPS.

Despite the limitations, the current study provides important evidence of the economic impact of CMRFCs in a nationally representative population and shows that common clusters such as overweight/obesity, diabetes, hypertension, and hyperlipidemia have a significant impact on the U.S. economy. Despite the clear clinical and economic toll for individuals and federal and state governments and private insurers, the prevalence of these CMRFCs is increasing significantly in almost every demographic group (2)(4). These risk factors may be preventable, and public health efforts need to be undertaken to prevent their alarming increases in prevalence. Efforts need to be taken to curb the underlying causes of their increasing prevalence, such as pervasive overweight/obesity and physical inactivity in the U.S. population and growing similar trends among children. In addition, aggressive management and treatment of CMRFCs may help prevent or delay the onset of overt CV disease. Unless these public health efforts are undertaken, the economic toll of CMRFCs will continue to grow unabated and drain resources from the U.S. economy.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

This research was supported by a research grant from Sanofi-Aventis.

Footnotes
  • 1

    Nonstandard abbreviations: CV, cardiovascular; CMRFC, cardiometabolic risk factor cluster; MEPS, Medical Expenditure Panel Survey; HC, Household Component; NHIS, National Health Interview Survey; ICD, International Classification of Diseases; CCC, clinical classification category; NCC, number of chronic conditions; VA, Veterans Administration.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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