Patrick W. Sullivan

Objective.: The Medical Expenditure Panel Survey (MEPS) was used to estimate the national influence of rheumatoid arthritis (RA) on employment, limitations in work or housework, inability to work or do housework, missed work days, days spent sick in bed, and annual wages.Methods.: MEPS is a nationally representative survey of the US population. Multiple logistic, negative binomial, and Heckman selection regression methods were used, controlling for age, sex, race, ethnicity, smoking status, income, education, and chronic comorbidity. RA was identified using International Classification of Diseases-9 code 714.Results.: In unadjusted descriptive statistics, individuals with RA were older, had more chronic conditions, missed more work days, spent more days sick in bed, had lower employment rates, had higher rates of limitations and inability to work, and received disability benefits at higher rates. After adjustment, multiple regression analyses showed individuals with RA were 53% less likely to be employed [OR 0.47, 95% CI 0.34–0.65], 3.3 times more likely to have limitations in work or housework (95% CI 2.35–4.64), 2.3 times more likely to be unable to work or do housework (95% CI 1.55–3.53), and spent 3.6 times as many days sick in bed as those without RA (95% CI 2.32–5.53). RA was associated with an expected loss of $8957 in annual earnings (95% CI $1881–$15,937). There was no statistically significant difference in missed work days or the level of wages.Conclusion.: In the most recent available national data for adults, RA was associated with reductions in employment, productivity, and function.

The prevalence of obesity and associated cardiometabolic risk factors such as diabetes, hyperlipidemia and hypertension is increasing significantly for all demographic groups. The 2000 and 2002 Medical Expenditure Panel Survey (MEPS), a nationally representative survey of the U.S. population, was used to estimate the marginal impact of obesity on health function, perception, and preferences for individuals with diabetes, hyperlipidemia, and hypertension using multivariate regression methods controlling for age, sex, race, ethnicity, education, income, insurance, smoking status, comorbidity, and proxy response. Three different instruments were used: SF-12 physical component scale (PCS-12) and mental component scale (MCS-12); EQ-5D index and visual analogue scale (VAS). Censored least absolute deviation was used for the EQ-5D and VAS (due to censoring) and ordinary least squares (OLS) was used for the PCS-12 and MCS-12. After controlling for sociodemographic characteristics, diabetes, hyperlipidemia, and hypertension were associated with significantly lower scores compared to normal weight individuals without the condition for all four instruments. Obesity significantly exacerbated this association. Controlling for comorbidity attenuated the negative association of obesity and cardiometabolic risk factors on instrument scores. In addition, scores decreased for increasing weight and number of risk factors. Obesity significantly exacerbates the deleterious association between diabetes, hyperlipidemia, and hypertension, and health function, health perception, and preference-based scores in the United States.

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.

The assessment of health-related quality of life (HRQL) for patients with hormone-refractory prostate cancer (HRPC) is of paramount importance because new treatments have a modest impact on survival but side effects of treatment and disease symptoms can significantly impact HRQL. This was an observational, non-interventional, multi-center, multi-national cohort study of patients with metastatic HRPC. Health-related quality of life was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ C30), the Functional Assessment of Cancer Therapy-Prostate (FACT-P) and the EQ-5D index. Mean changes from baseline to month 3, 6, and 9 were computed and tested using paired t-tests. FACT-P PCS, EQ-5D index and 10 of 14 EORTC domains were statistically significantly lower (P < 0.05) than the baseline scores at the 3, 6 and 9 month visits. The domains that did not reach statistical significance were cognitive functioning, insomnia, diarrhea and financial difficulties. These data demonstrate that patients with metastatic HRPC experience rapid, significant deterioration in HRQL, highlighting the need for effective palliative therapy for men with HRPC.

To determine whether diabetes mellitus (DM) with minor depression is associated with poorer levels of mental and physical functioning compared with DM without depression. Retrospective database study. US adults participating in the 2001 Medical Expenditure Panel Survey were included in these analyses. Main outcome measures were differences in health status, physical and cognitive limitations, and the Short-Form 12 (SF-12) Mental Component Summary (MCS) and Physical Component Summary (PCS) for US adults with DM stratified by minor depression status and evaluated using univariate and multivariate analyses to control for demographic, behavioral, and clinical covariates. A total of 1572 respondents with DM were included (1443 without depression, 129 with depression). Compared with people with DM and without depression, those with DM and minor depression were younger (P = .04); were more likely to be female, white, and smokers; and to have physical and cognitive limitations and lower SF-12 MCS and PCS scores (all P < .01). In multivariate analyses, minor depression was independently associated with lower self-reported health status, MCS scores, and more cognitive limitations. People with DM and minor depression have lower mental functional scores, more cognitive limitations, and lower self-reported health status scores compared with people with DM and without depression, differences that may adversely affect self-care activities. Primary and DM care providers should screen for and be aware of depression in their patients with DM.

Objective:Research has shown that risk factors for cardiovascular disease often cluster together, most notably overweight/obesity, diabetes, hyperlipidemia, and hypertension. The impact of cardiometabolic risk factor clusters on health‐related quality of life (HRQL) is not well understood. The purpose of this study was to examine and quantify the impact of cardiometabolic risk factor clusters on HRQL as measured by the SF (Short Form)‐12 Mental Component Scale (MCS‐12), SF‐12 Physical Component Scale (PCS‐12), EQ‐5D index (a generic quality of life index), and Visual Analogue Scale.Research Methods and Procedures:The Medical Expenditure Panel Survey is a nationally representative survey of the U.S. population. From 2000 to 2002, detailed information on sociodemographic characteristics and health conditions were collected for 36,697 adults with complete responses. Controlling for comorbidity and sociodemographic characteristics, this study estimated the marginal impact of cardiometabolic risk factor clusters on MCS‐12, PCS‐12, EQ‐5D index, and Visual Analogue Scale scores. Cardiometabolic risk factor clusters were defined as the presence of BMI ≥25 kg/m²and at least two of the following: diabetes, hyperlipidemia, and hypertension. Using BMI ≥30 kg/m²as the cut‐off was also examined.Results:The marginal impact of cardiometabolic risk factor clusters was highly statistically significant across all four HRQL measures and seemed to be clinically significant for all but the MCS‐12. The PCS‐12 showed a greater decrease in HRQL associated with physical function compared with mental function‐related domains of the MCS‐12.Discussion:Common cardiometabolic risk factor clusters such as overweight/obesity, diabetes, hypertension, and hyperlipidemia have a significant and negative impact on HRQL in the United States.

Background Health status measures provide a numeric score representing a profile of health status across several domains, such as physical and mental health. The SF-12 and its longer form, the SF-36, are examples of generic health status measures. Although these Background. Previous mapping algorithms estimating EQ-5D index scores from the SF-12 were based on preferences from a UK community sample. However, preferences based on the general US population are most appropriate for costeffectiveness analyses done from the societal perspective in the United States. Objective. To provide a mapping algorithm for estimating EQ-5D index scores from the SF-12 based on a nationally representative sample and using preferences based on the general US population.Methods. The Medical Expenditure Panel Survey (MEPS) 2002 and 2000 data were used as independent derivation and validation sets to estimate the relationship between SF-12 scores and EQ-5D index scores, controlling for sociodemographic characteristics and comorbidity burden. Prediction equations for end-users who only have access to SF-12 scores were derived and compared. The empirical performance of censored least absolute deviations (CLAD), Tobit, and ordinary least squares (OLS) analytic methods were compared by calculating the mean prediction error in the validation set.Results. The fully specified CLAD model resulted in the lowest mean prediction error, followed by OLS and Tobit. The CLAD prediction equation based only on SF-12 scores performed better than the fully specified OLS and Tobit models.Conclusion. The current research provides an algorithm for mapping EQ-5D index scores from the SF-12. This algorithm may provide analysts with an avenue to obtain appropriate preference-based health-related quality-of-life scores for use in cost-effectiveness analyses when only SF-12 data are available.