summarizing all the main points of this research medical journal article Data tables can be found on the bottom Abstract

Data tables can b Show more Need help summarizing all the main points of this research medical journal article Data tables can be found on the bottom Abstract Objective The prevalence of obesity and associated cardiometabolic risk factors such as diabetes hyperlipidemia and hypertension is increasing significantly for all demographic groups. Research design and methods 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. Results 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. Conclusions Obesity significantly exacerbates the deleterious association between diabetes hyperlipidemia and hypertension and health function health perception and preference-based scores in the United States. Keywords Diabetes _ Health-related quality of life _ Hyperlipidemia _ Hypertension _ Obesity Background There is an epidemic of obesity in the United States and much of the world [13]. At the same time the incidence of diabetes hypertension and hyperlipidemia is increasing dramatically [3]. The prevalence of these cardiometabolic risk factors has increased significantly for both men and women and for all race ethnic age and education groups [4 5]. Cardiometabolic risk factors result in a substantially increased risk of cardiovascular disease [5 6] and mortality [7]. Individually diabetes hypertension and obesity have been shown to be negatively correlated with health function health-related quality of life (HRQL) and preferencebased scores such as the EQ-5D index [816]. In addition there have been studies examining the individual and joint effects of body mass index (BMI) and cardiometabolic risk factors [1719]. However there are limited data on the marginal impact of obesity on health function health perception and preference-based HRQL associated with common cardiometabolic risk factors such as diabetes hypertension and hyperlipidemia. P. W. Sullivan (&) _ V. H. Ghushchyan Understanding HRQL is of paramount importance to the evaluation of disease and treatment. Generic measures such as the SF-12 provide an assessment of generic health status focusing on broad domains such as physical and mental function. In addition preference-based HRQL measures such as the EQ-5D index facilitate an assessment of the value of a variety of health interventions through the calculation of quality-adjusted life years (QALYs) and subsequent cost-effectiveness analysis [11]. Rating scale methods such as the visual analogue scale (VAS) provide important information on the individuals subjective perception of their generic health status. The purpose of the current study was to examine the marginal impact of obesity on health function health perception and preference-based HRQL associated with common cardiometabolic risk factors such as diabetes hypertension and hyperlipidemia as measured by the SF- 12 VAS and EQ-5D index in a nationally representative sample of US adults. A secondary aim of the study was to examine the marginal impact of obesity on health function health perception and preference-based HRQL associated with increasing numbers of common cardiometabolic risk factors. Methods Medical Expenditure Panel Survey The Medical Expenditure Panel Survey (MEPS) is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) and the National Center for Health Statistics (NCHS). The MEPS Household Component (HC) a nationally representative survey of the U.S. civilian noninstitutionalized population contains detailed information on demographic and socioeconomic attributes health conditions insurance status smoking status HRQL missed work and use and cost of medical care services [20]. 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 2000 and 2002 MEPS public use data on 43221 individuals was used in this research (the pooled 2000 and 2002 data were used to provide a larger sample and because they each contain data on unique individuals that do not overlap) [20]. The sample design of the MEPS-HC survey includes stratification clustering multiple stages of selection and disproportionate sampling [21]. MEPS sampling weights incorporate adjustment for the complex sample design and reflect survey non-response and population totals from the Current Population Survey. Self-reported information from the MEPS-HC survey was used for the assessment of sociodemographic characteristics BMI and medical conditions. BMI was calculated from respondents estimates of current body weight and height [20]. 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] 9 703. Based on medical and pharmacy utilization and self-report medical conditions were mapped to three-digit ICD-9 codes. Then 259 mutually exclusive clinical classification categories (CCC) were mapped from ICD-9 codes in order to provide clinically homogenous groupings. The ICD-9 to CCC crosswalk is available at www.meps.ahrq.gov. The current research used CCC 49 Diabetes Mellitus Without Complication and CCC50 Diabetes Mellitus With Complication to identify diabetes and CCC 053 Disorders of Lipid Metabolism and CCC 098 Essential Hypertension to identify individuals with hyperlipidemia and hypertension respectively. Each condition (including diabetes hyperlipidemia and hypertension) was categorized into six dichotomous (yes/no) mutually exclusive variables: normal weight without the condition (reference) normal weight with the condition overweight without the condition overweight with the condition obese without the condition and obese with the condition. In addition a second set of analyses focused on the combination of conditions and weight categories. Individuals were categorized into 12 mutually exclusive groups: normal weight with no conditions normal weight with 1 condition normal weight with 2 conditions normal weight with all 3 conditions and the same respective 4 categories for overweight and obese. Further several comorbidity and sociodemographic characteristics were identified in order to control for confounding in the statistical analyses. Education was categorized: high school not completed high school completed other degree bachelors degree and masters or PhD. Race was categorized as Caucasian black American Indian or other. Ethnicity was categorized as Hispanic or non-Hispanic. Race describes or relates to biological descent while ethnicity is related to cultural heritage. Age was grouped in the following categories: 1829 3039 4049 5059 6069 7079 and C80 years. Smoking status included current smoker and not current smoker. Insurance status included public insurance private insurance and no insurance. In order to examine the mediating effects of chronic comorbidity on obesity and diabetes hyperlipidemia and hypertension a measure of chronic comorbidity was constructed from all reported CCC codes. The total number of reported conditions minus the condition of interest (i.e. diabetes) were added together to create a count variable called number of chronic conditions (NCC). 1064 Qual Life Res (2008) 17:10631071 123 MEPS allows the use of proxy respondents for individuals who cannot complete the self-administered questionnaire (most commonly the wife daughter or mother of the individual). There is evidence that the use of proxy respondents systematically bias health and quality of life estimates downward (worse ratings). Hence all multivariate analyses control for proxy response. Instruments Two health function measures were used based on the SF- 12 Health Survey. The SF-12 is a general health status instrument with 12 questions producing two summary scores the physical component summary (PCS-12) and the mental component summary (MCS-12). The PCS-12 and MCS-12 are scored so that higher scores represent better physical and mental function. Scores are also standardized so that the mean score is 50 and standard deviation is 10 in the general population. QualityMetric has developed a proprietary method for imputing PCS-12 and MCS-12 scores if data are missing and MEPS has incorporated this imputation method. In addition the EQ-5D index was included as a preference- based measure. The EQ-5D consists of a five-item descriptive system that measures five dimensions of health status (mobility self care usual activities pain/discomfort and anxiety/depression) with three levels per dimension (no problem some problems and extreme problems) resulting in a total of 243 possible unique health states. A multi-attribute value function (MAVF) is used to map preferences for these health states and derive a scoring algorithm. The scoring algorithm for the EQ-5D index descriptive system used in this research is based on U.S. community preferences. The EQ-5D index scores are calculated based on responses to the five-item questionnaire using this scoring algorithm. The EQ-5D has been used as a HRQL and preference measure in a wide variety of diseases and conditions in over 600 publications and its construct validity reliability and responsiveness have been extensively documented. In addition the EQ-5D questionnaire included a single item question asking about selfperceived health using a visual analogue scale (VAS). This question asked respondents to rate their current overall health on a scale that ranges from 0 through 100 where 0 represents worst possible health and 100 represents best possible health. Data analysis All analyses incorporated MEPS sampling and variance adjustment weights to ensure nationally representative estimates. In order to examine the marginal impact of obesity on each condition for the outcome variable of interest a separate regression was constructed for each condition controlling for sociodemographic characteristics. For example to examine the marginal impact of obesity on diabetes as measured by the EQ-5D index the index was regressed on normal weight with diabetes (normal weight without diabetes was the reference) obese without diabetes and obese with diabetes controlling for overweight without diabetes overweight with diabetes age sex race ethnicity education income smoking insurance status and proxy response. In order to examine the marginal impact of obesity we included overweight with and without diabetes in the regression in order to ensure that the comparison was between normal weight without diabetes (the reference) and obese with/without diabetes. The same approach was followed for each condition separately. In order to examine the mediating effects of chronic comorbidity the same regression analyses were conducted with the inclusion of the NCC variable to control for the total number of reported chronic conditions. An additional set of analyses examined the impact of multiple conditions and weight category on outcomes (normal weight with no conditions normal weight with 1 condition normal weight with 2 conditions normal weight with all 3 conditions and the same respective four categories for overweight and obese). For example to examine the marginal impact of combinations of conditions and weight category on EQ-5D index scores the index was regressed on 11 of the 12 dichotomous variables (normal weight with no conditions as the reference) controlling for age sex race ethnicity education income smoking insurance status and proxy response. After these initial analyses additional multivariate regressions were conducted controlling for all reported chronic conditions (NCC). The analysis of each outcome variable required a different methodological approach depending on the statistical properties of the data. Ordinary least squares (OLS) regression analysis was utilized for both the PCS-12 and MCS-12. However EQ-5D index scores exhibit a ceiling effect with a significant number of respondents rating themselves in full health [11]. In the current MEPS sample 46% of respondents rated themselves in full health (i.e. no problem on each of the five items of the EQ-5D scale) resulting in an EQ-5D index score of 1.0. Given the properties of EQ-5D index scores failure to account for censoring using OLS regression will result in biased and inconsistent estimates. For these cases the Tobit model provides an unbiased and consistent alternative. However the Tobit model is only appropriate when errors are normally distributed with constant variance. When these assumptions are violated the censored least absolute deviations estimator (CLAD) is a robust alternative to maximum likelihood estimation for the Tobit model. Prior Qual Life Res (2008) 17:10631071 1065 123 published research has shown that these assumptions are violated when analyzing the EQ-5D index in MEPS [11]. As a result this research used CLAD to estimate EQ-5D index and VAS scores. CLAD was performed in STATA using an add-in that was adjusted to incorporate sample weights [22]. Results Table 1 shows the unadjusted prevalence of obesity diabetes hyperlipidemia and hypertension by sex age race education level ethnicity and income. Obesity diabetes and hypertension are more prevalent for increasing age lower levels of educational attainment and income Hispanic black and American Indian populations. In unadjusted analyses the number of chronic conditions was higher while EQ-5D PCS-12 MCS-12 and VAS scores were lower for obese compared to normal weight individuals (Table 2). There was also a clear trend of higher comorbidity and lower scores for obese individuals with the conditions than normal weight individuals with the respective conditions. Individuals with diabetes and obesity seemed to fare the worst on all measures. After controlling for sociodemographic characteristics in the multivariate regressions individuals with diabetes 123 hyperlipidemia and hypertension had lower scores compared to normal weight individuals without the respective condition on all four measures (Table 3a). All conditions were statistically significant except hyperlipidemia for both the PCS-12 and MCS-12. Obesity significantly exacerbated the deleterious impact of each condition on all four measures (all P.05). In general the magnitude of the association was greater for PCS-12 scores compared to MCS-12 scores. The inclusion of all reported chronic conditions (NCC) in the multivariate regressions appeared to attenuate the negative association between obesity and health function health perception and preference-based HRQL for all three conditions (diabetes hyperlipidemia and hypertension) (Table 3b). In addition in multivariate analysis controlling for sociodemographic characteristics scores were lower for increasing numbers of cardiometabolic risk factors and obesity appeared to exacerbate this association (all coefficients P .05) (Fig. 1). Similarly the inclusion of NCC in
the multivariate regression attenuated the negative association of increasing numbers of cardiometabolic risk factors and obesity (all coefficients P .05 except the following: normal weight with 2 risk factors on PCS-12 normal weight with 2 and 3 risk factors on VAS and obese with 3 risk factors on VAS) (Fig. 2). To provide perspective on the significance of the results the minimum clinically important difference (MCID) is 123 typically used in conjunction with statistical significance and effect size [23]. The SF-36 MCID is 35 points [24]. While the EQ-5D MCID has not been established it may be 0.036 [10] which is comparable to the MCID of other utility instruments: 0.03 for the SF-6D [25] and Health Utilities Index [26]. ) 1068 Qual Life Res (2008) 17:10631071 123 Discussion The results of this nationally representative study in the United States show that diabetes hyperlipidemia and hypertension are associated with negative health function health perception and preference-based HRQL and that obesity exacerbates this association. In addition the results show that scores are lower with greater numbers of cardiometabolic risk factors and obesity again exacerbates this relationship. The results also suggest that the negative association between diabetes hyperlipidemia hypertension and obesity with health function health perception and preference-based HRQL is attenuated by comprehensively controlling for other chronic conditions. Previous published research has examined the impact of BMI on health function health perception and HRQL. In general these studies have found a negative correlation between BMI and physical health function and HRQL but not with mental health function [8 9 18]. In addition similar to the current study adjustment for comorbidity attenuated the negative association between BMI and health function and HRQL. There are also several studies that examine the individual impact of diabetes hyperlipidemia and hypertension. These studies find a negative association between diabetes [11 13] and hypertension [11 1416 27 28] but the impact of hyperlipidemia was not consistent. Some studies show a negative impact from hyperlipidemia [11] while others show no difference or a positive effect [28 29]. The individual impact of hyperlipidemia on health function health perception and preference-based HRQL is unclear and may be confounded by comorbidity and other sociodemographic factors. Combinations of cardiometabolic risk factors such as diabetes hypertension hyperlipidemia obesity and smoking status have been shown to have a deleterious additive impact on health function perceived health and preference- based scores [17 19 30]. In summary previous research has consistently shown the deleterious impact of these conditions alone and in combination on health function health perception and preference-based HRQL. In addition several studies have shown that the appropriate control of chronic comorbidity in multivariate models attenuates the impact of BMI. Lastly previous research has shown that cardiometabolic risk factors exhibit their impact primarily on physical function. The results of the current study are consistent with previous research findings but add new insight into the marginal impact of obesity health function health perception and preference-based HRQL associated with diabetes hypertension and hyperlipidemia. It is clear that diabetes and hypertension have a deleterious impact and obesity significantly exacerbates this effect. Similar to previous research the results for hyperlipidemia were less clear. Estimates of hyperlipidemia for normal weight individuals were very small in magnitude and not statistically significant or were positive and significant (PCS-12 after controlling for all reported chronic conditions). These results are counterintuitive and may reflect unobserved confounding factors. Future research is needed to examine this finding. However the results clearly demonstrate that obesity with hyperlipidemia has a negative association with physical function health perception and preference-based HRQL. This is an interesting and novel finding that should be explored in future research. The results of the current research are also consistent with previous findings that cardiometabolic risk factors have a more pronounced association with physical function as opposed to mental function. This research is not without limitations. The results of this study are generalizable to the United States but may not be relevant for other populations. The diabetes overweight/ obesity hypertension and hyperlipidemia prevalence estimates presented here are consistent with other survey-based national-level estimates in the U.S. However there are several reasons why the prevalence rates may underestimate national prevalence. Similar to the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) MEPS is based on self-reporting. Previous research has shown that self-reported conditions may be underreported [31] and that the extent may vary by race and ethnicity. In addition overweight respondents may tend to underestimate their weight and overestimate their height [32 33]. Unlike the National Health and Nutrition Examination Survey (NHANES) MEPS does not contain information on laboratory values and undiagnosed diabetes hypertension or hyperlipidemia. Estimates suggest that up to 35% of individuals with diabetes have not been diagnosed [34]. This downward bias may result in smaller magnitude and significance in the estimates reported in this research because the comparison group is normal weight without the respective condition (which may include individuals who have the condition or are not normal weight). In addition other data sources that contain more specific laboratory data (such as fasting glucose levels) do not contain the rich array of health function health perception and preferencebased instruments and sociodemographic data available in MEPS. Another potential limitation is the necessary exclusion of individuals without complete data on all fields in the multivariate analyses. MEPS does impute PCS-12 and MCS-12 scores based on the QualityMetric imputation algorithm. However missing items on the EQ-5D were not imputed and this may affect the studys results. Likewise no effort was made to impute VAS scores. It is also important to note that because of the cross-sectional nature of the MEPS data no cause-effect relationship can be observed. All results reported are associations. Qual Life Res (2008) 17:10631071 1069 Show less

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