The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
THE RELATIONSHIP BETWEEN BODY MASS INDEX AND QUALITY OF LIFE IN COMMUNITY-LIVING OLDER ADULTS LIVING IN THE UNITED STATES F.G. BOTTONE JR1, K. HAWKINS1, S. MUSICH1, Y. CHENG1, R.J. OZMINKOWSKI2, R.J. MIGLIORI3, C.S. YEH4
1. Advanced Analytics, OptumInsight, Ann Arbor, MI 48108, USA; 2. OptumHealth Care Solutions, Ann Arbor, MI 48108, USA; 3. UnitedHealth Group Alliances, Minnetonka, MN 55343, USA; 4. AARP Services, Washington, DC20049, USA. Corresponding author: Frank G. Bottone, Jr., PhD, LDN, Advanced Analytics, OptumInsight, 315 E. Eisenhower Parkway, Suite 305, Ann Arbor, MI 48108, USA
Abstract: Background: Carrying excess weight is associated with various chronic conditions especially in older adults, and can have a negative influence on the quality of life of this population. Objective: The objective of this study was to estimate the independent (i.e. adjusted for demographic, socioeconomic and health status differences) impact of Body Mass Index (BMI) on health-related quality of life. Design: A mail survey was sent to 60,000 older adults living in 10 states. Methods: The survey assessed quality of life using the average physical component scores (PCS) and mental component scores (MCS) obtained from the Veterans Rand 12-item (VR-12) health status tool embedded in the survey. Ordinary least squares (OLS) regression techniques were used to estimate the independent impact of each BMI category on quality of life, compared to the impact of other chronic conditions. Results: A total of 22,827 (38%) eligible sample members responded to the survey. Of those, 2.2% were underweight, 38.5% had a normal BMI, 37.0% were overweight, 18.5% were obese and 1.9% were morbidly obese. Following OLS regression techniques, respondents’ PCS values were statistically significantly lower for the underweight, overweight, obese and morbidly obese BMI categories, compared to the normal BMI group. Compared with all other chronic conditions, being morbidly obese (-6.0 points) had the largest negative impact on the PCS. Underweight was the only BMI category with a statistically significantly lower MCS value. Conclusions: The greatest negative impacts of the various BMI categories on quality of life were on physical rather than mental aspects, especially for those in the underweight, obese and morbidly obese categories, more so than many other chronic conditions.
Key words: Medicare, quality of life, obesity, underweight.
Introduction
Based on data from the National Health and Nutrition Examination Survey (NHANES), the Centers for Disease Control and Prevention (CDC) currently estimates that 78.4% of men and 68.6% of women 60 years of age or older are overweight or obese (1, 2). Overweight and obesity are risk factors for mortality (1, 3, 4), disability (5), mobility problems (6) and a variety of chronic diseases such as cardiovascular disease (7), cancer (8), depression (9), diabetes (10) and osteoarthritis (11). The negative impact of carrying excess weight on quality of life has been demonstrated using generic (12-16) and obesity specific quality of life measures with relatively consistent results (17-21). These studies have shown that the greatest impact of carrying excess weight appears to be on physical rather than mental aspects of quality of life. Carrying excess weight can be especially burdensome in older adults, influencing their ability to complete activities of daily living and is associated with functional decline (4, 22, 23) and decreased quality of life (6, 13, 16, 24-28). The primary objective of this study was to examine the independent impact (i.e. adjusted for demographic, socioeconomic and health status differences) that each BMI category has on health-related quality of life in adults (65 years or older). The secondary objective was to compare the impact of weight on quality of life with that of other chronic
Received November 8, 2012 Accepted for publication January 7, 2013
conditions.
Methods
Sample Selection Approximately 9 million people purchase supplemental insurance plans to defray the out-of-pocket expenses from copayments, coinsurance and deductibles that Medicare does not cover in entirety (27). In 2011, about three million Medicare beneficiaries were covered by an AARP® Medicare Supplement Insurance Plan insured by UnitedHealthcare Insurance Company (for New York residents, UnitedHealthcare Insurance Company of New York) (27). These plans are offered in all 50 states, Washington DC and various US territories. A randomly selected sample of 60,000 eligible members with such a plan were surveyed between 2008 and 2011 (e.g. 15,000 per year). Eligible sample members were 65 years of age or older on December 31 of the year in which they were surveyed, were required to be enrolled in such a plan when the survey was fielded in May and June of the year in which they were surveyed and live in one of the ten states surveyed (Arizona, California, Colorado, Florida, Ohio, North Carolina, New Jersey, New York, Missouri and Texas). Those excluded from the study did not meet the eligibility criteria noted above or did not respond to the survey.
495
The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
BODY WEIGHT AND QUALITY OF LIFE
Data Source Data for this study was collected from the Health Update Survey (HUS). The HUS is a self-administered mail survey that includes the same questions as the Medicare Health Outcomes Survey (HOS), which is a commonly used tool to evaluate the health status and quality of life in adults with Medicare Advantage plans. The HOS contains self-reported height, weight and questions on health outcomes designed for older adults, providing an opportunity to estimate the burden of weight on quality of life in the Medigap population. The HOS was designed by CMS to gather valid, reliable and clinically meaningful health status data from older adults for use in quality improvement and other activities. For our use with a Medigap sample, we simply renamed the HOS as the HUS, as requested by the Centers for Medicare and Medicaid Services (CMS) so as not to confuse it with the HOS instruments used with Medicare managed care plans. We were not permitted by CMS to change the questions in the HUS survey. The HUS instrument includes all of the questions from the Veterans RAND 12-item (VR-12) health status/quality of life survey, in addition to several questions on demographics, chronic conditions and health concerns (29). The VR-12 is identical to the generic quality of life survey, SF-12. The VR12 is extensively used and has been included in the Health Plan Employer Data and Information Set (HEDIS) and is an integral part of the Medicare HOS, which is used to monitor the Medicare Advantage Program. The SF-12 (and by extension the VR-12) is highly correlated with the SF-36 and the VR-12 has been widely used and validated in older adults (30, 31) including obese populations making it an ideal general health survey for this study (28, 32). De-identified data from survey respondents were divided into the following five standard BMI categories based on their self-reported height and weight collected on the surveys: underweight (BMI at or below 18.5), normal-weight (BMI 18.6–24.9), overweight (BMI 25–29.9), obese (class I and II, BMI 30–39.9), and morbidly obese (class III, BMI 40 or greater) (33). Missing BMI resulting from either missing height or weight was maintained as a separate category. Insufficient numbers were available to allow for further categorization by obesity class (i.e. obesity class I-III).
Health-Related Quality of Life Since the BMI categories were evaluated relative to various chronic conditions, no single disease-specific quality of life instrument could be used. We therefore relied on the VR-12, a generic quality of life measurement tool to facilitate these comparisons. The outcome variables of interest included the VR 12’s physical component score (PCS) and mental component score (MCS). The scores summarize physical and mental health status, measures commonly used to evaluate health-related quality of life. These summary scores were calculated from eight VR-12 subscale scores, which measure: physical functioning, the ability to handle physical roles, bodily
496
pain, general health, vitality, social functioning, the ability to handle emotional roles and mental health. For each subscale, a score was calculated if at least 50% of the items in the scale were completed (this is commonly referred to as the “halfscale” rule) (34). As in other studies, the physical and mental component summary scores were then calculated when all eight subscale scores were not missing (35). The algorithms used to calculate scale scores were the same as those described elsewhere (29). Physical and mental component scores may range from zero (the worst possible quality of life) to 100 (the best possible quality of life). To allow our results to be compared with the other samples (e.g. from the US population or other Medicare samples), the physical and mental component scores were standardized to have a mean of 50 and a standard deviation of 10; this standardization process has been recommended elsewhere (36). A score of 50 represents the national average, with a score 10 points above or below 50 representing a difference of one standard deviation from the national average for the PCS or MCS. Covariates Demographic questions on the HUS survey instrument included those such as age, gender, race, marital status and living arrangements. Socioeconomic questions included race, income and education level. Questions about health status included those on the existence of common comorbid health conditions (e.g., diabetes, hypertension, respiratory disorders), experiencing a fall/trouble walking and smoking status. The HUS also included a question about whether the survey was completed by the person it was addressed or by someone else. This may be a proxy for dexterity due to declining functional status, cognitive issues or other serious illness (37). Variables denoting missing responses to these survey questions were included in the analyses to avoid loss of sample members in the statistical analyses. Multivariate logistic regression analyses were used to confirm the relationships between the BMI categories and all of the previously mentioned demographic, socioeconomic and health status variables.
Statistical Analysis The first analysis was descriptive, and categorized sample respondents by demographics, socioeconomics and clinical characteristics and compared underweight, overweight, obese and morbidly obese BMI categories to the normal weight group using univariate techniques. Chi-square and Student t tests were used to test for differences in categorical and continuous variables, respectively. All analyses were performed using SAS software (version 9.1; Cary, NC). The second analysis used multivariate ordinary least squares (OLS) regression techniques to estimate the independent impact of each BMI category on the quality of life measures, controlling for patient demographics, socioeconomics and other health status metrics across the weight groups as described
The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
JNHA: NUTRITION
previously (38). The results of these analyses were used to identify which covariates (BMI category, patient demographics, socioeconomics and health status metrics) had a significant impact on quality of life and the magnitude of that impact. The latter was important because it allowed the authors to rank which covariates had the largest impact on quality of life in relation to the impact of each BMI category.
Ethical Considerations This study was performed in accordance with the principles outlined in the Declaration of Helsinki (39) and in compliance with the “Protection of Human Subjects and Animals in Research” as described in the recommendations of the International Committee of Medical Journal Editors (40). Participants were notified in writing with the survey that participation was voluntary and that their responses would only be used in a blinded fashion in aggregate. Results
Sample Characteristics The demographic, socioeconomic and health status characteristics of the eligible survey respondents are detailed in Table 1 according to BMI category. There were 21,771 eligible respondents included in this survey for a response rate of 36.3%. Respondents were primarily female, White, living in a metropolitan area and had an average age of 77 years. Over half of the respondents were overweight or obese. Specifically, the respondents had the following BMI categories: 2.2% were underweight, 38.5% had a normal BMI, 37.0% were overweight, 18.5% were obese and 1.9% were morbidly obese. An additional 1.9% were missing BMI information. Based on the demographic information, those in the morbidly obese category had lower education levels and were more likely to report having hypertension, angina, congestive heart failure, arthritis and diabetes. Conversely, in our population, those reporting underweight were mostly frail older women, who reported having osteoporosis, arthritis and respiratory problems and who were smokers and at high risk of falling/trouble walking.
Unadjusted prevalence of chronic health conditions by weight category The prevalence of chronic health conditions is common in older adults and increases with weight. In our study, the prevalence of hypertension was 60.5% overall and increased with each BMI category ranging from 45.8% for those who were underweight to 81.8% for those who were morbidly obese (Figure 1). Similarly, the prevalence of arthritis of the hip or knee (37.4% overall) and that of diabetes (17.7% overall) increased with each BMI category from those who were underweight to those who were morbidly obese. Meanwhile, respiratory disorders were most common in the underweight and morbidly obese categories, and followed a U-shaped curve similar to that seen with all-cause mortality and age similar to
that seen in the Tromsø study which reported the impact of underweight and other weight categories on disease prevalence (41). Similar to the chronic conditions noted, the variable “advised to exercise by their physician” was reported by 45.3% of respondents overall and increased with each BMI category. Risk of falling/trouble walking was reported in 37.0% of respondents overall and was more likely to be reported in those who were underweight, obese or morbidly obese, relative to those who were normal weight. These results were confirmed using multivariate logistic regression modeling, which adjusted for the demographic, socioeconomic and health status measures (data not shown). Unadjusted impact of BMI categories on Quality of life Prior to adjusting the PCS and MCS scores for demographic, socioeconomic and health status differences using OLS regression techniques, the overall mean of the population was 44.02 and 54.69 for the PCS and MCS, respectively. As expected in an older population, this is lower than the adjusted national average of 50 across all age groups. Across BMI categories, relative to the normal weight group, the PCS values were statistically significantly reduced for all of the weight categories except overweight. The reductions in MCS values were less dramatic except in the underweight category, where it was significant (Table 1). Figure 1 Prevalence of select chronic diseases in relation to BMI category in the study population
Adjusted impact of BMI categories on quality of life Quality of life was assessed using the average PCS and MCS obtained from the VR-12 health status tool. The OLS regression controlled for patient demographics, socioeconomics and other health status metrics. The results were used to identify which covariates (BMI categories, patient demographics, socioeconomics and health status metrics) had statistically significant impacts on physical and mental quality of life and the magnitude of those impacts relative to the respective reference groups. Various BMI categories, patient demographic, socioeconomic and health status metrics either
497
The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
BODY WEIGHT AND QUALITY OF LIFE Table 1 Characteristics of the Study Population Overall
Characteristic Age
Gender Race
Education
Salary Marital Status
MSA
Living Arrangement Completed the survey
Comorbid Condition
Other Risks Outcome Measure
Total (%) Group
65-74 75-84 85+ Male Female Non-white White High school Some college or two year degree Four years of college or more 0-$19,999 $20,000-$39,999 $40,000-79,999 $80,000 or more Married Not Married Metropolitan area Micropolitan area Other area Owned by self Owned by someone else Rented for money Other Person to whom addressed Someone else completed Hypertension Angina Congestive Heart Failure Myocardial Infarction Other Heart Condition Stroke Respiratory Disease Digestive Disorders Arthritis of the Hip or Knee Arthritis of the Hand or Wrist Osteoporosis Diabetes Cancer Advised to exercise Smoke Risk of falling/trouble walking PCS MCS
Underweight
21,771 (100%) 491 (2.3%) Percent (%) Percent (%)
42.4 36.7 19.5 40.8 58.2 4.2 94.1 40.8 26.1 29.2 16.1 24.1 22.1 14.2 51.3 44.7 87.2 5.7 7.1 74.5 5.2 10.6 5.1 86.3 6.5 60.5 13.1 6.5 9.0 24.4 6.7 12.7 4.0 37.4 34.2 21.2 17.7 19.1 45.3 6.7 37.1 44.02 54.69
27.5* 33.2 38.7* 12.6* 87.4* 5.9 93.1 45.6* 27.1 23.8* 27.7* 24.0 13.4* 8.8* 31.0* 65.8* 84.3* 9.0* 6.7 64.0* 7.1 15.7* 7.7 78.8* 12.6 45.8* 10.0 8.4* 8.4 30.8* 7.1 21.4 4.7 28.3 34.6 45.2* 9.2 18.1 33.6* 17.1* 49.7* 40.09* 51.31*
Missing BMI
417 (1.9%) Percent (%)
14.4* 16.8* 25.2 17.5* 40.3* 4.3 54.4* 52.0* 17.7* 20.9* 18.9 17.0* 12.2* 8.6* 39.6* 51.1 85.6 7.0 7.4 59.5* 5.8 13.2 9.4* 72.9* 7.7 54.4 10.8 8.4* 7.0 26.4 7.0 13.7 3.8 40.8* 34.1 20.9* 21.1* 13.9* 20.1* 5.5 22.5* 41.43* 51.97*
Normal Weight (Reference Group) 8,372 (38.5%) Percent (%)
35.2 37.6 26.6 31.6 68.2 4.4 94.8 39.0 25.7 31.3 16.2 22.7 21.1 14.5 46.9 49.0 87.6 5.6 6.7 72.8 5.3 11.6 5.5 85.7 6.8 52.8 10.8 5.2 7.4 24.1 6.1 11.6 4.6 31.6 33.4 28.5 10.7 19.7 40.4 7.3 36.5 45.10 54.59
Overweight
Obese
Morbidly Obese
8,062 (37.0%) Percent (%)
4,027 (18.5%) Percent (%)
402 (1.9%) Percent (%)
44.7* 38.3 16.5* 52.9* 47.0* 3.7 95.2 39.5 26.0 30.8 14.1* 24.5* 23.5* 16.0* 57.0* 39.3* 87.2 5.3 7.5 77.4* 4.5 9.1* 4.7 87.5* 5.9 62.6* 14.6* 6.4* 10.2* 24.4 6.8 11.3 3.3* 36.9* 32.4 15.5* 18.1* 19.6 46.5* 6.2 34.6* 44.94 55.11*
55.6* 34.7* 9.2* 42.5* 57.3 4.2 94.6 44.8* 27.8 24.1* 17.7 26.7* 23.4* 12.0* 53.2* 43.4* 87.0 6.1 6.9 75.4* 5.8 10.2 4.6 87.7* 6.4 72.7* 15.1* 8.4* 10.2* 23.9 7.1 15.6* 3.9 48.9* 38.1* 15.1* 29.6* 17.9 55.4* 5.6* 41.4* 41.50* 54.91
61.4* 29.4* 7.7 32.3 67.7 5.5 93.5 48.8* 25.9 21.1 22.4* 27.9 22.1 7.5* 48.8 47.3 86.6 5.7 7.7 70.6 6.7 14.7 3.7 86.1 7.0 81.8* 16.4* 12.4* 9.0 25.6 8.2* 21.1* 5.7 62.9* 46.5* 17.7* 42.0* 15.7 65.4* 4.2 54.2* 35.60* 53.20*
Notes: *Statistically significant (p<0.01) from the normal weight (reference group). Those with missing outcome variables (quality of life scores) were excluded (n=1,056 or 4.6%). Those with missing BMI values (n=417) are shown for comparison. For brevity, the percentage of those with missing values for each category are not shown but were included in the analysis, thus categories may not total 100%. Not married includes: divorced, separated, widowed and never married. Digestive disorders included Crohn’s disease, ulcerative colitis or inflammatory bowel disease. Respiratory disease included emphysema, asthma and chronic obstructive pulmonary disease (COPD). Advised to exercise included: Advised to start, increase or maintain physical activity. Abbreviations: MSA, Metropolitan Statistical Area. Location of residence and survey hear were included in the descriptive analyses, but, for brevity, results are not shown in the table.
positively or negatively affected physical and mental components of quality of life as illustrated in Table 2. For ease of comparison, the impact each BMI category on the two components is illustrated graphically in Figure 2.
Physical health standpoint From a physical standpoint, relative to normal weight, all the BMI categories (underweight, overweight, obese and morbidly obese) had a significant negative impact on the PCS relative to normal weight, with scores ranging between -0.6 to -6.0 points.
498
Of the weight categories, being morbidly obese (-6.0 points) had the largest impact on the PCS compared with all other chronic conditions including congestive heart failure (-3.5), arthritis of the hip (-4.0) and respiratory problems (-4.1). Being morbidly obese had the second largest negative impact on physical quality of life compared with all the characteristics measured—only risk of falling/trouble walking (-7.0) had a more negative impact. In addition, being obese (-2.6 points) or underweight (-2.1 points) were among the top 10 variables that negatively affected quality of life from a physical standpoint.
The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
JNHA: NUTRITION
Other factors with relatively large decreases in quality of life from a physical standpoint included being 85 years of age or older or having someone else complete the survey. Factors associated with an increased physical quality of life included those with higher education and those with higher annual household income. Table 2 Independent impact of weight, demographic and health status characteristics on quality of life
Dependent Variable
Characteristic
Physical Component Score (PCS) Parameter Estimate (Relative to reference group)
Intercept Underweight Overweight Obese Morbidly Obese Missing BMI 75-84 85+ Male Non-white Some college or two year degree Four years of college or more Salary: $20,000-$39,999 Salary: $40,000-79,999 Salary: $80,000 or more Not Married Micropolitan area Other area Owned by someone else Rented for money Other living situation Someone else completed the survey Hypertension Angina Congestive Heart Failure Myocardial Infarction Other Heart Condition Stroke Respiratory Disease Digestive Disorders Arthritis of the Hip or Knee Arthritis of the Hand or Wrist Osteoporosis Diabetes Cancer Advised to start, increase or maintain physical activity Smoke Risk of falling/trouble walking
53.0** -2.1** -0.6** -2.6** -6.0** -0.8 -2.1** -4.9** 0.3 -0.2 0.8** 1.5** 0.8** 1.8** 3.0** 0.9** -0.3 -0.3 -1.0** -0.4 -0.6 -4.0** -1.4** -1.6** -3.5** -1.0** -1.8** -1.5** -4.1** -1.8** -4.0** -1.4** -1.8** -1.7** -1.0** -0.1 -0.9** -7.0**
Mental Component Score (MCS) Parameter Estimate (Relative to reference group) 55.0** -1.9** 0.3 0.6** -0.2 -0.8 0.4* 0.0 -0.2 -0.3 0.5** 0.4* 1.1** 1.6** 1.9** 0.0 -0.4 0.1 -0.5 -0.5* -1.0** -4.9** -0.1 -0.8 -1.5** 0.0 -0.8** -0.9** -1.0** -3.0** 0.2 -0.8** -0.8** -0.5** -0.1 0.0 -1.2** -3.1**
Reference groups: Age: 65 to 74; Gender: Female; Race: White; Education: High school degree; Annual income: $0 to $19,999.; Marital status: Married; Metropolitan Statistical Area: Metropolitan; Living arrangements: Owned or being bought by you; Weight category: Normal weight; Completed survey: Person to whom addressed; health status conditions: Not present. * p<0.01; ** p<0.001 statistically significant relative to the reference group. Notes: Digestive disorders included Crohn’s disease, ulcerative colitis or inflammatory bowel disease. Values are adjusted for demographic, socioeconomic and health status differences based on the OLS regression model. The OLS models adjusted for state of residence, however, for brevity the relative impacts are not shown. Respiratory disease included emphysema, asthma and chronic obstructive pulmonary disease (COPD). Not married includes: divorced, separated, widowed and never married. Abbreviations: BMI, Body Mass Index. Location of residence and survey year were included in the regression modeling, but, for brevity, results are not shown in the table.
Mental health standpoint The chronic condition with the greatest negative impact on quality of life from a mental health standpoint was having digestive disorders (-3.0), more so than the weight categories. Of the BMI categories, only underweight was significantly
associated with a decrease in mental quality of life (-1.9 points). Being obese was associated with a significant increase in mental quality of life; however, the magnitude was small (+0.6 points). Being overweight or morbidly obese did not have a significant impact on mental components of quality of life. This may be a reflective of a more relaxed attitude toward body image and body weight. As with the physical quality of life component, there was in increase in mental quality of life associated with higher income and a decrease associated with risk of falling/trouble walking and someone else completing the survey. Figure 2 Change in PCS and MCS summary scores relative to normal weight following OLS regression techniques to estimate the independent impact of each BMI category on quality of life
Discussion
Overweight and obesity are nearing epidemic proportions in the United States, affecting people of all ages. In our study, 57.4% of adults 65 years and older were overweight, obese or morbidly obese. This is lower than the 64.7% reported in the Medicare Advantage program using the same self-reported survey (42). This is also lower than the 65% seen in adults 65 or older in the 2011 Behavioral Risk Factor Surveillance System (BRFSS) survey and the 73% reported in those 60 years or older in the NHANES survey (1, 43). Higher obesity rates are seen in the BRFSS and the NHANES surveys as they are administered by health care professionals. These differences from the Medicare Advantage population are somewhat to be expected because the Medigap population generally has a higher socioeconomic status and are healthier on average (e.g., lower prevalence of chronic conditions) than Medicare Advantage members as illustrated by studies utilizing the Medicare HOS dataset. Those with multiple illnesses and fewer economic resources often select Medicare Advantage plans to reduce out-of-pocket expenses. Like the Medicare HOS dataset, our study relied on self-reported height and weight data, which
499
The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
BODY WEIGHT AND QUALITY OF LIFE
may slightly underestimate BMI (44). The impact of obesity and morbid obesity on the physical component of quality of life was significant in this population. A decline in physical quality of life was seen with increasing weight category from normal weight to morbid obesity. For the extreme BMI categories, morbid obesity and underweight, the negative impact of obesity was greater than the impact of many chronic conditions. Of the chronic conditions, morbid obesity had the greatest negative impact on physical quality of life, and was second only to risk of falling/trouble walking, a significant health risk in older adults that can have a large impact on quality of life. Furthermore, obesity is associated with an increased likelihood of other chronic conditions such as diabetes, cardiovascular disease and arthritis, which might result in a decreased ability to perform activities of daily living and/or decreased mobility. This might explain, at least in part, their decreased physical quality of life. These results are not surprising given that numerous studies report the impact of excess weight on quality of life. In the study by Wee et al. consisting of adults (24-95 years of age) living in Asia, the authors measured the independent impact of each BMI category on quality of life similar to the present study (28). The adjusted impact of obesity and morbidly obesity on PCS scores was 0.8 and 2.1 points lower than normal weight individuals, respectively. From a mental health standpoint, MCS values were similar across BMI categories relative to normal weight, except for the underweight category, which reported a decrease of 1.3 points. However, few studies on the impact of weight on physical and mental quality of life focus on older adults (13, 16, 26, 45, 46). Similarly, while other studies have utilized regression methods to adjust for some confounding variables (46, 47), these studies have focused on populations including a full age range from younger to older (24, 28). Our study is in general alignment (i.e., lower scores associated with obesity) with those studies focusing on older adults while also controlling for common demographic and health risk differences (13, 16). The impact of obesity on quality of life reported herein are in alignment (i.e., lower scores associated with obesity) with those using other quality of life surveys in older populations such as the Quality of Wellbeing Score (13) and the Health Status Questionnaire-12 (16) controlling for common demographic and health risk differences. These results are comparable to that seen in our study where the average adjusted PCS values -2.6 and -6.0 points relative to normal weight individuals following OLS regression techniques. In addition to focusing on older adults, our study measured the independent impact (i.e. adjusted for demographic, socioeconomic and health status differences) that each BMI category has on health-related quality of life. Other studies that include the impact of chronic conditions categorize them as 1, 2 or 3 comorbid conditions (25) or use them separately as control variables (28), in either case, they were not the focus of the study. Unlike these other studies, we compared the independent
500
impact of various chronic conditions common in older adults with that of the BMI categories. Compared to these chronic conditions, being morbidly obese had the largest impact on quality of life PCS component compared with all other chronic conditions and was second only to risk of falling/trouble walking when all the factors are considered. Similar to morbid obesity, being underweight had a significant negative impact on physical and mental quality of life in older adults as reported here and elsewhere (16, 24, 46, 48). While those on either end of the spectrum of BMI category had increased risk of falling/trouble walking and respiratory problems, other reasons for the decreased physical quality of life among these groups likely differ. Based on demographic information, in this study, the negative impact of underweight on mental quality of life might also be explained by the number of smokers, those with respiratory disorders and osteoporosis and those at risk of falling/trouble walking. Limitations include the relatively low response rate, and the fact that BMI was based on self-reported height and weight. Additionally, the study sample consisted only of beneficiaries 65 years of age or greater enrolled in an AARP Medicare Supplement plan; therefore, the results may not be generalized to all Medicare beneficiaries. Lastly, the information on comorbid conditions was self-reported, leaving open the possibility of misclassifying these problems. The strengths of this study include the fact that no other studies from the United States have reported the impact of various BMI categories on quality of life in older adults. Based on the position statement by American Society for Nutrition (ASN) and the North American Association for the Study of Obesity (NAASO), moderate weight loss of 5%-10% can improve the risk for various chronic diseases in older adults (49) while other studies have shown improvements in quality of life with moderate weight loss in this population (50-54). Recently, the U.S. Preventive Service Task Force recommended that clinicians screen all adults for obesity, while offering intensive counseling and behavioral interventions to promote sustained weight loss in obese adults (55). Medicare now covers intensive behavioral therapy under certain restrictions (in a primary care setting by a qualified primary care physician or other primary care practitioner or a registered dietitian if physician supervised, etc.) (56). Programs might consider incorporating nutrition interventions such as nutrition counseling by a registered dietitian, to improve quality of life among older adults. Although underweight is less prevalent, interventions, including screening, aimed at identifying and improving the quality of life for this population should also be considered.
See COI form for complete disclosures: This research work was funded by the Medicare Supplement Health Insurance Program. The investigators retained full independence in the conduct of this research. See manuscript for details.
1.
References
Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA. 2010;303(3):235-41.
The Journal of Nutrition, Health & Aging© Volume 17, Number 6, 2013
2. 3.
4.
5.
6. 7. 8.
9.
10.
11.
12.
13.
14.
15. 16.
17.
18. 19. 20. 21. 22.
23.
24. 25. 26. 27.
28. 29.
30.
JNHA: NUTRITION
CDC. Centers for Disease Control and Prevention 2009. Health Data Interactive Data Sources. 2009 [cited 2011 August 29, 2011]; Available from: http://www.cdc.gov /nchs/data_access/hdi/hdi_data_sources.htm. Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA. 2007;298(17):2028-37. Lang IA, Llewellyn DJ, Alexander K, Melzer D. Obesity, physical function, and mortality in older adults. J Am Geriatr Soc. 2008;56(8):1474-8. Hubert HB, Bloch DA, Fries JF. Risk factors for physical disability in an aging cohort: the NHANES I Epidemiologic Followup Study. J Rheumatol. 1993;20(3):480-8. Houston DK, Ding J, Nicklas BJ, Harris TB, Lee JS, Nevitt MC, et al. Overweight and obesity over the adult life course and incident mobility limitation in older adults: the health, aging and body composition study. Am J Epidemiol. 2009;169(8):927-36. Holvoet P, Kritchevsky SB, Tracy RP, Mertens A, Rubin SM, Butler J, et al. The metabolic syndrome, circulating oxidized LDL, and risk of myocardial infarction in wellfunctioning elderly people in the health, aging, and body composition cohort. Diabetes. 2004;53(4):1068-73. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. JAMA. 2005;293(15):1861-7. Scott KM, McGee MA, Wells JE, Oakley Browne MA. Obesity and mental disorders in the adult general population. J Psychosom Res. 2008;64(1):97-105. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282(16):1523-9. Anandacoomarasamy A, Caterson ID, Leibman S, Smith GS, Sambrook PN, Fransen M, et al. Influence of BMI on health-related quality of life: comparison between an obese adult cohort and age-matched population norms. Obesity (Silver Spring). 2009;17(11):2114-8. Coons SJ, Rao S, Keininger DL, Hays RD. A comparative review of generic quality-of-life instruments. Pharmacoeconomics. 2000;17(1):13-35. Groessl EJ, Kaplan RM, Barrett-Connor E, Ganiats TG. Body mass index and quality of well-being in a community of older adults. Am J Prev Med. 2004;26(2):126-9. Hawthorne G, Richardson J, Osborne R. The Assessment of Quality of Life (AQoL) instrument: a psychometric measure of health-related quality of life. Qual Life Res. 1999;8(3):209-24. Osborne RH, Hawthorne G, Lew EA, Gray LC. Quality of life assessment in the community-dwelling elderly: validation of the Assessment of Quality of Life (AQoL) Instrument and comparison with the SF-36. J Clin Epidemiol. 2003;56(2):138-47. Yan LL, Daviglus ML, Liu K, Pirzada A, Garside DB, Schiffer L, et al. BMI and healthrelated quality of life in adults 65 years and older. Obes Res. 2004;12(1):69-76. Kolotkin RL, Crosby RD, Kosloski KD, Williams GR. Development of a brief measure to assess quality of life in obesity. Obes Res. 2001;9(2):102-11. Kolotkin RL, Head S, Hamilton M, Tse CK. Assessing Impact of Weight on Quality of Life. Obes Res. 1995;3(1):49-56. Le Pen C, Levy E, Loos F, Banzet MN, Basdevant A. "Specific" scale compared with "generic" scale: a double measurement of the quality of life in a French community sample of obese subjects. J Epidemiol Community Health. 1998;52(7):445-50. Mannucci E, Ricca V, Barciulli E, Di Bernardo M, Travaglini R, Cabras PL, et al. Quality of life and overweight: the obesity related well-being (Orwell 97) questionnaire. Addict Behav. 1999;24(3):345-57. Zabelina DL, Erickson AL, Kolotkin RL, Crosby RD. The effect of age on weight-related quality of life in overweight and obese individuals. Obesity (Silver Spring). 2009;17(7):1410-3. Himes CL, Reynolds SL. Effect of obesity on falls, injury, and disability. J Am Geriatr Soc. 2012;60(1):124-9. Jensen GL, Hsiao PY. Obesity in older adults: relationship to functional limitation. Curr Opin Clin Nutr Metab Care. 2010;13(1):46-51. Hopman WM, Berger C, Joseph L, Barr SI, Gao Y, Prior JC, et al. The association between body mass index and health-related quality of life: data from CaMos, a stratified population study. Qual Life Res. 2007;16(10):1595-603. Huang IC, Frangakis C, Wu AW. The relationship of excess body weight and healthrelated quality of life: evidence from a population study in Taiwan. Int J Obes (Lond). 2006;30(8):1250-9. Lynch CP, McTigue KM, Bost JE, Tinker LF, Vitolins M, Adams-Campbell L, et al. Excess weight and physical health-related quality of life in postmenopausal women of diverse racial/ethnic backgrounds. J Womens Health (Larchmt). 2010;19(8):1449-58. Medpac. Medpac Report. 2011 [June 14, 2011]; June:[Available from: http://www.medpac.gov/documents/Jun11_EntireReport.pdf. Wee HL, Wu Y, Thumboo J, Lee J, Tai ES. Association of body mass index with ShortForm 36 physical and mental component summary scores in a multiethnic Asian population. Int J Obes (Lond). 2010;34(6):1034-43. Selim AJ, Rogers W, Fleishman JA, Qian SX, Fincke BG, Rothendler JA, et al. Updated U.S. population standard for the Veterans RAND 12-item Health Survey (VR-12). Qual Life Res. 2009;18(1):43-52. Resnick B, Nahm ES. Reliability and validity testing of the revised 12-item Short-Form Health Survey in older adults. J Nurs Meas. 2001;9(2):151-61.
31.
32.
33. 34. 35.
36.
37.
38. 39. 40. 41. 42. 43. 44. 45.
46.
47. 48.
49. 50.
51. 52. 53.
54.
55. 56.
501
Ware J, Jr., Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-33. Wee CC, Davis RB, Hamel MB. Comparing the SF-12 and SF-36 health status questionnaires in patients with and without obesity. Health and quality of life outcomes. 2008;6:11. NHLBI. National Heart, Lung, and Blood Institute (NHLBI) Obesity Task Force. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults—the evidence report. Obes Res. 1998;6((Suppl 2)):51-209. Rogers WH, Qian S, Kazis L. Imputing the Physical and Mental Summary Scores (PCS and MCS) for the MOS SF-36 and the Veterans SF-36 Health Survey in the presence of Missing Data. 2004 [December 4, 2012]; Available from: http://hosonline.org/surveys/hos/ download/HOS_Veterans_36_Imputation.pdf. Kosinski MK, Bayliss MS, Bjorner JB, Ware JE. Improving estimates of SF-36® health survey scores for respondents in missing data. Medical Outcomes Trust Monitor. 2000;5:8-10. Ware JE, Kosinski, M., Turner-Bowker, D.M., and Gandek, B. SF-12v2™: How to Score Version 2 of the SF-12® Health Survey. Lincoln, RI: QualityMetric Incorporated. 2002. Neumann PJ, Araki SS, Gutterman EM. The use of proxy respondents in studies of older adults: lessons, challenges, and opportunities. J Am Geriatr Soc. 2000;48(12):1646-54. Hawkins K, Bottone FG, Jr., Ozminkowski RJ, Musich S, Bai M, Migliori RJ, et al. The prevalence of hearing impairment and its burden on the quality of life among adults with Medicare Supplement Insurance. Qual Life Res. 2011. World_Medical_Association. World Medical Association Declaration of Helsinki. Recommendations guiding physicians in biomedical research involving human subjects. Cardiovascular research. 1997;35(1):2-3. ICMJE. International Committee of Medical Journal Editors (ICMJE): Uniform Requirements for Manuscripts Submitted to Biomedical Journals: writing and editing for biomedical publication. Haematologica. 2004;89(3):264. Kvamme JM, Wilsgaard T, Florholmen J, Jacobsen BK. Body mass index and disease burden in elderly men and women: the Tromsø Study. European journal of epidemiology. 2010;25(3):183-93. CMS. Medicare HOS 2009 Medicare Advantage Baseline Report. June 2010. 2010 [March 24, 2011]; Available from: http://www.hosonline.org/surveys/hos/ download/HOS_SampleBaseline_C12.pdf. Hassan MK, Joshi AV, Madhavan SS, Amonkar MM. Obesity and health-related quality of life: a cross-sectional analysis of the US population. Int J Obes Relat Metab Disord. 2003;27(10):1227-32. Gorber SC, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Rev. 2007;8(4):307-26. Okoro CA, Hootman JM, Strine TW, Balluz LS, Mokdad AH. Disability, arthritis, and body weight among adults 45 years and older. Obes Res. 2004;12(5):854-61. Renzaho A, Wooden M, Houng B. Associations between body mass index and healthrelated quality of life among Australian adults. Qual Life Res. 2010;19(4):515-20. Garcia-Poma A, Segami MI, Mora CS, Ugarte MF, Terrazas HN, Rhor EA, et al. Obesity is independently associated with impaired quality of life in patients with rheumatoid arthritis. Clin Rheumatol. 2007;26(11):1831-5. Garner RE, Feeny DH, Thompson A, Bernier J, McFarland BH, Huguet N, et al. Bodyweight, gender, and quality of life: a population-based longitudinal study. Qual Life Res. 2011. Villareal DT, Apovian CM, Kushner RF, Klein S. Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society. Obes Res. 2005;13(11):1849-63. Kolotkin RL, Crosby RD, Williams GR, Hartley GG, Nicol S. The relationship between health-related quality of life and weight loss. Obes Res. 2001;9(9):564-71. Kolotkin RL, Norquist JM, Crosby RD, Suryawanshi S, Teixeira PJ, Heymsfield SB, et al. One-year health-related quality of life outcomes in weight loss trial participants: comparison of three measures. Health and quality of life outcomes. 2009;7:53. O'Keefe KL, Kemmeter PR, Kemmeter KD. Bariatric surgery outcomes in patients aged 65 years and older at an American Society for Metabolic and Bariatric Surgery Center of Excellence. Obes Surg. 2010;20(9):1199-205. Villareal DT, Banks M, Sinacore DR, Siener C, Klein S. Effect of weight loss and exercise on frailty in obese older adults. Arch Intern Med. 2006;166(8):860-6. Villareal DT, Chode S, Parimi N, Sinacore DR, Hilton T, Armamento-Villareal R, et al. Weight loss, exercise, or both and physical function in obese older adults. The New England journal of medicine. 2011;364(13):1218-29. USPSTF. Screening for Obesity in Adults, Topic Page. November 2011. U.S. Preventive Services Task Force. 2011 [cited 2012 5/22/2012]; Available from: http://www.uspreventiveservicestaskforce.org/uspstf/uspsobes.htm. Final Coverage Decision Memorandum for Intensive Behavioral Therapy for Obesity [database on the Internet]. 2011. Available from: http://www.cms.gov/medicare-coveragedatabase/details/nca-decision-memo.aspx?NCAId=253&ver=1&b c=AAAAAAAAIAAA&.
