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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 58:M1018-M1030 (2003)
© 2003 The Gerontological Society of America

Prevalence and Correlates of Overweight and Obesity Among Older Adults: Findings From the Canadian National Population Health Survey

Mark S. Kaplan1, Nathalie Huguet1, Jason T. Newsom2, Bentson H. McFarland3 and Joan Lindsay4

1 School of Community Health and the
2 Institute on Aging, Portland State University, Oregon.
3 Department of Psychiatry, Oregon Health & Science University, Portland.
4 Centre for Chronic Disease Prevention and Control, Health Canada, Ottawa.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 Appendix
 References
 
Background. The prevalence of obesity among elderly persons in industrialized countries ranges from 15% to 20%. Little is known about variations of overweight within subgroups of the elderly population. This study examined the factors associated with overweight and obesity among older men and women.

Methods. Data for 12,823 community-dwelling persons aged 65 and older from the 1996–1997 Canadian National Population Health Survey were examined. Predictors of overweight (body mass index [BMI] = 25.0–29.9 kg/m2) and obesity (BMI = >30 kg/m2) relative to normal weight (BMI = 20.0–24.9 kg/m2) were examined using logistic regression analyses. Analyses were stratified by gender. The predictor variables included age, education, marital status, place of birth, region, smoking status, alcohol use, chronic conditions, physical activity, functional limitations, self-rated health, social support, and psychological distress.

Results. Overall, 39% and 13% of Canadian older adults were classified as overweight and obese, respectively. Some of the risk factors for overweight were male gender, low education, being married, Canadian born, residence in the Atlantic provinces, no use of alcohol, comorbidity, physical inactivity, and limited functional status. Risk factors for obesity were similar to those for overweight except for being unmarried; American, European, and Australian born; lower and higher levels of alcohol use; poor self-rated health; and psychological distress.

Conclusions. The results could lead to more effective weight-control interventions that are designed to promote increased physical activity and healthy eating habits among obese older individuals.

OBESITY is now recognized as a major and rapidly growing public health problem. Medical authorities around the world are concerned with promoting optimal weight for all adults to preserve the quality of their lives throughout the life span (1–6). The prevalence of obesity among elderly persons in industrialized countries varies greatly, but estimates suggest an overall prevalence ranging from 15% to 20% (7,8). A recent report by the Centers for Disease Control and Prevention found that, between 1991 and 1998, the obesity rate for the U.S. population aged 60–69 years and 70 years and older rose 45% and 29%, respectively (7). More recently, Flegal and colleagues reported a 12% and 11% increase between 1988 and 2000 in the prevalence of obesity among men and women aged 60 to 74 years, respectively (9). In fact, the oldest age group experienced the largest obesity gain relative to younger study participants. Because all age groups had substantially higher obesity rates (7), it is likely that as more aging baby boomers enter late adulthood, their obesity rates will be higher than those of the current elderly cohort. Equally important, obesity in the older population will contribute greatly to health care utilization and cost (10).

Extremes of weight (very high [>30] or very low [<18] body mass indices [kg/m2]) are associated with poorer health status in elderly populations (11). There is some evidence, nonetheless, that obesity may have a more pronounced impact on morbidity than on mortality in later life (12,13). In fact, obesity is a serious condition associated with debilitating and life-threatening medical conditions, such as type 2 diabetes mellitus (14,15), hypertension (14,15), coronary artery disease (14–17), osteoarthritis (14), certain cancers (18,19), and diminished functional status (15,16,20–22). Studies have also found associations between older adult obesity and depression (23) and diminished quality of life (24).

Many of the health problems related to overweight and obesity among older persons are the same as among younger age groups. However, while more information is available on the physical, social, and economic factors associated with higher body mass index (BMI) scores in younger people (25), relatively little data exist on how patterns of overweight and obesity vary within subgroups of the elderly population (14). Using data from the Longitudinal Study of Aging and the Assets and Health Dynamics of the Oldest Old Survey, Himes (15) found that older women are less likely than older men to be overweight but more likely to be obese. The author also found that education was negatively associated with overweight and obesity but marital status was not related to body size. In another study involving data from the Epidemiologic Follow-up Study of National Health and Nutrition Examination Survey I, Launer and colleagues (16) demonstrated that lower education and never smoking were associated with higher BMI.

The present cross-sectional study addressed a wide array of potential risk and protective factors for overweight and obesity (as defined by the BMI) in a population-based sample of older adults. Using data from the Canadian National Population Health Survey (NPHS), we examined the prevalence and correlates of overweight and obesity among older adults (65 years of age and older). The NPHS offers the strengths of a population-based survey that includes a wider array of factors than examined in previous obesity research. The present study was designed to determine the prevalences of overweight and obesity in community-dwelling older men and women, and their association with demographic, behavioral, health, and psychosocial factors. The objectives of this study were to: (a) present representative prevalence data for overweight and obesity among older adults; (b) examine the independent prediction abilities of a variety of psychosocial, sociodemographic, behavioral, and health factors, including gender, age, education, marital status, smoking, health status, functional limitations, social support, place of residence, and psychological distress; (c) determine whether moderate use of alcohol lowers the risk of overweight and obesity; (d) determine if educational attainment is associated with overweight and obesity; (e) examine regional variations in overweight and obesity; and (f) determine if reporting higher levels of psychological distress is associated with overweight and obesity and whether this relationship is independent of other predictors such as perceived social support. Information on the prevalence of overweight and obesity risk factors in the older adult population, as well as other factors affecting their distribution, is crucial for designing effective clinical and community interventions to reduce overweight and obesity.


   METHODS
Top
 Abstract
 Methods
Results
 Discussion
 Appendix
 References
 
We analyzed data from the 1996–1997 NPHS (second wave), a large, population-based longitudinal study conducted by Statistics Canada. Analyses were conducted with the NPHS public use microdata file. The questionnaire comprised two components: the general, which was administered to all household members, and the health, which was administered thereafter to selected respondents. Using a multistage, stratified, random sampling procedure, the NPHS surveyed 73,402 households across Canada by telephone in the health component of the study. The overall response rate at the household level was 82.6% (26). Of the 13,744 people aged 65 and older who were selected as respondents (from the completed general component), 13,404 completed the health component, for a response rate of 97.5% (F. Brisebois, personal communication, 2001). Analyses in the present study were conducted using the 1996–1997 cross-sectional health file for persons aged 65 and older who had complete data (N = 12,823).

Using multiple logistic regression, we employed an array of putative factors to predict the likelihood of overweight and obesity relative to normal weight among older adults. We calculated BMI (weight in kilograms divided by the square of height in meters) based on self-reported weight and height. Following modified criteria of the World Health Organization, we classified participants as obese if their BMI was greater than or equal to 30 kg/m2 (27). Overweight was defined as a BMI range of 25.0 to 29.9 kg/m2 (28). A BMI range of 20.0 to 24.9 kg/m2 (normal weight) was used as the reference category (29–33). Explanatory variables consisted of gender, age (5-year groupings), education (equal or less than secondary, greater than secondary), marital status, place of birth (Canada, United States/Europe/Australia, other), geographic region (Atlantic, Quebec, Ontario, Prairie, British Columbia), smoking status (never or former versus current), alcohol use (abstainer [none], infrequent [<1 drink per week], moderate [1 drink per week to 2 drinks per day], heavy [3 or more drinks per day]), comorbidity (the number of chronic conditions, including asthma, arthritis or rheumatism, back problems, high blood pressure, chronic bronchitis or emphysema, diabetes, heart disease, effects of a stroke, bowel disorder, Alzheimer's disease, cataracts, or glaucoma), physical activity (inactive respondents were those who participated in physical activity lasting more than 15 minutes fewer than 11 times per month), functional limitations (need for help with instrumental or basic activities of daily living), self-rated health status (fair/poor versus excellent/very good/good), perceived social support, and nonspecific psychological distress. Social support was measured with 4 items that reflected whether the respondents felt that they had someone they could confide in, count on, give them advice, and make them feel loved and cared for. The total social support score was derived from the sum of all affirmative responses (yes versus no) to the 4 items. The scale, derived from the previous literature and especially designed for the NPHS, taps major distinct dimensions of social support such as instrumental, informational, appraisal, and emotional (34). Following Kessler and colleagues (35), psychological distress was assessed by 6 items on a 5-point Likert scale, ranging from "all of the time" to "none of the time." The respondents indicated the frequency in the past month they had felt "so sad that nothing could cheer you up," "nervous," "restless or fidgety," "hopeless," "worthless," or "everything was an effort."

All analyses were adjusted for the complex design of the survey and for the analytic weights provided by the NPHS with the use of SUDAAN statistical software (release 8.0; Research Triangle Institute, Research Triangle Park, NC). SUDAAN weights percentages and logistic regression estimates using the sampling weights in order to estimate population values accurately. Adjustments to standard errors for significance tests were then made using jackknife estimates to account for the NPHS sampling design. A multivariate logistic regression modeling procedure was used to determine the independent effect of specific predictor variables on overweight and obesity. All predictor variables were entered into the logistic models simultaneously. This strategy allowed the predictive ability of each variable to be assessed while controlling for all other variables (36). Gender was used as a stratification variable. We present adjusted odds ratios (ORs), their associated confidence limits, and p values.


   RESULTS
Top
 Abstract
 Methods
 Results
Discussion
 Appendix
 References
 
Figures 1A–Q (see Appendix) display the prevalence of underweight (BMI < 20.0), normal weight (BMI = 20.0–24.9), overweight (BMI = 25.0–29.9), and obesity (BMI > 29.9) within categories of the independent variables. Overall, 8.5%, 40.0%, 38.7%, and 12.8% of respondents were classified as underweight, normal weight, overweight, and obese, respectively. The figures also present the gender-stratified distribution of the 4 BMI categories by personal, health, and lifestyle behavior measures. There were apparent differences between men and women, particularly for the underweight category. Compared with men, the percentage of women who were classified as underweight was higher across most of the independent variables. On the other hand, proportionately more men than women reported that they were overweight. Furthermore, there appears to be a lack of consistency between men and women in the prevalence rate of obesity across categories of most of the independent variables examined.



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  		Figure 1. Prevalence of underweight (UW), normal weight (NW), overweight (OW), and obesity (OB) by demographic, health, and psychosocial characteristics (N = 12,823). Percentages are weighted for sampling design effects. A, Body mass index by gender; B, overall body mass index by age; C, men and women overall body mass index by age

 
Turning to the multivariate logistic regression analysis, Table 1 gives the adjusted ORs for the relationship between the independent variables and overweight. Overall, men were 57% more likely than women to be overweight. Several factors were significantly predictive of overweight: younger (aged 65–69 years); less educated; being married (among women); American, European, or Australian born; living in Atlantic provinces; not smoking; abstainer or infrequent alcohol use; comorbidity (among women); sedentary lifestyle; functional limitation in women; lower level of social support (for women); and psychological distress in women.


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  		Table 1. Logistic Regression Analyses Predicting Overweight, BMI 25–29.9 kg/m2 (n = 8,802).

 
Table 2 summarizes the odds of being obese. Overall, men were 37% more likely to be obese than women. Obesity was also more common among younger senior adults; less educated; unmarried; American, European, or Australian born; those from Atlantic provinces; nonsmokers; infrequent and heavier users of alcohol; physically inactive; with comorbid conditions; functional limitations; poorer self-rated health; and reporting psychological distress. A separate analysis that included income as a predictor was also performed. The inclusion of this variable did not substantially change the magnitude or statistical significance of the other ORs in the model. We present the analysis without income because inclusion of this variable led to a loss of more than 25% of the sample.


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  		Table 2. Logistic Regression Analyses Predicting Obesity, BMI >= 30 kg/m2 (n = 5,980).

 
Particularly striking is the fact that obesity was more common among men who reported higher levels of social support and less likely among women with higher levels of social support. A test of the interaction between gender and social support showed this difference to be significant (p <.001). In addition, psychological distress was associated with lower risk of being overweight for men and higher risk of being overweight for women, and this difference was statistically significant (p <.001). Because single marital status among older adults is primarily due to widowhood, we investigated whether social support, physical activity, and comorbidity interacted with marital status in predicting obesity (data not shown) following procedures suggested by Jaccard (37), Turner and Turner (38), and Camacho and colleagues (39). Only social support (OR, 0.55; 95% confidence interval [CI], 0.42–0.73) and exercise (OR, 1.44; 95% CI, 1.20–1.72) significantly interacted with marital status. Plots of these interactions suggested that physical activity has a stronger relationship to obesity for unmarried respondents. Social support was associated with greater risk of obesity for married individuals but was associated with lower risk of obesity for unmarried individuals. Separate analyses by gender suggested that these interactions occurred only for males (OR for social support, 0.50; 95% CI, 0.34–0.74; OR for physical activity, 3.00; 95% CI, 1.98–4.52).


   DISCUSSION
Top
 Abstract
 Methods
 Results
 Discussion
Appendix
 References
 
Significant variables were found in several predictor domains, supporting an interpretation that overweight and obesity in older adulthood are complex conditions influenced by many factors. Among the most important predictors of obesity were demographic factors, such as gender, education, age, marital status, and place of birth. For instance, those aged over 80 years were approximately 92% less likely to be obese than younger (aged 65 to 69) subjects. Women, those with postsecondary education, and those of non-European origin were less likely to be obese. Unmarried women appear to be more susceptible to obesity but seem to be protected from being overweight. In addition, among the most important social and health variables were the presence of one or more chronic conditions and sedentary life style, increasing the risk of obesity by approximately 74% and 113%, respectively. Few substantive differences were found between the predictors of overweight and obesity.

The most striking finding was that social support was associated with a lower likelihood of obesity for women and a higher likelihood of obesity for men. This finding may be a result of different social normative pressures experienced by women and men. Cultural norms for men emphasize big appetites and inactivity, whereas those for women stress careful dieting (40), healthy body weight (41), and self-care (42). Greater social involvement with others may facilitate transmission of these normative values. Although social support is typically positively associated with better health outcomes (43), relationships between health behaviors and social involvement have been less frequently investigated separately for older men and women.

Some of our results are consistent with other studies of obesity in the older adult population. First, our finding that obesity is associated with functional decline in advanced age is similar to the patterns reported in previous studies (22,44). Second, moderate drinking of alcohol appears to be protective in men and women. Interestingly, our results revealed a potential U-shaped relationship between alcohol use and the risk for obesity. However, the small number of heavy drinkers may contribute to insufficient statistical power to detect this difference. This finding is consistent with other reports that moderate drinkers (2 to 3 drinks per day) with physically active lifestyles may be at a lower risk for obesity (45). Third, our finding of an association between obesity and psychological distress among women agrees with a recent community-based prospective study conducted in California on the relationship between obesity and negative affect (23).

Some limitations must be considered in interpreting our findings. First, most survey methods used to calculate a person's BMI are based on self-reported data for height and weight. Bias may be particularly serious among older adults, with some data indicating that misreporting is greatest in the oldest age group (46,47). Overweight participants in self-report studies tend to underestimate their weight, and all participants tend to overestimate their height (46–48). Studies in which weight and height were measured by health professionals found that 22.5% of adults were obese—more than one-third higher than rates in the self-report Behavioral Risk Factor Surveillance System. Despite these potential biases in self-report of weight overall, it is difficult to gauge the impact of these biases in the context of a multivariate model, and it is possible that underestimates of weight overall will not impact the factors that are predictive of overweight or obesity. Second, it is unclear whether the BMI is the most appropriate measurement of body weight for elderly persons. That is, the BMI may underestimate body fat in persons who have lost muscle mass (49). Third, the possibility of selective survival must be considered in the analyses of the data from the very old and oldest subgroups. That is, a large percentage of the population was overweight, exceeding 30% in the men and women across the full age range. In comparison, the percentage of subjects who were obese declined with age in the men from 18.5% to 5.0% across the 25-year age range, whereas in the women the prevalence of obesity was 15.1% in the 65–79 age group and only 5.6% in women over age 80. Fourth, the 1996–1997 NPHS did not include dietary information such as food frequency, types of food consumed, or eating behavior. Of course, the absence of data on food frequency and eating behavior is limiting. Fifth, differential effects of psychological stress, social support, and self-rated health status on overweight and obesity status in men versus women may be attributed to the lack of validated gender neutral questionnaire items. Despite the limitations detailed above, this study provides a unique opportunity to examine the distribution of overweight and obesity risk factors in a large population-based sample of persons aged 65 and older.

Although our data can be used to portray more accurately the risk and protective factors for overweight and obesity, longitudinal studies are needed to confirm these findings and to assess the effects over time of higher BMI scores on quality of life, functional abilities, and psychosocial status (15,31). Investment in prospective studies of obesity, its predictors, and its health consequences will be of considerable value in light of the accelerating prevalence of overweight and obesity among aging baby boomers. Given the aging population, which includes younger cohorts with higher rates of overweight, concerted attention to obesity is needed at all ages (50). The prevalence of obesity among older adults, in particular, underscores the importance of prevention and health promotion efforts that address specific subgroups of the elderly population. For example, our analysis of independent predictors suggests that efforts are needed to intervene in older women with lower educational status or among those who suffer from chronic illness. Prevention and treatment need to be empirically structured to address the differential risks for obesity among the elderly subgroups identified in our study. Indeed, identifying specific at-risk subgroups as targets for clinical intervention and public health action is considered to be good practice. Such knowledge could lead to more effective weight-control interventions that are designed to promote increased physical activity and healthy eating habits among obese older individuals (51).


   APPENDIX
Top
 Abstract
 Methods
 Results
 Discussion
 Appendix
References
 



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  		Figure 1. (Continued ). D, body mass index by education; E, body mass index by marital status; F, body mass index by place of birth

 


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  		Figure 1. (Continued ). G, overall body mass index by region of residence; H, men and women body mass by region of residence; I, body mass index by smoking status

 


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  		Figure 1. (Continued ). J, overall body mass index by alcohol use; K, men and women body mass index by alcohol use; L, body mass index by comorbidity

 


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  		Figure 1. (Continued ). M, body mass index by physical activity; N, body mass index by functional limitation; O, body mass index by self-rated health

 


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  		Figure 1. (Continued ). P, body mass index by social support; Q, body mass index by psychological distress

 

   Acknowledgments
 
This research was undertaken with grants from the National Institute of Mental Health (MH 59719), the Alcoholic Beverage Medical Research Foundation, and the Canadian Embassy in Washington, D.C. This analysis is based on Statistics Canada's National Population Health Survey 1996–1997, Household Component, Public Use Microdata Files, which contain anonymized data. All computations on these microdata were prepared by Portland State University, and the responsibility for the use and interpretation of these data is entirely that of the authors.

Address correspondence to Mark S. Kaplan, DrPH, School of Community Health, Portland State University, Portland, P.O. Box 751, Portland, OR 97207. E-mail: kaplanm{at}pdx.edu

Received September 20, 2002

Accepted December 20, 2002


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 Discussion
 Appendix
 References
 

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