Weight, height and body mass index distributions in geographically and ethnically diverse samples of older persons
Lenore J. Launer
Keywords: Anthropometry, Elderly people, Screening, Epidemiology.
Introduction
Height and weight are two of the most easily obtained anthropometric measurements. In combination, they have been used to demonstrate the health risks associated with overweight as well as underweight and are used extensively in screening and monitoring programmes [1]. However, much of what is known about these relationships relates to children, adolescents, and middle-aged adults; little is known about older people [2].
The WHO Expert Committee on Physical Status: The Use and Interpretation of Anthropometry recently formulated guidelines for data obtained from people aged over 60 [3]. Data were obtained on height, weight and body mass index (BMI; weight in kg/height in [m.sup.2]) in older persons from geographically and ethnically diverse populations. In this report, we examine those data with regard to differences in sex-specific distributions by geographic region/ethnic group, age, and reported health status. These factors produce different distributions of anthropometric data in studies of younger populations. Geographic region/ethnic group differences may reflect differences in early childhood experiences and life-style during adulthood, as well as genetic background [4, 5]. Differences by age may emerge that are related to survival, or to physiological, cohort and health status factors [6-9]. Health status may be related to the distribution of weight and BMI because of its association with the risk for, and the consequences of, disease [2]. How these factors may influence distributions of anthropometric data for older populations is not clear.
Methods
On the basis of literature, personal contacts and suggestions from colleagues, 13 groups were identified with candidate data-sets based on surveys of randomly selected community-dwelling elderly people. Twelve groups contributed data, 11 of whom had at least one sample that included individuals aged 70-79 years. Some groups contributed data collected from multiple sites and data from each site are presented separately. The number of older persons in individual studies ranged from 68 to over 4000. Complete details on the design and results of individual studies are available from the investigators (Appendix). As is reported in Table I, the studies in general do not include institutionalized residents. Some studies explicitly excluded this group, while others included the institutionalized group in the larger study, but did not collect anthropometric data from them. For all studies, it is likely that the most disabled are underrepresented owing to non-response.
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From the eligible 24 studies, we compared 19 study sites (described more fully in Table I) that used standard methods to measure weight (to the nearest 0.1 kg to 1.0kg) and height (to the nearest 0.1 cm). To assess geographic/ethnic differences, comparisons were limited to persons aged 70-79 years old. To assess age trends within and between studies, three age groups were defined (60-69 years old, 70-79 years old, 80+ years old). Comparisons between these age groups were limited to eight studies with complete data for these age strata. We also compared the BMI distributions of persons aged 70-79 by three levels of reported global health status: poor/fair, good, and very good/excellent. Since not all studies collected data on health status, used the same question, or had adequate numbers ([greater than or equal to]10 per health status category), comparisons by health status were limited to five studies.
Data on height, weight and BMI are presented separately for men and women. Distributions were compared using the mean and standard deviation. The proportion in each sample that fell within the BMI categories of <20 (underweight), 20-24.99, 25-29.99, and [greater than or equal to]30 (overweight) was also calculated. These categories were in general use at the time of data collection [10].
Results
Variation by geographic/ethnic group in 70-79-year-old men and women
Height and weight: As expected, men were taller than women. In general, the rank order of samples with regard to height was similar in men and women; men and women in Guatemala were the shortest and people in Sweden the tallest. The mean (SD) height for men in the Guatemalan sample was 1.56 (0.06)m and in the Swedish sample 1.74 (0.06) m; the mean height for women was 1.40 (0.07) m and 1.61 (0.05) m respectively for Guatemala and Sweden (Table II). There was a strong linear association between height and weight in men (weighted r= 0.79; p < 0.001) and women (weighted r = 0.73; p = 0.01). However, the Mediterranean samples [Italy (n = 2), Greece (n = 1), and Greeks living in Australia (n= 1)] showed relatively greater weight for height.
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BMI: Women had a higher mean BMI and standard deviation than men in most samples (Table II), which may reflect greater variability in weight among women. The highest mean BMI was generally found in the samples of Mediterranean origin, as expected from the data presented in Table II. Mean BMI was highest among Australian women of Greek origin [30.7 (5.1)] and women in Barbados [29.2 (6.9)].
Underweight and overweight: Geographic/ethnic differences in the prevalence of underweight (BMI < 20) and overweight (BMI[greater than or equal to]30) were large (Figures 1 and 2). Among the 18 studies for which data were available, the prevalence of overweight in 70-79-year-old men ranged from 0% in the Asian and West African samples to 35% in the Spata, Greece sample. The prevalence of underweight went in the opposite direction (range: 1.0% in the Australian sample of Greek origin to 58% in the Guatemalan sample). A similar pattern was observed for women. The range of overweight among women was 2.5% in the Hong Kong samples to 53.5% in the Barbados sample, and of underweight from 0% in the Greek sample to 53.8% in the Nigerian sample.
Variation by age
Height: Mean height decreased with age in each of the eight studies which measured height in all three age strata (Table III). Mean height in 60-69-year-old men ranged from 1.9 cm to 6.7 cm more than that of the 80+ year-old men; similar differences were seen in women. In other data with self-reported rather than measured height, differences by age were smaller, suggesting the potential for bias in self-reported data on height from older persons (data not shown).
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BMI: The mean BMI tended to decrease with age more for women than for men (Table IV). We have shown that height declined with age; for BMI to decline with age, weight must also decline and to a greater extent than height. In general, this decline resulted in a shift in the distribution for the entire sample rather than a skewed tail of lower weight.
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Underweight and overweight: Consistent with the shift in BMI distributions by age, in most studies the proportion of the sample with a BMI < 20 increased with age. For example the prevalence of underweight in women was 1.5 times higher in the Brazil sample and 6.0 times higher in the Italy/17 sample in the 80+ year-old group than in the 60-69-year-old groups. Patterns were similar for the men (data not shown).
Variation by reported health status in 70-79-year-old men and women
Across studies, no one range of BMI was consistently associated with either excellent/very good health or fair/ poor health (Table V). Rather, the distribution by health status overlapped considerably within studies and related to the underlying distribution of BMI in the sample.
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Discussion
Anthropometry is often used as an indicator of nutritional and health status, in particular for infants, children and adults; little is known about its value for predicting the health status of older people. As a first step towards evaluating the use of anthropometric indicators in persons older than 60 years, we undertook a comparison of distributions of weight, height and BMI from geographically diverse populations. There are some common findings relating to patterns of weight and height by age and sex. However, these distributions differed widely by geographic region/ ethnic groups and by health status.
Some caution is needed when interpreting these comparisons. Although most studies randomly recruited their participants and included more than 1000 respondents, several studies were based on small samples of < 15 per age-sex stratum. These small samples may not be representative of the older persons living in their community. In addition, the distributions based on these smaller samples may not be stable. Therefore, the size of the sample should be taken into account when evaluating the comparison. For instance, the regression association of height and weight from data in Table II were weighted by sample size.
Given the limitations of sample size and representativeness, these comparisons highlight the differences in height and BMI distributions by geographic region/ ethnic group and by age, as well as in the prevalence of underweight and overweight. Sources of these differences reflect multiple factors including the effects of genetic potential, early growth and nutritional status, differences in socio-economic status and health behaviours, biological changes in body composition that accompany ageing, the high prevalence of chronic diseases in old age, and differential loss in older samples due to mortality. The exploration of the contribution of each of these factors as explanatory variables is beyond the scope of this descriptive paper but has begun in some of the studies.
These sources of differences in distribution will affect the extent to which an anthropometric variable is predictive of, or associated with, a given outcome. More data on older people describing the relations of anthropometry to health outcomes and body composition are needed before the utility of anthropometry in screening and monitoring programmes for older people can be assessed. If anthropometric data prove to be useful indicators of health in older people, then the systematic differences in the distributions across populations will have to be accounted for if standard cut-off points for screening, monitoring and evaluation purposes are being considered. Similarly, the differences across populations will need to be considered when selecting a set of standard curves to facilitate comparisons within and across samples.
Acknowledgement
This study was undertaken as a part of the preparatory activities for the WHO Expert Committee on Physical Status: The Use and Interpretation of Anthropometry, Chair: Prof. J-P. Habicht; Secretary: Dr M. de Onis; Chair of the Subcommittee on Adults over 60 years of age: Dr P. B. Eveleth.
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(*) Contributing authors listed in Appendix 1.
References
[1.] Mason JB, Habicht JP, Tabatabai H, Valverde V. Nutritional surveillance. Geneva: WHO, 1984. [2.] Van Itallie TB. Health implications of overweight and underweight. Ann Intern Med 1985;103:(suppl;part 2):977-1077. [3.] Physical status: the use and interpretation of anthropometry (Report of a WHO Expert Committee). WHO Tech Rep Ser 1995. [4.] Eveleth PB, Tanner JM. World-wide variation in human growth. 2nd edn. Cambridge: Cambridge University Press, 1990. [5.] Ravussin E, Swinburn BA. Pathophysiology of obesity. Lancet 1992;340:404-8. [6.] Borkan GA, Hults DE, Glynn RJ. Role of longitudinal change and secular trend in age differences in male body dimensions. Hum Biol 1983;55:629-41. [7.] Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R. Studies in the distribution of body fat: 1. Effects of age, sex, obesity. g Gerontol 1989;44:66-73. [8.] Shimokata H, Andres R, Coon PJ, Elahi D, Muller DC, Tobin JD. Studies in the distribution of body fat: II. Longitudinal effects of change in weight. Int J Obes 1989;13:455-64. [9.] Fischer J, Johnson MA. Low body weight and weight loss in the aged. Perspect Pract 1990,90:1697-706. [10.] Garrow JS. Treat obesity seriously–a clinical manual. Edinburgh: Churchill Livingstone, 1981.Authors’ addresses
L. J. Launer National Institute of Public Health and Environmental Protection, RIVM/CCM, PO Box 1, 3720 BA, Bilthoven, The Netherlands
T. Harris Epidemiology, Demography and Biostatistics Program, National Institute on Aging, National Institutes of Health, USA
Received in revised form 11 December 1995
Appendix. Contributors to the Ad Hoc Committee on the Statistics of Anthropometry and Aging
B. H.-H. Hsu-Hage, M. L. Wahlqvist. Department of Medicine, Monash Medical Centre, Monash University, Melbourne, Australia (Melbourne Chinese Health Study).
R. Sichieri, D. C. Coltinho, M. M. Leao. Centro Nacional de Epidemiologia e Informacao, Hospital do Apareiho Locomotor, Brasilia-DF Brazil (Brazilian National Survey of Health and Nutrition, 1989).
Kayou Ge. Institute of Nutrition and Food Hygiene, Beijing, China (Chinese Nationwide Nutrition Survey).
A. Rissanen, M. Heliovaara. The Social Insurance Institution of Finland, Helsinki, Finland (The Mini-Finland Health Survey).
N. W. Solomons, M. Mazariegos, I. Mendoza. Cessiam, Hospital de Ojos y Oidos, Guatemala City, Guatemala (Nutritional Assessment of Guatemalan Ambulatory Elderly).
S. C. Ho. Department of Community and Family Medicine, The Chinese University of Hong Kong, Hong Kong (Longitudinal Study of Health and Social Support in the Hong Kong Chinese Elderly Cohort).
A. Ferro-Luzzi, G. Maiani, C. Scaccini. National Institute of Nutrition and WHO Coordinating Centre in Nutrition, Rome, Italy (Nutrition in Old Age in Italy).
G. Enzi, M. R. A. Gatto, E. M. Inelmen. Department of Internal Medicine, Institute of Geriatrics, University of Padova, Padova and Institute of Hygiene, University of Bologna, Bologna, Italy (Italian Nutrition Examination Survey of the Elderly).
D. E. Grobbee. Department of Epidemiology and Biostatistics, Erasmus University Medical School, Rotterdam, the Netherlands (Rotterdam Study).
J. H. Madans, J. J. Feldman, T. Harris. National Center for Health Statisics, Hyattsville, MD, USA (NHANES I Epidemiologic Follow-up Study).
Coordinating Centre, M. L. Wahlqvist (chairman). Department of Medicine, Monash Medical Centre, Monash University, Melbourne, Australia (IUNS Study of Food Habits in Later Life).
R. S. Cooper for the ICSHIB Investigators, Department of Preventive Medicine, Loyola University Medical School, Maywood, IL, USA (The International Collaborative Study on Hypertension in Blacks).
M-C. Chang, A. Hermalin, J. Liang. Institute of Gerontology, University of Michigan, Ann Arbor, MI, USA (1989 Survey of Health and Living Status of the Elderly in Taiwan).
Epidemiology, Demography and Biostatistics Program. National Institute on Aging, National Institutes of Health, Bethesda MD, USA (Established Populations for Epidemiologic Studies of the Elderly).
W. A. van Staveren, C. P. G. M. de Groot, J. G. A. G. Hautvast. Department of Human Nutrition, Wageningen Agricultural University, Wageningen, the Netherlands (Euronut Seneca Study on Nutrition and the Elderly).
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