Age and Ageing 1996.25-300-306
Weight Height and Body Mass Index
Distributions in Geographically and Ethnically
Diverse Samples of Older Persons
LENORE J. LAUNER, TAMARA HARRIS on behalf of the Ad Hoc
Committee on the Statistics of Anthropometry and Aging*
Summary
We compared anthropometric data (height, weight and body mass index) from 19 geographically and ethnically
varied samples of community-dwelling elderly people. Participants were stratified into three age groups, 60—69,
70-79 and 80 years or older. We present age-group-specific means and standard deviations for height, weight
and body mass index (BMI, weight/height2) and the prevalence of underweight (BMI < 20) and overweight
(BMI Ss 30).
Across studies there are large differences in the prevalence of overweight and underweight, but in all studies
mean height and BMI decreased with age. In general, mean BMI among 70-79-year-old women is greater than
that for men of a similar age, and the Mediterranean samples are heavier for height than samples from Western
Europe, Asia, Africa and the United States.
The comparisons suggest that the sensitivity and specificity of a fixed cut-off for underweight and overweight
are likely to differ by sex, age, and geographic location in samples of older persons.
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 )
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
•Contributing authors listed in Appendix 1.
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 communitydwelling 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.
From the eligible 24 studies, we compared 19 study sites
International Collaborative
Study on Hypertension in
Blacks
Nutritional Assessment of
Guatemalan Ambulatory Elderly
(Guatemala)
Longitudinal Study of Health
and Social Support in the Hong
Kong Chinese Elderly Cohort
Nutrition in Old Age in Italy
(Italy/17)
Italian Nutrition Examination
Survey of the Elderly (Padova,
Italy)"
Rotterdam Study
National Health Examination
Follow-up Survey (U.S.
National White)
Food Habits in Later Life:
A Cross Cultural Study
Melbourne Chinese Health Study
(Chinese, Australia)
Brazilian National Survey of
Health and Nutrition, 1989
Chinese Nationwide Nutrition
Survey 1982
Mini-Finland Health Survey
Name of study
3752
3695
111
186
264
441
70
204
192
129
283
60-97
65-95
60-103
60-86
60-79
70-104
60-95
70-96
70-94
69-91
60-80+
60-80+
60-80+
Italy: 17 locations
Italy: 5 locations
Rotterdam, The Netherlands
United States national sample
Australia: Melbourne (Anglo Celtics)
Australia: Melbourne (Greeks)
China: Beijing
China: Tianjin
Greece: Spata
Sweden: Johanneberg
Barbados: 1 rural location
Nigeria: 2 rural locations
Cameroon: 2 rural locations
1248
921
977
70-100
Hong Kong sample
2126
202
60-90+
Finland national survey
1
1764
60-103
60-94
China national survey
1
4419
68
Sample
size
Guatemala: one rural
location
60-108
Brazil national survey
1
1
60-80
Age range
(years)
Australia: Melbourne (Chinese)
Location
1
No. of sites
in analysis
Population sample from census
or government data; excludes
institutionalized
Telephone, population or
electoral registers; excludes
institutionalized
Population register
Household sample; excludes
institutionalized
Electoral rolls; excludes
institutionalized and severely
Electoral rolls; excludes
institutionalized
Household and old-age housing
Census; excludes institutionalized
Population register; no
anthropometric collection in
institutionalized
Household sample
Household sample
Telephone lists
Sampling frame
Table I. Sample characteristics of study sites included in the comparison of anthropometric data from geographically/ethnically diverse samples of older persons
en
Z
O
in
m
v
r
D
m
O
•n
o
en
M
r
en
z
a
m
O
X
H
>
Z
D
O
X
H
X
181
30
13
9
478
894
900
75
217
246
187
634
24
297
26
64
11
180
31
21
16
273
656
560
51
Women
30
54
241
479
206
698
26
386
20
59
9
31
44
Men
1.56(0.06)
1.61 (0.04)
1.61 (0.08)
1.62(0.06)
1.62(0.07)
1.63(0.10)
1.64(0.10)
1.64(0.07)
1.66(0.06)
1.65(0.06)
1.65 (0.08)
1.66(0.06)
1.66(0.06)
1.67(0.08)
1.67(0.06)
1.69(0.06)
1.72(0.08)
1.73 (0.06)
1.74(0.06)
Mean (SD)2
Men
Body Mass Index (weight in kg/height in m ); SD: Standard deviation;
Guatemala
Beijing, China
China (National)
Hong Kong
Italy (17 sites)
Brazil (National)
Anglos, Australia
Padova, Italy
Spata, Greece
Greek, Australia
Chinese, Australia
Tianjin, China
Barbados
Nigeria
Cameroon
Finland (National)
US (National, White)
Rotterdam, NL
Johanneberg, Sweden
Name of study
Number
Height (m)
, < 10.
1.55(0.06)
1.59(0.05)
1.59(0.07)
1.61 (0.05)
3
1.53 (0.06)
1.59(0.07)
1.58(0.07)
3
1.40(0.07)
1.54(0.04)
1.48(0.06)
1.48(0.06)
1.51 (0.07)
1.50(0.08)
1.66(0.08)
1.51 (0.06)
1.51 (0.06)
1.50(0.05)
Women
Mean (SD)
49.3 (7.7)
63.1 (9.2)
54.1 (9.0)
55.7 (10.0)
67.3(12.5)
61.3(15.1)
71.6(12.0)
71.6(11.3)
75.7(13.8)
76.4(10.9)
62.6 (8.9)
61.4(10.6)
69.9(15.4)
57.2 (9.7)
63.2(7.9)
73.3 (12.4)
75.7(12.2)
77.0(10.5)
76.9(10.5)
Mean (SD)
Men
Weight (kg)
64.3 (11.7)
64.9(12.7)
68.7(11.0)
62.5 (12.0)
3
52.2(10.9)
73.2(18.0)
58.1 (10.0)
3
41.6(9.0)
55.1 (9.0)
45.5 (8.6)
49.4 (9.8)
64.7(13.2)
56.6(18.0)
70.9 (8.7)
65.1 (13.0)
64.8 (10.6)
68.9(11.3)
Women
Mean (SD)
Table II. Anthropometric characteristics of 70-79-year-old men and women from geographically/ethnically diverse samples
20.2 (2.2)
24.2 (3.5)
21.0(3.3)
21.2(3.4)
25.5 (4.3)
22.9 (5.0)
26.6(3.7)
26.5 (3.8)
27.5 (4.4)
28.0(3.6)
23.0(2.7)
22.2(3.3)
25.3 (5.6)
20.6 (2.9)
22.6 (2.7)
25.6(3.7)
25.6(3.7)
25.8(3.3)
25.4(3.2)
Mean (SD)
Men
BMI1
26.8 (4.5)
25.7 (4.9)
27.1 (4.3)
24.1 (4.5)
3
22.2(4.1)
29.2 (6.9)
21.5 (6.0)
3
21.4(4.4)
23.0(3.5)
20.7 (3.6)
22.4 (4.0)
28.5 (5.4)
25.0(7.4)
25.6(3.0)
28.4(5.3)
28.3 (4.5)
30.7(5.1)
Women
Mean (SD)
50
50
X
M
50
C
2
r
WEIGHT, HEIGHT AND BMI IN SAMPLES OF OLDER PERSONS
(described more fully in Table I) that used standard methods
to measure weight (to the nearest 0.1 kg to 1.0 kg) 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 7079 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 (^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), 2024.99, 25-29.99, and >30 (overweight) was also calculated.
These categories were in general use at the time of data
collection [10].
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.
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 ^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.
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
70
60
50
3°3
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 80-1year-old men; similar differences were seen in women.
In other data with self-reported rather than measured
40
30
20
10
Underweight (%)
0
Nigeria
Cameroon
Guatemala
Chinese Australian
Hong Kong
Tianjin, China
Beijing, China
Brazil (National)
Johanneberg Sweden
Rotterdam, NL
US (National, White)
Italy (17 sites)
Finland (National)
Padova, Italy
Barbados
Anglo Australian
Greek Australian
Spata, Greece
0
10
Overweight (%)
"B
20
30
40
50
60
70
* = prevalence is 0%
Figure 1. Comparison of anthropometric characteristics of older persons from geographically/ethnically diverse samples.
Percentage overweight (BMI ^30) and underweight (BMI < 20) among 70-79-year-old men.
L. J. LAUNER, T. HARRIS ET AL.
70
60
50
40
30
20
10
10
20
30
40
50
60
Underweight (%)
0
Nigeria
Cameroon
Guatemala
Chinese Australian
Hong Kong
Tianjin, China
Beijing. China
Brazil (National)
Johanneberg, Sweden
Rotterdam, NL
US National. White
Italy (17 sites)
Finland (National)
Padova, Italy
Barbados
Anglo Australian
Greek Australian
Spata, Greece
70
Overweight (%)
+ = prevalence is 0%
* = <10 in the strata
Figure 2. Comparison of anthropometric characteristics of older persons from geographically/ethnically diverse samples.
Percentage overweight (BMI^30) and underweight (BMI < 20) among 70-79-year-old women.
Table III. Comparison of anthropometric characteristics of older persons from geographically/ethnically diverse samples: height
(m) by age strata
60-69 years
80-89 years
70-79 years
Name of study
Men
Mean (SD)
Women
VIean (SD)
Men
Mean (SD)
Women
Mean (SD)
Men
Mean (SD)
Guatemala
China (National)
Italy (17 sites)
Brazil (National)
Padova, Italy
Finland (National)
US (National, White)
Rotterdam, NL
1.55 (0.05)
1.62 (0.07)
1.64(0.07)
1.65 (0.11)
1.66(0.06)
1.70(0.12)
1.74(0.05)
1.75 (0.06)
1.43 (0.06)
1.51 (0.06)
1.53 (0.07)
1.52(0.07)
1.53 (0.06)
1.57(0.06)
1.61 (0.05)
1.63 (0.06)
1.56(0.06)
1.61 (0.08)
1.62(0.07)
1.63 (0.10)
1.64(0.07)
1.69(0.06)
1.72(0.08)
1.73 (0.06)
1.40(0.07)
1.48(0.06)
1.51 (0.07)
1.50(0.08)
1.51 (0.06)
1.55(0.06)
1.59(0.05)
1.59(0.07)
1.53(0.07)
1.55 (0.07)
1.60(0.07)
1.62(0.09)
1.62(0.07)
1.67(0.06)
1.71 (0.05)
1.71 (0.06)
1
kVomen
VIean (SD)
.40
.46
.48
.49
.51
.53
.58
.57
(0.06)
(0.07)
(0.07)
(0.09)
(0.07)
(0.06)
(0.05)
(0.06)
Table IV. Comparison of anthropometric characteristics of older persons from geographically/ethnically diverse samples: body
mass index by age strata
70-79 years
60-69 years
80-89 years
Name of study
Men
Mean (SD)
Women
Mean (SD)
Men
Mean (SD)
Women
Mean (SD)
Men
Mean (SD)
Women
Mean (SD)
Guatemala
China (National)
Italy (17 sites)
Brazil (National)
Padova, Italy
Finland (National)
US (National, White)
Rotterdam, NL
21.3 (2.6)
20.8 (3.0)
26.6 (4.6)
23.7 (5.4)
26.9 (3.7)
26.0(3.7)
26.4 (4.0)
25.8 (2.9)
22.4(3.3)
21.7(3.9)
28.6 (4.5)
25.8 (6.7)
29.0 (5.0)
27.8 (4.5)
26.5 (5.3)
26.8 (2.9)
20.2 (2.2)
21.0(3.3)
25.5 (4.3)
22.9 (5.0)
26.5 (3.8)
25.6 (3.7)
25.6 (3.7)
25.8(3.3)
21.4(4.4)
20.7 (3.6)
28.5 (5.4)
25.0 (7.4)
28.4(5.3)
26.8 (4.5)
25.7 (4.9)
27.1 (4.3)
19.6(2.3)
21.0(2.6)
25.1 (3.7)
22.4(4.1)
25.1 (3.6)
24.3 (3.9)
24.6(3.1)
24.9 (3.4)
20.7(4.1)
19.6(3.1)
27.0 (5.0)
23.9 (4.9)
26.6 (4.7)
25.6 (4.0)
24.4 (5.0)
26.9 (3.4)
WEIGHT, HEIGHT AND BMI IN SAMPLES OF OLDER PERSONS
3°5
Table V. Comparison of anthropometric characteristics of older persons from geographically/ethnically diverse samples: body
mass index by strata of self-reported health status
Self-reported health
Excellent/very good
Name of study
Hong Kong
Men
Women
Padova, Italy
Men
Women
Tianjin, China
Men
Women
Finland (National)
Men
Women
US (National, White)
Men
Women
Fairly good
Poor
Mean (SD)
n
Mean (SD)
n
Mean (SD)
13
17
21.8(3.8)
22.3 (4.2)
112
221
21.7(3.2)
22.8 (4.0)
117
239
20.7 (3.5)
22.2 (4.0)
31
27
26.1 (3.4)
25.9(3.6)
305
411
26.5 (3.7)
28.3 (5.0)
165
236
26.2 (4.0)
27.9(5.6)
78
68
22.0 (3.0)
22.0 (4.0)
82
88
22.3 (3.4)
21.6(3.7)
16
20
22.9(3.6)
23.9 (4.6)
19
27
24.4 (2.3)
27.5 (4.5)
168
332
26.2 (3.8)
26.8 (4.5)
85
117
25.2 (4.0)
26.4 (4.9)
181
284
26.0 (3.8)
25.5 (4.3)
224
272
25.5 (3.4)
25.7 (4.2)
219
295
25.4 (4.2)
25.8 (6.0)
n
SD: Standard deviation.
height, differences by age were smaller, suggesting the
potential for bias in self-reported data on height from
older persons (data not shown).
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.
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+ yearold 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.
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
3 o6
L. J. LAUNER, T. HARRIS ET AL.
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.
A cknowledgemen t
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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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).