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Note: This article was published with errors, which have now been corrected. This file contains the uncorrected text for reference purposes. Results from this text should not be cited.
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The prevalence rates for overweight and obesity were 39.0% (95% CI, 37.7–40.3) and 20.8% (95% CI, 18.4–23.1), respectively, defined by BMI, and 30.5% (95% CI, 26.4–34.5) and 25.5% (95% CI, 23.8–27.2) by waist circumference (Box 1). By either measure, approximately 60% of the population was overweight or obese. The prevalence of obesity by waist circumference was higher in women (34.1%) than in men (26.8%) (P < 0.01). Using BMI, however, the difference was not significant.
The prevalence of obesity defined by BMI showed a steady increase up to the age group 55–64 years, after which the prevalence fell. The corresponding peak in the prevalence of obesity defined by waist circumference occurred at an older age (65–74 years). Mean BMI and waist circumference were 26.9 kg/m2 (95% CI, 26.6–27.2 kg/m2) and 96.0 cm (95.0–97.0 cm), respectively, for men, and 26.4 kg/m2 (25.9–26.9 kg/m2) and 84.2 cm (82.4–85.9 cm) for women. Mean BMI and waist circumference according to age group are shown in Box 2; it is apparent that, for BMI, the increase with age was much more pronounced in women than men, while, for waist circumference, the association with age was similar between the sexes. The age-standardised prevalence of obesity defined by BMI has risen from 7.1% in 1980 to 18.4% in 2000.
The associations between obesity and potential risk factors are shown in Boxes 3 and 4, with television viewing time strongly associated with obesity in both sexes. Physical activity time was related to obesity defined by BMI or waist circumference in women, while in men it was only associated with obesity defined by waist circumference. Lower educational attainment was consistently predictive of obesity in each sex. Increasing income increased the risk of obesity in women. Although no such association was significant for men, both the BMI and waist circumference data suggested that middle-income men tended to be more obese than the lowest income group. Men with occupations in skill levels 2 and 3 had the lowest risk of obesity, suggesting a "U"-shaped relationship, although no significant associations between occupation and obesity were observed for women.
Comparing the association of television viewing and physical activity time with obesity, television viewing clearly showed the stronger relationship (Box 5). Within each tertile of physical activity, the odds of being obese were highly dependent on television viewing time. While increased physical activity decreased the odds of obesity among each tertile of television viewing time, its influence was not as strong as that of television viewing time.
A previous analysis of respondents versus non-respondents to the physical examination found small differences in the proportion of English speakers, those born in the United Kingdom and those suspecting they had diabetes (P < 0.05).7
These nationally representative AusDiab data show alarming rates of overweight and obesity. The obesity rates reported here are considerably higher than those of previous Australian urban studies in 1980, 1989 and 1995.13-15 In 1980, 7.1% of the population aged 25–64 years living in major cities in Australia were obese.13 Using the same age and geographical restrictions, the prevalence (defined by BMI) in our study was 18.4%, showing a 2.5-fold rise over 20 years. Across the four surveys in the last 20 years, the prevalence appears to have stabilised in men since 1995, but a continuing rise is apparent for women.
The consequences of high rates of overweight and obesity are likely to be profound, with the AusDiab data also showing that Australia now has one of the highest rates of type 2 diabetes in the developed world.4
The high prevalence of obesity found is still considerably lower than the reported rate for 1999–2000 from the United States2 (27.5% in men and 33.4% in women), but comparable with those reported from the UK16 (17% in men and 21% in women) and (West) Germany17 (19.4% in men and 20.9% in women), ranking Australia as one of the most severely affected Europid populations.
Waist circumference provides an alternative measure of adiposity that correlates reasonably well with BMI,18 but appears to be a better indicator of visceral fat, type 2 diabetes and cardiovascular disease.19 There are few published studies of the age and sex distribution of obesity according to waist circumference. In the second MONICA survey20 only two centres (Germany, Czech Republic) had higher mean waist circumferences for men than in our study, and four centres (Germany, Czech Republic, Spain and Yugoslavia) had higher mean waist circumferences for women.
The age and sex pattern of BMI is consistent with a number of other studies.2,21 Both the prevalence of obesity and mean BMI are lower in young women than in young men, but a more rapid rise in BMI in women results in women overtaking men by the age of 35–44 years for obesity prevalence and by age 55–64 years for mean BMI. The impact of age on waist circumference is almost identical for men and for women, suggesting that the factors leading to increasing waist circumference with age are much more similar between men and women than are the factors affecting BMI. Increasing peripheral fat in women related to childbirth and the menopause may well be the critical difference.1
We found that physical activity time and television viewing time were the strongest correlates with obesity. Television viewing time showed a significant positive association with both measures of obesity in men and women, after adjustment for physical activity time and other risk factors. A corresponding negative association was seen for increased physical activity time, although it was not a significant predictor of BMI in men. Of particular note is the strength of the relationship of obesity with television viewing time — even those in the top tertile of physical activity showed a high risk of obesity if they were also in the top tertile of television viewing time. This dominant effect of television viewing time has been reported previously in another population-based Australian study.22 The relative imprecision of recall of physical activity time in comparison with television viewing time may explain part of this finding. Another explanation may be the reduction in incidental (non-structured) physical activity associated with television viewing, which, in an inactive society, has the potential to significantly reduce total energy expenditure.23 The association between television viewing time and obesity is important for health education and public health programs. While most such programs focus on increasing the time spent engaged in physical activity, it may be more achievable to recommend reducing the time spent in completely sedentary activities such as watching television. Indeed, a recent interventional trial has shown that measures to limit television viewing in children can be effective in controlling obesity.24
Together with television viewing time and physical activity time, energy intake has an important impact on obesity. From 1983 to 1995, Australian data show that there has been a significant increase in energy intake among adults and children.25 Television viewing itself has been linked to higher energy intake (take-away meals) among women,26 and this may relate to habits of eating during television viewing.
Of the socioeconomic factors examined, lower educational attainment showed the most consistent relationship with obesity. This finding is supported by other studies,21 although the cause is not clear. The association of obesity with income was sex specific. In men, minor trends for middle-income groups to be more obese and the least affluent to be thin were observed, although these were not significant. Women, by contrast, showed a strong positive graded association between income and obesity.
When interpreting these results, some caution should be exercised. As the AusDiab study was cross-sectional, causality cannot be determined from the associations observed. For example, obese people may be less active as a consequence of their obesity. This is unlikely to be the entire explanation for the associations reported, as decreased physical activity has been linked to obesity in prospective studies.27 The level of response to the study should also be considered, as well as the small differences between responders and non-responders.7 Additionally, even though AusDiab was designed to provide estimates representative of the adult Australian population, the exclusion criteria may have resulted in under-representation of some population groups (eg, Indigenous and rural Australians).
In conclusion, Australia has been shown to have alarming rates of both central and general obesity. This urgently demands action on many levels to prevent further rises in the prevalence of diseases such as type 2 diabetes. The strong relationship we found between obesity and surrogate indices of energy expenditure needs to be confirmed in prospective studies, but suggests that reducing time spent in sedentary activities could be an important target for preventing and treating obesity.
1: Age-specific prevalence (%) of (A) overweight and (B) obesity defined by body mass index (BMI) (n = 11 067) and by waist circumference (n = 11 059) among Australian adults
|
25–34 y |
35–44 y |
45–54 y |
55–64 y |
65–74 y |
75+ y |
Total |
||||
A |
Age-specific prevalence (%) of overweight |
||||||||||
BMI* |
|
|
|
|
|
|
|
||||
Men |
43.7 |
46.8 |
51.1 |
48.9 |
53.6 |
50.8 |
48.2 |
||||
Women |
22.6 |
25.7 |
32.1 |
35.4 |
37.4 |
36.4 |
29.9 |
||||
Total |
33.5 |
36.3 |
41.7 |
42.2 |
44.8 |
42.4 |
39.0 |
||||
Waist circumference† |
|
|
|
|
|
|
|
||||
Men |
26.6 |
25.7 |
31.0 |
31.0 |
30.7 |
27.5 |
28.5 |
||||
Women |
19.6 |
21.8 |
21.5 |
25.6 |
27.7 |
22.8 |
22.6 |
||||
Total |
23.3 |
23.8 |
26.3 |
28.3 |
29.1 |
24.7 |
25.5 |
||||
B |
Age-specific prevalence (%) of obesity |
||||||||||
BMI‡ |
|
|
|
|
|
|
|
||||
Men |
17.4 |
17.8 |
20.8 |
25.5 |
19.9 |
12.7 |
19.3 |
||||
Women |
12.4 |
19.5 |
26.9 |
32.8 |
29.4 |
15.6 |
22.2 |
||||
Total |
15.0 |
18.6 |
23.8 |
29.1 |
25.1 |
14.4 |
20.8 |
||||
Waist circumference§ |
|
|
|
|
|
|
|
||||
Men |
14.0 |
24.9 |
27.6 |
36.0 |
40.7 |
36.5 |
26.8 |
||||
Women |
17.2 |
25.8 |
38.3 |
48.0 |
51.2 |
42.3 |
34.1 |
||||
Total |
15.5 |
25.4 |
32.9 |
42.0 |
46.5 |
39.9 |
30.5 |
||||
* Overweight defined as a BMI of 25.0–29.9 kg/m2. |
|||||||||||
2: Mean body mass index (BMI) (A) and mean waist circumference (B) by age group for Australian men and women (bars = 95% CIs)

3: Association between obesity (measured using body mass index [BMI]* [n = 4996] and waist circumference* [n = 4984]) and potential risk factors among Australian men
|
Body mass index |
Waist circumference |
|||||||||
|
n |
Adjusted odds ratio† (95% CI) |
n |
Adjusted odds ratio† (95% CI) |
|||||||
Smoking status |
|
|
|
|
|||||||
Non/ex-smoker |
4048 |
1.00 |
4041 |
1.00 |
|||||||
Smoker |
865 |
0.71 (0.48–1.04) |
860 |
0.63 (0.51–0.78)‡ |
|||||||
Physical activity§ |
|
|
|
|
|
|
|||||
Lowest quintile |
946 |
1.00 |
946 |
1.00 |
|
||||||
Highest quintile |
1021 |
0.70 (0.46–1.06) |
1022 |
0.56 (0.42–0.75)‡ |
|||||||
Television viewing¶ |
|
|
|
|
|
||||||
Lowest quintile |
784 |
1.00 |
783 |
1.00 |
|||||||
Highest quintile |
1094 |
1.86 (1.30–2.67)‡ |
1093 |
1.97 (1.48–2.63)‡ |
|||||||
Education |
|
|
|
|
|
||||||
University/Further education |
2089 |
1.00 |
2086 |
1.00 |
|||||||
Completed high school |
901 |
1.14 (0.92–1.42) |
900 |
0.93 (0.69–1.27) |
|||||||
Some high school completed |
1693 |
2.19 (1.6–3.01)‡ |
1684 |
1.65 (1.17–2.33)‡ |
|||||||
Primary school/never attended school |
309 |
2.40 (1.59–3.61)‡ |
310 |
2.31 (1.69–3.15) ‡ |
|||||||
Country of birth |
|
|
|
|
|||||||
Australia/New Zealand |
3727 |
1.00 |
3713 |
1.00 |
|||||||
United Kingdom/Northern Ireland |
612 |
0.92 (0.65–1.29) |
611 |
0.89 (0.68–1.16) |
|||||||
Rest of world |
653 |
0.85 (0.67–1.08) |
656 |
0.60 (0.40–0.92)‡ |
|||||||
Weekly income (A$) |
|
|
|
|
|
||||||
1500+ |
502 |
1.00 |
502 |
1.00 |
|||||||
800–1499 |
1541 |
1.18 (0.80–1.74) |
1533 |
1.30 (0.93–1.81) |
|||||||
600–799 |
1162 |
1.12 (0.78–1.60) |
1157 |
1.29 (0.91–1.82) |
|||||||
400–599 |
1133 |
1.07 (0.77–1.50) |
1134 |
1.23 (0.84–1.80) |
|||||||
200–399 |
571 |
1.06 (0.70–1.62) |
573 |
0.97 (0.69–1.36) |
|||||||
0–199 |
37 |
0 .68 (0.22–2.07) |
36 |
0.51 (0.17–1.54) |
|||||||
Occupation** |
|
|
|
|
|
||||||
Skill level 1 |
1243 |
1.00 |
1244 |
1.00 |
|||||||
Skill level 2 |
540 |
0.65 (0.46–0.93)‡ |
534 |
0.74 (0.49–1.14) |
|||||||
Skill level 3 |
658 |
0.48 (0.32–0.72)‡ |
659 |
0.43 (0.26–0.70)‡ |
|||||||
Skill level 4 |
604 |
0.95 (0.63–1.43) |
602 |
0.84 (0.58–1.21) |
|||||||
Skill level 5 |
304 |
0.93 (0.52–1.63) |
304 |
0.92 (0.46–1.84) |
|||||||
Others |
1629 |
0.56 (0.35–0.89)‡ |
1623 |
1.07 (0.66–1.72) |
|||||||
* Obesity defined as BMI ≥ 30 kg/m2, or waist circumference ≥ 102 cm. † Model adjusted for age and all other risk factors in the table. |
|||||||||||
4: Association between obesity (measured using body mass index [BMI]* [n = 6071] and waist circumference* [n = 6075]) and each of the potential risk factors among Australian women
|
Body mass index |
Waist circumference |
|||||||||
|
n |
Adjusted odds ratio† (95% CI) |
n |
Adjusted odds ratio† (95% CI) |
|||||||
Smoking status |
|
|
|
|
|||||||
Non/ex-smoker |
5123 |
1.00 |
5128 |
1.00 |
|||||||
Smoker |
857 |
0.70 (0.51–0.97)‡ |
858 |
1.03 (0.76–1.39) |
|||||||
Physical activity§ |
|
|
|
||||||||
Lowest quintile |
1254 |
1.00 |
1255 |
1.00 |
|||||||
Highest quintile |
1221 |
0.47 (0.31–0.72)‡ |
1221 |
0.53 (0.34–0.80)‡ |
|||||||
Television viewing¶ |
|
|
|
|
|||||||
Lowest quintile |
992 |
1.00 |
994 |
1.00 |
|||||||
Highest quintile |
1292 |
1.82 (1.19–2.76)‡ |
1295 |
2.27 (1.55–3.32)‡ |
|||||||
Education |
|
|
|
|
|||||||
University/Further education |
1985 |
1.00 | 1985 |
1.00 |
|||||||
Completed high school |
1185 |
1.04 (0.77–1.40) |
1185 |
1.31 (1.01–1.70)‡ |
|||||||
Some high school completed |
2481 |
1.48 (1.19–1.83)‡ |
2487 |
1.47 (1.19–1.82)‡ |
|||||||
Primary school/never attended school |
419 |
2.12 (1.18–3.80)‡ |
417 |
2.68 (1.64–4.36)‡ |
|||||||
Country of birth |
|
|
|
||||||||
Australia/New Zealand |
4672 |
1.00 |
4677 |
1.00 |
|||||||
United Kingdom/Northern Ireland |
644 |
0.95 (0.68–1.34) |
646 |
1.01 (0.69–1.49) |
|||||||
Rest of world |
754 |
0.80 (0.66–0.97)‡ |
751 |
0.72 (0.57–0.92)‡ |
|||||||
Weekly income (A$) |
|
|
|
|
|||||||
1500+ |
1034 |
1.00 |
1034 |
1.00 |
|||||||
800–1499 |
2046 |
0.85 (0.67–1.08) |
2046 |
0.93 (0.74–1.19) |
|||||||
600–799 |
1270 |
0.87 (0.66–1.15) |
1274 |
0.79 (0.62–1.02) |
|||||||
400–599 |
1064 |
0.57 (0.40–0.82)‡ |
1062 |
0.62 (0.46–0.83)‡ |
|||||||
200–399 |
499 |
0.67 (0.48–0.93)‡ |
501 |
0.59 (0.37–0.94)‡ |
|||||||
0–199 |
20 |
0.63 (0.19–2.11) |
20 |
0.46 (0.13–1.65) |
|||||||
Occupation** |
|
|
|
|
|||||||
Skill level 1 |
990 |
1.00 |
989 |
1.00 |
|||||||
Skill level 2 |
390 |
0.82 (0.41–1.65) |
389 |
0.80 (0.48–1.34) |
|||||||
Skill level 3 |
354 |
0.88 (0.55–1.40) |
355 |
0.93 (0.58–1.48) |
|||||||
Skill level 4 |
886 |
1.11 (0.72–1.71) |
884 |
0.74 (0.53–1.05) |
|||||||
Skill level 5 |
549 |
0.84 (0.45–1.57) |
551 |
0.90 (0.60–1.36) |
|||||||
Others |
2869 |
0.94 (0.54–1.63) |
2874 |
1.08 (0.71–1.64) |
|||||||
* Obesity defined as BMI ≥ 30 kg/m2, or waist circumference ≥ 88 cm. |
|||||||||||
We are grateful to the following for their support of the study: the then Commonwealth Department of Health and Aged Care, Eli Lilly (Aust) Pty Ltd, Janssen-Cilag (Aust) Pty Ltd, Abbott Australasia Pty Ltd, Merck-Lipha s.a., Alphapharm Pty Ltd, Merck Sharp & Dohme (Aust), Roche Diagnostics, Servier Laboratories (Aust) Pty Ltd, SmithKline Beecham International, Pharmacia and Upjohn Pty Ltd, BioRad Laboratories Pty Ltd, HITECH Pathology Pty Ltd, the Australian Kidney Foundation, Diabetes Australia (Northern Territory), Queensland Health, South Australian Department of Human Services, Tasmanian Department of Health and Human Services, Territory Health Services, Victorian Department of Human Services and the Health Department of Western Australia.
For their invaluable contribution to the field activities of AusDiab, we are grateful to Annie Allman, Adam Meehan, Claire Reid, Alison Stewart, Robyn Tapp and Fay Wilson.
Finally, our thanks goes to the local collaborating centres, including Sir Charles Gairdner Hospital (Western Australia), the Prince of Wales Hospital (New South Wales), the Menzies Centre for Population Health Research (Tasmania), the Queen Elizabeth Hospital (South Australia), the Menzies School of Health Research (Northern Territory), Queensland Health, the Monash Medical Centre Department of Nephrology (Victoria), and the Centre for Eye Research Australia (Victoria).
Department of Epidemiology, International Diabetes Institute, Caulfield, VIC.
Adrian J Cameron, MPH, Epidemiologist; Paul Z Zimmet, MD, FRACP, FAFPHM, Director; David W Dunstan, PhD, Research Fellow; Marita Dalton, GradDipEpidemiol, Epidemiologist; Jonathan E Shaw, MD, MRCP, Director of Research.Department of Medicine and Public Health, University of Western Australia, Perth, WA.
Timothy A Welborn, MB BS, PhD, Clinical Professor.School of Population Health, University of Queensland, Brisbane, QLD.
Neville Owen, PhD, Director.School of Health Sciences, Deakin University, Burwood, VIC.
Jo Salmon, PhD, Research Fellow; Damien Jolley, MSc, Associate Dean.Correspondence: Mr Adrian J Cameron, Department of Epidemiology, International Diabetes Institute, 250 Kooyong Road, Caulfield, VIC 3162. acameronATidi.org.au
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©The Medical Journal of Australia 2003 www.mja.com.au Print ISSN: 0025-729X Online ISSN: 1326-5377
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