Overweight, obesity and girth of Australian preschoolers: prevalence and socio-economic correlates



(1) To determine the prevalence of overweight and obesity in Australian 4–5-year-old children. (2) To investigate associations between socio-economic characteristics and (a) overweight/obesity and (b) waist circumference.


Cross-sectional population survey.


Wave 1 (2004) of the Longitudinal Study of Australian Children.


Nationally representative sample of 4983 4–5-year-old children (2537 boys and 2446 girls; mean age 56.9 months (s.d. 2.64 months; range 51–67 months)).

Main outcome measures:

Prevalence of overweight and obesity (International Obesity TaskForce definitions) and waist circumference (cm).


Prevalence estimates were obtained as weighted percentages. Uni- and multivariable ordinal logistic regression (using the proportional odds model) were used to assess associations between potential predictors and the risk of higher child body mass index status and a multivariable linear regression model to assess relationships between the same potential predictors and waist circumference.


15.2% of Australian preschoolers are estimated to be overweight and 5.5% obese. In univariate analyses, seven of the 12 variables were associated with higher odds of being in a heavier body mass index category. In a multivariable regression model, speaking a language other than English (particularly for boys), indigenous status and lower disadvantage quintile were the clearest independent predictors of higher body mass index status, with children in the lowest quintile of social disadvantage having 47% higher odds (95% CI 14, 92%) of being in a heavier body mass index category compared to those in the highest quintile. Waist circumference was not related to any socio-economic variable.


This nationally representative survey confirms high rates of overweight and obesity in preschoolers throughout Australia. The recent emergence of a substantial socio-economic gradient should bring new urgency to public health measures to combat the obesity epidemic.


The rising prevalence of overweight and obese school-aged children and adolescents has become a major public health concern. In primary school children, the prevalence continues to increase steadily, with suggestions that the steepest increases are now occurring in children of low socio-economic circumstances.1 Prevalence among preschool children has received much less attention, although it appears from a small number of international reports that overweight and obesity have also become common in this age group2, 3, 4 following a sharp rise in prevalence since the 1970s.5, 6 Even at this young age, patterns of physical activity and nutritional behaviour may be established and those who are already overweight or obese at school entry typically remain so during their primary school years.7 Population surveys of the preschool age group are needed not only to document overall trends in adiposity, but also to detect any emerging patterns of subgroup susceptibility.

Child population surveys typically report excess adiposity in terms of excess body mass index (BMI), either as overweight and obese using International Obesity TaskForce (IOTF) cutpoints8 or as ‘at-risk for overweight’ and overweight using Centers for Disease Control and Prevention (CDC) definitions.9 Another indicator of adiposity suitable for large-scale child field studies is waist circumference, which provides information additional to BMI about cardiovascular and metabolic risk and tracks strongly into adulthood.10 In turn, adult waist circumference is far more sensitive than triglycerides, blood pressure, high-density lipoprotein or BMI in identifying concurrent insulin resistance.11 Relative increases in waist circumference in the last decade appear to have been even greater than changes in BMI in both teenagers12 and preschoolers,13 leading to the conclusion that ‘body mass index is a poor proxy for central fatness’.12 Although waist percentiles have now been presented for school-aged children and adolescents in a number of countries,14, 15, 16, 17, 18, 19 only two studies have reported waist percentiles for preschool-aged children,13, 19 neither of which explored possible socio-economic factors.

In developed countries, an inverse relationship between socio-economic status and overweight/obesity in adult women has long been recognized.20 A recent systematic review reported that greater weight gain in both men and women was consistently linked with lower-ranked occupations and fairly consistently with lower education, but longitudinal studies reporting income data were few and its relationships with weight gain were inconsistent.21 Socio-economic relationships have not been consistently demonstrated for children internationally22 and were not evident in pooled data from three large surveys of Australian school children as recently as 1995–1997.23 However, repeated national samples of 5–10 year-old English children between 1974 and 2003 strongly suggest the recent emergence of a socio-economic gradient not evident in earlier samples,24 with children from low-income families responsible for much of the continuing acceleration of overweight/obesity prevalence between 1997 and 2003.1 Regarding waist circumference, only one report has examined differences by ethnicity19 and none has examined variability in waist circumference by broader measures of socio-economic status.

In this paper, we aim to (1) report estimated prevalence of overweight and obesity in Australian preschool children and (2) investigate associations between socio-economic variables and each of BMI status and waist circumference. To this end, we analysed nationally representative cross-sectional data collected in 2004 in the first wave of the Longitudinal Study of Australian Children (LSAC).


Study design and sample

The sample comprised all children in the 4–5-year-old cohort from the first wave of LSAC (data collection March–November 2004) with complete height and weight data. The sampling design and field methods have been described elsewhere.25 Briefly, LSAC employed a two-stage clustered sampling design stratified by both a state and a city/‘rest of state’ division and clustered by postcode within each stratum, so that about one out of 10 Australian postcodes were included in the study. The sampling frame was the Health Insurance Commission Medicare database, in which 98% of all 4-year-old Australian children are enrolled. Children born between March 1999 and February 2000 were randomly selected to achieve a cohort aged between 4.3 and 5.2 years at interview with all birth months represented. Of the 10 596 children included in the sampling frame, 8391 were still resident within that postcode and could be contacted and of these 4983 (59%) took part.


Trained professional interviewers conducted a 2½ h face-to-face interview in the child's home with the study child's primary caregiver (usually the biological mother), who also completed a written questionnaire. Written informed consent was obtained for each participating child, and the study was approved by the Australian Institute of Family Studies Ethics Committee.

Anthropometric measures

Children's weight was measured in light clothing to the nearest 50 g using glass bathroom scales (Salter Australia, Code 79985), height to the nearest 0.1 cm using a portable rigid stadiometer (Invicta, Code IPO955), and waist circumference horizontally around the navel, to the nearest 0.1 cm, using a non-stretch dressmaker's tape. The averages of two height and two waist circumference measurements were used in analyses; where the two differed by more than 0.5 cm a third measurement was taken and the average of the two closest was used. Children were classified as non-overweight, overweight but not obese, or obese according to the IOTF age- and sex-specific criteria8 for BMI (kg/m2) for 4.5-year-olds (51 and <57 months), 5.0-year-olds (57 and <63 months) and 5.5-year-olds (63 months of age). To check whether conclusions were robust to BMI classification, children were also classified as normal weight, at-risk for overweight (85% and <95th percentile) and overweight (95% percentile) using the CDC definitions.9 Mothers self-reported their own height and weight, and were classified by BMI as non-overweight (<25 kg/m2), overweight (25 and <30 kg/m2) or obese (30 kg/m2).26


Parent-reported child variables were sex (male/female), child's age in months (for waist circumference only, as both BMI classifications take age into account), number of siblings in the household (0, 1, 2, 3), whether the study child was the oldest in the household (yes/no, a proxy for first-born status), language other than English spoken at home by the child (yes/no), and the child's indigenous status (yes/no). Maternal variables were highest completed educational level (27 (ASCO) categories (1–3 (most skilled), 4–7, 8–9 (least skilled) no parent in the household working), family type (one- or two-parent home) and family income (a continuous variable; gross family income was equivalized to household size by taking the midpoint of the 15 income brackets in the LSAC data set and dividing by the square root of the number of people residing in the house28). Neighbourhood or geographical variables were state of residence, location of residence (urban/rural) and Socio-Economic Indexes for Areas (SEIFA29) disadvantage index at the postcode of residence level. SEIFA values are standardized scores by geographic area compiled from 2001 census data to numerically summarize the social and economic conditions of Australia (national mean 1000, s.d. 100; higher values represent greater advantage). SEIFA values were analysed using categories determined by the quintiles in the distribution of the general population.

Statistical analysis

Analyses were conducted using Stata release 9.1 (StataCorp, Texas, USA, 2005). Weights were applied to take account of the design (selection probabilities) and response patterns, as specified in the LSAC data set, and 95% confidence intervals (CI) and standard errors (s.e.) were estimated using first-order Taylor linearization to account for the weights and the clustered design. In order to enable all cases to be used in multivariable analyses, we employed multiple imputation with the method of chained equations to account for missing data.30 Apart from mother's BMI status, which was missing for 23% (n=1114) of the sample, imputation was carried out for a further four covariates with small numbers of missing cases (6%) (indigenous status (n=2), mother's education (n=42), income (n=313) and occupation (n=13)). Prevalence estimates for the categories of normal, overweight and obese were obtained as weighted percentages overall and within categories of each covariate. Univariate ordinal logistic regression (using the proportional odds model) was used to assess associations between each potential predictor and the risk of higher BMI status in the child (non-overweight, overweight, obese). Ordinal and continuous covariates were tested for departure from linearity and are presented as linear effects where the test result was non-significant at the 5% level.

In the multivariable proportional odds logistic regression, P-values and 95% CIs were obtained using Wald tests adjusted for the survey design,31 with appropriate combination over the multiply imputed data sets.32 Interactions of each predictor with sex were investigated and reported in the final model if statistical significance was less than 5%. The Brant test33 was used for each predictor in the model (ignoring missing values) to test the proportional odds assumption that the regression lines for the comparison of categories were parallel; the assumption was upheld for all predictors. Maternal BMI status was entered into a final multivariable model to determine its independent contribution to child BMI status over and above the socio-economic contributors. The multivariable analysis was repeated using the CDC BMI classification.

A multivariable linear regression model was investigated with waist circumference as a continuous outcome and potential predictors as listed above, but also including age. Linear regression analysis assumptions were checked and not found to be violated.


BMI was available for 4934 (99.0%) children, comprising 2509 boys and 2425 girls, with a mean age of 56.9 months (s.d. 2.64, range 51–67). The estimated proportions of children in the non-overweight, overweight and obese categories were 79.3% (95% CI 77.9, 80.6), 15.2% (14.1, 16.4) and 5.5% (4.7, 6.3).

Table 1 shows the socio-economic characteristics of the sample and results of the univariate and multivariate logistic regressions. The univariate analyses (crude odds ratios) indicated that female sex, language other than English spoken at home, indigenous status and less skilled parent occupation category were all associated with increased likelihood of being in a heavier versus a lighter BMI category. In addition, the data suggest that the child's log odds of being in a heavier BMI category increased approximately linearly with decreasing maternal education, family income and SEIFA disadvantage index. When these analyses were repeated using CDC cutpoints, associations between BMI status and child sex weakened (OR 0.96 (0.84, 1.09), P=0.5), whereas associations with state of residence strengthened. Conclusions about all other variables did not change (analyses available on request).

Table 1 Estimated population prevalence of normal weight, overweight and obese children by socio-economic variables

Adjusting for all covariates in the multivariable logistic regression model (Table 1, adjusted odds ratios), indigenous status and lower SEIFA disadvantage index were the clearest independent predictors of higher BMI status (P=0.03 and P=0.003, respectively). Indigenous children are estimated to have 1.5 times greater odds of being in a higher weight category compared to a lower category. The only statistically significant interaction effect with sex was that boys who speak a language other than English had nearly three times the odds of being in a heavier weight category compared to English-speaking boys, whereas the difference was substantially smaller among girls (Table 1, final rows).

The estimated population proportions of mothers in the non-overweight, overweight and obese categories were 55.7, 26.2 and 18.2%. In univariate analyses, having an overweight mother nearly doubled (OR 1.93 (95% CI 1.58, 2.75)) and an obese mother nearly tripled (OR 2.75 (95% CI 2.15, 3.52)) the odds that a child would be in a heavier BMI category, compared to a child with a non-overweight mother. In the final multivariable model, adjusting for mother's BMI status in addition to all other covariates had minimal impact on the relationships between socio-economic status and child weight category or on the relationship with maternal BMI status (adjusted OR overweight vs non-overweight 1.9 (95% CI 1.6, 2.4), adjusted OR obese vs non-overweight 2.6 (95% CI 2.1, 3.3); P<0.0001).

Waist circumference was available for 4902 (98.4%) of the 4983 participants. Mean girth for the whole sample was 54.6 cm (s.e. 0.09), and was virtually identical for boys 54.6 cm (s.e. 0.11) and girls 54.6 cm (s.e. 0.12), with a slightly wider variation in girls. Table 2 (results of the linear regression analysis) indicates that age was the only independent predictor and that no socio-economic factor independently predicted waist circumference. These results were unaffected by inclusion of maternal BMI status in the model.

Table 2 Adjusted coefficient estimates (with 95% CI) for linear regression of child's waist circumference in centimetres on socio-demographic variables

Analysing the BMI data using the CDC cutpoints produced similar results. Results of the regression analysis including mother's BMI status were little different when restricted to the 3643 cases who had complete data on all covariates, suggesting that the assumptions underlying the multiple imputation method were not having a major influence on findings.


This large national survey confirms a high prevalence (>20%) of overweight/obesity in Australian preschoolers in 2004. Although high, right across the social spectrum, the associations between higher risk of overweight/obesity and indicators of lower socio-economic status at several levels – the mother (lower maternal education), the family (lower income, less-skilled occupations, immigrant and indigenous status) and the community (scores indicative of greater disadvantage) – are disturbing. Children in the bottom quintile of the disadvantage index had a 47% higher odds (95% CI 14, 92%) of being in a heavier weight category than those in the top quintile. In terms of overall population impact, this corresponds to a difference between the bottom and top quintiles (in terms of proportions of overweight/obese children) of about 8%. However, other factors appeared less strong, and the causal pathways are likely to be complex. As in other studies, maternal overweight and obesity were strongly predictive of child BMI status, but this did not alter the conclusions regarding socio-economic status. Despite the wide range of measured waist circumference, the study did not show relationships between children's girth and their socio-economic status.

Strengths of the study include its large-scale, nationally representative design and the recency of the data, enabling firm conclusions to be drawn about preschool obesity throughout today's Australia. It collected indicators of several different aspects of socio-economic status, all of which pointed to similar conclusions, and obtained direct measures of height, weight and girth using standardized measurement techniques.

Weaknesses include the response rate of 59%, as missing data cannot be fully accounted for even when non-response weightings are used. However, the estimated prevalence is very similar to a recent regional South Australian survey of this age group which achieved a higher capture rate (70–80%).34 Children of lower socio-economic status postcodes, non-English speaking backgrounds and lone-parent families are known to be underrepresented in LSAC and children with mothers who completed year 12 were overrepresented. This may have led to some loss of precision, but there is no reason to expect that these selection biases would affect the direction of the association. Finally, these data are cross-sectional so causal relationships cannot be inferred. Subsequent waves of LSAC will support examination of trajectories of BMI and truncal adiposity against multiple indicators of socio-economic status.

It has been known for some years that the prevalence of overweight/obesity has increased sharply in both disadvantaged35, 36 and general6, 37, 38 preschool child populations. Social gradients in the prevalence of childhood overweight/obesity in school-aged children, not evident in Australia23 or internationally24 in the late 1990s, have only recently been reported.1, 39 Studies focusing on children 6 years of age or younger have also recently reported relationships between obesity and lower socio-economic status. These have either used only a limited range of socio-economic factors,40, 41, 42, 43, 44, 45 have not utilized representative population samples,46 or have had sample sizes that are small47 or limited to low-income families.48 This study, to our knowledge the first to examine relationships between such a broad range of indicators of social disadvantage and overweight/obesity in a large national study, confirms and extends these previous findings in the published literature.

In univariate analyses, all indicators of lower socio-economic status were implicated in a greater risk of childhood obesity, hinting at the complexity of the multiple behavioural patterns that probably promote obesity. In the multivariable analysis, indigenous status, language other than English (particularly for boys) and lower SEIFA disadvantage index score exhibited the clearest independent associations with obesity risk. As the disadvantage index is calculated from census data variables (pooled by postcode of residence) regarding low-income, low educational attainment, high unemployment and prevalence of low-skilled occupations in that postcode, it is not surprising that there is some redundancy when these variables are included with the disadvantage index in the multivariable model. However, it is of great interest that the relationship between preschool obesity and the collective neighbourhood summary variable appears to outweigh the relationship with the individual's and family's characteristics. This implies that additional neighbourhood factors related to social disadvantage, over and above the limitations brought by individual families to that neighbourhood, may be obesogenic for children and highlights the importance of a better understanding of external determinants of disadvantage in this epidemic.21, 49 Relationships between childhood obesity and immigrant status have been demonstrated in several countries with different ethnic groups, with at least one study concluding that this increased risk is due, not to the nationality, but to higher prevalence of known risk factors of overweight in immigrant groups, such as lower maternal education and higher levels of physical inactivity.44 Why these relationships should be more marked for boys than girls is not clear.

It is also not clear why there should be relationships between disadvantage and BMI, but not disadvantage and girth. This could mean that, at this age, there is truly no relationship, suggesting that any body mass accumulation that is sensitive to deprivation occurs at other body sites. Alternatively, it could mean that girth variability at this age may be too great to detect the gradient. This is possible, given that many children are passing through adiposity rebound at 4–5 years, when a small change in maturational age can create substantial changes in both BMI and skinfolds. This could induce greater variability in waist girth (heavily affected by very labile subcutaneous abdominal fat) than in BMI, which has other more stable components (lean mass, skeletal mass, etc). Although the field workers underwent training in the child measurements, most had not previously undertaken anthropometric measures. This may have resulted in increased measurement error, which might obscure associations, although we saw little sign of any ‘trends’ in the point estimates. We would support more detailed measurement of body composition in future studies of preschool children to shed light on this unexpected finding.

In conclusion, population responses to childhood obesity should include both universal (relating to the high prevalence across all sectors) and targeted (to those of greater social disadvantage) components, and should focus on preschool- as well as school-aged children. Future research should address which components of social disadvantage promote obesity in young children and how these components operate, ideally through longitudinal and intervention (rather than cross-sectional) studies. Particularly important research gaps include the need for a better understanding of how social disadvantage alters obesogenic behaviours, how it relates to the intergenerational transmission of obesity and of specific neighbourhood characteristics that promote healthy and unhealthy weight gain in young children.


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This paper uses confidentialized unit record files from the Longitudinal Study of Australian Children (LSAC) survey. The LSAC project was initiated and is funded by the Commonwealth Department of Families, Community Services and Indigenous Affairs is managed by the Australian Institute of Family Studies. The findings and views reported in this paper, however, are those of the authors and should not be attributed to either FaCSIA or the Australian Institute of Family Studies. Analysis and writing of this paper was funded by Australian National Health and Medical Research Council Project Grant 334308. We thank all the parents and children who took part in wave 1 of LSAC.

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Wake, M., Hardy, P., Canterford, L. et al. Overweight, obesity and girth of Australian preschoolers: prevalence and socio-economic correlates. Int J Obes 31, 1044–1051 (2007). https://doi.org/10.1038/sj.ijo.0803503

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  • child, preschool
  • waist circumference
  • socio-economic factors
  • national prevalence
  • cross-sectional studies

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