Morbidity patterns among the underweight, overweight and obese between 2 and 18 years: population-based cross-sectional analyses



No study has documented how symptomatic morbidity varies across the body mass index (BMI) spectrum (underweight, normal weight, overweight and obese) or across the entire child and adolescent age range.


To (1) quantify physical and psychosocial morbidities experienced by 2–18-year-olds according to BMI status and (2) explore morbidity patterns by age.

Design, setting and participants:

Cross-sectional data from two Australian population studies (the Longitudinal Study of Australian Children and the Health of Young Victorians Study) were collected during 2000–2006. Participants were grouped into five age bands: 2–3 (n=4606), 4–5 (n=4983), 6–7 (n=4464), 8–12 (n=1541) and 13–18 (n=928) years.

Main measures:

Outcomes—Parent- and self-reported global health; physical, psychosocial and mental health; special health-care needs; wheeze; asthma and sleep problems. Exposure—measured BMI (kg m−2) categorised using standard international cutpoints.


The variation in comorbidities across BMI categories within and between age bands was examined using linear and logistic regression models.


Comorbidities varied with BMI category for all except sleep problems, generally showing the highest levels for the obese category. However, patterns differed markedly between age groups. In particular, poorer global health and special health-care needs were associated with underweight in young children, but obesity in older children. Prevalence of poorer physical health varied little by BMI in 2–5-year-olds, but from 6 to 7 years was increasingly associated with obesity. Normal-weight children tended to experience the best psychosocial and mental health, with little evidence that the U-shaped associations of these variables with BMI status varied by age. Wheeze and asthma increased slightly with BMI at all ages.


Deviation from normal weight is associated with health differences in children and adolescents that vary by morbidity and age. As well as lowering risks for later disease, promoting normal body weight appears central to improving the health and well-being of the young.


Child and adolescent obesity is a priority health concern internationally,1 as it strongly predicts adult obesity,2 which is clearly associated with a heavy health burden.3 However, the range of physical and psychosocial health problems that may be associated with overweight and obesity during childhood and adolescence has not been confirmed or quantified. Such information must be considered alongside projections of future adult outcomes4 to understand the total burden of child and adolescent obesity in developed countries.

To date, the literature has largely focused on asymptomatic complications (such as insulin resistance and hypertension),5, 6 associated health-risk behaviours (such as dieting and eating-disordered patterns)7 and self-perceptions (such as body image and self esteem)8 in school-aged children and adolescents. An emerging but piecemeal literature suggests that health-related quality of life (HRQoL) is not affected by overweight/obesity in 4–5-year-olds,9 and that HRQoL’s emotional and school functioning domains vary relatively little with increasing body mass index (BMI) throughout childhood and adolescence.10, 11 However, older children with high BMI tend to have poorer physical and social functioning10, 11 with these associations marginal in overweight children, modest in community samples of obese children and very marked in help-seeking tertiary clinical samples.10, 11, 12

No study has comprehensively documented the symptomatic physical or mental health problems that might prompt individuals, families or health professionals to treat or manage BMI in children and adolescents. Health problems putatively related to high BMI include asthma,13, 14 sleep problems15 and special health-care needs,16, 17, 18 all of which are very prevalent16, 19, 20, 21 and incur high levels of health burden and cost.16, 21 However, the extent to which they may cluster among obese children and at what ages remains unclear, and all these findings need to be extended and compared in population studies that span the entire child and adolescent age range.

A further issue is that the epidemiology of childhood BMI may be changing across its entire range. Underweight may also be on the rise in preschoolers (Wake et al, under preparation), primary school children22 and adolescents,23 the net effect being reduced proportions of normal-weight children. Other than the small numbers with anorexia nervosa,24 little is known about the health status of thin children and adolescents. In Dutch 5–14-year-olds, no association was found between mental health and BMI status including underweight,25 whereas obesity, but not underweight, was associated with poorer global and physical health, more health visits and more school absenteeism.26

The advent of standardised cut-off points for childhood underweight27 using the same metric as the International Obesity TaskForce (IOTF) overweight/obesity classification28 now makes it possible to study how common morbidities may vary across the 2–18-year age range and the full BMI spectrum. Here, we address this question drawing on two contemporaneous large-scale, population-based Australian longitudinal studies with comparable physical and psychosocial health measures spanning the entire 2–18-year-old age range and the full BMI spectrum. We aimed to:

  1. 1

    Quantify the physical and psychosocial health of underweight, overweight and obese children and adolescents aged 2–18 years, compared with their normal-weight peers and

  2. 2

    Explore whether and how patterns of associations between BMI status and morbidity patterns vary by age.

Materials and methods

Design and sample

To gain complete age coverage of childhood and adolescence, we used data from (i) waves 1 and 2 (2004 and 2006) of the Longitudinal Study of Australian Children (LSAC) and (ii) waves 2 and 3 (2000 and 2005) of the Health of Young Victorians Study (HOYVS).

LSAC is a national study that recruited 5107 Australian infants aged 0–1 year and 4983 preschool children aged 4–5 years in 2004. This paper draws on wave 2 infant cohort data (that is, at age 2–3 years, the first wave that included BMI data) and waves 1 (4–5 years) and 2 (6–7 years) data from the preschool cohort. LSAC used a two-stage cluster sampling design with Australian postcodes as the primary sampling units, stratified by state of residence and urban versus rural status, and children enrolled on the Medicare Australia database as the secondary sampling units.29 Of those who were resident in the sampled postcodes and contactable, response rates were 64% for the infant cohort and 59% for the preschool cohort. In all, 4606 of the infants (90.2%) and 4464 (89.6%) of the preschool children participated in wave 2. The LSAC sample is considered broadly representative of the Australian population, although children with highly educated parents are slightly overrepresented in wave 1, whereas children in single-parent families, non-English speaking families and families living in rental accommodation are underrepresented in wave 1 and have lower retention in wave 2.30, 31

HOYVS was a longitudinal population-based cohort study established in 1997. Sampling and methods have been reported previously.32 Briefly, 24 elementary schools were selected from across the state of Victoria, Australia (population 4.6 million in 1997), using a stratified two-stage random sampling design based on school education sector (government, Catholic or independent) and school class level. The baseline response rate for students in the first (‘Prep’) through the fourth (grade 3) school year in 1997 was 83.2% (1943 out of 2336 identified children, age range: 5.0–10.7 years). The achieved sample mirrored Victorian census data for age distribution, sex, ethnicity (parental county of birth) and proportion of indigenous persons.

Children were resurveyed 3 years later (wave 2, 2000) when in grades 3 through 6 (n=1575; response rate 81.0%; age range: 8.4–13.8 years). We excluded children with missing age data (n=8) or aged 13 (n=26), resulting in 1541 children in the 8–12-year age band.

Wave 3 was conducted a further five years later (September 2005–December 2006) when adolescents were in grades 8 through 12 (n=929; response rate 47.8%; age range: 13.6–19.4 years). One 19-year-old participant was excluded to constitute a 13–18-year age band (n=928). The baseline characteristics of those retained and lost to follow-up at Wave 3 were similar with regard to gender and socio-economic status (Socio-Economic Indexes for Areas Disadvantage Index, see below), but those lost had less educated mothers, were older, had higher BMI Z-score and a higher proportion were overweight or obese.33

Both studies were approved by the relevant Ethics Committees, with HOYVS also approved by relevant education authorities of participating schools. Written informed consent was provided by parents in both studies, and also by adolescents in wave 3 of HOYVS.

Anthropometric measures (primary exposure) were taken by trained field workers using similar protocols in both studies. Weight was measured in light clothing using digital scales to the nearest 50 g in LSAC (Salter Australia (Springvale, Victoria, Australia) code 79985 and HoMedics (Melbourne, Victoria, Australia) digital BMI bathroom scales) and to the nearest 100 g in HOYVS (Tanita (Tokyo, Japan), Model 1597 in 2000, Model THD-646 in 2005). Height was measured to the nearest 0.1 cm using portable rigid stadiometers (Invicta (Leicester, UK), Model IPO955). Only one height measurement was taken in wave 2 of HOYVS. In LSAC and wave 3 of HOYVS, height was measured twice and the two measurements averaged. If they differed by >0.5 cm, a third measurement was taken; LSAC used the average of the two closest, whereas HOYVS wave 3 used the median of all three measurements. BMI was then calculated (kg m−2) and participants classified as underweight, normal, overweight or obese according to the international age- and gender-specific cutpoints defined by Cole et al.27, 28

Potential comorbidities (outcomes) are summarised in Table 1 and cover global health, physical and psychosocial health status, mental health, special health-care needs, asthma and wheeze, and sleep problems. These outcomes were selected because of their availability and consistency across all the ages and both studies, and because all are putatively associated with BMI status as outlined in the Introduction.

Table 1 Measures of potential comorbidities (‘outcomes’) of child and adolescent overweight/obesity

Demographic variables were the child/adolescent’s sex (male/female), age and the Socio-Economic Indexes for Areas Disadvantage Index,34 a measure of neighbourhood disadvantage, for the most recent postcode of residence categorised into quintiles. Socio-Economic Indexes for Areas Indexes are standardised scores compiled from census data for geographic areas to numerically summarise the distribution of Australian social and economic conditions (national mean 1000, s.d. 100; higher values represent greater advantage).

Statistical analysis

For each age band, the distribution of comorbidities across the four BMI categories was summarised into percentages for categorical variables and means for continuous variables and presented in tabular and graphical form.

Regarding aim 1, we examined variation in morbidity level within each age band across the four BMI categories, using unadjusted linear regression (continuous outcomes) and logistic regression (categorical outcomes) methods to assess the evidence for (unstructured) differences between categories.

Regarding aim 2 (exploring whether and how morbidity patterns vary by age), we used interaction terms in linear and logistic regression models to test for differences in the pattern of variation with BMI category between age categories, adjusting for multiple measures on the same individual by using robust ‘information sandwich’ standard errors.35 Comparisons were adjusted for sex and socio-economic indexes for area, but we report the simpler unadjusted results as adjustment made no substantial difference, because there was no major imbalance on these factors between the BMI categories. Analyses were conducted using Stata release 11.1 (StataCorp (College Station, TX, USA), 2007).


Of those retained within the specified age, BMI was available for 4522 (98.2%) children aged 2–3 years, 4934 (99.0%) aged 4–5 years, 4423 (99.1%) aged 6–7 years, 1540 (99.9%) aged 8–12 years and 920 (99.1%) aged 13–18 years. Table 2 shows the characteristics of the sample. The prevalence of underweight was highest in the toddlers (5.3%) and lowest in the teenagers (4.6%), whereas, conversely, obesity was most prevalent in teenagers (6.1%) and least prevalent in toddlers (4.4%).

Table 2 Demographic characteristics of children and adolescents with body mass index data

Morbidity levels across the BMI categories within each age band are presented in Table 3 (continuous data outcomes) and Table 4 (categorical data outcomes). These tables report (i) P-values from tests of association between BMI and morbidity within age bands and (ii) P-values for interaction between BMI and age in their association with morbidity.

Table 3 Morbidity levels (continuous outcomes) by BMI status and age
Table 4 Morbidities (categorical data) by BMI status and age

There was strong evidence (P<0.001) that the pattern of risk of poorer global health across BMI categories varied between age groups, with these differing patterns very evident visually in Figure 1. In the youngest age groups, risk was primarily elevated in the underweight category, while as age increased an elevated risk of poorer global health progressively emerged in the obese and to some extent the overweight categories. There was similarly strong evidence (P<0.001) that the risk of poor physical health (Pediatric Quality of Life Inventory) across BMI categories varied with age: although there was little difference in physical health between BMI categories amongst younger children, poorer physical health was associated with obesity in older children.

Figure 1

Morbidities by BMI status for the five age groups. (a) Physical health (PedsQL Physical Summary). (b) Psychosocial health (PedsQL Psychosocial Summary). (c) Mental health (SDQ Total). (d) Global health good/fair/poor. (e) Special health-care needs. (f) Wheeze. (g) Asthma. (h) Sleep problem. P-value for interaction test based on four categories of BMI crossed with age categories, using linear regression. Special health-care needs, wheeze, asthma and sleep data not available for 8–12-year-olds. SDQ total data not available for 2–3- or 8–12-year-olds. SDQ, Strengths and Difficulties Questionnaire; PedsQL, Pediatric Quality of Life Inventory.

Children in the normal-weight group generally had the best psychosocial (Pediatric Quality of Life Inventory) and mental (Strengths and Difficulties Questionnaire) health, with poorer health for the obese category and, to a lesser extent, the overweight and underweight categories. There was no evidence of differential trends across age bands for either psychosocial (P=0.55) or mental (P=0.51) health.

There was a suggestive evidence (P=0.03) of different patterns of age-related risk of special health-care needs across weight categories but the variation in prevalence of this outcome was difficult to interpret. In the two preschool age groups, underweight children had the highest rates of special health-care needs, but in the school-aged children, underweight children had the lowest rates. In all the groups, special needs were more prevalent in overweight and obese than in normal-weight children.

The pattern of association between BMI categories and both wheeze and asthma were similar at every age, that is, increasing modestly across the BMI categories, with underweight children experiencing the lowest incidence. There was little evidence of an age interaction for either wheeze (P=0.88) or asthma (P=0.20). Rates of sleep problems were not clearly associated with BMI status in any age band.

We ran several post-hoc analyses (data not shown) to examine the possibility that the differing patterns of morbidity by age might reflect sampling and/or secular trend differences in the cohorts. First, the older (HOYVS) children were from the single state of Victoria, whereas the younger (LSAC) children were a national Australian sample; conclusions were unchanged when we reran the analyses with the LSAC Victorian subsample only. Second, the HOYVS 8–12-year-old data were collected in 2000, whereas the LSAC data were collected in 2004–2006. To check whether secular trend might be an issue, we examined patterns of morbidity by BMI for 8–9-year-olds in both HOYVS and the subsequent 2008 LSAC wave. Again, patterns were very similar.


Principal findings

Patterns of comorbidity varied across BMI categories for all outcomes except sleep problems, with obese children and adolescents generally showing the highest levels. However, patterns varied greatly both by morbidity type and age. In particular, poorer global health and special health-care needs were associated with underweight in young children, but with overweight and obesity in older children. Poorer physical health varied little by BMI in children aged 2–5 years, but from 6 to 7 years was increasingly associated with overweight and obesity. Wheeze and asthma increased slightly with increasing BMI category at all ages. Associations with psychological health were weaker but, at all ages, the best psychosocial and mental health was experienced by normal-weight children, and the worst by obese children.

Strengths of the study

These analyses utilised large, contemporary Australian population-based samples spanning ages 2–18 years, all studied within a narrow time window (2000–2006) and all recruited using clustered geographic sampling. Height and weight were measured using virtually identical protocols in the three cohorts, with BMI classified using a single metric for the four categories and the entire age spectrum. This high degree of consistency provides confidence that the changing patterns of association are not due to measurement error in the primary exposure of BMI. Similarly, there was a very strong consistency across the entire age range in the standardised outcome measures of potential comorbidity.

Study limitations

Five age bands comprised three separate groups of individuals measured once or twice; however, the analytical approach ensured that there was a benefit from these repeated measures on the same individuals, in the form of enhanced precision for the comparisons between age groups. Loss to follow-up resulted in some underrepresentation of families with a single parent, mothers with lower education levels, non-English speaking background or living in rental accommodation in the later waves; although morbidity prevalence rates may not fully generalise to these groups, substantial numbers in these groups were still present in the sample and internal associations between weight and morbidities are unlikely to be biased. All associations were cross-sectional, limiting the scope for conclusions about causality. It remains possible that some of the age differences reflected secular trend and/or the use of more than one cohort; however, our post-hoc analyses suggest that our findings would probably have been similar had we been able to study our 11 000 participants in a single national sample, combining all morbidities and measured BMI across the age range of 2–18 years.

Although our primary exposure (BMI) was measured, the outcome measures of potential comorbidities were all either parent- or self-reported. This is appropriate for subjective constructs, such as HRQoL and special health-care needs, but it is possible that, had we measured asthma and sleep objectively, our conclusions might have differed. However, it would be difficult if not impossible to objectively measure these consistently given the limitations of their measurement in field studies, the very large number of participants and the wide age range (including very young children) that we see as a particular strength of the study. All our measures are standardised, validated and widely used in epidemiological research. Further, parent- and self-reported morbidities are likely to be highly relevant to help-seeking and thus the health-care burden and costs of BMI-related morbidity in children and adolescents.

Interpretation in light of other studies

Our study confirms previous findings11, 12 that obese children experience lower HRQoL than their normal-weight peers, but goes further in demonstrating that this association is weak or absent in very young children, emerges convincingly only in the school years and then steadily strengthens with age. Our findings are consistent with the more fragmented literature reporting that overweight/obese older children and adolescents report poorer global health,26 greater primary health-care needs26 and higher prevalence of wheeze/asthma36 than children of normal weight. The lack of association between sleep problems and BMI status in this study may reflect either limitations with our sleep measure (problems rather than duration) or a true lack of association, with recent research producing conflicting findings as to whether short sleep duration does15, 37, 38 or does not39, 40 predict childhood obesity.

Underweight was associated with substantially greater morbidity than obesity in preschoolers. This may well reflect broader societal stereotypes of health and might partly explain why parents and practitioners seem less concerned about obesity in their preschool children;41 fear of underweight might actively hinder attempts to address excess adiposity in this age group. Conversely, underweight school-aged children and adolescents were among the most healthy in their age groups. Thus, underweight 13–18-year-olds had the least special health-care needs and asthma and the best global health. Their physical and mental health was not dissimilar to normal-weight individuals, but they showed slightly lower psychosocial HRQoL from the late primary school years onwards. The long-term outcomes of thinness in healthy individuals are as yet unknown. On the one hand, deliberate caloric restriction in healthy young adults may enhance health and longevity, with trials currently under way.42 Conversely, those with underweight in the context of eating disorders are known to have worse mental health that continues well into young adulthood,43 and several large longitudinal studies have indicated that healthy underweight (baseline BMI <18.5 kg m−2), as well as obese, adults have higher subsequent cardiovascular mortality.44, 45, 46


These findings have obvious implications for policy. In young children, lack of obesity impacts coupled with heightened concern about underweight is likely to impede efforts to systematically address early-onset obesity. Conversely, reductions in global and physical health are already strongly evident among obese adolescents, reinforcing the need for effective preventive strategies throughout childhood47 and indeed adolescence.

The great variation of relationships with BMI status by type of morbidity and by age throughout childhood and adolescence suggests that a range of different mechanisms are involved. Future longitudinal analyses within these and other cohorts should shed light on the temporal nature of these associations, their causal pathways and their specific mechanisms. This information may provide population-based insights into the intricate emerging balance between human metabolism, growth, health and ageing from the earliest years.

Taken as a whole, this study shows how profoundly deviation from normal weight is linked to changes in health in children and adolescents. Promoting a normal body weight has the potential to affect not just risks for later life disease but appears central to improving the health and well-being of the young.


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We thank all the parents and children who took part in LSAC and HOYVS, and the major contributions of all field workers in both studies. This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The third wave of the Health of Young Victorians Study (HOYVS) was funded by the Australian National Health and Medical Research Council (NHMRC) Project Grant 334303, and the second wave by grants-in-aid from the National Heart Foundation, Murdoch Childrens Research Institute and the Financial Markets Foundation for Children. MW is supported by the NHMRC Career Development Award 546405, GP by the NHMRC Senior Principal Research Fellowship 454360 and EW by the Jack Brockoff Foundation and NHMRC Child and Adolescent Obesity Prevention Capacity Building Grant. MCRI research is supported by the Victorian Government’s Operational Infrastructure Support Program.

Author Contributions

All the authors had access to the data and take responsibility for the integrity of the data and the accuracy of the data analysis. MW is the study guarantor. MW, EW, GP, JW, Kylie Hesketh and Timothy Olds conceived the third wave of the Health of Young Victorians Study, obtained funding and directed the study. LC carried out the preliminary analyses and SC carried out the final analyses under the supervision of JC. MW and SC wrote the paper, with critical input from the other authors.

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Correspondence to M Wake.

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The authors declare no conflict of interest.

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The findings and views reported are those of the authors and should not be attributed to FaHCSIA, AIFS or the ABS. The funding bodies had no role in the conduct of the studies or development of this manuscript. The researchers were independent of the funders.

Ethical approval

LSAC: Written informed consent was obtained at wave 1, and the study was approved by the Australian Institute of Family Studies Ethics Committee. HOYVS: Each wave was approved by the Royal Children's Hospital Ethics in Human Research Committee and the authorities responsible for each education sector, and parents provided written consent (plus written adolescent consent for wave 3).

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Wake, M., Clifford, S., Patton, G. et al. Morbidity patterns among the underweight, overweight and obese between 2 and 18 years: population-based cross-sectional analyses. Int J Obes 37, 86–93 (2013).

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  • overweight
  • underweight
  • comorbidity
  • child
  • adolescent
  • cross-sectional studies

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