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Despite the strongly established link between socio-economic status (SES) and health across most stages of the life-course, the evidence for a socio-economic gradient in adolescent health outcomes is less consistent. This paper examines associations between household, school, and neighbourhood SES measures with body composition outcomes in 16 year old South African Black urban adolescents from the 1990 born Birth to Twenty (Bt20) cohort. Multivariable regression analyses were applied to data from a sub-sample of the Bt20 cohort (n = 346, 53% male) with measures taken at birth and 16 years of age to establish socio-economic, biological, and demographic predictors of fat mass, lean mass, and body mass index (BMI). Results were compared with earlier published evidence of health inequality at ages 9-10 years in Bt20. Consistent predictors of higher fat mass and BMI in fully adjusted models were being female, born post term, having a mother with post secondary school education, and having an obese mother. Most measures of SES were only weakly associated with body composition, with an inconsistent direction of association. This is in contrast to earlier findings with Bt20 9-10 year olds where SES inequalities in body composition were observed. Findings suggest targeting obesity interventions at females in households where a mother has a high BMI. © 2012 Elsevier B.V. All rights reserved.
Birth to Twenty (Bt20) is a longitudinal cohort study of 3273 singleton births occurring in 1990 to permanently resident mothers in Johannesburg–Soweto, South Africa (Richter et al., 2004, 2007). At ages 9–10 years, a sub-sample from the cohort (n = 429) was enrolled into a longitudinal study assessing factors influencing bone health and body composition. Because the sub-cohort are predominantly African Black this paper focuses on the African Black group because the African White group were not present in sufficient numbers to analyse as a separate group. Bone health participants had more detailed health and SES assessments than the Bt20 cohort. For instance neighbourhood and school measures of SES were not collected on the main cohort at age 16 years. Those African Black participants with data on household/neighbourhood/school SES and anthropometric and dual-energy X-ray absorptiometry (DXA) data at 16 years were included in current analyses (n = 346, 53% male). Ethical approval was granted by the ethics committees of the University of the Witwatersrand, South Africa for the primary data collection and Loughborough University, UK for the secondary analyses of the data. The primary caregiver gave written informed consent for their adolescent to participate and the adolescent provided written ascent to participate at 16 years. During infancy and at age 16 years household SES measures were caregiver assessed using a questionnaire that was based on standard measures used by the Demographic and Health Surveys (www.measuredhs.com). Measures included caregiver’s education and occupation, home ownership and type, and household consumer durable ownership. These are measures that have been commonly used to assess the association between SES and child health or fertility in developing countries according to a review of 67 studies (Bollen et al., 2001). In addition to the most commonly used measures of SES, marital status and water and sanitation provision were also tested in this study. Those not marrying or cohabiting are likely to live in households that have less adults and which are more likely to be female headed. Having less adults in the household results in a higher dependency ratio and potentially reduced resources for expenditure on children. Female headed households and higher dependency ratios have been shown to be associated with higher risk of chronic poverty in the South African context (Roberts, 2001). Bt20 collected information on the type of water and toilet facility and whether it was shared, sole, or other type of access. In infancy, information was only available on whether the household had inside provision of water and toilet facilities, outside facilities, or a mixture of both. At age 16 years, more detailed information was available to allow households to be split into whether they had hot or cold tap water or another source of water as well as whether this source was sole or shared. At age 16 years more detailed information was also available regarding the toilet facilities so that households could be distinguished by whether they had access to a flush versus other type of toilet and whether that access was sole or shared. Sole use of water and toilet facilities is preferable and would indicate a higher SES in this context. Similarly hot water and a flush toilet would be considered the optimal water and sanitation provision. Measures of water and sanitation provision not only capture the SES environment but also disease risk. Where facilities are shared or less optimal (i.e. no availability of hot water) the risk for morbidity will be higher and this may have direct influences on body composition outcomes that are separate from SES effects. At 16 years of age neighbourhood and school SES were self-assessed using a culturally relevant questionnaire, which was developed by consulting community leaders and Bt20 adolescents/caregivers using focus group discussions and in-depth interviews in 2005/2006 (Sheppard et al., 2010). Based on the findings of this earlier formative work, neighbourhood was defined for all participants as an area approximately 20 min walk or 2 km from home in any direction. The neighbourhood SES questionnaire included questions relating to: (1) economic aspects of neighbourhoods including neighbourhood wealth, perceived inequalities in wealth, type, condition, and spacing of housing, infrastructure and service provision type, condition of roads, and neighbourhood problems (e.g. traffic congestion, illegal dumping), (2) social aspects of neighbourhoods including safety, crime, activities for young people, neighbourhood friends, peer pressure, noise, and religious involvement, and (3) school environment with questions on school type, facilities, class sizes, out of school activities, and problems (e.g. poor academic standards, alcohol and drug consumption, weapons). To enable a more parsimonious analysis of SES measures and to avoid problems of multicollinearity, principal components analysis (PCA) was used to construct neighbourhood SES indices. A theory based approach was used to develop seven neighbourhood SES indices as well as two household SES indices and PCA confirmed the appropriateness of grouping these variables together. In each case, the first component scores were extracted and the statistical assumption that all Eigenvalues be greater than 1 was met. The first components explained between 27 and 91% of variation. In most cases only one Eigenvalue was greater than 1. However, where there were two Eigenvalues greater than 1, the scree plots showed a clear inflection between the first and second component in all cases meaning that 1 component was extracted for the analysis. Furthermore, the second components for any of the indices were hard to interpret in a theory driven approach to construction because they included both positive and negative values and there was no clear substantive reason that could be found for the variables that were assigned negative values versus positive values in any of the indices. Further details about the composition and fit of the neighbourhood indices have previously been reported (Griffiths et al., 2012). Neighbourhood indices created were; (1) neighbourhood economic index, (2) neighbourhood need for more services and facilities index, (3) neighbourhood problem index, (4) neighbourhood crime prevention index, and (5) neighbourhood social support and happiness index. Two variables (How safe do you feel in the neighbourhood and how much crime is there in the neighbourhood?) did not load well onto any index and were thus retained as individual variables. There were also two school indices identified; (6) school environment index and (7) school problems index. In addition to these neighbourhood and school measures, household questionnaire data were used to construct two indices that measured ownership of consumer durables, the first during infancy and the second at age 16 years. Regression factor scores were extracted for each index and tertiles for each index were created. A variable was created to identify adolescents who transitioned from one index of consumer durables tertile to another between infancy and 16 years. Birth weight and weight and height at 16 years were assessed using standard techniques (Lohman et al., 1991). Weight was measured using digital scales and height using a stadiometer (Holtain, UK). Low birthweight was defined as a birthweight less than 2.5 kg. Body mass index was calculated as weight (Kg)/height (m)2 and adolescents were classified as normal weight, overweight, or obese using Cole et al.’s international age specific cut-off points. At 16 years of age a fan-beam densitometer (model QDR 4500A; Hologic Inc, Bedford, Massachusetts) was used to obtain DXA readings of body composition. Total body fat mass (FM) and lean mass (LM) were assessed using the adult software version 8.21 (Hologic Inc) to enable longitudinal follow up with comparable software into adulthood. DXA scans used recommended standardised patient positioning and scan analysis. Caregivers reported the ethnicity of the adolescent as recorded on the official birth notification. Individuals born before 37 weeks gestation were classified as preterm and after 41 weeks as post term. Adolescent’s parity and mother’s marital status and age were self reported during infancy. Adolescent’s reported smoking status (current, previous smoker, or never smoked) at age 16 years and assessed their own pubertal development with the use of picture cards detailing the different stages of the Tanner scales for breasts and genitalia or pubic hair development (Tanner, 1962). Maternal weight and height were available from data that were collected between 2002 and 2004 when the cohort were aged 12–14 years. Maternal BMI was calculated in the same way as for adolescents, but overweight and obesity were defined using internationally accepted cut-offs of >25 kg/m2 and >30 kg/m2, respectively. General linear regression was used with outcomes of BMI, FM, and LM, as well as binary logistic regression models that dichotomised adolescents into those who were overweight and obese compared to those who were not. Fig. 1 shows the independent variables included in the analysis of body composition outcomes at age 16 years broken down by characteristics of the child, mother, household, neighbourhood, or school (displayed on the vertical axis) by age of measurement (horizontal axis). For a number of variables used in the analyses there was missing information for some cases because of incomplete responses at a data assessment period where the variables were recorded. This was especially problematic for variables like maternal BMI measured at ages 12–14 years where 61 cases did not have this information recorded. To exclude cases that had missing information on any one of the predictor variables in Fig. 1 would have reduced the sample size by close to a third. To exclude variables with large numbers of missing cases would have excluded important information available for testing the papers key hypotheses. A decision was therefore made to incorporate this missing information into analyses by creating a missing category where relevant for predictor variables that were included in the analysis. Where a missing category shows a significant association with the outcome, this suggests that the missing group are different to the reference group for that variable. In adjusted models, of all of the missing categories entered, only the missing category for term birth was significantly different to the reference group in all three models. This group had a very small sample size. The models were re-estimated without the missing cases for this variable and no changes to substantive findings were observed. The models with all missing cases included are therefore presented in the paper. Multicolinearity checks were undertaken for all variables including for missing cases in the adjusted models using VIF statistics. This ensured that including missing cases did not introduce problems of multicolinearity to the analyses if the same cases were missing for a number of variables. Variables measured in infancy and age 16 years that were tested for an association with body composition outcomes at age 16 years in the urban South African Birth to Twenty cohort. Initial unadjusted regression analyses explored relationships between each of the measures shown in Fig. 1 and each of the outcomes (results not shown). Subsequently multivariable regression analyses adjusted for all variables that had shown a relationship (p < 0.1) with the relevant outcome in the unadjusted analysis. Height was included as a covariate in all FM and LM models to correct for body size. This approach does not have the same flaws as percentage body fat or lean tissue or fat and lean mass indices, which include the fat or lean mass component in both the numerator and denominator and therefore over adjusts for weight (Cole et al., 2008). In addition, sex was included as a covariate in all adjusted models. This means that an initial step of the modelling process (labelled step 0) models sex and height (for FM and LM only) as a predictor of the outcomes. Regression models were then built in steps that added to this initial step 0; (1) significant infancy variables from the initial unadjusted analysis, (2) significant year 16 household and neighbourhood and school SES variables, (3) significant infancy and year 16 variables, and (4) model 3 plus significant other variables. This approach allowed for the effects of the infancy variables and the year 16 variables to be interpreted separately, and subsequently for any mediating effect of the year 16 variables on the association between the infancy variables and the outcomes as well as any mediating effect of the other variables on the association between infancy and year 16 SES variables and the outcomes to be investigated. Where both maternal BMI and weight status measured when the cohort were aged 12–14 years (i.e. normal, overweight, obese) were significantly associated with an outcome only weight status was taken forward to the multivariable analysis. In the FM model, there was a problem of multicolinearity between the missing category of the maternal BMI variable and the pubertal status variable. Because the pubertal status variable was not significant in adjusted analysis and the maternal BMI variable was, pubertal status was not included in the adjusted regression model. The VIF statistics confirmed that there were no further problems with multicolinearity between the predictor variables included in any of the models (results not shown). All analyses were conducted using SPSS 16.0 (Chicago, IL).
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