The Scottish Government

« Previous | Contents | Next »

Listen

CHAPTER ONE INTRODUCTION

Introduction

1.1 This report draws on data from the first sweep of the Growing Up in Scotland ( GUS) study. The Sweep 1 Report highlighted the persistence of inequalities between advantaged and disadvantaged families which impact on parents and their children (Anderson et al, 2007). Within the first sweep, there were clear inter-relationships between the age of mother at birth, socio-economic classification, household income and area deprivation. This paper explores the contribution of specific measures of advantage and disadvantage in relation to a number of specific health related behaviours for parent and child and, in doing so, seeks to identify the characteristics of more vulnerable and more resilient families in ways that can support policy making, service development and delivery to ensure that all children have the best start in life, whatever their circumstances.

Background

1.2 The intractable nature of social and health inequalities pose difficult challenges for government policy. Nonetheless, both the UK and Scottish Governments have expressed a strong commitment to lessening such inequality and have done so across policy domains. Early years policies in particular aim to give children the best possible start in life, whatever their circumstances, and seek to target resources to those most in need alongside a more universal approach to supporting families. Ameliorating social and health inequalities requires an understanding of the complex relationship between social circumstances, health related behaviours and the provision and uptake of services, including health and education. The interrelationship between structural factors, for example, relating to socio-economic position, cultural processes, relating for example, to social group, family or area, and individual behaviours is complex, and demands careful analysis of the factors that influence people's lives and health related behaviours. While the baseline data from the first sweep of GUS (Anderson et al, 2007) revealed some clearly differentiated aspects of experience for mothers and children by measures of advantage and disadvantage, this briefing will extend this analysis to focus in more detail on the interrelatedness of different factors.

1.3 The notion of resilience has become popular in policy terms, suggesting, as it does, that understanding factors that promote positive outcomes, in the face of disadvantage, may help governments meet the policy challenges of persisting disadvantage (Hill, M et al 2007). A resilience perspective would ask what social processes seem to foster more positive outcomes for those in disadvantageous circumstances. Identifying such processes might lead to the development of more specific policies and practices that actively support resilience through specific initiatives to alter the trajectories of those experience disadvantage. In relation to GUS, this would involve identifying those displaying positive behaviours or outcomes for parents and young children, who would, overall, be characterised as disadvantaged.

About the Study

1.2 The Growing Up in Scotland study ( GUS) is an important longitudinal research project aimed at tracking the lives of a cohort of Scottish children from the early years, through childhood and beyond. Its principal aim is to provide information to support policy-making, but it is also intended to be a broader resource that can be drawn on by academics, voluntary sector organisations and other interested parties. Focusing initially on a cohort of 5,217 children aged 0-1 years old and a cohort of 2,859 children aged 2-3 years old, the first wave of fieldwork began in April 2005. This report is one of a series that provide key findings from the first sweep of the survey.

1.3 GUS is based on a cohort or longitudinal design involving the recruitment of a 'panel' of children (and their families) who will be revisited on a number of occasions over an extended period of time. Members of the panel were identified in the first instance from Child Benefit records. For the first year of the study, interviewers sought to contact the 'main carer' of the child named in the Child Benefit records. In virtually all cases (99%), this proved to be the child's natural mother. As well as information on informal support, the first interview also collected data on pregnancy, birth and early parenting, childcare, child health and development, and parental health.

Measures of advantage and disadvantage

1.4 Table 1 details the variables which were used to measure different aspects of advantage and disadvantage across the GUS sample. Many of these were selected because of the variance demonstrated according to these characteristics in relation to a wide range of maternal behaviour, service use and child outcomes in the sweep 1 overview report (Anderson et al, 2007). Others (for example receipt of benefits and housing tenure) were chosen on the basis of their use as deprivation measures in other research. ¹

Table 1 Selected measures of advantage and disadvantage

Format of the Report

1.5 This report begins, in the next chapter, by examining patterns of advantage and disadvantage in order to map out the complex interrelationship between different factors. Chapter 3 then examines the interrelationship between social disadvantage and the following specific maternal behaviours: breastfeeding, attendance at ante-natal classes and smoking. Chapter 4 takes a resilience perspective by focussing on mothers considered to be disadvantaged (lone and young mothers) and comparing those who demonstrate more 'positive' behaviours with those demonstrating more 'negative' behaviours. Chapter 5 will offer some conclusions and recommendations based on these analyses.

Description of the analysis

1.6 The fact that there is a relationship between key independent variables such as family type, age of mother at birth of sample child, and level of mother's education, means that it is difficult to establish the key drivers of differences in the observations contained in simple descriptive analysis. For example, is the relationship between age of mother and breastfeeding simply a function of the fact that younger mothers are more likely to be in lower income households or have fewer qualifications? By using multivariate analysis (logistic regression) to look at the impact of a number of variables simultaneously on a mother's propensity to breastfeed, for example, we can find out whether the circumstances of younger mothers are distinct once other factors, such as educational qualifications, are controlled. The results of these analyses are presented in chapters 3 and 4 of the report.

1.7 The regression results are presented as odds ratios for each independent variable, all of which have a significance value and 95% confidence intervals attached. Odds ratios estimate the effect of each individual independent variable on the outcome variable, adjusted for all other independent variables in the regression model. Logistic regression compares the odds of a reference category (shown in the tables in brackets) with that of the other categories. An odds ratio of greater than one indicates that the group in question is more likely to demonstrate this characteristic than is the chosen reference category, an odds ratio of less than one means they are less likely. For example, in Table 2, which contains the results of the regression model seeking to identify measures of disadvantage related to the sample child having been breastfed, the category of maternal educational qualifications at Higher grade returns an odds ratio of 2.65. This indicates that the odds of mothers with educational qualifications at Higher grade having breastfed the sample child are 2.65 times greater than they are for mothers who have no qualifications (the reference category). Categories which have a significance value of greater than 0.05 are not considered to be significant.

1.8 As well as significance scores, odds ratios and confidence intervals, the regression tables display the results of two statistical tests carried out with the regression analysis which help to evaluate how well the models predicted the outcome variable - Nagelkerke's R ² and Hosmer and Lemeshow's Goodness of Fit test. Nagelkerke's R ² is most often quoted in logistic regression as a measure of strength of association ranging from 0 to 1. The closer the R ² value is to 1, the better the model is at accurately predicting the value of the outcome variable. A value closer to 0, suggests that there are important explanatory factors which are not included in the model. If the result of the Hosmer and Lemeshow Goodness of Fit test is not significant (p>0.05) the model's prediction of the outcome variable is not significantly different from the observed values of the outcome variable and the model is predicting the dependent variable well, or has 'good fit'. Further notes on the regression analysis are included in Appendix A.

1.9 Analysis of data from each of the Growing Up in Scotland cohorts must be undertaken separately (because together the cohorts do not represent a coherent or real population and results would be misleading). For the purposes of space and simplicity, all analysis in this report uses only data collected from natural mothers in the birth cohort. Larger numbers in the birth cohort also allow more detailed analysis of the selected sub-groups.

« Previous | Contents | Next »