care, health and development Child: Review Article bs_bs_banner
doi:10.1111/cch.12181
Developmental vulnerability – don’t investigate without a model in mind S. Woolfenden,* K. Williams,† V. Eapen,‡ F. Mensah,§ A. Hayen,‡ A. Siddiqi¶ and L. Kemp‡ *Sydney Children’s Hospital Network, Sydney, NSW, Australia †Royal Children’s Hospital Melbourne, Victoria, Australia ‡University of New South Wales, Sydney, NSW, Australia §Murdoch Child Health Research Institute, Melbourne, Victoria, Australia, and ¶University of Toronto, Toronto, Ontario, Canada Accepted for publication 30 June 2014
Keywords analytical models, developmental vulnerability, early child development, research methods, theoretical models Correspondence: Sue Woolfenden, Community Paediatrician Sydney Children’s Hospital Network, High St, Randwick, NSW 2031, Australia E-mail: susan.woolfenden@sesiahs .health.nsw.gov.au
Abstract Children who are developmentally vulnerable are at risk of a difficult start to school, and ongoing educational challenges which may adversely impact on long term health outcomes. Clinicians, researchers and service providers need a thorough understanding of both risk and protective factors and their complex interplay to understand their impact on early childhood development, in order to plan effective and comprehensive prevention and interventions strategies. In this opinion piece we recommend that investigation of developmental vulnerability should only proceed if underpinned by both a theoretical model through which the interaction between risk and protective factors may be investigated, and analytical models that are appropriate to assess these impacts.
This article was published online on 4 August 2014. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected [24 November 2014]
Introduction An estimated 10–25% of preschool aged children in high income countries are identified as ‘developmentally vulnerable’ making them ill-equipped for a successful start to school and its subsequent demands (Hertzman & Boyce 2010; Goldfeld et al. 2012). Children who are developmentally vulnerable risk not achieving their potential and experiencing more adversity
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over their life course. Developmental vulnerability has its origins in a child’s genes, intrauterine environments, and cumulative exposure to biological and environmental risk and protective factors (Patrianakos-Hoobler et al. 2009; Hertzman 2010). Despite our increasing understanding that experience shapes biology and impacts on a child’s developmental outcomes, as researchers we tend to examine only one, or a few risk factors, at one level of interest. A systematic review of how
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socioeconomic status was analysed in studies of early childhood development found that a clear statement of the conceptual background to research was often lacking, there was a lack of consensus as to which analytical models to use and a tendency of the literature to focus only on single risk factors in one bioecological level (Nicholson et al. 2004). Examining the risks for developmental vulnerability in such a linear and unidirectional manner, fails to consider what happens in ‘real life’ for a child. Child specific theoretical models are needed that examine the multiple and diverse factors that influence early child development, the role of a child’s inherent ability to adapt to stress and the cumulative impact of these factors over time (Maggi et al. 2010; Nicholson et al. 2004; NRC&IM 2004; Hertzman 2010; Walker et al. 2011; Marmot 2010). These models should be linked to appropriate study designs and analysis. If not, we run the risk of overemphasizing the importance of one risk factor over another and not recognizing their complex interplays.
Bioecological model The bioecological model focuses on the transactional relationships between the child, and their innate biological sensitivity to the environment (differential reactivity) (Sameroff 2009; Walker et al. 2011). Risk and protective factors are classified from ‘proximal to distal’ or similar nomenclature that implies an environmental hierarchy. Key factors from review of the developmental vulnerability literature, including systematic and narrative reviews, government policy documents, randomized controlled trials and observational studies, are listed in Box 1 (Sameroff and Seifer 1983; Sameroff et al. 1987; Garcia Coll 1990; Guralnick 1997; Zeanah et al. 1997; Miller 1998; Runyan et al. 1998; Roberts et al. 1999; Leventhal & Brooks-Gunn 2000; Shonkoff & Phillips 2000; Shonkoff et al. 2000; Petterson & Albers 2001; Bradley & Corwyn 2002; Anderson et al. 2003; Arnold & Doctoroff 2003; Koenen et al. 2003; Shonkoff 2003; Koller et al. 2004; NRC&IM 2004; Tamis-LeMonda et al. 2004; NICHD 2005; Sohr-Preston & Scaramella 2006; Siddiqi et al. 2007; Goldberg et al. 2008; Lucas et al. 2008; Sarkadi et al. 2008; Halle et al. 2009; Patrianakos-Hoobler et al. 2009; Sameroff 2009; Hertzman 2010; Marmot 2010; Waldfogel & Washbrook 2010; AIHW 2011; Braveman et al. 2011; Miller et al. 2011; Walker et al. 2011; Petanjek & Kostovic 2012; Wuermli et al. 2012; Essex et al. 2013) [Correction added on 24 November 2014, after first online publication: The citation ‘Sameroff and Seifer 1983’ was initially omitted and has now been added in the above sentence.]. Proximal factors are those that directly impact on acquisition of developmental skills, for example parents reading to their child.
© 2014 John Wiley & Sons Ltd, Child: care, health and development, 41, 3, 337–345
Box 1. Risk and protective factors for developmental vulnerability
Child/Biological factors • Genetic factors, including epigenetic phenomena • Adverse in utero environments • Low birthweight • Male gender • Prematurity • Not breastfed • Significant acute and chronic illness Parent factors • Maternal depression • Parental sensitivity and responsiveness • Parental substance abuse • Poor maternal nutrition • Single parenthood Family factors • Socioeconomic disadvantage • Poverty • Housing instability • Food and energy insecurity • Large family size • Low maternal education • Unemployment • Family stress, violence and/or chaos • Stimulating home environment Cultural, service and neighbourhood factors • Minority ethnicity • Social isolation • Social capital, the connections between individuals and entities in society or a community • High-quality early childhood service including preschool antenatal and postnatal home visiting • Neighbourhood affluence and order • Environmental toxins, e.g. Lead National and international factors • Global and national financial crises • Policies regarding early childhood and low income family support
Distal factors are those that impact on a family’s capacity to do so such as poverty, parental education and availability of early childhood services. The ‘dose’ of these co-occurring risk and
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exert their influence on an outcome and moderators, are characteristics of a child, family or environment that alter the potential impact of a risk factor. For example, the effects of distal factors such as socioeconomic disadvantage are often mediated by the impact of more proximal factors such as stimulation at home and positive parent behaviours (Mistry et al. 2010). In this case the distal factor of socioeconomic disadvantage would be considered to have an indirect effect and the proximal factor such as parenting a direct effect. Further, maternal depression has been found to be a mediator between family socioeconomic disadvantage and parenting for effects on child development (Linver et al. 1999). In turn, the impact of household food insecurity on developmental vulnerability appears to stem not from a direct effect but rather as an indirect effect mediated by maternal depression, home environments and parenting practices (Zaslow et al. 2009; Miller et al. 2011). Examples of moderation where when the effect of one risk factor is modified by another are the positive impact of high quality preschool and neighbourhood affluence for children who are socially disadvantaged (Leventhal & Brooks-Gunn 2000; Bradley & Corwyn 2002; Marmot 2010).
Figure 1. Example of a bioecological model. Source: Zubrick et al.
(2000).
protective factors and their interaction is then examined (Fig. 1) (Zubrick et al. 2000; Walker et al. 2011). Distal and proximal risk and protective factors may have a direct effect on outcome or can act as a mediator and/or moderator on other factors thereby indirectly affecting outcomes. Mediators are proximal factors through which distal factors
Life course models Life course models emphasize the importance of the cumulative and dynamic relationship of multiple risk and protective factors on an individual’s developmental pathway or trajectory from conception to death and into the next generation (Fig. 2)
Deveelopmental Progreess
Developmental Trajectory
Biological Suscepbility IUGR Prematurity M Maternal l Smoking Poverty
Breaseedingg Good Parental mental health Family Support Home vising High SES Strong culture
Optimum Development Being Read to High quality childcare or preschool Family SSupport High SES Strong Culture
Parental Sensivity Smulaon at home Family Support High SES Strong Culture
Developmental Vulnerability
Advocacy, Pro child policies, Social Capital; Universal Primary Health Care
Birth 6 mo Figure 2. Example of life course model. Source: Halfon et al. (2014).
Biological Suscepbility P Poor smulaon No preschool Poverty
Biological Suscepbility P Poor child hild health Maternal depression Harsh parenng Poverty
Infancy 12 mo
18 mo
Toddler 24 mo Age
3 yrs
Preschool 5 yrs
Modificaon of Halfon 2002
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(Hertzman & Wiens 1996; Maggi et al. 2010; Walker et al. 2011; Halfon et al. 2013). From conception to mid-adolescence there are an enormous number of developmental skills acquired as a result of a complex interplay between the child, their genes and environment, resulting in growth and modification of neural pathways and thus cognitive functioning (Halfon et al. 2013). Exposure to cumulative risk without buffering of protective factors can result in ‘biological embedding’, through changes in neurobiology and neurochemistry, particularly during ‘critical’ or ‘sensitive periods’, mediated through the hypothalamicpituitary-adrenal (HPA) pathways (Siddiqi et al. 2007; Maggi et al. 2010). Examples include demonstrated altered brain architecture found in children from clearly abusive environments or who have a history of sensory deprivation, and emerging evidence of the association between the impact of less extreme adverse environments on brain architecture linked with language and reading (Hackman et al. 2010; Marmot 2010). Risk and protective factors have their maximum effect at different times, especially in the first 5 years of life, depending brain activity and growth at the time of exposure (Nicholson et al. 2004; Marmot 2010). The brain has capacity to continue changing across the life course, but this becomes more difficult the further we move away from childhood, although it appears that mid-adolescence is also a key period (Shonkoff et al. 2000; Siddiqi et al. 2007; Shonkoff et al. 2009; Wise 2009; Power et al. 2013) Much of what we know from birth cohorts on the association of socioeconomic disadvantage and adverse early childhood and later adult outcomes has been informed by life course models (Power et al. 2013).
Composite models As they have evolved, bioecological and life course models have increasingly taken on complimentary aspects of each other to reflect what goes on in ‘real life’ of a growing child a child, i.e. the dynamic, cumulative interplay, over time, of risk and protective factors from family to ecological levels (Bronfenbrenner & Ceci 1994; Nicholson et al. 2004; Horn et al. 2009; Halfon et al. 2013). These models place the child in the centre of a bioecological framework with an emphasis on the balance between the toxic impact of risk factors and the buffering impact of protective factors at multiple levels of that child’s environment and its neurobiological effect over the life course on the subsequent developmental trajectory. For example a stimulating home environment and positive parenting behaviours can go some way to mitigate the impact of biological and environmental adversity; however, it is worth noting that on their own they are not sufficient to redress disparities in child
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development outcomes caused by poverty (Britto & Brooks-Gunn 2001; Sylva et al. 2008). There are a number of these composite models including the ‘Team-ECD (Early Childhood Development)’ model developed for the World Health Organization Commission on the Social Determinants of Health (Fig. 3) (Siddiqi et al. 2007) and Shonkoff’s Bio developmental Framework (Shonkoff 2010).
How can we design studies that reflect these theoretical models? It is clear that we need to be able to conceptualize and analyse the co-occurrence of risk and protective factors at one point and their cumulative impact over time. No factor acts in isolation, and there is interaction at multiple bioecological levels, and at multiple time points along the life course. The importance of a risk factor can vary depending on the age of a child, reflecting sensitive periods in child development. Further, there may be a lag time between the exposure to the risk factor and its impact on early childhood development (Zeanah et al. 1997; Msall et al. 1998). We need rigorous and relevant epidemiological and statistical techniques that can analyse these pathways (Starfield & Shi 1999). Cross-sectional studies allow us to apply the bioecological model, as we can examine multiple factors over multiple levels at one point in time. For a life course approach we need longitudinal study designs that follow up children and their environments over time, ideally with antenatal recruitment and measures of biological risk including genetic studies, detailed pregnancy and birth data and other biological and environmental measures (Golding 2008, 2009). The method of data analysis used in studies that investigate early childhood development also can impact on how well the interaction between risk and protective factors can be examined and how the theoretical models can be applied. Regression models of single or multiple explanatory variables have been used extensively in early childhood development research. These measure the importance of key risk and protective factors, such as parental income, and allow for exploration of how these factors work together. However trajectories over time cannot be examined and risk factors compete with each other in the design if there is no clear statement of underlying theoretical framework (Burchinal et al. 2000; Kawachi et al. 2002). A cumulative risk index of postulated risk factors such as family and social demographic risk factors (that is the sum of individual risk factors) is a robust way of conceptualizing the impact risk factors may have within bioecological model and it can also be extended over a life course (Sameroff et al. 1987) [Correction added on 24 November 2014, after first online publication: The
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Figure 3. Example of composite model – Team-ECD model. Source: Irwin et al. (2007).
citation ‘Sameroff et al. 1987’ was wrongly represented as ‘Sameroff and Seifer 1983’ and has now been corrected in the above sentence.]. Risk variables across levels can be dichotomized (present/absent) and then grouped into a single score and their effect analysed (Sameroff et al. 1987) [Correction added on 24 November 2014, after first online publication: The citation ‘Sameroff et al. 1987’ was wrongly represented as ‘Sameroff and Seifer 1983’ and has now been corrected in the above sentence.]. This method can be applied in longitudinal and cross-sectional studies and suits smaller sample sizes. Its advantage is its simplicity and the fact that the ‘dose’ of a number of significant risk factors can be investigated rather than focusing on one risk factor at a time (Burchinal et al. 2000). The limitation with such cumulative indices is that they do not allow for investigation of risk factors working as mediators and moderators. In addition, all risk factors are given equal weight which is not true in ‘real life’. This is a significant disadvantage when compared with a regression model, where different weights are given to different explanatory factors, on the basis of finding the best model fit. Although the above models are very useful in quantifying risk they do not allow for the pathways of risk or the timing
and duration of risk factors to be investigated. Analytical approaches such as Structural Equation Modelling and Multilevel Modelling provide a means to do this. Structural Equation Modelling (SEM) or Confirmatory factor analysis is implemented by applying Factor Analysis to a number of proposed risk models that take into account potential mediators and moderators. Models are based on a proposed hypothesis and the ‘goodness of fit’ is measured to see which model fits best. For example, it has been demonstrated that confirmatory factor analysis could be used to develop a more accurate risk prediction model of the impact of multiple weighted risks on child development outcomes than a cumulative risk index where all risk factors are assumed to have an equal weight (Hall et al. 2010). Multilevel Modelling is another method that uses ‘nesting’ of proximal individual level risk factors (micro units) within distal group level risk factors (macro units). This makes it possible to simultaneously examine the impact of group effects of distal risk factors and individual risk factors on an individual outcome. One can examine individual and group variability, their relationships and their impact longitudinally. This uses
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regression techniques, where variables including time can be grouped into hierarchical levels (Diez Roux 2002, 2004). The literature evaluating the impact of Sure Start in the United Kingdom is an example of using multilevel models to examine child and family variables, the community variables within which they are nested and their impact on early childhood development outcomes (Belsky et al. 2006).
Acknowledgements Professor Craig Mellis (editorial support), Professor Steven Zubrick, Professor Neal Halfon, Professor Sven Silburn and Professor Graham Vimpani for giving permission to reproduce their models.
References Conclusion This opinion piece has briefly described the theoretical models, study designs and analytical models that can be used to investigate developmental vulnerability in children that reflect the ‘real life’ of the child using bioecological and life course approaches. We argue that it is essential to have a clear a priori statement of the conceptual theoretical model and a study design and analysis plan that reflect this model to address research questions and inform service delivery. Where possible composite theoretical model, longitudinal study designs and multilevel modelling should be considered and even small research projects or simple descriptive audits of our clinical populations and services would benefit from such a systematic approach. Just as we consider the internal and external validity of our study designs we need to consider the contextual ‘validity’ of our research in terms of a child’s ‘real life’.
Key messages • Children who are developmentally vulnerable risk not achieving their true human capability over their life course. • Developmental vulnerability has its origins in a child’s biological risks, and early childhood experiences and environment and the complex transactional relationship between these. • Examining the risks for developmental vulnerability only in a linear and unidirectional manner, fails to consider what happens in ‘real life’ for a child. • There are theoretical models, study designs and analytical models that reflect the ‘real life’ of the child using bioecological and life course approaches. • Any research or service planning done to understand or address developmental vulnerability, needs a clear statement of the conceptual theoretical model underlying the research or programme and that the study/programme design and analysis/evaluation reflect this model.
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doi:10.1111/cch.12181
Developmental vulnerability – don’t investigate without a model in mind S. Woolfenden,* K. Williams,† V. Eapen,‡ F. Mensah,§ A. Hayen,‡ A. Siddiqi¶ and L. Kemp‡ *Sydney Children’s Hospital Network, Sydney, NSW, Australia †Royal Children’s Hospital Melbourne, Victoria, Australia ‡University of New South Wales, Sydney, NSW, Australia §Murdoch Child Health Research Institute, Melbourne, Victoria, Australia, and ¶University of Toronto, Toronto, Ontario, Canada Accepted for publication 30 June 2014
Keywords analytical models, developmental vulnerability, early child development, research methods, theoretical models Correspondence: Sue Woolfenden, Community Paediatrician Sydney Children’s Hospital Network, High St, Randwick, NSW 2031, Australia E-mail: susan.woolfenden@sesiahs .health.nsw.gov.au
Abstract Children who are developmentally vulnerable are at risk of a difficult start to school, and ongoing educational challenges which may adversely impact on long term health outcomes. Clinicians, researchers and service providers need a thorough understanding of both risk and protective factors and their complex interplay to understand their impact on early childhood development, in order to plan effective and comprehensive prevention and interventions strategies. In this opinion piece we recommend that investigation of developmental vulnerability should only proceed if underpinned by both a theoretical model through which the interaction between risk and protective factors may be investigated, and analytical models that are appropriate to assess these impacts.
This article was published online on 4 August 2014. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected [24 November 2014]
Introduction An estimated 10–25% of preschool aged children in high income countries are identified as ‘developmentally vulnerable’ making them ill-equipped for a successful start to school and its subsequent demands (Hertzman & Boyce 2010; Goldfeld et al. 2012). Children who are developmentally vulnerable risk not achieving their potential and experiencing more adversity
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over their life course. Developmental vulnerability has its origins in a child’s genes, intrauterine environments, and cumulative exposure to biological and environmental risk and protective factors (Patrianakos-Hoobler et al. 2009; Hertzman 2010). Despite our increasing understanding that experience shapes biology and impacts on a child’s developmental outcomes, as researchers we tend to examine only one, or a few risk factors, at one level of interest. A systematic review of how
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socioeconomic status was analysed in studies of early childhood development found that a clear statement of the conceptual background to research was often lacking, there was a lack of consensus as to which analytical models to use and a tendency of the literature to focus only on single risk factors in one bioecological level (Nicholson et al. 2004). Examining the risks for developmental vulnerability in such a linear and unidirectional manner, fails to consider what happens in ‘real life’ for a child. Child specific theoretical models are needed that examine the multiple and diverse factors that influence early child development, the role of a child’s inherent ability to adapt to stress and the cumulative impact of these factors over time (Maggi et al. 2010; Nicholson et al. 2004; NRC&IM 2004; Hertzman 2010; Walker et al. 2011; Marmot 2010). These models should be linked to appropriate study designs and analysis. If not, we run the risk of overemphasizing the importance of one risk factor over another and not recognizing their complex interplays.
Bioecological model The bioecological model focuses on the transactional relationships between the child, and their innate biological sensitivity to the environment (differential reactivity) (Sameroff 2009; Walker et al. 2011). Risk and protective factors are classified from ‘proximal to distal’ or similar nomenclature that implies an environmental hierarchy. Key factors from review of the developmental vulnerability literature, including systematic and narrative reviews, government policy documents, randomized controlled trials and observational studies, are listed in Box 1 (Sameroff and Seifer 1983; Sameroff et al. 1987; Garcia Coll 1990; Guralnick 1997; Zeanah et al. 1997; Miller 1998; Runyan et al. 1998; Roberts et al. 1999; Leventhal & Brooks-Gunn 2000; Shonkoff & Phillips 2000; Shonkoff et al. 2000; Petterson & Albers 2001; Bradley & Corwyn 2002; Anderson et al. 2003; Arnold & Doctoroff 2003; Koenen et al. 2003; Shonkoff 2003; Koller et al. 2004; NRC&IM 2004; Tamis-LeMonda et al. 2004; NICHD 2005; Sohr-Preston & Scaramella 2006; Siddiqi et al. 2007; Goldberg et al. 2008; Lucas et al. 2008; Sarkadi et al. 2008; Halle et al. 2009; Patrianakos-Hoobler et al. 2009; Sameroff 2009; Hertzman 2010; Marmot 2010; Waldfogel & Washbrook 2010; AIHW 2011; Braveman et al. 2011; Miller et al. 2011; Walker et al. 2011; Petanjek & Kostovic 2012; Wuermli et al. 2012; Essex et al. 2013) [Correction added on 24 November 2014, after first online publication: The citation ‘Sameroff and Seifer 1983’ was initially omitted and has now been added in the above sentence.]. Proximal factors are those that directly impact on acquisition of developmental skills, for example parents reading to their child.
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Box 1. Risk and protective factors for developmental vulnerability
Child/Biological factors • Genetic factors, including epigenetic phenomena • Adverse in utero environments • Low birthweight • Male gender • Prematurity • Not breastfed • Significant acute and chronic illness Parent factors • Maternal depression • Parental sensitivity and responsiveness • Parental substance abuse • Poor maternal nutrition • Single parenthood Family factors • Socioeconomic disadvantage • Poverty • Housing instability • Food and energy insecurity • Large family size • Low maternal education • Unemployment • Family stress, violence and/or chaos • Stimulating home environment Cultural, service and neighbourhood factors • Minority ethnicity • Social isolation • Social capital, the connections between individuals and entities in society or a community • High-quality early childhood service including preschool antenatal and postnatal home visiting • Neighbourhood affluence and order • Environmental toxins, e.g. Lead National and international factors • Global and national financial crises • Policies regarding early childhood and low income family support
Distal factors are those that impact on a family’s capacity to do so such as poverty, parental education and availability of early childhood services. The ‘dose’ of these co-occurring risk and
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exert their influence on an outcome and moderators, are characteristics of a child, family or environment that alter the potential impact of a risk factor. For example, the effects of distal factors such as socioeconomic disadvantage are often mediated by the impact of more proximal factors such as stimulation at home and positive parent behaviours (Mistry et al. 2010). In this case the distal factor of socioeconomic disadvantage would be considered to have an indirect effect and the proximal factor such as parenting a direct effect. Further, maternal depression has been found to be a mediator between family socioeconomic disadvantage and parenting for effects on child development (Linver et al. 1999). In turn, the impact of household food insecurity on developmental vulnerability appears to stem not from a direct effect but rather as an indirect effect mediated by maternal depression, home environments and parenting practices (Zaslow et al. 2009; Miller et al. 2011). Examples of moderation where when the effect of one risk factor is modified by another are the positive impact of high quality preschool and neighbourhood affluence for children who are socially disadvantaged (Leventhal & Brooks-Gunn 2000; Bradley & Corwyn 2002; Marmot 2010).
Figure 1. Example of a bioecological model. Source: Zubrick et al.
(2000).
protective factors and their interaction is then examined (Fig. 1) (Zubrick et al. 2000; Walker et al. 2011). Distal and proximal risk and protective factors may have a direct effect on outcome or can act as a mediator and/or moderator on other factors thereby indirectly affecting outcomes. Mediators are proximal factors through which distal factors
Life course models Life course models emphasize the importance of the cumulative and dynamic relationship of multiple risk and protective factors on an individual’s developmental pathway or trajectory from conception to death and into the next generation (Fig. 2)
Deveelopmental Progreess
Developmental Trajectory
Biological Suscepbility IUGR Prematurity M Maternal l Smoking Poverty
Breaseedingg Good Parental mental health Family Support Home vising High SES Strong culture
Optimum Development Being Read to High quality childcare or preschool Family SSupport High SES Strong Culture
Parental Sensivity Smulaon at home Family Support High SES Strong Culture
Developmental Vulnerability
Advocacy, Pro child policies, Social Capital; Universal Primary Health Care
Birth 6 mo Figure 2. Example of life course model. Source: Halfon et al. (2014).
Biological Suscepbility P Poor smulaon No preschool Poverty
Biological Suscepbility P Poor child hild health Maternal depression Harsh parenng Poverty
Infancy 12 mo
18 mo
Toddler 24 mo Age
3 yrs
Preschool 5 yrs
Modificaon of Halfon 2002
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(Hertzman & Wiens 1996; Maggi et al. 2010; Walker et al. 2011; Halfon et al. 2013). From conception to mid-adolescence there are an enormous number of developmental skills acquired as a result of a complex interplay between the child, their genes and environment, resulting in growth and modification of neural pathways and thus cognitive functioning (Halfon et al. 2013). Exposure to cumulative risk without buffering of protective factors can result in ‘biological embedding’, through changes in neurobiology and neurochemistry, particularly during ‘critical’ or ‘sensitive periods’, mediated through the hypothalamicpituitary-adrenal (HPA) pathways (Siddiqi et al. 2007; Maggi et al. 2010). Examples include demonstrated altered brain architecture found in children from clearly abusive environments or who have a history of sensory deprivation, and emerging evidence of the association between the impact of less extreme adverse environments on brain architecture linked with language and reading (Hackman et al. 2010; Marmot 2010). Risk and protective factors have their maximum effect at different times, especially in the first 5 years of life, depending brain activity and growth at the time of exposure (Nicholson et al. 2004; Marmot 2010). The brain has capacity to continue changing across the life course, but this becomes more difficult the further we move away from childhood, although it appears that mid-adolescence is also a key period (Shonkoff et al. 2000; Siddiqi et al. 2007; Shonkoff et al. 2009; Wise 2009; Power et al. 2013) Much of what we know from birth cohorts on the association of socioeconomic disadvantage and adverse early childhood and later adult outcomes has been informed by life course models (Power et al. 2013).
Composite models As they have evolved, bioecological and life course models have increasingly taken on complimentary aspects of each other to reflect what goes on in ‘real life’ of a growing child a child, i.e. the dynamic, cumulative interplay, over time, of risk and protective factors from family to ecological levels (Bronfenbrenner & Ceci 1994; Nicholson et al. 2004; Horn et al. 2009; Halfon et al. 2013). These models place the child in the centre of a bioecological framework with an emphasis on the balance between the toxic impact of risk factors and the buffering impact of protective factors at multiple levels of that child’s environment and its neurobiological effect over the life course on the subsequent developmental trajectory. For example a stimulating home environment and positive parenting behaviours can go some way to mitigate the impact of biological and environmental adversity; however, it is worth noting that on their own they are not sufficient to redress disparities in child
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development outcomes caused by poverty (Britto & Brooks-Gunn 2001; Sylva et al. 2008). There are a number of these composite models including the ‘Team-ECD (Early Childhood Development)’ model developed for the World Health Organization Commission on the Social Determinants of Health (Fig. 3) (Siddiqi et al. 2007) and Shonkoff’s Bio developmental Framework (Shonkoff 2010).
How can we design studies that reflect these theoretical models? It is clear that we need to be able to conceptualize and analyse the co-occurrence of risk and protective factors at one point and their cumulative impact over time. No factor acts in isolation, and there is interaction at multiple bioecological levels, and at multiple time points along the life course. The importance of a risk factor can vary depending on the age of a child, reflecting sensitive periods in child development. Further, there may be a lag time between the exposure to the risk factor and its impact on early childhood development (Zeanah et al. 1997; Msall et al. 1998). We need rigorous and relevant epidemiological and statistical techniques that can analyse these pathways (Starfield & Shi 1999). Cross-sectional studies allow us to apply the bioecological model, as we can examine multiple factors over multiple levels at one point in time. For a life course approach we need longitudinal study designs that follow up children and their environments over time, ideally with antenatal recruitment and measures of biological risk including genetic studies, detailed pregnancy and birth data and other biological and environmental measures (Golding 2008, 2009). The method of data analysis used in studies that investigate early childhood development also can impact on how well the interaction between risk and protective factors can be examined and how the theoretical models can be applied. Regression models of single or multiple explanatory variables have been used extensively in early childhood development research. These measure the importance of key risk and protective factors, such as parental income, and allow for exploration of how these factors work together. However trajectories over time cannot be examined and risk factors compete with each other in the design if there is no clear statement of underlying theoretical framework (Burchinal et al. 2000; Kawachi et al. 2002). A cumulative risk index of postulated risk factors such as family and social demographic risk factors (that is the sum of individual risk factors) is a robust way of conceptualizing the impact risk factors may have within bioecological model and it can also be extended over a life course (Sameroff et al. 1987) [Correction added on 24 November 2014, after first online publication: The
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Figure 3. Example of composite model – Team-ECD model. Source: Irwin et al. (2007).
citation ‘Sameroff et al. 1987’ was wrongly represented as ‘Sameroff and Seifer 1983’ and has now been corrected in the above sentence.]. Risk variables across levels can be dichotomized (present/absent) and then grouped into a single score and their effect analysed (Sameroff et al. 1987) [Correction added on 24 November 2014, after first online publication: The citation ‘Sameroff et al. 1987’ was wrongly represented as ‘Sameroff and Seifer 1983’ and has now been corrected in the above sentence.]. This method can be applied in longitudinal and cross-sectional studies and suits smaller sample sizes. Its advantage is its simplicity and the fact that the ‘dose’ of a number of significant risk factors can be investigated rather than focusing on one risk factor at a time (Burchinal et al. 2000). The limitation with such cumulative indices is that they do not allow for investigation of risk factors working as mediators and moderators. In addition, all risk factors are given equal weight which is not true in ‘real life’. This is a significant disadvantage when compared with a regression model, where different weights are given to different explanatory factors, on the basis of finding the best model fit. Although the above models are very useful in quantifying risk they do not allow for the pathways of risk or the timing
and duration of risk factors to be investigated. Analytical approaches such as Structural Equation Modelling and Multilevel Modelling provide a means to do this. Structural Equation Modelling (SEM) or Confirmatory factor analysis is implemented by applying Factor Analysis to a number of proposed risk models that take into account potential mediators and moderators. Models are based on a proposed hypothesis and the ‘goodness of fit’ is measured to see which model fits best. For example, it has been demonstrated that confirmatory factor analysis could be used to develop a more accurate risk prediction model of the impact of multiple weighted risks on child development outcomes than a cumulative risk index where all risk factors are assumed to have an equal weight (Hall et al. 2010). Multilevel Modelling is another method that uses ‘nesting’ of proximal individual level risk factors (micro units) within distal group level risk factors (macro units). This makes it possible to simultaneously examine the impact of group effects of distal risk factors and individual risk factors on an individual outcome. One can examine individual and group variability, their relationships and their impact longitudinally. This uses
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regression techniques, where variables including time can be grouped into hierarchical levels (Diez Roux 2002, 2004). The literature evaluating the impact of Sure Start in the United Kingdom is an example of using multilevel models to examine child and family variables, the community variables within which they are nested and their impact on early childhood development outcomes (Belsky et al. 2006).
Acknowledgements Professor Craig Mellis (editorial support), Professor Steven Zubrick, Professor Neal Halfon, Professor Sven Silburn and Professor Graham Vimpani for giving permission to reproduce their models.
References Conclusion This opinion piece has briefly described the theoretical models, study designs and analytical models that can be used to investigate developmental vulnerability in children that reflect the ‘real life’ of the child using bioecological and life course approaches. We argue that it is essential to have a clear a priori statement of the conceptual theoretical model and a study design and analysis plan that reflect this model to address research questions and inform service delivery. Where possible composite theoretical model, longitudinal study designs and multilevel modelling should be considered and even small research projects or simple descriptive audits of our clinical populations and services would benefit from such a systematic approach. Just as we consider the internal and external validity of our study designs we need to consider the contextual ‘validity’ of our research in terms of a child’s ‘real life’.
Key messages • Children who are developmentally vulnerable risk not achieving their true human capability over their life course. • Developmental vulnerability has its origins in a child’s biological risks, and early childhood experiences and environment and the complex transactional relationship between these. • Examining the risks for developmental vulnerability only in a linear and unidirectional manner, fails to consider what happens in ‘real life’ for a child. • There are theoretical models, study designs and analytical models that reflect the ‘real life’ of the child using bioecological and life course approaches. • Any research or service planning done to understand or address developmental vulnerability, needs a clear statement of the conceptual theoretical model underlying the research or programme and that the study/programme design and analysis/evaluation reflect this model.
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