The far-reaching benefits of a good start in life

 

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Content type
Webinar
Event date

13 November 2015, 01:00PM to 02:00PM

Presenters

Yvonne Kelly

Location

Online

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This webinar was held on 13 November 2015.

Lifecourse research has shown that what happens in childhood and adolescence is predictive of many aspects of health and wellbeing throughout life. Children and young people who have a good start in life not only have healthier and happier childhoods, but also enjoy far-reaching benefits in adulthood.

The influences on childhood and adolescent health and development are complex, and their importance often changes during the first two decades of life. Multiple social changes, including family composition, social identities and the use of technologies, might have different influences on development and wellbeing for children, young people, their families and the communities they are growing up in. Past and current research can be used to help forecast likely trends in health and wellbeing, giving important clues relevant for the future of families.

In this webinar, Yvonne Kelly presented an overview of lifecourse research in the UK that documents influences on children’s and young people’s health and wellbeing. Prevention and intervention strategies from different international contexts that aim to improve the life chances of children and young people were discussed.

This event was presented in partnership with Family & Relationship Services Australia (FRSA).

Family & Relationship Services Australia

   

The feature image is by Heather Katsoulis, CC BY-SA 2.0.

Audio transcript (edited)

Webinar facilitated & speaker introduced by Jacqueline Homel

HOMEL

Good afternoon everyone and welcome to today's webinar: The far-reaching benefits of a good start in life. This event is presented by the CFCA information exchange and Family and Relationship Services Australia. My name is Jacqueline Homel and I am a research fellow here at the Australian Institute of Family Studies. Today we will hear about the complex influences of what happens in childhood and adolescence for health and wellbeing throughout life. The webinar will present an overview of life course research using data from the UK that document such influences, and will discuss prevention and intervention strategies that aim to improve the life chances of children and young people.

Before I introduce our speaker I would like to acknowledge the traditional custodians on the land on which we are meeting. In Melbourne the traditional custodians are the Wurundjeri people of the Kulin Nation. I pay my respects to their elders past and present, and to the elders from other communities who may be participating today.

It is now my pleasure to introduce today's speaker. Dr Yvonne Kelly is a professor of life course epidemiology at University College, London and deputy director of the international centre for life course studies. Her work on health and development during childhood and adolescence makes use of longitudinal data sets including the millennium cohort study, the UK household longitudinal study and the 1958 and 1970 birth cohort studies.

Before we begin, I need to alert you to some brief housekeeping details. One of the core functions of the CFCA information exchange is to share knowledge, so I would like to remind everyone that you can submit questions via the chat box at any time during the webinar. There will be a limited amount of time for questions at the end of today's presentation and we will try to respond to as many as possible. Please remember also that this webinar is being recorded and the audio, transcript and presentation slides will be made available on the CFCA website and the YouTube channel in due course. Accessible versions will also be available. 
So without further ado, please join me in giving Yvonne a very warm virtual welcome.

KELLY

Thank you Jacqui for that very kind introduction. My name is Yvonne Kelly from University College, London. The talk I will give today has at it's very core the influence of social circumstances on human health and development, and by that I mean the gross inequalities that are the product of the social environments in which children and young people grow and develop, and the influences on that on health throughout the life course. These inequalities are unfair and unjust and I firmly believe that as researchers, policy makers and practitioners we all bear the responsibility to try to do something to reduce extant inequalities in health throughout the life course.

This slide just gives an overview of the talk. I will be talking about the life course approach, how that can help inform policy to prevent and intervene on to improve human development. I'll be talking about some of the proximal and distal influences on healthy development throughout the life course. I will be also referring to social change, things that are happening at the societal level and how they might influence children's health and development, and family relationships. I will also be referring throughout the talk to prevention and intervention strategies, and some of those particularly from the UK setting.

Okay so this - as you can see - this is an adaptation from a slide from Keith Godfrey, who's based at South Hampton University in the UK. It's a very, very nice slide. I think it depicts some of these issues in terms of both life course research, the life course approach, but also opportunities for prevention and intervention strategies. On the bottom of the slide you can see a depiction of plasticity reducing over the life course, and that refers particularly to neural plasticity. So the ability or the impact of I suppose aging on neural networks. Neural plasticity and in turn the corollary of that or coupled with that is an increase in inadequate responses to stressful events. Stressful events are experienced in response to various stimuli. Stressful responses are normal in terms of acute responses with flight and fight responses. But when people are exposed to chronic stressful situations, those flight and fight responses are somewhat blunted, and have detrimental impacts both on human development but also on health and the risk of disease.

You can see at the top of this graph the different points of intervention. So the idea here is that if we intervene or if programs intervene during pregnancy and during childhood we can increase functional capability and resilience. So resilience to adverse events that will occur throughout life, and we can intervene at any point in the life course, of course. But the paybacks of that, if we think of the classic approach of James Heffernan, the economic pay backs at the societal paybacks to intervening at different points in the life course might be most beneficial if we do that earlier. It's during the pregnancy and early years, and during adolescence that are golden opportunities for us to intervene and improve life chances. 

Now to do this kind of life course research and to generate evidence for the long-term impacts of early life circumstances, we typically employ longitudinal data. Those data cover a wide range of topics from the social environment, very rich social and economic environments, contextual factors, which are essential in trying to unpick the complex relationships that occur as a result of social circumstances in which people live.

They also typically collect data on different aspects of human development and health. So by that I mean both physical health and physical development, as well as social and emotional development, and cognitive development, and things linked to intellectual ability. Just mapped out here on these slides, some of the very prominent studies are based in the UK, so the UK has a long rich history of longitudinal research, with the 1946, 58 and 70 birth cohorts, and the more recent millennium cohort study, as well as panel studies such as the British household panel study and understanding society. The US also has quite rich longitudinal data resources, as well as various countries within Northern Europe. Sweden and Finland and Denmark have very rich linked data sources, registry data sources, which are used for longitudinal research. And, of course, hear in Australia things like the Longitudinal Study of Australian Children LSAC and Hilda are very rich resources, as well as the Dunedin birth cohort in New Zealand.

These studies essentially track the same people. So for cohort studies in particular, they track the same people. And if they're birth cohort studies, we take information from the period of infancy, and people have followed up right throughout their life courses. These studies typically collect data on thousands of participants. The British birth cohort studies have sample sizes typically around the sort of 15,000, 18,000 mark. Here in Australia I think the LSAC study has about 10,000 participants, for example.

So the idea is that we follow people from early in their life courses, through adolescence, into their adult lives, and ultimately into old age. So really tracking those experiences. It allows us to unpack, as I said, those very complex dynamics between the social environments, human behaviours and individuals, and their risk of various health and disease outcomes, as well as social participation outcomes.

So there are literally thousands of examples of how early life experiences, whether they're social circumstances or other things that happen during childhood, for example smoking during pregnancy or being breastfed, are linked to children's development and health. This first slide I guess in the classical epidemiological language, the exposure variable of interest is early linguistic ability. This paper by Ingrid Shune shows that for children with delays in their early linguistic ability, their increased risk of poor mental health into adulthood is increased about twofold. So for men and for women with early linguistic problems, they have about a twofold chance of serious mental health problems during adulthood. Early linguistic ability of course isn't just linked to poor mental health. It links to a range of other markers of wellbeing such as educational attainments. Educational attainments themselves link to a whole range of other social outcomes. The sorts of jobs we get or our occupations, the kind of wealth and income that we generate as a result of those occupations. So early linguistic ability sets people onto various pathways in life, various pathways of exposure, to different sorts of social environments, and those different social environments have with them the exposures to different risks, which help to shape health and development right throughout life. 

This next slide again takes up this idea of pathways of exposure. So here it's a nice graph from the CDC in the United States and it shows life expectancy by educational qualifications at age 25. So you can see men on the left and women on the right. The years of 1996 and 2006, just for a sort of a temporal comparison there. So a comparison over that decade, and as you can see for men with the fewest qualifications compared to those with a Bachelor degree – so men with no high school qualifications compared to a Bachelor degree or more – is about a difference of about 6 years in life expectancy, which is huge. And for women, on the right hand side, the differences were seven or eight years. So these links between educational attainment and later life expectancy are a product of these different exposures to various social circumstances and life events, all of which correlate with educational attainment and the sort of things that we're exposed to as a result of our social environments.

Now it isn't just all about education and early linguistic ability, and the links with those things. There are continuities and pathways of exposure to do with other elements of human behaviour and human development. So this slide I wanted to show you picks up on one reflection of health behaviours, which is the risk of obesity. This is data from the 1978 birth cohort by a colleague Russell Finer, and it shows the increased risk of being obese if one is obese during late childhood - so at age ten here - and during the adolescent years at age 16. And as you can see, if you are – these are average effects of course. We often get a bit tangled up in life course research and epidemiology when we're trying to translate the results to individual risk. But these are population level data. These are population average risk ratios. I must say that.

So you can see that on average obese 10-year-olds are about four times more likely to be obese at age 30 and age 16 there is an increased risk too. Not just obesity that tracks throughout life - poor mental health does as well, and you can see on the right hand side of the graph there that 16 year-olds with poor mental health were much more likely, about five times more likely to have poor mental health in adult years.

So these ideas about tracking throughout life, about pathways of exposure, but of course in life course research we also think about sensitive periods and about accumulation. So, for example, the example of a sensitive period, exposure could be something during pregnancy. So a mother smoking during pregnancy is associated with a reduction in birth weight and that reduction in birth weight is irreversible. Low birth weights are then related to a whole range of health and developmental consequences throughout the childhood years, which then have knock-on effects for health right throughout the life course. An example of accumulation might be to do with the environments, the physical environments in which we live. So, for example, exposure to environmental pollutants, and with chronic exposure to environmental pollutants will increase incrementally in a step-wise fashion the risk of cardiovascular disease and respiratory disease throughout life.

So there are these different ideas in life course research using a life course approach where we're talking about social environments and exposures which might operate during a sensitive period. So a critical period in development then might operate in a cumulative way across the life course. Or then might set individuals or populations or subgroups onto various pathways of exposure during their life course. And all of these different mechanisms relate in different ways to the likelihood of the increased risk of poor developmental outcomes, poor health outcome, poor social outcomes throughout life.

So now I'm just going to talk a bit about some of the influences on human health and development, and as I said before I'm thinking about physical development as well as social and emotional development and intellectual development. Within the framework of the departments of health, which you can see here, a classic slide that many of you will be familiar with from Michael Marmot's work, you can see on the left hand slide, left hand side of the slide - sorry, jetlag kicking in here - some of the structural determinants of health. So on the very left hand side we have the sort of broad social and political context at a global and at a country level, and then also on the left hand side we have these, what we might think of as stratifying variables. So age, gender, ethnicity, which will operate through racist discrimination, for example, and some of the structural effects of belonging to an ethnic group.

We also have socio-economic position. So, as I've already touched on, education linking to occupations and to your wealth and income. So we think of those as the things which structure our exposures to various intermediary factors or proximal factors on the right hand side of the slide, which would be here as conceptualised, separated into material pathways. So, for example, housing, the sorts of places in which we live and the sort of housing that we occupy. Behavioural factors, which when we're thinking about families and children and adolescents and different family members, could be to do with relationships. How people interact. It also could be to do with - so things like parenting. But it also could be related to things like health behaviours. So whether or not we smoke or drink alcohol and so on.

Psycho-social factors are also a key proximal influence on human health and development right throughout life. So the way those very important transactions and interactions that we have on an interpersonal level and encompassing all of these structural and intermediary factors, social cohesion and isolation. So again going back to relationships here more at the kind of planning interim. It's personal relationships as well as at the community level.

On the right hand side of the graph we have these impacting on health, and then health and developments linking back to the structural determinants of health. So the idea of health selection coming into play there. We have both social causation sweeping from left to right and then the idea of kind of health selection coming from right to left on this graph.

Now most of the theories and sort of conceptual frameworks which we use for healthy development in humans are based on ideas generated by people like Urie Bronfenbrenner, so the bioecological model of human development, which was then further developed by Conger and colleagues in terms of the family stress model. Here we have depicted quite nicely another well-known schema from Dahlgren and Whitehead. You can see this rainbow picture. Here we have the individual centre. Here, particularly for the purposes of what I'm going to talk about next. So we might have that as the child within the family context and then the next sphere of influence could be the community, school, community level institutions and so on, fanning out in these different interacting spheres of influences, and there are transactions and interactions of individuals and families and communities backwards and forwards across these different spheres of human development.

Now taking this, operationalising this into a concrete example in some recent work that myself and colleagues at UCL have been doing. We have taken various factors from children's environments, which we call - here we've called - risk factors, and they could be to do with the individual children. So for example this slide shows the frequency of exposure. These are dated from the millennium cohort study on about 18,000 children. Different spheres of influence on the children's life are picked up on here. We're trying to tap into those. So at the individual level, whether the child was born at low birth weight, whether they were breastfed, and in their early childhood whether they had regular bed times, and I'll come back to the ideas about sleep a little bit later in this presentation.

Then at the family level, things to do with the parents and the families. So whether there's a sole parent’s household, whether the mum had mental health problems - whether she was depressed - educational qualifications, poverty within the family, and other things to do with education of the parents. And then at the community level, whether these families are living in deprived neighbourhoods or not. So by sort of using an index of multiple depravation. What we've done, very simply, is we've added up those risk factor scores, and this slide shows the frequency of children in the millennium cohort study being exposed to these different levels of risk factors.

On the left hand side you can see about 30 per cent of cohort participants were exposed to none of these risk factors and then you can see that one risk factor was about 25 per cent. Just going over to the right hand side of the graph you can see that children exposed to five or more of these risk factors are about ten per cent of the population, which is fairly substantial. About two per cent of these - of the NCS participants being exposed to seven or more of these risk factors. So children really growing up with a constellation of disadvantages in difficult circumstances, and I'm going to show you now a couple of slides to show how these things relate to elements of their development.

So this first slide shows verbal ability scores by risk factor score. So on the X axis we've got a risk factor as you've just seen. Zero through to seven plus. On the Y axis we've got months ahead and months behind in terms of verbal ability scores throughout the early part of the life course. So ages three, five and seven, and as you can see the gap between children with zero scores and those with multiple scores, so seven plus, at age three there's about 19 months. So a substantial already - preschool. This is - at the time, these children were born in the year 2000. So these 3-year-olds were sort of going into their pre-school educational settings. Most of them into nursery education or being looked after by child minders, or being looked after by family members. In the pre-school years, already at age three, more than 18-month gap when we're looking across these different risk factor scores. By age 5 on school entry the gaps widened a little bit to about 20 months, and by age seven about 22 months. So these children have now been in formal education for about 2 years and there's just under a 2-year gap in their verbal ability scores.

So the next wave of data that are available for these children are when they're age 11. If you were a live audience that I could see in a room in front of me I would ask you to predict what was going to happen next in terms of this gradient, this gap in verbal ability scores. I'd ask you to tell me whether you thought that the gap had narrowed, whether it remained the same or whether it widened, and I'd also be encouraging you to raise your hands. As we can't do that, we're working in a virtual environment, I'll just have to imagine what you might be saying. The next slide shows to me a really shocking, a stunning widening in the gap. By age 11, these children are at the end of their primary school education in the UK setting and there is now more than a 5-year, 63-month difference across the piece, which is absolutely astonishing really. Going back to the earlier slides about how educational detainments, of course, these vulnerability - we access most of our intellectual contents through our verbal abilities and verbal reasoning. So these delays, these gaps in verbal ability have profound impacts on future educational qualifications, attainments, and I've already shown you how things like educational attainments relate to life expectancy.

It's not just all about verbal abilities. This slide shows again a similar slide showing how these risk factor scores relate to another marker of child developments - social and emotional difficulties. And these are clinically relevant social and emotional difficulties. So these are relating to things like hyperactivity and conduct disorders, peer problems and emotional symptoms linked to depression, and you can see that as we add up exposure to these risk factor scores, the risk of children having clinically relevant social and emotional difficulties increases massively. So these are as rated by their parents, their mothers and their teachers. You can see for children with zero risk factors, around 4 or 5 per cent of them having these clinically relevant scores up to 30, 33 per cent of those with multiple scores. So really strong, shocking inequalities in both verbal abilities and social emotional difficulties, and we could carry on. I could show you gradients all day relating to various aspects of child development in terms of various elements of social environments. But I'll move on to some of these risk factors, some of these exposures in a bit more detail.

I'll just touch for a few moments on sleep. Sleep is essential for all of us, and having just flown across several time zones I'm feeling this most acutely myself at the moment. Sleep is essential for a range of physical and mental functioning. It impacts directly on immune systems. If we don't get enough sleep or good quality sleep, immune system functioning is impaired. We pick up more infections if we don't get enough sleep. But the kind of essential running repairs to our musculoskeletal system is impaired. So we get more aches and pains. I'm just having a little stretch now. If we don't get enough sleep we are more emotionally labile. So for children that links directly to self-regulation, and social and emotional development. Sleep is also related to memory consolidation. So having sleep is like an investment really for being able to learn and being able to do well in the intellectual sphere. Sleep also impacts on endocrine function – relating, for example, to the risk of obesity. Short and fragmented sleep is linked to increased appetite through various hormonal control systems. So sleep relates to a decreased risk of becoming overweight and obese. These kind of range of different markers of healthy developments, which sleep is relevant for and the amount of sleep that we need varies both across individuals and it varies across the life course.

This slide shows the average number of hours of sleep that children and young people need. So just starting on the left hand side of the graph, there you can see that for infants, infants generally require somewhere between 14 and 18 hours sleep in a 24-hour period. This comes down quite sharply of course into earlier mid-childhood, around the age of between 7 and 10. Children on average require somewhere around 10 or 11 hours sleep. And still into the adolescent years, into the teen years, there you can see that individuals generally require about nine hours sleep. Whether or not adolescents and teenagers actually get those 9-hour sleep-filled nights is another thing, of course. But they're, on average, for healthy development, that's what's required.

But it isn't just all about the duration and the amount of sleep that we get. It's about the regularity of sleeping routines as well. If we chop and change around - again linking back to travelling across time zones - if we chop and change around the times that we sleep, so the regularity of bed times and the times at which we go to sleep, we induce a kind of jet lag effect. It messes around with the circadian rhythm, with our body clocks, which again impacts on function, and I'm going to show you an example now of how this relates to children's social and emotional development.

In this paper we took a quasi-experimental approach using some difference analysis. What we show here on the left hand side of the graph is the change in social and emotional difficulty score for children whose bed times change from being non-regular to being regular, and as you can see they're between the ages of 5 and 7, and 3 and 7. You can see that there's a downshift, so a negative score - negative is good in terms of this particular measure of social and emotional development – so that children who change from not having regular bed times to having regular bed times there appear to be improvements in the social and emotional difficulty scores. On the right hand side of the graph, the opposite reflection - it doesn't look such a strong relationship or a strong association, but you can see there on the right hand side of the graph that children who change from having regular bed times to not having regular bed times, so a kind of breaking the habit, you can see that there's a worsening of scores to a positive score.

Taking the same principal in terms of this quasi-experimental approach a difference in difference analysis, we looked at the change and the frequency of parents reading to their children. We know that being read to, reading stories to our children, telling them stories is a very intimate - a set of very intimate interactions between caregiver and child. It's a chance to bond. It's a chance to really consolidate many aspects of a caregiver and child's relationship.

So we wondered what it would look like if we took this approach of looking at what happens to children's social and emotional scores when the frequency of them being read to changes. On the left hand side of the graph you can see that there are improvements in social and emotional scores for children who change from being read to less than weekly, I think, sorry, less than daily to daily. I wasn't looking. And on the right hand side for children who are being read to less than weekly to weekly, you can see that there are improvements in their social and emotional scores. Those two examples both are looking at changes in sleep and changes in reading frequency to children help us or could help us get a handle on prevention strategies and interventions in those sort of early years settings.

Moving to infancy now. A whole range of research, not just done by our group but by other groups too shows the beneficial links between being breastfed as a child and a range of health and developmental outcomes. So including from sort of gastrointestinal and other infections through to social and emotional regulation, and cognitive function, in particular. This slide taps into the idea, back to the idea of sort of children's school readiness and their cognitive function. You can see this is the percentage of children with a good standard of development in terms of the foundation stage profiles as assessed by their teachers. You can see that for children who were never breastfed – they're on the left hand side of the slide – only 30 per cent of them attained a good standard of development by age five, as assessed by their teachers. You can see that as the duration of breastfeeding goes up you kind of get that incremental increase. That's all, sort of, classic dose response relationship. You can see with around 60 per cent of children who are breastfed for 4 months or more having a good standard in terms of their foundation stage profiles.

Work by Amanda Sacka has gone on to show the longer-term benefits of breastfeeding in terms of social ability, but I'm not going to show you that particular example here. I don't have a slide about it. But she shows that being breastfed – using data from the 1958 and 1970 cohorts – being breastfed is associated with promotion of upward social mobility and a protection against downward social mobility.

Now thinking about the future and thinking about what we know in terms of life course research and the influences on human development, how we might take some of those things to help us to shape policies into the future is really quite tricky. None of us can forecast what's going to happen in the future. But we do have a feel for some of the changes, which have occurred in recent decades, and we can take some of that to help us predict what some aspects of how associations might look between various exposures and markers of human development in future years. There are a number of caveats around that, and perhaps I'll touch on those in a bit more detail over the next few moments.

So, for example, we know in terms of human health, we know that there have been increases in the likelihood of overweight and obesity of recent decades, globally. So including - and including Australia - the proportion of children in the UK who are overweight or obese has increased massively over the last couple of decades. By the time children are leaving primary school in the UK, the levels of obesity are around 2 in 10, and Australia is, I think, the levels aren't quite so high in Australia, but they're certainly not that far behind rates for the US and the UK.

In the UK context, there's also increases in poor mental health in children and young people. The UK young people and children do particularly badly in terms of mental health and some risky behaviour such as binge drinking compared to their European counterparts. This is happening amid a whole range of social changes. There are increases in economic inequality. There have been changes over recent decades in labour force participation amongst women and there are changes to do with family compositions. So, for example, in the UK, a few decades ago about 90 or 95 per cent of children were born into married unions and now that figure is about just over 50 per cent.

How does that relate to aspects of children's health and development? On average children born to sole parents and those in co-habiting relationships appear to do a bit less well on various markers of development compared to children born into married unions. However, when we take account of recent research coming out of the UK and other contexts, it shows that when we take account of economic disadvantage, any disadvantage experienced by children of co-habitees and lone parents tends to disappear. So the importance of social context for outcomes appears to be particularly important there.

There are also massive demographic changes occurring across many different contexts. In the UK, the proportion of ethnic minority members of the population has increased massively over recent decades and is projected to increase more, and what might that mean in terms of the profile of health development in the UK, for example? This slide shows the association between a mixed and a non-mixed ethnic identity. It's important that children - for children's health and development - in terms of their ethnic identity and other forms of their social identities. So this slide shows the social and emotional difficulty scores for children from mixed and non-mixed ethnic identities. On average children's social and emotional difficulty scores reduce over their first decade of life and you can see here on the left hand side of the graph of the Bangladeshi group. For non-mixed Bangladeshi children, you can see exactly that relationship or that association - that pattern, rather, panning out over the first decade of life. You can see an incremental decrease in their social and emotional difficulty score. Contrast that with the upward shift in social and emotional difficulty score for Bangladeshi children with mixed identities.

On the right hand side of the graph there you can see the same relationships for Pakistani children. So for Pakistani children from non-mixed unions, you can see the classic kind of decreased social and emotional difficulties score and then again a suggestion of an upward shift for Pakistani children by the time they're aged 11 in terms of their social and emotional difficulties.

And why might this be? We don't see this for children from, for example, the black Caribbean mixed and non-mixed groups. For both of those groups we see a decrease in social and emotional difficulty scores over the first decade of life. The working hypothesis here is because it's to do with recency of migration. In the UK, the Bangladeshi and Pakistani groups mostly migrated during the 1980s and 90s, and mixed unions are far less common in the Bangladeshi and Pakistani groups, for example, compared to the black Caribbean group with the white majority group. So it might be to do with how that plays out in terms of social norms and stigmas within groups.

Another huge change that we have all been a party to over the last couple of decades, the technological changes that have occurred to do with digital technologies, and this is just an example from some work that we did with colleagues at the University of Essex, my colleague Kara Bouchelard, which shows the association between social network use and the risk of poor mental health in adolescence. So in early adolescence, using data from the UK household longitudinal study, and, as you can see here the number of hours as reported by young people themselves spent on social networking sites is related to the risk of poor mental health outcomes. So young people who are spending four hours or more on social networking sites are about double, just over double the risk of poor mental health compared to those who were using social network sites for less than an hour a day.

I must say here, however, we also in early analysis with this paper, we separated out those young people who had reported no excess. So sort of zero hours per day on social networking sites and their mental health – they were a relatively small group. So in a statistical sense they were too small to kind of look at separately. But their mental health – the suggestion is that their mental health was worse than those who engaged in social networking sites for a small amount of time per day. 

But what might that look like in the future? And is this relevant for the future when increasingly more of us spend time interacting with digital technologies, hand held devices and so on and so forth. These data are by their very nature a few years old now. Higher proportions of children, young people and adults using digital technologies and interacting in a virtual manner, what might that mean for human health and development as time moves on? It's not really particularly easy to say. If everybody's using digital technologies several hours a day then it's likely that we won’t be able to see links between these sorts of ways between interacting in relation to mental health.

I'm going to go back now to the idea of prevention and intervention. Back to this graph from Godfrey, which just touches on those different opportunities for intervening across the life course with the potential paybacks. On the Y axis, there is the risk of chronic disease. You don't have to take too much notice of that, but the idea here is, of course, that you can bump people off these trajectories into more positive outcomes. Going back to the graph of verbal abilities, you can see that there are various key points here. So pregnancy and the very early years - so before children are into preschool - clearly that's a key point in which we could intervene to try to stop that already 19 month gap in verbal abilities occurring.

Another key point here, of course, as we moved on would be age 7. So could we intervene at the school level, the family level there, in the early childhood years to prevent that massive widening of the gap between age 7 and 11? We counter this. These are average effects. It isn't deterministic. It isn't set in stone. But if children are exposed to these particular numbers of risk factors, of course we will have these particular outcomes.

Another point of intervention. Here this graph shows the risk of childhood obesity. Ages 5 and ages 11. You can see one striking thing here: that for children born, so it's children and families in the lowest two quintiles of the income distribution, at age five were about twice as likely to be obese compared to their better off counterparts. By age 11 that risk has tripled. So by age 11 the poorest kids are about 3 times as likely to be obese compared to their well off counterparts. There are key intervening points potentially there between ages 5 and 11 in terms of various behavioural and kind of things relating to lifestyle perhaps that we could focus on again in that early years point.

But also going back to pregnancy, we know that there are curious factors during the pregnancy period related to the risk of obesity developing in the first place, and these things are distributed unequally across socially stratified groups.

I'm showing this example of early puberty for many reasons, but really one of the key reasons is that early puberty is a well-established risk factor for many sorts of detrimental outcomes during the life course. It's linked to poor mental health in adolescence and into adulthood. This is early puberty in women. The marker here is menstruation by age 11. Early menstruation is also linked to a range of chronic disease risks. So the risk of cardiovascular diseases and a range of cancers and life expectancy.

Now this slide shows the pronounced inequalities. Again using income as a stratifying variable. You can see that 11-year-old girls in the poorest households are about 2 and a half times more likely to have begun to menstruate by age 11. The average in the UK for menstruation is about 12.9 years currently. There have been declines in the age of menstruation over the last 100 years or so. But it's currently around 12 and a half, 12.9 years.

Now I wanted to show you this social gradient, this income gradient, and the risk of puberty for the main reason of seeing what happens when we adjust for, in the statistical sense, different markers of stressful events going on in children's lives. So we take in a range of markers for the social environments and when we adjust for those, when we take account of those, we can see that some of our gradients reduce, and on the right hand side of the graph we additionally adjust for adiposity. So for BMI and for the fat mass index too in separate analyses, you can see that there is a further reduction in that inequality. But it's still there. So clearly there are a range of the different mechanisms influencing the onset of early puberty with this study. We've just managed to test 2 of those so that the unequal distribution of adiposity across socially stratified groups and the unequal distribution of exposure to psycho-social risk across income groups.

Thinking more broadly now about intervention programs. Clearly - there's some of them listed here on the slide - clearly the content and the manner in which these intervention programs are delivered into - that is absolutely key. So intervention programs will focus on different elements of children's healthy development. Perhaps the most successful ones are those which look across the piece and look at health as well as social development and intellectual development, as these things are inextricably linked and heavily intertwined. You know co-occurring really interventions that span all three domains of human development are very important and probably most likely to be successful.

The manner of delivery is also important. Different interventions' evaluations pick up on this all of the time. Some interventions are delivered by professionals in home settings, others in centre settings and so on and so forth. Other interventions are delivered by voluntary organisations, some by health professionals, some of them by statutory bodies. So there's these completely different mixtures both in the mode of delivery, the contents of the delivery, and these clearly have implications for evaluation programs. Evaluation of interventions is incredibly complex. Some of the evaluations which have taken place for programs like in the US the Head Start programs and early Head Start, and the Nurse-Family Partnership were based on an RCT design. So randomised control design, and they were able to demonstrate short- and long-term impacts. For example in the US, the Head Start program - the original Head Start program from some decades back - had long-lasting impacts on things to do with educational attainments and employment, and interactions with the criminal justice system. The Sure Start intervention program in the UK took a lot of its inspiration I guess from Head Start and Early Head Start with a broad aim of reducing social exclusion.

Sure Start was delivered on an area level. So on a community neighbourhood level basis, and the early intervention - or sorry the early evaluation of the Sure Start Program showed some benefits in the early years for children up to the age of 3. There was sort of a bit of a mixed bag I guess in some of the benefits, and not finding benefits for other outcomes. So it appears that there were some positive impacts - for example, on some of the indices of behavioural development, and some of the indices of relationships between parents and between parents and children. 

So I think the consensus view is that the evaluation was inconclusive in terms of clear benefits. We won't know. Most of the Sure Start program has now been rolled back so we won't know particularly too much about the long-term impacts of those early Sure Start programs in the UK setting.

Okay well I think time is probably running quite short now so I'll wrap up and thank you for your attention and thank you for logging on and taking part in this webinar.

WEBINAR CONCLUDED

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Slide outline

  1. The far reaching benefits of a good start in life
    • Yvonne Kelly
    • International Centre for Lifecourse Studies in Society and Health (ICLS)
    • www.ucl.ac.uk/icls    @icls_info
  2. The lifecourse approach
    • Influences on human development
    • Social change – what the future might hold
    • Prevention and intervention
  3. Evidence from longitudinal research
    • How early life social circumstances and early development link to life chances
    • Cohort and panel studies from UK, Northern Europe, US, Australia, New Zealand
  4. Lifecourse strategy for disease prevention
    • Diagram: This slide is adapted from one by Keith Godfrey and colleagues on non communicable diseases. Looking across the lifecourse, from left to right, plasticity is greatest in infancy and although possible throughout life, it becomes less and less likely with ageing. The corollary is that individuals are less able to respond to environmental insults and challenges as they get older.

The lifecourse approach highlights that there is no once and for all intervention. It is often emphasized that the greatest pay-back is from interventions in infancy. But early interventions to increase capabilities still need to be reinforced throughout childhood and early adulthood. And in later life, the focus may turn from prevention to cure.

  • Source: Adapted from Godfrey et al DOI: http://dx.doi.org/10.1016/j.tem.2009.12.008
  1. Predictive effect of linguistic development in early childhood on adult mental health at age 34
    • Diagram: How the odds of poor mental health correlate with poor receptive language at age 5.
      • Women 2.2, 1.46
      • Men 2.94, 1.77
      • Source: Schoon et al Pediatrics 2010;126:e73-79
  2. Life expectancy by highest qualification at age 25 for men and women USA 1996 and 2006
    • Diagram: Shows that life expectancy is increased the higher your qualification.
      • Source: CDC/NCHS, Health, United States, 2011, Figure 32. Data from the National Health Interview Survey Linked Mortality File.
  3. Risks of obesity and psychological distress at age 30: Childhood (age 10), Adolescence (age 16)
    • Obesity (risk of adult BMI>30)
    • Childhood obesity (BMI >95th centile)
    • 4.2 (2.8 -; 6.2)
    • Adolescent obesity (BMI >95th centile)
    • 11.6 (8.9 - 15.5)
    • Psychological distress (risk of adult distress on malaise inventory)
    • Age 10, high score on Rutter parent scale
    • 1.5 (1.4 - 1.7)
    • Age 16, high score on malaise inventory self report
    • 5.5 (3.3 - 9.2)
    • Source: Viner and Barker 2005
  4. CSDH conceptual framework for action on the social determinants of health
    • Diagram: CSDH conceptual framework for action on the social determinants of health 
      This diagram lists some of the influences on human health and development, including physical development, social and emotional development and intellectual development.
    • Source: Solar O, Irwin A (2010): A Conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). Geneva: World Health Organization.
  5. General socio-economic, cultural and environmental conditions
    • Diagram: General socio-economic, cultural and environmental conditions 
      This diagram shows the different influencing spheres of human development, including individual lifestyle factors, social and community networks, and general socio-economic, cultural and environmental conditions.
    • Source: Dahlgren and Whitehead, 1991
  6. Percentage of children exposed to risk factors at age 7
    • Diagram shows that exposure to risk factors decrease with age. Just under 30% of 3 year olds have none of the risk factors. Just over 10% of 3 year olds have 5 or more risk factors.
    • Risk factors included:
      • low birth weight
      • not breastfed
      • maternal depression
      • lone parent
      • family poverty
      • parental unemployment
      • few maternal educational qualifications
      • damp housing
      • social housing
      • area deprivation
  7. Verbal months ahead or behind at ages 3, 5 & 7 by number of risk factors
    • Diagram: Verbal months ahead or behind at ages 3, 5 & 7 by number of risk factors 
      This diagram shows verbal ability scores by risk factor scores through the early part of the life course. The gap between children with zero risk factor scores and those with 7+ scores at age 3 is about 19 months. By age 5, the gap widens to about 20 months, and by age 7 the gap is about 22 months.
    • MI results (Nov 2015). Risk factors included:
      • low birth weight
      • not breastfed
      • maternal depression
      • lone parent
      • family income below 60% of the median
      • parental unemployment
      • maternal educational qualifications
      • damp housing
      • "social" housing
      • area deprivation (lowest quintile of IMD)
      • overcrowding
  8. Verbal months ahead or behind at ages 3, 5, 7 & 11 by number of risk factors
    • Diagram: Verbal months ahead or behind at ages 3, 5, 7 & 11 by number of risk factors 
      This diagram shows verbal ability scores by risk factor scores at ages 3, 5, 7 & 11. The gap between children with zero risk factor scores and those with 7+ scores at age 11 is more than a 5-year gap.
    • MI results (Nov 2015). Risk factors included:
      • low birth weight
      • not breastfed
      • maternal depression
      • lone parent
      • family income below 60% of the median
      • parental unemployment
      • maternal educational qualifications
      • damp housing
      • "social" housing
      • area deprivation (lowest quintile of IMD)
      • overcrowding
  9. Clinically relevant behavioural problems at age 7, by number of risk factors
    • Diagram: Clinically relevant behavioural problems at age 7, by number of risk factors 
      This diagram shows how risk factor scores relate to social and emotional difficulties. The risk of children having clinically relevant social and emotional difficulties increases dramatically as exposure to risk factor scores increases.
    • MI results (Nov 2015). Risk factors included:
      • low birth weight
      • never breastfed
      • mother symptoms of depression
      • single parent
      • family income below 60% of the median
      • both parents jobless
      • mother no qualifications
      • damp housing
      • social housing
      • deprived area = lowest quintile of IMD
    • Sweep 4 survey weights used.
  10. Number of hours sleep needed
    • Diagram: Shows that hours of sleep needed decrease as age increases, from 15 hours of sleep needed at birth to 9 hours of sleep needed at age 17.
  11. The effects of changes in the regularity of bedtimes on behavioural difficulties scores, difference in differences
    • Diagram:
      • Change between ages 3 and 7 Non-regular to regular: -0.63 to -1.01
      • Change between ages 5 and 7 Regular to non-regular: 0.1 to 0.42
  12. Change in frequency of parent/carer reading to children from age 3 to age 5
    • Diagram:
      • Change to reading daily: -0.38
      • Change to reading weekly: -0.56
  13. Foundation Stage Profile: Good level of overall achievement (%) by duration of breastfeeding
    • Diagram:
      • Never 37.2
      • < 2 months 48.7
      • 2-3.9 months 55.7
      • ≥ 4months  59.7
  14. Predicted total difficulties score comparing Bangladeshi children with Pakistani children, mixed and not mixed
    • Diagram: Shows that Pakistani mixed children experience rates of change in problem behaviours that are significantly different from those of their non-mixed counterparts
  15. Association between social networking use and poor mental health in early adolescence
Hours spent on social network sitesPoor mental health (%)
< 17.4
1 - 38.1
4 +15.2
  • Diagram: Relationship between Odds of poor mental health and Hours per day chatting on social network sites
    • Column 1 Unadjusted Adjusted 1-3 hours 1.1 1.32 > 4 hours 2.25 2.38
  1. Lifecourse strategy for disease prevention
    • Diagram: This slide is adapted from one by Keith Godfrey and colleagues on non communicable diseases. Looking across the lifecourse, from left to right, plasticity is greatest in infancy and although possible throughout life, it becomes less and less likely with ageing. The corollary is that individuals are less able to respond to environmental insults and challenges as they get older. The lifecourse approach highlights that there is no once and for all intervention. It is often emphasized that the greatest pay-back is from interventions in infancy. But early interventions to increase capabilities still need to be reinforced throughout childhood and early adulthood. And in later life, the focus may turn from prevention to cure. 
      To make this more concrete, I have an example from recent preliminary work by myself in collaboration with my colleagues Yvonne Kelly and Mel Bartley. We looked at children’s verbal ability over the early years according to a number of the risk factors discussed earlier when I talked about the ecological model of health across the lifecourse.
    • Source: Adapted from Godfrey et al DOI: http://dx.doi.org/10.1016/j.tem.2009.12.008
  2. Verbal months ahead or behind at ages 3, 5, 7 & 11 by number of risk factors
    • Diagram: Verbal months ahead or behind at ages 3, 5, 7 & 11 by number of risk factors 
      This diagram shows verbal ability scores by risk factor scores at ages 3, 5, 7 & 11. More risk factors at every age results in even greater delayed verbal ability, and this trend is stronger in the older children.
    • MI results (Nov 2015). Risk factors included:
      • low birth weight
      • not breastfed
      • maternal depression
      • lone parent
      • family income below 60% of the median
      • parental unemployment
      • maternal educational qualifications
      • damp housing
      • "social" housing
      • area deprivation (lowest quintile of IMD)
      • overcrowding
  3. Income gap in the risk of obesity at 5 & 11 years of age
    • Diagram: Income gap in the risk of obesity at 5 & 11 years of age 
      This diagram shows the risk of childhood obesity at 5 & 11 years of age. Children born in the lowest 2 quintiles of the income distribution are twice as likely to be obese compared to their well-off counterparts at age 5. By age 11, the poorest children are 3 times as likely to be obese as their well-off counterparts.
  4. Social inequalities, psychosocial stress and ‘early’ menstruation
    • Diagram: Social inequalities, psychosocial stress and 'early' menstruation 
      Diagram shows 2 mechanisms influencing the onset of early puberty in women. Eleven-year-old girls in the poorest households are about 2.5 times more likely to have begun to menstruate by age 11.
    • Source: Kelly et al. forthcoming
  5. Intervention programmes
    • Sure Start, Nurse-Family Partnership, Strengthening Families Strengthening Communities, Head Start
    • Content
    • Delivery
    • Evaluation
  6. ICLS Lifecourse studies in society and health
    • Bridging social and biological sciences
    • www.ucl.ac.uk.icls
    • @icls_info
    • ESRC Economic & social research council
 

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