There are many pressures on young adults to establish their careers, start families, and buy their first house. The current generation of young adults is making these major life changes in the context of financial and political upheaval. The stresses and strains of this life stage mean that this is the time when many young people experience problems with their mental health and wellbeing. Our research will measure mental health and wellbeing in young adults to explore the best ways to support young adults during this turbulent phase of their lives.

There is a large body of research validating self-report medical data with that obtained through linkage, for example (1–3). There is however far less research that has investigated the validity of educational data self-reports, with studies restricted to small samples (4) or single courses (5). The accuracy of official measures of education such as the UK national Pupil Database is cited as one of the main advantages to conducting data linkage in cohort studies, and therefore determining the accuracy of self-reports when compared to these official measures is of critical importance for assessing the benefits of data linkage. Furthermore, the direction and magnitude of mis-reporting has been found to vary with individual characteristics(5) which will means that measurement error will contain not just random noise but also directional bias.

ALSPAC holds data on G1 participants’ education via data linkage only. Retrospective collection of participants education through self-report responses to questionnaires would allow the accuracy of this data to be compared to the linked data. Given the rich data that ALSPAC holds on its participants it may also be possible to investigate how misreporting varies across a range of individual characteristics including sex, intelligence, and family socioeconomic position. We note however that this analysis into misreporting may be restricted due to the non-random nature of cohort attrition in ALSPAC. Below we highlight some of the research questions that could be addressed through the collection of self-report education data collected by ALSPAC.

The importance of this data collection is demonstrated by recent changes in data linkage surrounding data sharing. The NPD recently decided to temporarily withdraw all data linkage due to data sharing and confidentiality issues, highlighting the risk that because linked data is not owned by cohort studies it can be pulled at short notice. Collecting self-report education data will therefore also provide ALSPAC with a safety net in the form of its own accurate educational records in the worst-case scenario that NPD data linkage is revoked permanently. While this self-report data will be only a sub-sample of the cohort due to attrition, it will ensure a larger sample than collection at a future occasion due to continued attrition.

As part of this data collection we will clean, code and deposit all data with full accompanying documentation and code for use by others.

1. Hafferty, J. D. et al. Self-reported medication use validated through record linkage to national prescribing data. J. Clin. Epidemiol. (2018). doi:10.1016/j.jclinepi.2017.10.013
2. Comino, E. J. et al. Validating self-report of diabetes use by participants in the 45 and up study: A record linkage study. BMC Health Serv. Res. (2013). doi:10.1186/1472-6963-13-481
3. Mars, B. et al. Using Data Linkage to Investigate Inconsistent Reporting of Self-Harm and Questionnaire Non-Response. Arch. Suicide Res. (2016). doi:10.1080/13811118.2015.1033121
4. Sticca, F. et al. Examining the accuracy of students’ self-reported academic grades from a correlational and a discrepancy perspective: Evidence from a longitudinal study. PLoS One (2017). doi:10.1371/journal.pone.0187367
5. Rosen, J. A., Porter, S. R. & Rogers, J. Understanding Student Self-Reports of Academic Performance and Course-Taking Behavior. AERA Open 3, 2332858417711427 (2017).

Obesity is associated with a range of poor health outcomes (e.g., high blood pressure), but not all obese individuals have these outcomes. The relationships of childhood growth and body mass index (BMI is used to define obesity) trajectories, that describe how BMI changes as a person ages, with adulthood obesity have been well-documented. However, few studies have investigated whether there are multiple different patterns of BMI change over age that all lead to adulthood obesity but have different health outcomes. For example, it has been proposed that there are two main BMI patterns that lead to adulthood obesity. The first is characterised by being big at all ages due to a healthy combination of fat and fat-free mass, while the second is characterised by low or normal BMI in infancy and subsequently an unhealthy level of fat accumulation in childhood. It is hypothesised that the first pattern doesn't incur any adverse health consequences, while the second pattern does. This project aims to test this idea that there exist multiple different BMI patterns that lead to adulthood obesity and that they have different consequences for cardio-metabolic health. The same analysis in normal weight adults will help explain the BMI pattern that leads to some normal weight adults having poor health prospects.

The UK policy toward providing fertility services tends to draw on ideas about ‘procreative liberty’. Often such policies combine pronatalism ideology (reproduction as an intrinsic good) with a commitment to individual autonomy. The concept of reproductive liberty, however, fails to acknowledge the necessity of government assistance in facilitating reproductive choice, and a gap is emerging between the language of ‘rights’ to particular services and access to those services.
Research to date exploring access to fertility services has focussed on the impact of involuntarily childlessness, but there has been scant attention to the related, but important, question of why people choose to have children in the first place. The lack of empirical literature exploring the reasons why people do reproduce is evidence in itself of the generally unquestioned acceptance of pronatalism, suggesting the decision to have children needs less scrutiny than the decision not to. The questions we propose to include in ALSPAC will explore the reasons people have for making reproductive decisions, with a view to facilitating a better understanding of the reasons behind reproductive decisions. This, in turn, will help us to build a more complete understanding of the relative importance of being able to access fertility services and the value people place on specific means of reproduction and parenting, which can feed into analysis of arguments about the funding of fertility services.

The term ‘effortful control’ refers to the ability to self-regulate one’s own behavioural, cognitive, or emotional responses as appropriate for the situation. The construct includes both the ability to direct attention to one particular source of interest and to be able to inhibit unhelpful responses and instead produce appropriate responses. Disruptions to healthy effortful control development in childhood have previously been found to have long-term consequences on life outcomes such as psychopathology and academic attainment.

Previous research has identified parenting behaviours, such as scaffolding and the promotion of child autonomy, opposed to over-control, as being associated with the development of child effortful control (Fay-Stammbach, Hawes, & Meredith, 2014). The development of child inhibitory control, for example, may be associated with greater maternal sensitivity and less harsh paternal discipline behaviours (Lucassen et al., 2015). Child executive attention, on the other hand, has been associated with maternal behaviours aimed at facilitating learning (Mezzacappa, Buckner, & Earls, 2011). However, despite the clear importance of parenting behaviours for the development of child executive attention and inhibitory control, research has infrequently investigated predictors of such parenting styles.

Understanding the nature of child effects on parents, and whether some parents are more vulnerable to the influence of these child effects, will help to inform further research on the role of parenting in child development as well as interventions aimed at improving child outcomes.

Cardiometabolic health is a term that refers to a collection of conditions, including cardiovascular disease (CVD), type 2 diabetes and metabolic syndrome. Together, these conditions are the major cause of preventable death globally. Researchers now know that the underlying processes for developing cardiometabolic diseases can begin at an early age. A large part cardiometabolic health is linked to lifestyle factors, and dietary habits throughout the life span are an important modifiable factor. Previous studies have shown that different dietary patterns and qualities of the diet can influence cardiometabolic health. Evidence also suggests that habitual ‘unhealthy’ eating patterns can lead to a chronic inflammatory state (because certain nutrients and foods produce an inflammatory response in the body), and chronic low-grade inflammation has been linked to numerous chronic diseases. Finally, dietary habits are intricately connected to obesity, and obesity is related to both cardiometabolic health and chronic inflammation.

Research into the relationship between diet quality, cardiometabolic health and low-grade inflammation in children and young adults is still very limited. It is particularly important to understand these relationships during this period because it is when dietary habits begin to stabilize and track into adulthood. Therefore, we plan to study if diet quality in childhood and adolescence are associated with cardiometabolic health and low-grade inflammation later in early adulthood.

We will use detailed reports of food intake collected when children were 7, 10 and 13 years old to measure four distinct aspects of diet quality which have been related to cardiometabolic health in adults i) a dietary pattern largely characterised by the amount of energy, fat, sugar and fibre ii) an adapted Inflammatory Dietary Score representing the inflammatory potential of the diet, iii) an adapted relative Mediterranean diet representing adherence to a Mediterranean-style diet, and iv) the adapted Alternative Healthy Eating Index-2010 representing compliance to dietary guidelines to reduce chronic disease risk. Cardiometabolic health will be assessed at 17 and 24 years by measuring glucose, insulin, triglyceride and cholesterol levels, blood pressure, body composition and adiposity indicators and cardiovascular structure. Chronic low-grade inflammation will be assessed at 24 years by measuring a number of key markers of inflammation in blood samples taken at this age. This study should help gain a clearer understanding of the complex relationship between different aspects of diet quality, chronic low-grade inflammation and cardiometabolic alternations in childhood and young adults, where research is severely lacking. The results will contribute to the scientific evidence needed to develop effective preventative measures and identify and manage high-risk groups.

Some children have particular difficulty comprehending written text: despite being able to read words quite well, they struggle to understand what they have read. So called “poor comprehenders” are not rare, with estimates varying between 5-10% of children in mid-childhood. There is now a large body of work documenting the nature of poor reading comprehension in mid-childhood and in particular, its association with spoken language abilities. However, we know relatively little about its precursors earlier in development, or its consequences for later childhood and beyond. The longitudinal data contained in ALSPAC will help us understand why it is that some children fail to comprehend, and what the consequences of this are for educational achievement and emotional well-being in the secondary school years and beyond. Our findings will help inform and specify educational interventions for children with poor reading comprehension.

The paper will be a tutorial guide on how to model a life-course trajectory using data from multiple cohorts where the cohorts cover different periods of the life-course. We would like to include an example of modelling weight from birth to mid-life.