In response to the COVID-19 pandemic we plan to send two standalone questionnaires out to ALSPAC participants to determine potential transmission (through questions on travel and symptoms) before the peak that is starting in the UK now. Exact content of the questionnaires is to be agreed but will also likely include measures of other symptoms (not just COVID but e.g. seasonal flu, anxiety), mental health (depression, well being), patterns of response to the pandemic (e.g. self isolating, working at home etc) and what indivdual's concerns are (financial, health, etc). We will follow up with our standard annual questionnaire to determine long term impact.

Inflammation has been implicated as a potential mechanism in the development of psychiatric illnesses such as schizophrenia and major depressive disorder (MDD). However, it is not fully understood whether inflammation causes mental illness, whether behaviours associated with mental illness cause increased inflammation, or whether mental illness and inflammation share common risk factors. This project therefore aims to i) investigate whether genetic risk for psychiatric disorders is associated with inflammation and ii) investigate whether inflammation explains the associations between genetic risk for psychiatric disorders and mental health outcomes in adolescence and early adulthood.
Results of this project will further improve our understanding of the role of inflammation in pathways to mental ill health.

Only a small proportion of pregnant individuals will continue to smoke during their pregnancy. Identifying the key traits which predict this behaviour will allow the development of more efficient screening procedures and interventions for this vulnerable group, improving outcomes for both mother and foetus. While both psychological vulnerabilities (such as depression) and socioeconomic risk factors (such as material deprivation) have been considered individually in their relationship with smoking during pregnancy, the relative importance of psychological versus socioeconomic factors has not been determined. In addition, very few studies have considered the complex inter-relationships between socioeconomic status (SES) and psychological wellbeing, and how these might contribute to smoking in pregnancy. The purpose of the present project is to use the ALSPAC dataset to address these two issues. Pregnant women in this dataset will be classed as either continuing smokers (smoked prior to pregnancy and continued during second and/or third trimesters), quit smokers (smoked prior to pregnancy but not during second or third trimesters), and never smokers (did not smoke prior to pregnancy or during pregnancy). Differences between these groups in psychological variables (depression, anxiety, and experience of stressful events during pregnancy) and socioeconomic variables (educational attainment, financial difficulties and neighbourhood deprivation) will be tested using multiple logistic regression. The second phase of this project will use network outcome analysis to map the complex inter-relationships between psychological and socioeconomic risk factors and smoking in pregnancy. This will allow us to identify the best targets for intervention.

What mothers eat during pregnancy may affect the development of allergy in their babies and children. This study will use data from two cohorts: the Healthy Start cohort and the ALSPAC cohort. The study will learn if a novel comprehensive measure of maternal diet during pregnancy affects development of eczema, food allergy, seasonal allergies/hay fever/allergic rhinitis and asthma in chidden. The new knowledge will help families and clinicians to eventually prevent the onset and progression of allergy.

The Clinical Interview Schedule – Revised (CIS-R) is a structured diagnostic measure developed from the Clinical Interview Schedule (CIS), a standardised interview designed to assess common mental disorders among community settings. The CIS was designed for use by clinical interviewers and required expert judgement to determine psychopathology. The CIS was standardised to enable interviewers without this expert knowledge to administer, the resulting CIS-R can thus be self-completed and returns results comparable to those from standardized interviews. The CIS-R has been validated across a number of populations and ages, and studies suggest that the instrument remains valid across a number of cultural settings and age groups. To date, little work has been done to validate the CIS-R administered using different modes of assessment.

Previously the CIS-R has been administered to ALSPAC participants via a computerised assessment during clinic time. To minimise participant burden during clinic time and maximise response rate to the CIS-R, it has been suggested that the assessment could be completed online, outside of the clinic. The CIS-R has not currently been validated as an online task, therefore we propose to investigate whether responses to this measure differ according to the setting in which it is completed, and whether it remains a valid diagnostic measure when completed online outside of the clinic setting.

Despite the common belief that teachers can impact various aspects of students' lives, the empirical evidence on the breadth and strength of their influence is relatively unclear. This project aims to examine the relative strength of the influence of teachers' characteristics and behaviours as perceived by multiple raters (i.e., teachers themselves, students, and parents) on multiple experiences and outcomes associated with the teacher (e.g., job satisfaction) and their students (e.g., academic achievement).

Ultimately, the similarity between parents and their children can come from two sources: nature and nurture. Recently, methods have been introduced to identify the effects of “genetic nurture” - effects of the parentally-provided environment which can be explained by the parents’ genes through their influences on parental behavior (rather than through genetic transmission to their children). We have developed an extension of these methods which may allow us to find specific genetic factors which make up this effect, for nuclear families with genotype data available on at least 2 members (e.g., sibling pairs or parent-offspring pairs). This method involves imputing missing parental genotypes from the available genotypes of each nuclear family. Because of its detailed longitudinal phenotype data and genotyped mothers and children, ALSPAC is an ideal dataset to examine these effects across a number of traits important to human health and disease. The results of this study will better help us understand the important role of the parental environment in childhood development and identify specific genetic and environmental risk factors (and their interaction) for physical and mental health outcomes in children.

Educational and career decisions for school leavers are complex and influenced by multiple factors. These can include: factors at the level of an individual, such as cognitive abilities, motivations and self-beliefs; factors related to the family environment, such as parental education and beliefs, and family income; factors relating to the school setting, such as type of schooling; and finally factors relating to the broader economic and cultural context. These factors, besides having a direct and specific impact on individuals' educational and professional path, interact in complex ways. For individuals with special educational needs and disabilities (SEND), education and career decisions are likely to be even more complex, and we know that career advice for individuals with SEND is often patchy and incomplete.

In order to gain a thorough understanding of the factors involved in the post-secondary educational and occupational choices of individuals with SEND, we plan to combine two complementary data analysis techniques: a variable centred approach, which examines which variables play an important role in predicting outcome across the range, and a person centred approach, which is focused on a holistic analysis of the combined effect of multilevel factors. This allows us to understand whether there are different profiles of individuals with SEND who show different post-secondary choices.