Childhood maltreatment has been consistently associated with poor health outcomes, such as obesity, cardiovascular health, mental health, substance misuse and risky sexual behaviours. There is no gold standard to assess childhood maltreatment, and both prospective and retrospective reports entail potential limitations. A poor agreement has been shown between measures of childhood maltreatment assessed prospectively (usually through parental report and official records) and retrospectively (often self-reported), suggesting that these forms of report of maltreatment identify different groups of individuals. The use of retrospective measures of childhood maltreatment might overestimate the associations with subjectively measured outcomes (e.g. self-reported, such as health behaviours and mental health) and underestimate the associations with objectively measured outcomes (e.g. physical health). However, few studies have information on both prospective and retrospective measures of childhood maltreatment and were able to compared the associations of different types of reports of maltreatment with subjectively and objectively measured health outcomes. This project aims to assess and compare associations of prospectively and retrospectively reported childhood maltreatment with key subjectively and objectively measured health outcomes in early adulthood (e.g. cardiovascular risk factors, mental health, and socioeconomic status).

Our original proposal, B2492, is about the joint development of health, skills and education. From the start, this involved studying the role of genetic and early-life environmental factors in shaping individuals’ health and education trajectories.

This proposal fits directly within our aim to study the “interactions between genes and family composition”, focussing on the effects of “interactions between genes and family composition on children’s test scores and health behaviours”. Indeed, parental separation implies a drastic change in the family structure and composition, less time investment, as well as mental and financial distress. This has been shown to have a number of consequences for child mental health outcomes. In this paper, we will quantify the extent to which the effects of father absence on child outcomes are modified by children’s genetic predisposition.

Background: Parent separation has been shown to have a number of consequences for the child mental health outcomes. On the other hand there are a number of studies on the effects of father involvement and paternity leave policy on children. In this paper, we would like to contribute to the literature by showing how the effects of father absence on child outcomes are modified by children’s genetic predisposition for such health outcomes as BMI and depression.

Research Questions: What happens to children’s education and health outcomes after fathers leave family? Does it harm more those with higher genetic risk for mental health issues and risky health behaviours? Are children with higher genetic predisposition for education more resilient to the absence of a father?

Increasing evidence suggests that social processes including weight-related stigma are key to explaining many consequences of overweight and obesity. For instance, people carrying genetic variants linked to obesity are at higher risk of depression, even where those variants have no known metabolic consequences(1). This strongly implicates social, not just biological, processes by which body weight affects mental health. Among the different facets of stigma is internalized weight stigma (self-attribution of negative obesity-related stereotypes) which may have especially negative consequences. For higher-weight individuals, it is linked to disordered eating(2), maladaptive coping(3), and worse quality of life(4). But it can also affect normal weight and underweight people, and predicts disordered eating and drive for thinness in non-overweight groups(5).
Despite strong theoretical work in this area, our empirical understanding of weight stigma’s causes and consequences is limited. This includes how socioeconomic factors, adiposity development across the lifecourse, and parental body weight influence weight stigma internalization, and the consequences for mental health and social functioning. This is because research has been almost entirely based on small, non-representative samples. In this project, we would measure internalized weight stigma among ALSPAC participants of all weight statuses using a validated questionnaire(6). This would, uniquely, allow investigation of its causes and consequences in a large sample of young people. We would also investigate if a proxy measurement of internalized weight stigma can be constructed from other items, for when purpose-designed items are unavailable. The data and results will support further research in a range of existing data sources.

Smoking is the leading cause of preventable death in the UK (Cornish et al., 2019) and represents both a risk of serious illness (Prescott, 2019; Bello et al., 2014) and a significant burden on public services and the economy (Ekpu and Brown, 2015). As a result, research which may have potential implications for smoking cessation interventions may be considered justified.

A number of studies have examined the relationship between financial stress and smoking behaviour and identified associations between these variables (Guillaumier et al., 2017; Siahpush, Borland and Scollo, 2013). However, the exact causal nature of this relationship may be considered somewhat obscure.

The proposed research plans to investigate the possibility of financial stress acting as an active predictor/risk factor of smoking behaviour. The longitudinal nature of the ALSPAC data facilitates research examining how changes in financial stress overtime may relate to later smoking behaviour. Although exact cause and effect may not be identified, tracking the relationship between these variables overtime may offer some form of greater insight into how they interact. This insight may be considered enough to make recommendations for further investigation and/or have implications for smoking cessation interventions.

Perinatal psychiatry is a relatively new, multidisciplinary field of psychiatry. Perinatal usually refers to the period immediately before and after birth, starting at the 20th to 28th week of gestation and ending 1 to 4 weeks after birth; while the postnatal period can be defined as the first 6-8 weeks after birth. Although knowledge is increasing, more research is needed to clarify the associations between parental and offspring mental illness.
The theory behind the potential associations between perinatal and postnatal risk factors and subsequent offspring mental health problems proposes that environmental stress, during pregnancy, such as life event exposure, may affect the neurodevelopment of the foetus and lead to an increased risk of psychopathology.
Among the perinatal and postnatal risk factors that have been investigated in the last years, maternal postnatal depression is one of the most relevant and studied risk factors, and thus most of the existing research is solely focused on this specific risk factor. Further, maternal postnatal depression is known to have an adverse effect on several aspects of child and adolescent development, including social, emotional, and cognitive function, and is associated with offspring depressive symptoms in adolescence and adulthood. Maternal postnatal depression might negatively affect bonding and parenting during infancy, which might affect offspring attachment style and increase the risk of psychotic experiences, among others.
In addition to postnatal depression, there are some other perinatal and postnatal risk factors that could be considered a form of psychological distress and that might reflect chronic maternal stress, which could affect the neurodevelopment of offspring. Among these risk factors, perinatal and postnatal maternal sleeping problems, family adversity, substance abuse, gestational age, or maternal age when birth could also play an important role in the development of subsequent mental health problems in adolescence, such as depressive and psychotic symptoms. However, to the best of our knowledge, this is the first study investigating a range of relevant perinatal and postnatal risk factors for subsequent psychopathology in adolescence.

Early life adversity can have a profound impact on children's cognitive development. But is that association specific or general? We want to test whether adversities group according to their type, and whether the longitudinal impact of different types of adversity are associated with different kinds of negative outcome.

Diseases such as COVID-19 are not defeated, and there remains an urgent need for improved modelling of disease incidence to support future early-warning systems; improved prevalence estimation; and understanding of long term impacts to vulnerable communities. Finding data to underpin such analyses, however, is extremely difficult indeed. Much COVID incidence goes completely unreported; even the largest studies are limited to tiny fractions of the population, and biased towards specific demographics; and generalized studies have to use either tip-of-the-iceberg medical statistics (ONS, NHS), fine-grained but unsustainable self-reporting technologies (e.g. KCL’s COVID Symptom Study app), or broad brush behavioural data (e.g. Google COVID-19 mobility reports, Social media data). With a lack of widespread adoption of track-and-trace systems in the UK, and (understandably) declining public engagement with self-reporting initiatives, new approaches are urgently required.

A solution is available however. Epidemics such as COVID-19 are now well recognized as being driven as much by behavioural factors as they are from clinical ones - behavioural factors that are richly embedded in the mass, anonymized retail transaction logs held (untapped) in CIVIC's private-sector partners' datasets. Through the interrogation of such data (e.g. health purchases from the UK's leading health retailer), CIVIC will address key epidemiological knowledge gaps in: determining dynamic estimates of untested COVID-19 via fine-grained pharmacy and self-medication datasets; advancing AI/Modelling knowledge by triangulating behavioural features embedded in >1.5 billion loyalty-card logs - that can act as predictors of future outbreak; and advancing the state-of-the-art in identification and mapping of key vulnerable communities across the UK (Olio, Fareshare, ONS).

In such modelling "ground-truth" labelling of target (independent) variables) is crucial. In stage 1 of CIVIC, analysis will occur at aggregated LSOA geographical levels, with data-points labelled with (a) time series of recorded incidence in each LSOA (NHS) and (b) time series of relevant 111-related activity. However in Stage-2, CIVIC will importantly, also investigate the potential of "data-linkage", an approach that holds potential to underpin finer-grained individual level analyses at scale. Such processes must be engrained with privacy-by-design, and underpinned by informed and participatory consent, if they are ever to contribute to public health.