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Experiencing multiple adverse childhood experiences (ACEs) is a risk factor for many adverse outcomes. We explore associations of ACEs with educational attainment and adolescent health and the role of family and socioeconomic factors in these associations. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a prospective cohort of children born in southwest England in 1991-1992, we assess associations of ACEs between birth and 16 years (sexual, physical, or emotional abuse; emotional neglect; parental substance abuse; parental mental illness or suicide attempt; violence between parents; parental separation; bullying; and parental criminal conviction, with data collected on multiple occasions between birth and age 16) with educational attainment at 16 years (n = 9,959) and health at age 17 years (depression, obesity, harmful alcohol use, smoking, and illicit drug use; n = 4,917). We explore the extent to which associations are robust to adjustment for family and socioeconomic factors (home ownership, mother and partner's highest educational qualification, household social class, parity, child's ethnicity, mother's age, mother's marital status, mother's depression score at 18 and 32 weeks gestation, and mother's partner's depression score at 18 weeks gestation) and whether associations differ according to socioeconomic factors, and we estimate the proportion of adverse educational and health outcomes attributable to ACEs or family or socioeconomic measures. Among the 9,959 participants (49.5% female) included in analysis of educational outcomes, 84% reported at least one ACE, 24% reported 4 or more ACEs, and 54.5% received 5 or more General Certificates of Secondary Education (GCSEs) at grade C or above, including English and Maths. Among the 4,917 participants (50.1% female) included in analysis of health outcomes, 7.3% were obese, 8.7% had depression, 19.5% reported smoking, 16.1% reported drug use, and 10.9% reported harmful alcohol use. There were associations of ACEs with lower educational attainment and higher risk of depression, drug use, and smoking. For example, odds ratios (ORs) for 4+ ACEs compared with no ACEs after adjustment for confounders were depression, 2.4 (1.6-3.8, p < 0.001); drug use, 3.1 (2.1-4.4, p < 0.001); and smoking, 2.3 (1.7-3.1, p < 0.001). Associations with educational attainment attenuated after adjustment but remained strong; for example, the OR after adjustment for confounders for low educational attainment comparing 4+ ACEs with no ACEs was 2.0 (1.7-2.4, p < 0.001). Associations with depression, drug use, and smoking were not altered by adjustment. Associations of ACEs with harmful alcohol use and obesity were weak. For example, ORs for 4+ ACEs compared with no ACEs after adjustment for confounders were harmful alcohol use, 1.4 (0.9-2.0, p = 0.10) and obesity, 1.4 (0.9-2.2, p = 0.13) We found no evidence that socioeconomic factors modified the associations of ACEs with educational or health outcomes. Population attributable fractions (PAFs) for the adverse educational and health outcomes range from 5%-15% for 4+ ACEs and 1%-19% for low maternal education. Using data from multiple questionnaires across a long period of time enabled us to capture a detailed picture of the cohort members' experience of ACEs; however, a limitation of our study is that this resulted in a high proportion of missing data, and our analyses assume data are missing at random. This study demonstrates associations between ACEs and lower educational attainment and higher risks of depression, drug use, and smoking that remain after adjustment for family and socioeconomic factors. The low PAFs for both ACEs and socioeconomic factors imply that interventions that focus solely on ACEs or solely on socioeconomic deprivation, whilst beneficial, would miss most cases of adverse educational and health outcomes. This interpretation suggests that intervention strategies should target a wide range of relevant factors, including ACEs, socioeconomic deprivation, parental substance use, and mental health.

Autistic people show diverse trajectories of autistic traits over time, a phenomenon labelled 'chronogeneity'. For example, some show a decrease in symptoms, whilst others experience an intensification of difficulties. Autism spectrum disorder (ASD) is a dimensional condition, representing one end of a trait continuum that extends throughout the population. To date, no studies have investigated chronogeneity across the full range of autistic traits. We investigated the nature and clinical significance of autism trait chronogeneity in a large, general population sample. Autistic social/communication traits (ASTs) were measured in the Avon Longitudinal Study of Parents and Children using the Social and Communication Disorders Checklist (SCDC) at ages 7, 10, 13 and 16 ( = 9744). We used Growth Mixture Modelling (GMM) to identify groups defined by their AST trajectories. Measures of ASD diagnosis, sex, IQ and mental health (internalising and externalising) were used to investigate external validity of the derived trajectory groups. The selected GMM model identified four AST trajectory groups: (i) Persistent High (2.3% of sample), (ii) Persistent Low (83.5%), (iii) Increasing (7.3%) and (iv) Decreasing (6.9%) trajectories. The Increasing group, in which females were a slight majority (53.2%), showed dramatic increases in SCDC scores during adolescence, accompanied by escalating internalising and externalising difficulties. Two-thirds (63.6%) of the Decreasing group were male. Clinicians should note that for some young people autism-trait-like social difficulties first emerge during adolescence accompanied by problems with mood, anxiety, conduct and attention. A converse, majority-male group shows decreasing social difficulties during adolescence.

ObjectiveEarly pubertal timing has been linked to heightened body dissatisfaction, but previous studies have focused on girls, with small sample sizes and lacking objective measures of pubertal timing. The objective of this study was to examine the association between pubertal timing (age at peak height velocity [aPHV] and age at menarche [AAM] for girls) and body dissatisfaction and self-image in mid-adolescence (age 14).Design and settingProspective cohort study in the UK.Participants6644 participants (41% male) from the Avon Longitudinal Study of Parents and Children.Outcome measuresOutcomes were measured using the Satisfaction and Dissatisfaction with Body Parts Scale and Self-Image Profile at age 14. Multivariable regression models were adjusted for socioeconomic status and prepubertal body mass index (BMI).ResultsIn boys, later aPHV was associated with higher body dissatisfaction (b=0.13 (95% CI 0.09 to 0.18)). In girls, later aPHV was associated with lower body dissatisfaction, but this was attenuated after adjusting for BMI (b=−0.03 (95% CI −0.07 to 0.01)). A negative association was found between AAM and body dissatisfaction (b=−0.06 (95% CI −0.09 to –0.02)). Later aPHV in girls was associated with increased odds of feeling good-looking (OR=1.09 (95% CI 1.01 to 1.19)) and lower odds of feeling different from others (OR=0.91 (95% CI 0.83 to 1.00)). No associations between aPHV and self-image were found in boys.ConclusionsThese findings highlight the need for targeted interventions for adolescent body dissatisfaction.

Background Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables (“auxiliary variables”) in imputation models, in addition to those required for the substantive analysis. Common advice is to take an inclusive approach to auxiliary variable selection (i.e. include all variables thought to be predictive of missingness and/or the missing values). There are no clear guidelines about the impact of this strategy when data may be MNAR. Methods We explore the impact of including an auxiliary variable predictive of missingness but, in truth, unrelated to the partially observed variable, when data are MNAR. We quantify, algebraically and by simulation, the magnitude of the additional bias of the MI estimator for the exposure coefficient (fitting either a linear or logistic regression model), when the (continuous or binary) partially observed variable is either the analysis outcome or the exposure. Here, “additional bias” refers to the difference in magnitude of the MI estimator when the imputation model includes (i) the auxiliary variable and the other analysis model variables; (ii) just the other analysis model variables, noting that both will be biased due to data MNAR. We illustrate the extent of this additional bias by re-analysing data from a birth cohort study. Results The additional bias can be relatively large when the outcome is partially observed and missingness is caused by the outcome itself, and even larger if missingness is caused by both the outcome and the exposure (when either the outcome or exposure is partially observed). Conclusions When using MI, the naïve and commonly used strategy of including all available auxiliary variables should be avoided. We recommend including the variables most predictive of the partially observed variable as auxiliary variables, where these can be identified through consideration of the plausible casual diagrams and missingness mechanisms, as well as data exploration (noting that associations with the partially observed variable in the complete records may be distorted due to selection bias).

Most of the evidence on the effects of internet use on mental health derives from cross-sectional research. We set out to explore prospective associations between internet use (hours online and specific internet experiences) and future mental health problems. Participants were 1,431 respondents from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK birth cohort, who completed a questionnaire on internet use (time online and ten different internet experiences) when they were aged 18 years. Outcomes included past year self-harm, assessed at 21 years and high levels of depression and anxiety symptoms, assessed at 22 years. Associations were investigated using logistic regression models and analyses were conducted separately for males and females. Females reporting high levels of internet use (number of hours online) were found to be at increased risk of depression at follow-up (highest tertile vs lowest tertile OR = 1.41, 95% CI 0.90 to 2.20), whereas males with high levels of internet use were at increased risk for self-harm (highest tertile vs lowest tertile OR = 2.53, 95%CI 0.93 to 6.90). There was no evidence to suggest an association between hours spent online and anxiety. With regards to the specific internet experiences, associations were found for females but not for males. In fully adjusted models, being bullied online (OR = 1.76, 95% CI 1.09 to 2.86) and meeting someone face to face (OR = 1.55, 95% CI 1.00 to 2.41) were associated with an increased risk of future depression. Being bullied online was also associated with an increased risk of future self-harm (OR = 2.42, 95% CI 1.41 to 4.15), along with receiving unwanted sexual comments or material, and coming across pornography and violent/gruesome material. Our findings highlight the importance of digital citizenship training to help teach young people to use technology safely and responsibly.

Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). Standard (default) MI procedures use simple linear covariate functions in the imputation model. We examine the bias that may be caused by acceptance of this default option and evaluate methods to identify problematic imputation models, providing practical guidance for researchers. Using simulation and real data analysis, we investigated how imputation model mis-specification affected MI performance, comparing results with complete records analysis (CRA). We considered scenarios in which imputation model mis-specification occurred because (i) the analysis model was mis-specified or (ii) the relationship between exposure and confounder was mis-specified. Mis-specification of the relationship between outcome and exposure, or between exposure and confounder, can cause biased CRA and MI estimates (in addition to any bias in the full-data estimate due to analysis model mis-specification). MI by predictive mean matching can mitigate model mis-specification. Methods for examining model mis-specification were effective in identifying mis-specified relationships. When using MI methods that assume data are MAR, compatibility between the analysis and imputation models is necessary, but not sufficient to avoid bias. We propose a step-by-step procedure for identifying and correcting mis-specification of imputation models. •Compatibility between analysis and imputation models is not sufficient to avoid bias.•Uncritical use of linear functions in the imputation model should be avoided.•It is important to check that each imputation model is correctly specified.

Depression is a common mental illness and research has focused on late childhood and adolescence in an attempt to prevent or reduce later psychopathology and/or social impairments. It is important to establish and study population-averaged trajectories of depressive symptoms across adolescence as this could characterise specific changes in populations and help identify critical points to intervene with treatment. Multilevel growth-curve models were used to explore adolescent trajectories of depressive symptoms in 9301 individuals (57% female) from the Avon Longitudinal Study of Parents and Children, a UK based pregnancy cohort. Trajectories of depressive symptoms were constructed for males and females using the short mood and feelings questionnaire over 8 occasions, between 10 and 22 years old. Critical points of development such as age of peak velocity for depressive symptoms (the age at which depressive symptoms increase most rapidly) and the age of maximum depressive symptoms were also derived. The results suggested that from similar initial levels of depressive symptoms at age 11, females on average experienced steeper increases in depressive symptoms than males over their teenage and adolescent years until around the age of 20 when levels of depressive symptoms plateaued and started to decrease for both sexes. Females on average also had an earlier age of peak velocity of depressive symptoms that occurred at 13.5 years, compared to males who on average had an age of peak velocity at 16 years old. Evidence was less clear for a difference between the ages of maximum depressive symptoms which were on average 19.6 years for females and 20.4 for males. Identifying critical periods for different population subgroups may provide useful knowledge for treating and preventing depression and could be tailored to be time specific for certain groups. Possible explanations and recommendations are discussed.

The negative control design can be used to provide evidence for whether a prenatal exposure–outcome association occurs by in utero mechanisms. Assortative mating has been suggested to influence results from negative control designs, although how and why has not yet been adequately explained. We aimed to explain why mutual adjustment of maternal and paternal exposure in regression models can account for assortative mating. We used directed acyclic graphs to show how bias can occur when modeling maternal and paternal effects separately. We empirically tested our claims using a simulation study. We investigated how increasing assortative mating influences the bias of effect estimates obtained from models that do and do not use a mutual adjustment strategy. In models without mutual adjustment, increasing assortative mating led to increased bias in effect estimates. The maternal and paternal effect estimates were biased by each other, making the difference between them smaller than the true difference. Mutually adjusted models did not suffer from such bias. Mutual adjustment for maternal and paternal exposure prevents bias from assortative mating influencing the conclusions of a negative control design. We further discuss issues that mutual adjustment may not be able to resolve.

Musculoskeletal conditions, including fractures, can have severe and long-lasting consequences. Higher body mass index in adulthood is widely acknowledged to be protective for most fracture sites. However, sources of bias induced by confounding factors may have distorted previous findings. Employing a lifecourse Mendelian randomisation (MR) approach by using genetic instruments to separate effects at different life stages, this investigation aims to explore how prepubertal and adult body size independently influence fracture risk in later life. Using data from a large prospective cohort, univariable and multivariable MR were conducted to simultaneously estimate the effects of age-specific genetic proxies for body size (n = 453,169) on fracture risk (n = 416,795). A two-step MR framework was additionally applied to elucidate potential mediators. Univariable and multivariable MR indicated strong evidence that higher body size in childhood reduced fracture risk (OR, 95% CI: 0.89, 0.82 to 0.96, P = 0.005 and 0.76, 0.69 to 0.85, P = 1 × 10 − 6 , respectively). Conversely, higher body size in adulthood increased fracture risk (OR, 95% CI: 1.08, 1.01 to 1.16, P = 0.023 and 1.26, 1.14 to 1.38, P = 2 × 10 − 6 , respectively). Two-step MR analyses suggested that the effect of higher body size in childhood on reduced fracture risk was mediated by its influence on higher estimated bone mineral density (eBMD) in adulthood. This investigation provides novel evidence that higher body size in childhood reduces fracture risk in later life through its influence on increased eBMD. From a public health perspective, this relationship is complex since obesity in adulthood remains a major risk factor for co-morbidities. Results additionally indicate that higher body size in adulthood is a risk factor for fractures. Protective effect estimates previously observed are likely attributed to childhood effects.

Background International research shows the significance and impact of intimate partner violence and abuse (IPVA) as a public health issue for young adults. There is a lack of qualitative research exploring pathways to IPVA. Methods The current mixed-methods study used qualitative interviews and analysis of longitudinal cohort data, to explore experiences of pathways to IPVA. Semi-structured Interviews alongside Life History Calendars were undertaken to explore 17 young women’s (19–25 years) experiences and perceptions of pathways to IPVA in their relationships. Thematic analysis was undertaken. Based on themes identified in the qualitative analysis, quantitative analysis was conducted in data from 2127 female and 1145 male participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort study. We fitted regression models to assess the association of child maltreatment, parental domestic violence, and peer-to-peer victimisation, by age 12, with loneliness during adolescence (ages 13–14), and the association of loneliness during adolescence with IPVA (age 18–21). Mediation analysis estimated the direct effects of maltreatment on IPVA, and indirect effects through loneliness. Findings All women interviewed experienced at least one type of maltreatment, parental domestic violence, or bullying during childhood. Nearly all experienced IPVA and most had been multi-victimised. Findings indicated a circular pathway: early trauma led to isolation and loneliness, negative labelling and being silenced through negative responses to help seeking, leading to increased experiences of loneliness and intensifying vulnerability to further violence and abuse in young adulthood. The pathway was compounded by intersectionality. Potential ways to break this cycle of loneliness included being heard and supported, especially by teachers. Quantitative analysis confirmed an association between child maltreatment and loneliness in adolescence, and an association between loneliness in adolescence and experience of IPVA in young adult relationships. Conclusion It is likely that negative labelling and loneliness mediate pathways to IPVA, especially among more disadvantaged young women. The impact of early maltreatment on young people’s wellbeing and own relationships is compounded by disadvantage, disability and ethnicity. Participants’ resilience was enabled by support in the community.