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PURPOSE: Current knowledge of the characteristics of puberty beyond age at menarche and thelarche is limited, particularly within population-based cohorts. Secular trends and concerns of the health effects of early puberty reinforce the value of contemporary studies characterizing the timing, tempo, duration, and synchronicity of puberty. METHODS: The Childhood to Adolescence Transition Study is a unique Australian cohort of individuals followed annually from late childhood to late adolescence, with up to eight assessments of pubertal stage from 9 to 19 years of age (N = 1,183; 636 females). At each assessment, females reported their Tanner Stage of breast and pubic hair development, while males reported on genital/pubic hair development. Nonlinear mixed-effects models characterized pubertal trajectories and were used to derive each individual's estimates of timing, tempo, and synchronicity. Parametric survival models were used to estimate the overall duration of puberty. RESULTS: Timing of mid-puberty (Tanner Stage 3) ranged from 12.5 to 13.5 years, with females developing approximately 6 months before males. Pubertal tempo (at mid-puberty) was similar across sex (between half and one Tanner Stage per year), but the overall duration of puberty was slightly shorter in males. Most females exhibited asynchronous changes of breast and pubic hair development. DISCUSSION: Estimates of pubertal timing and tempo are consistent with reports of cohorts from two or more decades ago, suggesting stabilization of certain pubertal characteristics in predominantly White populations. However, our understanding of the duration of puberty and individual differences in pubertal characteristics (e.g., synchronicity of physical changes) remains limited.

BACKGROUND: Early childhood interventions have the potential to reduce children's developmental inequities. We aimed to estimate the extent to which household income supplements for lower-income families in early childhood could close the gap in children's developmental outcomes and parental mental health. METHODS: Data were drawn from a nationally representative birth cohort, the Longitudinal Study of Australian Children (N = 5107), which commenced in 2004 and conducted follow-ups every two years. Exposure was annual household income (0-1 year). Outcomes were children's developmental outcomes, specifically social-emotional, physical functioning, and learning (bottom 15% versus top 85%) at 4-5 years, and an intermediate outcome, parental mental health (poor versus good) at 2-3 years. We modelled hypothetical interventions that provided a fixed-income supplement to lower-income families with a child aged 0-1 year. Considering varying eligibility scenarios and amounts motivated by actual policies in the Australian context, we estimated the risk of poor outcomes for eligible families under no intervention and the hypothetical intervention using marginal structural models. The reduction in risk under intervention relative to no intervention was estimated. RESULTS: A single hypothetical supplement of AU$26,000 (equivalent to ∼USD$17,350) provided to lower-income families (below AU$56,137 (∼USD$37,915) per annum) in a child's first year of life demonstrated an absolute reduction of 2.7%, 1.9% and 2.6% in the risk of poor social-emotional, physical functioning and learning outcomes in children, respectively (equivalent to relative reductions of 12%, 10% and 11%, respectively). The absolute reduction in risk of poor mental health in eligible parents was 1.0%, equivalent to a relative reduction of 7%. Benefits were similar across other income thresholds used to assess eligibility (range, AU$73,329-$99,864). CONCLUSIONS: Household income supplements provided to lower-income f

BACKGROUND: Prevention is key to reducing socioeconomic inequities in children's mental health problems, especially given limited availability and accessibility of services. We investigated the potential to reduce inequities for disadvantaged children by improving parental mental health and preschool attendance in early childhood. METHODS: Data from the nationally representative birth cohort, Longitudinal Study of Australian Children (N = 5107, commenced in 2004), were used to examine the impact of socioeconomic disadvantage (0-1 year) on children's mental health problems (10-11 years). Using an interventional effects approach, we estimated the extent to which inequities could be reduced by improving disadvantaged children's parental mental health (4-5 years) and their preschool attendance (4-5 years). RESULTS: Disadvantaged children had a higher prevalence of elevated mental health symptoms (32.8%) compared with their nondisadvantaged peers (18.7%): confounder-adjusted difference in prevalence is 11.6% (95% confidence interval: 7.7% to 15.4%). Improving disadvantaged children's parental mental health and their preschool attendance to the level of their nondisadvantaged peers could reduce 6.5% and 0.3% of socioeconomic differences in children's mental health problems, respectively (equivalent to 0.8% and 0.04% absolute reductions). If these interventions were delivered in combination, a 10.8% (95% confidence interval: 6.9% to 14.7%) higher prevalence of elevated symptoms would remain for disadvantaged children. CONCLUSIONS: Targeted policy interventions that improve parental mental health and preschool attendance for disadvantaged children are potential opportunities to reduce socioeconomic inequities in children's mental health problems. Such interventions should be considered within a broader, sustained, and multipronged approach that includes addressing socioeconomic disadvantage itself.

BACKGROUND: Lower maternal education is associated with higher body mass index (BMI) and higher chronic inflammation in offspring. Childhood adversity potentially mediates these associations. We examined the extent to which addressing childhood adversity could reduce socioeconomic inequities in these outcomes. METHODS: We analysed data from two early-life longitudinal cohorts: the Longitudinal Study of Australian Children (LSAC; n=1873) and the UK Avon Longitudinal Study of Parents and Children (ALSPAC; n=7085). EXPOSURE: low/medium (below university degree) versus high maternal education, as a key indicator of family socioeconomic position (0-1 year). OUTCOMES: BMI and log-transformed glycoprotein acetyls (GlycA) (LSAC: 11-12 years; ALSPAC: 15.5 years). Mediator: multiple adversities (≥2/<2) indicated by family violence, mental illness, substance abuse and harsh parenting (LSAC: 2-11 years; ALSPAC: 1-12 years). A causal mediation analysis was conducted. RESULTS: Low/medium maternal education was associated with up to 1.03 kg/m2 higher BMI (95% CI: 0.95 to 1.10) and up to 1.69% higher GlycA (95% CI: 1.68 to 1.71) compared with high maternal education, adjusting for confounders. Causal mediation analysis estimated that decreasing the levels of multiple adversities in children with low/medium maternal education to be like their high maternal education peers could reduce BMI inequalities by up to 1.8% and up to 3.3% in GlycA. CONCLUSIONS: Our findings in both cohorts suggest that slight reductions in socioeconomic inequities in children's BMI and inflammation could be achieved by addressing childhood adversities. Public health and social policy efforts should help those affected by childhood adversity, but also consider underlying socioeconomic conditions that drive health inequities.

Researchers faced with incomplete data are encouraged to consider whether their data are 'missing completely at random' (MCAR), 'missing at random' (MAR) or 'missing not at random' (MNAR) when planning their analysis. However, there are two major problems with this classification as originally defined by Rubin in the 1970s. First, when there are missing data in multiple variables, the plausibility of the MAR assumption is difficult to assess using substantive knowledge and is more stringent than is generally appreciated. Second, although MCAR and MAR are sufficient conditions for consistent estimation with specific methods, they are not necessary conditions and therefore this categorization does not directly determine the best approach for handling the missing data in an analysis. How best to handle missing data depends on the assumed causal relationships between variables and their missingness, and what these relationships imply in terms of the 'recoverability' of the target estimand (the population parameter that encodes the answer to the underlying research question). Recoverability is defined as whether the estimand can be consistently estimated from the patterns and associations in the observed data without needing to invoke external information on the extent to which the distribution of missing values might differ from that of observed values. In this manuscript we outline an approach for deciding which method to use to handle multivariable missing data in an analysis, using directed acyclic graphs to depict missingness assumptions and determining the implications in terms of recoverability of the target estimand.

BACKGROUND: With continuous outcomes, the average causal effect is typically defined using a contrast of expected potential outcomes. However, in the presence of skewed outcome data, the expectation (population mean) may no longer be meaningful. In practice the typical approach is to continue defining the estimand this way or transform the outcome to obtain a more symmetric distribution, although neither approach may be entirely satisfactory. Alternatively the causal effect can be redefined as a contrast of median potential outcomes, yet discussion of confounding-adjustment methods to estimate the causal difference in medians is limited. In this study we described and compared confounding-adjustment methods to address this gap. METHODS: The methods considered were multivariable quantile regression, an inverse probability weighted (IPW) estimator, weighted quantile regression (another form of IPW) and two little-known implementations of g-computation for this problem. Methods were evaluated within a simulation study under varying degrees of skewness in the outcome and applied to an empirical study using data from the Longitudinal Study of Australian Children. RESULTS: Simulation results indicated the IPW estimator, weighted quantile regression and g-computation implementations minimised bias across all settings when the relevant models were correctly specified, with g-computation additionally minimising the variance. Multivariable quantile regression, which relies on a constant-effect assumption, consistently yielded biased results. Application to the empirical study illustrated the practical value of these methods. CONCLUSION: The presented methods provide appealing avenues for estimating the causal difference in medians.

BACKGROUND: Digital methods that enable early caries identification can streamline data collection in research and optimize dental examinations for young children. Intraoral scanners are devices used for creating 3D models of teeth in dentistry and are being rapidly adopted into clinical workflows. Integrating fluorescence technology into scanner hardware can support early caries detection. However, the performance of caries detection methods using 3D models featuring color and fluorescence in primary teeth is unknown. OBJECTIVE: This study aims to assess the diagnostic agreement between visual examination (VE), on-screen assessment of 3D models in approximate natural colors with and without fluorescence, and application of an automated caries scoring system to the 3D models with fluorescence for caries detection in primary teeth. METHODS: The study sample will be drawn from eligible participants in a randomized controlled trial at the Royal Children's Hospital, Melbourne, Australia, where a dental assessment was conducted, including VE using the International Caries Detection and Assessment System (ICDAS) and intraoral scan using the TRIOS 4 (3Shape TRIOS A/S). Participant clinical records will be collected, and all records meeting eligibility criteria will be subject to an on-screen assessment of 3D models by 4 dental practitioners. First, all primary tooth surfaces will be examined for caries based on 3D geometry and color, using a merged ICDAS index. Second, the on-screen assessment of 3D models will include fluorescence, where caries will be classified using a merged ICDAS index that has been modified to incorporate fluorescence criteria. After 4 weeks, all examiners will repeat the on-screen assessment for all 3D models. Finally, an automated caries scoring system will be used to classify caries on primary occlusal surfaces. The agreement in the total number of caries detected per person between methods will be assessed using a Bland-Altman analysis and intrac

BACKGROUND: Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investigated in the context of a case-cohort study with a time-to-event outcome, it is unclear how this approach performs with a binary outcome. METHODS: We conducted a simulation study to assess and compare the performance of approaches using only MI, only IPW, and a combination of MI and IPW, for handling intended and unintended missingness in the case-cohort setting. We also applied the approaches to a case study. RESULTS: Our results show that the combined approach is approximately unbiased for estimation of the exposure effect when the sample size is large, and was the least biased with small sample sizes, while MI-only and IPW-only exhibited larger biases in both sample size settings. CONCLUSIONS: These findings suggest that a combined MI/IPW approach should be preferred to handle intended and unintended missing data in case-cohort studies with binary outcomes.

BACKGROUND: Mortality statistics using a single underlying cause of death (UC) are key health indicators. Rising multimorbidity and chronic disease mean that deaths increasingly involve multiple conditions. However, additional causes reported on death certificates are rarely integrated into mortality indicators, partly due to complexities in data and methods. This study aimed to assess trends and patterns in cause-related mortality in Australia, integrating multiple causes (MC) of death. METHODS: Deaths (n = 1 773 399) in Australia (2006-17) were mapped to 136 ICD-10-based groups and MC indicators applied. Age-standardized cause-related rates (deaths/100 000) based on the UC (ASRUC) were compared with rates based on any mention of the cause (ASRAM) using rate ratios (RR = ASRAM/ASRUC) and to rates based on weighting multiple contributing causes (ASRW). RESULTS: Deaths involved on average 3.4 causes in 2017; the percentage with >4 causes increased from 20.9 (2006) to 24.4 (2017). Ischaemic heart disease (ASRUC = 73.3, ASRAM = 135.8, ASRW = 63.5), dementia (ASRUC = 51.1, ASRAM = 98.1, ASRW = 52.1) and cerebrovascular diseases (ASRUC = 39.9, ASRAM = 76.7, ASRW = 33.5) ranked as leading causes by all methods. Causes with high RR included hypertension (ASRUC = 2.2, RR = 35.5), atrial fibrillation (ASRUC = 8.0, RR = 6.5) and diabetes (ASRUC = 18.5, RR = 3.5); the corresponding ASRW were 12.5, 12.6 and 24.0, respectively. Renal failure, atrial fibrillation and hypertension ranked among the 10 leading causes by ASRAM and ASRW but not by ASRUC. Practical considerations in working with MC data are discussed. CONCLUSIONS: Despite the similarities in leading causes under the three methods, with integration of MC several preventable diseases emerged as leading causes. MC analyses offer a richer additional perspective for population health monitoring and policy development.

BACKGROUND: Academic difficulties are common in adolescents with mental health problems. Although earlier childhood emotional problems, characterised by heightened anxiety and depressive symptoms are common forerunners to adolescent mental health problems, the degree to which mental health problems in childhood may contribute independently to academic difficulties has been little explored. METHODS: Data were drawn from a prospective cohort study of students in Melbourne, Australia (N = 1239). Data were linked with a standardised national assessment of academic performance at baseline (9 years) and wave three (11 years). Depressive and anxiety symptoms were assessed at baseline and wave two (10 years). Regression analyses estimated the association between emotional problems (9 and/or 10 years) and academic performance at 11 years, adjusting for baseline academic performance, sex, age and socioeconomic status, and hyperactivity/inattention symptoms. RESULTS: Students with depressive symptoms at 9 years of age had lost nearly 4 months of numeracy learning two years later after controlling for baseline academic performance and confounders. Results were similar for anxiety symptoms. Regardless of when depressive symptoms occurred there were consistent associations with poorer numeracy performance at 11 years. The association of depressive symptoms with reading performance was weaker than for numeracy if they were present at wave two. Persistent anxiety symptoms across two waves led to nearly a 4 month loss of numeracy learning at 11 years, but the difference was not meaningful for reading. Findings were similar when including hyperactivity/inattention symptoms. CONCLUSIONS: Childhood anxiety and depression are not only forerunners of later mental health problems but predict academic achievement. Partnerships between education and health systems have the potential to not only improve childhood emotional problems but also improve learning.

BACKGROUND: Populations willing to participate in randomized trials may not correspond well to policy-relevant target populations. Evidence of effectiveness that is complementary to randomized trials may be obtained by combining the 'target trial' causal inference framework with whole-of-population linked administrative data. METHODS: We demonstrate this approach in an evaluation of the South Australian Family Home Visiting Program, a nurse home visiting programme targeting socially disadvantaged families. Using de-identified data from 2004-10 in the ethics-approved Better Evidence Better Outcomes Linked Data (BEBOLD) platform, we characterized the policy-relevant population and emulated a trial evaluating effects on child developmental vulnerability at 5 years (n = 4160) and academic achievement at 9 years (n = 6370). Linkage to seven health, welfare and education data sources allowed adjustment for 29 confounders using Targeted Maximum Likelihood Estimation (TMLE) with SuperLearner. Sensitivity analyses assessed robustness to analytical choices. RESULTS: We demonstrated how the target trial framework may be used with linked administrative data to generate evidence for an intervention as it is delivered in practice in the community in the policy-relevant target population, and considering effects on outcomes years down the track. The target trial lens also aided in understanding and limiting the increased measurement, confounding and selection bias risks arising with such data. Substantively, we did not find robust evidence of a meaningful beneficial intervention effect. CONCLUSIONS: This approach could be a valuable avenue for generating high-quality, policy-relevant evidence that is complementary to trials, particularly when the target populations are multiply disadvantaged and less likely to participate in trials.

INTRODUCTION: Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity analysis under plausible alternative missing data assumptions is required. This is particularly pertinent with multiple imputation (MI), which is often justified by assuming data are MAR. The objective of this scoping review is to examine the use of MI in observational studies that address causal questions, with a focus on if and how (a) missingness assumptions are expressed and assessed, (b) missingness assumptions are used to justify the choice of a complete case analysis and/or MI for handling missing data and (c) sensitivity analyses under alternative plausible assumptions about the missingness mechanism are conducted. METHODS AND ANALYSIS: We will review observational studies that aim to answer causal questions and use MI, published between January 2019 and December 2021 in five top general epidemiology journals. Studies will be identified using a full text search for the term 'multiple imputation' and then assessed for eligibility. Information extracted will include details about the study characteristics, missing data, missingness assumptions and MI implementation. Data will be summarised using descriptive statistics. ETHICS AND DISSEMINATION: Ethics approval is not required for this review because data will be collected only from published studies. The results will be disseminated through a peer reviewed publication and conference presentations. TRIAL REGISTRATION NUMBER

BACKGROUND: Despite recent advances in causal inference methods, outcome regression remains the most widely used approach for estimating causal effects in epidemiological studies with a single-point exposure and outcome. Missing data are common in these studies, and complete-case analysis (CCA) and multiple imputation (MI) are two frequently used methods for handling them. In randomised controlled trials (RCTs), it has been shown that MI should be conducted separately by treatment group. In observational studies, causal inference is now understood as the task of emulating an RCT, which raises the question of whether MI should be conducted by exposure group in such studies. METHODS: We addressed this question by evaluating the performance of seven methods for handling missing data when estimating causal effects with outcome regression. We conducted an extensive simulation study based on an illustrative case study from the Victorian Adolescent Health Cohort Study, assessing a range of scenarios, including seven outcome generation models with exposure-confounder interactions of differing strength. RESULTS: The simulation results showed that MI by exposure group led to the least bias when the size of the smallest exposure group was relatively large, followed by MI approaches that included the exposure-confounder interactions. CONCLUSIONS: The findings from our simulation study, which was designed based on a real case study, suggest that current practice for the conduct of MI in causal inference may need to shift to stratifying by exposure group where feasible, or otherwise including exposure-confounder interactions in the imputation model.

Personality reliably predicts life outcomes ranging from social and material resources to mental health and interpersonal capacities. However, little is known about the potential intergenerational impact of parent personality prior to offspring conception on family resources and child development across the first thousand days of life. We analysed data from the Victorian Intergenerational Health Cohort Study (665 parents, 1030 infants; est. 1992), a two-generation study with prospective assessment of preconception background factors in parental adolescence, preconception personality traits in young adulthood (agreeableness, conscientiousness, emotional stability, extraversion, and openness), and multiple parental resources and infant characteristics in pregnancy and after the birth of their child. After adjusting for pre-exposure confounders, both maternal and paternal preconception personality traits were associated with numerous parental resources and attributes in pregnancy and postpartum, as well as with infant biobehavioural characteristics. Effect sizes ranged from small to moderate when considering parent personality traits as continuous exposures, and from small to large when considering personality traits as binary exposures. Young adult personality, well before offspring conception, is associated with the perinatal household social and financial context, parental mental health, parenting style and self-efficacy, and temperamental characteristics of offspring. These are pivotal aspects of early life development that ultimately predict a child's long-term health and development.

BACKGROUND: The potential for early interventions to reduce the later prevalence of common mental disorders (CMD) first experienced in adolescence is unclear. AIMS: To examine the course of CMD and evaluate the extent to which the prevalence of CMD could be reduced by preventing adolescent CMD, or by intervening to change four young adult processes, between the ages of 20 and 29 years, that could be mediating the link between adolescent and adult disorder. METHOD: This was a prospective cohort study of 1923 Australian participants assessed repeatedly from adolescence (wave 1, mean age 14 years) to adulthood (wave 10, mean age 35 years). Causal mediation analysis was undertaken to evaluate the extent to which the prevalence of CMD at age 35 years in those with adolescent CMD could be reduced by either preventing adolescent CMD, or by intervening on four young adult mediating processes: the occurrence of young adult CMD, frequent cannabis use, parenting a child by age 24 years, and engagement in higher education and employment. RESULTS: At age 35, 19.2% of participants reported CMD; a quarter of these participants experienced CMD during both adolescence and young adulthood. In total, 49% of those with CMD during both adolescence and young adulthood went on to report CMD at age 35 years. Preventing adolescent CMD reduced the population prevalence at age 35 years by 3.9%. Intervening on all four young adult processes among those with adolescent CMD, reduced this prevalence by 1.6%. CONCLUSIONS: In this Australian cohort, a large proportion of adolescent CMD resolved by adulthood, and by age 35 years, the largest proportion of CMD emerged among individuals without prior CMD. Time-limited, early intervention in those with earlier adolescent disorder is unlikely to substantially reduce the prevalence of CMD in midlife.

BACKGROUND: In case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome investigations. Analysis is conducted using only the cases and the subcohort, with inverse probability weighting (IPW) used to account for the unequal sampling probabilities resulting from the study design. Like all epidemiological studies, case-cohort studies are susceptible to missing data. Multiple imputation (MI) has become increasingly popular for addressing missing data in epidemiological studies. It is currently unclear how best to incorporate the weights from a case-cohort analysis in MI procedures used to address missing covariate data. METHOD: A simulation study was conducted with missingness in two covariates, motivated by a case study within the Barwon Infant Study. MI methods considered were: using the outcome, a proxy for weights in the simple case-cohort design considered, as a predictor in the imputation model, with and without exposure and covariate interactions; imputing separately within each weight category; and using a weighted imputation model. These methods were compared to a complete case analysis (CCA) within the context of a standard IPW analysis model estimating either the risk or odds ratio. The strength of associations, missing data mechanism, proportion of observations with incomplete covariate data, and subcohort selection probability varied across the simulation scenarios. Methods were also applied to the case study. RESULTS: There was similar performance in terms of relative bias and precision with all MI methods across the scenarios considered, with expected improvements compared with the CCA. Slight underestimation of the standard error was seen throughout but the nominal level of coverage (95%) was generally achieved. All MI methods showed a similar increase in precision as the subcohort selection probability increased, irrespective of the scenario. A similar pattern of results was seen

BACKGROUND: Mechanisms underlying the adiposity-cancer relationship are incompletely understood. We quantified the mediating roles of C-reactive protein (CRP), leptin, fasting insulin, and estradiol in the effect of adiposity on estrogen receptor (ER)-positive breast, endometrial, and colorectal cancer risk in postmenopausal women. METHODS: We used a case-cohort study within the Women's Health Initiative Observational Study, analyzed as a cumulative sampling case-control study. The study included 188 breast cancer cases, 98 endometrial cancer cases, 193 colorectal cancer cases, and 285 controls. Interventional indirect and direct effects on the risk ratio (RR) scale were estimated using causal mediation analysis. RESULTS: For breast cancer, the total effect RR for BMI ≥30 versus ≥18.5-<25 kg/m2 was 1.87 (95%CI,1.11-3.13). The indirect effect RRs were 1.38 (0.79-2.33) through leptin and CRP, 1.58 (1.17-2.43) through insulin, and 1.11 (0.98-1.30) through estradiol. The direct effect RR was 0.82 (0.39-1.68). For endometrial cancer, the total effect RR was 2.12 (1.12-4.00). The indirect effect RRs were 1.72 (0.85-3.98) through leptin and CRP, 1.42 (0.96-2.26) through insulin, and 1.24 (1.03-1.65) through estradiol. The direct effect RR was 0.70 (0.23-2.04). For colorectal cancer, the total effect RR was 1.70 (1.03-2.79). The indirect effect RRs were 1.04 (0.61-1.72) through leptin and CRP, 1.36 (1.00-1.88) through insulin, and 1.02 (0.88-1.17) through estradiol. The direct effect RR was 1.16 (0.58-2.43). CONCLUSION: Leptin, CRP, fasting insulin, and estradiol appear to mediate the effect of high BMI on cancer risk to different extents, with likely varying degrees of importance between cancers. These insights might be important in developing interventions to modify obesity-associated cancer risk in postmenopausal women.

BACKGROUND AND AIMS: Alcohol consumption is common in adolescence and young adulthood and may continue into pregnancy, posing serious risk to early fetal development. We examine the frequency of periconception alcohol use (prior to pregnancy awareness) and the extent to which adolescent and young adult alcohol use prospectively predict periconception use. DESIGN: A longitudinal, population-based study. SETTING: Victoria, Australia. PARTICIPANTS: A total of 289 women in trimester three of pregnancy (age 29-35 years; 388 pregnancies). MEASURES: The main exposures were binge [≥ 4.0 standard drinks (SDs)/day] and frequent (≥ 3 days/week) drinking in adolescence (mean age = 14.9-17.4 years) and young adulthood (mean age 20.7-29.1 years). Outcomes were frequency (≥ 3 days/week, ≥ monthly, never) and quantity (≥ 4.0 SDs, ≥ 0.5 and < 4.0 SDs, none) of periconception drinking. FINDINGS: Alcohol use was common in young adulthood prior to pregnancy (72%) and in the early weeks of pregnancy (76%). The proportions drinking on most days and binge drinking were similar at both points. Reflecting a high degree of continuity in alcohol use behaviours, most women who drank periconceptionally had an earlier history of frequent (77%) and/or binge (85%) drinking throughout the adolescent or young adult years. Young adult binge drinking prospectively predicted periconception drinking quantity [odds ratio (OR) = 3.7, 95% confidence interval (CI) = 1.9-7.4], compared with women with no prior history. Similarly, frequent young adult drinking prospectively predicted frequent periconception drinking (OR = 30.7, 95% CI = 12.3-76.7). CONCLUSIONS: Women who engage in risky (i.e. frequent and binge) drinking in their adolescent and young adult years are more likely to report risky drinking in early pregnancy prior to pregnancy recognition than women with no prior history of risky drinking.

Longitudinal cohorts can provide timely and cost-efficient evidence about the best points of health service and preventive interventions over the life course. Working systematically across cohorts has the potential to further exploit these valuable data assets, such as by improving the precision of estimates, enhancing (or appropriately reducing) confidence in the replicability of findings, and investigating interrelated questions within a broader theoretical model. In this conceptual review, we explore the opportunities and challenges presented by multi-cohort approaches in life course research. Specifically, we: 1) describe key motivations for multi-cohort work and the analytic approaches that are commonly used in each case; 2) flag some of the scientific and pragmatic challenges that arise when adopting these approaches; and 3) outline emerging directions for multi-cohort work in life course research. Harnessing their potential while thoughtfully considering limitations of multi-cohort approaches can contribute to the robust and granular evidence base needed to promote health and wellbeing over the life span.

To better understand how health risk processes are linked to adrenarche, measures of adrenarcheal timing and tempo are needed. Our objective was to describe and classify adrenal trajectories, in terms of timing and tempo, in a population of children transitioning to adolescence with repeated measurements of salivary dehydroepiandrosterone (DHEA), DHEA-sulphate, and testosterone. We analysed data from the Childhood to Adolescence Transition Study (CATS), a longitudinal study of 1239 participants, recruited at 8-9 years old and followed up annually. Saliva samples were assayed for adrenal hormones. Linear mixed-effect models with subject-specific random intercepts and slopes were used to model longitudinal hormone trajectories by sex and derive measures of adrenarcheal timing and tempo. The median values for all hormones were higher at each consecutive study wave for both sexes, and higher for females than males. For all hormones, between-individual variation in hormone levels at age 9 (timing) was moderately large and similar for females and males. Between-individual variation in hormone progression over time (tempo) was of moderate magnitude compared with the population average age-slope, which itself was small compared with overall hormone level at each age. This suggests that between-individual variation in tempo was less important for modelling hormone trajectories. Between-individual variation in timing was more important for determining relative adrenal hormonal level in childhood than tempo. This finding suggests that adrenal hormonal levels at age 8-9 years can be used to predict relative levels in early adolescence (up to 13 years).

BACKGROUND: The relationship between childhood adversity and inflammation is well-established. Examination of positive experiences can provide a more complete understanding of intervention opportunities. We investigated associations of adverse and positive experiences, and their intersection, with inflammation in children and adolescents. METHODS: Data sources: Longitudinal Study of Australian Children (LSAC; N = 1237) and Avon Longitudinal Study of Parents and Children (ALSPAC; N = 3488). Exposures: Adverse and positive experiences assessed repeatedly (LSAC: 0-11 years; ALSPAC: 0-15 years). Outcomes: Inflammation quantified by high sensitivity C-reactive protein (hsCRP) and glycoprotein acetyls (GlycA) (LSAC: 11-12 years; ALSPAC: 15.5 years). Analyses: Linear regression on the log-transformed outcomes estimated the relative difference in inflammatory markers with adverse/positive experiences, adjusting for socio-demographics and concurrent positive/adverse experiences, respectively. RESULTS: Most associations were in the expected direction but differed in magnitude by exposure, outcome and cohort. Across both cohorts, adverse experiences were associated with up to 7.3% higher hsCRP (95% CI: -18.6%, 33.2%) and up to 2.0% higher GlycA (95% CI: 0.5%, 3.5%); while positive experiences were associated with up to 22.1% lower hsCRP (95% CI: -49.0%, 4.7%) and 1.3% lower GlycA (95% CI: -2.7%, 0.2%). In LSAC, the beneficial effect of positive experiences on inflammation was more pronounced among those with fewer concurrent adverse experiences. CONCLUSION: Across two cohorts, we found small but directionally consistent associations between adverse experiences and higher inflammation, and positive experiences and lower inflammation, particularly for GlycA. Future research should give further consideration to positive experiences to complement the current focus on adversity and inform the design and evaluation of early life interventions.

Three-level data arising from repeated measures on individuals clustered within higher-level units are common in medical research. A complexity arises when individuals change clusters over time, resulting in a cross-classified data structure. Missing values in these studies are commonly handled via multiple imputation (MI). If the three-level, cross-classified structure is modeled in the analysis, it also needs to be accommodated in the imputation model to ensure valid results. While incomplete three-level data can be handled using various approaches within MI, the performance of these in the cross-classified data setting remains unclear. We conducted simulations under a range of scenarios to compare these approaches in the context of an acute-effects cross-classified random effects substantive model, which models the time-varying cluster membership via simple additive random effects. The simulation study was based on a case study in a longitudinal cohort of students clustered within schools. We evaluated methods that ignore the time-varying cluster memberships by taking the first or most common cluster for each individual; pragmatic extensions of single- and two-level MI approaches within the joint modeling (JM) and the fully conditional specification (FCS) frameworks, using dummy indicators (DI) and/or imputing repeated measures in wide format to account for the cross-classified structure; and a three-level FCS MI approach developed specifically for cross-classified data. Results indicated that the FCS implementations performed well in terms of bias and precision while JM approaches performed poorly. Under both frameworks approaches using the DI extension should be used with caution in the presence of sparse data.

Item does not contain fulltext, Semi-continuous variables are characterized by a point mass at one value and a continuous range of values for remaining observations. An example is alcohol consumption quantity, with a spike of zeros representing non-drinkers and positive values for drinkers. If multiple imputation is used to handle missing values for semi-continuous variables, it is unclear how this should be implemented within the standard approaches of fully conditional specification (FCS) and multivariate normal imputation (MVNI). This question is brought into focus by the use of categorized versions of semi-continuous exposure variables in analyses (eg, no drinking, drinking below binge level, binge drinking, heavy binge drinking), raising the question of how best to achieve congeniality between imputation and analysis models. We performed a simulation study comparing nine approaches for imputing semi-continuous exposures requiring categorization for analysis. Three methods imputed the categories directly: ordinal logistic regression, and imputation of binary indicator variables representing the categories using MVNI (with two variants). Six methods (predictive mean matching, zero-inflated binomial imputation, and two-part imputation methods with variants in FCS and MVNI) imputed the semi-continuous variable, with categories derived after imputation. The ordinal and zero-inflated binomial methods had good performance across most scenarios, while MVNI methods requiring rounding after imputation did not perform well. There were mixed results for predictive mean matching and the two-part methods, depending on whether the estimands were proportions or regression coefficients. The results highlight the need to consider the parameter of interest when selecting an imputation procedure.

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators’ distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Vict

Abstract Focus of Presentation Utilising data from multiple cohorts to address causal questions in health research has become increasingly widespread due to a number of advantages. These include improved precision of estimates, in particular to investigate effect heterogeneity as well as rare events and exposures, and the ability to examine the replicability of findings. However, undertaking causal inference in multi-cohort studies also faces several challenges, which makes clear causal thinking even more important than in single-cohort studies. We propose the use of the “target trial” framework for the conduct of causal inference in multi-cohort studies. Findings Using two case studies, the first considering the effect of maternal mental health on emotional reactivity and the second examining the influence of exposure to adversity on inflammatory outcomes in childhood, we describe and demonstrate how the target trial approach enables clear definition of the target estimand and systematic consideration of sources of bias. Considering the target trial as the reference point allows the identification of potential biases within each study, so that analysis can be planned to reduce them. Furthermore, the interpretation of findings is assisted by an understanding of the unavoidable biases that may be compounded when pooling data from multiple cohorts, or that may explain discrepant findings across cohorts. Conclusions/Implications Use of the target trial framework in multi-cohort studies helps strengthen causal inferences through improved analysis design and clarity in the interpretation of findings. Key messages The target trial framework, already well-established for casual inference in single-coho

Randomized trials involving independent and paired observations occur in many areas of health research, for example in paediatrics, where studies can include infants from both single and twin births. Multiple imputation (MI) is often used to address missing outcome data in randomized trials, yet its performance in trials with independent and paired observations, where design effects can be less than or greater than one, remains to be explored. Using simulated data and through application to a trial dataset, we investigated the performance of different methods of MI for a continuous or binary outcome when followed by analysis using generalized estimating equations to account for clustering due to the pairs. We found that imputing data separately for independent and paired data, with paired data imputed in wide format, was the best performing MI method, producing unbiased point and standard error estimates for the treatment effect throughout. Ignoring clustering in the imputation model performed well in settings where the design effect due to the inclusion of paired data was close to one, but otherwise led to moderately biased variance estimates. Including a random cluster effect in the imputation model led to slightly biased point estimates for binary outcome data and variance estimates that were too small in some settings. Based on these results, we recommend researchers impute independent and paired data separately where feasible to do so. The exception is if the design effect due to the inclusion of paired data is close to one, where ignoring clustering may be appropriate.

BACKGROUND: Adiposity increases endometrial cancer risk, possibly through inflammation, hyperinsulinemia, and increasing estrogens. We aimed to quantify the mediating effects of adiponectin (anti-inflammatory adipocytokine); IL6, IL1-receptor antagonist, TNF receptor 1 and 2, and C-reactive protein (inflammatory status biomarkers); C-peptide (hyperinsulinemia biomarker); and free estradiol and estrone (estrogen biomarkers) in the adiposity-endometrial cancer link in postmenopausal women. METHODS: We used data from a case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC). Eligible women did not have cancer, hysterectomy, and diabetes; did not use oral contraceptives or hormone therapy; and were postmenopausal at recruitment. Mediating pathways from adiposity to endometrial cancer were investigated by estimating natural indirect (NIE) and direct (NDE) effects using sequential mediation analysis. RESULTS: The study included 163 cases and 306 controls. The adjusted OR for endometrial cancer for body mass index (BMI) ≥30 versus ≥18.5-<25 kg/m2 was 2.51 (95% confidence interval, 1.26-5.02). The ORsNIE were 1.95 (1.01-3.74) through all biomarkers [72% proportion mediated (PM)] decomposed as: 1.35 (1.06-1.73) through pathways originating with adiponectin (33% PM); 1.13 (0.71-1.80) through inflammation beyond (the potential influence of) adiponectin (13% PM); 1.05 (0.88-1.24) through C-peptide beyond adiponectin and inflammation (5% PM); and 1.22 (0.89-1.67) through estrogens beyond preceding biomarkers (21% PM). The ORNDE not through biomarkers was 1.29 (0.54-3.09). Waist circumference gave similar results. CONCLUSIONS: Reduced adiponectin and increased inflammatory biomarkers, C-peptide, and estrogens mediated approximately 70% of increased odds of endometrial cancer in women with obesity versus normal weight. IMPACT: If replicated, these results could have implications for identifying targets for intervention to reduce endometria

Many analyses of longitudinal cohorts require incorporating sampling weights to account for unequal sampling probabilities of participants, as well as the use of multiple imputation (MI) for dealing with missing data. However, there is no guidance on how MI and sampling weights should be implemented together. We simulated a target population based on the Australian Bureau of Statistics Estimated Resident Population and drew 1000 random samples dependent on three design variables to mimic the Longitudinal Study of Australian Children. The target analysis was the weighted prevalence of overweight/obesity over childhood. We evaluated the performance of several MI approaches available in Stata, based on multivariate normal imputation (MVNI), fully conditional specification (FCS) and twofold FCS: a weighted imputation model, imputing missing data separately for each quintile sampling weight grouping, including the design stratum indicator in the imputation model, and using sampling weights as a covariate in the imputation model. Approaches based on available cases and inverse probability weighting (IPW), with time-varying weights, were also compared. We observed severe issues of convergence with FCS and twofold FCS. All MVNI-based approaches performed similarly, producing minimal bias and nominal coverage, except for when imputation was conducted separately for each quintile sampling weight group. IPW performed equally as well as MVNI-based approaches in terms of bias, however, was less precise. In similar longitudinal studies, we recommend using MVNI with the design stratum as a covariate in the imputation model. If this is unknown, including the sampling weight as a covariate is an appropriate alternative.

Semi-continuous variables are characterized by a point mass at one value and a continuous range of values for remaining observations. An example is alcohol consumption quantity, with a spike of zeros representing non-drinkers and positive values for drinkers. If multiple imputation is used to handle missing values for semi-continuous variables, it is unclear how this should be implemented within the standard approaches of fully conditional specification (FCS) and multivariate normal imputation (MVNI). This question is brought into focus by the use of categorized versions of semi-continuous exposure variables in analyses (eg, no drinking, drinking below binge level, binge drinking, heavy binge drinking), raising the question of how best to achieve congeniality between imputation and analysis models. We performed a simulation study comparing nine approaches for imputing semi-continuous exposures requiring categorization for analysis. Three methods imputed the categories directly: ordinal logistic regression, and imputation of binary indicator variables representing the categories using MVNI (with two variants). Six methods (predictive mean matching, zero-inflated binomial imputation, and two-part imputation methods with variants in FCS and MVNI) imputed the semi-continuous variable, with categories derived after imputation. The ordinal and zero-inflated binomial methods had good performance across most scenarios, while MVNI methods requiring rounding after imputation did not perform well. There were mixed results for predictive mean matching and the two-part methods, depending on whether the estimands were proportions or regression coefficients. The results highlight the need to consider the parameter of interest when selecting an imputation procedure.

Objective Children from socioeconomically disadvantaged backgrounds have poorer learning outcomes. These inequities are a significant public health issue, tracking forward to adverse health outcomes in adulthood. We examined the potential to reduce socioeconomic gaps in children's reading skills through increasing home reading and preschool attendance among disadvantaged children. Methods We drew on data from the nationally representative birth cohort of the Longitudinal Study of Australian Children (N = 5107) to examine the impact of socioeconomic disadvantage (0-1 year) on children's reading skills (8-9 years). An interventional effects approach was applied to estimate the extent to which improving the levels of home reading (2-5 years) and preschool attendance (4-5 years) of socioeconomically disadvantaged children to be commensurate with their advantaged peers, could potentially reduce socioeconomic gaps in children's reading skills. Results Socioeconomically disadvantaged children had a higher risk of poor reading outcomes compared to more advantaged peers: absolute risk difference = 20.1% (95% confidence interval [CI]: 16.0%-24.2%). Results suggest that improving disadvantaged children's home reading and preschool attendance to the level of their advantaged peers could eliminate 6.5% and 2.1% of socioeconomic gaps in reading skills, respectively. However, large socioeconomic gaps would remain, with disadvantaged children maintaining an 18.3% (95% CI: 14.0%-22.7%) higher risk of poor reading outcomes in absolute terms.

Conclusion

BACKGROUND: Use of social networking in later childhood and adolescence has risen quickly. The consequences of these changes for mental health are debated but require further empirical evaluation. METHODS: Using data from the Childhood to Adolescence Transition Study (n = 1,156), duration of social networking use was measured annually at four time points from 11.9 to 14.8 years of age (≥1 h/day indicating high use). Cross-sectional and prospective relationships between social networking use and depressive and anxiety symptoms were examined. RESULTS: In adjusted (age, socioeconomic status, prior mental health history) cross-sectional analyses, females with high social networking use had greater odds of depressive (odds ratio [OR]: 2.15; 95% confidence interval [CI]: 1.58-2.91) and anxiety symptoms (OR: 1.99; 95% CI: 1.32-3.00) than those that used a few minutes at most, while males with high social networking use had 1.60 greater odds of reporting depressive symptoms (95% CI: 1.09-2.35). For females, an increased odds of depressive symptoms at age 14.8 was observed for high social networking use at one previous wave and at two or three previous waves, even after adjustment (OR: 1.76; 95% CI: 1.11-2.78; OR: 2.06, 95% CI: 1.27-3.37, respectively) compared to no wave of high use. CONCLUSIONS: Our results suggest weak to moderate increased odds of depression and anxiety in girls and boys with high social networking use versus low/normal use. These findings indicate that prevention programs for early mental health problems might benefit from targeting social networking use in early adolescence.

BACKGROUND: Murine studies demonstrate that maternal prenatal gut microbiota influences brain development and behaviour of offspring. No human study has related maternal gut microbiota to behavioural outcomes during early life. This study aimed to evaluate relationships between the prenatal faecal microbiota, prenatal diet and childhood behaviour. METHODS: A sub-cohort of 213 mothers and 215 children were selected from a longitudinal pre-birth cohort. Maternal prenatal exposure measures collected during the third trimester included the faecal microbiota (generated using 16S rRNA amplicon sequencing), and dietary intake. The behavioural outcome used the Childhood Behaviour Checklist at age two. Models were adjusted for prenatal diet, smoking, perceived stress, maternal age and sample batch. FINDINGS: We found evidence that the alpha diversity of the maternal faecal microbiota during the third trimester of pregnancy predicts child internalising behaviour at two years of age (-2·74, (-4·71, -0·78), p = 0·01 (Wald test), R2=0·07). Taxa from butyrate-producing families, Lachnospiraceae and Ruminococcaceae, were more abundant in mothers of children with normative behaviour. A healthy prenatal diet indirectly related to decreased child internalising behaviours via higher alpha diversity of maternal faecal microbiota. INTERPRETATION: These findings support animal studies showing that the composition of maternal prenatal gut microbiota is related to offspring brain development and behaviour. Our findings highlight the need to evaluate potential impacts of the prenatal gut microbiota on early life brain development. FUNDING: This study was funded by the National Health and Medical Research Council of Australia (1082307, 1147980), Murdoch Children's Research Institute, Barwon Health and Deakin University.

Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two general approaches are available in standard computer packages: MI based on the posterior distribution of incomplete variables under a multivariate (joint) model, and fully conditional specification (FCS), which imputes missing values using univariate conditional distributions for each incomplete variable given all the others, cycling iteratively through the univariate imputation models. In the context of longitudinal or clustered data, it is not clear whether these approaches result in consistent estimates of regression coefficient and variance component parameters when the analysis model of interest is a linear mixed effects model (LMM) that includes both random intercepts and slopes with either covariates or both covariates and outcome contain missing information. In the current paper, we compared the performance of seven different MI methods for handling missing values in longitudinal and clustered data in the context of fitting LMMs with both random intercepts and slopes. We study the theoretical compatibility between specific imputation models fitted under each of these approaches and the LMM, and also conduct simulation studies in both the longitudinal and clustered data settings. Simulations were motivated by analyses of the association between body mass index (BMI) and quality of life (QoL) in the Longitudinal Study of Australian Children (LSAC). Our findings showed that the relative performance of MI methods vary according to whether the incomplete covariate has fixed or random effects and whether there is missingnesss in the outcome variable. We showed that compatible imputation and analysis models resulted in consistent estimation of both regression parameters and variance components via simulation. We illustrate our findings with the analysis of LSAC data.

Prader-Willi syndrome (PWS) is a rare disorder caused by the loss of expression of genes on the paternal copy of chromosome 15q11-13. The main molecular subtypes of PWS are the deletion of 15q11-13 and non-deletion, and differences in neurobehavioral phenotype are recognized between the subtypes. This study aimed to investigate growth trajectories in PWS and associations between PWS subtype (deletion vs. non-deletion) and height, weight and body mass index (BMI). Growth data were available for 125 individuals with PWS (63 males, 62 females), of which 72 (57.6%) had the deletion subtype. There was a median of 28 observations per individual (range 2-85), producing 3565 data points distributed from birth to 18 years of age. Linear mixed models with cubic splines, subject-specific random effects and an autoregressive correlation structure were used to model the longitudinal growth data whilst accounting for the nature of repeated measures. Height was similar for males in both PWS subtypes, with non-deletion females being shorter than deletion females for older ages. Weight and BMI were estimated to be higher in the deletion subtype compared to the non-deletion subtype, with the size of difference increasing with advancing age for weight. These results suggest that individuals with deletion PWS are more prone to obesity.

Adiposity increases estrogen receptor (ER)-positive postmenopausal breast cancer risk. While mechanisms underlying this relationship are uncertain, dysregulated sex-steroid hormone production and insulin signaling are likely pathways. Our aim was to quantify mediating effects of fasting insulin and free estradiol in the adiposity and ER-positive postmenopausal breast cancer association. We used data from a case-cohort study of sex hormones and insulin signaling nested within the Melbourne Collaborative Cohort Study. Eligible women, at baseline, were not diagnosed with cancer, were postmenopausal, did not use hormone therapy and had no history of diabetes or diabetes medication use. Women with ER-negative disease or breast cancer diagnosis within the first follow-up year were excluded. We analyzed the study as a cumulative sampling case-control study with 149 cases and 1,029 controls. Missing values for insulin and free estradiol were multiply imputed with chained equations. Interventional direct (IDE) and indirect (IIE) effects were estimated using regression-based multiple-mediator approach. For women with body mass index (BMI) >30 kg/m2 compared to women with BMI 18.5-25 kg/m2 , the risk ratio (RR) of breast cancer was 1.75 (95% confidence interval [CI] 1.05-2.91). The estimated IDE (RR) not through the mediators was 1.03 (95% CI 0.43-2.48). Percentage mediated effect through free estradiol was 72% (IIE-RR 1.56; 95% CI 1.11-2.19). There was no evidence for an indirect effect through insulin (IIE-RR 1.12; 95% CI 0.68-1.84; 28% mediated). Our results suggest that circulating free estradiol plays an important mediating role in the adiposity-breast cancer relationship but does not explain all of the association.

INTRODUCTION: No prospective studies have examined the prevalence, antecedents or concurrent characteristics associated with self-harm in non-treatment-seeking primary school-aged children. METHODS: In this cohort study from Melbourne, Australia we assessed 1239 children annually from age 8-9 years (wave 1) to 11-12 years (wave 4) on a range of health, social, educational and family measures. Past-year self-harm was assessed at wave 4. We estimated the prevalence of self-harm and used multivariable logistic regression to examine associations with concurrent and antecedent factors. RESULTS: 28 participants (3% of the 1059 with self-harm data; 18 girls [3%], 10 boys [2%]) reported self-harm at age 11-12 years. Antecedent (waves 1-3) predictors of self-harm were: persistent symptoms of depression (sex-age-socioeconomic status adjusted odds ratio [aOR]: 7.8; 95% confidence intervals [CI] 2.6 to 24) or anxiety (aOR: 5.1; 95%CI 2.1 to 12), frequent bullying victimisation (aOR: 24.6; 95%CI 3.8 to 158), and recent alcohol consumption (aOR: 2.9; 95%CI 1.2 to 7.1). Concurrent (wave 4) associations with self-harm were: having few friends (aOR: 8.7; 95%CI 3.2 to 24), poor emotional control (aOR: 4.2; 95%CI 1.9 to 9.6), antisocial behaviour (theft-aOR: 3.1; 95%CI 1.2 to 7.9; carrying a weapon-aOR: 6.9; 95%CI 3.1 to 15), and being in mid-puberty (aOR: 6.5; 95%CI 1.5 to 28) or late/post-puberty (aOR: 14.4; 95%CI 2.9 to 70). CONCLUSIONS: The focus of intervention efforts aimed at preventing and reducing adolescent self-harm should extend to primary school-aged children, with a focus on mental health and peer relationships during the pubertal transition.

INTRODUCTION: The effects of electronic media use on health has received much attention but less is known about links with academic performance. This study prospectively examines the effect of media use on academic performance in late childhood. MATERIALS AND METHODS: 1239 8- to 9-year-olds and their parents were recruited to take part in a prospective, longitudinal study. Academic performance was measured on a national achievement test at baseline and 10-11 years of age. Parents reported on their child's duration of electronic media use. RESULTS: After control for baseline reading, watching more than two hours of television per day at 8-9 years of age predicted a 12-point lower performance in reading at 10-11 years, equivalent to the loss of a third of a year in learning. Using a computer for more than one hour a day predicted a similar 12-point lower numeracy performance. Regarding cross-sectional associations (presumed to capture short-term effects) of media use on numeracy, after controlling for prior media exposure, watching more than two hours of television per day at 10-11 years was concurrently associated with a 12-point lower numeracy score and using a computer for more than one hour per day with a 13-point lower numeracy performance. There was little evidence for concurrent effects on reading. There was no evidence of short- or long-term associations between videogame use and academic performance. DISCUSSION: Cumulative television use is associated with poor reading and cumulative computer use with poorer numeracy. Beyond any links between heavy media use and health risks such as obesity, physical activity and mental health, these findings raise a possibility of additional risks of both television and computer use for learning in mid-childhood. These findings carry implications for parents, teachers and clinicians to consider the type and timing of media exposure in developing media plans for children.

In mice, the maternal microbiome influences fetal immune development and postnatal allergic outcomes. Westernized populations have high rates of allergic disease and low rates of gastrointestinal carriage of Prevotella, a commensal bacterial genus that produces short chain fatty acids and endotoxins, each of which may promote the development of fetal immune tolerance. In this study, we use a prebirth cohort (n = 1064 mothers) to conduct a nested case-cohort study comparing 58 mothers of babies with clinically proven food IgE mediated food allergy with 258 randomly selected mothers. Analysis of the V4 region of the 16S rRNA gene in fecal samples shows maternal carriage of Prevotella copri during pregnancy strongly predicts the absence of food allergy in the offspring. This association was confirmed using targeted qPCR and was independent of infant carriage of P. copri. Larger household size, which is a well-established protective factor for allergic disease, strongly predicts maternal carriage of P. copri.

BACKGROUND: There is increasing evidence that parental determinants of offspring early life development begin well before pregnancy. OBJECTIVES: We established the Victorian Intergenerational Health Cohort Study (VIHCS) to examine the contributions of parental mental health, substance use, and socio-economic characteristics before pregnancy to child emotional, physical, social, and cognitive development. POPULATION: Men and women were recruited from the Victorian Adolescent Health Cohort (VAHCS), an existing cohort study beginning in 1992 that assessed a representative sample of 1943 secondary school students in Victoria, Australia, repeatedly from adolescence (wave 1, mean age 14 years) to adulthood (wave 10, mean age 35 years). METHODS: Victorian Adolescent Health Cohort participants with children born between 2006 and 2013 were recruited to VIHCS and invited to participate during trimester three, at 2 months postpartum, and 1 year postpartum. Parental mental health, substance use and socio-economic characteristics were assessed repeatedly throughout; infant characteristics were assessed postnatally and in infancy. Data will be supplemented by linkage to routine datasets. A further follow-up is underway as children reach 8 years of age. PRELIMINARY RESULTS: Of the 1307 infants born to VAHCS participants between 2006 and 2013, 1030 were recruited to VIHCS. At VIHCS study entry, 18% of recruited parents had preconception common mental disorder in adolescence and young adulthood, 18% smoked daily in adolescence and young adulthood, and 6% had not completed high school. Half of VIHCS infants were female (48%), 4% were from multiple births, and 7% were preterm (<37 weeks' gestation). CONCLUSIONS: Victorian Intergenerational Health Cohort Study is a prospective cohort of 1030 children with up to nine waves of preconception parental data and three waves of perinatal parental and infant data. These will allow examination of continuities of parental health and health risks

Obesity predicts adverse microvasculature from childhood, potentially via inflammatory pathways. We investigated whether inflammation mediates associations between obesity and microvascular parameters. In 1054 children (mean age 11 years) and 1147 adults (44 years) from a cross-sectional study, we measured BMI (z-scores for children) and WHtR, Glycoprotein acetyls (GlycA), an inflammatory marker, and retinal arteriolar and venular calibre. Causal mediation analysis methods decomposed a "total effect" into "direct" and "indirect" components via a mediator, considering continuous and categorical measures and adjusting for potential confounders. Compared to normal-weight BMI children, those with overweight or obesity had narrower arteriolar calibre (total effects -0.21 to -0.12 standard deviation (SD)): direct (not mediated via GlycA) effects were similar. Children with overweight or obesity had 0.25 to 0.35 SD wider venular calibre, of which 19 to 25% was mediated via GlycA. In adults, those with obesity had 0.07 SD greater venular calibre, which was completely mediated by GlycA (indirect effect: 0.07 SD, 95% CI -0.01 to 0.16). Similar findings were obtained with other obesity measures. Inflammation mediated associations between obesity and retinal venules, but not arterioles from mid-childhood, with higher mediation effects observed in adults. Interventions targeting inflammatory pathways may help mitigate adverse impacts of obesity on the microvasculature.

BACKGROUND: Three-level data arising from repeated measures on individuals who are clustered within larger units are common in health research studies. Missing data are prominent in such longitudinal studies and multiple imputation (MI) is a popular approach for handling missing data. Extensions of joint modelling and fully conditional specification MI approaches based on multilevel models have been developed for imputing three-level data. Alternatively, it is possible to extend single- and two-level MI methods to impute three-level data using dummy indicators and/or by analysing repeated measures in wide format. However, most implementations, evaluations and applications of these approaches focus on the context of incomplete two-level data. It is currently unclear which approach is preferable for imputing three-level data. METHODS: In this study, we investigated the performance of various MI methods for imputing three-level incomplete data when the target analysis model is a three-level random effects model with a random intercept for each level. The MI methods were evaluated via simulations and illustrated using empirical data, based on a case study from the Childhood to Adolescence Transition Study, a longitudinal cohort collecting repeated measures on students who were clustered within schools. In our simulations we considered a number of different scenarios covering a range of different missing data mechanisms, missing data proportions and strengths of level-2 and level-3 intra-cluster correlations. RESULTS: We found that all of the approaches considered produced valid inferences about both the regression coefficient corresponding to the exposure of interest and the variance components under the various scenarios within the simulation study. In the case study, all approaches led to similar results. CONCLUSION: Researchers may use extensions to the single- and two-level approaches, or the three-level approaches, to adequately handle incomplete three-level data.

BACKGROUND: Preterm birth (PTB) and small for gestational age (SGA) are increasingly prevalent, with major consequences for health and development into later life. There is emerging evidence that some risk processes begin before pregnancy. We report on associations between maternal and paternal common mental disorders (CMD) before and during pregnancy and offspring PTB and SGA. METHODS: 398 women with 609 infants and 267 men with 421 infants were assessed repeatedly for CMD symptoms before pregnancy between age 14 and 29 and during pregnancy. Associations between preconception and antenatal CMD symptoms and offspring gestational age/PTB and size for gestational age/SGA were estimated using linear and Poisson regression. FINDINGS: In men, persistent preconception CMD across adolescence and young adulthood predicted offspring PTB after adjustment for ethnicity, education, BMI and adolescent substance use (adjusted RR 7·0, 95% CI 1·8,26·8), corresponding to a population attributable fraction of 31% of preterm births. In women, antenatal CMD symptoms predicted offspring PTB (adjusted RR 4·4, 95% CI 1·4,14·1). There was little evidence of associations with SGA. INTERPRETATION: This first report of an association between paternal preconception mental health and offspring gestational age, while requiring replication in larger samples, complements earlier work on stress in animals, and further strengthens the case for expanding preconception mental health care to both men and women. FUNDING: National Health and Medical Research Council (Australia), Victorian Health Promotion Foundation, Australian Rotary Health, Colonial Foundation, Perpetual Trustees, Financial Markets Foundation for Children (Australia), Royal Children's Hospital Foundation, Murdoch Children's Research Institute, Australian Research Council.