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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: 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.

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.

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.

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.

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.

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.

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.

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

Mechanisms underlying adiposity-colorectal cancer (CRC) association are incompletely understood. Using UK Biobank data, we investigated the role of C-reactive protein (CRP), hemoglobin-A1c (HbA1c) and (jointly) sex hormone-binding globulin (SHBG) and testosterone, in explaining this association. Total effect of obesity versus normal-weight (based on waist circumference, body mass index, waist-hip ratio) on CRC risk was decomposed into natural direct (NDE) and indirect (NIE) effects using sequential mediation analysis. After a median follow-up of 7.1 years, 2070 incident CRC cases (men = 1,280; postmenopausal women = 790) were recorded. For men, the adjusted risk ratio (RR) for waist circumference (≥102 vs. ≤94 cm) was 1.37 (95% confidence interval [CI], 1.19-1.58). The RRsNIE were 1.08 (95% CI: 1.01-1.16) through all biomarkers, 1.06 (95% CI: 1.01-1.11) through pathways influenced by CRP, 0.99 (95% CI: 0.97-1.01) through HbA1c beyond (the potential influence of) CRP and 1.03 (95% CI: 0.99-1.08) through SHBG and testosterone combined beyond CRP and HbA1c. The RRNDE was 1.26 (95% CI: 1.09-1.47). For women, the RR for waist circumference (≥88 vs. ≤80 cm) was 1.27 (95% CI: 1.07-1.50). The RRsNIE were 1.08 (95% CI: 0.94-1.22) through all biomarkers, 1.08 (95% CI: 0.99-1.17) through CRP, 1.00 (95% CI: 0.98-1.02) through HbA1c beyond CRP and 1.00 (95% CI: 0.92-1.09) through SHBG and testosterone combined beyond CRP and HbA1c. The RRNDE was 1.18 (95% CI: 0.96-1.45). For men and women, pathways influenced by CRP explained a small proportion of the adiposity-CRC association. Testosterone and SHBG also explained a small proportion of this association in men. These results suggest that pathways marked by these obesity-related factors may not explain a large proportion of the adiposity-CRC association.

The not-at-random fully conditional specification (NARFCS) procedure provides a flexible means for the imputation of multivariable missing data under missing-not-at-random conditions. Recent work has outlined difficulties with eliciting the sensitivity parameters of the procedure from expert opinion due to their conditional nature. Failure to adequately account for this conditioning will generate imputations that are inconsistent with the assumptions of the user. In this paper, we clarify the importance of correct conditioning of NARFCS sensitivity parameters and develop procedures to calibrate these sensitivity parameters by relating them to more easily elicited quantities, in particular, the sensitivity parameters from simpler pattern mixture models. Additionally, we consider how to include the missingness indicators as part of the imputation models of NARFCS, recommending including all of them in each model as default practice. Algorithms are developed to perform the calibration procedure and demonstrated on data from the Avon Longitudinal Study of Parents and Children, as well as with simulation studies.

The association between aging and cancer is complex. Recent studies have developed measures of biological aging based on DNA methylation and called them "age acceleration." We aimed to assess the associations of age acceleration with risk of and survival from seven common cancers. Seven case-control studies of DNA methylation and colorectal, gastric, kidney, lung, prostate and urothelial cancer and B-cell lymphoma nested in the Melbourne Collaborative Cohort Study were conducted. Cancer cases, vital status and cause of death were ascertained through linkage with cancer and death registries. Conditional logistic regression and Cox models were used to estimate odds ratios (OR) and hazard ratios (HR) and 95% confidence intervals (CI) for associations of five age acceleration measures derived from the Human Methylation 450 K Beadchip assay with cancer risk (N = 3,216 cases) and survival (N = 1,726 deaths), respectively. Epigenetic aging was associated with increased cancer risk, ranging from 4% to 9% per five-year age acceleration for the 5 measures considered. Heterogeneity by study was observed, with stronger associations for risk of kidney cancer and B-cell lymphoma. An associated increased risk of death following cancer diagnosis ranged from 2% to 6% per five-year age acceleration, with no evidence of heterogeneity by cancer site. Cancer risk and mortality were increased by 15-30% for the fourth versus first quartile of age acceleration. DNA methylation-based measures of biological aging are associated with increased cancer risk and shorter cancer survival, independently of major health risk factors.

BACKGROUND: Ecological evidence suggests vitamin D insufficiency (VDI) due to lower ambient ultraviolet radiation (UVR) exposure may be a risk factor for IgE-mediated food allergy. However, there are no studies relating directly measured VDI during early infancy to subsequent challenge-proven food allergy. OBJECTIVE: To prospectively investigate the association between VDI during infancy and challenge-proven food allergy at 1 year. METHODS: In a birth cohort (n = 1074), we used a case-cohort design to compare 25-hydroxyvitamin D3 (25(OH)D3 ) levels among infants with food allergy vs a random subcohort (n = 274). The primary exposures were VDI (25(OH)D3 <50 nM) at birth and 6 months of age. Ambient UVR and time in the sun were combined to estimate UVR exposure dose. IgE-mediated food allergy status at 1 year was determined by formal challenge. Binomial regression was used to examine associations between VDI, UVR exposure dose and food allergy and investigate potential confounding. RESULTS: Within the random subcohort, VDI was present in 45% (105/233) of newborns and 24% (55/227) of infants at 6 months. Food allergy prevalence at 1 year was 7.7% (61/786), and 6.5% (53/808) were egg-allergic. There was no evidence of an association between VDI at either birth (aRR 1.25, 95% CI 0.70-2.22) or 6 months (aRR 0.93, 95% CI 0.41-2.14) and food allergy at 1 year. CONCLUSIONS: There was no evidence that VDI during the first 6 months of infancy is a risk factor for food allergy at 1 year of age. These findings primarily relate to egg allergy, and larger studies are required.