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Superfluid vortices pinned to nuclear lattice sites or magnetic flux tubes in a neutron star evolve abruptly through a sequence of metastable spatial configurations, punctuated by unpinning avalanches associated with rotational glitches, as the stellar crust spins down electromagnetically. The metastable configurations are approximately but not exactly axisymmetric, causing the emission of persistent, quasimonochromatic, current quadrupole gravitational radiation. The characteristic gravitational wave strain h0 as a function of the spin frequency f and distance D from the Earth is bounded above by $h_0 = 1.2\substack{+1.3 \\ -0.9} \times 10^{-32} (f/30\,\,{\rm Hz})^{2.5} (D/1\,\,{\rm kpc})^{-1}$, corresponding to a Poissonian spatial configuration (equal probability per unit area, i.e. zero inter-vortex repulsion), and bounded below by $h_0 = 1.8\substack{+2.0 \\ -1.5} \times 10^{-50} (f/30\,\,{\rm Hz})^{1.5} (D/1\,\,{\rm kpc})^{-1}$, corresponding to a regular array (periodic separation, i.e. maximum inter-vortex repulsion). N-body point vortex simulations predict an intermediate scaling, $h_0 = 7.3\substack{+7.9 \\ -5.4} \times 10^{-42} (f/30\,\,{\rm Hz})^{1.9} (D/1\,\,{\rm kpc})^{-1}$, which reflects a balance between the randomizing but spatially correlated action of superfluid vortex avalanches and the regularizing action of inter-vortex repulsion. The scaling is calibrated by conducting simulations with Nv ≤ 5 × 103 vortices and extrapolated to the astrophysical regime Nv ∼ 1017(f/30 Hz). The scaling is provisional, pending future computational advances to raise Nv and include three-dimensional effects such as vortex tension and turbulence.

Every year, an estimated 21 million girls aged 15-19 years become pregnant in low-income and middle-income countries (LMICs). Policy responses have focused on reducing the adolescent birth rate whereas efforts to support pregnant adolescents have developed more slowly. We did a systematic review of interventions addressing any health-related outcome for pregnant adolescents and their newborn babies in LMICs and mapped its results to a framework describing high-quality health systems for pregnant adolescents. Although we identified some promising interventions, such as micronutrient supplementation, conditional cash transfers, and well facilitated group care, most studies were at high risk of bias and there were substantial gaps in evidence. These included major gaps in delivery, abortion, and postnatal care, and mental health, violence, and substance misuse-related outcomes. We recommend that the fields of adolescent, maternal, and sexual and reproductive health collaborate to develop more adolescent-inclusive maternal health care and research, and specific interventions for pregnant adolescents. We outline steps to develop high-quality, evidence-based care for the millions of pregnant adolescents and their newborns who currently do not receive this.

Solar flares result from the sudden release of energy deposited by subphotospheric motions into the magnetic field of the corona. The deposited energy accumulates secularly between events. One may interpret the observed event statistics as resulting from a state-dependent Poisson process in which the instantaneous flare rate is a function of the stress in the system and a flare becomes certain as the stress approaches a threshold set by the microphysics of the flare trigger. If the system is driven fast, and if the threshold is static and uniform globally, a cross-correlation is predicted between the size of a flare and the forward waiting time to the next flare. This cross-correlation is broadly absent from the Geostationary Operational Environmental Satellite (GOES) soft X-ray flare database. One also predicts higher cross-correlations in active regions where the shapes of the waiting time and size distributions match. Again, there is no evidence for such an association in the GOES data. The data imply at least one of the following: (i) the threshold at which a flare is triggered varies in time; (ii) the rate at which energy is driven into active regions varies in time; (iii) historical flare catalogs are incomplete; or (iv) the description of solar flares as resulting from a buildup and release of energy, once a threshold is reached, is incomplete.

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: 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: 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: 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: Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters. Standard analysis methods for these trials such as a linear mixed model with estimation via maximum likelihood or restricted maximum likelihood (REML) rely on asymptotic properties and have been shown to yield inflated type I error when applied to studies with a small number of clusters. Small-sample methods such as the Kenward-Roger approximation in combination with REML can potentially improve estimation of the fixed effects such as the treatment effect. A Bayesian approach may also be promising for such multilevel models but has not yet seen much application in cluster randomized trials. METHODS: We conducted a simulation study comparing the performance of REML with and without a Kenward-Roger approximation to a Bayesian approach using weakly informative prior distributions on the intracluster correlation parameters. We considered a continuous outcome and a range of stepped wedge trial configurations with between 4 and 40 clusters. To assess method performance we calculated bias and mean squared error for the treatment effect and correlation parameters and the coverage of 95% confidence/credible intervals and relative percent error in model-based standard error for the treatment effect. RESULTS: Both REML with a Kenward-Roger standard error and degrees of freedom correction and the Bayesian method performed similarly well for the estimation of the treatment effect, while intracluster correlation parameter estimates obtained via the Bayesian method were less variable than REML estimates with different relative levels of bias. CONCLUSIONS: The use of REML with a Kenward-Roger approximation may be sufficient for the analysis of stepped wedge cluster randomized trials with a small number of clusters. However, a Bayesian approach with weakly informative prior distributions on the intraclus

BACKGROUND: As of mid-2021, Australia's only nationwide coronavirus disease 2019 (COVID-19) epidemic occurred in the first 6 months of the pandemic. Subsequently, there has been limited transmission in most states and territories. Understanding community spread during the first wave was hampered by initial limitations on testing and surveillance. To characterize the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody seroprevalence generated during this time, we undertook Australia's largest national SARS-CoV-2 serosurvey. METHODS: Between June 19 and August 6, 2020, residual specimens were sampled from people undergoing general pathology testing (all ages), women attending antenatal screening (20-39 years), and blood donors (20-69 years) based on the Australian population's age and geographic distributions. Specimens were tested by Wantai total SARS-CoV-2-antibody assay. Seroprevalence estimates adjusted for test performance were produced. The SARS-CoV-2 antibody-positive specimens were characterized with microneutralization assays. RESULTS: Of 11 317 specimens (5132 general pathology; 2972 antenatal; 3213 blood-donors), 71 were positive for SARS-CoV-2-specific antibodies. Seroprevalence estimates were 0.47% (95% credible interval [CrI], 0.04%-0.89%), 0.25% (CrI, 0.03%-0.54%), and 0.23% (CrI, 0.04%-0.54%), respectively. No seropositive specimens had neutralizing antibodies. CONCLUSIONS: Australia's seroprevalence was extremely low (<0.5%) after the only national COVID-19 wave thus far. These data and the subsequent limited community transmission highlight the population's naivety to SARS-CoV-2 and the urgency of increasing vaccine-derived protection.

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).

Rapidly identifying and isolating people with acute SARS-CoV-2 infection has been a core strategy to contain COVID-19 in Australia, but a proportion of infections go undetected. We estimated SARS-CoV-2 specific antibody prevalence (seroprevalence) among blood donors in metropolitan Melbourne following a COVID-19 outbreak in the city between June and September 2020. The aim was to determine the extent of infection spread and whether seroprevalence varied demographically in proportion to reported cases of infection. The design involved stratified sampling of residual specimens from blood donors (aged 20-69 years) in three postcode groups defined by low (<3 cases/1,000 population), medium (3-7 cases/1,000 population) and high (>7 cases/1,000 population) COVID-19 incidence based on case notification data. All specimens were tested using the Wantai SARS-CoV-2 total antibody assay. Seroprevalence was estimated with adjustment for test sensitivity and specificity for the Melbourne metropolitan blood donor and residential populations, using multilevel regression and poststratification. Overall, 4,799 specimens were collected between 23 November and 17 December 2020. Seroprevalence for blood donors was 0.87% (90% credible interval: 0.25-1.49%). The highest estimates, of 1.13% (0.25-2.15%) and 1.11% (0.28-1.95%), respectively, were observed among donors living in the lowest socioeconomic areas (Quintiles 1 and 2) and lowest at 0.69% (0.14-1.39%) among donors living in the highest socioeconomic areas (Quintile 5). When extrapolated to the Melbourne residential population, overall seroprevalence was 0.90% (0.26-1.51%), with estimates by demography groups similar to those for the blood donors. The results suggest a lack of extensive community transmission and good COVID-19 case ascertainment based on routine testing during Victoria's second epidemic wave. Residual blood donor samples provide a practical epidemiological tool for estimating seroprevalence and information on popula

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

BackgroundThere is increasing evidence that patterns of pubertal maturation are associated with different patterns of health risk. This study aimed to explore the associations between anthropometric measures and salivary androgen concentrations in pre-adolescent children.MethodsWe analysed a stratified random sample (N=1151) of pupils aged 8–9 years old from 43 primary schools in Melbourne, Australia from the Childhood to Adolescence Transition Study. Saliva samples were assayed for dehydroepiandrosterone (DHEA), DHEA-sulfate and testosterone. Anthropometric measures included height, weight, body mass index (BMI) and waist circumference. Associations between (1) anthropometric measures and each androgen, and (2) hormone status with obesity and parental report of pubertal development were investigated using linear regression modelling with general estimating equations.ResultsGreater height, weight, BMI and waist circumference were positively associated with higher androgen concentrations, after adjusting for sex and socioeconomic status. Being overweight or obese was associated with higher testosterone and DHEA concentrations compared with the normal BMI category. Those who were obese were more likely (OR=2.7, 95% CI 1.61 to 4.43, p<0.001) to be in the top tertile of age-adjusted androgen status in both sexes.ConclusionThis study provides clear evidence for an association between obesity and higher androgen levels in mid-childhood. The adrenal transition may be a critical time period for weight management intervention strategies in order to manage the risk for metabolic problems in later life for high-risk individuals.

OBJECTIVE: Determine the sensitivity and specificity of neonatal jaundice visual estimation by primary healthcare workers (PHWs) and physicians as predictors of hyperbilirubinaemia. DESIGN: Multicentre observational cohort study. SETTING: Hospitals in Chandigarh and Delhi, India; Dhaka, Bangladesh; Durban, South Africa; Kumasi, Ghana; La Paz, Bolivia. PARTICIPANTS: Neonates aged 1-20 days (n=2642) who presented to hospitals for evaluation of acute illness. Infants referred for any reason from another health facility or those needing immediate cardiopulmonary resuscitation were excluded. OUTCOME MEASURES: Infants were evaluated for distribution (head, trunk, distal extremities) and degree (mild, moderate, severe) of jaundice by PHWs and physicians. Serum bilirubin level was determined for infants with jaundice, and analyses of sensitivity and specificity of visual estimations of jaundice used bilirubin thresholds of >260 µmol/L (need for phototherapy) and >340 µmol/L (need for emergency intervention in at-risk and preterm babies). RESULTS: 1241 (47.0%) neonates had jaundice. High sensitivity for detecting neonates with serum bilirubin >340 µmol/L was found for 'any jaundice of the distal extremities (palms or soles) OR deep jaundice of the trunk or head' for both PHWs (89%-100%) and physicians (81%-100%) across study sites; specificity was more variable. 'Any jaundice of the distal extremities' identified by PHWs and physicians had sensitivity of 71%-100% and specificity of 55%-95%, excluding La Paz. For the bilirubin threshold >260 µmol/L, 'any jaundice of the distal extremities OR deep jaundice of the trunk or head' had the highest sensitivity across sites (PHWs: 58%-93%, physicians: 55%-98%). CONCLUSIONS: In settings where serum bilirubin cannot be measured, neonates with any jaundice on the distal extremities should be referred to a hospital for evaluation and management, where delays in serum bilirubin measurement and appropriate treatment are anticipated follow

OBJECTIVES: To estimate SARS-CoV-2-specific antibody seroprevalence after the first epidemic wave of coronavirus disease 2019 (COVID-19) in Sydney. SETTING, PARTICIPANTS: People of any age who had provided blood for testing at selected diagnostic pathology services (general pathology); pregnant women aged 20-39 years who had received routine antenatal screening; and Australian Red Cross Lifeblood plasmapheresis donors aged 20-69 years. DESIGN: Cross-sectional study; testing of de-identified residual blood specimens collected during 20 April - 2 June 2020. MAIN OUTCOME MEASURE: Estimated proportions of people seropositive for anti-SARS-CoV-2-specific IgG, adjusted for test sensitivity and specificity. RESULTS: Thirty-eight of 5339 specimens were IgG-positive (general pathology, 19 of 3231; antenatal screening, 7 of 560; plasmapheresis donors, 12 of 1548); there were no clear patterns by age group, sex, or location of residence. Adjusted estimated seroprevalence among people who had had general pathology blood tests (all ages) was 0.15% (95% credible interval [CrI], 0.04-0.41%), and 0.29% (95% CrI, 0.04-0.75%) for plasmapheresis donors (20-69 years). Among 20-39-year-old people, the age group common to all three collection groups, adjusted estimated seroprevalence was 0.24% (95% CrI, 0.04-0.80%) for the general pathology group, 0.79% (95% CrI, 0.04-1.88%) for the antenatal screening group, and 0.69% (95% CrI, 0.04-1.59%) for plasmapheresis donors. CONCLUSIONS: Estimated SARS-CoV-2 seroprevalence was below 1%, indicating that community transmission was low during the first COVID-19 epidemic wave in Sydney. These findings suggest that early control of the spread of COVID-19 was successful, but efforts to reduce further transmission remain important.

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.

Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the imputation process. These problems frequently occur when imputation models contain large numbers of variables, especially with the popular approach of multivariate imputation by chained equations. This paper describes common causes of failure of the imputation procedure including perfect prediction and collinearity, focusing on issues when using Stata software. We outline a number of strategies for addressing these issues, including imputation of composite variables instead of individual components, introducing prior information and changing the form of the imputation model. These strategies are illustrated using a case study based on data from the Longitudinal Study of Australian Children.

BACKGROUND: Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice. METHODS: Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies. RESULTS: From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only. CONCLUSIONS: As chronic and progress

For cluster randomized trials (CRTs) with a small number of clusters, the matched-pair (MP) design, where clusters are paired before randomizing one to each trial arm, is often recommended to minimize imbalance on known prognostic factors, add face-validity to the study, and increase efficiency, provided the analysis recognizes the matching. Little evidence exists to guide decisions on when to use matching. We used simulation to compare the efficiency of the MP design with the stratified and simple designs, based on the mean confidence interval width of the estimated intervention effect. Matched and unmatched analyses were used for the MP design; a stratified analysis was used for the stratified design; and analyses without and with post-stratification adjustment for factors that would otherwise have been used for restricted allocation were used for the simple design. Results showed the MP design was generally the most efficient for CRTs with 10 or more pairs when the correlation between cluster-level outcomes within pairs (matching correlation) was moderate to strong (0.3-0.5). There was little gain in efficiency for the MP or stratified designs compared to simple randomization when the matching correlation was weak (0.05-0.1). For trials with four pairs of clusters, the simple and stratified designs were more efficient than the MP design because greater degrees of freedom were available for the analysis, although an unmatched analysis of the MP design recovered precision for weak matching correlations. Practical guidance on choosing between the MP, stratified, and simple designs is provided.

Medical research often involves using multi-item scales to assess individual characteristics, disease severity, and other health-related outcomes. It is common to observe missing data in the scale scores, due to missing data in one or more items that make up that score. Multiple imputation (MI) is a popular method for handling missing data. However, it is not clear how best to use MI in the context of scale scores, particularly when they are assessed at multiple waves of data collection resulting in large numbers of items. The aim of this article is to provide practical advice on how to impute missing values in a repeatedly measured multi-item scale using MI when inference on the scale score is of interest. We evaluated the performance of five MI strategies for imputing missing data at either the item or scale level using simulated data and a case study based on four waves of the Longitudinal Study of Australian Children (LSAC). MI was implemented using both multivariate normal imputation and fully conditional specification, with two rules for calculating the scale score. A complete case analysis was also performed for comparison. Based on our results, we caution against the use of a MI strategy that does not include the scale score in the imputation model(s) when the scale score is required for analysis.

BACKGROUND: There is increasing evidence that patterns of pubertal maturation are associated with different patterns of health risk. This study aimed to explore the associations between anthropometric measures and salivary androgen concentrations in pre-adolescent children. METHODS: We analysed a stratified random sample (N=1151) of pupils aged 8-9 years old from 43 primary schools in Melbourne, Australia from the Childhood to Adolescence Transition Study. Saliva samples were assayed for dehydroepiandrosterone (DHEA), DHEA-sulfate and testosterone. Anthropometric measures included height, weight, body mass index (BMI) and waist circumference. Associations between (1) anthropometric measures and each androgen, and (2) hormone status with obesity and parental report of pubertal development were investigated using linear regression modelling with general estimating equations. RESULTS: Greater height, weight, BMI and waist circumference were positively associated with higher androgen concentrations, after adjusting for sex and socioeconomic status. Being overweight or obese was associated with higher testosterone and DHEA concentrations compared with the normal BMI category. Those who were obese were more likely (OR=2.7, 95% CI 1.61 to 4.43, p<0.001) to be in the top tertile of age-adjusted androgen status in both sexes. CONCLUSION: This study provides clear evidence for an association between obesity and higher androgen levels in mid-childhood. The adrenal transition may be a critical time period for weight management intervention strategies in order to manage the risk for metabolic problems in later life for high-risk individuals.

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.

Aim This paper describes the use of the single patient therapy plan (SPTP). The SPTP has been designed to assess the efficacy at an individual level of a commercially available cannabinoid product, cannabidiol, in reducing seizure frequency in paediatric patients with intractable epilepsy. Methods The SPTP is a randomised, double‐blind, placebo‐controlled N‐of‐1 trial designed to assess the efficacy of treatment in a neurology outpatient setting. The primary objective of the SPTP is to assess the efficacy of cannabidiol in reducing seizure frequency in each patient with intractable epilepsy, with change in seizure frequency being the primary outcome of interest. The analysis adopts a Bayesian approach, which provides results in the form of posterior probabilities that various levels of benefit (based on the primary outcome measure, seizure frequency) have been achieved under active treatment compared to placebo, accompanied by decision rules that provide thresholds for deciding whether treatment has been successful in the individual patient. The SPTP arrangement is most accurately considered part of clinical practice rather than research, since it is aimed at making clinical treatment decisions for individual patients and is not testing a hypothesis or collecting aggregate data. Therefore, Human Research Ethics Committee approval was considered not to be required, although it is recommended that hospital Clinical Ethics Committees provide ethical oversight. Conclusion: These SPTP resources are made available so that they may inform clinical practice in the treatment of severe epilepsy or adapted for use in other conditions.

Little is known about early predictors of later cystic fibrosis (CF) structural lung disease. This study examined early predictors of progressive structural lung abnormalities in children who completed the Australasian CF Bronchoalveolar Lavage (ACFBAL) clinical trial at age 5-years and participated in an observational follow-up study (CF-FAB).Eight Australian and New Zealand CF centres participated in CF-FAB and provided follow-up chest computed-tomography (CT) scans for children who had completed the ACFBAL study with baseline scans at age 5-years. CT scans were annotated using PRAGMA-CF scoring. Ordinal regression analysis and linear regression were used to investigate associations between PRAGMA-CF (Perth-Rotterdam Annotated Grid Morphometric Analysis for CF) outcomes at follow-up and variables measured during the ACFBAL study.99 out of 157 ACFBAL children (mean±sd age 13±1.5 years) participated in the CF-FAB study. The probability of bronchiectasis at follow-up increased with airway disease severity on the baseline CT scan. In multiple regression (retaining factors at p<0.05) the extent of bronchiectasis at follow-up was associated with baseline atelectasis (OR 7.2, 95% CI 2.4-22; p≤ 0.001), bronchoalveolar lavage (BAL) log2 interleukin (IL)-8 (OR 1.2, 95% CI 1.05-1.5; p=0.010) and body mass index z-score (OR 0.49, 95% CI 0.24-1.00; p=0.05) at age 5 years. Percentage trapped air at follow-up was associated with BAL log2 IL-8 (coefficient 1.3, 95% CI 0.57-2.1; p<0.001) at age 5 years.The extent of airway disease, atelectasis, airway inflammation and poor nutritional status in early childhood are risk factors for progressive structural lung disease in adolescence.

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: High folate status in pregnancy has been implicated in the increased prevalence of allergic disease, but there are no published data relating directly measured folate status in pregnancy to challenge-proven food allergy among offspring. The study aim was to examine the association between red blood cell (RBC) folate status in trimester three of pregnancy and allergic disease among offspring. METHODS: Red blood cell folate levels were measured at 28-32 weeks' gestation in a prospective birth cohort (n = 1074). Food allergy outcomes were assessed in 1-year-old infants by skin prick testing and subsequent food challenge. Eczema was assessed by questionnaire and clinical review. High trimester three RBC folate was defined as greater than (>) 1360 nmol/L. Binomial regression was used to examine associations between trimester three RBC folate and allergic outcomes, adjusting for potential confounders. RESULTS: Red blood cell folate levels were measured in 88% (894/1064) of pregnant women. The mean concentration was 1695.6 nmol/L (standard deviation 415.4) with 82% (731/894) >1360 nmol/L. There was no evidence of either linear or non-linear relationships between trimester three RBC folate and allergic outcomes, nor evidence of associations between high RBC folate and food allergy (adjusted risk ratio (aRR) 2.89, 95% CI 0.90-9.35), food sensitization (aRR 1.72, 95% CI 0.85-3.49), or eczema (aRR 0.97, 95% CI 0.67-1.38). CONCLUSION: The majority of pregnant women in this study had high RBC folate levels. There was no evidence of associations between trimester three RBC folate and food allergy, food sensitization, or eczema among the offspring, although larger studies are required.

BACKGROUND: A few studies have investigated the antecedents and outcomes of infants who demonstrate IgE sensitization to foods that they clinically tolerate. Improved understanding of this sensitized-tolerant phenotype may inform strategies for the prevention of food allergy. METHODS: In an Australian birth cohort (n = 1074), assembled using an unselected antenatal sampling frame, participants were categorized as nonsensitized (NS), sensitizedtolerant (ST), or food allergic (FA) based on skin prick testing and food challenge at 12 months of age. Environmental exposures were recorded throughout. Cord blood regulatory T-cell populations were measured at birth. Subsequent childhood allergic disease was assessed by parent report, clinical examination, and repeat skin prick testing. RESULTS: The covariates of interest varied between NS (n = 698), ST (n = 27), and FA (n = 61) groups as follows, suggesting that across these measures, the ST group was more similar to the NS than the FA group: family history of eczema NS 44.6%, ST. 44.6%, FA 65.6%; pet ownership at 12 months: NS 71.5%, ST 81.5%, FA 45.8%; eczema during the first 12 months: NS 19%, ST 32%, FA 64%; and aeroallergen sensitization at 4 years: NS 19.1%, ST 28.6%, FA 44.4%. At birth, a higher proportion of activated regulatory T cells was associated with ST (OR = 2.89, 95% CI 1.03-8.16, P = .045). CONCLUSION: Food-sensitized-tolerance in infancy appears to be associated with a similar pattern of exposures, immunity, and outcomes to nonsensitized infants. In addition, we found some evidence that an elevated proportion of activated regulatory T cells at birth was specific to the sensitized-tolerant infants, which may be relevant to suppression of clinical disease.

There are now a growing number of applications of multilevel regression and poststratification (MRP) in population health and epidemiological studies. MRP uses multilevel regression to model individual survey responses as a function of demographic and geographic covariates. Estimated mean outcome values for each demographic-geographic respondent subtype are then weighted by the proportions of each subtype in the population to produce an overall population-level estimate. We recently reported an extensive case study of a large nationwide survey and found that MRP performed favorably compared to conventional survey sampling weights for the estimation of population descriptive quantities in a highly selected sample. In this study, we aimed to evaluate, by way of a simulation experiment, both the accuracy and precision of MRP versus survey sampling weights in the context of large population health studies. While much of the research into MRP has been focused on U.S. political and social science, we considered an alternative population structure of smaller size and with notably fewer geographic subsets. We explored the impact on MRP performance of sample size, model misspecification, interactions, and the addition of a geographic-level covariate. MRP was found to achieve generally superior performance in both accuracy and precision at both the national and state levels. Results were generally robust to model misspecification, and MRP performance was further improved by the inclusion of a geographic-level covariate. These findings offer further evidence that MRP provides a promising analytic approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

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

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: Maternal mental health during pregnancy and postpartum predicts later emotional and behavioural problems in children. Even though most perinatal mental health problems begin before pregnancy, the consequences of preconception maternal mental health for children's early emotional development have not been prospectively studied. METHODS: We used data from two prospective Australian intergenerational cohorts, with 756 women assessed repeatedly for mental health problems before pregnancy between age 13 and 29 years, and during pregnancy and at 1 year postpartum for 1231 subsequent pregnancies. Offspring infant emotional reactivity, an early indicator of differential sensitivity denoting increased risk of emotional problems under adversity, was assessed at 1 year postpartum. RESULTS: Thirty-seven percent of infants born to mothers with persistent preconception mental health problems were categorised as high in emotional reactivity, compared to 23% born to mothers without preconception history (adjusted OR 2.1, 95% CI 1.4-3.1). Ante- and postnatal maternal depressive symptoms were similarly associated with infant emotional reactivity, but these perinatal associations reduced somewhat after adjustment for prior exposure. Causal mediation analysis further showed that 88% of the preconception risk was a direct effect, not mediated by perinatal exposure. CONCLUSIONS: Maternal preconception mental health problems predict infant emotional reactivity, independently of maternal perinatal mental health; while associations between perinatal depressive symptoms and infant reactivity are partially explained by prior exposure. Findings suggest that processes shaping early vulnerability for later mental disorders arise well before conception. There is an emerging case for expanding developmental theories and trialling preventive interventions in the years before pregnancy.

Objectives To describe a well-established marker of cardiovascular risk, carotid intima-media thickness (IMT) and related measures (artery distensibility and elasticity) in children aged 11-12 years old and mid-life adults, and examine associations within parent-child dyads. Design Cross-sectional study (Child Health CheckPoint), nested within a prospective cohort study, the Longitudinal Study of Australian Children (LSAC). Setting Assessment centres in seven Australian major cities and eight selected regional towns, February 2015 to March 2016. Participants Of all participating CheckPoint families (n=1874), 1489 children (50.0% girls) and 1476 parents (86.8% mothers) with carotid IMT data were included. Survey weights and methods were applied to account for LSAC's complex sample design and clustering within postcodes and strata. Outcome measures Ultrasound of the right carotid artery was performed using standardised protocols. Primary outcomes were mean and maximum far-wall carotid IMT, quantified using semiautomated edge detection software. Secondary outcomes were carotid artery distensibility and elasticity. Pearson's correlation coefficients and multivariable linear regression models were used to assess parent-child concordance. Random effects modelling on a subset of ultrasounds (with repeated measurements) was used to assess reliability of the child carotid IMT measure. Results The average mean and maximum child carotid IMT were 0.50 mm (SD 0.06) and 0.58 mm (SD 0.05), respectively. In adults, average mean and maximum carotid IMT were 0.57 mm (SD 0.07) and 0.66 mm (SD 0.10), respectively. Mother-child correlations for mean and maximum carotid IMT were 0.12 (95% CI 0.05 to 0.23) and 0.10 (95% CI 0.03 to 0.21), respectively. For carotid artery distensibility and elasticity, mother-child correlations were 0.19 (95% CI 0.10 to 0.25) and 0.11 (95% CI 0.02 to 0.18), respectively. There was no strong evidence of father-child correlation in any measure. Conclusions We

OBJECTIVES: To describe the distribution of albuminuria among Australian children aged 11-12 years and their parents, and assess its intergenerational concordance within parent-child dyads. DESIGN: Population-based cross-sectional study (the Child Health CheckPoint), nested within the Longitudinal Study of Australian Children. SETTING: Assessment centres (seven Australian cities and eight regional towns) and home visits across Australia, February 2015 to March 2016. PARTICIPANTS: Of all participating CheckPoint families (n=1874), 1557 children (46.2% girls) and 1454 parents (85.5% mothers) provided random urine samples at the visit; samples from menstruating females were excluded. OUTCOME MEASURES: Urine albumin-to-creatinine ratio (ACR) and its components (urine albumin and creatinine concentration); albuminuria was defined as an ACR ≥3.4 mg/mmol. Pearson's correlation coefficients and multivariable linear regression models assessed parent-child concordance, using log-transformed data due to skewing. Survey weights and methods were applied to account for the complex sample design. RESULTS: The median ACR for children was 1.03 mg/mmol (IQR 0.65-1.97) and 1.01 mg/mmol (IQR 0.60-2.09) for adults. The median ACR was higher in girls (1.20, IQR 0.71-2.65) than boys (0.90, IQR 0.61-1.65) and in mothers (1.13, IQR 0.63-2.33) than fathers (0.66, IQR 0.41-1.05). Albuminuria was detected in 15.1% of children (girls 20.8%, boys 10.1%) and 13.5% of adults (15.1% mothers, 4.0% fathers) had albuminuria. There was a small correlation between parent and child ACR (Pearson correlation coefficient 0.06, 95% CI 0.01 to 0.12). CONCLUSIONS: Albuminuria is common among Australian children and adults, which is of concern because it predicts risk for kidney and cardiovascular disease, and mortality. The weak concordance among intergenerational pairs for urine ACR suggests either that genetic heritability is low or that it becomes evident only at later offspring life stages.

Background High-throughput nuclear magnetic resonance profiling of circulating metabolites is suggested as an adjunct for cardiovascular risk evaluation. The relationship between metabolites and subclinical atherosclerosis remains unclear, particularly among children. Therefore, we examined the associations of metabolites with carotid intima-media thickness ( cIMT ) and arterial pulse wave velocity ( PWV ). Methods and Results Data from two independent population-based studies was examined; (1) cross-sectional associations with cIMT and PWV in 1178 children (age 11-12 years, 51% female) and 1316 parents (mean age 45 years, 87% female) from the CheckPoint study (Australia); and (2) longitudinal associations in 4249 children (metabolites at 7-8 years, PWV at 10-11 years, 52% female), and cross-sectional associations in 4171 of their mothers (mean age 48 years, cIMT data) from ALSPAC (The Avon Longitudinal Study of Parents and Children; UK ). Metabolites were measured by the same nuclear magnetic resonance platform in both studies, comprising of 69 biomarkers. Biophysical assessments included body mass index, blood pressure, cIMT and PWV . In linear regression analyses adjusted for age, sex, body mass index, and blood pressure, there was no evidence of metabolite associations in either children or adults for cIMT at a 10% false discovery threshold. In CheckPoint adults, glucose was positively, and some high-density lipoprotein-cholesterol derived measures and amino acids (glutamine, histidine, tyrosine) inversely associated with PWV. Conclusions These data suggest that in children circulating metabolites have no consistent association with cIMT and PWV once adjusted for body mass index and blood pressure. In their middle-aged parents, some evidence of metabolite associations with PWV were identified that warrant further investigation.

OBJECTIVES: 'Growing Up in Australia: The Longitudinal Study of Australian Children' (LSAC) is Australia's only nationally representative children's longitudinal study, focusing on social, economic, physical and cultural impacts on health, learning, social and cognitive development. LSAC's first decade collected wide-ranging repeated psychosocial and administrative data; here, we describe the Child Health CheckPoint, LSAC's dedicated biophysical module. DESIGN, SETTING AND PARTICIPANTS: LSAC recruited a cross-sequential sample of 5107 infants aged 0-1 year and a sample of 4983 children aged 4-5 years in 2004, since completing seven biennial visits. CheckPoint was a cross-sectional wave that travelled Australia in 2015-2016 to reach LSAC's younger cohort at ages 11-12 years between LSAC waves 6 and 7. Parent-child pairs participated in comprehensive assessments at 15 Assessment Centres nationwide or, if unable to attend, a shorter home visit. MEASURES: CheckPoint's intergenerational, multidimensional measures were prioritised to show meaningful variation within normal ranges and capture non-communicable disease (NCD) phenotype precursors. These included anthropometry, physical activity, fitness, time use, vision, hearing, and cardiovascular, respiratory and bone health. Biospecimens included blood, saliva, buccal swabs (also from second parent), urine, hair and toenails. The epidemiology and parent-child concordance of many measures are described in separate papers. RESULTS: 1874 (54% of eligible) parent-child pairs and 1051 second parents participated. Participants' geographical distribution mirrored the broader Australian population; however, mean socioeconomic position and parental education were higher and fewer reported non-English-speaking or Indigenous backgrounds. Application of survey weights partially mitigates that the achieved sample is less population representative than previous waves of LSAC due to non-random attrition. Completeness was uniformly high

BACKGROUND: Improving oxygen systems may improve clinical outcomes for hospitalised children with acute lower respiratory infection (ALRI). This paper reports the effects of an improved oxygen system on mortality and clinical practices in 12 general, paediatric, and maternity hospitals in southwest Nigeria. METHODS AND FINDINGS: We conducted an unblinded stepped-wedge cluster-randomised trial comparing three study periods: baseline (usual care), pulse oximetry introduction, and stepped introduction of a multifaceted oxygen system. We collected data from clinical records of all admitted neonates (<28 days old) and children (28 days to 14 years old). Primary analysis compared the full oxygen system period to the pulse oximetry period and evaluated odds of death for children, children with ALRI, neonates, and preterm neonates using mixed-effects logistic regression. Secondary analyses included the baseline period (enabling evaluation of pulse oximetry introduction) and evaluated mortality and practice outcomes on additional subgroups. Three hospitals received the oxygen system intervention at 4-month intervals. Primary analysis included 7,716 neonates and 17,143 children admitted during the 2-year stepped crossover period (November 2015 to October 2017). Compared to the pulse oximetry period, the full oxygen system had no association with death for children (adjusted odds ratio [aOR] 1.06; 95% confidence interval [CI] 0.77-1.46; p = 0.721) or children with ALRI (aOR 1.09; 95% CI 0.50-2.41; p = 0.824) and was associated with an increased risk of death for neonates overall (aOR 1.45; 95% CI 1.04-2.00; p = 0.026) but not preterm/low-birth-weight neonates (aOR 1.30; 95% CI 0.76-2.23; p = 0.366). Secondary analyses suggested that the introduction of pulse oximetry improved oxygen practices prior to implementation of the full oxygen system and was associated with lower odds of death for children with ALRI (aOR 0.33; 95% CI 0.12-0.92; p = 0.035) but not for children, preterm

OBJECTIVES: Nuclear magnetic resonance (NMR) metabolomics is high throughput and cost-effective, with the potential to improve the understanding of disease and risk. We examine the circulating metabolic profile by quantitative NMR metabolomics of a sample of Australian 11-12 year olds children and their parents, describe differences by age and sex, and explore the correlation of metabolites in parent-child dyads. DESIGN: The population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children. SETTING: Blood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015-March 2016. PARTICIPANTS: 1180 children and 1325 parents provided a blood sample and had metabolomics data available. This included 1133 parent-child dyads (518 mother-daughter, 469 mother-son, 68 father-daughter and 78 father-son). OUTCOME MEASURES: 228 metabolic measures were obtained for each participant. We focused on 74 biomarkers including amino acid species, lipoprotein subclass measures, lipids, fatty acids, measures related to fatty acid saturation, and composite markers of inflammation and energy homeostasis. RESULTS: We identified differences in the concentration of specific metabolites between childhood and adulthood and in metabolic profiles in children and adults by sex. In general, metabolite concentrations were higher in adults than children and sex differences were larger in adults than in children. Positive correlations were observed for the majority of metabolites including isoleucine (CC 0.33, 95% CI 0.27 to 0.38), total cholesterol (CC 0.30, 95% CI 0.24 to 0.35) and omega 6 fatty acids (CC 0.28, 95% CI 0.23 to 0.34) in parent-child comparisons. CONCLUSIONS: We describe the serum metabolite profiles from mid-childhood and adulthood in a population-based sample, together with a parent-child concordance. Differences in profiles by age and sex were observed.

We aimed to examine the association between parental occupational social contact and hygiene factors on type 1 diabetes (T1D) risk and possible mediation of these effects through child enteroviral infection. We interviewed 333 incident T1D cases and 660 controls from 2008–2011 in Melbourne, Australia. Enteroviral indices (ribonucleic acid by reverse transcription polymerase chain reaction and Coxsackie B virus antibody levels) in peripheral blood were measured in nested case control samples. Parent occupational social contact was assessed by the number of well or sick children, adults or animals contacted daily through work. Higher parental occupational social contact was strongly associated with reduced T1D risk with evidence of dose response (contact with the well or sick score, Adjusted odds ratio (AOR) per category: 0.73 (95% Confidence Interval (CI): 0.66, 0.81); P<0.001 or AOR 0.63 (95% CI: 0.53, 0.75); P<0.001) respectively). Nine of the ten parental social contact indices, were significant mediated through one or more enteroviral indices. The strength of association between enterovirus presence and T1D onset increased with child age (1.2 fold increase per year; P = 0.05). Lower child hand hygiene enhanced the adverse effect of low parental occupational contact with the sick; Synergy Index 5.16 (95% CI: 3.61, 7.36). The interaction between hand washing and parental occupational contact is more consistent with protection against parental enteroviral shedding than the sharing of a protective infectious agent or microbiome.

Investigators in large-scale population health studies face increasing difficulties in recruiting representative samples of participants. Nonparticipation, item nonresponse, and attrition, when follow-up is involved, often result in highly selected samples even in well-designed studies. We aimed to assess the potential value of multilevel regression and poststratification, a method previously used to successfully forecast US presidential election results, for addressing biases due to nonparticipation in the estimation of population descriptive quantities in large cohort studies. The investigation was performed as an extensive case study using baseline data (2013-2014) from a large national health survey of Australian males (Ten to Men: The Australian Longitudinal Study on Male Health). Analyses were performed in the open-source Bayesian computational package RStan. Results showed greater consistency and precision across population subsets of varying sizes when compared with estimates obtained using conventional survey sampling weights. Estimates for smaller population subsets exhibited a greater degree of shrinkage towards the national estimate. Multilevel regression and poststratification provides a promising analytical approach to addressing potential participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

With incomplete data, the "missing at random" (MAR) assumption is widely understood to enable unbiased estimation with appropriate methods. While the need to assess the plausibility of MAR and to perform sensitivity analyses considering "missing not at random" (MNAR) scenarios has been emphasized, the practical difficulty of these tasks is rarely acknowledged. With multivariable missingness, what MAR means is difficult to grasp, and in many MNAR scenarios unbiased estimation is possible using methods commonly associated with MAR. Directed acyclic graphs (DAGs) have been proposed as an alternative framework for specifying practically accessible assumptions beyond the MAR-MNAR dichotomy. However, there is currently no general algorithm for deciding how to handle the missing data given a specific DAG. Here we construct "canonical" DAGs capturing typical missingness mechanisms in epidemiologic studies with incomplete data on exposure, outcome, and confounding factors. For each DAG, we determine whether common target parameters are "recoverable," meaning that they can be expressed as functions of the available data distribution and thus estimated consistently, or whether sensitivity analyses are necessary. We investigate the performance of available-case and multiple-imputation procedures. Using data from waves 1-3 of the Longitudinal Study of Australian Children (2004-2008), we illustrate how our findings can guide the treatment of missing data in point-exposure studies.

We aimed to examine the association between parental occupational social contact and hygiene factors on type 1 diabetes (T1D) risk and possible mediation of these effects through child enteroviral infection. We interviewed 333 incident T1D cases and 660 controls from 2008-2011 in Melbourne, Australia. Enteroviral indices (ribonucleic acid by reverse transcription polymerase chain reaction and Coxsackie B virus antibody levels) in peripheral blood were measured in nested case control samples. Parent occupational social contact was assessed by the number of well or sick children, adults or animals contacted daily through work. Higher parental occupational social contact was strongly associated with reduced T1D risk with evidence of dose response (contact with the well or sick score, Adjusted odds ratio (AOR) per category: 0.73 (95% Confidence Interval (CI): 0.66, 0.81); P<0.001 or AOR 0.63 (95% CI: 0.53, 0.75); P<0.001) respectively). Nine of the ten parental social contact indices, were significant mediated through one or more enteroviral indices. The strength of association between enterovirus presence and T1D onset increased with child age (1.2 fold increase per year; P = 0.05). Lower child hand hygiene enhanced the adverse effect of low parental occupational contact with the sick; Synergy Index 5.16 (95% CI: 3.61, 7.36). The interaction between hand washing and parental occupational contact is more consistent with protection against parental enteroviral shedding than the sharing of a protective infectious agent or microbiome.