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

Refereed/Peer-reviewed Background: Across the life course, socioeconomic disadvantage disproportionately afflicts those with genetic predispositions to inflammatory diseases. We describe how socioeconomic disadvantage and polygenic risk for high BMI magnify the risk of obesity across childhood, and using causal analyses, explore the hypothetical impact of intervening on socioeconomic disadvantage to reduce adolescent obesity. Methods: Data were drawn from a nationally representative Australian birth cohort, with biennial data collection between 2004 and 2018 (research and ethics committee approved). We generated a polygenic risk score for BMI using published genome-wide association studies. We measured early-childhood disadvantage (age 2–3 years) with a neighbourhood census-based measure and a family-level composite of parent income, occupation, and education. We used generalised linear regression (Poisson-log link) to estimate the risk of overweight or obesity (BMI ≥85th percentile) at age 14–15 years for children with early-childhood disadvantage (quintiles 4–5) versus average (quintile 3) and least disadvantage (quintiles 1–2), for those with high and low polygenic risk separately. Findings: For 1607 children (n=796 female, n=811 male; 31% of the original cohort [N=5107]), polygenic risk and disadvantage were both associated with overweight or obesity; effects of disadvantage were more marked as polygenic risk increased. Of children with polygenic risk higher than the median (n=805), 37% of children living in disadvantage at age 2–3 years had an overweight or obese BMI by adolescence, compared with 26% of those with least disadvantage. For genetically vulnerable children, causal analyses indicated that early neighbourhood intervention to lessen disadvantage (to quintile 1–2) would reduce risk of adolescent overweight or obesity by 23% (risk ratio 0·77; 95% CI 0·57–1·04); estimates for improving family environments were similar (0·59; 0·43–0·80). Interpretation: Actions addressing socioeconomic disadvantage could mitigate polygenic risk for developing obesity. This study benefits from population-representative longitudinal data but is limited by sample size.

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.

Background Antimicrobial resistance is increasing globally, largely due to high rates of antibiotic use and misuse. Factors that influence frequent antibiotic use in children are poorly understood. Objectives This study describes rates of antibiotic use in Australian children and investigates parental factors including knowledge, attitudes and behaviours that influence antibiotic use. Methods An online questionnaire relating to antibiotic use was administered as part of the Royal Children’s Hospital National Child Health Poll to a randomly recruited nationwide sample of parents or guardians of children aged 0–17 years in Australia. Data on antibiotic use in children and parental knowledge of appropriate indications for antibiotics and behaviours were collected. Standard binary logistic regression was used to assess associations between parent demographics and behaviour with antibiotic administration. Results The survey was completed by 2157 parents (64% completion rate), of which 1131 (52%) reported having given oral antibiotics to one or more of their children in the preceding 12 months. Of the 3971 children represented overall, 1719 (43%) had received at least one course of antibiotics. The average number of courses per child was 0.86 overall and 1.96 courses per child among those with reported antibiotic use. Notably, 194/1131 (17%) parents reported giving antibiotics to their child without a prescription. Poor parental knowledge of antibiotic indications was associated with antibiotic use. Conclusions Reducing excessive use of antibiotics in children is necessary in the global strategy for preventing antimicrobial resistance. This study identified areas for public health interventions to educate parents and increase regulation of access to antibiotics.

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 (

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

Background Inflammation is one of key mechanisms linking childhood experiences to later chronic disease risk. Childhood adversity is associated with inflammation, but little is known about positive experiences. We examine how adverse and positive experiences are associated with inflammatory markers in late childhood, and whether they have an interaction effect. Methods Data sources: Longitudinal Study of Australian Children (LSAC; N = 1237) and Avon Longitudinal Study of Parents and Children (ALSPAC; N = 3488). Exposures: Adverse and positive experiences assessed from 0 to 11 (LSAC) and 0-14 years (ALSPAC). Adversity indicators included parent legal problems, family violence, mental illness, substance abuse, harsh parenting, parental divorce, neighbourhood violence, family member death, and bullying victimization. Positive experiences included positive parenting practice, trusting and supportive relationships, supportive neighbourhood and home learning environments, social engagement and enjoyment. Outcomes: Inflammation quantified by high-sensitivity C-reactive protein (hsCRP) and glycoprotein acetyls (GlycA). Analyses: Linear regression was used to estimate relative change in inflammatory markers, adjusted for sociodemographics. Outcomes were log-transformed. Results Exposure to adversity was associated with higher levels of inflammation (e.g., CRP: β = 8.8%, 95% CI= -16.5% to 34.2% in LSAC), whereas exposure to positive experiences was associated with lower levels (e.g., CRP: β=-18.9%, 95% CI= -45.8% to 7.9% in LSAC), after adjusting for sociodemographics. There was no interaction effect of adverse and positive experiences on inflammation. Conclusions Adverse and positive experiences have independent and small effects on children’s inflammation across two cohorts. Key messages Positive experiences are critical to inform interventions to improve inflammatory outcomes for children who face adversity.

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.

IntroductionRecruiting a representative sample of participants is becoming increasingly difficult in large-scale health surveys. Multilevel regression and poststratification (MRP) has been shown to be effective in estimating population descriptive quantities in non-representative samples. We performed a simulation study, previously applied to an Australian population, this time to a US population, to assess MRP performance.MethodsData were extracted from the 2017 Current Population Survey representing a population of US adult males aged 18–55 years. Simulated datasets of non-representative samples were generated. State-level prevalence estimates for a dichotomous outcome using MRP were compared with the use of sampling weights (with and without raking adjustment). We also investigated the impact on MRP performance of sample size, model misspecification, interactions and the addition of a geographic-level covariate.ResultsMRP was found to achieve generally superior performance, with large gains in precision vastly outweighing the increased accuracy observed for sampling weights with raking adjustment. MRP estimates were generally robust to model misspecification. We found a tendency of MRP to over-pool between-state variation in the outcome, particularly for the least populous states and small sample sizes. The inclusion of a state-level covariate appeared to mitigate this and further improve MRP performance.DiscussionMRP has been shown to be effective in estimating population descriptive quantities in two different populations. This provides promising evidence for the general applicability of MRP to populations with different geographic structures. MRP appears to be a valuable analytic strategy for addressing potential participation bias from large-scale health surveys.

Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

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.