Household income supplements in early childhood to reduce inequities in children's development.

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

Addressing Child Mental Health Inequities Through Parental Mental Health and Preschool Attendance.

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.

Socioeconomic disadvantage and polygenic risk for high BMI magnify obesity risk across childhood: a longitudinal, population, cohort study.

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. FUNDING: Australian National Health and Medical Research Council.

The effect of adverse and positive experiences on inflammatory markers in Australian and UK children.

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

Serological testing of blood donors to characterise the impact of COVID-19 in Melbourne, Australia, 2020.

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 population patterns of infection, against which the effectiveness of ongoing responses to the pandemic can be assessed.

Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2-Specific Antibodies in Australia After the First Epidemic Wave in 2020: A National Survey.

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.

Better together: Advancing life course research through multi-cohort analytic approaches.

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

Seroprevalence of SARS-CoV-2-specific antibodies in Sydney after the first epidemic wave of 2020.

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.

Multilevel regression and poststratification for estimating population quantities from large health studies: a simulation study based on US population structure.

INTRODUCTION: Recruiting 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. METHODS: Data 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. RESULTS: MRP 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. DISCUSSION: MRP 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 Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies.

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.

A nationwide parent survey of antibiotic use in Australian children.

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.

Multilevel regression and poststratification as a modeling approach for estimating population quantities in large population health studies: A simulation study.

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.

Multilevel Regression and Poststratification: A Modeling Approach to Estimating Population Quantities From Highly Selected Survey Samples.

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.

The Australian longitudinal study on male health sampling design and survey weighting: implications for analysis and interpretation of clustered data.

BACKGROUND: The Australian Longitudinal Study on Male Health (Ten to Men) used a complex sampling scheme to identify potential participants for the baseline survey. This raises important questions about when and how to adjust for the sampling design when analyzing data from the baseline survey. METHODS: We describe the sampling scheme used in Ten to Men focusing on four important elements: stratification, multi-stage sampling, clustering and sample weights. We discuss how these elements fit together when using baseline data to estimate a population parameter (e.g., population mean or prevalence) or to estimate the association between an exposure and an outcome (e.g., an odds ratio). We illustrate this with examples using a continuous outcome (weight in kilograms) and a binary outcome (smoking status). RESULTS: Estimates of a population mean or disease prevalence using Ten to Men baseline data are influenced by the extent to which the sampling design is addressed in an analysis. Estimates of mean weight and smoking prevalence are larger in unweighted analyses than weighted analyses (e.g., mean = 83.9 kg vs. 81.4 kg; prevalence = 18.0 % vs. 16.7 %, for unweighted and weighted analyses respectively) and the standard error of the mean is 1.03 times larger in an analysis that acknowledges the hierarchical (clustered) structure of the data compared with one that does not. For smoking prevalence, the corresponding standard error is 1.07 times larger. Measures of association (mean group differences, odds ratios) are generally similar in unweighted or weighted analyses and whether or not adjustment is made for clustering. CONCLUSIONS: The extent to which the Ten to Men sampling design is accounted for in any analysis of the baseline data will depend on the research question. When the goals of the analysis are to estimate the prevalence of a disease or risk factor in the population or the magnitude of a population-level exposure-outcome association, our advice is to adopt an analysis that respects the sampling design.