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Recently, a meningococcal vaccine for group B was approved and deployed into clinical practice. In this trial, the effect of widespread use of this vaccine on the nasopharyngeal carriage of meningococcus group B was assessed in more than 24,000 adolescents in Australia.

Cluster randomized trials (cRCT) to assess vaccine effectiveness incorporate indirect effects of vaccination, helping to inform vaccination policy. To calculate the sample size for a cRCT, an estimate of the intracluster correlation coefficient (ICC) is required. For infectious diseases, shared characteristics and social mixing behaviours may increase susceptibility and exposure, promote transmission and be a source of clustering. We present ICCs from a school-based cRCT assessing the effectiveness of a meningococcal B vaccine (Bexsero, GlaxoSmithKline) on reducing oropharyngeal carriage of Neisseria meningitidis ( Nm ) in 34,489 adolescents from 237 schools in South Australia in 2017/2018. We also explore the contribution of shared behaviours and characteristics to these ICCs. The ICC for carriage of disease-causing Nm genogroups (primary outcome) pre-vaccination was 0.004 (95% CI: 0.002, 0.007) and for all Nm was 0.007 (95%CI: 0.004, 0.011). Adjustment for social behaviours and personal characteristics reduced the ICC for carriage of disease-causing and all Nm genogroups by 25% (to 0.003) and 43% (to 0.004), respectively. ICCs are also reported for risk factors here, which may be outcomes in future research. Higher ICCs were observed for susceptibility and/or exposure variables related to Nm carriage (having a cold, spending ≥1 night out socializing or kissing ≥1 person in the previous week). In metropolitan areas, nights out socializing was a highly correlated behaviour. By contrast, smoking was a highly correlated behaviour in rural areas. A practical example to inform future cRCT sample size estimates is provided.

Children born very prematurely are deprived of maternal docosahexaenoic acid. This study shows an IQ at 5 years of age that was 3.5 points higher among children who had received neonatal DHA supplementation.

The effects of SARS-Cov-2 infection can extend beyond the acute phase of the illness, often described as Long COVID, post-COVID condition (PCC) or Post-acute sequelae of COVID (PASC). Post-acute sequelae (PAS) are also likely to be a problem for a small proportion of children and adolescents following influenza infection. However, there is no comprehensive ongoing data collection in Australian children and adolescents, and global data on both PCC during the SARS-Cov-2 Omicron variant period and PAS following influenza is limited. This study aims to determine the cumulative incidence of PCC in Australian children and adolescents five years after the start of the COVID-19 pandemic. Secondary aims include identifying the cumulative incidence of PAS in children and adolescents following influenza infection. This longitudinal cohort study will recruit children and adolescents aged 0-18 years in South Australia who tested positive for SARS-Cov-2 or influenza in the previous 2 months. Following consent, participants will complete an online baseline survey and then at 3, 6, and 12 months post-infection. The survey has been adapted from the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) Paediatric COVID-19 follow-up survey. The survey includes validated assessment tools such as the Pediatric Quality of Life Inventory (PedsQL), Multidimensional Fatigue Scale, and the Malmö Postural Orthostatic Tachycardia Syndrome (POTS) Score questionnaire. PCC following COVID-19 and PAS following influenza infection will be identified according to an adapted World Health Organization definition of PCC in children and adolescents. This study addresses gaps in understanding PCC and PAS following influenza in children and adolescents during Omicron circulation. Whilst it is no longer feasible to prospectively compare post-acute sequelae in children and adolescents who have never had COVID-19, this design allows a comparison with another common viral infection, influenza, informing clinical management of children post-infection.

To describe (1) infant feeding practices during initial hospitalisation and up to 6 months corrected age (CA) in infants born late preterm with mothers intending to breastfeed, (2) the impact of early feeding practices on hospital length of stay and (3) maternal and infant factors associated with duration of breastfeeding. We conducted a prospective cohort study of infants born at 34.sup.+0 to 36.sup.+6 weeks gestational age during 2018-2020. Families were followed up until the infant reached 6 months of age (corrected for prematurity). Feeding practices during the birth hospitalisation, length of initial hospital stay, and the prevalence of exclusive or any breastfeeding at 6 weeks, 3 months, and 6 months CA were examined. Associations between maternal and infant characteristics and breastfeeding at 6 weeks, 3 months and 6 months CA were assessed using multivariable logistic regression models. 270 infants were enrolled, of these, 30% were multiple births. Overall, 78% of infants received only breastmilk as their first feed, and 83% received formula during the hospitalisation. Seventy-four per cent of infants were exclusively breastfed at discharge, 41% at 6 weeks CA, 35% at 3 months CA, and 29% at 6 months CA. The corresponding combined exclusive and partial breastfeeding rates (any breastfeeding) were 72%, 64%, and 53% of babies at 6 weeks CA, 3 months CA, and 6 months CA, respectively. The mean duration of hospitalisation was 2.9 days longer (95% confidence interval (CI) 0.31, 5.43 days) in infants who received any formula compared with those receiving only breastmilk (adjusted for GA, maternal age, multiple birth, site, and neonatal intensive care unit admission). In multivariable models, receipt of formula as the first milk feed was associated with a reduction in exclusive breastfeeding at 6 weeks CA (odds ratio = 0.22; 95% CI 0.09 to 0.53) and intention to breastfeed >6 months with an increase (odds ratio = 4.98; 95% CI 2.39 to 10.40). Intention to breastfeed >6 months remained an important predictor of exclusive breastfeeding at 3 and 6 months CA. Our study demonstrates that long-term exclusive breastfeeding rates were low in a cohort of women intending to provide breastmilk to their late preterm infants, with approximately half providing any breastmilk at 6 months CA. Formula as the first milk feed and intention to breastfeed >6 months were significant predictors of breastfeeding duration. Improving breastfeeding outcomes may require strategies to support early lactation and a better understanding of the ongoing support needs of this population.

Background Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Methods Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Results Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Conclusions Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

Background Randomisation forms the foundation of clinical trials, but its implementation can be prone to error. Often randomisation errors affect few participants and do not seriously compromise the integrity of the trial. However, in some cases randomisation errors can have widespread consequences and call into question the validity of trial conclusions. Published articles may be retracted as a result. Valuable insight can be gained from studying past errors to minimise the risk of similar errors and their disastrous consequences impacting future trials. The aims of this article are to (i) describe examples of major failures of randomisation, and (ii) provide guidance on how to avoid them in practice. Methods Major failures of randomisation were defined as inadvertent errors that affected many trial participants and occurred during the process of designing the randomisation scheme, generating the randomisation schedule, allocating participants to treatment groups, or providing the assigned treatment. Examples of major failures of randomisation were drawn from author experience and through a review of the published literature, which included a systematic search of the Retraction Watch Database for serious randomisation problems that led to the retraction of a published article. Practice points to avoid such errors were developed by consensus among the authors. Results Examples are provided of seven broad types of major failures of randomisation: randomisation schedule followed incorrectly, randomisation schedule sorted incorrectly, randomisation schedule too short, clusters handled incorrectly, incorrect or unknown treatment provided at randomisation, poorly designed randomisation scheme, and programming errors in adaptive randomisation schemes. Practice points for avoiding such errors are presented, including suggestions for written documentation, staff training, and thorough testing of the randomisation process prior to trial commencement. Conclusions Randomisation is of fundamental importance in clinical trials. Greater consideration should be given to the potential for major failures of randomisation and strategies to avoid them. When major failures of randomisation do occur, greater transparency in reporting is needed.

Background Observational evidence suggests both low and high iodine intakes in pregnancy are associated with poorer neurodevelopment in children. This raises concern that blanket recommendations for iodine supplementation in pregnancy may negatively impact child neurodevelopment in women with sufficient iodine intake from food alone. Methods PoppiE (Prenatal Iodine Supplementation and Early Childhood Neurodevelopment) is a multi-centre, parallel, two-arm, clinician, researcher and participant blinded randomised controlled trial. Seven hundred fifty-four consenting pregnant women ≤ 13 weeks of gestation with an iodine intake of > 165 μg/day from food will be randomised to receive a multivitamin and mineral supplement containing 20 µg/day (intervention) or 200 µg/day (control) of iodine from enrolment until delivery. The primary outcome is the developmental quotient of infants at 24 months of age as assessed with the Cognitive Scale Score of the Bayley Scales of Infant Development, 4th Edition, to be analysed using linear regression with generalised estimating equations to account for multiple births. In this article, we comprehensively detail the planned statistical analyses of the PoppiE trial, including approaches to intercurrent events, methods for handling missing data and planned sensitivity analyses. Conclusions PoppiE is the first trial to examine the effect of prenatal iodine supplementation on early childhood development in women with sufficient iodine intake from food. At the time of writing (February 2025), recruitment into the trial is complete and data collection is due to conclude in July 2026. The statistical analysis plan was finalised before the database lock, which will ensure study conclusions are not subject to bias due to data-driven analyses. Trial registration ClinicalTrials.gov NCT04586348. Registered on October 14, 2020.

Background Prebiotic dietary supplementation has been shown to improve glucose homeostasis in type 2 diabetes patients. The aim of this analysis was to determine whether pre-pregnancy body mass index (BMI) modifies the effect of prebiotic supplementation from mid-pregnancy on reducing the risk of gestational diabetes mellitus (GDM). Methods In a double-blinded, randomised controlled trial, pregnant women < 21 gestational weeks were randomly assigned (1:1) to consume daily prebiotics (14.2 g galacto-oligosaccharides and fructo-oligosaccharides) or placebo (8.7 g maltodextrin) powder. An effect modification analysis was performed to assess the heterogeneity of the effect of prebiotic supplementation in relation to pre-pregnancy BMI on GDM diagnosis. Results Between June 2016 and November 2021, 329 women were assigned to the prebiotic group (50.4%), and 323 (49.5%) were assigned to the placebo group. Overall, 288 of 652 women (44.2%) were classified as overweight/obese prior to pregnancy (BMI ≥ 25 kg/m 2 ). The distribution was balanced, with 146/329 (44.4%) randomised to the prebiotics group and 142/323 (44.0%) in the placebo group. Pre-pregnancy BMI modified the effect of prebiotic supplementation, with the intervention reducing GDM rates in women with a BMI ≥ 25 kg/m 2 (prebiotic group 11.0% vs. control group 21.8%; adjusted relative risk 0.50; 95% confidence interval (CI) 0.28 to 0.89) but not in women with a pre-pregnancy BMI < 25 kg/m 2 (7.7–7% vs. 4.4%; adjusted relative risk 1.72; 95% CI 0.70 to 4.19; interaction p  = 0.02). Conclusion Pre-pregnancy BMI was found to modify the effect of prebiotic supplementation on GDM rates, with benefits observed in overweight and obese women. Our results highlight a target population for future randomised controlled trials to further investigate the effects of prebiotic supplementation during pregnancy on reducing the risk of GDM.  Trial registration Primary and secondary infant allergic disease outcomes of this randomised controlled trial have been previously published. The trial was registered with the Australian New Zealand Clinical Trial Registry: ACTRN12615001075572, https://www.anzctr.org.au/on13/10/2015 .