A growing body of literature highlights that racial discrimination has negative impacts on child health, although most studies have been limited to an examination of direct forms of racism using cross-sectional data. We aim to provide further insights on the impact of early exposure to racism on child health using longitudinal data among Indigenous children in Australia and multiple indicators of racial discrimination. We used data on 1239 Indigenous children aged 5–10 years from Waves 1–6 (2008–2013) of Footprints in Time, a longitudinal study of Indigenous children across Australia. We examined associations between three dimensions of carer-reported racial discrimination (measuring the direct experiences of children and vicarious exposure by their primary carer and family) and a range of physical and mental health outcomes. Analysis was conducted using multivariate logistic regression within a multilevel framework. Two-fifths (40%) of primary carers, 45% of families and 14% of Indigenous children aged 5–10 years were reported to have experienced racial discrimination at some point in time, with 28–40% of these experiencing it persistently (reported at multiple time points). Primary carer and child experiences of racial discrimination were each associated with poor child mental health status (high risk of clinically significant emotional or behavioural difficulties), sleep difficulties, obesity and asthma, but not with child general health or injury. Children exposed to persistent vicarious racial discrimination were more likely to have sleep difficulties and asthma in multivariate models than those with a time-limited exposure. The findings indicate that direct and persistent vicarious racial discrimination are detrimental to the physical and mental health of Indigenous children in Australia, and suggest that prolonged and more frequent exposure to racial discrimination that starts in the early lifecourse can impact on multiple domains of health in later life. Tackling and reducing racism should be an integral part of policy and intervention aimed at improving the health of Australian Indigenous children and thereby reducing health disparities between Indigenous and non-Indigenous children.

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression.

The pattern of association between socioeconomic factors and health outcomes has primarily depicted better health for those who are higher in the social hierarchy. Although this is a ubiquitous finding in the health literature, little is known about the interplay between these factors among indigenous populations. We begin to bridge this knowledge gap by assessing evidence on social gradients in indigenous health in Australia. We reveal a less universal and less consistent socioeconomic status patterning in health among Indigenous Australians, and discuss the plausibility of unique historical circumstances and social and cultural characteristics in explaining these patterns. A more robust evidence base in this field is fundamental to processes that aim to reduce the pervasive disparities between indigenous and nonindigenous population health.

Introduction: The Australian Aboriginal population experiences significantly poorer health than the non-Aboriginal population. The contribution of environmental risk factors in remote communities to this health disparity is poorly understood. Objective: To describe and quantify major environmental risk factors and associated health outcomes in remote Aboriginal communities in Western Australia. Methods: The association between environmental health indicators, community infrastructure and reported health outcomes was analysed using linear and logistic regression of survey data. Results: Housing/overcrowding was significantly associated with increased reports of hearing/eyesight (OR 3.01 95 % CI 1.58–5.73), skin (OR 2.71 95 % CI 1.31–5.60), gastrointestinal (OR 3.51 95 % CI 1.49–8.26) and flu/colds (OR 2.47 95 % CI 1.27–4.78) as health concerns. Dust was significantly associated with hearing/eyesight (OR 3.16 95 % CI 1.82–5.48), asthma/respiratory (OR 2.48 95 % CI 1.43–4.29) and flu/colds (OR 3.31 95 % CI 1.88–5.86) as health concerns. Conclusion: Poor environmental health is prevalent in remote Aboriginal communities and requires further delineation to inform environmental health policy.