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Risk exposures and predictions of child development outcomes typically estimate the independent effects of individual exposures. As a rule though, children are not exposed piecemeal to individual or single risks but, rather, they are exposed to clusters of risk. Many of these clusters of risks are better thought of as comprising a developmental “circumstance” with a substantial duration, over which period, additional risk exposures also accumulate. In this paper we examined the distribution of 16 single risk exposures for low language ability using latent class analysis across a sample of approximately 4000 children from the Longitudinal Study of Australian Children. The best fitting model identified six distinct classes. 46% of children were in a Developmentally Enabled group, 20% were in a group typified as Working Poor families, 10% of children were in group typified as Overwhelmed group, 9% of children were in a group defined by Child Developmental Delay, 8% of children were in a group defined by Low Human Capital, and 7% of children were in a group defined by Resource Poor non-English Speaking background families. These groups had quantitatively and qualitatively distinct patterns of risk factors and showed different onward trajectories of receptive vocabulary. Our results demonstrate a range of multiple risk profiles in a population-representative sample of Australian children and highlight the mix of risk factors faced by children. Children with distinct patterns of risk factors have different onward trajectories of receptive vocabulary development. [ABSTRACT FROM AUTHOR]

Introduction: A Job Exposure Matrix (JEM) for psychosocial job stressors allows assessment of these exposures at a population level. JEMs are particularly useful in situations when information on psychosocial job stressors were not collected individually and can help eliminate the biases that may be present in individual self-report accounts. This research paper describes the development of a JEM in the Australian context. Methods: The Household Income Labour Dynamics in Australia (HILDA) survey was used to construct a JEM for job control, job demands and complexity, job insecurity, and fairness of pay. Population median values of these variables for all employed people (n = 20,428) were used to define individual exposures across the period 2001 to 2012. The JEM was calculated for the Australian and New Zealand Standard Classification of Occupations (ANZSCO) at the four-digit level, which represents 358 occupations. Both continuous and binary exposures to job stressors were calculated at the 4-digit level. We assessed concordance between the JEM-assigned and individually-reported exposures using the Kappa statistic, sensitivity and specificity assessments. We conducted regression analysis using mental health as an outcome measure. Results: Kappa statistics indicate good agreement between individually-reported and JEM-assigned dichotomous measures for job demands and control, and moderate agreement for job insecurity and fairness of pay. Job control, job demands and security had the highest sensitivity, while specificity was relatively high for the four exposures. Regression analysis shows that most individually reported and JEM measures were significantly associated with mental health, and individually-reported exposures produced much stronger effects on mental health than the JEM-assigned exposures. Discussion: These JEM-based estimates of stressors exposure provide a conservative proxy for individual-level data, and can be applied to a range of health and organisational outcomes. [ABSTRACT FROM AUTHOR]