Commentary on Nutrition and older Indigenous Australians : service delivery implications in remote communities. A narrative review

Indigenous Australians are the most socially and economically disadvantaged population group in Australia and have the poorest health status. The statistics describe and highlight the degree of sicknesses and disadvantage along with lower life expectancy, elevated mortality rate and increased risk of cardiovascular disease, cancer, diabetes, respiratory disease and kidney disease. While these statistics reflect poor health status and a high level of illness within Indigenous communities, it is known that individual, family and community behaviours play a key role in Indigenous health and wellbeing outcomes. These behavioural issues include use of tobacco, alcohol and other substances along with lack of physical activity and poor nutrition. The paper Nutrition and older Indigenous Australians: Service delivery implications in remote communities. A narrative view explores some of the issues specific to nutrition. Bronwyn Fredericks was invited to provide this commentary by the Editor of the Australasian Journal on Aging.

A review of modern computational algorithms for Bayesian optimal design

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

Beliefs about infant regulation, early infant behaviors and maternal postnatal depressive symptoms

Background: Young infants may have irregular sleeping and feeding patterns. Such regulation difficulties are known correlates of maternal depressive symptoms. Parental beliefs regarding their role in regulating infant behaviours also may play a role. We investigated the association of depressive symptoms with infant feeding/sleeping behaviours, parent regulation beliefs, and the interaction of the two. Method: In 2006, 272 mothers of infants aged up to 24 weeks completed a questionnaire about infant behaviour and regulation beliefs. Participants were recruited from general medical practices and child health clinics in Brisbane, Australia. Depressive symptomology was measured using the Edinburgh Postnatal Depression Scale (EPDS). Other measures were adapted from the ALSPAC study. Results: Regression analyses were run controlling for partner support, other support, life events, and a range of demographic variables. Maternal depressive symptoms were associated with infant sleeping and feeding problems but not regulation beliefs. The most important infant predictor was sleep behaviours with feeding behaviours accounting for little additional variance. An interaction between regulation beliefs and sleep behaviours was found. Mothers with high regulation beliefs were more susceptible to postnatal depressive symptoms when infant sleep behaviours were problematic. Conclusion: Mothers of young infants who expect greater control are more susceptible to depressive symptoms when their infant presents challenging sleep behaviour.