La Trobe University

About: La Trobe University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 13370 authors who have published 41291 publications receiving 1138269 citations. The organization is also known as: LaTrobe University & LTU.

Randomised controlled trial of midwife led debriefing to reduce maternal depression after operative childbirth

Abstract: Objective: To assess the effectiveness of a midwife led debriefing session during the postpartum hospital stay in reducing the prevalence of maternal depression at six months postpartum among women giving birth by caesarean section, forceps, or vacuum extraction. Design: Randomised controlled trial. Setting: Large maternity teaching hospital in Melbourne, Australia. Participants: 1041 women who had given birth by caesarean section (n= 624) or with the use of forceps (n= 353) or vacuum extraction (n= 64). Main outcome measures: Maternal depression (score ≥13 on the Edinburgh postnatal depression scale) and overall health status (comparison of mean scores on SF-36 subscales) measured by postal questionnaire at six months postpartum. Results: 917 (88%) of the women recruited responded to the outcome questionnaire. More women allocated to debriefing scored as depressed six months after birth than women allocated to usual postpartum care (81 (17%) v 65 (14%)), although this difference was not significant (odds ratio=1.24, 95% confidence interval 0.87 to 1.77). They were also more likely to report that depression had been a problem for them since the birth, but the difference was not significant (123 (28%) v 94 (22%); odds ratio=1.37, 1.00 to 1.86). Women allocated to debriefing had poorer health status on seven of the eight SF-36 subscales, although the difference was significant only for role functioning (emotional): mean scores 73.32 v 78.98, t= −2.31, 95% confidence interval −10.48 to −0.84). Conclusions: Midwife led debriefing after operative birth is ineffective in reducing maternal morbidity at six months postpartum. The possibility that debriefing contributed to emotional health problems for some women cannot be excluded.

Visual analysis of single-case time series: Effects of variability, serial dependence, and magnitude of intervention effects.

Abstract: Visual analysis is the dominant method of analysis for single-case time series. The literature assumes that visual analysts will be conservative judges. We show that previous research into visual analysis has not adequately examined false alarm and miss rates or the effect of serial dependence. In order to measure false alarm and miss rates while varying serial dependence, amount of random variability, and effect size, 37 students undertaking a postgraduate course in single-case design and analysis were required to assess the presence of an intervention effect in each of 27 AB charts constructed using a first-order autoregressive model. Three levels of effect size and three levels of variability, representative of values found in published charts, were combined with autocorrelation coefficients of 0, 0.3 and 0.6 in a factorial design. False alarm rates were surprisingly high (16% to 84%). Positive autocorrelation and increased random variation both significantly increased the false alarm rates and interacted in a nonlinear fashion. Miss rates were relatively low (0% to 22%) and were not significantly affected by the design parameters. Thus, visual analysts were not conservative, and serial dependence did influence judgment.

Conflict among individual mitochondrial proteins in resolving the phylogeny of eutherian orders.

Abstract: The phylogenetic relationship among primates, ferungulates (artiodactyls + cetaceans + perissodactyls + carnivores), and rodents was examined using proteins encoded by the H strand of mtDNA, with marsupials and monotremes as the outgroup Trees estimated from individual proteins were compared in detail with the tree estimated from all 12 proteins (either concatenated or summing up log-likelihood scores for each gene) Although the overall evidence strongly suggests ((primates, ferungulates), rodents), the ND1 data clearly support another tree, ((primates, rodents), ferungulates) To clarify whether this contradiction is due to (1) a stochastic (sampling) error; (2) minor model-based errors (eg, ignoring site rate variability), or (3) convergent and parallel evolution (specifically between either primates and rodents or ferungulates and the outgroup), the ND1 genes from many additional species of primates, rodents, other eutherian orders, and the outgroup (marsupials + monotremes) were sequenced The phylogenetic analyses were extensive and aimed to eliminate the following artifacts as possible causes of the aberrant result: base composition biases, unequal site substitution rates, or the cumulative effects of both Neither more sophisticated evolutionary analyses nor the addition of species changed the previous conclusion That is, the statistical support for grouping rodents and primates to the exclusion of all other taxa fluctuates upward or downward in quite a tight range centered near 95% confidence These results and a site-by-site examination of the sequences clearly suggest that convergent or parallel evolution has occurred in ND1 between primates and rodents and/or between ferungulates and the outgroup While the primate/rodent grouping is strange, ND1 also throws some interesting light on the relationships of some eutherian orders, marsupials, and montremes In these parts of the tree, ND1 shows no apparent tendency for unexplained convergences

Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming?

Abstract: Models that couple habitat suitability with demographic processes offer a potentially improved approach for estimating spatial distributional shifts and extinction risk under climate change. Applying such an approach to five species of Australian plants with contrasting demographic traits, we show that: (i) predicted climate-driven changes in range area are sensitive to the underlying habitat model, regardless of whether demographic traits and their interaction with habitat patch configuration are modeled explicitly; and (ii) caution should be exercised when using predicted changes in total habitat suitability or geographic extent to infer extinction risk, because the relationship between these metrics is often weak. Measures of extinction risk, which quantify threats to population persistence, are particularly sensitive to life-history traits, such as recruitment response to fire, which explained approximately 60% of the deviance in expected minimum abundance. Dispersal dynamics and habitat patch structure have the strongest influence on the amount of movement of the trailing and leading edge of the range margin, explaining roughly 40% of modeled structural deviance. These results underscore the need to consider direct measures of extinction risk (population declines and other measures of stochastic viability), as well as measures of change in habitat area, when assessing climate change impacts on biodiversity. Furthermore, direct estimation of extinction risk incorporates important demographic and ecosystem processes, which potentially influence species’ vulnerability to extinction due to climate change.

The fourth-corner solution – using predictive models to understand how species traits interact with the environment

Abstract: Summary An important problem encountered by ecologists in species distribution modelling (SDM) and in multivariate analysis is that of understanding why environmental responses differ across species, and how differences are mediated by functional traits. We describe a simple, generic approach to this problem – the core idea being to fit a predictive model for species abundance (or presence/absence) as a function of environmental variables, species traits and their interaction. We show that this method can be understood as a model-based approach to the fourth-corner problem – the problem of studying the environment–trait association using matrices of abundance or presence/absence data across species, environmental data across sites and trait data across species. The matrix of environment–trait interaction coefficients is the fourth corner. We illustrate that compared with existing approaches to the fourth-corner problem, the proposed model-based approach has advantages in interpretability and its capacity to perform model selection and make predictions. To illustrate the method we used a generalized linear model with a LASSO penalty, fitted to data sets from four different studies requiring different models, illustrating the flexibility of the proposed approach. Predictive performance of the model is compared with that of fitting SDMs separately to each species, and in each case, it is shown that the trait model, despite being much simpler, had comparable predictive performance, even significantly outperforming separate SDMs in some cases.