Understanding Overrepresentation of Indigenous Children in Child Welfare Data: An Application of the Drake Risk and Bias Models.

Child welfare data collected for administrative purposes are often used as a source of information for understanding the population impact of child abuse and neglect (CA/N). This study used administrative data linked at the individual level for a cohort of Aotearoa New Zealand (NZ) children to follow and extend a model developed by Drake et al. In this investigation, we aimed to build an understanding of the high representation of indigenous NZ children in administratively sourced measures of CA/N. Variation in rate ratios (RRs) within infant mortality and birth outcomes considered as possible proxies for actual CA/N RRs leaves open a range of interpretations. Our findings indicate that a more nuanced interpretation of the overrepresentation of indigenous children in administratively recorded maltreatment statistics is required. Rather than considering risk and bias as competing explanations, we suggest an acknowledgment of the impact of colonization and the existence of systemic bias generating increased risk as key drivers. As linked administrative data are increasingly used for research and evaluation, and considered for use in supporting decision making, there is a need for a deeper understanding of the drivers of administratively recorded CA/N in order to effectively address the needs of indigenous populations.

Predictive modeling: potential application in prevention services.

INTRODUCTION: In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. METHODS: Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. RESULTS: Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Maori children. CONCLUSIONS: Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias.

Measuring fit of sequence data to phylogenetic model: gain of power using marginal tests.

Testing fit of data to model is fundamentally important to any science, but publications in the field of phylogenetics rarely do this. Such analyses discard fundamental aspects of science as prescribed by Karl Popper. Indeed, not without cause, Popper (Unended quest: an intellectual autobiography. Fontana, London, 1976) once argued that evolutionary biology was unscientific as its hypotheses were untestable. Here we trace developments in assessing fit from Penny et al. (Nature 297:197-200, 1982) to the present. We compare the general log-likelihood ratio (the G or G (2) statistic) statistic between the evolutionary tree model and the multinomial model with that of marginalized tests applied to an alignment (using placental mammal coding sequence data). It is seen that the most general test does not reject the fit of data to model (P approximately 0.5), but the marginalized tests do. Tests on pairwise frequency (F) matrices, strongly (P < 0.001) reject the most general phylogenetic (GTR) models commonly in use. It is also clear (P < 0.01) that the sequences are not stationary in their nucleotide composition. Deviations from stationarity and homogeneity seem to be unevenly distributed amongst taxa; not necessarily those expected from examining other regions of the genome. By marginalizing the 4( t ) patterns of the i.i.d. model to observed and expected parsimony counts, that is, from constant sites, to singletons, to parsimony informative characters of a minimum possible length, then the likelihood ratio test regains power, and it too rejects the evolutionary model with P << 0.001. Given such behavior over relatively recent evolutionary time, readers in general should maintain a healthy skepticism of results, as the scale of the systematic errors in published trees may really be far larger than the analytical methods (e.g., bootstrap) report.

Phylogenetic methodology for detecting protein interactions.

Detecting protein-protein interactions and assigning proteins to functional complexes are key challenges of modern biology. The rise of genomics has lead to evidence that correlated patterns of presence/absence and/or fusing of proteins in any organism suggest these proteins interact. Unfortunately, methods based on such data work best with divergent genomes, whereas major sequencing efforts in vertebrates, for example, are yielding alignments of the same set of proteins sampled from the same set of taxa (species). Using vertebrate mitochondrial genomes to illustrate a novel method, we associate proteins based on vectors of their evolutionary tree edge (branch or internode) lengths. This approach is based on the expectation that molecular coevolution is greatest between proteins that interact in some way. Mitochondrial DNA-encoded proteins are associated into groups largely consistent with the complexes they come from. This association is apparently not due to the tree structure or mutation processes, leaving coevolution as the best explanation. We show that it is important that the tree used to derive the edge-length vector is estimated accurately in terms of both topology and edge lengths. Although more complex substitution models reduce systematic error, they also inflate stochastic error. This makes the use of less complex substitution models preferable in some circumstances. We describe a method to estimate correlations of pairwise evolutionary distances, which adjusts for non-independent correlations due to shared evolutionary history. Associations of proteins based on their edge-length vectors are visualized and assessed using a variety of hierarchical clustering and multidimensional scaling methods. New formula for estimating the fit of data to model, including the average percent standard deviation of distances on least squares trees, are presented. Use of edge-length vectors is compared and contrasted with correlated distance methods, correlated rates methods, and site-specific evidence of coevolution.

Estimating changes in mutational mechanisms of evolution.

By considering three DNA sequences simultaneously there is sufficient information to recover a full Markov model with three transition matrices from the root to each of the sequences. It is necessary to have relatively long sequences because, for nucleotides, the full model requires 39 parameters that are estimated from 63 observable values. This triplet Markov method is evaluated for the protein coding genes of mammalian vertebrate mitochondrial genomes, and, in addition, version for two-state-characters (such as R/Y coding) is implemented. A key finding is that some changes in mutational mechanism differentially affect the mutation rate between pairs of nucleotides: there does not appear to be a universal change in "rate" of evolution. It remains to be explored whether detecting changes in certain nucleotide interchanges can be localized to a particular part of the DNA replication/repair system. In order to estimate divergence dates it may eventually be advantageous to use the nucleotide interchanges that show little rate change.

Four new mitochondrial genomes and the increased stability of evolutionary trees of mammals from improved taxon sampling.

We have sequenced four new mitochondrial genomes to improve the stability of the tree for placental mammals; they are two insectivores (a gymnure, Echinosorex gymnurus and Formosan shrew Soriculus fumidus); a Formosan lesser horseshoe bat (Rhinolophus monoceros); and the New Zealand fur seal (Arctocephalus forsteri). A revision to the hedgehog sequence (Erinaceus europaeus) is also reported. All five are from the Laurasiatheria grouping of eutherian mammals. On this new data set there is a strong tendency for the hedgehog and its relative, the gymnure, to join with the other Laurasiatherian insectivores (mole and shrews). To quantify the stability of trees from this data we define, based on nuclear sequences, a major four-way split in Laurasiatherians. This ([Xenarthra, Afrotheria], [Laurasiatheria, Supraprimates]) split is also found from mitochondrial genomes using either protein-coding or RNA (rRNA and tRNA) data sets. The high similarity of the mitochondrial and nuclear-derived trees allows a quantitative estimate of the stability of trees from independent data sets, as detected from a triplet Markov analysis. There are significant changes in the mutational processes within placental mammals that are ignored by current tree programs. On the basis of our quantitative results, we expect the evolutionary tree for mammals to be resolved quickly, and this will allow other problems to be solved.

Very fast algorithms for evaluating the stability of ML and Bayesian phylogenetic trees from sequence data.

Evolutionary trees sit at the core of all realistic models describing a set of related sequences, including alignment, homology search, ancestral protein reconstruction and 2D/3D structural change. It is important to assess the stochastic error when estimating a tree, including models using the most realistic likelihood-based optimizations, yet computation times may be many days or weeks. If so, the bootstrap is computationally prohibitive. Here we show that the extremely fast "resampling of estimated log likelihoods" or RELL method behaves well under more general circumstances than previously examined. RELL approximates the bootstrap (BP) proportions of trees better that some bootstrap methods that rely on fast heuristics to search the tree space. The BIC approximation of the Bayesian posterior probability (BPP) of trees is made more accurate by including an additional term related to the determinant of the information matrix (which may also be obtained as a product of gradient or score vectors). Such estimates are shown to be very close to MCMC chain values. Our analysis of mammalian mitochondrial amino acid sequences suggest that when model breakdown occurs, as it typically does for sequences separated by more than a few million years, the BPP values are far too peaked and the real fluctuations in the likelihood of the data are many times larger than expected. Accordingly, several ways to incorporate the bootstrap and other types of direct resampling with MCMC procedures are outlined. Genes evolve by a process which involves some sites following a tree close to, but not identical with, the species tree. It is seen that under such a likelihood model BP (bootstrap proportions) and BPP estimates may still be reasonable estimates of the species tree. Since many of the methods studied are very fast computationally, there is no reason to ignore stochastic error even with the slowest ML or likelihood based methods.