Health, Death, and Indigenous Australians in the Coronial system

This paper details research conducted in Queensland during the first year of operation of the new Coroners Act 2003. Information was gathered from all completed investigations between December 2003 and December 2004 across five categories of death: accidental, suicide, natural, medical and homicide. It was found that 25 percent of the total number of Indigenous deaths recorded in 2004 were reported to, and investigated by, the Coroner, in comparison to 9.4 percent of non-Indigenous deaths. Moreover, Indigenous people were found to be over-represented in each category of death, except in death in a medical setting, where they were absent.

Teaching cause-of-death certification : lessons from international experience

The accuracy of cause-of-death statistics substantially depends on the quality of cause-of-death information in death certificates, primarily completed by medical doctors. Deficiencies in cause-of-death certification have been observed across the world, and over time. Despite educational interventions targeting to improve the quality of death certification, their intended impacts are rarely evaluated. This review aims to provide empirical evidence that could guide the modification of existing educational programs, or the development of new interventions, which are necessary to improve the capacity of certifiers as well as the quality of cause-of-death certification, and thereby, the quality of mortality statistics.

Quality of cause-of-death reporting using ICD-10 drowning codes : a descriptive study of 69 countries

Background: The systematic collection of high-quality mortality data is a prerequisite in designing relevant drowning prevention programmes. This descriptive study aimed to assess the quality (i.e., level of specificity) of cause-of-death reporting using ICD-10 drowning codes across 69 countries.---------- Methods: World Health Organization (WHO) mortality data were extracted for analysis. The proportion of unintentional drowning deaths coded as unspecified at the 3-character level (ICD-10 code W74) and for which the place of occurrence was unspecified at the 4th character (.9) were calculated for each country as indicators of the quality of cause-of-death reporting.---------- Results: In 32 of the 69 countries studied, the percentage of cases of unintentional drowning coded as unspecified at the 3-character level exceeded 50%, and in 19 countries, this percentage exceeded 80%; in contrast, the percentage was lower than 10% in only 10 countries. In 21 of the 56 countries that report 4-character codes, the percentage of unintentional drowning deaths for which the place of occurrence was unspecified at the 4th character exceeded 50%, and in 15 countries, exceeded 90%; in only 14 countries was this percentage lower than 10%.---------- Conclusion: Despite the introduction of more specific subcategories for drowning in the ICD-10, many countries were found to be failing to report sufficiently specific codes in drowning mortality data submitted to the WHO.

Survival Power: An Empirical Analysis of Power in a Life and Death Situation

This paper seeks to identify what antecedents of power make it more or less likely for people to survive in a life-threatening situation.In particular, we look at the Titanic disaster as the life or death situation. Maritime disasters can be interpreted as quasi-natural experiments because every person is affected by the shock. True human nature becomes apparent in such a dangerous situation. Five antecedents of power are distinguished: physical strength, economic resources, nationality, social and moral factors. This empirical analysis supports the notion that power is a key determinant in extreme situations of life or death.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Infant wrapping as an evidence-based strategy to encourage settling and the utilisation of the supine sleep position

Objective: To identify knowledge, attitudes and practices of child health nurses relating to infant wrapping as an effective settling/sleep strategy. Methods: A pre-test/post-test intervention design was used to explore knowledge, attitudes and practices relating to wrapping in a sample of child health nurses (n=182): a) pre-test survey; b) educational intervention incorporating evidence relating to infant wrapping; SIDS&KIDS endorsed infant wrapping pamphlet; Safe Sleeping recommendations. Emphasis was placed on infant wrapping as an effective settling strategy for parents to use as an alternative to prone positioning; c) post-test survey to evaluate intervention effectiveness. Results: Pretest results identified wide variation in nurses’ knowledge, attitudes and practices of infant wrapping as a settling/sleep strategy. The intervention increased awareness of wrapping guidelines and self-reported practices relating to parent advice. Significant positive changes in nurses’ awareness of wrapping guidelines (p<0.001); to wrap in supine position only (p<0.001); and parental advice to use wrapping as an alternative strategy to prone positioning to assist settling/sleep (p<0.01), were achieved post-test. Conclusions: Managing unsettled infants and promoting safe sleeping practices are issues routinely addressed by child health nurses working with parents of young infants. Queensland has a high incidence of prone sleeping. Infant wrapping is an evidence-based strategy to improve settling and promote supine sleep consistent with public health recommendations. Infant wrapping guidelines are now included in Queensland Health’s state policy and Australian SIDSandKids information relating to safe infant sleeping. In communicating complex health messages to parents, health professionals have a key role in reinforcing safe sleeping recommendations and offering safe, effective settling/sleep strategies to address the non-recommended use of prone positioning for unsettled infants.

Noblesse oblige? Determinants of survival in a life and death situation

This paper explores what determines the survival of people in a life–and-death situation. The sinking of the Titanic allows us to inquire whether pro-social behavior matters in such extreme situations. This event can be considered a quasi-natural experiment. The empirical results suggest that social norms such as ‘women and children first’ are persevered during such an event. Women of reproductive age and crew members had a higher probability of survival. Passenger class, fitness, group size, and cultural background also mattered.

Correcting mean-field approximations for birth-death-movement processes

On the microscale, migration, proliferation and death are crucial in the development, homeostasis and repair of an organism; on the macroscale, such effects are important in the sustainability of a population in its environment. Dependent on the relative rates of migration, proliferation and death, spatial heterogeneity may arise within an initially uniform field; this leads to the formation of spatial correlations and can have a negative impact upon population growth. Usually, such effects are neglected in modeling studies and simple phenomenological descriptions, such as the logistic model, are used to model population growth. In this work we outline some methods for analyzing exclusion processes which include agent proliferation, death and motility in two and three spatial dimensions with spatially homogeneous initial conditions. The mean-field description for these types of processes is of logistic form; we show that, under certain parameter conditions, such systems may display large deviations from the mean field, and suggest computationally tractable methods to correct the logistic-type description.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Textbook portrayal of grieving stereotypes fails to cohere : a review

JANIS Balodis's Engine appears to have an educative purpose. Following the tragic death of her brother Stevie in a car crash, Natasha and her family struggle to cope with the devastation this sudden trauma has dealt them. Overlooked by her grieving parents, Natasha expresses her emotions by skipping school, self-harming and, as Engine unfolds, trying to enlist her Grumpop to help her finish rebuilding the car that could have saved Stevie's life by eliminating his need to catch a ride with a car full of friends. The symbolic action that drives Engine - rebuilding the car to rebuild Natasha and Grumpop's lives in the wake of trauma and guilt - is full of potential. It gives the design team, particularly designer Justin Nardella and composer Matt Hill, a strong premise to construct the garage that transforms into a home, a school, a church, a part, and the road on which the accident took place as the play unfolds.

Death by Drowning : Gary Bryson's Turtle [A Review]

Accused of being autobiographical, as many debut novels often are, Turtle, upon first reading and further prying, does read as a story wrenched out of Gary Bryson’s own life. In a recent interview with Mandy Sayer, however, he was quick to deny all sorts of archetypal allegations. “Any resemblance to turtles living or dead”, Bryson explained, “is entirely coincidental”. Regardless of the many parallels that align author with protagonist—both were born and raised in a grey-skied Glasgow, both grew up in self-described dysfunctional families, and both returned to the colourless city to attend their mothers’ funerals—the narrative combines bruising black comedy with moments of magic realism. The result is an unlikely but often surprising concoction of twists and turns, each of which mixes the fallibility of memory with the slippery nature of truth. This playfulness between the material world and its metaphorical counterpart raises questions, not only about the curse that poisons its characters, but about the ethical implications of blurring fact and fiction...