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.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.

Bearing damage analysis by calculation of capacitive coupling between inner and outer races of a ball bearing

Bearing damage in modern inverter-fed AC drive systems is more common than in motors working with 50 or 60 Hz power supply. Fast switching transients and common mode voltage generated by a PWM inverter cause unwanted shaft voltage and resultant bearing currents. Parasitic capacitive coupling creates a path to discharge current in rotors and bearings. In order to analyze bearing current discharges and their effect on bearing damage under different conditions, calculation of the capacitive coupling between the outer and inner races is needed. During motor operation, the distances between the balls and races may change the capacitance values. Due to changing of the thickness and spatial distribution of the lubricating grease, this capacitance does not have a constant value and is known to change with speed and load. Thus, the resultant electric field between the races and balls varies with motor speed. The lubricating grease in the ball bearing cannot withstand high voltages and a short circuit through the lubricated grease can occur. At low speeds, because of gravity, balls and shaft voltage may shift down and the system (ball positions and shaft) will be asymmetric. In this study, two different asymmetric cases (asymmetric ball position, asymmetric shaft position) are analyzed and the results are compared with the symmetric case. The objective of this paper is to calculate the capacitive coupling and electric fields between the outer and inner races and the balls at different motor speeds in symmetrical and asymmetrical shaft and balls positions. The analysis is carried out using finite element simulations to determine the conditions which will increase the probability of high rates of bearing failure due to current discharges through the balls and races.

An explorative qualitative analysis of participants' experience of using kava versus placebo in an RCT. (Clinical report)

Many randomised controlled trials (RCT) have been conducted using Piper methysticum (kava), however no qualitative research exploring the experience of taking kava during a clinical trial has previously been reported. ---------- Patients and methods: A qualitative research component (in the form of semi structured and open ended written questions) was incorporated into an RCT to explore the experiences of those participating in a clinical trial of kava. The written questions were provided to participants at weeks 2 and 3 (after randomisation, after each controlled phase). The researcher and participants were blinded as to whether they were taking kava or placebo. Two open ended questions were posed to elucidate their experiences from taking either kava or placebo. Thematic analysis was undertaken and researcher triangulation employed to ensure analytical rigour. Key themes after the kava phases were a reduction in anxiety and stress, and calming or relaxing mental effects. Other themes related to improvement in sleep and in somatic anxiety symptoms. ---------- Results: Kava use did not cause any serious adverse reactions although a few respondents reported nausea or other gastrointestinal side effects. This represents the first documented qualitative investigation of the experience of taking kava during a clinical trial. The primary themes involved anxiolytic and calming effects, with only a minor theme reflecting side effects. Our exploratory qualitative data was consistent with the significant quantitative results revealed in the study and provides additional support to suggest the trial results did not exclude any important positive or negative effects (at least as experienced by the trial participants).

Which components of heart failure programs are effective? A meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary component of heart failure management in 9,560 patients (Abridged Cochrane Review)

Aims--Telemonitoring (TM) and structured telephone support (STS) have the potential to deliver specialised management to more patients with chronic heart failure (CHF), but their efficacy is still to be proven. Objectives To review randomised controlled trials (RCTs) of TM or STS on all- cause mortality and all-cause and CHF-related hospitalisations in patients with CHF, as a non-invasive remote model of specialised disease-management intervention.--Methods and Results--Data sources:We searched 15 electronic databases and hand-searched bibliographies of relevant studies, systematic reviews, and meeting abstracts. Two reviewers independently extracted all data. Study eligibility and participants: We included any randomised controlled trials (RCT) comparing TM or STS to usual care of patients with CHF. Studies that included intensified management with additional home or clinic visits were excluded. Synthesis: Primary outcomes (mortality and hospitalisations) were analysed; secondary outcomes (cost, length of stay, quality of life) were tabulated.--Results: Thirty RCTs of STS and TM were identified (25 peer-reviewed publications (n=8,323) and five abstracts (n=1,482)). Of the 25 peer-reviewed studies, 11 evaluated TM (2,710 participants), 16 evaluated STS (5,613 participants) and two tested both interventions. TM reduced all-cause mortality (risk ratio (RR 0•66 [95% CI 0•54-0•81], p<0•0001) and STS showed similar trends (RR 0•88 [95% CI 0•76-1•01], p=0•08). Both TM (RR 0•79 [95% CI 0•67-0•94], p=0•008) and STS (RR 0•77 [95% CI 0•68-0•87], p<0•0001) reduced CHF-related hospitalisations. Both interventions improved quality of life, reduced costs, and were acceptable to patients. Improvements in prescribing, patient-knowledge and self-care, and functional class were observed.--Conclusion: TM and STS both appear effective interventions to improve outcomes in patients with CHF.

Differentiation of Rice Tungro Bacilliform Virus Strains by Restriction Analysis and DNA Hybridization

Rice tungro bacilliform virus (RTBV) is one of the two viruses that cause tungro disease. Four RTBV strains maintained in the greenhouse for 4 years, G1, G2, Ic, and L, were differentiated by restriction fragment length polymorphism (RFLP) analysis of the native viral DNA. Although strains G1 and Ic had identical restriction patterns when cleaved with Pst1, BamHI, EcoRI, and EcoRV, they can be differentiated from strains G2 and L by EcoRI and EcoRV digestion. These same endonucleases also differentiate strain G2 from strain L. When total DNA extracts from infected plants were used instead of viral DNA, and digested with EcoRV, identical restriction patterns for each strain (G2 and L) were obtained from roots, leaves, and leaf sheaths of infected plants. The restriction patterns were consistent from plant to plant, in different varieties, and at different times after inoculation. This technique can be used to differentiate RTBV strains and determine the variability of a large number of field samples.

Development of the response coefficient-root function method for the transient vibration serviceability design of flat concrete floors

The vibration serviceability limit state is an important design consideration for two-way, suspended concrete floors that is not always well understood by many practicing structural engineers. Although the field of floor vibration has been extensively developed, at present there are no convenient design tools that deal with this problem. Results from this research have enabled the development of a much-needed, new method for assessing the vibration serviceability of flat, suspended concrete floors in buildings. This new method has been named, the Response Coefficient-Root Function (RCRF) method. Full-scale, laboratory tests have been conducted on a post-tensioned floor specimen at Queensland University of Technology’s structural laboratory. Special support brackets were fabricated to perform as frictionless, pinned connections at the corners of the specimen. A series of static and dynamic tests were performed in the laboratory to obtain basic material and dynamic properties of the specimen. Finite-element-models have been calibrated against data collected from laboratory experiments. Computational finite-element-analysis has been extended to investigate a variety of floor configurations. Field measurements of floors in existing buildings are in good agreement with computational studies. Results from this parametric investigation have led to the development of new approach for predicting the design frequencies and accelerations of flat, concrete floor structures. The RCRF method is convenient tool to assist structural engineers in the design for the vibration serviceability limit-state of in-situ concrete floor systems.

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

Daily average temperature and mortality among the elderly : a meta-analysis and systematic review of epidemiological evidence

The impact of climate change on the health of vulnerable groups such as the elderly has been of increasing concern. However, to date there has been no meta-analysis of current literature relating to the effects of temperature fluctuations upon mortality amongst the elderly. We synthesised risk estimates of the overall impact of daily mean temperature on elderly mortality across different continents. A comprehensive literature search was conducted using MEDLINE and PubMed to identify papers published up to December 2010. Selection criteria including suitable temperature indicators, endpoints, study-designs and identification of threshold were used. A two-stage Bayesian hierarchical model was performed to summarise the percent increase in mortality with a 1°C temperature increase (or decrease) with 95% confidence intervals in hot (or cold) days, with lagged effects also measured. Fifteen studies met the eligibility criteria and almost 13 million elderly deaths were included in this meta-analysis. In total, there was a 2-5% increase for a 1°C increment during hot temperature intervals, and a 1-2 % increase in all-cause mortality for a 1°C decrease during cold temperature intervals. Lags of up to 9 days in exposure to cold temperature intervals were substantially associated with all-cause mortality, but no substantial lagged effects were observed for hot intervals. Thus, both hot and cold temperatures substantially increased mortality among the elderly, but the magnitude of heat-related effects seemed to be larger than that of cold effects within a global context.

Analysis of productive performance of crop and animal production systems : an integrated analytical framework

This article presents a two-stage analytical framework that integrates ecological crop (animal) growth and economic frontier production models to analyse the productive efficiency of crop (animal) production systems. The ecological crop (animal) growth model estimates "potential" output levels given the genetic characteristics of crops (animals) and the physical conditions of locations where the crops (animals) are grown (reared). The economic frontier production model estimates "best practice" production levels, taking into account economic, institutional and social factors that cause farm and spatial heterogeneity. In the first stage, both ecological crop growth and economic frontier production models are estimated to calculate three measures of productive efficiency: (1) technical efficiency, as the ratio of actual to "best practice" output levels; (2) agronomic efficiency, as the ratio of actual to "potential" output levels; and (3) agro-economic efficiency, as the ratio of "best practice" to "potential" output levels. Also in the first stage, the economic frontier production model identifies factors that determine technical efficiency. In the second stage, agro-economic efficiency is analysed econometrically in relation to economic, institutional and social factors that cause farm and spatial heterogeneity. The proposed framework has several important advantages in comparison with existing proposals. Firstly, it allows the systematic incorporation of all physical, economic, institutional and social factors that cause farm and spatial heterogeneity in analysing the productive performance of crop and animal production systems. Secondly, the location-specific physical factors are not modelled symmetrically as other economic inputs of production. Thirdly, climate change and technological advancements in crop and animal sciences can be modelled in a "forward-looking" manner. Fourthly, knowledge in agronomy and data from experimental studies can be utilised for socio-economic policy analysis. The proposed framework can be easily applied in empirical studies due to the current availability of ecological crop (animal) growth models, farm or secondary data, and econometric software packages. The article highlights several directions of empirical studies that researchers may pursue in the future.

Telemonitoring or Structured Telephone Support Programmes for Patients with Chronic Heart Failure: Systematic Review and Meta-Analysis

Objective: To determine whether remote monitoring (structured telephone support or telemonitoring) without regular clinic or home visits improves outcomes for patients with chronic heart failure. Data sources: 15 electronic databases, hand searches of previous studies, and contact with authors and experts. Data extraction: Two investigators independently screened the results. Review methods: Published randomised controlled trials comparing remote monitoring programmes with usual care in patients with chronic heart failure managed within the community. Results: 14 randomised controlled trials (4264 patients) of remote monitoring met the inclusion criteria: four evaluated telemonitoring, nine evaluated structured telephone support, and one evaluated both. Remote monitoring programmes reduced the rates of admission to hospital for chronic heart failure by 21% (95% confidence interval 11% to 31%) and all cause mortality by 20% (8% to 31%); of the six trials evaluating health related quality of life three reported significant benefits with remote monitoring, and of the four studies examining healthcare costs with structured telephone support three reported reduced cost and one no effect. Conclusion: Programmes for chronic heart failure that include remote monitoring have a positive effect on clinical outcomes in community dwelling patients with chronic heart failure.

Safeguarding rural Australia: addressing masculinity and violence in rural settings : unintentional violent harm including injuries in an Australian rural context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. This report focuses on our examination of extant data which have been sourced with respect to unintentional serious and violent harm, including injuries, to males living in regional and remote Australia . and which were available in public data bases at production. Such harm typically might be caused by, for example, transport accidents, occupational exposures and hazards, burns and so on. Thus unintentional violent harm can cause physical trauma the consequences of which can lead to chronic conditions including psychological harm or substance abuse.

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

Non-invasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for non-invasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform non-invasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.