An ingenious decision support system for remote health monitoring of pregnant women

Maternal deaths have been a critical issue for women living in rural and remote areas. The need to travel long distances, the shortage of primary care providers such as physicians, specialists and nurses, and the closing of small hospitals have been problems identified in many rural areas. Some research work has been undertaken and a few techniques have been developed to remotely measure the physiological condition of pregnant women through sophisticated ultrasound equipment. There are numerous ways to reduce maternal deaths, and an important step is to select the right approaches to achieving this reduction. One such approach is the provision of decision support systems in rural and remote areas. Decision support systems (DSSs) have already shown a great potential in many health fields. This thesis proposes an ingenious decision support system (iDSS) based on the methodology of survey instruments and identification of significant variables to be used in iDSS using statistical analysis. A survey was undertaken with pregnant women and factorial experimental design was chosen to acquire sample size. Variables with good reliability in any one of the statistical techniques such as Chi-square, Cronbach’s á and Classification Tree were incorporated in the iDSS. The decision support system was developed with significant variables such as: Place of residence, Seeing the same doctor, Education, Tetanus injection, Baby weight, Previous baby born, Place of birth, Assisted delivery, Pregnancy parity, Doctor visits and Occupation. The ingenious decision support system was implemented with Visual Basic as front end and Microsoft SQL server management as backend. Outcomes of the ingenious decision support system include advice on Symptoms, Diet and Exercise to pregnant women. On conditional system was sent and validated by the gynaecologist. Another outcome of ingenious decision support system was to provide better pregnancy health awareness and reduce long distance travel, especially for women in rural areas. The proposed system has qualities such as usefulness, accuracy and accessibility.

The emerging role of private health care provision in China : a critical analysis of the current health system

China continues to face great challenges in meeting the health needs of its large population. The challenges are not just lack of resources, but also how to use existing resources more efficiently, more effectively, and more equitably. Now a major unaddressed challenge facing China is how to reform an inefficient, poorly organized health care delivery system. The objective of this study is to analyze the role of private health care provision in China and discuss the implications of increasing private-sector development for improving health system performance. This study is based on an extensive literature review, the purpose of which was to identify, summarize, and evaluate ideas and information on private health care provision in China. In addition, the study uses secondary data analysis and the results of previous study by the authors to highlight the current situation of private health care provision in one province of China. This study found that government-owned hospitals form the backbone of the health care system and also account for most health care service provision. However, even though the public health care system is constantly trying to adapt to population needs and improve its performance, there are many problems in the system, such as limited access, low efficiency, poor quality, cost inflation, and low patient satisfaction. Currently, private hospitals are relatively rare, and private health care as an important component of the health care system in China has received little policy attention. It is argued that policymakers in China should recognize the role of private health care provision for health system performance, and then define and achieve an appropriate role for private health care provision in helping to respond to the many challenges facing the health system in present-day China.

Effective discrimination of acoustic emission source signals for structural health monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage‐related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Health monitoring of buildings during construction and service stages using vibration characteristics

Columns and walls in buildings are subjected to a number of load increments during the construction and service stages. The combination of these load increments and poor quality construction can cause defects in these structural components. In addition, defects can also occur due to accidental or deliberate actions by users of the building during construction and service stages. Such defects should be detected early so that remedial measures can be taken to improve life time serviceability and performance of the building. This paper uses micro and macro model upgrading methods during construction and service stages of a building based on the mass and stiffness changes to develop a comprehensive procedure for locating and detecting defects in columns and walls of buildings. Capabilities of the procedure are illustrated through examples.

Assessment of “change in flexibility method” in structural health monitoring systems

The modern structural diagnosis process is rely on vibration characteristics to assess safer serviceability level of the structure. This paper examines the potential of change in flexibility method to use in damage detection process and two main practical constraints associated with it. The first constraint addressed in this paper is reduction in number of data acquisition points due to limited number of sensors. Results conclude that accuracy of the change in flexibility method is influenced by the number of data acquisition points/sensor locations in real structures. Secondly, the effect of higher modes on damage detection process has been studied. This addresses the difficulty of extracting higher order modal data with available sensors. Four damage indices have been presented to identify their potential of damage detection with respect to different locations and severity of damage. A simply supported beam with two degrees of freedom at each node is considered only for a single damage cases throughout the paper.

Small business entity tax concessions : through the eyes of the practitioner

The often competing imperatives of equity, simplicity and efficiency in the income tax regime, particularly the notion of simplicity, has been most evident within Australia’s small business sector over the last decade. In an attempt to provide tax simplification and reduce the tax compliance burden faced by Australian small businesses, provisions collectively referred to as the ‘simplified tax system’ or STS were introduced. The STS was designed to provide eligible small businesses with the option of adopting a range of ‘simplified’ tax measures designed to simplify their tax affairs whilst at the same time, reducing their tax compliance costs. Ultimately, a low take-up rate and accompanying criticisms led to a remodelled and rebadged concessionary regime known as the ‘Small Business Entity’ (SBE) regime which came into effect from 1 July 2007. This paper, through a pilot study, investigates the SBE regime though the eyes of the practitioner. In line the Australian Federal Government’s objective of simplification and reduced compliance costs, the purpose of the study was to (1) determine the extent to which the SBE concessions are being adopted by tax practitioners on behalf of their clients, (2) gain an understanding as to which individual SBE tax concessions are most favoured by practitioners, (3) determine the primary motivation as to why tax practitioners recommend particular SBE concessions to their clients, and (4) canvass the opinions of practitioners as to whether they believed that the introduction of the SBE concessions had met their stated objective of reducing tax compliance costs for small businesses. The findings of this research indicate that, while there is a perception that the SBE concessions are worth embracing, contrary to the policy intent, the reasons behind adopting the concessions was the opportunity to minimise a clients’ tax liability. It was revealed that adopting particular concessions had nothing to do with compliance costs savings and, in fact, the SBE concessions merely added another layer of complexity to an already cumbersome and complex tax code, which resulted in increased compliance costs for their small businesses clients. Further, the SBE concessions allowed tax practitioners the opportunity to engage in effective tax minimisation, thereby fulfilling the client advocacy role of the tax practitioner in maximising their clients’ tax preferences.

Health matters for subscribers to community-supported agriculture

There are a variety of reasons and motivations for people to subscribe to community-supported agriculture (CSA) schemes, many of which include social, ethical, environmental, and economical benefits. The global rise of food allergies and food related health issues in recent years has led to a growing number of initiatives particularly in developing countries to raise more awareness of the current situation amongst individuals, organisations, and government bodies, and to plan for its implications for the existing food and health systems. Based on a mixed method research conducted in Australia, this paper argues that personal health matters are one of the key motivators for consumers to seek out alternative food systems, particularly CSA initiatives. In addition, it presents the willingness for consumers to seek out information about the food they consume and proposes that technology plays a key role in being used as a conduit to share and investigate information relating to alternative food systems. Further research is required to determine the variety of benefits and opportunities alternative food systems can provide consumers with food related health issues.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Health professionals’ perspectives on information provision for patients with brain tumours and their families

A significant number of patients diagnosed with primary brain tumours report unmet information needs. Using concept mapping methodology, this study aimed to identify strategies for improving information provision, and to describe factors that health professionals understood to influence their provision of information to patients with brain tumours and their families. Concept mapping is a mixed methods approach that uses statistical methods to represent participants’ perceived relationships between elements as conceptual maps. These maps, and results of associated data collection and analyses, are used to extract concepts involved in information provision to these patients. Thirty health professionals working across a range of neuro-oncology roles and settings participated in the concept mapping process. Participants rated a care coordinator as the most important strategy for improving brain tumour care, with psychological support as a whole rated as the most important element of care. Five major themes were identified as facilitating information provision: health professionals’ communication skills, style and attitudes; patients’ needs and preferences; perceptions of patients’ need for protection and initiative; rapport and continuity between patients and health professionals; and the nature of the health care system. Overall, health professionals conceptualised information provision as ‘individualised’, dependent on these interconnected personal and environmental factors.

Educational stress among Chinese adolescents : measurement, risk factors and associations with mental health

Academic pressure among adolescents is a major risk factor for poor mental health and suicide and other harmful behaviours. While this is a worldwide phenomenon, it appears to be especially pronounced in China and other East Asian countries. Despite a growing body of research into adolescent mental health in recent years, the multiple constructs within the ‘educational stress’ phenomenon have not been clearly articulated in Chinese contexts. Further, the individual, family, school and peer influencing factors for educational stress and its associations with adolescent mental health are not well understood. An in-depth investigation may provide important information for the ongoing educational reform in Mainland China with a special focus on students’ mental health and wellbeing. The primary goal of this study was to examine the relative contribution of educational stress to poor mental health, in comparison to other well-known individual, family, school and peer factors. Another important task was to identify significant risk factors for educational stress. In addition, due to the lack of a culturally suitable instrument for educational stress in this population, a new tool – the Educational Stress Scale for Adolescents (ESSA) was initially developed in this study and tested for reliability and validity. A self-administered questionnaire was used to collect information from convenient samples of secondary school students in Shandong, China. The pilot survey was conducted with 347 students (grades 8 and 11) to test the psychometric properties of the ESSA and other scales or questions in the questionnaire. Based on factor analysis and reliability and validity testing, the 16-item scale (the ESSA) with five factors showed adequate to good internal consistency, 2-week test-retest reliability, and satisfactory concurrent and predictive validity. Its factor structure was further demonstrated in the main survey with a confirmatory factor analysis illustrating a good fit of the proposed model based on a confirmatory factor analysis. The reliabilities of other scales and questions were also adequate to be used in this study. The main survey was subsequently conducted with a sample of 1627 secondary school (grades 7-12) students to examine the influencing factors of educational stress and its associations with mental health outcomes, including depression, happiness and suicidal behaviours. A wide range of individual, family, school and peer factors were found to have a significant association with the total ESSA and subscale scores. Most of the strong factors for academic stress were school or study-related, including rural school location, low school connectedness, perceived poor academic grades and frequent emotional conflicts with teachers and peers. Unexpectedly, family and parental factors, such as parental bonding, family connectedness and conflicts with parents were found to have little or no association with educational stress. Educational stress was the most predictive variable for depression, but was not strongly associated with happiness. It had a strong association with suicide ideation but not with suicide attempts. Among five subscales of the ESSA, ‘Study despondency’ score had the strongest associations with these mental health measures. Surprising, two subscales, ‘Self-expectation’ and ‘Worry about grades’ showed a protective effect on suicidal behaviours. An additional analysis revealed that although academic pressure was the most commonly reported reason for suicidal thinking, the occurrence of problems in peer relationships such as peer teasing and bullying, and romantic problems had a much stronger relationship with actual attempts. This study provides some insights into the nature and health implications of educational stress among Chinese adolescents. Findings in this study suggest that interventions on educational stress should focus on school environment and academic factors. Intervention programs focused on educational stress may have a high impact on the prevalence of common mental disorders such as depression. Efforts to increase perceived happiness however should cover a wider range of individual, family and school factors. The importance of healthy peer relationships should be adequately emphasised in suicide prevention. In addition, the newly developed scale (the ESSA) demonstrates sound psychometric properties and is expected to be used in future research into academic-related stress among secondary school adolescents.

An exploratory study to examine the phenomenon and practice of ambulance ramping at hospitals within the Queensland Health Southern Districts and the Queensland Ambulance Service

Background: Ambulance Ramping, defined anecdotally as a practice where patients brought to emergency departments by ambulance experience delays to admission, has become more frequent in Australian emergency departments over the last few years. Previous research has shown a link between emergency department overcrowding, ambulance diversion and adverse outcomes for patients. However, there is very little research about Ambulance Ramping. The literature has no consistent definition of Ambulance Ramping, no description of how it is managed, and limited research on the effects it has on patient and service delivery outcomes...

Respiratory health effects of diesel particulate matter

Particulate matter (PM) emissions involve a complex mixture of solid and liquid particles suspended in a gas, where it is noted that PM emissions from diesel engines are a major contributor to the ambient air pollution problem. Whilst epidemiological studies have shown a link between increased ambient PM emissions and respiratory morbidity and mortality, studies of this design are not able to identify the PM constituents responsible for driving adverse respiratory health effects. This review explores in detail the physico-chemical properties of diesel particulate matter (DPM), and identifies the constituents of this pollution source that are responsible for the development of respiratory disease. In particular, this review shows that the DPM surface area and adsorbed organic compounds play a significant role in manifesting chemical and cellular processes that if sustained can lead to the development of adverse respiratory health effects. The mechanisms of injury involved included: inflammation, innate and acquired immunity, and oxidative stress. Understanding the mechanisms of lung injury from DPM will enhance efforts to protect at-risk individuals from the harmful respiratory effects of air pollutants.