Ageing, learning, technology, and health management

The world’s population is ageing rapidly. Ageing has an impact on all aspects of human life, including social, economic, cultural, and political. Understanding ageing is therefore an important issue for the 21st century. This chapter will consider the active ageing model. This model is based on optimising opportunities for health, participation, and security in order to enhance quality of life. There is a range of exciting options developing for personal health management, for and by the ageing population, that make use of computer technology, and these should support active ageing. Their use depends however on older people learning to use computer technology effectively. The ability to use such technology will allow them to access relevant health information, advice, and support independently from wherever they live. Such support should increase rapidly in the future. This chapter is a consideration of ageing and learning, ageing and use of computer technology, and personal health management using computers.

Policy analysis of disaster health management in China

Humankind has been dealing with all kinds of disasters since the dawn of time. The risk and impact of disasters producing mass casualties worldwide is increasing, due partly to global warming as well as to increased population growth, increased density and the aging population. China, as a country with a large population, vast territory, and complex climatic and geographical conditions, has been plagued by all kinds of disasters. Disaster health management has traditionally been a relatively arcane discipline within public health. However, SARS, Avian Influenza, and earthquakes and floods, along with the need to be better prepared for the Olympic Games in China has brought disasters, their management and their potential for large scale health consequences on populations to the attention of the public, the government and the international community alike. As a result significant improvements were made to the disaster management policy framework, as well as changes to systems and structures to incorporate an improved disaster management focus. This involved the upgrade of the Centres for Disease Control and Prevention (CDC) throughout China to monitor and better control the health consequences particularly of infectious disease outbreaks. However, as can be seen in the Southern China Snow Storm and Wenchuan Earthquake in 2008, there remains a lack of integrated disaster management and efficient medical rescue, which has been costly in terms of economics and health for China. In the context of a very large and complex country, there is a need to better understand whether these changes have resulted in effective management of the health impacts of such incidents. To date, the health consequences of disasters, particularly in China, have not been a major focus of study. The main aim of this study is to analyse and evaluate disaster health management policy in China and in particular, its ability to effectively manage the health consequences of disasters. Flood has been selected for this study as it is a common and significant disaster type in China and throughout the world. This information will then be used to guide conceptual understanding of the health consequences of floods. A secondary aim of the study is to compare disaster health management in China and Australia as these countries differ in their length of experience in having a formalised policy response. The final aim of the study is to determine the extent to which Walt and Gilson’s (1994) model of policy explains how disaster management policy in China was developed and implemented after SARS in 2003 to the present day. This study has utilised a case study methodology. A document analysis and literature search of Chinese and English sources was undertaken to analyse and produce a chronology of disaster health management policy in China. Additionally, three detailed case studies of flood health management in China were undertaken along with three case studies in Australia in order to examine the policy response and any health consequences stemming from the floods. A total of 30 key international disaster health management experts were surveyed to identify fundamental elements and principles of a successful policy framework for disaster health management. Key policy ingredients were identified from the literature, the case-studies and the survey of experts. Walt and Gilson (1994)’s policy model that focuses on the actors, content, context and process of policy was found to be a useful model for analysing disaster health management policy development and implementation in China. This thesis is divided into four parts. Part 1 is a brief overview of the issues and context to set the scene. Part 2 examines the conceptual and operational context including the international literature, government documents and the operational environment for disaster health management in China. Part 3 examines primary sources of information to inform the analysis. This involves two key studies: • A comparative analysis of the management of floods in China and Australia • A survey of international experts in the field of disaster management so as to inform the evaluation of the policy framework in existence in China and the criteria upon which the expression of that policy could be evaluated Part 4 describes the key outcomes of this research which include: • A conceptual framework for describing the health consequences of floods • A conceptual framework for disaster health management • An evaluation of the disaster health management policy and its implementation in China. The research outcomes clearly identified that the most significant improvements are to be derived from improvements in the generic management of disasters, rather than the health aspects alone. Thus, the key findings and recommendations tend to focus on generic issues. The key findings of this research include the following: • The health consequences of floods may be described in terms of time as ‘immediate’, ‘medium term’ and ‘long term’ and also in relation to causation as ‘direct’ and ‘indirect’ consequences of the flood. These two aspects form a matrix which in turn guides management responses. • Disaster health management in China requires a more comprehensive response throughout the cycle of prevention, preparedness, response and recovery but it also requires a more concentrated effort on policy implementation to ensure the translation of the policy framework into effective incident management. • The policy framework in China is largely of international standard with a sound legislative base. In addition the development of the Centres for Disease Control and Prevention has provided the basis for a systematic approach to health consequence management. However, the key weaknesses in the current system include: o The lack of a key central structure to provide the infrastructure with vital support for policy development, implementation and evaluation. o The lack of well-prepared local response teams similar to local government based volunteer groups in Australia. • The system lacks structures to coordinate government action at the local level. The result of this is a poorly coordinated local response and lack of clarity regarding the point at which escalation of the response to higher levels of government is advisable. These result in higher levels of risk and negative health impacts. The key recommendations arising from this study are: 1. Disaster health management policy in China should be enhanced by incorporating disaster management considerations into policy development, and by requiring a disaster management risk analysis and disaster management impact statement for development proposals. 2. China should transform existing organizations to establish a central organisation similar to the Federal Emergency Management Agency (FEMA) in the USA or the Emergency Management Australia (EMA) in Australia. This organization would be responsible for leading nationwide preparedness through planning, standards development, education and incident evaluation and to provide operational support to the national and local government bodies in the event of a major incident. 3. China should review national and local plans to reflect consistency in planning, and to emphasize the advantages of the integrated planning process. 4. Enhance community resilience through community education and the development of a local volunteer organization. China should develop a national strategy which sets direction and standards in regard to education and training, and requires system testing through exercises. Other initiatives may include the development of a local volunteer capability with appropriate training to assist professional response agencies such as police and fire services in a major incident. An existing organisation such as the Communist Party may be an appropriate structure to provide this response in a cost effective manner. 5. Continue development of professional emergency services, particularly ambulance, to ensure an effective infrastructure is in place to support the emergency response in disasters. 6. Funding for disaster health management should be enhanced, not only from government, but also from other sources such as donations and insurance. It is necessary to provide a more transparent mechanism to ensure the funding is disseminated according to the needs of the people affected. 7. Emphasis should be placed on prevention and preparedness, especially on effective disaster warnings. 8. China should develop local disaster health management infrastructure utilising existing resources wherever possible. Strategies for enhancing local infrastructure could include the identification of local resources (including military resources) which could be made available to support disaster responses. It should develop operational procedures to access those resources. Implementation of these recommendations should better position China to reduce the significant health consequences experienced each year from major incidents such as floods and to provide an increased level of confidence to the community about the country’s capacity to manage such events.

Information support for health management in regional Sri Lanka : health managers’ perspectives

Good management, supported by accurate, timely and reliable health information, is vital for increasing the effectiveness of Health Information Systems (HIS). When it comes to managing the under resourced health systems of developing countries, information-based decision making is particularly important. This paper reports findings of a self-report survey that investigated perceptions of local health managers (HMs) of their own regional HIS in Sri Lanka. Data were collected through a validated, pre-tested postal questionnaire, and distributed among a selected group of HMs to elicit their perceptions of the current HIS in relation to information generation, acquisition and use, required reforms to the information system and application of information and communication technology (ICT). Results based on descriptive statistics indicated that the regional HIS was poorly organised and in need of reform; that management support for the system was unsatisfactory in terms of relevance, accuracy, timeliness and accessibility; that political pressure and community and donor requests took precedence over vital health information when management decisions were made; and use of ICT was unsatisfactory. HIS strengths included user-friendly paper formats, a centralised planning system and an efficient disease notification system; weaknesses were lack of comprehensiveness, inaccuracy, and lack of a feedback system. Responses of participants indicated that HIS would be improved by adopting an internationally accepted framework and introducing ICT applications. Perceived barriers to such improvements were high initial cost of educating staff to improve computer literacy, introduction of ICTs, and HIS restructure. We concluded that the regional HIS of Central Province, Sri Lanka had failed to provide much needed information support to HMs. These findings are consistent with similar research in other developing countries and reinforce the need for further research to verify causes of poor performance and to design strategic reforms to improve HIS in regional Sri Lanka.

Progress and challenges of disaster health management in China : a scoping review

Background Despite the importance of an effective health system response to various disasters, relevant research is still in its infancy, especially in middle- and low-income countries. Objective This paper provides an overview of the status of disaster health management in China, with its aim to promote the effectiveness of the health response for reducing disaster-related mortality and morbidity. Design A scoping review method was used to address the recent progress of and challenges to disaster health management in China. Major health electronic databases were searched to identify English and Chinese literature that were relevant to the research aims. Results The review found that since 2003 considerable progress has been achieved in the health disaster response system in China. However, there remain challenges that hinder effective health disaster responses, including low standards of disaster-resistant infrastructure safety, the lack of specific disaster plans, poor emergency coordination between hospitals, lack of portable diagnostic equipment and underdeveloped triage skills, surge capacity, and psychological interventions. Additional challenges include the fragmentation of the emergency health service system, a lack of specific legislation for emergencies, disparities in the distribution of funding, and inadequate cost-effective considerations for disaster rescue. Conclusions One solution identified to address these challenges appears to be through corresponding policy strategies at multiple levels (e.g. community, hospital, and healthcare system level).

The role of the health surveillance program in the Queensland coal mining industry

The Queensland Coal Industry Employees Health Scheme was implemented in 1993 to provide health surveillance for all Queensland coal industry workers. Tt1e government, mining employers and mining unions agreed that the scheme should operate for seven years. At the expiry of the scheme, an assessment of the contribution of health surveillance to meet coal industry needs would be an essential part of determining a future health surveillance program. This research project has analysed the data made available between 1993 and 1998. All current coal industry employees have had at least one health assessment. The project examined how the centralised nature of the Health Scheme benefits industry by identi~)jng key health issues and exploring their dimensions on a scale not possible by corporate based health surveillance programs. There is a body of evidence that indicates that health awareness - on the scale of the individual, the work group and the industry is not a part of the mining industry culture. There is also growing evidence that there is a need for this culture to change and that some change is in progress. One element of this changing culture is a growth in the interest by the individual and the community in information on health status and benchmarks that are reasonably attainable. This interest opens the way for health education which contains personal, community and occupational elements. An important element of such education is the data on mine site health status. This project examined the role of health surveillance in the coal mining industry as a tool for generating the necessary information to promote an interest in health awareness. The Health Scheme Database provides the material for the bulk of the analysis of this project. After a preliminary scan of the data set, more detailed analysis was undertaken on key health and related safety issues that include respiratory disorders, hearing loss and high blood pressure. The data set facilitates control for confounding factors such as age and smoking status. Mines can be benchmarked to identify those mines with effective health management and those with particular challenges. While the study has confirmed the very low prevalence of restrictive airway disease such as pneu"moconiosis, it has demonstrated a need to examine in detail the emergence of obstructive airway disease such as bronchitis and emphysema which may be a consequence of the increasing use of high dust longwall technology. The power of the Health Database's electronic data management is demonstrated by linking the health data to other data sets such as injury data that is collected by the Department of l\1mes and Energy. The analysis examines serious strain -sprain injuries and has identified a marked difference between the underground and open cut sectors of the industry. The analysis also considers productivity and OHS data to examine the extent to which there is correlation between any pairs ofJpese and previously analysed health parameters. This project has demonstrated that the current structure of the Coal Industry Employees Health Scheme has largely delivered to mines and effective health screening process. At the same time, the centralised nature of data collection and analysis has provided to the mines, the unions and the government substantial statistical cross-sectional data upon which strategies to more effectively manage health and relates safety issues can be based.

Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

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.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Synchronised integrated online e-health profiles

Web-based social networking applications have become increasingly important in recent years. The current applications in the healthcare sphere can support the health management, but to date there is no patient-controlled integrator. This paper proposes a platform called Multiple Profile Manager (MPM) that enables a user to create and manage an integrated profile that can be shared across numerous social network sites. Moreover, it is able to facilitate the collection of personal healthcare data, which makes a contribution to the development of public health informatics. Here we want to illustrate how patients and physicians can be benefited from enabling the platform for online social network sites. The MPM simplifies the management of patients' profiles and allows health professionals to obtain a more complete picture of the patients' background so that they can provide better health care. To do so, we demonstrate a prototype of the platform and describe its protocol specification, which is an XMPP (Extensible Messaging and Presence Protocol) [1] extension, for sharing and synchronising profile data (vCard²) between different social networks.

Safety, courts and crime: occupational safety and health prosecutions in the Magistrates' courts

This paper reports on an empirically based study of occupational safety and health prosecutions in the Magistrates' courts in the State of Victoria, Australia. It examines the way in which the courts construct occupational safety and health issues during prosecutions against alleged offenders, and then theorises the role of the criminal law in health and safety regulation. The paper argues that courts, inspectors, prosecutors and defence counsel are involved in filtering or reshaping occupational safety and health issues during the prosecution process, both pre-trial and in court. An analysis of the pattern of investigation of health and safety offences shows that they are constructed by focusing on 'events', in most cases incidents resulting in injury or death. This 'event focus' ensures that the attention of the parties is drawn to the details of the incident and away from the broader context of the event. This broader context includes the way in which work is organised at the workplace and the quality of occupational safety and health management (the micro context), and the pressures within capitalist production systems for occupational safety and health to be subordinated to production imperatives (the macro context). In particular, during the court-based sentencing process, defence counsel is able to adopt a range of 'isolation' techniques that isolate the incident from its micro and macro contexts, thereby individualising and decontextualising the incident. The paper concludes that the legal system plays a key role in decontextualising and individualising health and safety issues, and that this process is part of the 'architecture' of the legal system, and a direct consequence of the 'form of law'.

Use of online health information to manage children’s health care: A prospective study investigating parental decisions

Background The use of the internet to access information is rapidly increasing; however, the quality of health information provided on various online sites is questionable. We aimed to examine the underlying factors that guide parents' decisions to use online information to manage their child's health care, a behaviour which has not yet been explored systematically. Methods Parents (N=391) completed a questionnaire assessing the standard theory of planned behaviour (TPB) measures of attitude, subjective norm, perceived behavioural control (PBC), and intention as well as the underlying TPB belief-based items (i.e., behavioural, normative, and control beliefs) in addition to a measure of perceived risk and demographic variables. Two months later, consenting parents completed a follow-up telephone questionnaire which assessed the decisions they had made regarding their use of online information to manage their child's health care during the previous 2 months. Results We found support for the TPB constructs of attitude, subjective norm, and PBC as well as the additional construct of perceived risk in predicting parents' intentions to use online information to manage their child's health care, with further support found for intentions, but not PBC, in predicting parents' behaviour. The results of the TPB belief-based analyses also revealed important information about the critical beliefs that guide parents' decisions to engage in this child health management behaviour. Conclusions This theory-based investigation to understand parents' motivations and online information-seeking behaviour is key to developing recommendations and policies to guide more appropriate help-seeking actions among parents.

Do health inquiries lead to health system change? What have we learnt from recent health inquiries and will the same mistakes happen again?

Since 2001 there have been numerous Commissions of Inquiry into health system failures across the world. While the Inquiries were established to examine poor patient outcomes, each has identified a range of leadership and management shortcomings that have contributed to a poor standard of patient care. While there is an acknowledgement that different heath systems have different contexts, this paper highlights a number of themes that are common across Inquiries. It will discuss a number of common system failures in Inquiries spanning from 2001 to 2013 and pose questions as to why these types of failures are likely to re-occur, as well as possible learnings for health service management and leadership to address a number of these common themes.

Personal health data - Privacy policy harmonization and global enforcement

This workshop is jointly organized by EFMI Working Groups Security, Safety and Ethics and Personal Portable Devices in cooperation with IMIA Working Group "Security in Health Information Systems". In contemporary healthcare and personal health management the collection and use of personal health information takes place in different contexts and jurisdictions. Global use of health data is also expanding. The approach taken by different experts, health service providers, data subjects and secondary users in understanding privacy and the privacy expectations others may have is strongly context dependent. To make eHealth, global healthcare, mHealth and personal health management successful and to enable fair secondary use of personal health data, it is necessary to find a practical and functional balance between privacy expectations of stakeholder groups. The workshop will highlight these privacy concerns by presenting different cases and approaches. Workshop participants will analyse stakeholder privacy expectations that take place in different real-life contexts such as portable health devices and personal health records, and develop a mechanism to balance them in such a way that global protection of health data and its meaningful use is realized simultaneously. Based on the results of the workshop, initial requirements for a global healthcare information certification framework will be developed.

A review on reliability models with covariates

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional reliability models, the lifetime of assets is estimated using failure time data. However, in most real-life situations and industry applications, the lifetime of assets is influenced by different risk factors, which are called covariates. The fundamental notion in reliability theory is the failure time of a system and its covariates. These covariates change stochastically and may influence and/or indicate the failure time. Research shows that many statistical models have been developed to estimate the hazard of assets or individuals with covariates. An extensive amount of literature on hazard models with covariates (also termed covariate models), including theory and practical applications, has emerged. This paper is a state-of-the-art review of the existing literature on these covariate models in both the reliability and biomedical fields. One of the major purposes of this expository paper is to synthesise these models from both industrial reliability and biomedical fields and then contextually group them into non-parametric and semi-parametric models. Comments on their merits and limitations are also presented. Another main purpose of this paper is to comprehensively review and summarise the current research on the development of the covariate models so as to facilitate the application of more covariate modelling techniques into prognostics and asset health management.