Analysis of caesarean section rates over time in a single Swiss centre using a ten-group classification system.

OBJECTIVE Caesarean section (CS) rates have risen over the past two decades. The aim of this observational study was to identify time-dependent variations in CS and vaginal delivery rates over a period of 11 years. METHOD All deliveries (13,701 deliveries during the period 1999-2009) at the University Women's Hospital Bern were analysed using an internationally standardised and approved ten-group classification system. Caesarean sections on maternal request (CSMR) were evaluated separately. RESULTS We detected an overall CS rate of 36.63% and an increase in the CS rate over time (p <0.001). Low-risk profile groups were the two largest populations and displayed low CS rates, with significantly decreasing relative size over time. The relative size of groups with induced labour increased significantly, but this did not have an impact on the overall CS rate. Pregnancies complicated by breech position, multiple pregnancies and abnormal lies did not have an impact on overall CS rate. The biggest contributor to a high CS rate was preterm delivery and the existence of a uterine scar from a previous CS. CSMR was 1.45% and did not have an impact on the overall CS rate. CONCLUSION The observational study identified wide variations in caesarean section and vaginal delivery rates across the groups over time, and a shift towards high-risk populations was noted. The biggest contributors to high CS rates were identified; namely, previous uterine scar and preterm delivery. Interventions aiming to reduce CS rates are planned.

Prognosis of bearing failure based on health state estimation

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.

Thinking spatially, acting collaboratively : a GIS-based health decision support system for improving the collaborative health-planning practice

The field of collaborative health planning faces significant challenges due to the lack of effective information, systems and the absence of a framework to make informed decisions. These challenges have been magnified by the rise of the healthy cities movement, consequently, there have been more frequent calls for localised, collaborative and evidence-driven decision-making. Some studies in the past have reported that the use of decision support systems (DSS) for planning healthy cities may lead to: increase collaboration between stakeholders and the general public, improve the accuracy and quality of the decision-making processes and improve the availability of data and information for health decision-makers. These links have not yet been fully tested and only a handful of studies have evaluated the impact of DSS on stakeholders, policy-makers and health planners. This study suggests a framework for developing healthy cities and introduces an online Geographic Information Systems (GIS)-based DSS for improving the collaborative health planning. It also presents preliminary findings of an ongoing case study conducted in the Logan-Beaudesert region of Queensland, Australia. These findings highlight the perceptions of decision-making prior to the implementation of the DSS intervention. Further, the findings help us to understand the potential role of the DSS to improve collaborative health planning practice.

Public sector strategy classification system for shared service identification

The purpose of this conceptual paper is to address the lack of consistent means through which strategies are identified and discussed across theoretical perspectives in the field of business strategy. A standardised referencing system is offered to codify the means by which strategies can be identified, from which new business services and information systems may be derived. This taxonomy was developed using qualitative content analysis study of government agencies’ strategic plans. This taxonomy is useful for identifying strategy formation and determining gaps and opportunities. Managers will benefit from a more transparent strategic design process that reduces ambiguity, aids in identifying and correcting gaps in strategy formulation, and fosters enhanced strategic analysis. Key benefits to academics are the improved dialogue in strategic management field and suggest that progress in the field requires that fundamentals of strategy formulation and classification be considered more carefully. Finally, the formalization of strategy can lead to the clear identification of new business services, which inform ICT investment decisions and shared service prioritisation.

Developing a framework for planning healthy communities : the Logan Beaudesert health decision support system

In the last few decades, the focus on building healthy communities has grown significantly (Ashton, 2009). There is growing evidence that new approaches to planning are required to address the challenges faced by contemporary communities. These approaches need to be based on timely access to local information and collaborative planning processes (Murray, 2006; Scotch & Parmanto, 2006; Ashton, 2009; Kazda et al., 2009). However, there is little research to inform the methods that can support this type of responsive, local, collaborative and consultative health planning (Northridge et al., 2003). Some research justifies the use of decision support systems (DSS) as a tool to support planning for healthy communities. DSS have been found to increase collaboration between stakeholders and communities, improve the accuracy and quality of the decision-making process, and improve the availability of data and information for health decision-makers (Nobre et al., 1997; Cromley & McLafferty, 2002; Waring et al., 2005). Geographic information systems (GIS) have been suggested as an innovative method by which to implement DSS because they promote new ways of thinking about evidence and facilitate a broader understanding of communities. Furthermore, literature has indicated that online environments can have a positive impact on decision-making by enabling access to information by a broader audience (Kingston et al., 2001). However, only limited research has examined the implementation and impact of online DSS in the health planning field. Previous studies have emphasised the lack of effective information management systems and an absence of frameworks to guide the way in which information is used to promote informed decisions in health planning. It has become imperative to develop innovative approaches, frameworks and methods to support health planning. Thus, to address these identified gaps in the knowledge, this study aims to develop a conceptual planning framework for creating healthy communities and examine the impact of DSS in the Logan Beaudesert area. Specifically, the study aims to identify the key elements and domains of information that are needed to develop healthy communities, to develop a conceptual planning framework for creating healthy communities, to collaboratively develop and implement an online GIS-based Health DSS (i.e., HDSS), and to examine the impact of the HDSS on local decision-making processes. The study is based on a real-world case study of a community-based initiative that was established to improve public health outcomes and promote new ways of addressing chronic disease. The study involved the development of an online GIS-based health decision support system (HDSS), which was applied in the Logan Beaudesert region of Queensland, Australia. A planning framework was developed to account for the way in which information could be organised to contribute to a healthy community. The decision support system was developed within a unique settings-based initiative Logan Beaudesert Health Coalition (LBHC) designed to plan and improve the health capacity of Logan Beaudesert area in Queensland, Australia. This setting provided a suitable platform to apply a participatory research design to the development and implementation of the HDSS. Therefore, the HDSS was a pilot study examined the impact of this collaborative process, and the subsequent implementation of the HDSS on the way decision-making was perceived across the LBHC. As for the method, based on a systematic literature review, a comprehensive planning framework for creating healthy communities has been developed. This was followed by using a mixed method design, data were collected through both qualitative and quantitative methods. Specifically, data were collected by adopting a participatory action research (PAR) approach (i.e., PAR intervention) that informed the development and conceptualisation of the HDSS. A pre- and post-design was then used to determine the impact of the HDSS on decision-making. The findings of this study revealed a meaningful framework for organising information to guide planning for healthy communities. This conceptual framework provided a comprehensive system within which to organise existing data. The PAR process was useful in engaging stakeholders and decision-making in the development and implementation of the online GIS-based DSS. Through three PAR cycles, this study resulted in heightened awareness of online GIS-based DSS and openness to its implementation. It resulted in the development of a tailored system (i.e., HDSS) that addressed the local information and planning needs of the LBHC. In addition, the implementation of the DSS resulted in improved decision- making and greater satisfaction with decisions within the LBHC. For example, the study illustrated the culture in which decisions were made before and after the PAR intervention and what improvements have been observed after the application of the HDSS. In general, the findings indicated that decision-making processes are not merely informed (consequent of using the HDSS tool), but they also enhance the overall sense of ‗collaboration‘ in the health planning practice. For example, it was found that PAR intervention had a positive impact on the way decisions were made. The study revealed important features of the HDSS development and implementation process that will contribute to future research. Thus, the overall findings suggest that the HDSS is an effective tool, which would play an important role in the future for significantly improving the health planning practice.

Water quality parameter estimation in steady-state distribution system

The maintenance of chlorine residual is needed at all the points in the distribution system supplied with chlorine as a disinfectant. The propagation and level of chlorine in a distribution system is affected by both bulk and pipe wall reactions. It is well known that the field determination of wall reaction parameter is difficult. The source strength of chlorine to maintain a specified chlorine residual at a target node is also an important parameter. The inverse model presented in the paper determines these water quality parameters, which are associated with different reaction kinetics, either in single or in groups of pipes. The weighted-least-squares method based on the Gauss-Newton minimization technique is used for the estimation of these parameters. The validation and application of the inverse model is illustrated with an example pipe distribution system under steady state. A generalized procedure to handle noisy and bad (abnormal) data is suggested, which can be used to estimate these parameters more accurately. The developed inverse model is useful for water supply agencies to calibrate their water distribution system and to improve their operational strategies to maintain water quality.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.