Is language a barrier for quality care in hospitals? A case series from an Emergency Department of a teaching hospital in Brisbane

Aim: The aim of this pilot study is to describe the use of an Emergency Department (ED) at a large metropolitan teaching hospital by patients who speak English or other languages at home. Methods: All data were retrieved from the Emergency Department Information System (EDIS) of this tertiary teaching hospital in Brisbane. Patients were divided into two groups based on the language spoken at home: patients who speak English only at home (SEO) and patients who do not speak English only or speak other language at home (NSEO). Modes of arrival, length of ED stay and the proportion of hospital admission were compared among the two groups of patients by using SPSS V18 software. Results: A total of 69,494 patients visited this hospital ED in 2009 with 67,727 (97.5%) being in the SEO group and 1,281 (1.80%) in the NSEO group. The proportion of ambulance utilisation in arrival mode was significantly higher among SEO 23,172 (34.2%) than NSEO 397 (31.0%), p <0.05. The NSEO patients had longer length of stay in the ED (M = 337.21, SD = 285.9) compared to SEO patients (M= 290.9, SD = 266.8), with 46.3 minutes (95%CI 62.1, 30.5, p <0.001) difference. The admission to the hospital among NSEO was 402 (31.9%) higher than SEO 17,652 (26.6%), p <0.001. Conclusion: The lower utilisation rates of ambulance services, longer length of ED stay and higher hospital admission rates in NSEO patients compared to SEO patients are consistent with other international studies and may be due to the language barriers.

Corneal nerve structure and function as markers of diabetic neuropathy

Diabetic neuropathy is a significant clinical problem that currently has no effective therapy, and in advanced cases, leads to foot ulceration and lower limb amputation. The accurate detection, characterisation and quantification of this condition are important in order to define at-risk patients, anticipate deterioration, monitor progression and assess new therapies. This thesis evaluates novel corneal methods of assessing diabetic neuropathy. Over the past several years two new non-invasive corneal markers have emerged, and in cross-sectional studies have demonstrated their ability to stratify the severity of this disease. Corneal confocal microscopy (CCM) allows quantification of corneal nerve parameters and non-contact corneal aesthesiometry (NCCA), the presumed functional correlate of corneal structure, assesses the sensitivity of the cornea. Both these techniques are quick to perform, produce little or no discomfort for the patient, and with automatic analysis paradigms developed, are suitable for clinical settings. Each has advantages and disadvantages over established techniques for assessing diabetic neuropathy. New information is presented regarding measurement bias of CCM images, and a unique sampling paradigm and associated accuracy determination method of combinations is described. A novel high-speed corneal nerve mapping procedure has been developed and application of this procedure in individuals with neuropathy has revealed regions of sub-basal nerve plexus that dictate further evaluation, as they appear to show earlier signs of damage than the central region of the cornea that has to date been examined. The discriminative capacity of corneal sensitivity measured by NCCA is revealed to have reasonable potential as a marker of diabetic neuropathy. Application of these new corneal markers for longitudinal evaluation of diabetic neuropathy has the potential to reduce dependence on more invasive, costly, and time-consuming assessments, such as skin biopsy.

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.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

The effect of implementing a modified early warning scoring (MEWS) system on the adequacy of vital sign documentation

Introduction and objectives Early recognition of deteriorating patients results in better patient outcomes. Modified early warning scores (MEWS) attempt to identify deteriorating patients early so timely interventions can occur thus reducing serious adverse events. We compared frequencies of vital sign recording 24 h post-ICU discharge and 24 h preceding unplanned ICU admission before and after a new observation chart using MEWS and an associated educational programme was implemented into an Australian Tertiary referral hospital in Brisbane. Design Prospective before-and-after intervention study, using a convenience sample of ICU patients who have been discharged to the hospital wards, and in patients with an unplanned ICU admission, during November 2009 (before implementation; n = 69) and February 2010 (after implementation; n = 70). Main outcome measures Any change in a full set or individual vital sign frequency before-and-after the new MEWS observation chart and associated education programme was implemented. A full set of vital signs included Blood pressure (BP), heart rate (HR), temperature (T°), oxygen saturation (SaO2) respiratory rate (RR) and urine output (UO). Results After the MEWS observation chart implementation, we identified a statistically significant increase (210%) in overall frequency of full vital sign set documentation during the first 24 h post-ICU discharge (95% CI 148, 288%, p value <0.001). Frequency of all individual vital sign recordings increased after the MEWS observation chart was implemented. In particular, T° recordings increased by 26% (95% CI 8, 46%, p value = 0.003). An increased frequency of full vital sign set recordings for unplanned ICU admissions were found (44%, 95% CI 2, 102%, p value = 0.035). The only statistically significant improvement in individual vital sign recordings was urine output, demonstrating a 27% increase (95% CI 3, 57%, p value = 0.029). Conclusions The implementation of a new MEWS observation chart plus a supporting educational programme was associated with statistically significant increases in frequency of combined and individual vital sign set recordings during the first 24 h post-ICU discharge. There were no significant changes to frequency of individual vital sign recordings in unplanned admissions to ICU after the MEWS observation chart was implemented, except for urine output. Overall increases in the frequency of full vital sign sets were seen.

The neglected dimension of community liveability : impact on social connectedness and active ageing in higher density accommodation

Purpose This thesis is about liveability, place and ageing in the high density urban landscape of Brisbane, Australia. As with other major developed cities around the globe, Brisbane has adopted policies to increase urban residential densities to meet the main liveability and sustainability aim of decreasing car dependence and therefore pollution, as well as to minimise the loss of greenfield areas and habitats to developers. This objective hinges on urban neighbourhoods/communities being liveable places, which residents do not have to leave for everyday living. Community/neighbourhood liveability is an essential ingredient in healthy ageing in place and has a substantial impact upon the safety, independence and well-being of older adults. It is generally accepted that ageing in place is optimal for both older people and the state. The optimality of ageing in place generally assumes that there is a particular quality to environments or standard of liveability in which people successfully age in place. The aim of this thesis was to examine if there are particular environmental qualities or aspects of liveability that test optimality and to better understand the key liveability factors that contribute to successful ageing in place. Method A strength of this thesis is that it draws on two separate studies to address the research question of what makes high density liveable for older people. In Chapter 3, the two methods are identified and differentiated as Method 1 (used in Paper 1) and Method 2 (used in Papers 2, 3, 4 and 5). Method 1 involved qualitative interviews with 24 inner city high density Brisbane residents. The major strength of this thesis is the innovative methodology outlined in the thesis as Method 2. Method 2 involved a case study approach employing qualitative and quantitative methods. Qualitative data was collected using semi-structured, in-depth interviews and time-use diaries completed by participants during the week of tracking. The quantitative data was gathered using Global Positioning Systems for tracking and Geographical Information Systems for mapping and analysis of participants’ activities. The combination of quantitative and qualitative analysis captured both participants’ subjective perceptions of their neighbourhoods and their patterns of movement. This enhanced understanding of how neighbourhoods and communities function and of the various liveability dimensions that contribute to active ageing and ageing in place for older people living in high density environments. Both studies’ participants were inner-city high density residents of Brisbane. The study based on Method 1 drew on a wider age demographic than the study based on Method 2. Findings The five papers presented in this thesis by publication indicate a complex inter-relationship of the factors that make a place liveable. The first three papers identify what is comparable and different between the physical and social factors of high density communities/neighbourhoods. The last two papers explore relationships between social engagement and broader community variables such as infrastructure and the physical built environments that are risk or protective factors relevant to community liveability, active ageing and ageing in place in high density. The research highlights the importance of creating and/or maintaining a barrier-free environment and liveable community for ageing adults. Together, the papers promote liveability, social engagement and active ageing in high density neighbourhoods by identifying factors that constitute liveability and strategies that foster active ageing and ageing in place, social connections and well-being. Recommendations There is a strong need to offer more support for active ageing and ageing in place. While the data analyses of this research provide insight into the lived experience of high density residents, further research is warranted. Further qualitative and quantitative research is needed to explore in more depth, the urban experience and opinions of older people living in urban environments. In particular, more empirical research and theory-building is needed in order to expand understanding of the particular environmental qualities that enable successful ageing in place in our cities and to guide efforts aimed at meeting this objective. The results suggest that encouraging the presence of more inner city retail outlets, particularly services that are utilised frequently in people’s daily lives such as supermarkets, medical services and pharmacies, would potentially help ensure residents fully engage in their local community. The connectivity of streets, footpaths and their role in facilitating the reaching of destinations are well understood as an important dimension of liveability. To encourage uptake of sustainable transport, the built environment must provide easy, accessible connections between buildings, walkways, cycle paths and public transport nodes. Wider streets, given that they take more time to cross than narrow streets, tend to .compromise safety - especially for older people. Similarly, the width of footpaths, the level of buffering, the presence of trees, lighting, seating and design of and distance between pedestrian crossings significantly affects the pedestrian experience for older people and impacts upon their choice of transportation. High density neighbourhoods also require greater levels of street fixtures and furniture for everyday life to make places more useable and comfortable for regular use. The importance of making the public realm useful and habitable for older people cannot be over-emphasised. Originality/value While older people are attracted to high density settings, there has been little empirical evidence linking liveability satisfaction with older people’s use of urban neighbourhoods. The current study examined the relationships between community/neighbourhood liveability, place and ageing to better understand the implications for those adults who age in place. The five papers presented in this thesis add to the understanding of what high density liveable age-friendly communities/ neighbourhoods are and what makes them so for older Australians. Neighbourhood liveability for older people is about being able to age in place and remain active. Issues of ageing in Australia and other areas of the developed world will become more critical in the coming decades. Creating livable communities for all ages calls for partnerships across all levels of government agencies and among different sectors within communities. The increasing percentage of older people in the community will have increasing political influence and it will be a foolish government who ignores the needs of an older society.

Human proximal tubule epithelial cells modulate autologous dendritic cell function

Background We have previously demonstrated that human kidney proximal tubule epithelial cells (PTEC) are able to modulate autologous T and B lymphocyte responses. It is well established that dendritic cells (DC) are responsible for the initiation and direction of adaptive immune responses and that these cells occur in the renal interstitium in close apposition to PTEC under inflammatory disease settings. However, there is no information regarding the interaction of PTEC with DC in an autologous human context. Methods Human monocytes were differentiated into monocyte-derived DC (MoDC) in the absence or presence of primary autologous activated PTEC and matured with polyinosinic:polycytidylic acid [poly(I:C)], while purified, pre-formed myeloid blood DC (CD1c+ BDC) were cultured with autologous activated PTEC in the absence or presence of poly(I:C) stimulation. DC responses were monitored by surface antigen expression, cytokine secretion, antigen uptake capacity and allogeneic T-cell-stimulatory ability. Results The presence of autologous activated PTEC inhibited the differentiation of monocytes to MoDC. Furthermore, MoDC differentiated in the presence of PTEC displayed an immature surface phenotype, efficient phagocytic capacity and, upon poly(I:C) stimulation, secreted low levels of pro-inflammatory cytokine interleukin (IL)-12p70, high levels of anti-inflammatory cytokine IL-10 and induced weak Th1 responses. Similarly, pre-formed CD1c+ BDC matured in the presence of PTEC exhibited an immature tolerogenic surface phenotype, strong endocytic and phagocytic ability and stimulated significantly attenuated T-cell proliferative responses. Conclusions Our data suggest that activated PTEC regulate human autologous immunity via complex interactions with DC. The ability of PTEC to modulate autologous DC function has important implications for the dampening of pro-inflammatory immune responses within the tubulointerstitium in renal injuries. Further dissection of the mechanisms of PTEC modulation of autologous immune responses may offer targets for therapeutic intervention in renal medicine.