Socioeconomic position, gender, health behaviours and biomarkers of cardiovascular disease and diabetes

Socio-economic gradients in cardiovascular disease (CVD) and diabetes have been found throughout the developed world and there is some evidence to suggest that these gradients may be steeper for women. Research on social gradients in biological risk factors for CVD and diabetes has received less attention and we do not know the extent to which gradients in biomarkers vary for men and women. We examined the associations between two indicators of socio-economic position (education and household income) and biomarkers of diabetes and cardiovascular disease (CVD) for men and women in a national, population-based study of 11,247 Australian adults. Multi-level linear regression was used to assess associations between education and income and glucose tolerance, dyslipidaemia, blood pressure (BP) and waist circumference before and after adjustment for behaviours (diet, smoking, physical activity, TV viewing time, and alcohol use). Measures of glucose tolerance included fasting plasma glucose and insulin and the results of a glucose tolerance test (2 h glucose) with higher levels of each indicating poorer glucose tolerance. Triglycerides and High Density Lipoprotein (HDL) Cholesterol were used as measures of dyslipidaemia with higher levels of the former and lower levels of the later being associated with CVD risk. Lower education and low income were associated with higher levels of fasting insulin, triglycerides and waist circumference in women. Women with low education had higher systolic and diastolic BP and low income women had higher 2 h glucose and lower HDL cholesterol. With only one exception (low income and systolic BP), all of these estimates were reduced by more than 20% when behavioural risk factors were included. Men with lower education had higher fasting plasma glucose, 2 h glucose, waist circumference and systolic BP and, with the exception of waist circumference, all of these estimates were reduced when health behaviours were included in the models. While low income was associated with higher levels of 2-h glucose and triglycerides it was also associated with better biomarker profiles including lower insulin, waist circumference and diastolic BP. We conclude that low socio-economic position is more consistently associated with a worse profile of biomarkers for CVD and diabetes for women.

Promoting student understanding through complex learning

The world’s increasing complexity, competitiveness, interconnectivity, and dependence on technology generate new challenges for nations and individuals that cannot be met by “continuing education as usual” (The National Academies, 2009). With the proliferation of complex systems have come new technologies for communication, collaboration, and conceptualization. These technologies have led to significant changes in the forms of mathematical thinking that are required beyond the classroom. This paper argues for the need to incorporate future-oriented understandings and competencies within the mathematics curriculum, through intellectually stimulating activities that draw upon multidisciplinary content and contexts. The paper also argues for greater recognition of children’s learning potential, as increasingly complex learners capable of dealing with cognitively demanding tasks.

Adherence to fish oil intervention in patients with chronic kidney disease

Objective: With growing recognition of the role of inflammation in the development of chronic and acute disease, fish oil is increasingly used as a therapeutic agent, but the nature of the intervention may pose barriers to adherence in clinical populations. Our objective was to investigate the feasibility of using a fish oil supplement in hemodialysis patients. ---------- Design: This was a nonrandomized intervention study.---------- Setting: Eligible patients were recruited at the Hemodialysis Unit of Wesley Hospital, Brisbane, Queensland, Australia. Patients The sample included 28 maintenance hemodialysis patients out of 43 eligible patients in the unit. Exclusion criteria included patients regularly taking a fish oil supplement at baseline, receiving hemodialysis for less than 3 months, or being unable to give informed consent.---------- Intervention: Eicosapentaenoic acid (EPA) was administered at 2000 mg/day (4 capsules) for 12 weeks. Adherence was measured at baseline and weekly throughout the study according to changes in plasma EPA, and was further measured subjectively by self-report.---------- Results: Twenty patients (74%) adhered to the prescription based on changes in plasma EPA, whereas an additional two patients self-reported good adherence. There was a positive relationship between fish oil intake and change in plasma EPA. Most patients did not report problems with taking the fish oil. Using the baseline data, it was not possible to characterize adherent patients.---------- Conclusions: Despite potential barriers, including the need to take a large number of prescribed medications already, 74% of hemodialysis patients adhered to the intervention. This study demonstrated the feasibility of using fish oil in a clinical population.

Using Enterprise 2.0 tools to facilitate knowledge transfer in complex engineering environments

An essential challenge for organizations wishing to overcome informational silos is to implement mechanisms that facilitate, encourage and sustain interactions between otherwise disconnected groups. Using three case examples, this paper explores how Enterprise 2.0 technologies achieve such goals, allowing for the transfer of knowledge by tapping into the tacit and explicit knowledge of disparate groups in complex engineering organizations. The paper is intended to be a timely introduction to the benefits and issues associated with the use of Enterprise 2.0 technologies with the aim of achieving the positive outcomes associated with knowledge management

Deliberative assessment of surrogate consent in dementia research

Background Research involving incapacitated persons with dementia entails complex scientific, legal, and ethical issues, making traditional surveys of layperson views on the ethics of such research challenging. We therefore assessed the impact of democratic deliberation (DD), involving balanced, detailed education and peer deliberation, on the views of those responsible for persons with dementia. Methods One hundred and seventy-eight community-recruited caregivers or primary decision-makers for persons with dementia were randomly assigned to either an all-day DD session group or a control group. Educational materials used for the DD session were vetted for balance and accuracy by an interdisciplinary advisory panel. We assessed the acceptability of family-surrogate consent for dementia research (“surrogate-based research”) from a societal policy perspective as well as from the more personal perspectives of deciding for a loved one or for oneself (surrogate and self-perspectives), assessed at baseline, immediately post-DD session, and 1 month after DD date, for four research scenarios of varying risk-benefit profiles. Results At baseline, a majority in both the DD and control groups supported a policy of family consent for dementia research in all research scenarios. The support for a policy of family consent for surrogate-based research increased in the DD group, but not in the control group. The change in the DD group was maintained 1 month later. In the DD group, there were transient changes in attitudes from surrogate or self-perspectives. In the control group, there were no changes from baseline in attitude toward surrogate consent from any perspective. Conclusions Intensive, balanced, and accurate education, along with peer deliberation provided by democratic deliberation, led to a sustained increase in support for a societal policy of family consent in dementia research among those responsible for dementia patients.

Modelling complex resource requirements in Business Process Management Systems

Real-world business processes are resource-intensive. In work environments human resources usually multitask, both human and non-human resources are typically shared between tasks, and multiple resources are sometimes necessary to undertake a single task. However, current Business Process Management Systems focus on task-resource allocation in terms of individual human resources only and lack support for a full spectrum of resource classes (e.g., human or non-human, application or non-application, individual or teamwork, schedulable or unschedulable) that could contribute to tasks within a business process. In this paper we develop a conceptual data model of resources that takes into account the various resource classes and their interactions. The resulting conceptual resource model is validated using a real-life healthcare scenario.

Wavelet based approaches and high resolution methods for complex process models

Many industrial processes and systems can be modelled mathematically by a set of Partial Differential Equations (PDEs). Finding a solution to such a PDF model is essential for system design, simulation, and process control purpose. However, major difficulties appear when solving PDEs with singularity. Traditional numerical methods, such as finite difference, finite element, and polynomial based orthogonal collocation, not only have limitations to fully capture the process dynamics but also demand enormous computation power due to the large number of elements or mesh points for accommodation of sharp variations. To tackle this challenging problem, wavelet based approaches and high resolution methods have been recently developed with successful applications to a fixedbed adsorption column model. Our investigation has shown that recent advances in wavelet based approaches and high resolution methods have the potential to be adopted for solving more complicated dynamic system models. This chapter will highlight the successful applications of these new methods in solving complex models of simulated-moving-bed (SMB) chromatographic processes. A SMB process is a distributed parameter system and can be mathematically described by a set of partial/ordinary differential equations and algebraic equations. These equations are highly coupled; experience wave propagations with steep front, and require significant numerical effort to solve. To demonstrate the numerical computing power of the wavelet based approaches and high resolution methods, a single column chromatographic process modelled by a Transport-Dispersive-Equilibrium linear model is investigated first. Numerical solutions from the upwind-1 finite difference, wavelet-collocation, and high resolution methods are evaluated by quantitative comparisons with the analytical solution for a range of Peclet numbers. After that, the advantages of the wavelet based approaches and high resolution methods are further demonstrated through applications to a dynamic SMB model for an enantiomers separation process. This research has revealed that for a PDE system with a low Peclet number, all existing numerical methods work well, but the upwind finite difference method consumes the most time for the same degree of accuracy of the numerical solution. The high resolution method provides an accurate numerical solution for a PDE system with a medium Peclet number. The wavelet collocation method is capable of catching up steep changes in the solution, and thus can be used for solving PDE models with high singularity. For the complex SMB system models under consideration, both the wavelet based approaches and high resolution methods are good candidates in terms of computation demand and prediction accuracy on the steep front. The high resolution methods have shown better stability in achieving steady state in the specific case studied in this Chapter.

Molecular characterisation of six badnavirus species associated with leaf streak disease of banana in East Africa

Banana leaf streak disease, caused by several species of Banana streak virus (BSV), is widespread in East Africa. We surveyed for this disease in Uganda and Kenya, and used rolling-circle amplification (RCA) to detect the presence of BSV in banana. Six distinct badnavirus sequences, three from Uganda and three from Kenya, were amplified for which only partial sequences were previously available. The complete genomes were sequenced and characterised. The size and organisation of all six sequences was characteristic of other badnaviruses, including conserved functional domains present in the putative polyprotein encoded by open reading frame (ORF) 3. Based on nucleotide sequence analysis within the reverse transcriptase/ribonuclease H-coding region of open reading frame 3, we propose that these sequences be recognised as six new species and be designated as Banana streak UA virus, Banana streak UI virus, Banana streak UL virus, Banana streak UM virus, Banana streak CA virus and Banana streak IM virus. Using PCR and species-specific primers to test for the presence of integrated sequences, we demonstrated that sequences with high similarity to BSIMV only were present in several banana cultivars which had tested negative for episomal BSV sequences.

Facilitating needs based cancer care for people with a chronic disease : evaluation of an intervention using a multi-centre interrupted time series design

Background: Palliative care should be provided according to the individual needs of the patient, caregiver and family, so that the type and level of care provided, as well as the setting in which it is delivered, are dependent on the complexity and severity of individual needs, rather than prognosis or diagnosis. This paper presents a study designed to assess the feasibility and efficacy of an intervention to assist in the allocation of palliative care resources according to need, within the context of a population of people with advanced cancer. ---------- Methods/design: People with advanced cancer and their caregivers completed bi-monthly telephone interviews over a period of up to 18 months to assess unmet needs, anxiety and depression, quality of life, satisfaction with care and service utilisation. The intervention, introduced after at least two baseline phone interviews, involved a) training medical, nursing and allied health professionals at each recruitment site on the use of the Palliative Care Needs Assessment Guidelines and the Needs Assessment Tool: Progressive Disease - Cancer (NAT: PD-C); b) health professionals completing the NAT: PD-C with participating patients approximately monthly for the rest of the study period. Changes in outcomes will be compared pre-and post-intervention.---------- Discussion: The study will determine whether the routine, systematic and regular use of the Guidelines and NAT: PD-C in a range of clinical settings is a feasible and effective strategy for facilitating the timely provision of needs based care.

Cochrane review summary for cancer nursing : drug therapy for cancer-related fatigue

Cancer-related fatigue (CRF) is one of themost debilitating symptoms in patients with cancer. It is prevalent at the time of diagnosis and during and after antineoplastic treatment and in patients with advanced disease. The multifactorial and complex nature of CRF makes it challenging for health professionals to identify a clear underlying mechanism and manage this symptom effectively. Often, the management plan for CRF (whether pharmacological or nonpharmacological) can be further complicated by the coexistence of other symptoms. This systematic review1 is therefore important in informing health professionals on the effectiveness of pharmacological management for CRF.

Best practice for prevention and treatment of cardiovascular disease through an equity lens : a review

Background: Despite declining rates of cardiovascular disease (CVD) mortality in developed countries, lower socioeconomic groups continue to experience a greater burden of the disease. There are now many evidence-based treatments and prevention strategies for the management of CVD and it is essential that their impact on the more disadvantaged group is understood if socioeconomic inequalities in CVD are to be reduced. Aims: To determine whether key interventions for CVD prevention and treatment are effective among lower socioeconomic groups, to describe barriers to their effectiveness and the potential or actual impact of these interventions on the socioeconomic gradient in CVD. Methods: Interventions were selected from four stages of the CVD continuum. These included smoking reduction strategies, absolute risk assessment, cardiac rehabilitation, secondary prevention medications, and heart failure self-management programmes. Electronic searches were conducted using terms for each intervention combined with terms for socioeconomic status (SES). Results: Only limited evidence was found for the effectiveness of the selected interventions among lower SES groups and there was little exploration of socioeconomic-related barriers to their uptake. Some broad themes and key messages were identified. In the majority of findings examined, it was clear that the underlying material, social and environmental factors associated with disadvantage are a significant barrier to the effectiveness of interventions. Conclusion: Opportunities to reduce socioeconomic inequalities occur at all stages of the CVD continuum. Despite this, current treatment and prevention strategies may be contributing to the widening socioeconomic-CVD gradient. Further research into the impact of best-practice interventions for CVD upon lower SES groups is required.

Lifestyle factors associated concurrently and prospectively with co-morbid cardiovascular disease in a population-based cohort of colorectal cancer survivors

Aims To assess self-reported lifetime prevalence of cardiovascular disease (CVD) among colorectal cancer survivors, and examine the cross-sectional and prospective associations of lifestyle factors with co-morbid CVD. Methods Colorectal cancer survivors were recruited (n = 1966). Data were collected at approximately 5, 12, 24 and 36 months post-diagnosis. Cross-sectional findings included six CVD categories (hypercholesterolaemia, hypertension, diabetes, heart failure, kidney disease and ischaemic heart disease (IHD)) at 5 months post-diagnosis. Longitudinal outcomes included the probability of developing (de novo) co-morbid CVD by 36 months post-diagnosis. Lifestyle factors included body mass index, physical activity, television (TV) viewing, alcohol consumption and smoking. Results Co-morbid CVD prevalence at 5 months post-diagnosis was 59%, and 16% of participants with no known CVD at the baseline reported de novo CVD by 36 months. Obesity at the baseline predicted de novo hypertension (odds ratio [OR] = 2.20, 95% confidence intervals [CI] = 1.09, 4.45) and de novo diabetes (OR = 6.55, 95% CI = 2.19, 19.53). Participants watching >4 h of TV/d at the baseline (compared with <2 h/d) were more likely to develop ischaemic heart disease by 36 months (OR = 5.51, 95% CI = 1.86, 16.34). Conclusion Overweight colorectal cancer survivors were more likely to suffer from co-morbid CVD. Interventions focusing on weight management and other modifiable lifestyle factors may reduce functional decline and improve survival.

The effects of cognitive ageing on the use of complex interfaces

This paper discusses an experiment investigating the effects of cognitive ageing and prior-experience with technology on using complex interfaces intuitively. Overall 37 participants, between the ages of 18 to 83, participated in this study. All participants were assessed for their cognitive abilities and prior-experience with technology. It was anticipated that the Central Executive function (a component of Working Memory) would emerge as one of the important cognitive functions in using complex interfaces. This was found to be the case with the strongest negative correlation occurring between sustained attention (one of the functions of the Central Executive), the time to complete the task and number of errors made by the participants.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Falls in Parkinson's disease : kinematic evidence for impaired head and trunk control

Changes in stride characteristics and gait rhythmicity characterize gait in Parkinson's disease and are widely believed to contribute to falls in this population. However, few studies have examined gait in PD patients who fall. This study reports on the complexities of walking in PD patients who reported falling during a 12-month follow-up. Forty-nine patients clinically diagnosed with idiopathic PD and 34 controls had their gait assessed using three-dimensional motion analysis. Of the PD patients, 32 (65%) reported at least one fall during the follow-up compared with 17 (50%) controls. The results showed that PD patients had increased stride timing variability, reduced arm swing and walked with a more stooped posture than controls. Additionally, PD fallers took shorter strides, walked slower, spent more time in double-support, had poorer gait stability ratios and did not project their center of mass as far forward of their base of support when compared with controls. These stride changes were accompanied by a reduced range of angular motion for the hip and knee joints. Relative to walking velocity, PD fallers had increased mediolateral head motion compared with PD nonfallers and controls. Therefore, head motion could exceed “normal” limits, if patients increased their walking speed to match healthy individuals. This could be a limiting factor for improving gait in PD and emphasizes the importance of clinically assessing gait to facilitate the early identification of PD patients with a higher risk of falling.