Sustainability of a multimodal intervention to promote lifestyle factors associated with the prevention of cardiovascular disease in midlife Australian women : a 5-year follow-up

We evaluated sustainability of an intervention to reduce women’s cardiovascular risk factors, determined the influence of self-efficacy, and described women’s current health. We used a mixed method approach that utilized forced choice and open-ended questionnaire items about health status, habits, and self-efficacy. Sixty women, average age 61, returned questionnaires. Women in the original intervention group continued health behaviors intended to reduce cardiovascular disease (CVD) at a higher rate than the control group, supporting the feasibility of a targeted intervention built around women’s individual goals. The role of self-efficacy in behavior change is unclear. The original intervention group reported higher self-reported health.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Examination of social demographic status, disease status, health behaviours and quality of life in older Australians using the theory of planned behaviour model

The increase of life expectancy worldwide during the last three decades has increased age-related disability leading to the risk of loss of quality of life. How to improve quality of life including physical health and mental health for older people and optimize their life potential has become an important health issue. This study used the Theory of Planned Behaviour Model to examine factors influencing health behaviours, and the relationship with quality of life. A cross-sectional mailed survey of 1300 Australians over 50 years was conducted at the beginning of 2009, with 730 completed questionnaires returned (response rate 63%). Preliminary analysis reveals that physiological changes of old age, especially increasing waist circumference and co morbidity was closely related to health status, especially worse physical health summary score. Physical activity was the least adherent behaviour among the respondents compared to eating healthy food and taking medication regularly as prescribed. Increasing number of older people living alone with co morbidity of disease may be the barriers that influence their attitude and self control toward physical activity. A multidisciplinary and integrated approach including hospital and non hospital care is required to provide appropriate services and facilities toward older people.

Delirium in early-stage Alzheimer's disease : enhancing cognitive reserve as a possible preventive measure

Delirium is a disorder of acute onset with fluctuating symptoms and is characterized by inattention, disorganized thinking, and altered levels of consciousness. The risk for delirium is greatest in individuals with dementia, and the incidence of both is increasing worldwide because of the aging of our population. Although several clinical trials have tested interventions for delirium prevention in individuals without dementia, little is known about the mechanisms for the prevention of delirium in early-stage Alzheimer’s disease (AD). The purpose of this article is to explore ways of preventing delirium and slowing the rate of cognitive decline in early-stage AD by enhancing cognitive reserve. An agenda for future research on interventions to prevent delirium in individuals with early-stage AD is also presented.

Social Participation of Community Dwelling Stroke Survivors [Published Abstract: From the Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke Conference 2009]

Disability following a stroke can impose various restrictions on patients’ attempts at participating in life roles. The measurement of social participation, for instance, is important in estimating recovery and assessing quality of care at the community level. Thus, the identification of factors influencing social participation is essential in developing effective measures for promoting the reintegration of stroke survivors into the community. Data were collected from 188 stroke survivors (mean age 71.7 years) 12 months after discharge from a stroke rehabilitation hospital. Of these survivors, 128 (61 %) had suffered a first ever stroke, and 81 (43 %) had a right hemisphere lesion. Most (n = 156, 83 %) were living in their own home, though 32 (17 %) were living in residential care facilities. Path analysis was used to test a hypothesized model of participation restriction which included the direct and indirect effects between social, psychological and physical outcomes and demographic variables. Participation restriction was the dependent variable. Exogenous independent variables were age, functional ability, living arrangement and gender. Endogenous independent variables were depressive symptoms, state self-esteem and social support satisfaction. The path coefficients showed functional ability having the largest direct effect on participation restriction. The results also showed that more depressive symptoms, low state self-esteem, female gender, older age and living in a residential care facility had a direct effect on participation restriction. The explanatory variables accounted for 71% of the variance in explaining participation restriction. Prediction models have empirical and practical applications such as suggesting important factors to be considered in promoting stroke recovery. The findings suggest that interventions offered over the course of rehabilitation should be aimed at improving functional ability and promoting psychological aspects of recovery. These are likely to enhance stroke survivors resume or maximize their social participation so that they may fulfill productive and positive life roles.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Immunogenicity and protective efficacy of an anti-Streptococcus pyogenes vaccine candidate in multiple animal species

Streptococcus pyogenes, also known as Group A Streptococcus (GAS) has been associated with a range of diseases from the mild pharyngitis and pyoderma to more severe invasive infections such as streptococcal toxic shock. GAS also causes a number of non-suppurative post-infectious diseases such as rheumatic fever, rheumatic heart disease and glomerulonephritis. The large extent of GAS disease burden necessitates the need for a prophylactic vaccine that could target the diverse GAS emm types circulating globally. Anti-GAS vaccine strategies have focused primarily on the GAS M-protein, an extracellular virulence factor anchored to GAS cell wall. As opposed to the hypervariable N-terminal region, the C-terminal portion of the protein is highly conserved among different GAS emm types and is the focus of a leading GAS vaccine candidate, J8-DT/alum. The vaccine candidate J8-DT/alum was shown to be immunogenic in mice, rabbits and the non-human primates, hamadryas baboons. Similar responses to J8-DT/alum were observed after subcutaneous and intramuscular immunization with J8-DT/alum, in mice and in rabbits. Further assessment of parameters that may influence the immunogenicity of J8-DT demonstrated that the immune responses were identical in male and female mice and the use of alum as an adjuvant in the vaccine formulation significantly increased its immunogenicity, resulting in a long-lived serum IgG response. Contrary to the previous findings, the data in this thesis indicates that a primary immunization with J8-DT/alum (50ƒÊg) followed by a single boost is sufficient to generate a robust immune response in mice. As expected, the IgG response to J8- DT/alum was a Th2 type response consisting predominantly of the isotype IgG1 accompanied by lower levels of IgG2a. Intramuscular vaccination of rabbits with J8-DT/alum demonstrated that an increase in the dose of J8-DT/alum up to 500ƒÊg does not have an impact on the serum IgG titers achieved. Similar to the immune response in mice, immunization with J8-DT/alum in baboons also established that a 60ƒÊg dose compared to either 30ƒÊg or 120ƒÊg was sufficient to generate a robust immune response. Interestingly, mucosal infection of naive baboons with a M1 GAS strain did not induce a J8-specific serum IgG response. As J8-DT/alum mediated protection has been previously reported to be due to the J8- specific antibody formed, the efficacy of J8-DT antibodies was determined in vitro and in vivo. In vitro opsonization and in vivo passive transfer confirmed the protective potential of J8-DT antibodies. A reduction in the bacterial burden after challenge with a bioluminescent M49 GAS strain in mice that were passively administered J8-DT IgG established that protection due to J8-DT was mediated by antibodies. The GAS burden in infected mice was monitored using bioluminescent imaging in addition to traditional CFU assays. Bioluminescent GAS strains including the ‘rheumatogenic’ M1 GAS could not be generated due to limitations with transformation of GAS, however, a M49 GAS strain was utilized during BLI. The M49 serotype is traditionally a ‘nephritogenic’ serotype associated with post-streptococcal glomerulonephritis. Anti- J8-DT antibodies now have been shown to be protective against multiple GAS strains such as M49 and M1. This study evaluated the immunogenicity of J8-DT/alum in different species of experimental animals in preparation for phase I human clinical trials and provided the ground work for the development of a rapid non-invasive assay for evaluation of vaccine candidates.