Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.

Harnessing ICT-based tool for improving the collaborative health planning debate : the case of Logan-Beaudesert, Australia

The field of collaborative health planning faces significant challenges posed by the lack of effective information, systems and a framework to organise that information. Such a framework is critical in order to make accessible and informed decisions for planning healthy cities. The challenges have been exaggerated by the rise of the healthy cities movement, as a result of which, there have been more frequent calls for localised, collaborative and evidence-based decision-making. Some studies suggest that the use of ICT-based tools in health planning may lead to: increased collaboration between stakeholder sand the community; improve the accuracy and quality of the decision making process; and, improve the availability of data and information for health decision-makers as well as health service planners. Research has justified the use of decision support systems (DSS) in planning for healthy cities as these systems have been found to improve the planning process. DSS are information communication technology (ICT) tools including geographic information systems (GIS) that provide the mechanisms to help decision-makers and related stake holders assess complex problems and solve these in a meaningful way. Consequently, it is now more possible than ever before to make use of ICT-based tools in health planning. However, knowledge about the nature and use of DSS within collaborative health planning is relatively limited. In particular, little research has been conducted in terms of evaluating the impact of adopting these tools upon stakeholders, policy-makers and decision-makers within the health planning field. This paper presents an integrated method that has been developed to facilitate an informed decision-making process to assist in the health planning process. Specifically, the paper describes the participatory process that has been adopted to develop an online GIS-based DSS for health planners. The literature states that the overall aim of DSS is to improve the efficiency of the decisions made by stakeholders, optimising their overall performance and minimizing judgmental biases. For this reason, the paper examines the effectiveness and impact of an innovative online GIS-based DSS on health planners. The case study of the online DSS is set within a unique settings-based initiative designed to plan for and improve the health capacity of Logan-Beaudesert area, Australia. This unique setting-based initiative is named the Logan-Beaudesert Health Coalition (LBHC).The paper outlines the impact occurred by implementing the ICT-based DSS. In conclusion, the paper emphasizes upon the need for the proposed tool for enhancing health planning.

Spatial distribution of suicide in Queensland, Australia

Background: There has been a lack of investigation into the spatial distribution and clustering of suicide in Australia, where the population density is lower than many countries and varies dramatically among urban, rural and remote areas. This study aims to examine the spatial distribution of suicide at a Local Governmental Area (LGA) level and identify the LGAs with a high relative risk of suicide in Queensland, Australia, using geographical information system (GIS) techniques.---------- Methods: Data on suicide and demographic variables in each LGA between 1999 and 2003 were acquired from the Australian Bureau of Statistics. An age standardised mortality (ASM) rate for suicide was calculated at the LGA level. GIS techniques were used to examine the geographical difference of suicide across different areas.---------- Results: Far north and north-eastern Queensland (i.e., Cook and Mornington Shires) had the highest suicide incidence in both genders, while the south-western areas (i.e., Barcoo and Bauhinia Shires) had the lowest incidence in both genders. In different age groups (≤24 years, 25 to 44 years, 45 to 64 years, and ≥65 years), ASM rates of suicide varied with gender at the LGA level. Mornington and six other LGAs with low socioeconomic status in the upper Southeast had significant spatial clusters of high suicide risk.---------- Conclusions: There was a notable difference in ASM rates of suicide at the LGA level in Queensland. Some LGAs had significant spatial clusters of high suicide risk. The determinants of the geographical difference of suicide should be addressed in future research.

Inequitable Provision of Optimal Services for Patients with Chronic Heart failure: A National Geo-Mapping Study

Objective: To compare the location and accessibility of current Australian chronic heart failure (CHF) management programs and general practice services with the probable distribution of the population with CHF. Design and setting: Data on the prevalence and distribution of the CHF population throughout Australia, and the locations of CHF management programs and general practice services from 1 January 2004 to 31 December 2005 were analysed using geographic information systems (GIS) technology. Outcome measures: Distance of populations with CHF to CHF management programs and general practice services. Results: The highest prevalence of CHF (20.3–79.8 per 1000 population) occurred in areas with high concentrations of people over 65 years of age and in areas with higher proportions of Indigenous people. Five thousand CHF patients (8%) discharged from hospital in 2004–2005 were managed in one of the 62 identified CHF management programs. There were no CHF management programs in the Northern Territory or Tasmania. Only four CHF management programs were located outside major cities, with a total case load of 80 patients (0.7%). The mean distance from any Australian population centre to the nearest CHF management program was 332 km (median, 163 km; range, 0.15–3246 km). In rural areas, where the burden of CHF management falls upon general practitioners, the mean distance to general practice services was 37 km (median, 20 km; range, 0–656 km). Conclusion: There is an inequity in the provision of CHF management programs to rural Australians.

Application of Geographic Modeling Techniques to Quantify Spatial Access to Health Services Before and After an Acute Cardiac Event: The Cardiac ARIA Project

Background: Access to cardiac services is essential for appropriate implementation of evidence-based therapies to improve outcomes. The Cardiac Accessibility and Remoteness Index for Australia (Cardiac ARIA) aimed to derive an objective, geographic measure reflecting access to cardiac services. Methods: An expert panel defined an evidence-based clinical pathway. Using Geographic Information Systems (GIS), a numeric/alpha index was developed at two points along the continuum of care. The acute category (numeric) measured the time from the emergency call to arrival at an appropriate medical facility via road ambulance. The aftercare category (alpha) measured access to four basic services (family doctor, pharmacy, cardiac rehabilitation, and pathology services) when a patient returned to their community. Results: The numeric index ranged from 1 (access to principle referral center with cardiac catheterization service ≤ 1 hour) to 8 (no ambulance service, > 3 hours to medical facility, air transport required). The alphabetic index ranged from A (all 4 services available within 1 hour drive-time) to E (no services available within 1 hour). 13.9 million (71%) Australians resided within Cardiac ARIA 1A locations (hospital with cardiac catheterization laboratory and all aftercare within 1 hour). Those outside Cardiac 1A were over-represented by people aged over 65 years (32%) and Indigenous people (60%). Conclusion: The Cardiac ARIA index demonstrated substantial inequity in access to cardiac services in Australia. This methodology can be used to inform cardiology health service planning and the methodology could be applied to other common disease states within other regions of the world.

Cardiac ARIA Index: Measuring Accessibility to Cardiovascular Services in Rural and Remote Australia via Applied Geographic Spatial Technology

Cardiovascular disease (CVD) continues to impose a heavy burden in terms of cost, disability and death in Australia. Evidence suggests that increasing remoteness, where cardiac services are scarce, is linked to an increased risk of dying from CVD. Fatal CVD events are reported to be between 20% and 50% higher in rural areas compared to major cities. The Cardiac ARIA project, with its extensive use of geographic Information Systems (GIS), ranks each of Australia’s 20,387 urban, rural and remote population centres by accessibility to essential services or resources for the management of a cardiac event. This unique, innovative and highly collaborative project delivers a powerful tool to highlight and combat the burden imposed by cardiovascular disease (CVD) in Australia. Cardiac ARIA is innovative. It is a model that could be applied internationally and to other acute and chronic conditions such as mental health, midwifery, cancer, respiratory, diabetes and burns services. Cardiac ARIA was designed to: 1. Determine by expert panel, what were the minimal services and resources required for the management of a cardiac event in any urban, rural or remote population locations in Australia using a single patient pathway to access care. 2. Derive a classification using GIS accessibility modelling for each of Australia’s 20,387 urban, rural and remote population locations. 3. Compare the Cardiac ARIA categories and population locations with census derived population characteristics. Key findings are as follows: • In the event of a cardiac emergency, the majority of Australians had very good access to cardiac services. Approximately 71% or 13.9 million people lived within one hour of a category one hospital. • 68% of older Australians lived within one hour of a category one hospital (Principal Referral Hospital with access to Cardiac Catheterisation). • Only 40% of indigenous people lived within one hour of the category one hospital. • 16% (74000) of indigenous people lived more than one hour from a hospital. • 3% (91,000) of people 65 years of age or older lived more than one hour from any hospital or clinic. • Approximately 96%, or 19 million, of people lived within one hour of the four key services to support cardiac rehabilitation and secondary prevention. • 75% of indigenous people lived within one hour of the four cardiac rehabilitation services to support cardiac rehabilitation and secondary prevention. Fourteen percent (64,000 persons) indigenous people had poor access to the four key services to support cardiac rehabilitation and secondary prevention. • 12% (56,000) of indigenous people were more than one hour from a hospital and only had access one the four key services (usually a medical service) to support cardiac rehabilitation and secondary prevention.

Can the Cardiac ARIA Index Improve Cardiac Care for Australia's Indigenous Population?

Background: Timely access to appropriate cardiac care is critical for optimising outcomes. Our aim was to derive an objective, comparable, geographic measure reflecting access to cardiac services for Australia's 20,387 population locations. Methods: An expert panel defined a single patient care pathway. Using geographic information systems (GIS) the numeric/alpha index was modelled in two phases. The acute phase index (numeric) ranged from 1 (access to tertiary centre with PCI ≤1 h) to 8 (no ambulance service, >3 h to medical facility, air transport required). The aftercare index was modelled into 5 alphabetic categories; A (Access to general practitioner, pharmacy, cardiac rehabilitation, pathology ≤1 h) to E (no services available within 1 h). Results: Approximately 70% or 13.9 million people lived within a CardiacARIAindex category 1A location. Disparity continues in access to category 1A cardiac services for 5.8 million (30%) of all Australians, 60% of Aboriginal and Torres Strait Islander people and 32% of people over 65 years of age. In a cardiac emergency only 40% of the Indigenous population reside within one hour of category 1 hospital. Approximately 30% (81,491 Indigenous persons) are more than one to three hours from basic cardiac services. Conclusion: Geographically, the majority of Australian's have timely access for survival of a cardiac event. The CardiacARIAindex objectively demonstrates that the healthcare system may not be providing for the needs of 60% of Indigenous people residing outside the 1A geographic radius. Innovative clinical practice maybe required to address these disparities.

Where not to have a Heart Attack in Australia!: The Cardiac ARIA Index

Background/aims: Access to appropriate health care following an acute cardiac event is important for positive outcomes. The aim of the Cardiac ARIA index was to derive an objective, comparable, geographic measure reflecting access to cardiac services across Australia. Methods: Geographic Information Systems (GIS) were used to model a numeric-alpha index based on acute management from onset of symptoms to return to the community. Acute time frames have been calculated to include time for ambulance to arrive, assess and load patient, and travel to facility by road 40–80 kph. Results: The acute phase of the index was modelled into five categories: 1 [24/7 percutaneous cardiac intervention (PCI) ≤1 h]; 2 [24/7 PCI 1–3 h, and PCI less than an additional hour to nearest accident and emergency room (A&E)]: 3 [Nearest A&E ≤3 h (no 24/7 PCI within an extra hour)]: 4 [Nearest A&E 3–12 h (no 24/7 PCI within an extra hour)]: 5 [Nearest A&E 12–24 h (no 24/7 PCI within an extra hour)]. Discharge care was modelled into three categories based on time to a cardiac rehabilitation program, retail pharmacy, pathology services, hospital, GP or remote clinic: (A) all services ≤30 min; (B) >30 min and ≤60 min; (C) >60 min. Examples of the index indicate that the majority of population locations within capital cities were category 1A; Alice Springs and Byron Bay were 3A; and the Northern Territory town of Maningrida had minimal access to cardiac services with an index ranking of 5C. Conclusion: The Cardiac ARIA index provides an invaluable tool to inform appropriate strategies for the use of scarce cardiac resources.

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.

Comparative effectiveness of population interventions to improve access to reperfusion for ST-segment–elevation myocardial infarction in Australia

Background Improving timely access to reperfusion is a major goal of ST-segment–elevation myocardial infarction care. We sought to compare the population impact of interventions proposed to improve timely access to reperfusion therapy in Australia. Methods and Results Australian hospitals, population, and road network data were integrated using Geographical Information Systems. Hospitals were classified into those that provided primary percutaneous coronary intervention (PPCI) or fibrinolysis. Population impact of interventions proposed to improve timely access to reperfusion (PPCI, fibrinolysis, or both) were modeled and compared. Timely access to reperfusion was defined as the proportion of the population capable of reaching a fibrinolysis facility ≤60 minutes or a PPCI facility ≤120 minutes from emergency medical services activation. The majority (93.2%) of the Australian population has timely access to reperfusion, mainly (53%) through fibrinolysis. Only 40.2% of the population had timely access to PPCI, and access to PPCI services is particularly limited in regional and nonexistent in remote areas. Optimizing the emergency medical services’ response or increasing PPCI services resulted in marginal improvement in timely access (1.8% and 3.7%, respectively). Direct transport to PPCI facilities and interhospital transfer for PPCI improves timely access to PPCI for 19.4% and 23.5% of the population, respectively. Prehospital fibrinolysis markedly improved access to timely reperfusion in regional and remote Australia. Conclusions Significant gaps in timely provision of reperfusion remain in Australia. Systematic implementation of changes in service delivery has potential to improve timely access to PPCI for a majority of the population and improve access to fibrinolysis to those living in regional and remote areas.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Understanding neighbourhood liveability for older urban Australians

As with other major developed cities, the sub-tropical and fastest growing Australian capital city of Brisbane has adopted policies designed to increase residential densities and meet the liveability and sustainability goal of decreasing car dependence and greenhouse gas emissions. This goal hinges on a pedestrian friendly environment and walkable proximity to satisfy everyday needs. While older people are particularly attracted to sub-tropical urban environments, there has been little empirical evidence linking liveability satisfaction and perceived and actual use of older people’s urban neighbourhood. Using qualitative (diaries and in-depth interviews) and quantitative (Global Positioning Systems and Geographical Information Systems mapping) liveability research data this paper explores whether high density supports liveability and is sustainable for older people living in a sub-tropical urban environment. This paper links satisfaction and perceived use of the sub-tropical urban Brisbane environment with actual mapped characteristics and use. Linking the two methods (both quantitative and qualitative) is important in obtaining a greater understanding of human behaviour and the lived world of older urban Australians and in providing a wider picture of sub-tropical urban neighbourhoods for a significant population group within those neighbourhoods. What emerges from the research is an uneven standard of design, provision of amenities and maintenance of the public realm which negatively impacts on local neighbourhood participation by older urban Australians. By highlighting these issues this research furthers the understanding of design factors which make the sub-tropical urban neighbourhood more liveable and sustainable for older people and will inform actionable and implementable policies, programs and designs.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.