Air pollution is ranked by the World Health Organisation as one of the top ten contributors to the global burden of disease and injury. Epidemiological studies have shown that exposure to air pollution is associated with cardiorespiratory diseases. However, most of the previous studies have looked at this issue using air pollution data from a single monitoring site or average values from a few monitoring sites in a city. There is increasing concern that the relationships between air pollution and mortality may vary with geographical area, particularly for a big city. This thesis consisted of three interlinked studies that aimed to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane, Australia. The first study evaluated the long-term air pollution trends in Brisbane, Australia. Air pollution data used in this study were provided by the Queensland Environmental Protection Agency (QEPA). The data comprised the daily average concentrations of particulate matter less then 10 µm in aerodynamic diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) between 1 January 1980 and 31 December 2004 in two monitoring sites (i.e. Eagle farm and Rocklea), and in other available monitoring sites between 1 January 1996 and 31 December 2004. Computerised data files of daily mortality between 1 January 1996 and 31 December 2004 in Brisbane city were provided by the Office of Economic and Statistical Research of the Queensland Treasury. Population data and the Socio-Economic Indexes for Areas (SEIFA) data in 2001 were obtained from the Australian Bureau of Statistics (ABS) for each statistical local area (SLA) of the Brisbane city. The long-term air pollution (the daily maximum 1-hour average or daily 24-hour average concentrations of NO2, O3 and PM10) trends were evaluated using a polynomial regression model in two monitoring sites (Eagle Farm and Rocklea) in Brisbane, Australia, between 1980 and 2003. The study found that there were significant up-and-down features for air pollution concentrations in both monitoring sites in Brisbane. Rocklea recorded a substantially higher number of days with concentrations above the relevant daily maximum 1-hour or 24-hour standards than that in Eagle Farm. Additionally, there was a significant spatial variation in air pollution concentrations between these areas. Therefore, the results indicated a need to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane. The second study examined the spatial variation of SO2 concentrations and cardiorespiratory mortality in Brisbane between 1999 and 2001. Air pollutant concentrations were estimated using geographical information systems (GIS) techniques at a SLA level. Spatial distribution analysis and a multivariable logistic regression model were employed to investigate the impact of gaseous air pollution on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The results of this study indicate that for every 1 ppb increase in annual average SO2 concentration, there was an estimated increase of 4.4 % (95 % confidence interval (CI): 1.4 - 7.6 %) and 4.8 % (95 % CI: 2.0 - 7.7 %) in cardiovascular and cardiorespiratory mortality, respectively. We estimated that the excess number of cardiorespiratory deaths attributable to SO2 was 312 (3.4% of total cardiorespiratory deaths) in Brisbane during the study period. Our results suggest that long-term exposure to SO2, even at low levels, is a significant hazard to population health. The final study examined the association of long-term exposure to gaseous air pollution (including NO2, O3 and SO2) with cardiorespiratory mortality in Brisbane, Australia, 1996 - 2004. The pollutant concentrations were estimated using GIS techniques at a SLA level. Logistic regression was used to investigate the impact of NO2, O3 and SO2 on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The study found that there was an estimated 3.1% (95% CI: 0.4 - 5.8%) and 0.5% (95% CI: -0.03 - 1.3 %) increase in cardiorespiratory mortality for 1 ppb increment in annual average concentration of SO2 and O3, respectively. However there was no significant relationship between NO2 and cardiorespiratory mortality observed in the multiple gaseous pollutants model. The results also indicated that long-term exposure to gaseous air pollutants in Brisbane, even at the levels lower than most cities in the world (especially SO2), were associated with cardiorespiratory mortality. Therefore, spatial patterns of gaseous air pollutants and their impact on health outcomes need to be assessed for an evaluation of long-term effects of air pollution on population health in metropolitan areas. This study examined the relationship between air pollution and health outcomes. GIS and relevant mapping technologies were used to display the spatial patterns of air pollution and cardiorespiratory mortality at a SLA level. The results of this study show that long-term exposure to gaseous air pollution was associated with cardiorespiratory mortality in Brisbane and this association appeared to vary with geographic area. These findings may have important public health implications in the control and prevention of air pollution-related health effects, since now many countries and governments have paid more attention to control wide spread air pollution and to protect our environment and human health.

The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.