Referral patterns and proximity to palliative care inpatient services by level of socio-economic disadvantage : a national study using spatial analysis

Background: A range of health outcomes at a population level are related to differences in levels of social disadvantage. Understanding the impact of any such differences in palliative care is important. The aim of this study was to assess, by level of socio-economic disadvantage, referral patterns to specialist palliative care and proximity to inpatient services. Methods: All inpatient and community palliative care services nationally were geocoded (using postcode) to one nationally standardised measure of socio-economic deprivation – Socio-Economic Index for Areas (SEIFA; 2006 census data). Referral to palliative care services and characteristics of referrals were described through data collected routinely at clinical encounters. Inpatient location was measured from each person’s home postcode, and stratified by socio-economic disadvantage. Results: This study covered July – December 2009 with data from 10,064 patients. People from the highest SEIFA group (least disadvantaged) were significantly less likely to be referred to a specialist palliative care service, likely to be referred closer to death and to have more episodes of inpatient care for longer time. Physical proximity of a person’s home to inpatient care showed a gradient with increasing distance by decreasing levels of socio-economic advantage. Conclusion: These data suggest that a simple relationship of low socioeconomic status and poor access to a referral-based specialty such as palliative care does not exist. Different patterns of referral and hence different patterns of care emerge.

Spatial analysis of notified cryptosporidiosis infections in Brisbane, Australia

Purpose: This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia. Methods: We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis. Results: Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%. Conclusions: There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.

Bayesian spatiotemporal analysis of socio-ecologic drivers of Ross River Virus transmission in Queensland, Australia

This study aims to examine the impact of socio-ecologic factors on the transmission of Ross River virus (RRV) infection and to identify areas prone to social and ecologic-driven epidemics in Queensland, Australia. We used a Bayesian spatiotemporal conditional autoregressive model to quantify the relationship between monthly variation of RRV incidence and socio-ecologic factors and to determine spatiotemporal patterns. Our results show that the average increase in monthly RRV incidence was 2.4% (95% credible interval (CrI): 0.1–4.5%) and 2.0% (95% CrI: 1.6–2.3%) for a 1°C increase in monthly average maximum temperature and a 10 mm increase in monthly average rainfall, respectively. A significant spatiotemporal variation and interactive effect between temperature and rainfall on RRV incidence were found. No association between Socio-economic Index for Areas (SEIFA) and RRV was observed. The transmission of RRV in Queensland, Australia appeared to be primarily driven by ecologic variables rather than social factors.

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.

Spatial clusters of suicide in Australia

Background: Understanding the spatial distribution of suicide can inform the planning, implementation and evaluation of suicide prevention activity. This study explored spatial clusters of suicide in Australia, and investigated likely socio-demographic determinants of these clusters. Methods: National suicide and population data at a statistical local area (SLA) level were obtained from the Australian Bureau of Statistics for the period of 1999 to 2003. Standardised mortality ratios (SMR) were calculated at the SLA level, and Geographic Information System (GIS) techniques were applied to investigate the geographical distribution of suicides and detect clusters of high risk in Australia. Results: Male suicide incidence was relatively high in the northeast of Australia, and parts of the east coast, central and southeast inland, compared with the national average. Among the total male population and males aged 15 to 34, Mornington Shire had the whole or a part of primary high risk cluster for suicide, followed by the Bathurst-Melville area, one of the secondary clusters in the north coastal area of the Northern Territory. Other secondary clusters changed with the selection of cluster radius and age group. For males aged 35 to 54 years, only one cluster in the east of the country was identified. There was only one significant female suicide cluster near Melbourne while other SLAs had very few female suicide cases and were not identified as clusters. Male suicide clusters had a higher proportion of Indigenous population and lower median socio-economic index for area (SEIFA) than the national average, but their shapes changed with selection of maximum cluster radii setting. Conclusion: This study found high suicide risk clusters at the SLA level in Australia, which appeared to be associated with lower median socio-economic status and higher proportion of Indigenous population. Future suicide prevention programs should focus on these high risk areas.

The bioeconomics of nutritional poverty and child labour

This paper provides a bio-economic foundation of fertility and child labor. Drawing on the clinical and physiological literature, the model highlights the interaction between work efforts of adults and children, their subsistence consumption, and fertility. The subsistence consumption requirements are endogenous to physical efforts. Parents engaged in physically demanding occupations (e.g. non-mechanized agriculture) are likely to suffer from energy deficiency, leading to reduced future work-capacity. Consumption smoothing occurs through bearing a large number of children who provide income support as adults. Although net cost of an additional child is positive, the cost is balanced by the additional income accruing though child employment. In contrast, parents in low-physical effort occupations are less likely to su¤er from nutritional deficiency, and thus tend to have lower fertility and child labor.

Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.

Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis

Background The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. Methods Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. Results In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996–2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001–2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III. Conclusions Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.

Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space–time SIR-based model

Background A pandemic strain of influenza A spread rapidly around the world in 2009, now referred to as pandemic (H1N1) 2009. This study aimed to examine the spatiotemporal variation in the transmission rate of pandemic (H1N1) 2009 associated with changes in local socio-environmental conditions from May 7–December 31, 2009, at a postal area level in Queensland, Australia. Method We used the data on laboratory-confirmed H1N1 cases to examine the spatiotemporal dynamics of transmission using a flexible Bayesian, space–time, Susceptible-Infected-Recovered (SIR) modelling approach. The model incorporated parameters describing spatiotemporal variation in H1N1 infection and local socio-environmental factors. Results The weekly transmission rate of pandemic (H1N1) 2009 was negatively associated with the weekly area-mean maximum temperature at a lag of 1 week (LMXT) (posterior mean: −0.341; 95% credible interval (CI): −0.370–−0.311) and the socio-economic index for area (SEIFA) (posterior mean: −0.003; 95% CI: −0.004–−0.001), and was positively associated with the product of LMXT and the weekly area-mean vapour pressure at a lag of 1 week (LVAP) (posterior mean: 0.008; 95% CI: 0.007–0.009). There was substantial spatiotemporal variation in transmission rate of pandemic (H1N1) 2009 across Queensland over the epidemic period. High random effects of estimated transmission rates were apparent in remote areas and some postal areas with higher proportion of indigenous populations and smaller overall populations. Conclusions Local SEIFA and local atmospheric conditions were associated with the transmission rate of pandemic (H1N1) 2009. The more populated regions displayed consistent and synchronized epidemics with low average transmission rates. The less populated regions had high average transmission rates with more variations during the H1N1 epidemic period.

Biorefineries – creating economic opportunities from biofuels and bio-products

Biorefineries, producing fuels, green chemicals and bio-products, offer great potential for improving the profitability and sustainability of tropical agricultural industries. Biomass from tropical crops like sugarcane, sweet sorghum, palm and cassava offer great potential because of the high biomass growth potential under favourable climatic conditions. Biorefineries aim to convert waste residues through biochemical and enzymatic processes to low cost fermentable sugars which are a platform for value-adding. Through subsequent fermentation utilising microbial biotechnologies or chemical synthesis, the sugars can be converted to fuels including ethanol and butanol, oils, organic acids such as lactic and levulinic acid and polymer precursors. Other biorefinery products can include food and animal feeds, plastics, fibre products and resins. Pretreatment technologies are a key to unlocking this potential and new technologies are emerging. This paper will address the opportunities available for tropical biorefineries to contribute to the future profitability of tropical agricultural industries. The importance of pretreatment technologies will be discussed.

Developing a rural land investment performance index for New Zealand

Agricultural production is one of the major industries in New Zealand and accounts for over 60% of all export trade. The farming industry comprises 70,000 entities ranging in size from small individual run farms to large corporate operations. The reliance of the New Zealand economy to the international rural sector has seen considerable volatility in the rural land markets over the past four decades, with significant shifts in rural land prices based on location, land use and underlying international rural commodity prices. With the increasing attention being paid to the rural sector, especially in relation to food production and bio-fuels, there has been an increasing corporate interest in rural land ownership in relatively low subsidised agricultural producing countries such as New Zealand and Australia. A factor that has limited this participation of institutional investors previously has been a lack of reliable and up-to-date investment performance data for this asset class. This paper is the initial starting phase in the development of a New Zealand South Island rural land investment performance index and covers the period 1990-2007. The research in this paper analyses all rural sales transactions in the South Island and develops a capital return index for rural property based on major rural property land use. Additional work on this index will cover both total return performance and geographic location.

The creative sector in New Zealand : mapping and economic role : report to New Zealand Trade and Enterprise

The New Zealand creative sector was responsible for almost 121,000 jobs at the time of the 2006 Census (6.3% of total employment). These are divided between • 35,751 creative specialists – persons employed doing creative work in creative industries • 42,300 support workers - persons providing management and support services in creative industries • 42,792 embedded creative workers – persons engaged in creative work in other types of enterprise The most striking feature of this breakdown is the fact that the largest group of creative workers are employed outside the creative industries, i.e. in other types of businesses. Even within the creative industries, there are fewer people directly engaged in creative work than in providing management and support. Creative sector employees earned incomes of approximately $52,000 per annum at the time of the 2006 Census. This is relatively uniform across all three types of creative worker, and is significantly above the average for all employed persons (of approximately $40,700). Creative employment and incomes were growing strongly over both five year periods between the 1996, 2001 and 2006 Censuses. However, when we compare creative and general trends, we see two distinct phases in the development of the creative sector: • rapid structural growth over the five years to 2001 (especially led by developments in ICT), with creative employment and incomes increasing rapidly at a time when they were growing modestly across the whole economy; • subsequent consolidation, with growth driven by more by national economic expansion than structural change, and creative employment and incomes moving in parallel with strong economy-wide growth. Other important trends revealed by the data are that • the strongest growth during the decade was in embedded creative workers, especially over the first five years. The weakest growth was in creative specialists, with support workers in creative industries in the middle rank, • by far the strongest growth in creative industries’ employment was in Software & digital content, which trebled in size over the decade Comparing New Zealand with the United Kingdom and Australia, the two southern hemisphere nations have significantly lower proportions of total employment in the creative sector (both in creative industries and embedded employment). New Zealand’s and Australia’s creative shares in 2001 were similar (5.4% each), but in the following five years, our share has expanded (to 5.7%) whereas Australia’s fell slightly (to 5.2%) – in both cases, through changes in creative industries’ employment. The creative industries generated $10.5 billion in total gross output in the March 2006 year. Resulting from this was value added totalling $5.1b, representing 3.3% of New Zealand’s total GDP. Overall, value added in the creative industries represents 49% of industry gross output, which is higher than the average across the whole economy, 45%. This is a reflection of the relatively high labour intensity and high earnings of the creative industries. Industries which have an above-average ratio of value added to gross output are usually labour-intensive, especially when wages and salaries are above average. This is true for Software & Digital Content and Architecture, Design & Visual Arts, with ratios of 60.4% and 55.2% respectively. However there is significant variation in this ratio between different parts of the creative industries, with some parts (e.g. Software & Digital Content and Architecture, Design & Visual Arts) generating even higher value added relative to output, and others (e.g. TV & Radio, Publishing and Music & Performing Arts) less, because of high capital intensity and import content. When we take into account the impact of the creative industries’ demand for goods and services from its suppliers and consumption spending from incomes earned, we estimate that there is an addition to economic activity of: • $30.9 billion in gross output, $41.4b in total • $15.1b in value added, $20.3b in total • 158,100 people employed, 234,600 in total The total economic impact of the creative industries is approximately four times their direct output and value added, and three times their direct employment. Their effect on output and value added is roughly in line with the average over all industries, although the effect on employment is significantly lower. This is because of the relatively high labour intensity (and high earnings) of the creative industries, which generate below-average demand from suppliers, but normal levels of demand though expenditure from incomes. Drawing on these numbers and conclusions, we suggest some (slightly speculative) directions for future research. The goal is to better understand the contribution the creative sector makes to productivity growth; in particular, the distinctive contributions from creative firms and embedded creative workers. The ideas for future research can be organised into the several categories: • Understanding the categories of the creative sector– who is doing the business? In other words, examine via more fine grained research (at a firm level perhaps) just what is the creative contribution from the different aspects of the creative sector industries. It may be possible to categorise these in terms of more or less striking innovations. • Investigate the relationship between the characteristics and the performance of the various creative industries/ sectors; • Look more closely at innovation at an industry level e.g. using an index of relative growth of exports, and see if this can be related to intensity of use of creative inputs; • Undertake case studies of the creative sector; • Undertake case studies of the embedded contribution to growth in the firms and industries that employ them, by examining taking several high performing noncreative industries (in the same way as proposed for the creative sector). • Look at the aggregates – drawing on the broad picture of the extent of the numbers of creative workers embedded within the different industries, consider the extent to which these might explain aspects of the industries’ varied performance in terms of exports, growth and so on. • This might be able to extended to examine issues like the type of creative workers that are most effective when embedded, or test the hypothesis that each industry has its own particular requirements for embedded creative workers that overwhelms any generic contributions from say design, or IT.

Design and construction of fixed bed pyrolysis system and plum seed pyrolysis for bio-oil production

This work investigated the production of bio oil from plum seed (Zyziphus jujuba) by fixed bed pyrolysis technology. A fixed bed pyrolysis system has been designed and fabricated for production of bio oil. The major components of the system are: fixed bed reactor, liquid condenser and liquid collector. Nitrogen gas was used to maintain the inert atmosphere in the reactor where the pyrolysis reaction takes place. The feedstock considered in this study is plum seed as it is available waste material in Bangladesh. The reactor is heated by means of a cylindrical biomass external heater. Rice husk was used as the energy source. The products are oil, char and gas. The parameters varied are reactor bed temperature, running time and feed particle size. The parameters are found to influence the product yields significantly. The maximum liquid yield of 39 wt% at 5200C for a feed particle size of 2.36-4.75 mm and a gas flow rate of 8 liter/min with a running time of 120 minute. The pyrolysis oil obtained at these optimum process conditions are analyzed for some of their properties as an alternative fuel. The density of the liquid was closer with diesel. The viscosity of the plum seed liquid was lower than that of the conventional fuels. The calorific value of the pyrolysis oil is one half of the diesel fuel.

China's foreign economic cooperation development : exporting Chinese construction services

Since the movement for economic reform started in China 20 years ago, the nation's GDP had grown on average from seven to nine per cent a year, making China's construction industry one of the largest in the world. This paper presents an overview of China's foreign economic cooperation development (FECD) in the context of exporting three major construction services namely; contracting, labour and design. The paper outlines the export market profile of Chinese contractors and discusses their current position in the international market. It then addresses challenges; they are facing in view of meeting the ambitious strategic targets set out by the Government for the FECD, which cover the export of construction services. Finally, the paper sheds some light on key exporting strategies currently adopted by Chinese contractors.

Creative city index [Report]

The CCI Creative City Index (CCI-CCI) is a new approach to the measurement and ranking of creative global cities. It is constructed over eight principal dimensions, each with multiple distinct elements. Some of these dimensions are familiar from other global city indexes, such as the MORI or GaWC indexes, which account for the size of creative industries, the scale of cultural amenities, or the flows of creative people and global connectedness. In addition to these indicators, the CCI-CCI contributes several new dimensions. These measure the demand side of creative participation, the attention economy, user-created content, and the productivity of socially networked consumers. Global creative cities can often seem alike, in respect of per-capita measures of factors such as public spending on cultural amenities, or the number of hotels and restaurants. This is to be expected when people and capital are relatively free to move, and where economic and political institutions are broadly comparable. However, we find that different cities can register far larger differences at the level of consumer-co-creation and especially digital creative ‘microproductivity’. To explain this finding, we review the logic and rationale of creative and global city index construction and present a review of previous and contemporary indexes. We set out the case for our new model of a creative city index by showing why greater attention to consumer co-creation and microproductivity are important, as well as examining how these factors have been previously overlooked. We show how we have CCI-CCI Creative City Index measured these additional factors and indicate the effect they have on creative and global city indexes. We then present the findings from a pilot study of six cities, two Australian, two German and two from the UK, to indicate how the new index is calculated and applied. Our results indicate much greater variance arising from the new arguments between cities.