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.

Environmental and pro-social norms : evidence on littering

The paper investigates the relationship between pro-social norms and its implications for improved environmentsl outcomes. This is an area, which has been neglected in the environmental economic literature. We provide empirical evidence to demonstrate a small but significant positive impact between perceived environmental cooperation (reduced public littering) and increased voluntary environmental morale. For this purpose we use European Value Survey (EVS) data for 30 European countries. We also demonstrate that Western European countries are more sensitive to perceived environmental cooperation than the public in Eastern Europe. Interestingly, the results also demonstrate that environmental morale is strongly correlated with several socio-economic and environmental variables. Several robustness tests are conducted to check the validity of the results.

The role of environmental factors in the spatial distribution of Japanese encephalitis in mainland China

Japanese encephalitis (JE) is the most common cause of viral encephalitis and an important public health concern in the Asia-Pacific region, particularly in China where 50% of global cases are notified. To explore the association between environmental factors and human JE cases and identify the high risk areas for JE transmission in China, we used annual notified data on JE cases at the center of administrative township and environmental variables with a pixel resolution of 1 km×1 km from 2005 to 2011 to construct models using ecological niche modeling (ENM) approaches based on maximum entropy. These models were then validated by overlaying reported human JE case localities from 2006 to 2012 onto each prediction map. ENMs had good discriminatory ability with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.82-0.91, and low extrinsic omission rate of 5.44-7.42%. Resulting maps showed JE being presented extensively throughout southwestern and central China, with local spatial variations in probability influenced by minimum temperatures, human population density, mean temperatures, and elevation, with contribution of 17.94%-38.37%, 15.47%-21.82%, 3.86%-21.22%, and 12.05%-16.02%, respectively. Approximately 60% of JE cases occurred in predicted high risk areas, which covered less than 6% of areas in mainland China. Our findings will help inform optimal geographical allocation of the limited resources available for JE prevention and control in China, find hidden high-risk areas, and increase the effectiveness of public health interventions against JE transmission.

Toward robust image detection of crown-of-thorns starfish for autonomous population monitoring

Robust texture recognition in underwater image sequences for marine pest population control such as Crown-Of-Thorns Starfish (COTS) is a relatively unexplored area of research. Typically, humans count COTS by laboriously processing individual images taken during surveys. Being able to autonomously collect and process images of reef habitat and segment out the various marine biota holds the promise of allowing researchers to gain a greater understanding of the marine ecosystem and evaluate the impact of different environmental variables. This research applies and extends the use of Local Binary Patterns (LBP) as a method for texture-based identification of COTS from survey images. The performance and accuracy of the algorithms are evaluated on a image data set taken on the Great Barrier Reef.

Wandering and the physical environment

Background/Rationale Guided by the need-driven dementia-compromised behavior (NDB) model, this study examined influences of the physical environment on wandering behavior. Methods Using a descriptive, cross-sectional design, 122 wanderers from 28 long-term care (LTC) facilities were videotaped 10 to 12 times; data on wandering, light, sound, temperature and humidity levels, location, ambiance, and crowding were obtained. Associations between environmental variables and wandering were evaluated with chi-square and t tests; the model was evaluated using logistic regression. Results In all, 80% of wandering occurred in the resident’s own room, dayrooms, hallways, or dining rooms. When observed in other residents’ rooms, hallways, shower/baths, or off-unit locations, wanderers were likely (60%-92% of observations) to wander. The data were a good fit to the model overall (LR [logistic regression] χ2 (5) = 50.38, P < .0001) and by wandering type. Conclusions Location, light, sound, proximity of others, and ambiance are associated with wandering and may serve to inform environmental designs and care practices.

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Estimates of crowding in long-term care: Comparing two approaches

Objective: Because studies of crowding in long-term care settings are lacking, the authors sought to: (1) generate initial estimates of crowding in nursing homes and assisted living facilities; and (2) evaluate two operational approaches to its measurement. ----- ----- Background: Reactions to density and proximity are complex. Greater density intensifies people's reaction to a situation in the direction (positive or negative) that they would react if the situation were to occur under less dense conditions. People with dementia are especially reactive to the environment. ----- ----- Methods: Using a cross-sectional correlational design in nursing homes and assisted living facilities involving 185 participants, multiple observations (N = 6,455) of crowding and other environmental variables were made. Crowding, location, and sound were measured three times per observation; ambiance was measured once. Data analyses consisted of descriptive statistics, t-tests, and one-way analysis of variance. ----- ----- Results: Crowding estimates were higher for nursing homes and in dining and activity rooms. Crowding also varied across settings and locations by time of day. Overall, the interaction of location and time affected crowding significantly (N = 5,559, df [47, 511], F = 105.69, p < .0001); effects were greater within location-by-hour than between location-by-hour, but the effect explained slightly less variance in Long-Term Care Crowding Index (LTC-CI) estimates (47.41%) than location alone. Crowding had small, direct, and highly significant correlations with sound and with the engaging subscale for ambiance; a similar, though inverse, correlation was seen with the soothing subscale for ambiance. ----- ----- Conclusions: Crowding fluctuates consistent with routine activities such as meals in long-term care settings. Furthermore, a relationship between crowding and other physical characteristics of the environment was found. The LTC-CI is likely to be more sensitive than simple people counts when seeking to evaluate the effects of crowding on the behavior of elders-particularly those with dementia-in long-term care settings. aging in place.

Efficiency of research performance of Australian universities : a reappraisal using a bootstrap truncated regression approach

The motivation of the study stems from the results reported in the Excellence in Research for Australia (ERA) 2010 report. The report showed that only 12 universities performed research at or above international standards, of which, the Group of Eight (G8) universities filled the top eight spots. While performance of universities was based on number of research outputs, total amount of research income and other quantitative indicators, the measure of efficiency or productivity was not considered. The objectives of this paper are twofold. First, to provide a review of the research performance of 37 Australian universities using the data envelopment analysis (DEA) bootstrap approach of Simar and Wilson (2007). Second, to determine sources of productivity drivers by regressing the efficiency scores against a set of environmental variables.

Examination of relationships and mediating effects of self-efficacy, locus of control, coping and the practice environment on caring efficacy and job satisfaction in Australian registered nurses

Background to the Problem: Improving nurses' self-efficacy and job satisfaction may improve the quality of nursing care to patients. Moreover, to work effectively and consistently with professional nursing standards, nurses have to believe they are able to make decisions about their practice. In order to identify what strategies and professional development programmes should be developed and implemented for registered nurses in the Australian context, a comprehensive profile of registered nurses and factors that affect nursing care in Australia needs to be available. However, at present, there is limited information available on a) the perceived caring efficacy and job satisfaction of registered nurses in Australia, and b) the relationships between the demographic variables general self-efficacy, work locus of control, coping styles, the professional nursing practice environment and caring efficacy and job satisfaction of registered nurses in Australia. This is the first study to 1) investigate relationships between caring efficacy and job satisfaction with factors such as general self-efficacy, locus of control and coping, 2) the nursing practice environment in the Australian context and 3) conceptualise a model of caring efficacy and job satisfaction in the Australian context. Research Design and Methods: This study used a two-phase cross-sectional survey design. A pilot study was conducted in order to determine the validity and reliability of the survey instruments and to assess the effectiveness of the participant recruitment process. The second study of the research involved investigating the relationships between the socio-demographic, dependent and independent variables. Socio-demographic variables included age, gender, level of education, years of experience, years in current job, employment status, geographical location, specialty area, health sector, state and marital status. Other independent variables in this study included general self-efficacy, work locus of control, coping styles and the professional nursing practice environment. The dependent variables were job satisfaction and caring efficacy. Results: A confirmatory factor analysis of the Brisbane Practice Environment Measure (B-PEM) was conducted. A five-factor structure of the B-PEM was confirmed. Relationships between socio-demographic variables, caring efficacy and job satisfaction, were identified at the bivariate and multivariable levels. Further, examination using structural equation modelling revealed general self-efficacy, work locus of control, coping style and the professional nursing practice environment contributed to caring efficacy and job satisfaction of registered nurses in Australia. Conclusion: This research contributes to the literature on how socio-demographic, personal and environmental variables (work locus of control, general self-efficacy and the nursing practice environment) influence caring efficacy and job satisfaction in registered nurses in Australia. Caring efficacy and job satisfaction may be improved if general self-efficacy is high in those that have an internal work locus of control. The study has also shown that practice environments that provide the necessary resources improve job satisfaction in nurses. The results have identified that the development and implementation of strategies for professional development and orientation programmes that enhance self-efficacy and work locus of control may contribute to better quality nursing practice and job satisfaction. This may further assist registered nurses towards focusing on improving their practice abilities. These strategies along with practice environments that provide the necessary resources for nurses to practice effectively may lead to better job satisfaction. This information is important for nursing leaders, healthcare organisations and policymakers, as the development and implementation of these strategies may lead to better recruitment and retention of nurses. The study results will contribute to the national and international literature on self-efficacy, job satisfaction and nursing practice.

Seasonal variation in cardiovascular disease risk factors in a subarctic population : the Tromso Study 1979-2008

Background Seasonal changes in cardiovascular disease (CVD) risk factors may be due to exposure to seasonal environmental variables like temperature and acute infections or seasonal behavioural patterns in physical activity and diet. Investigating the seasonal pattern of risk factors should help determine the causes of the seasonal pattern in CVD. Few studies have investigated the seasonal variation in risk factors using repeated measurements from the same individual, which is important as individual and population seasonal patterns may differ. Methods The authors investigated the seasonal pattern in systolic and diastolic blood pressure, heart rate, body weight, total cholesterol, triglycerides, high-density lipoprotein cholesterol, C reactive protein and fibrinogen. Measurements came from 38 037 participants in the population-based cohort, the Tromsø Study, examined up to eight times from 1979 to 2008. Individual and population seasonal patterns were estimated using a cosinor in a mixed model. Results All risk factors had a highly statistically significant seasonal pattern with a peak time in winter, except for triglycerides (peak in autumn), C reactive protein and fibrinogen (peak in spring). The sizes of the seasonal variations were clinically modest. Conclusions Although the authors found highly statistically significant individual seasonal patterns for all risk factors, the sizes of the changes were modest, probably because this subarctic population is well adapted to a harsh climate. Better protection against seasonal risk factors like cold weather could help reduce the winter excess in CVD observed in milder climates.

The role of representative design in talent development : a comment on “Talent identification and promotion programmes of Olympic athletes”

A recent comment in the Journal of Sports Sciences (MacNamara & Collins, 2011) highlighted some major concerns with the current structure of talent identification and development (TID) programmes of Olympic athletes (e.g. Gulbin, 2008; Vaeyens, Gullich, Warr, & Philippaerts, 2009). In a cogent commentary, MacNamara and Collins (2011) provided a short review of the extant literature, which was both timely and insightful. Specifically, they criticised the ubiquitous one-dimensional ‘physically-biased’ attempts to produce world class performers, emphasising the need to consider a number of key environmental variables in a more multi-disciplinary perspective. They also lamented the wastage of talent, and alluded to the operational and opportunistic nature of current talent transfer programmes. A particularly compelling aspect of the comment was their allusion to high profile athletes who had ‘failed’ performance evaluation tests and then proceeded to succeed in that sport. This issue identifies a problem with current protocols for evaluating performance and is a line of research that is sorely needed in the area of talent development. To understand the nature of talent wastage that might be occurring in high performance programmes in sport, future empirical work should seek to follow the career paths of ‘successful’ and ‘unsuccessful’ products of TID programmes, in comparative analyses. Pertinent to the insights of MacNamara and Collins (2011), it remains clear that a number of questions have not received enough attention from sport scientists interested in talent development, including: (i) why is there so much wastage of talent in such programmes? And (ii), why are there so few reported examples of successful talent transfer programmes? These questions highlight critical areas for future investigation. The aim of this short correspondence is to discuss these and other issues researchers and practitioners might consider, and to propose how an ecological dynamics underpinning to such investigations may help the development of existing protocols...

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Spatial prediction of N2O emissions in pasture : a Bayesian model averaging analysis

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

Correlates of adults' participation in physical activity : review and update

Purpose To review and update the evidence relating to the personal, social, and environmental factors associated with physical activity (PA) in adults. Methods Systematic review of the peer-reviewed literature to identify papers published between 1998 and 2000 with PA (and including exercise and exercise adherence). Qualitative reports or case studies were not included. Results Thirty-eight new studies were located. Most confirmed the existence of factors already known to be correlates of PA. Changes in status were noted in relation to the influence of marital status, obesity, smoking, lack of time, past exercise behavior, and eight environmental variables. New studies were located which focused on previously understudied population groups such as minorities, middle and older aged adults, and the disabled. Conclusion The newly reported studies tend to take a broader “ecological” approach to understanding the correlates of PA and are more focused on environmental factors. There remains a need to better understand environmental influences and the factors that influence different types of PA. As most of the work in this field still relies on cross-sectional studies, longitudinal and intervention studies will be required if causal relationships are to be inferred.