Spatial analysis of suicide mortality in Australia: investigation of metropolitan-rural-remote differentials of suicide risk across states/territories

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.

Views of Young People in Rural Australia on SPARX, a Fantasy World Developed for New Zealand Youth With Depression

Background

A randomized control trial demonstrated that a computerized cognitive behavioral therapy (cCBT) program (Smart, Positive, Active, Realistic, X-factor thoughts [SPARX]) was an appealing and efficacious treatment for depression for adolescents in New Zealand. Little is known about the acceptability of computerized therapy programs for rural Australians and the suitability of computerized programs developed in one cultural context when used in another country. Issues such as accents and local differences in health care access might mean adjustments to programs are required.

Objective

This study sought to explore the acceptability of SPARX by youth in rural Australia and to explore whether and how young people would wish to access such a program.

Methods

Focus groups and semistructured interviews were conducted with 16 young people attending two youth-focused community services in a small, rural Tasmanian town. An inductive data-driven approach was used to identify themes using the interview transcripts as the primary data source. Interpretation was supported by demographic data, observer notes, and content analysis.

Results

Participants reported that young people want help for mental health issues but they have an even stronger need for controlling how they access services. In particular, they considered protecting their privacy in their small community to be paramount. Participants thought computerized therapy was a promising way to increase access to treatment for youth in rural and remote areas if offered with or without therapist support and via settings other than school. The design features of SPARX that were perceived to be useful, included the narrative structure of the program, the use of different characters, the personalization of an avatar, “socialization” with the Guide character, optional journaling, and the use of encouraging feedback. Participants did not consider (New Zealand) accents off-putting. Young people believed the SPARX program would appeal to those who play computer games generally, but may be less appealing for those who do not.

Conclusions

The findings suggest that computerized therapy offered in ways that support privacy and choice can improve access to treatment for rural youth. Foreign accents and style may not be off-putting to teenage users when the program uses a playful fantasy genre, as it is consistent with their expectation of fantasy worlds, and it is in a medium with which they already have a level of competence. Rather, issues of engaging design and confidential access appeared to be more important. These findings suggest a proven tool once formally assessed at a local level can be adopted cross-nationally.

A spatial analysis of functional outcomes and quality of life outcomes after pediatric injury

Background

Changes in health-related quality of life (HRQoL) are more regularly being monitored during the first year after injury. Monitoring changes in HRQoL using spatial cluster analysis can potentially identify concentrations of geographic areas with injury survivors with similar outcomes, thereby improving how interventions are delivered or in how outcomes are evaluated.

Methods

We used a spatial scan statistic designed for oridinal data to test two different spatial cluster analysis of very low, low, high, and very high HRQoL scores. Our study was based on HRQoL scores returned by children treated for injury at British Columbia Children’s Hospital and discharged to the Vancouver Metropolitan Area. Spatial clusters were assessed at 4 time periods – baseline (based on pre-injury health as reported prior to discharge from hospital), and one, four, and twelve months after discharge. Outcome data were measured used the PedsQL™ outcome scale. Outcome values of very low, low, high, and very high HRQoL scores were defined by classifying PedsQL™ scores into quartiles. In the first test, all scores were assessed for clustering without specifying whether the response score was from a baseline or follow-up response. In the second analysis, we built a space-time model to identify whether HRQoL responses could be identified at specific time points.

Results

Among all participants, geographic clustering of response scores were observed globally and at specific time periods. In the purely spatial analysis, five significant clusters of ‘very low’ PedsQL physical and psychosocial health outcomes were identified within geographic zones ranging in size from 1 to 21 km. A space-time analysis of outcomes identified significant clusters of both ‘very low’ and ‘low’ outcomes between survey months within zones ranging in size from 3 to 5 km.

Conclusion

Monitoring patient health outcomes following injury is important for planning and targeting interventions. A common theme in the literature is that future prevention efforts may benefit from identifying those most a risk of developing ongoing problems after injury in effort to target resources to those most in need. Spatial scan statistics are tools that could be applied for identifying concentrations of poor recovery outcomes. By classifying outcomes as a categorical variable, clusters of ‘potentially low’ outcomes can also be mapped, thereby identifying populations whose recovery status may decrease.

Electronic supplementary material

The online version of this article (doi:10.1186/s40621-014-0016-1) contains supplementary material, which is available to authorized users.

Risk assessment in mental health: introducing a traffic light system in a community mental health team

Aims:

To reports a study in which action research approach was utilised to introduce a new system of risk assessment, based on traffic lights, into a community mental health team.

Background:

Risk management is a serious concern in community mental healthcare where there is less direct, real-time supervision of clients than in other settings, and because inadequate management of risk can have fatal consequences when service users are a risk to themselves and/or others.

Design:

An action research design was undertaken, using three phases of Look, Think and Act.

Methods:

Data were collected between January and March of 2012. In the action research phases, qualitative data were collected in focus groups with the team’s multi-disciplinary mental health professionals. Data were transcribed verbatim and analysed thematically, which involved agreement of themes and interpretations by two researchers. The Look, Think and Act phases guided the development of the project; team members worked collaboratively on the traffic light system, implemented and evaluated it.

Findings:

Themes were constructed that were discussed across the focus groups. These themes were: Ease of use; Risk identification and management; Legal status; Different teams’ views of risk; Post-implementation evaluation.

Conclusion:

Action research has been used to implement change in mental health risk management. Others internationally would benefit from considering a Traffic Light System, and in using action research to implement it.

Application of scan statistics to detect suicide clusters in Australia

BACKGROUND

Suicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example.

METHODS AND FINDINGS

Scan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection.

CONCLUSIONS

These findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters.

Assessing the impact of socioeconomic variables on small area variations in suicide outcomes in England

Ecological studies of suicide and self-harm have established the importance of area variables (e.g., deprivation, social fragmentation) in explaining variations in suicide risk. However, there are likely to be unobserved influences on risk, typically spatially clustered, which can be modeled as random effects. Regression impacts may be biased if no account is taken of spatially structured influences on risk. Furthermore a default assumption of linear effects of area variables may also misstate or understate their impact. This paper considers variations in suicide outcomes for small areas across England, and investigates the impact on them of area socio-economic variables, while also investigating potential nonlinearity in their impact and allowing for spatially clustered unobserved factors. The outcomes are self-harm hospitalisations and suicide mortality over 6,781 Middle Level Super Output Areas.