A Spatial Analysis of Suicide Displacement at a High-Risk Cliff-Based Location Following Installation of a Means Restriction Initiative.

Means restriction interventions are recognised as highly effective for the deterrence of suicide attempts by jumping. While such interventions can lead to significant reductions in suicide, it is unclear whether these reductions represent a displacement effect, whereby individuals are instead choosing to attempt suicide at other nearby locations which offer the same means. The potential displacement of suicides as an unintended consequence of means restriction has been relatively unexplored to date. The only studies exploring displacement effects have focused on bridges, which are relatively easily contained sites; no studies have yet explored displacement effects at cliff-based high risk suicide locations (hotspots). Using Australian coronial data for the period of 2006-2019, we undertook joinpoint and kernel density analysis of suicides by jumping at a well-known cliff-based hotspot in Sydney, Australia, to determine whether there was evidence of displacement to local and broader surrounding cliffs following the installation of a multi-component harm minimization intervention (the Gap Park Masterplan). While slight decreases were noted in the immediate area subject to the structural intervention in the post-implementation period, alongside slight increases in the surrounding cliffs, there was no evidence for statistically significant changes. While kernel density analyses did not identify the emergence of any new hotspot locations in the post-implementation period, three existing hotspot sites of concern were found in our total area of interest, with greater than expected growth in the density of one of the hotspots. While we found no persuasive evidence of displacement, ongoing monitoring of the cliff-based location where the structural interventions were implemented is needed to ensure the ongoing safety of the area.

Spatial Errors in Automated Geocoding of Incident Locations in Australian Suicide Mortality Data.

BACKGROUND: There is increasing interest in the spatial analysis of suicide data to identify high-risk (often public) locations likely to benefit from access restriction measures. The identification of such locations, however, relies on accurately geocoded data. This study aims to examine the extent to which common completeness and positional spatial errors are present in suicide data due to the underlying geocoding process. METHODS: Using Australian suicide mortality data from the National Coronial Information System for the period of 2008-2017, we compared the custodian automated geocoding process to an alternate multiphase process. Descriptive and kernel density cluster analyses were conducted to ascertain data completeness (address matching rates) and positional accuracy (distance revised) differences between the two datasets. RESULTS: The alternate geocoding process initially improved address matching from 67.8% in the custodian dataset to 78.4%. Additional manual identification of nonaddress features (such as cliffs or bridges) improved overall match rates to 94.6%. Nearly half (49.2%) of nonresidential suicide locations were revised more than 1,000 m from data custodian coordinates. Spatial misattribution rates were greatest at the smallest levels of geography. Kernel density maps showed clear misidentification of hotspots relying solely on autogeocoded data. CONCLUSION: Suicide incidents that occur at nonresidential addresses are being erroneously geocoded to centralized fall-back locations in autogeocoding processes, which can lead to misidentification of suicide clusters. Our findings provide insights toward defining the nature of the problem and refining geocoding processes, so that suicide data can be used reliably for the detection of suicide hotspots. See video abstract at, http://links.lww.com/EDE/B862.

Hotel Suicides in Australia 2006-2017.

Background: This study investigated the frequency, characteristics, and geospatial clustering of hotel suicides in Australia to inform suicide prevention efforts. Aims: (1) To determine the proportion of suicide deaths that occurred in hotels, (2) to determine differences in demographic characteristics of hotel deaths compared to other locations, (3) to assess level of planning, and (4) to determine whether these deaths form geographic clusters amenable to targeted suicide prevention activities. Methods: Archival data on suicide mortality were used to examine associations between incident location (hotels, home, away from home), demographic characteristics, and suicide means. Kernel density visualization was used to assess geospatial clustering of hotel suicides, and the degree of planning involved was assessed using the modified Suicide Intent Scale. Results: Hotels accounted for 2% of all suicide deaths and 6.2% of suicides occurring away from home. Females were over-represented (p < .0001), as were deaths by drug overdoses (p < .0001) and falls (p < .0001). Approximately 40% of incidents occurred within seven geospatial clusters. 85% of those who died were state residents, with a median distance from home of 13.0 km. Most individuals checked in to the hotel alone, for short stays, and displayed a high degree of suicidal planning. Limitations: Coronial records had limited information on narrative circumstances of deaths; other indicators of risk may not have been identified. A comparison against a general population of hotel guests, rather than all other suicide deaths would be more useful in terms of preventative activities, however these data were not readily available. Conclusion: This study identified characteristics, behaviors, and geographic locations associated with hotel suicides to inform training of hotel staff to recognize and respond to signs of risk. Males of working age who live locally and arrive alone for short stays could be considered at a higher risk of suicide, and prevention efforts should be prioritized in the identified high-risk areas.

Spatial inequities of mental health nurses in rural and remote Australia.

Despite an increased burden from chronic mental health conditions, access to effective mental health services in rural and remote areas is limited, and these services remain spatially undefined. We examine the spatial availability of mental health nurses across local government areas in Australia and identify gaps in mental health service delivery capacity in a finer-grained level than the state/territory data. A spatial distribution of mental health nurses was conducted. We utilized the 2017 National Health Workforce Dataset which was aggregated to LGA level based on the 2018 Australian Bureau Statistics (ABS) Data. The availability of mental health nurses was measured using the full time equivalent (FTE) rates per 100 000 population. We calculated the proportion of LGAs with zero total FTE rates based on remoteness categories. We also compared the mean of total FTE rates based on remoteness categories using analysis of variance. A spatial distribution of mental health nurses was visualized using GIS software for total FTE rates. Our analysis included 544 LGA across Australia, with 24.8% being defined as remote and very remote. The mean total FTE for mental health nurses per 100 000 populations is 56.6 (±132.2) with a median of 17.4 (IQR: 61.8). A wide standard deviation reflects unequal distribution of mental health nurses across LGAs. The availability of total FTE rates for mental health nurses per 100 000 populations is significantly lower in remote and very remote LGAs in comparison with major cities. As many as 35.1% of LGAs across Australia have no FTE for mental health nurses with 46% are remote and very remote. Our study reflects the existing unequal distribution of mental health nurses between metropolitan/urban setting and rural and remote areas. We suggest three broad strategies to address these spatial inequities: improving supply and data information systems; revisiting task-shifting strategies, retraining the existing health workforce to develop skills necessary for mental health care to rural and remote communities; and incorporating the provision of mental health services within expanding innovative delivery models including consumer-led, telemedicine and community-based groups.