Association between mental health workforce supply and clusters of high and low rates of youth suicide: An Australian study using suicide mortality data from 2016 to 2020.

OBJECTIVE: To examine the association between mental health workforce supply and spatial clusters of high versus low incidence of youth suicide. METHODS: A cross-sectional analysis of spatial suicide clusters in young Australians (aged 10-25) from 2016 to 2020 was conducted using the scan statistic and suicide data from the National Coronial Information System. Mental health workforce was extracted from the 2020 National Health Workforce Dataset by local government areas. The Geographic Index of Relative Supply was used to estimate low and moderate-to-high mental health workforce supply for clusters characterised by a high and low incidence of suicide (termed suicide hotspots and coldspots, respectively). Univariate and multivariate logistic regression was used to determine the association between suicide clusters and a range of sociodemographic characteristics including mental health workforce supply. RESULTS: Eight suicide hotspots and two suicide coldspots were identified. The multivariate analysis showed low mental health workforce supply was associated with increased odds of being involved in a suicide hotspot (adjusted odds ratio = 8.29; 95% confidence interval = 5.20-13.60), followed by residential remoteness (adjusted odds ratio = 2.85; 95% confidence interval = 1.68-4.89), and illicit drug consumption (adjusted odds ratio = 1.97; 1.24-3.11). Both coldspot clusters occurred in areas with moderate-to-high mental health workforce supply. CONCLUSION: Findings highlight the potential risk and protective roles that mental health workforce supply may play in the spatial distributions of youth suicide clusters. These findings have important implications for the provision of postvention and the prevention of suicide clusters.

A systematic review on the effect of work-related stressors on mental health of young workers.

PURPOSE: There is no review on the effect of work-related stressors on mental health of young workers. We systematically reviewed epidemiological evidence on this relationship. METHODS: The review searched eight databases: Embase, PubMed, Web of Science, Cinahl, Cochrane Library, Informit, PsycINFO, and Scopus from their respective start dates until May 2017. Studies that have examined a mental health outcome in relation to a work-related stressor as exposure in young workers were included. The review was reported based on the PRISMA statement. RESULTS: Three cross-sectional studies and six longitudinal cohort studies were included. Cross-sectional evidence showed that adverse work conditions including working overtime, job boredom, low skill variety, low autonomy, high job insecurity, and lack of reward were associated with poor mental health of young workers. Longitudinal evidence showed that high job demands, low job control, effort-reward imbalance, and low work support (men only) were associated with poor mental health. There was evidence on the contemporaneous relationship between two or more adverse work conditions and poor mental health. CONCLUSIONS: Although more research (particularly high-quality longitudinal studies) is warranted in this area, our review indicates that work-related stressors have a negative impact on the mental health of young workers. The current review suggests that workplace interventions and policy are required to improve the quality of work for young workers.

Understanding the characteristics and mechanisms underlying suicide clusters in Australian youth: a comparison of cluster detection methods.

AIMS: There is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: (1) determine the overlap in suicide clusters among each method, (2) compare the spatial and temporal parameters associated with different suicide clusters and (3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members. METHODS: Suicide data were obtained from the National Coronial Information System. N = 3027 Australians, aged 10-24 who died by suicide in 2006-2015 were included. Suicide clusters were determined using: (1) poisson scan statistics, (2) a systematic search of coronial inquests and (3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: (1) statistically significant, (2) perceived to be significant and (3) characterised by social links among three or more suicide descendants. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members. RESULTS: Eight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% (N = 7) in clusters from the scan statistic, 32.5% (N = 13) in clusters from coronial inquest and 56.8% (N = 21) in clusters from the descriptive network analysis. CONCLUSION: Each method identified markedly different suicide clusters. Evidence of social links between cluster members typically involved clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.

Complaints about dental practitioners: an analysis of 6 years of complaints about dentists, dental prosthetists, oral health therapists, dental therapists and dental hygienists in Australia.

BACKGROUND: Previous research has found dental practitioners at elevated risk of complaint compared with other health professions. This study aimed to describe the frequency, nature and risk factors for complaints involving dental practitioners. METHODS: We assembled a national dataset of complaints about registered health practitioners in Australia between January 2011 and December 2016. We classified complaints into 23 issues across three domains: health, performance and conduct. We compared rates of complaints about dental practitioners and other health practitioners. We used negative binomial regression analysis to identify factors associated with complaints. RESULTS: Dental practitioners made up 3.5% of health practitioners, yet accounted for approximately 10% of complaints. Dental practitioners had the highest rate of complaints among fourteen health professions (42.7 per 1000 practitioners per year) with higher rates among dentists and dental prosthetists than allied dental practitioners. Male practitioners were at a higher risk of complaints. Most complaints about dentists related to treatments and procedures (59%). Around 4% of dentists received more than one complaint, accounting for 49% of complaints about dentists. In 60% of closed cases no regulatory action was required. Around 13% of complaints resulted in restrictive actions, such as conditions on practice. CONCLUSION: Improved understanding of patterns may assist regulatory boards and professional associations to ensure competent practice and protect patient safety.

Clusters of suicides and suicide attempts: detection, proximity and correlates.

BACKGROUND: A suicide cluster is defined as a higher number of observed cases occurring in space and/or time than would typically be expected. Previous research has largely focused on identifying clusters of suicides, while there has been comparatively limited research on clusters of suicide attempts. We sought to identify clusters of both types of behaviour, and having done that, identify the factors that distinguish suicide attempts inside a cluster from those that were outside a cluster. METHODS: We used data from Western Australia from 2000 to 2011. We defined suicide attempts as admissions to hospital for deliberate self-harm and suicides as deaths due to deliberate self-harm. Using an analytic strategy that accounted for the repetition of attempted suicide within a cluster, we performed spatial-temporal analysis using Poisson discrete scan statistics to detect clusters of suicide attempts and clusters of suicides. Logistic regression was then used to compare clustered attempts with non-clustered attempts to identify risk factors for an attempt being in a cluster. RESULTS: We detected 350 (1%) suicide attempts occurring within seven spatial-temporal clusters and 12 (0.6%) suicides occurring within two spatial-temporal clusters. Both of the suicide clusters were located within a larger but later suicide attempt cluster. In multivariate analysis, suicide attempts by individuals who lived in areas of low socioeconomic status had higher odds of being in a cluster than those living in areas of high socioeconomic status [odds ratio (OR) = 29.1, 95% confidence interval (CI) = 6.3-135.5]. A one percentage-point increase in the proportion of people who had changed address in the last year was associated with a 60% increase in the odds of the attempt being within a cluster (OR = 1.60, 95% CI = 1.29-1.98) and a one percentage-point increase in the proportion of Indigenous people in the area was associated with a 7% increase in the suicide being within a cluster (OR = 1.07, 95% CI = 1.00-1.13). Age, sex, marital status, employment status, method of harm, remoteness, percentage of people in rented accommodation and percentage of unmarried people were not associated with the odds of being in a suicide attempt cluster. CONCLUSIONS: Early identification of and responding to suicide clusters may reduce the likelihood of subsequent clusters forming. The mechanisms, however, that underlie clusters forming is poorly understood.