Occupational class suicide risk: 12-year study of national coronial data.

BACKGROUND: Previous research showed that the Global Financial Crisis (GFC) was associated with a widening disparity in suicide rates between lower-class occupations and the highest-class occupations in Australia. There has been no research investigating whether this trend continued post-GFC. AIMS: This study aimed to investigate suicide rates by occupational class among employed Australians aged 15 years and over, between 2007 and 2018. METHOD: A population-level retrospective mortality study was conducted using data from the National Coronial Information System. Adjusted suicide rates were calculated over the period 2007 to 2018. Negative binomial regression models were used to assess the relationship between occupational class, gender and time, comparing post-GFC years (2010-2012, 2013-2015 and 2016-2018) with GFC years (2007-2009). RESULTS: Relative to the GFC period of 2007-2009, a significant reduction in suicide disparity between managers and other occupation groups was only observed among male labourers (rate ratios (RR) = 0.65, 95% CI 0.49-0.86) and male technicians/trades workers (RR = 0.73, 95% CI 0.56-0.96) for the period 2013-2015. CONCLUSION: Skilled manual and lower-skilled occupational classes remain at elevated risk of suicide in Australia. While a decreasing divergence in suicide rates was only observed between labourer and manager occupational classes post-GFC, this trend was not maintained over the later part of the study period (2016-2018). There is a need to further understand the relationship between contextual factors associated with suicide among the employed population, especially during periods of economic downturn.

Exploring Sociodemographic Correlates of Suicide Stigma in Australia: Baseline Cross-Sectional Survey Findings from the Life-Span Suicide Prevention Trial Studies.

The risk of suicidal behaviour in Australia varies by age, sex, sexual preference and Indigenous status. Suicide stigma is known to affect suicide rates and help-seeking for suicidal crises. The aim of this study was to investigate the sociodemographic correlates of suicide stigma to assist in prevention efforts. We surveyed community members and individuals who had attended specific emergency departments for suicidal crisis. The respondents were part of a large-scale suicide prevention trial in New South Wales, Australia. The data collected included demographic characteristics, measures of help-seeking and suicide stigma. The linear regression analyses conducted sought to identify the factors associated with suicide stigma. The 5426 participants were predominantly female (71.4%) with a mean (SD) age of 41.7 (14.8) years, and 3.9% were Indigenous. Around one-third of participants reported a previous suicide attempt (n = 1690, 31.5%) with two-thirds (n = 3545, 65.3%) seeking help for suicidal crisis in the past year. Higher stigma scores were associated with Indigenous status (ß 0.123, 95%CI 0.074-0.172), male sex (ß 0.527, 95%CI 0.375-0.626) and regional residence (ß 0.079, 95%CI 0.015-0.143). Lower stigma scores were associated with younger age (ß -0.002, 95%CI -0.004--0.001), mental illness (ß -0.095, 95%CI -0.139 to -0.050), male bisexuality (ß -0.202, 95%CI -0.351 to -0.052) and males who glorified suicide (ß -0.075, 95%CI -0.119 to -0.031). These results suggested that suicide stigma differed across the community, varying significantly by sex, sexual orientation and Indigenous status. Targeted educational programs to address suicide stigma could assist in suicide prevention efforts.

The application of spatial analysis to understanding the association between area-level socio-economic factors and suicide: a systematic review.

BACKGROUND: Little is known about what impact the use of different spatial methodological approaches may have on understanding the relationship between area-level socio-economic factors and suicide. METHODS: In this systematic review, we searched PubMed, Embase, CINAHL and PsycInfo for original empirical studies examining the relationship between socio-economic factors and suicide with a spatial lens, published up to January 22, 2022. Data on applied spatial methods, indicators of socio-economic factors, and risk of suicide related to socio-economic factors were extracted. The protocol for this systematic review was registered with PROSPERO (CRD42021251387). RESULTS: A systematic search yielded 6290 potentially relevant results; 58 studies met the inclusion criteria for review. Of the 58 included studies, more than half of the studies (n = 34; 58.6%) used methods that accounted for spatial effects in analyses of the association between socio-economic factors and suicide or examined spatial autocorrelation, while 24 (41.4%) studies applied univariate and multivariate models without considering spatial effects. Bayesian hierarchical models and spatial regression models were commonly used approaches to correct for spatial effects. The risk of suicide relating to socio-economic factors varied greatly by local areas and between studies using various socio-economic indicators. Areas with higher deprivation, higher unemployment, lower income, and lower education level were more likely to have higher suicide risk. There was no significant difference in results between studies using conventional versus spatial statistic methods. CONCLUSION: An increasing number of studies have applied spatial methods, including Bayesian spatial models and spatial regression models, to explore the relationship between area-level socio-economic factors and suicide. This review of spatial studies provided further evidence that area-level socio-economic factors are generally inversely associated with suicide risk, with or without accounting for spatial autocorrelation.

Suicide among health professionals in Australia: A retrospective mortality study of trends over the last two decades.

OBJECTIVE: To examine the relative risk of suicide among healthcare professionals compared to other occupations and examine changes in suicide rates over time. METHODS: Suicide cases were identified using the National Coronial Information System and were included if they were recorded as a death by intentional self-harm between 2001 and 2017 and were by an employed adult aged 20-69 with a known occupation at the time of death. Suicide methods were reported descriptively. Workforce data at the population level was extracted from the Australian Bureau of Statistics 2011 Census. Age-standardised suicide rates per 100,000 person-years for each of the four occupational groups were calculated using direct standardisation and using the Australian Bureau of Statistics population-level data from the 2011 Census. Negative binomial regression was used to estimate suicide risk by healthcare employment status and profession, to investigate differences by sex and to examine trends in suicide rates over time, using rate ratios and 95% confidence intervals. RESULTS: Healthcare professionals were at increased risk of suicide compared to other occupations (rate ratio = 1.30, 95% confidence interval = [1.19, 1.42], p < 0.001), controlling for age, sex and year of death. Nurses and midwives were identified as being at significantly increased risk of suicide (rate ratio = 1.95, 95% confidence interval = [1.73, 2.19], p < 0.001). Suicide rates among female medical practitioners increased substantially over time (p = 0.01). CONCLUSION: Health professionals are at significantly increased risk of suicide, though the relative risk of different groups is changing over time. There has been a substantial increase in the risk of suicide among female medical practitioners with rates of suicide in this group more than doubling over the last two decades. Findings highlight the need for targeted suicide prevention initiatives for healthcare professionals.

Suicide among emergency service workers: a retrospective mortality study of national coronial data, 2001-2017.

BACKGROUND: Emergency service workers (ESW) are known to be at increased risk of mental disorders but population-level and longitudinal data regarding their risk of suicide are lacking. METHOD: Suicide data for 2001-2017 were extracted from the Australian National Coronial Information Service (NCIS) for two occupational groups: ESW (ambulance personnel, fire-fighters and emergency workers, police officers) and individuals employed in all other occupations. Age-standardised suicide rates were calculated and risk of suicide compared using negative binomial regression modelling. RESULTS: 13 800 suicide cases were identified among employed adults (20-69 years) over the study period. The age-standardised suicide rate across all ESW was 14.3 per 100 000 (95% CI 11.0-17.7) compared to 9.8 per 100 000 (95% CI 9.6-9.9) for other occupations. Significant occupational differences in the method of suicide were identified (p < 0.001). There was no evidence for increased risk of suicide among ESW compared to other occupations once age, gender and year of death were accounted for (RR = 0.99, 95% CI 0.84-1.17; p = 0.95). In contrast, there was a trend for ambulance personnel to be at elevated risk of suicide (RR = 1.41, 95% CI 1.00-2.00, p = 0.053). CONCLUSION: Whilst age-standardised suicide rates among ESW are higher than other occupations, emergency service work was not independently associated with an increased risk of suicide, with the exception of an observed trend in ambulance personnel. Despite an increased focus on ESW mental health and wellbeing over the last two decades, there was no evidence that rates of suicide among ESW are changing over time.

Machine learning algorithms to classify self-harm behaviours in New South Wales Ambulance electronic medical records: A retrospective study.

BACKGROUND: There is increasing interest in suicide surveillance solutions to identify non-fatal suicidal and self-harming behaviours in the Australian community not currently captured through national administrative datasets. OBJECTIVE: The aim of the present study was to develop machine learning models to classify self-harm related behaviours using unstructured clinical note text from New South Wales (NSW) Ambulance data and compare their performance via traditional methods. METHODS: Primary data were derived from NSW Ambulance electronic medical records (eMRs) for potential self-harm related NSW Ambulance attendances for the period 2013-2019. Data included paramedic clinical notes detailing the nature of the attendance, clinical outcome, and narrative information. We assessed sensitivity, specificity, positive predictive value, negative predictive value, F-score, and the Matthews correlation coefficient (MCC) for four algorithms (Support Vector Machine, random forest, decision tree, and logistic regression). RESULTS: The performance of these algorithms was compared using the MCC measure. In a test sample of 3157 ambulance attendances (1349 self-harm related behaviours and 1808 unrelated), the MCC for classification of self-harm related behaviour ranged from +0.681 to +0.730. The Support Vector Machine (sensitivity = 82.7%, specificity = 89.6%, MCC = 0.730) and the logistic regression (sensitivity = 83.1%, specificity = 89.3%, MCC = 0.727) models performed best. CONCLUSIONS: This study demonstrates that machine learning models can be applied to paramedic notes within unstructured medical records to classify self-harm related behaviours. The resulting model could be used to compliment current manual abstraction of self-harm behaviours and provide more timely approximations to be used for self-harm surveillance.