Participatory model building for suicide prevention in Canada

Thompson, Laura H.; Lang, Justin J.; Olibris, Brieanne; Gauthier-Beaupré, Amélie; Cook, Heather; Gillies, Dakota; Orpana, Heather

doi:10.1186/s13033-020-00359-6

Cited by 6 publications

(1 citation statement)

References 48 publications

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“…Understanding suicide and suicidal behaviour in Canada demands a recognition of its multifaceted nature [8,9]. A suicide attempt is an outcome influenced by a labyrinth of interwoven factors: from individual-level physical or psychological stressors [10,11], genetic predispositions [12,13], and past traumatic experiences [14,15] to broader societal determinants like economic instability [16], social networks [17,18], cultural nuances [19,20], and systemic healthcare disparities [21,22].…”

Section: Introductionmentioning

confidence: 99%

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Zahan,

Mikuliak,

Osgood

2024

Preprint

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Suicide dynamics form a complex system deeply influenced by interrelated factors, necessitating advanced methodologies for comprehensive analysis. This study pioneers the application of Particle Markov Chain Monte Carlo (PMCMC) methods in suicide research, recognized for their enhanced sampling efficiency over traditional techniques in complex, high-dimensional models. PMCMC is particularly adept at handling non-Gaussian distributions and navigating parameter spaces efficiently, preventing the entrapment in local optima. Utilizing PMCMC, our research simulated a broad spectrum of potential outcomes, clarifying the probable scenarios and their likelihoods, thus enriching the predictive accuracy and understanding of suicide dynamics. These methods facilitated the exploration of dynamic interactions among key risk factors such as mental health issues, trauma, substance use, social isolation, and access to harmful means, whose complex interplays challenge predictive modeling. The study extends previous applications of PMCMC in complex systems like H1N1, opioid crises, and COVID-19 to the domain of suicide, suggesting its potential in enhancing decision-making and intervention strategies. However, limitations due to the model's simplified assumptions and the specificity of the data to certain populations underscore the necessity for broader application to validate findings across varied demographics. In summary, while PMCMC offers robust capabilities for the dynamic modeling of suicide, it requires careful parameter selection and consideration of computational demands. Future research should continue to leverage this approach in more complex settings, enhancing our ability to predict and mitigate suicide risks effectively.

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Section: Introductionmentioning

confidence: 99%

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Zahan,

Mikuliak,

Osgood

2024

Preprint

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System dynamics models of depression at the population level: a scoping review

Graham

Gariépy

Orpana

2023

Health Res Policy Sys

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Aims Depression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have been used to model infectious and chronic disease, but rarely applied to mental health. This scoping review aimed to identify population-based SD models of depression and report on their modelling strategies and applications to policy and decision-making to inform research in this emergent field. Methods We searched articles in MEDLINE, Embase, PsychInfo, Scopus, MedXriv, and abstracts from the System Dynamics Society from inception to October 20, 2021 for studies of population-level SD models of depression. We extracted data on model purpose, elements of SD models, results, and interventions, and assessed the quality of reporting. Results We identified 1899 records and found four studies that met the inclusion criteria. Studies used SD models to assess various system-level processes and interventions, including the impact of antidepressant use on population-level depression in Canada; the impact of recall error on lifetime estimates of depression in the USA; smoking-related outcomes among adults with and without depression in the USA; and the impact of increasing depression incidence and counselling rates on depression in Zimbabwe. Studies included diverse stocks and flows for depression severity, recurrence, and remittance, but all models included flows for incidence and recurrence of depression. Feedback loops were also present in all models. Three studies provided sufficient information for replicability. Conclusions The review highlights the usefulness of SD models to model the dynamics of population-level depression and inform policy and decision-making. These results can help guide future applications of SD models to depression at the population-level.

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Outcomes of community-based suicide prevention program in primary health care of Iran

Azizi

Fakhari

Farahbakhsh

et al. 2021

Int J Ment Health Syst

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Background Suicidal management and prevention in communities, especially in its first stages, is an effective intervention for the health systems. However, in numerous societies most cases go undetected. Primary Health Care (PHC) is an effective place for the management of Suicide Prevention Programs (SPP). In Malekan County, a health community assessment found suicide as the most important health problem. A regional SPP was performed for suicide prevention during 2014–2017. Methods This study was carried out in six steps: (1) Establishing a research team, (2) Improving a registry for suicidal behaviors (SBs), (3) Identifying local determinants of SBs, (4) Training healthcare providers, (5) Follow-up and monitoring of SBs, and (6) Public awareness campaigns. Our ultimate goal was to lower the rates of suicide, and suicide attempt (SA) by 15 and 20 %, respectively. Multiple logistic regression was used to estimate the adjusted odds ratios and the 95% confidence intervals. Results A total of 821 SAs and 32 suicides were identified. The gender distribution for suicides was 70% males whereas SAs were 64% among females. The majority of suicides occurred in spring 18 (56.25%) while summer was the most common season among SAs 288 (35.8%). Almost 62 and 75% of suicides and SAs have used hanging and poisoning methods, respectively. Hanging increased suicide risk significantly (OR: 8.5, 95% CI 2.9–76.99). During the study, 93 life-skill and parenting education sessions were held. The incidence rates of suicide and SA decreased from 11.22, and 203 per 100,000 in 2013 to 2.63, and 157 in 2017, respectively. Similarly, the re-attempt to SAs ratio decreased from 12% to 2013 to 6.7% in 2017. Moreover, more than 8% of SBs were collected from adjacent Counties. Conclusions At the study end, suicide, SA, and re-attempt were lowered by 75%, 22%, and 42%, respectively. The practical framework that achieved in this study could be used as a basis for developing future SPPs and suicide researches in the Iranian context. Furthermore, the various socio-economic and socio-cultural challenges highlight the need to consider a wide range of contextual factors when developing an SPP.

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Participatory model building for suicide prevention in Canada

Cited by 6 publications

References 48 publications

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

System dynamics models of depression at the population level: a scoping review

Outcomes of community-based suicide prevention program in primary health care of Iran

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