Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
Department of Electrical Engineering and Computer Engineering, Laval University, Quebec, Quebec, Canada.
BMJ Open. 2023 Feb 27;13(2):e066423. doi: 10.1136/bmjopen-2022-066423.
Suicide has a complex aetiology and is a result of the interaction among the risk and protective factors at the individual, healthcare system and population levels. Therefore, policy and decision makers and mental health service planners can play an important role in suicide prevention. Although a number of suicide risk predictive tools have been developed, these tools were designed to be used by clinicians for assessing individual risk of suicide. There have been no risk predictive models to be used by policy and decision makers for predicting population risk of suicide at the national, provincial and regional levels. This paper aimed to describe the rationale and methodology for developing risk predictive models for population risk of suicide.
A case-control study design will be used to develop sex-specific risk predictive models for population risk of suicide, using statistical regression and machine learning techniques. Routinely collected health administrative data in Quebec, Canada, and community-level social deprivation and marginalisation data will be used. The developed models will be transformed into the models that can be readily used by policy and decision makers. Two rounds of qualitative interviews with end-users and other stakeholders were proposed to understand their views about the developed models and potential systematic, social and ethical issues for implementation; the first round of qualitative interviews has been completed. We included 9440 suicide cases (7234 males and 2206 females) and 661 780 controls for model development. Three hundred and forty-seven variables at individual, healthcare system and community levels have been identified and will be included in least absolute shrinkage and selection operator regression for feature selection.
This study is approved by the Health Research Ethnics Committee of Dalhousie University, Canada. This study takes an integrated knowledge translation approach, involving knowledge users from the beginning of the process.
自杀的病因复杂，是个体、医疗保健系统和人群各级别的风险因素和保护因素相互作用的结果。因此，政策制定者和决策者以及精神卫生服务规划者可以在预防自杀方面发挥重要作用。虽然已经开发出许多自杀风险预测工具，但这些工具旨在供临床医生用于评估个体自杀风险。目前还没有预测模型供政策制定者和决策者用于预测国家、省和地区各级人群的自杀风险。本文旨在描述开发人群自杀风险预测模型的原理和方法。
将使用病例对照研究设计，使用统计回归和机器学习技术，为人群自杀风险开发性别特异性风险预测模型。将使用加拿大魁北克省的常规健康管理数据以及社区层面的社会剥夺和边缘化数据。开发的模型将转换为政策制定者和决策者可以轻松使用的模型。计划与最终用户和其他利益相关者进行两轮定性访谈，以了解他们对开发模型的看法，以及实施过程中的潜在系统、社会和伦理问题；第一轮定性访谈已经完成。我们纳入了 9440 例自杀病例（7234 名男性和 2206 名女性）和 661780 名对照，用于模型开发。已确定个体、医疗保健系统和社区各级别的 347 个变量，并将其纳入最小绝对收缩和选择算子回归中进行特征选择。
这项研究已获得加拿大达尔豪斯大学健康研究伦理委员会的批准。这项研究从一开始就采用综合知识转化方法，让知识使用者参与其中。
