Abstract：
BACKGROUND: Suicide is a significant public health issue. Many risk prediction tools have been developed to estimate an individual\'s risk of suicide. Risk prediction models can go beyond individual risk assessment; one important application of risk prediction models is population health planning. Suicide is a result of the interaction among the risk and protective factors at the individual, health care system, and community levels. Thus, policy and decision makers can play an important role in suicide prevention. However, few prediction models for the population risk of suicide have been developed.
OBJECTIVE: This study aims to develop and validate prediction models for the population risk of suicide using health administrative data, considering individual-, health system-, and community-level predictors.
METHODS: We used a case-control study design to develop sex-specific risk prediction models for suicide, using the health administrative data in Quebec, Canada. The training data included all suicide cases (n=8899) that occurred from January 1, 2002, to December 31, 2010. The control group was a 1% random sample of living individuals in each year between January 1, 2002, and December 31, 2010 (n=645,590). Logistic regression was used to develop the prediction models based on individual-, health care system-, and community-level predictors. The developed model was converted into synthetic estimation models, which concerted the individual-level predictors into community-level predictors. The synthetic estimation models were directly applied to the validation data from January 1, 2011, to December 31, 2019. We assessed the performance of the synthetic estimation models with four indicators: the agreement between predicted and observed proportions of suicide, mean average error, root mean square error, and the proportion of correctly identified high-risk regions.
RESULTS: The sex-specific models based on individual data had good discrimination (male model: C=0.79; female model: C=0.85) and calibration (Brier score for male model 0.01; Brier score for female model 0.005). With the regression-based synthetic models applied in the validation data, the absolute differences between the synthetic risk estimates and observed suicide risk ranged from 0% to 0.001%. The root mean square errors were under 0.2. The synthetic estimation model for males correctly predicted 4 of 5 high-risk regions in 8 years, and the model for females correctly predicted 4 of 5 high-risk regions in 5 years.
CONCLUSIONS: Using linked health administrative databases, this study demonstrated the feasibility and the validity of developing prediction models for the population risk of suicide, incorporating individual-, health system-, and community-level variables. Synthetic estimation models built on routinely collected health administrative data can accurately predict the population risk of suicide. This effort can be enhanced by timely access to other critical information at the population level.
OBJECTIVE: This study aims to develop and validate prediction models for the population risk of suicide using health administrative data, considering individual-, health system-, and community-level predictors.
METHODS: We used a case-control study design to develop sex-specific risk prediction models for suicide, using the health administrative data in Quebec, Canada. The training data included all suicide cases (n=8899) that occurred from January 1, 2002, to December 31, 2010. The control group was a 1% random sample of living individuals in each year between January 1, 2002, and December 31, 2010 (n=645,590). Logistic regression was used to develop the prediction models based on individual-, health care system-, and community-level predictors. The developed model was converted into synthetic estimation models, which concerted the individual-level predictors into community-level predictors. The synthetic estimation models were directly applied to the validation data from January 1, 2011, to December 31, 2019. We assessed the performance of the synthetic estimation models with four indicators: the agreement between predicted and observed proportions of suicide, mean average error, root mean square error, and the proportion of correctly identified high-risk regions.
RESULTS: The sex-specific models based on individual data had good discrimination (male model: C=0.79; female model: C=0.85) and calibration (Brier score for male model 0.01; Brier score for female model 0.005). With the regression-based synthetic models applied in the validation data, the absolute differences between the synthetic risk estimates and observed suicide risk ranged from 0% to 0.001%. The root mean square errors were under 0.2. The synthetic estimation model for males correctly predicted 4 of 5 high-risk regions in 8 years, and the model for females correctly predicted 4 of 5 high-risk regions in 5 years.
CONCLUSIONS: Using linked health administrative databases, this study demonstrated the feasibility and the validity of developing prediction models for the population risk of suicide, incorporating individual-, health system-, and community-level variables. Synthetic estimation models built on routinely collected health administrative data can accurately predict the population risk of suicide. This effort can be enhanced by timely access to other critical information at the population level.
摘要：
背景：自杀是一个重要的公共卫生问题。已经开发了许多风险预测工具来估计个人的自杀风险。风险预测模型可以超越个体风险评估，风险预测模型的一个重要应用是人口健康规划。自杀是个体风险和保护因素相互作用的结果，卫生保健系统,和社区层面。因此，政策和决策者可以在预防自杀方面发挥重要作用。然而,针对人群自杀风险的预测模型很少。
目的：本研究旨在使用卫生行政数据开发和验证人群自杀风险的预测模型，考虑到个人-，卫生系统-，和社区层面的预测因子。
方法：我们使用病例对照研究设计来开发针对自杀的性别特异性风险预测模型，使用魁北克的卫生行政数据，加拿大。训练数据包括2002年1月1日至2010年12月31日发生的所有自杀病例（n=8899）。对照组是在2002年1月1日至2010年12月31日之间每年的1％的生活个体随机抽样（n=645,590）。采用Logistic回归建立了基于个体的预测模型，医疗保健系统-，和社区层面的预测因子。将开发的模型转换为综合估计模型，将个人水平的预测因子与社区水平的预测因子相协调。综合估计模型直接应用于2011年1月1日至2019年12月31日的验证数据。我们用四个指标评估了综合估计模型的性能：预测和观察到的自杀比例之间的一致性，平均平均误差，均方根误差，以及正确识别的高风险区域的比例。
结果：基于个体数据的性别特异性模型具有良好的辨别（男性模型：C=0.79；女性模型：C=0.85）和校准（男性模型的Brier得分0.01；女性模型的Brier得分0.005）。通过在验证数据中应用基于回归的合成模型，综合风险估计值和观察到的自杀风险之间的绝对差异为0%~0.001%.均方根误差小于0.2。男性的综合估计模型在8年内正确预测了5个高危地区中的4个，女性模型在5年内正确预测了5个高危地区中的4个。
结论：使用链接的卫生管理数据库，这项研究证明了建立人群自杀风险预测模型的可行性和有效性，融入个人-，卫生系统-，和社区层面的变量。基于常规收集的卫生管理数据建立的综合估计模型可以准确预测人群自杀风险。可以通过及时获取人口一级的其他关键信息来加强这一努力。
目的：本研究旨在使用卫生行政数据开发和验证人群自杀风险的预测模型，考虑到个人-，卫生系统-，和社区层面的预测因子。
方法：我们使用病例对照研究设计来开发针对自杀的性别特异性风险预测模型，使用魁北克的卫生行政数据，加拿大。训练数据包括2002年1月1日至2010年12月31日发生的所有自杀病例（n=8899）。对照组是在2002年1月1日至2010年12月31日之间每年的1％的生活个体随机抽样（n=645,590）。采用Logistic回归建立了基于个体的预测模型，医疗保健系统-，和社区层面的预测因子。将开发的模型转换为综合估计模型，将个人水平的预测因子与社区水平的预测因子相协调。综合估计模型直接应用于2011年1月1日至2019年12月31日的验证数据。我们用四个指标评估了综合估计模型的性能：预测和观察到的自杀比例之间的一致性，平均平均误差，均方根误差，以及正确识别的高风险区域的比例。
结果：基于个体数据的性别特异性模型具有良好的辨别（男性模型：C=0.79；女性模型：C=0.85）和校准（男性模型的Brier得分0.01；女性模型的Brier得分0.005）。通过在验证数据中应用基于回归的合成模型，综合风险估计值和观察到的自杀风险之间的绝对差异为0%~0.001%.均方根误差小于0.2。男性的综合估计模型在8年内正确预测了5个高危地区中的4个，女性模型在5年内正确预测了5个高危地区中的4个。
结论：使用链接的卫生管理数据库，这项研究证明了建立人群自杀风险预测模型的可行性和有效性，融入个人-，卫生系统-，和社区层面的变量。基于常规收集的卫生管理数据建立的综合估计模型可以准确预测人群自杀风险。可以通过及时获取人口一级的其他关键信息来加强这一努力。
