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Predicting the Population Risk of Suicide Using Routinely Collected Health Administrative Data in Quebec, Canada:

JianLi Wang1, Fatemeh Gholi Zadeh Kharrat2, Geneviève Gariépy3

  • 1Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada.

JMIR Public Health and Surveillance
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PubMed
Summary

This study developed accurate suicide risk prediction models using health administrative data for population health planning. These synthetic estimation models can reliably identify high-risk regions for targeted suicide prevention efforts.

Keywords:
CanadaQuebecadolescentadolescentsanxietycase-controldepressiondevelopmentfemalehealth administrative datamalemental healthpopulation risk predictionpreventionsuicidesuicide preventionteenteenagerteenagersteensvalidation

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Services Research

Background:

  • Suicide is a major public health concern requiring effective prevention strategies.
  • Existing suicide risk prediction tools often focus on individual assessment, limiting their use in population health planning.
  • There is a need for models that predict population-level suicide risk by integrating individual, health system, and community factors.

Purpose of the Study:

  • To develop and validate sex-specific prediction models for population suicide risk.
  • To utilize health administrative data, incorporating individual, health system, and community-level predictors.
  • To convert individual-level predictors into community-level predictors using synthetic estimation models for broader application.

Main Methods:

  • A case-control study design was employed using health administrative data from Quebec, Canada.
  • Sex-specific logistic regression models were developed using suicide cases and a control sample from 2002-2010.
  • Synthetic estimation models were created and validated on data from 2011-2019, assessing prediction accuracy and identification of high-risk regions.

Main Results:

  • The developed sex-specific models demonstrated good discrimination and calibration.
  • Synthetic estimation models showed minimal absolute differences (0-0.001%) between predicted and observed suicide risks.
  • Models accurately identified a high proportion of high-risk regions over the validation period.

Conclusions:

  • Linked health administrative data can be effectively used to develop valid suicide risk prediction models for population health planning.
  • Synthetic estimation models derived from routinely collected data accurately predict population suicide risk.
  • Enhancing these models requires timely access to additional population-level data for improved suicide prevention strategies.