Cargando…

A case–control study on predicting population risk of suicide using health administrative data: a research protocol

INTRODUCTION: 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, JianLi, Gholi Zadeh Kharrat, Fatemeh, Pelletier, Jean-François, Rochette, Louis, Pelletier, Eric, Lévesque, Pascale, Massamba, Victoria, Brousseau-Paradis, Camille, Mohammed, Mada, Gariépy, Geneviève, Gagné, Christian, Lesage, Alain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972456/
https://www.ncbi.nlm.nih.gov/pubmed/36849211
http://dx.doi.org/10.1136/bmjopen-2022-066423
_version_ 1784898328983502848
author Wang, JianLi
Gholi Zadeh Kharrat, Fatemeh
Pelletier, Jean-François
Rochette, Louis
Pelletier, Eric
Lévesque, Pascale
Massamba, Victoria
Brousseau-Paradis, Camille
Mohammed, Mada
Gariépy, Geneviève
Gagné, Christian
Lesage, Alain
author_facet Wang, JianLi
Gholi Zadeh Kharrat, Fatemeh
Pelletier, Jean-François
Rochette, Louis
Pelletier, Eric
Lévesque, Pascale
Massamba, Victoria
Brousseau-Paradis, Camille
Mohammed, Mada
Gariépy, Geneviève
Gagné, Christian
Lesage, Alain
author_sort Wang, JianLi
collection PubMed
description INTRODUCTION: 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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.
format Online
Article
Text
id pubmed-9972456
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-99724562023-03-01 A case–control study on predicting population risk of suicide using health administrative data: a research protocol Wang, JianLi Gholi Zadeh Kharrat, Fatemeh Pelletier, Jean-François Rochette, Louis Pelletier, Eric Lévesque, Pascale Massamba, Victoria Brousseau-Paradis, Camille Mohammed, Mada Gariépy, Geneviève Gagné, Christian Lesage, Alain BMJ Open Mental Health INTRODUCTION: 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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. BMJ Publishing Group 2023-02-27 /pmc/articles/PMC9972456/ /pubmed/36849211 http://dx.doi.org/10.1136/bmjopen-2022-066423 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Mental Health
Wang, JianLi
Gholi Zadeh Kharrat, Fatemeh
Pelletier, Jean-François
Rochette, Louis
Pelletier, Eric
Lévesque, Pascale
Massamba, Victoria
Brousseau-Paradis, Camille
Mohammed, Mada
Gariépy, Geneviève
Gagné, Christian
Lesage, Alain
A case–control study on predicting population risk of suicide using health administrative data: a research protocol
title A case–control study on predicting population risk of suicide using health administrative data: a research protocol
title_full A case–control study on predicting population risk of suicide using health administrative data: a research protocol
title_fullStr A case–control study on predicting population risk of suicide using health administrative data: a research protocol
title_full_unstemmed A case–control study on predicting population risk of suicide using health administrative data: a research protocol
title_short A case–control study on predicting population risk of suicide using health administrative data: a research protocol
title_sort case–control study on predicting population risk of suicide using health administrative data: a research protocol
topic Mental Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9972456/
https://www.ncbi.nlm.nih.gov/pubmed/36849211
http://dx.doi.org/10.1136/bmjopen-2022-066423
work_keys_str_mv AT wangjianli acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT gholizadehkharratfatemeh acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT pelletierjeanfrancois acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT rochettelouis acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT pelletiereric acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT levesquepascale acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT massambavictoria acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT brousseauparadiscamille acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT mohammedmada acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT gariepygenevieve acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT gagnechristian acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT lesagealain acasecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT wangjianli casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT gholizadehkharratfatemeh casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT pelletierjeanfrancois casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT rochettelouis casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT pelletiereric casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT levesquepascale casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT massambavictoria casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT brousseauparadiscamille casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT mohammedmada casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT gariepygenevieve casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT gagnechristian casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol
AT lesagealain casecontrolstudyonpredictingpopulationriskofsuicideusinghealthadministrativedataaresearchprotocol