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Mental health
Protocol
A case–control study on predicting population risk of suicide using health administrative data: a research protocol
  1. JianLi Wang1,
  2. Fatemeh Gholi Zadeh Kharrat2,
  3. Jean-François Pelletier3,
  4. Louis Rochette4,
  5. Eric Pelletier4,
  6. Pascale Lévesque4,
  7. Victoria Massamba4,
  8. Camille Brousseau-Paradis3,
  9. Mada Mohammed1,
  10. Geneviève Gariépy5,6,
  11. Christian Gagné2,
  12. Alain Lesage7
  1. 1Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
  2. 2Department of Electrical Engineering and Computer Engineering, Laval University, Quebec, Quebec, Canada
  3. 3Department of Psychiatry, University of Montreal, Montreal, Québec, Canada
  4. 4Institut national de sante publique du Quebec (INSPQ), Quebec City, Quebec, Canada
  5. 5Public Health Agency of Canada, Ottawa, Ontario, Canada
  6. 6Department of Social and Preventive Medicine, University of Montreal, Montreal, Québec, Canada
  7. 7Institut universitaire en sante mentale de Montreal, Montreal, Québec, Canada
  1. Correspondence to Dr JianLi Wang; jianli.wang{at}dal.ca

Abstract

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.

  • Suicide & self-harm
  • PUBLIC HEALTH
  • PSYCHIATRY
  • HEALTH SERVICES ADMINISTRATION & MANAGEMENT
http://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://doi.org/10.1136/bmjopen-2022-066423

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    Footnotes

    • Contributors JLW drafted the manuscript. JLW, FGZK, J-FP, LR, EP, PL, GG, CG and AL were involved in study design, conceptualisation and funding application. JLW, FGZK, J-FP, LR, EP, PL, VM, CB-P, MM, GG, CG and AL were involved in manuscript review, discussion, revision and final approval.

    • Funding This study is supported by a New Frontiers for Research Funds grant (2019-00471) from Tri-Agency Institutional Programs Secretariat, Government of Canada and by a Tier I Canada Research Chair award to JLW.

    • Disclaimer The funders play no role in the design and operation of this study.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.