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Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case–control study
  1. Shabbar I Ranapurwala1,2,
  2. Vanessa E Miller2,
  3. Timothy S Carey3,4,
  4. Bradley N Gaynes5,
  5. Alexander P Keil1,
  6. Kate Vinita Fitch1,2,
  7. Monica E Swilley-Martinez1,2,
  8. Andrew L Kavee3,
  9. Toska Cooper2,
  10. Samantha Dorris2,
  11. David B Goldston6,
  12. Lewis J Peiper7,
  13. Brian W Pence1,2
  1. 1 Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
  2. 2 Injury Prevention Research Center, University of North Carolina, Chapel Hill, North Carolina, USA
  3. 3 Cecil G Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
  4. 4 Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
  5. 5 Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
  6. 6 Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA
  7. 7 Division of Adult Correction – Prisons, North Carolina Department of Public Safety, Raleigh, North Carolina, USA
  1. Correspondence to Dr Shabbar I Ranapurwala, Epidemiology, University of North Carolina at Chapel Hill Department of Epidemiology, Chapel Hill, NC 27510, USA; sirana{at}email.unc.edu

Abstract

Background Suicide deaths have been increasing for the past 20 years in the USA resulting in 45 979 deaths in 2020, a 29% increase since 1999. Lack of data linkage between entities with potential to implement large suicide prevention initiatives (health insurers, health institutions and corrections) is a barrier to developing an integrated framework for suicide prevention.

Objectives Data linkage between death records and several large administrative datasets to (1) estimate associations between risk factors and suicide outcomes, (2) develop predictive algorithms and (3) establish long-term data linkage workflow to ensure ongoing suicide surveillance.

Methods We will combine six data sources from North Carolina, the 10th most populous state in the USA, from 2006 onward, including death certificate records, violent deaths reporting system, large private health insurance claims data, Medicaid claims data, University of North Carolina electronic health records and data on justice involved individuals released from incarceration. We will determine the incidence of death from suicide, suicide attempts and ideation in the four subpopulations to establish benchmarks. We will use a nested case–control design with incidence density-matched population-based controls to (1) identify short-term and long-term risk factors associated with suicide attempts and mortality and (2) develop machine learning-based predictive algorithms to identify individuals at risk of suicide deaths.

Discussion We will address gaps from prior studies by establishing an in-depth linked suicide surveillance system integrating multiple large, comprehensive databases that permit establishment of benchmarks, identification of predictors, evaluation of prevention efforts and establishment of long-term surveillance workflow protocols.

  • Surveillance
  • Suicide/Self?Harm
  • Case-Control Study
  • Prisoners
  • Mental Health

Data availability statement

Data may be obtained from a third party and are not publicly available. The data used in this study are not publicly available but can be obtained upon request from the entities noted under the 'Data sets and linkage' section in the Methods section of this manuscript.

https://doi.org/10.1136/injuryprev-2022-044609

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    Data availability statement

    Data may be obtained from a third party and are not publicly available. The data used in this study are not publicly available but can be obtained upon request from the entities noted under the 'Data sets and linkage' section in the Methods section of this manuscript.

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    Footnotes

    • Twitter @episinister

    • Correction notice This article has been corrected since it was published Online First. An author name has been amended.

    • Contributors All authors made significant contributions and reviewed and edited multiple drafts of the manuscript and provided approval for the final submitted version. In addition, SR and BWP conceptualised the study, secured funding, wrote the original draft of the study and this protocol, secured data use agreements and provide supervision to all aspects of the study. VM developed manuscript outline, edited the original draft of this protocol manuscript and helps with data collection and analysis. SD provides project and funding management and data collection support. TSC reviewed and edited the original draft of the protocol and supported data collection from electronic health records. BNG conceptualised the study, reviewed and edited the original study proposal and provides clinical and scientific expertise. APK provides machine learning expertise and wrote the original draft of machine learning methods for the study and this protocol. CVF conducts data analysis and linkage. DG provides clinical and scientific expertise and reviewed, edited and wrote the original study protocol. ALK provides data linkage and curation expertise. MS-M conducts data cleaning, curation and analysis. TC provides project and funding management and data collection support. LP supports data collection and liaisons the work with North Carolina Department of Public Safety.

    • Funding This work is supported by funding from the National Institute of Health’s National Institute of Mental Health (grant number: R01MH124752) (BWP and SR, Multiple Principle Investigators).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

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