RT Journal Article
SR Electronic
T1 Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case–control study
JF Injury Prevention
JO Inj Prev
FD BMJ Publishing Group Ltd
SP injuryprev-2022-044609
DO 10.1136/injuryprev-2022-044609
A1 Shabbar I Ranapurwala
A1 Vanessa E Miller
A1 Timothy S Carey
A1 Bradley N Gaynes
A1 Alexander P Keil
A1 Kate Vinita Fitch
A1 Monica E Swilley-Martinez
A1 Andrew L Kavee
A1 Toska Cooper
A1 Samantha Dorris
A1 David B Goldston
A1 Lewis J Peiper
A1 Brian W Pence
YR 2022
UL http://injuryprevention.bmj.com/content/early/2022/06/23/injuryprev-2022-044609.abstract
AB 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.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.