Evaluation of the Risk Identification for Suicide and Enhanced Care Model in a Native American Community

Abstract

ImportanceThere are many prognostic models of suicide risk, but few have been prospectively evaluated, and none has been developed specifically for Native American populations.ObjectiveTo prospectively validate a statistical risk model implemented in a community setting and evaluate whether use of this model was associated with improved reach of evidence-based care and reduced subsequent suicide-related behavior among high-risk individuals.Design, Setting, and ParticipantsThis prognostic study, done in partnership with the White Mountain Apache Tribe, used data collected by the Apache Celebrating Life program for adults aged 25 years or older identified as at risk for suicide and/or self-harm from January 1, 2017, through August 31, 2022. Data were divided into 2 cohorts: (1) individuals and suicide-related events from the period prior to suicide risk alerts being active (February 29, 2020) and (2) individuals and events from the time after alerts were activated.Main Outcomes and MeasuresAim 1 focused on a prospective validation of the risk model in cohort 1. Aim 2 compared the odds of repeated suicide-related events and the reach of brief contact interventions among high-risk cases between cohort 2 and cohort 1.ResultsAcross both cohorts, a total of 400 individuals identified as at risk for suicide and/or self-harm (mean [SD] age, 36.5 [10.3] years; 210 females [52.5%]) had 781 suicide-related events. Cohort 1 included 256 individuals with index events prior to active notifications. Most index events (134 [52.5%]) were for binge substance use, followed by 101 (39.6%) for suicidal ideation, 28 (11.0%) for a suicide attempt, and 10 (3.9%) for self-injury. Among these individuals, 102 (39.5%) had subsequent suicidal behaviors. In cohort 1, the majority (220 [86.3%]) were classified as low risk, and 35 individuals (13.3%) were classified as high risk for suicidal attempt or death in the 12 months after their index event. Cohort 2 included 144 individuals with index events after notifications were activated. For aim 1, those classified as high risk had a greater odds of subsequent suicide-related events compared with those classified as low risk (odds ratio [OR], 3.47; 95% CI, 1.53-7.86; P = .003; area under the receiver operating characteristic curve, 0.65). For aim 2, which included 57 individuals classified as high risk across both cohorts, during the time when alerts were inactive, high-risk individuals were more likely to have subsequent suicidal behaviors compared with when alerts were active (OR, 9.14; 95% CI, 1.85-45.29; P = .007). Before the active alerts, only 1 of 35 (2.9%) individuals classified as high risk received a wellness check; after the alerts were activated, 11 of 22 (50.0%) individuals classified as high risk received 1 or more wellness checks.Conclusions and RelevanceThis study showed that a statistical model and associated care system developed in partnership with the White Mountain Apache Tribe enhanced identification of individuals at high risk for suicide and was associated with a reduced risk for subsequent suicidal behaviors and increased reach of care.

For the Next Seven Generations: An Indigenous Strengths-Based Developmental Science

“…Collaborations with the White Mountain Apache have also produced and refined theories of suicidal behavior that accounts for individual, social, and cultural factors (Cwik et al, 2015;Tingey et al, 2015). Data from this collaboration was even used to validate and implement a machine learning algorithm that classified individuals as low or high-risk for suicidal behaviors to alert Celebrating Life staff (Haroz et al, 2023). Implementing the algorithm-driven alerts was associated with greater service utilization and lower rates of subsequent suicidal behaviors among high-risk individuals (Haroz et al, 2023).…”

“…Data from this collaboration was even used to validate and implement a machine learning algorithm that classified individuals as low or high-risk for suicidal behaviors to alert Celebrating Life staff (Haroz et al, 2023). Implementing the algorithm-driven alerts was associated with greater service utilization and lower rates of subsequent suicidal behaviors among high-risk individuals (Haroz et al, 2023). With holistic programming like Celebrating Life, the White Mountain Apache are assuring that the appropriate structures are in place for Indigenous youth to live meaningful lives -a process that developmental scientists are privileged to be a part of.…”