Identifying risk factors for suicidal ideation across a large community healthcare system.

BACKGROUND

Suicide is the tenth leading cause of death in the United States. Several studies have leveraged electronic health record (EHR) data to predict suicide risk in veteran and military samples; however, few studies have investigated suicide risk factors in a large-scale community health population.

METHODS

Clinical data was queried for 9,811 patients from the Penn Medicine Health System who had completed a Patient Health Questionnaire-9 (PHQ-9) documented in the EHR between January 2017 and June 2019. Patient demographics, PHQ-9 scores, and psychiatric comorbidities were extracted from the EHR. Univariate and multivariable logistic regressions were applied to determine significant risk factors associated with suicide ideation responses from the PHQ-9.

RESULTS

One-quarter (25.8%% of patients endorsed suicide ideation. Univariate analysis found 22 risk factors of suicide ideation. Multivariable logistic regression found significant positive associations (Odds Ratio, (95% Confidence Interval)) with the following: younger ages less than 18 years: 2.1, (1.69, 2.60) and 19-24 years: 1.55, (1.29, 1.87)), single marital status (1.22, (1.08, 1.38)), African American (1.22, (1.08, 1.38)), non-commercial insurance (1.16, (1.03, 1.31)), multiple comorbidities (1 comorbidity (1.65, (1.32, 2.07); 2 comorbidities (2.07, (1.61, 2.64)), 3+ comorbidities (2.49, (1.87, 3.33))), bipolar disorders (Type I: 1.38, (1.14, 1.67) and Type II: 1.94, (1.52, 2.49)), depressive disorders (1.70, (1.49, 1.94)), obsessive compulsive disorder (OCD) (1.43, (1.08, 1.90)), and stress disorders (1.53, (1.33, 1.76)).

CONCLUSION

Community EHR information can be used to predict suicidal ideation. This information can be used to design tools for identifying patients at risk for suicide in real-time.