title: Predictors of suicide in patient charts among patients with depression in the Veterans Health Administration health system: importance of prescription drug and alcohol abuse.
abstract: OBJECTIVE
  To identify factors recorded in electronic medical chart progress notes associated with suicide among patients who had received treatment for depression.
  
  
  METHODS
  The retrospective study sample consisted of 324 randomly selected US Veterans Health Administration (VHA) patients treated for depression who died by suicide from April 1, 1999, to September 30, 2004, stratified by geographic region, gender, and year of depression cohort entry and 312 control patients with depression who were alive on the date of suicide death (index date) and were from the same stratum as the matched suicide patient. In addition to constructing variables from administrative data, variables were abstracted from electronic medical chart notes in the year prior to the index date in 5 categories: clinical symptoms and diagnoses, substance use, life stressors, behavioral/ideation measures (eg, suicide attempts), and treatments received. Logistic regression was used to assess the associations.
  
  
  RESULTS
  Even after we adjusted for administratively available data, suicidal behaviors and substance-related variables were the strongest independent predictors of suicide. Prescription drug misuse had an odds ratio (OR) of 6.8 (95% CI, 2.5-18.5); history of suicide attempts, 6.6 (95% CI, 1.7-26.4); and alcohol abuse/dependence, 3.3 (95% CI, 1.9-5.7). Difficulty with access to health care was a predictor of suicide (OR = 2.9; 95% CI, 1.3-6.3). Receipt of VHA substance abuse treatment was protective (OR = 0.4; 95% CI, 0.1-0.9).
  
  
  CONCLUSIONS
  Prescription drug and alcohol misuse assessments should be prioritized in suicide assessments among depressed patients. Additionally, behavioral measures noted in electronic chart records may be useful in health system monitoring and surveillance and can potentially be accessed using word search or natural language processing approaches.
authors.affiliation: Center for Statistical Consultation and Research, University of Michigan, 3555 Rackham, Ann Arbor, MI 48109-1070, USA. myrakim@umich.edu
authors.forename: Hyungjin Myra
authors.initials: HM
authors.lastname: Kim
authors.forename: Eric G
authors.initials: EG
authors.lastname: Smith
authors.forename: Dara
authors.initials: D
authors.lastname: Ganoczy
authors.forename: Heather
authors.initials: H
authors.lastname: Walters
authors.forename: Clare M
authors.initials: CM
authors.lastname: Stano
authors.forename: Mark A
authors.initials: MA
authors.lastname: Ilgen
authors.forename: Amy S B
authors.initials: AS
authors.lastname: Bohnert
authors.forename: Marcia
authors.initials: M
authors.lastname: Valenstein
mesh_terms: D000368:Aged; D000437:Alcoholism / Q000150:complications / Q000523:psychology*; D003863:Depression / Q000150:complications / Q000523:psychology*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D063487:Prescription Drug Misuse; D012189:Retrospective Studies; D012307:Risk Factors; D013405:Suicide / Q000523:psychology*; D014481:United States; D014493:United States Department of Veterans Affairs / Q000706:statistics & numerical data