© 2019, Society of General Internal Medicine (This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply). Background: Health care systems struggle to identify risk factors for suicide. Adverse social determinants of health (SDH) are strong predictors of suicide risk, but most electronic health records (EHR) do not include SDH data. Objective: To determine the prevalence of SDH documentation in the EHR and how SDH are associated with suicide ideation and attempt. Design: This cross-sectional analysis included EHR data spanning October 1, 2015–September 30, 2016, from the Veterans Integrated Service Network Region 4. Participants: The study included all patients with at least one inpatient or outpatient visit (n = 293,872). Main Measurements: Adverse SDH, operationalized using Veterans Health Administration (VHA) coding for services and International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes, encompassed seven types (violence, housing instability, financial/employment problems, legal problems, familial/social problems, lack of access to care/transportation, and nonspecific psychosocial needs). We defined suicide morbidity by ICD-10 codes and data from the VHA’s Suicide Prevention Applications Network. Logistic regression assessed associations of SDH with suicide morbidity, adjusting for socio-demographics and mental health diagnoses (e.g., major depression). Statistical significance was assessed with p <.01. Key Results: Overall, 16.4% of patients had at least one adverse SDH indicator. Adverse SDH exhibited dose-response-like associations with suicidal ideation and suicide attempt: each additional adverse SDH increased odds of suicidal ideation by 67% (AOR = 1.67, 99%CI = 1.60–1.75; p <.01) and suicide attempt by 49% (AOR = 1.49, 99%CI = 1.33–1.68; p <.01). Independently, each adverse SDH had strong effect sizes, ranging from 1.86 (99%CI = 1.58–2.19; p <.01) for legal issues to 3.10 (99%CI = 2.74–3.50; p <.01) for non-specific psychosocial needs in models assessing suicidal ideation and from 1.58 (99%CI = 1.10–2.27; p <.01) for employment/financial problems to 2.90 (99%CI = 2.30–4.16; p <.01) for violence in models assessing suicide attempt. Conclusions: SDH were strongly associated with suicidal ideation and suicide attempt even after adjusting for mental health diagnoses. Integration of SDH data in EHR could improve suicide prevention.