Current MHRN Projects

Impact of buprenorphine on suicidal behavior

Funder: NIMH

Grant Number: U19MH121738-02S2  (supplement to main MHRN cooperative agreement)

Grant period: 9/17/2020 – 8/31/2022

Brief Narrative: This large observational study will evaluate the effects of initiating buprenorphine treatment on subsequent suicidal behavior among people with opioid use disorder, including those with and without co-occurring mental health conditions or other known risk factors for suicidal behavior. We will use comprehensive health records data from four large health systems serving a combined member/patient population of approximately 11 million. Analyses will examine the overall effect of buprenorphine treatment on subsequent suicide attempts or death, heterogeneity of effects in patient subgroups, and specificity of effects to buprenorphine vs other medications.

Lead Site: KPWA


Evaluation of health system Zero Suicide programs


Mental health impacts of COVID-19


MHRN III Administrative Core

Funder: NIMH

Grant Number: U19MH121738

Grant Period: 09/23/2019 – 06/30/2020

Narrative:​ Practice-based research has the potential to dramatically improve the speed, efficiency, relevance, and impact of mental health clinical and services research.  Mental Health Research Network (MHRN) III will include 14 research centers embedded in health systems serving a combined population of over 25 million patients in 16 states.  MHRN infrastructure will be enhanced to support a next-generation practice-based network, including:

  • Increased engagement of patients, health system leaders, and other stakeholders in network governance
  • An expanded public, open-source library of software tools and other technical resources
  • More formal processes for conducting feasibility pilot projects and rapid response to stakeholder queries
  • Expanded outreach to external stakeholders and research partners

MHRN III Methods Core


Mindfulness intervention to prevent perinatal depression


Health system implementation of suicide risk prediction models