Impact of antidepressants on the risk of suicide in patients with depression in real-life conditions: A decision analysis model

A. Cougnard, H. Verdoux, A. Grolleau, Y. Moride, B. Begaud, M. Tournier

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Background. The impact of antidepressant drug treatment (ADT) on the risk of suicide is uncertain. The aim of this study was to determine in a real-life setting whether ADT is associated with an increased or a reduced risk of suicide compared to absence of ADT (no-ADT) in patients with depression. Method. A decision analysis method was used to estimate the number of suicides prevented or induced by ADT in children and adolescents (10-19 years old), adults (20-64 years old) and the elderly (≥65 years) diagnosed with major depression. The impact of gender and parasuicide history on the findings was explored within each age group. Sensitivity analyses were used to assess the robustness of the models. Results. Prescribing ADT to all patients diagnosed with depression would prevent more than one out of three suicide deaths compared to the no-ADT strategy, irrespective of age, gender or parasuicide history. Sensitivity analyses showed that persistence in taking ADT would be the main characteristic influencing the effectiveness of ADT on suicide risk. Conclusions. Public health decisions that contribute directly or indirectly to reducing the number of patients with depression who are effectively administered ADT may paradoxically induce a rise in the number of suicides.

Original languageEnglish (US)
Pages (from-to)1307-1315
Number of pages9
JournalPsychological medicine
Volume39
Issue number8
DOIs
StatePublished - Aug 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Applied Psychology
  • Psychiatry and Mental health

Keywords

  • Antidepressant
  • Decision analysis
  • Depression
  • Suicide

Fingerprint

Dive into the research topics of 'Impact of antidepressants on the risk of suicide in patients with depression in real-life conditions: A decision analysis model'. Together they form a unique fingerprint.

Cite this