Drug Safety

, Volume 41, Issue 4, pp 363–376 | Cite as

Mixed Approach Retrospective Analyses of Suicide and Suicidal Ideation for Brand Compared with Generic Central Nervous System Drugs

  • Ning Cheng
  • Md. Motiur Rahman
  • Yasser Alatawi
  • Jingjing Qian
  • Peggy L. Peissig
  • Richard L. Berg
  • C. David Page
  • Richard A. Hansen
Original Research Article

Abstract

Introduction

Several different types of drugs acting on the central nervous system (CNS) have previously been associated with an increased risk of suicide and suicidal ideation (broadly referred to as suicide). However, a differential association between brand and generic CNS drugs and suicide has not been reported.

Objectives

This study compares suicide adverse event rates for brand versus generic CNS drugs using multiple sources of data.

Methods

Selected examples of CNS drugs (sertraline, gabapentin, zolpidem, and methylphenidate) were evaluated via the US FDA Adverse Event Reporting System (FAERS) for a hypothesis-generating study, and then via administrative claims and electronic health record (EHR) data for a more rigorous retrospective cohort study. Disproportionality analyses with reporting odds ratios and 95% confidence intervals (CIs) were used in the FAERS analyses to quantify the association between each drug and reported suicide. For the cohort studies, Cox proportional hazards models were used, controlling for demographic and clinical characteristics as well as the background risk of suicide in the insured population.

Results

The FAERS analyses found significantly lower suicide reporting rates for brands compared with generics for all four studied products (Breslow–Day P < 0.05). In the claims- and EHR-based cohort study, the adjusted hazard ratio (HR) was statistically significant only for sertraline (HR 0.58; 95% CI 0.38–0.88).

Conclusion

Suicide reporting rates were disproportionately larger for generic than for brand CNS drugs in FAERS and adjusted retrospective cohort analyses remained significant only for sertraline. However, even for sertraline, temporal confounding related to the close proximity of black box warnings and generic availability is possible. Additional analyses in larger data sources with additional drugs are needed.

Notes

Compliance with Ethical Standards

Conflicts of interest

In the past 3 years, Richard A. Hansen has provided expert testimony for Daiichi Sankyo. Richard Hansen and Jingjing Qian also have received grant funding from US FDA Grant U01FD005272 and contract HHSF2232015101102C, and these grants are directly related to the general topic of this manuscript. Dr. Hansen also received funding from National Institute of Health grant 2R01GM097618-04, which is related to but did not directly fund this work. Ning Cheng, Md. Motiur Rahman, Yasser Alatawi, Jingjing Qian, Peggy L. Peissig, Richard L. Berg, and David Page have no conflicts of interest that are directly relevant to the content of this study. Views expressed in written materials or publications and by speakers do not necessarily reflect the official policies of the Department of Health and Human Services; nor does any mention of trade names, commercial practices, or organization imply endorsement by the United States Government.

Funding

This study was supported by the U.S. Food and Drug Administration through Grant U01FD005272 and contract HHSF2232015101102C. A National Institute of Health Grant 2R01GM097618-04 provided related support but did not directly fund this study.

Ethics approval

The study was approved by the Auburn University Institutional Review Board for research involving human subjects (protocol 14-465 EP1410).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • Ning Cheng
    • 1
  • Md. Motiur Rahman
    • 1
  • Yasser Alatawi
    • 1
  • Jingjing Qian
    • 1
  • Peggy L. Peissig
    • 2
  • Richard L. Berg
    • 2
  • C. David Page
    • 3
    • 4
  • Richard A. Hansen
    • 1
  1. 1.Department of Health Outcomes Research and Policy, Harrison School of PharmacyAuburn UniversityAuburnUSA
  2. 2.Biomedical Informatics Research Center, Marshfield Clinic Research InstituteMarshfieldUSA
  3. 3.Department of Biostatistics and Medical Informatics, School of Medicine and Public HealthUniversity of WisconsinMadisonUSA
  4. 4.Department of Computer ScienceUniversity of WisconsinMadisonUSA

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