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Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time

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  • Matthew Large
  • Muthusamy Kaneson
  • Nicholas Myles
  • Hannah Myles
  • Pramudie Gunaratne
  • Christopher Ryan

Abstract

Objective: It is widely assumed that the clinical care of psychiatric patients can be guided by estimates of suicide risk and by using patient characteristics to define a group of high-risk patients. However, the statistical strength and reliability of suicide risk categorization is unknown. Our objective was to investigate the odds of suicide in high-risk compared to lower-risk categories and the suicide rates in high-risk and lower-risk groups. Method: We located longitudinal cohort studies where psychiatric patients or people who had made suicide attempts were stratified into high-risk and lower-risk groups for suicide with suicide mortality as the outcome by searching for peer reviewed publications indexed in PubMed or PsychINFO. Electronic searches were supplemented by hand searching of included studies and relevant review articles. Two authors independently extracted data regarding effect size, study population and study design from 53 samples of risk-assessed patients reported in 37 studies. Results: The pooled odds of suicide among high-risk patients compared to lower-risk patients calculated by random effects meta-analysis was of 4.84 (95% Confidence Interval (CI) 3.79–6.20). Between-study heterogeneity was very high (I2 = 93.3). There was no evidence that more recent studies had greater statistical strength than older studies. Over an average follow up period of 63 months the proportion of suicides among the high-risk patients was 5.5% and was 0.9% among lower-risk patients. The meta-analytically derived sensitivity and specificity of a high-risk categorization were 56% and 79% respectively. There was evidence of publication bias in favour of studies that inflated the pooled odds of suicide in high-risk patients. Conclusions: The strength of suicide risk categorizations based on the presence of multiple risk factors does not greatly exceed the association between individual suicide risk factors and suicide. A statistically strong and reliable method to usefully distinguish patients with a high-risk of suicide remains elusive.

Suggested Citation

  • Matthew Large & Muthusamy Kaneson & Nicholas Myles & Hannah Myles & Pramudie Gunaratne & Christopher Ryan, 2016. "Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
  • Handle: RePEc:plo:pone00:0156322
    DOI: 10.1371/journal.pone.0156322
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    Cited by:

    1. Kathryn Turner & Anthony R. Pisani & Jerneja Sveticic & Nick O’Connor & Sabine Woerwag-Mehta & Kylie Burke & Nicolas J. C. Stapelberg, 2022. "The Paradox of Suicide Prevention," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    2. Tara Hunt & Coralie Wilson & Peter Caputi & Ian Wilson & Alan Woodward, 2018. "Patterns of Signs That Telephone Crisis Support Workers Associate with Suicide Risk in Telephone Crisis Line Callers," IJERPH, MDPI, vol. 15(2), pages 1-13, January.
    3. Konrad Michel, 2021. "Suicide Models and Treatment Models Are Separate Entities. What Does It Mean for Clinical Suicide Prevention?," IJERPH, MDPI, vol. 18(10), pages 1-9, May.
    4. Karl Andriessen & Lennart Reifels & Karolina Krysinska & Jo Robinson & Georgia Dempster & Jane Pirkis, 2019. "Dealing with Ethical Concerns in Suicide Research: A Survey of Australian Researchers," IJERPH, MDPI, vol. 16(7), pages 1-12, March.
    5. Frank Iorfino & Nicholas Ho & Joanne S Carpenter & Shane P Cross & Tracey A Davenport & Daniel F Hermens & Hannah Yee & Alissa Nichles & Natalia Zmicerevska & Adam Guastella & Elizabeth Scott & Ian B , 2020. "Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-16, December.
    6. Seo-Eun Cho & Zong Woo Geem & Kyoung-Sae Na, 2021. "Development of a Suicide Prediction Model for the Elderly Using Health Screening Data," IJERPH, MDPI, vol. 18(19), pages 1-10, September.
    7. India Bellairs-Walsh & Sadhbh J. Byrne & Sarah Bendall & Yael Perry & Karolina Krysinska & Ashleigh Lin & Maria Michail & Michelle Lamblin & Tina Yutong Li & Sarah Hetrick & Jo Robinson, 2021. "Working with Young People at Risk of Suicidal Behaviour and Self-Harm: A Qualitative Study of Australian General Practitioners’ Perspectives," IJERPH, MDPI, vol. 18(24), pages 1-19, December.

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