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Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil

Author

Listed:
  • Vinícius Serafini Roglio
  • Eduardo Nunes Borges
  • Francisco Diego Rabelo-da-Ponte
  • Felipe Ornell
  • Juliana Nichterwitz Scherer
  • Jaqueline Bohrer Schuch
  • Ives Cavalcante Passos
  • Breno Sanvicente-Vieira
  • Rodrigo Grassi-Oliveira
  • Lisia von Diemen
  • Flavio Pechansky
  • Felix Henrique Paim Kessler

Abstract

Background: Suicide is a severe health problem, with high rates in individuals with addiction. Considering the lack of studies exploring suicide predictors in this population, we aimed to investigate factors associated with attempted suicide in inpatients diagnosed with cocaine use disorder using two analytical approaches. Methods: This is a cross-sectional study using a secondary database with 247 men and 442 women hospitalized for cocaine use disorder. Clinical assessment included the Addiction Severity Index, the Childhood Trauma Questionnaire, and the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, totalling 58 variables. Descriptive Poisson regression and predictive Random Forest algorithm were used complementarily to estimate prevalence ratios and to build prediction models, respectively. All analyses were stratified by gender. Results: The prevalence of attempted suicide was 34% for men and 50% for women. In both genders, depression (PRM = 1.56, PRW = 1.27) and hallucinations (PRM = 1.80, PRW = 1.39) were factors associated with attempted suicide. Other specific factors were found for men and women, such as childhood trauma, aggression, and drug use severity. The men's predictive model had prediction statistics of AUC = 0.68, Acc. = 0.66, Sens. = 0.82, Spec. = 0.50, PPV = 0.47 and NPV = 0.84. This model identified several variables as important predictors, mainly related to drug use severity. The women's model had higher predictive power (AUC = 0.73 and all other statistics were equal to 0.71) and was parsimonious. Conclusions: Our findings indicate that attempted suicide is associated with depression, hallucinations and childhood trauma in both genders. Also, it suggests that severity of drug use may be a moderator between predictors and suicide among men, while psychiatric issues shown to be more important for women.

Suggested Citation

  • Vinícius Serafini Roglio & Eduardo Nunes Borges & Francisco Diego Rabelo-da-Ponte & Felipe Ornell & Juliana Nichterwitz Scherer & Jaqueline Bohrer Schuch & Ives Cavalcante Passos & Breno Sanvicente-Vi, 2020. "Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.
  • Handle: RePEc:plo:pone00:0232242
    DOI: 10.1371/journal.pone.0232242
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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Rosana E Norman & Munkhtsetseg Byambaa & Rumna De & Alexander Butchart & James Scott & Theo Vos, 2012. "The Long-Term Health Consequences of Child Physical Abuse, Emotional Abuse, and Neglect: A Systematic Review and Meta-Analysis," PLOS Medicine, Public Library of Science, vol. 9(11), pages 1-31, November.
    3. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
    4. Andrea Miranda-Mendizabal & Pere Castellví & Oleguer Parés-Badell & Itxaso Alayo & José Almenara & Iciar Alonso & Maria Jesús Blasco & Annabel Cebrià & Andrea Gabilondo & Margalida Gili & Carolina Lag, 2019. "Gender differences in suicidal behavior in adolescents and young adults: systematic review and meta-analysis of longitudinal studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(2), pages 265-283, March.
    5. Laura Acion & Diana Kelmansky & Mark van der Laan & Ethan Sahker & DeShauna Jones & Stephan Arndt, 2017. "Use of a machine learning framework to predict substance use disorder treatment success," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    6. Justin Christopher Yang & Andres Roman-Urrestarazu & Carol Brayne, 2018. "Binge alcohol and substance use across birth cohorts and the global financial crisis in the United States," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-18, June.
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