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Predicting posttraumatic stress disorder following a natural disaster.

ABSTRACT: Earthquakes are a common and deadly natural disaster, with roughly one-quarter of survivors subsequently developing posttraumatic stress disorder (PTSD). Despite progress identifying risk factors, limited research has examined how to combine variables into an optimized post-earthquake PTSD prediction tool that could be used to triage survivors to mental health services. The current study developed a post-earthquake PTSD risk score using machine learning methods designed to optimize prediction. The data were from a two-wave survey of Chileans exposed to the 8.8 magnitude earthquake that occurred in February 2010. Respondents (n = 23,907) were interviewed roughly three months prior to and again three months after the earthquake. Probable post-earthquake PTSD was assessed using the Davidson Trauma Scale. We applied super learning, an ensembling machine learning method, to develop the PTSD risk score from 67 risk factors that could be assessed within one week of earthquake occurrence. The super learner algorithm had better cross-validated performance than the 39 individual algorithms from which it was developed, including conventional logistic regression. The super learner also had a better area under the receiver operating characteristic curve (0.79) than existing post-disaster PTSD risk tools. Individuals in the top 5%, 10%, and 20% of the predicted risk distribution accounted for 17.5%, 32.2%, and 51.4% of all probable cases of PTSD, respectively. In addition to developing a risk score that could be implemented in the near future, these results more broadly support the utility of super learning to develop optimized prediction functions for mental health outcomes.

SUBMITTER: Rosellini AJ

PROVIDER: S-EPMC5726547 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Predicting posttraumatic stress disorder following a natural disaster.

Rosellini Anthony J AJ Dussaillant Francisca F Zubizarreta José R JR Kessler Ronald C RC Rose Sherri S

Journal of psychiatric research 20170908

Earthquakes are a common and deadly natural disaster, with roughly one-quarter of survivors subsequently developing posttraumatic stress disorder (PTSD). Despite progress identifying risk factors, limited research has examined how to combine variables into an optimized post-earthquake PTSD prediction tool that could be used to triage survivors to mental health services. The current study developed a post-earthquake PTSD risk score using machine learning methods designed to optimize prediction. T ...[more]

PMID: 28950110

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Project description:ImportancePsychiatric interventions offered after natural disasters commonly address subsyndromal symptom presentations, but often remain insufficient to reduce the burden of chronic posttraumatic stress disorder (PTSD).ObjectiveTo simulate a comparison of a stepped care case-finding intervention (stepped care [SC]) vs a moderate-strength single-level intervention (usual care [UC]) on treatment effectiveness and incremental cost-effectiveness in the 2 years after a natural disaster.Design, setting, and participantsThis study, which simulated treatment scenarios that start 4 weeks after landfall of Hurricane Sandy on October 29, 2012, and ending 2 years later, created a model of 2 642 713 simulated agents living in the areas of New York City affected by Hurricane Sandy.InterventionsUnder SC, cases were referred to cognitive behavioral therapy, an evidence-based therapy that aims to improve symptoms through problem solving and by changing thoughts and behaviors; noncases were referred to Skills for Psychological Recovery, an evidence-informed therapy that aims to reduce distress and improve coping and functioning. Under UC, all patients were referred only to Skills for Psychological Recovery.Main outcomes and measuresThe reach of SC compared with UC for 2 years, the 2-year reduction in prevalence of PTSD among the full population, the 2-year reduction in the proportion of PTSD cases among initial cases, and 10-year incremental cost-effectiveness.ResultsThis population of 2 642 713 simulated agents was initialized with a PTSD prevalence of 4.38% (115 751 cases) and distributions of sex (52.6% female and 47.4% male) and age (33.9% aged 18-34 years, 49.0% aged 35-64 years, and 17.1% aged ≥65 years) that were comparable with population estimates in the areas of New York City affected by Hurricane Sandy. Stepped care was associated with greater reach and was superior to UC in reducing the prevalence of PTSD in the full population: absolute benefit was clear at 6 months (risk difference [RD], -0.004; 95% CI, -0.004 to -0.004), improving through 1.25 years (RD, -0.015; 95% CI, -0.015 to -0.014). Relative benefits of SC were clear at 6 months (risk ratio, 0.905; 95% CI, 0.898-0.913), with continued gains through 1.75 years (risk ratio, 0.615; 95% CI, 0.609-0.662). The absolute benefit of SC among cases was much stronger, emerging at 3 months (RD, -0.006; 95% CI, -0.007 to -0.005) and increasing through 1.5 years (RD, -0.338; 95% CI, -0.342 to -0.335). Relative benefits of SC among cases were equivalent to those observed in the full population. The incremental cost-effectiveness of SC compared with UC was $3428.71 to $6857.68 per disability-adjusted life year avoided, and $0.80 to $1.61 per PTSD-free day.Conclusions and relevanceThe results of this simulation study suggest that SC for individuals with PTSD in the aftermath of a natural disaster is associated with greater reach than UC, more effectiveness than UC, and is well within the range of acceptability for cost-effectiveness. Results should be considered in light of limitations inherent to agent-based models.

Dataset Information

Predicting posttraumatic stress disorder following a natural disaster.

Publications

Predicting posttraumatic stress disorder following a natural disaster.

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