Browsing UMB Open Access Articles by Subject "suicide"
Now showing items 1-4 of 4
Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiencyObjectives: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case-control study of vitamin D deficiency predicting subsequent suicide. Methods: We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm. Results: The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic. Conclusions: In addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor. © 2021 The Authors.
Preventing Suicide Among Working-Age Adults: The Correlates of Help-Seeking BehaviorWe aimed to identify the correlates with not seeking help among working-age adults with suicidal ideation. By adapting the integrated model of suicide help-seeking, we examined help-seeking behavior in the following 3 stages: problem recognition, decision to seek help, and sources of help. We used a sample of working-age adults between 26 and 64 years old, who reported suicidal ideation in the past year (N = 1414). Data were drawn from the 2011 and 2012 National Survey on Drug Use and Health, and multinomial logistic regression analyses were applied. Findings suggested that being male, being nonwhite, being employed full-time, having lower levels of general mental health needs, and not having health insurance were associated with not seeking help. Results also indicated how each factor was related in the help-seeking pathway. Strategies to help problem recognition can be effective in enhancing help-seeking behavior among men, racial/ethnic minorities, and those without serious clinical conditions. Help-seeking interventions for working-age adults with suicidal ideation should also consider that race/ethnic minorities and those with lower levels of functional impairment might rely on alternative sources of help, such as family, friends, and religious advisors. Copyright The Author(s) 2019.
Toxoplasma gondii, Suicidal Behavior, and Intermediate Phenotypes for Suicidal BehaviorWithin the general literature on infections and suicidal behavior, studies on Toxoplasma gondii (T. gondii) occupy a central position. This is related to the parasite's neurotropism, high prevalence of chronic infection, as well as specific and non-specific behavioral alterations in rodents that lead to increased risk taking, which are recapitulated in humans by T. gondii's associations with suicidal behavior, as well as trait impulsivity and aggression, mental illness and traffic accidents. This paper is a detailed review of the associations between T. gondii serology and suicidal behavior, a field of study that started 15 years ago with our publication of associations between T. gondii IgG serology and suicidal behavior in persons with mood disorders. This "legacy" article presents, chronologically, our primary studies in individuals with mood disorders and schizophrenia in Germany, recent attempters in Sweden, and in a large cohort of mothers in Denmark. Then, it reviews findings from all three meta-analyses published to date, confirming our reported associations and overall consistent in effect size [ranging between 39 and 57% elevation of odds of suicide attempt in T. gondii immunoglobulin (IgG) positives]. Finally, the article introduces certain links between T. gondii and biomarkers previously associated with suicidal behavior (kynurenines, phenylalanine/tyrosine), intermediate phenotypes of suicidal behavior (impulsivity, aggression) and state-dependent suicide risk factors (hopelessness/dysphoria, sleep impairment). In sum, an abundance of evidence supports a positive link between suicide attempts (but not suicidal ideation) and T. gondii IgG (but not IgM) seropositivity and serointensity. Trait impulsivity and aggression, endophenotypes of suicidal behavior have also been positively associated with T. gondii seropositivity in both the psychiatrically healthy as well as in patients with Intermittent Explosive Disorder. Yet, causality has not been demonstrated. Thus, randomized interventional studies are necessary to advance causal inferences and, if causality is confirmed, to provide hope that an etiological treatment for a distinct subgroup of individuals at an increased risk for suicide could emerge.
Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration SystemThere is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model. Copyright 2020 Kessler, et. al.