Dataset Information

Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach.

ABSTRACT:

Aim

Understanding the differential mental health effects of traumatic experiences is important to identify particularly vulnerable subpopulations. We examined the heterogeneous associations between disaster-related traumatic experiences and postdisaster mental health, using a novel machine learning-based causal inference approach.

Methods

Data were from a prospective cohort study of Japanese older adults in an area severely affected by the 2011 Great East Japan Earthquake. The baseline survey was conducted 7 months before the disaster and the 2 follow-up surveys were conducted 2.5 and 5.5 years after (n = 1150 to n = 1644 depending on the exposure-outcome combinations). As disaster-related traumatic experiences, we assessed complete home loss and loss of loved ones. Using the generalized random forest algorithm, we estimated conditional average treatment effects (CATEs) of the disaster damages on postdisaster mental health outcomes to examine the heterogeneous associations by 51 predisaster characteristics of the individuals.

Results

We found that, even when there was no population average association between disaster-related trauma and subsequent mental health outcomes, some subgroups experienced severe impacts. We also identified and compared characteristics of the most and least vulnerable groups (ie, top versus bottom deciles of the estimated CATEs). While there were some unique patterns specific to each exposure-outcome combination, the most vulnerable group tended to be from lower socioeconomic backgrounds with preexisting depressive symptoms for many exposure-outcome combinations.

Conclusions

We found considerable heterogeneity in the association between disaster-related traumatic experiences and subsequent mental health problems.

SUBMITTER: Shiba K

PROVIDER: S-EPMC9102396 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach.

Shiba Koichiro K Daoud Adel A Kino Shiho S Nishi Daisuke D Kondo Katsunori K Kawachi Ichiro I

Psychiatry and clinical neurosciences 20220121 4

<h4>Aim</h4>Understanding the differential mental health effects of traumatic experiences is important to identify particularly vulnerable subpopulations. We examined the heterogeneous associations between disaster-related traumatic experiences and postdisaster mental health, using a novel machine learning-based causal inference approach.<h4>Methods</h4>Data were from a prospective cohort study of Japanese older adults in an area severely affected by the 2011 Great East Japan Earthquake. The bas ...[more]

PMID: 34936171

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Project description:BackgroundTraumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge.Methods and findingsThe Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2).ConclusionsTDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients.Trial registrationClinicalTrials.gov Identifier NCT01565551.

Project description:BackgroundPrevious research has shown that exposure to bullying is linked to long-term adverse mental health consequences. However, prospective studies examining the persistence of bullying, using information from repeated time points, are limited. The aim of this study was to examine, firstly, the extent to which exposure to bullying among adolescents in Sweden changes between grades 9 (age 15-16) and 11 (age 17-18) (i.e., before and after the transition from lower to upper secondary school); secondly, whether being bullied in grade 9 or 11 is associated with depression and anxiety symptoms at age 20-21; and thirdly, if being bullied in both grade 9 and 11 is linked to an even higher likelihood of subsequent depression and anxiety symptoms. Potential differences by gender were investigated throughout.MethodsData was derived from the Swedish cohort study Futura01 involving individuals attending grade 9 in the school year 2016/17 (n = 2323). We utilised self-reported information from three survey waves conducted in 2017, 2019, and 2022, and linked registry information on sociodemographic characteristics. Bullying was assessed using a single item in waves 1 and 2. Depression and anxiety symptoms were measured using the Patient Health Questionnaire-4 (PHQ-4) in wave 3. Gender stratified binary logistic regressions were performed.ResultsAmong those who were bullied in grade 9, 22.6% of males and 35.8% of females continued to experience bullying in grade 11. For females, exposure to bullying in grade 9 or 11 was associated with an increased likelihood of reporting depression and anxiety symptoms at age 20-21, with the highest odds for those bullied at both time points. For males, only one statistically significant association was identified - specifically, between being bullied in grade 9 and subsequent depression symptoms.ConclusionsFor a majority of adolescents who experience bullying in lower secondary school, but not all, the transition to upper secondary school proves to be beneficial as the bullying typically does not persist. However, bullying can have long-term health effects, in particular for females. These findings emphasise the importance of effective measures to address bullying within schools.

Dataset Information

Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach.

Aim

Methods

Results

Conclusions

Publications

Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach.

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