Data_Sheet_1_A machine-learning model to predict suicide risk in Japan based on national survey data.DOCX

ObjectiveSeveral prognostic models of suicide risk have been published; however, few have been implemented in Japan using longitudinal cohort data. The aim of this study was to identify suicide risk factors for suicidal ideation in the Japanese population and to develop a machine-learning model to predict suicide risk in Japan.Materials and MethodsData was obtained from Wave1 Time 1 (November 2016) and Time 2 (March 2017) of the National Survey for Stress and Health in Japan, were incorporated into a suicide risk prediction machine-learning model, trained using 65 items related to trauma and stress. The study included 3,090 and 2,163 survey respondents >18 years old at Time 1 and Time 2, respectively. The mean (standard deviation, SD) age was 44.9 (10.9) years at Time 1 and 46.0 (10.7) years at Time 2. We analyzed the participants with increased suicide risk at Time 2 survey. Model performance, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were also analyzed.ResultsThe model showed a good performance (AUC = 0.830, 95% confidence interval = 0.795–0.866). Overall, the model achieved an accuracy of 78.8%, sensitivity of 75.4%, specificity of 80.4%, positive predictive value of 63.4%, and negative predictive value of 87.9%. The most important risk factor for suicide risk was the participants' Suicidal Ideation Attributes Scale score, followed by the Sheehan Disability Scale score, Patient Health Questionnaire-9 scores, Cross-Cutting Symptom Measure (CCSM-suicidal ideation domain, Dissociation Experience Scale score, history of self-harm, Generalized Anxiety Disorder-7 score, Post-Traumatic Stress Disorder check list-5 score, CCSM-dissociation domain, and Impact of Event Scale-Revised scores at Time 1.ConclusionsThis prognostic study suggests the ability to identify patients at a high risk of suicide using an online survey method. In addition to confirming several well-known risk factors of suicide, new risk measures related to trauma and trauma-related experiences were also identified, which may help guide future clinical assessments and early intervention approaches.</p

Factors associated with disconcordance between self-reports and claims records.

<p>Factors associated with disconcordance between self-reports and claims records.</p

Characteristics of study subjects with and without consent to data linkage.^†

<p>Note:</p>†<p>All <i>p</i>-values for the chi-square tests between patients with or without consent were <0.001.</p>‡<p>A total of 16 participants had missing data for educational level and 43 participants had missing data for marital status.</p><p>Characteristics of study subjects with and without consent to data linkage.^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112257#nt102" target="_blank">†</a>}</p

Concordance between Patient Self-Reports and Claims Data on Clinical Diagnoses, Medication Use, and Health System Utilization in Taiwan

<div><p>Purpose</p><p>The aim of this study was to evaluate the concordance between claims records in the National Health Insurance Research Database and patient self-reports on clinical diagnoses, medication use, and health system utilization.</p><p>Methods</p><p>In this study, we used the data of 15,574 participants collected from the 2005 Taiwan National Health Interview Survey. We assessed positive agreement, negative agreement, and Cohen's kappa statistics to examine the concordance between claims records and patient self-reports.</p><p>Results</p><p>Kappa values were 0.43, 0.64, and 0.61 for clinical diagnoses, medication use, and health system utilization, respectively. Using a strict algorithm to identify the clinical diagnoses recorded in claims records could improve the negative agreement; however, the effect on positive agreement and kappa was diverse across various conditions.</p><p>Conclusion</p><p>We found that the overall concordance between claims records in the National Health Insurance Research Database and patient self-reports in the Taiwan National Health Interview Survey was moderate for clinical diagnosis and substantial for both medication use and health system utilization.</p></div

Concordance between self-reports and claims records, by disease diagnoses, medication use, and health system utilization.

<p>Concordance between self-reports and claims records, by disease diagnoses, medication use, and health system utilization.</p

Comparison of the concordance between claims records and self-reports using different algorithms.

<p>Comparison of the concordance between claims records and self-reports using different algorithms.</p

Data_Sheet_1_Examining the Benefits of Greenness on Reducing Suicide Mortality Rate: A Global Ecological Study.pdf

ObjectiveThis study applied an ecological-based analysis aimed to evaluate on a global scale the association between greenness exposure and suicide mortality.MethodsSuicide mortality data provided by the Institute for Health Metrics and Evaluation and the Normalized Difference Vegetation Index (NDVI) were employed. The generalized additive mixed model was applied to evaluate with an adjustment of covariates the association between greenness and suicide mortality. Sensitivity tests and positive-negative controls also were used to examine less overt insights. Subgroup analyses were then conducted to investigate the effects of greenness on suicide mortality among various conditions.ResultsThe main finding of this study indicates a negative association between greenness exposure and suicide mortality, as greenness significantly decreases the risk of suicide mortality per interquartile unit increment of NDVI (relative risk = 0.69, 95%CI: 0.59–0.81). Further, sensitivity analyses confirmed the robustness of the findings. Subgroup analyses also showed a significant negative association between greenness and suicide mortality for various stratified factors, such as sex, various income levels, urbanization levels, etc.ConclusionsGreenness exposure may contribute to a reduction in suicide mortality. It is recommended that policymakers and communities increase environmental greenness in order to mitigate the global health burden of suicide.</p

sj-docx-1-aut-10.1177_13623613221125620 – Supplemental material for Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach

Supplemental material, sj-docx-1-aut-10.1177_13623613221125620 for Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach by Yi-Ling Chien, Yu-Jen Chen, Wan-Ling Tseng, Yung-Chin Hsu, Chi-Shin Wu, Wen-Yih Isaac Tseng and Susan Shur-Fen Gau in Autism</p

Table_1_Exploring the Potential Relationship Between Global Greenness and DALY Loss Due to Depressive Disorders.DOCX

ObjectivePrior studies have shown that greenness can reduce the burden of depressive disorders. However, most were focused on local-scale analyses while limited evaluated globally. We aimed to investigate the association between greenness and the burden of depressive disorders using data from 183 countries worldwide.MethodsWe used the normalized difference vegetation index (NDVI) to estimate greenness. Country-level disability-adjusted life year (DALY) loss due to depressive disorders was used to represent depressive disorder burdens. A generalized linear mixed model was applied to assess the relationship between greenness and depressive disorders after controlling for covariates. Stratified analyses were conducted to determine the effects of greenness across several socio-demographic levels.ResultsThe findings showed a significant negative association between greenness and the health burden of depressive disorders with a coefficient of −0.196 (95% CI: −0.356, −0.035) in the DALY changes per interquartile unit increment of NDVI. The stratified analyses suggested beneficial effects of greenness on depressive disorders across sex, various age groups especially for those aged ConclusionsOur study noted that greenness exposure was significant negative association with the burden of depressive disorders. The findings should be viewed as recommendations for relevant authorities in supporting environmental greenness enhancement to reduce the mental burdens.</p

Demographics and clinical characteristics participants.

<p>Abbreviations: Amph: amphetamine; ASPD: antisocial personality disorder.</p>#<p><i>P</i><.05 between MMT and OD-combined groups.</p>‡<p><i>P</i><.05 between OD-long and OD-short subgroups.</p><p>*binge drinking (≥5 drinks over one occasion in the prior month; none met criteria of alcohol abuse or dependence).</p>∧<p>self-reported “problems” with law authority including previous jail time for non-drug related violations.</p