Some data suggest a higher incidence of diagnosis of autoimmune inflammatory rheumatic diseases (AIRDs) among patients with a history of COVID-19 compared with uninfected patients. However, these studies had methodological shortcomings. To investigate the effect of COVID-19 on long-term risk for incident AIRD over various follow-up periods. Binational, longitudinal, propensity-matched cohort study. Nationwide claims-based databases in South Korea (K-COV-N cohort) and Japan (JMDC cohort). 10 027 506 Korean and 12 218 680 Japanese patients aged 20 years or older, including those with COVID-19 between 1 January 2020 and 31 December 2021, matched to patients with influenza infection and to uninfected control patients. The primary outcome was onset of AIRD (per appropriate codes from the International Classification of Diseases, 10th Revision) 1, 6, and 12 months after COVID-19 or influenza infection or the respective matched index date of uninfected control patients. Between 2020 and 2021, among the 10 027 506 Korean participants (mean age, 48.4 years [SD, 13.4]; 50.1% men), 394 274 (3.9%) and 98 596 (0.98%) had a history of COVID-19 or influenza, respectively. After propensity score matching, beyond the first 30 days after infection, patients with COVID-19 were at increased risk for incident AIRD compared with uninfected patients (adjusted hazard ratio, 1.25 [95% CI, 1.18 to 1.31]) and influenza-infected control patients (adjusted hazard ratio, 1.30 [CI, 1.02 to 1.59]). The risk for incident AIRD was higher with more severe acute COVID-19. Similar patterns were observed in the Japanese cohort. Referral bias due to the pandemic; residual confounding. SARS-CoV-2 infection was associated with increased risk for incident AIRD compared with matched patients without SARS-CoV-2 infection or with influenza infection. The risk for incident AIRD was higher with greater severity of acute COVID-19. National Research Foundation of Korea.

Background: Suicide is the second-leading cause of death among adolescents and is associated with clusters of suicides. Despite numerous studies on this preventable cause of death, the focus has primarily been on single nations and traditional statistical methods. Objective: This study aims to develop a predictive model for adolescent suicidal thinking using multinational data sets and machine learning (ML). Methods: We used data from the Korea Youth Risk Behavior Web-based Survey with 566,875 adolescents aged between 13 and 18 years and conducted external validation using the Youth Risk Behavior Survey with 103,874 adolescents and Norway’s University National General Survey with 19,574 adolescents. Several tree-based ML models were developed, and feature importance and Shapley additive explanations values were analyzed to identify risk factors for adolescent suicidal thinking. Results: When trained on the Korea Youth Risk Behavior Web-based Survey data from South Korea with a 95% CI, the XGBoost model reported an area under the receiver operating characteristic (AUROC) curve of 90.06% (95% CI 89.97-90.16), displaying superior performance compared to other models. For external validation using the Youth Risk Behavior Survey data from the United States and the University National General Survey from Norway, the XGBoost model achieved AUROCs of 83.09% and 81.27%, respectively. Across all data sets, XGBoost consistently outperformed the other models with the highest AUROC score, and was selected as the optimal model. In terms of predictors of suicidal thinking, feelings of sadness and despair were the most influential, accounting for 57.4% of the impact, followed by stress status at 19.8%. This was followed by age (5.7%), household income (4%), academic achievement (3.4%), sex (2.1%), and others, which contributed less than 2% each. Conclusions: This study used ML by integrating diverse data sets from 3 countries to address adolescent suicide. The findings highlight the important role of emotional health indicators in predicting suicidal thinking among adolescents. Specifically, sadness and despair were identified as the most significant predictors, followed by stressful conditions and age. These findings emphasize the critical need for early diagnosis and prevention of mental health issues during adolescence.

Background: To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model to predict adolescent substance use across three national datasets. Objective: This study aims to develop a generalizable predictive model for adolescent substance use using multinational datasets and ML. Methods: The study used the Korea Youth Risk Behavior Web-Based Survey (KYRBS) from South Korea (n=1,098,641) to train ML models. For external validation, we used the Youth Risk Behavior Survey (YRBS) from the United States (n=2,511,916) and Norwegian nationwide Ungdata surveys (Ungdata) from Norway (n=700,660). After developing various ML models, we evaluated the final model’s performance using multiple metrics. We also assessed feature importance using traditional methods and further analyzed variable contributions through SHapley Additive exPlanation values. Results: The study used nationwide adolescent datasets for ML model development and validation, analyzing data from 1,098,641 KYRBS adolescents, 2,511,916 YRBS participants, and 700,660 from Ungdata. The XGBoost model was the top performer on the KYRBS, achieving an area under receiver operating characteristic curve (AUROC) score of 80.61% (95% CI 79.63-81.59) and precision of 30.42 (95% CI 28.65-32.16) with detailed analysis on sensitivity of 31.30 (95% CI 29.47-33.20), specificity of 99.16 (95% CI 99.12-99.20), accuracy of 98.36 (95% CI 98.31-98.42), balanced accuracy of 65.23 (95% CI 64.31-66.17), F1-score of 30.85 (95% CI 29.25-32.51), and area under precision-recall curve of 32.14 (95% CI 30.34-33.95). The model achieved an AUROC score of 79.30% and a precision of 68.37% on the YRBS dataset, while in external validation using the Ungdata dataset, it recorded an AUROC score of 76.39% and a precision of 12.74%. Feature importance and SHapley Additive exPlanation value analyses identified smoking status, BMI, suicidal ideation, alcohol consumption, and feelings of sadness and despair as key contributors to the risk of substance use, with smoking status emerging as the most influential factor. Conclusions: Based on multinational datasets from South Korea, the United States, and Norway, this study shows the potential of ML models, particularly the XGBoost model, in predicting adolescent substance use. These findings provide a solid basis for future research exploring additional influencing factors or developing targeted intervention strategies.