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Supplemental material, sj-docx-1-tab-10.1177_1759720X231152653 for Systemic lupus erythematosus is associated with increased risk of Parkinson’s disease by In Young Kim, Yeonghee Eun, Jaejoon Lee, Kyungdo Han, Da Hye Kim, Ju-Hong Min, Hoon-Suk Cha, Eun-Mi Koh, Dong Wook Shin and Hyungjin Kim in Therapeutic Advances in Musculoskeletal Disease

Korea's population is aging at a faster pace than any other major country, and the adverse impact of this trend on the economy is predicted to be significant. This paper focuses on the macroeconomic effects of population aging with particular attention paid to the pace of aging in Korea. According to our analysis, it is difficult to offset the decline in the labor supply driven by rapid population aging, even if the labor force participation rate of the working-age population rises to a significantly high level. We suggest a re-orientation of policy directions to correspond to the behavioral changes of economic agents. Policies must focus on promoting labor force participation among the elderly while pushing towards human capital advancement and higher productivity.

Supplemental material, sj-docx-1-tab-10.1177_1759720X221116409 for Cardiovascular risk associated with allopurinol or benzbromarone treatment in patients with gout by Yeonghee Eun, Heewon Han, Kyunga Kim, Seonyoung Kang, Seulkee Lee, Hyungjin Kim, Jaejoon Lee, Eun-Mi Koh and Hoon-Suk Cha in Therapeutic Advances in Musculoskeletal Disease

Additional file 1: Supplementary Methods. Table S1: Clinical variables excluded in adjusting multiple comparisons. Table S2: Number of users of each TNFi. Table S3: Reasons for discontinuation of tumour necrosis factor alpha inhibitors and duration until discontinuation in two groups that were excluded from the drug survival analysis. Table S4: Comparison of the divided clusters with radiographic classification (radiographic axial SpA and non-radiographic axial SpA). Table S5: Comparison of the divided clusters with the division of non-radiographic axial SpA into radiographic and clinical arms according to the Assessment of SpondyloArthritis International Society criteria for axial SpA. Table S6: Comparison of the divided clusters with grouping according to HLA-B27 positivity.

Additional file 1: Text S1. Detailed methods for dividing test dataset and feature importance analysis, and additional analysis with features reported from the feature importance analysis.

Additional file 1: Supplementary Table S1. Hazard ratios and 95% confidence intervals for incident gout according to age at menarche. Supplementary Table S2. Hazard ratios and 95% confidence intervals for incident gout according to parity and oral contraceptives use. Supplementary Table S3. Hazard ratios and 95% confidence intervals for incident gout according to parity, breastfeeding and oral contraceptives use who had parity (n = 1,047,956). Supplementary Table S4. Hazard ratios and 95% confidence intervals for incident gout according to age at menopause. Supplementary Table S5. Hazard ratios and 95% confidence intervals for incident gout according to hormone replacement therapy.

Additional file 3: Table S1. List of input features. Table S2. Chosen hyperparameter sets for each model.

Additional file 2: Figure S1-3. Prediction performance of machine learning methods by varying hyperparameters in RA patients. Figure S4-6. Prediction performance of machine learning methods by varying hyperparameters in AS patients. Figure S7. Performance of RF-method and logistic regression models in RA patients divided by the type of bDMARDs. Figure S8. Result of the feature importance analysis of the best-performing RF-method model in RA patients divided by the type of bDMARDs. Figure S9. Performance of RF-method and logistic regression models in AS patients divided by the type of bDMARDs. Figure S10. Feature importance analysis of the best-performing RF-method model in AS patients divided by the type of bDMARDs. Figure S11. Correlation analysis between input features that reported the feature importance analysis. Figure S12. Linear regression analysis with dot plot results for input features. Figure S13. Prediction performances of logistic regression models using patient self-reported scales and their combinations.

Additional file 2: Figure S1: Results of multiple correspondence analysis (MCA). The first factorial plane with the x-axis and y-axis represents the first and second most important dimensions, respectively. Visualisation of the coordinates of each variable category. Each symbol represents the presence or absence of the variables. Figure S2: Results of sensitivity analyses. Drug survival probabilities of tumour necrosis factor alpha inhibitors (TNFi) in each group: (A) including patients who stopped TNFi for reasons other than inefficacy or adverse event; (B) including patients who stopped TNFi owing to remission. Figure S3: Drug survival probability of tumour necrosis factor alpha inhibitors (TNFi) according to (A) radiographic classification and (B) human leukocyte antigen (HLA)-B27 positivity. Figure S4: Results of factor analysis of mixed data (FAMD) at the individual level divided by cluster analysis. In this cluster analysis, age at disease onset and duration of disease symptoms were used as continuous values. Figure S5: Graph of average silhouette width according to the number of clusters.