{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T22:31:51Z","timestamp":1772663511977,"version":"3.50.1"},"reference-count":53,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"unspecified","delay-in-days":292,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Epidemiol Psychiatr Sci"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n\t  <jats:sec id=\"S2045796025100231_sec1a\">\n\t    <jats:title>Aims<\/jats:title>\n\t    <jats:p>The epidemiology and age-specific patterns of lifetime suicide attempts (LSA) in China remain unclear. We aimed to examine age-specific prevalence and predictors of LSA among Chinese adults using machine learning (ML).<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S2045796025100231_sec2b\">\n\t    <jats:title>Methods<\/jats:title>\n\t    <jats:p>We analyzed 25,047 adults in the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR-2024), stratified into three age groups (18\u201324, 25\u201344,\u00a0\u2265\u00a045\u00a0years). Thirty-seven candidate predictors across six domains\u2014sociodemographic, physical health, mental health, lifestyle, social environment, and self-injury\/suicide history\u2014were assessed. Five ML models\u2014random forest, logistic regression, support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Naive Bayes\u2014were compared. SHapley Additive exPlanations (SHAP) were used to quantify feature importance.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S2045796025100231_sec3c\">\n\t    <jats:title>Results<\/jats:title>\n\t    <jats:p>The overall prevalence of LSA was 4.57% (1,145\/25,047), with significant age differences: 8.10% in young adults (18\u201324), 4.67% in adults aged 25\u201344, and 2.67% in older adults (\u226545). SVM achieved the best test-set performance across all ages [area under the curve (AUC) 0.88\u20130.94, sensitivity 0.79\u20130.87, specificity 0.81\u20130.88], showing superior calibration and net clinical benefit. SHAP analysis identified both shared and age-specific predictors. Suicidal ideation, adverse childhood experiences, and suicide disclosure were consistent top predictors across all ages. Sleep disturbances and anxiety symptoms stood out in young adults; marital status, living alone, and perceived stress in mid-life; and functional limitations, poor sleep, and depressive symptoms in older adults.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S2045796025100231_sec4d\">\n\t    <jats:title>Conclusions<\/jats:title>\n\t    <jats:p>LSA prevalence in Chinese adults is relatively high, with a clear age gradient peaking in young adulthood. Risk profiles revealed both shared and age-specific predictors, reflecting distinct life-stage vulnerabilities. These findings support age-tailored suicide prevention strategies in China.<\/jats:p>\n\t  <\/jats:sec>","DOI":"10.1017\/s2045796025100231","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T11:53:56Z","timestamp":1760961236000},"source":"Crossref","is-referenced-by-count":1,"title":["Age-specific prevalence and predictors of lifetime suicide attempts using machine learning in Chinese adults: a nationwide multi-centre survey"],"prefix":"10.1017","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6595-4273","authenticated-orcid":false,"given":"Yu","family":"Wu","sequence":"first","affiliation":[{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panliang","family":"Zhong","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Wu","sequence":"additional","affiliation":[{"name":"Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Zheng","sequence":"additional","affiliation":[{"name":"Peking University, Chengze Yuan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"S2045796025100231_ref15","doi-asserted-by":"publisher","DOI":"10.1001\/jamapsychiatry.2020.1042"},{"key":"S2045796025100231_ref25","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.30143"},{"key":"S2045796025100231_ref34","first-page":"4544","article-title":"A Safe Feature Elimination Rule for L(1)-Regularized Logistic Regression","volume":"44","author":"Pan","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"S2045796025100231_ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2765321"},{"key":"S2045796025100231_ref3","doi-asserted-by":"publisher","DOI":"10.1097\/01.jom.0000175155.50789.98"},{"key":"S2045796025100231_ref48","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-025-21399-y"},{"key":"S2045796025100231_ref40","doi-asserted-by":"publisher","DOI":"10.1037\/a0018697"},{"key":"S2045796025100231_ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"S2045796025100231_ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2015.09.007"},{"key":"S2045796025100231_ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.genhosppsych.2024.01.010"},{"key":"S2045796025100231_ref43","unstructured":"WHO. 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