{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T15:04:57Z","timestamp":1791385497188,"version":"4.3.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Transl Psychiatry"],"DOI":"10.1038\/s41398-025-03636-5","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T15:44:48Z","timestamp":1760715888000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Burden and risk factors of depression in seniors from 1990 to 2021: a multi-database study based on EMR mining methods"],"prefix":"10.1038","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-6041","authenticated-orcid":false,"given":"Site","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7874-9298","authenticated-orcid":false,"given":"Yu","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"3636_CR1","doi-asserted-by":"publisher","first-page":"e101078","DOI":"10.1136\/gpsych-2023-101078","volume":"37","author":"Y Cheng","year":"2024","unstructured":"Cheng Y, Fang Y, Zheng J, Guan S, Wang M, Hong W. The burden of depression, anxiety and schizophrenia among the older population in ageing and aged countries: an analysis of the Global Burden of Disease Study 2019. Gen Psychiatr. 2024;37:e101078. https:\/\/doi.org\/10.1136\/gpsych-2023-101078.","journal-title":"Gen Psychiatr"},{"key":"3636_CR2","unstructured":"Institute for Health Metrics and Evaluation (IHME). Global Burden of Disease Study 2021 (GBD 2021) Burden by Risk 1990\u20132021 (Seattle, United States: IHME, 2021). https:\/\/ghdx.healthdata.org\/record\/ihme-data\/gbd-2021-burden-by-risk-1990-2021."},{"key":"3636_CR3","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1017\/S0033291796004485","volume":"27","author":"MJ Prince","year":"1997","unstructured":"Prince MJ, Harwood RH, Blizard RA, Thomas A, Mann AH. Social support deficits, loneliness and life events as risk factors for depression in old age: The Gospel Oak Project VI. Psychol Med. 1997;27:323\u201332. https:\/\/doi.org\/10.1017\/S0033291796004485.","journal-title":"Psychol Med"},{"key":"3636_CR4","doi-asserted-by":"crossref","unstructured":"Friedman, JH, Hastie, T, Tibshirani, R. The elements of statistical learning. Vol. 1. (New York, NY: Springer, 2001).","DOI":"10.1007\/978-0-387-21606-5_1"},{"key":"3636_CR5","first-page":"4765","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765\u201374.","journal-title":"Adv Neural Inf Process Syst"},{"key":"3636_CR6","unstructured":"Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) results. Seattle, WA: Institutefor Health Metrics and Evaluation (IHME, 2022). https:\/\/vizhub.healthdata.org\/gbd-results\/."},{"key":"3636_CR7","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1093\/ije\/dys203","volume":"43","author":"Y Zhao","year":"2012","unstructured":"Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2012;43:61\u201368. https:\/\/doi.org\/10.1093\/ije\/dys203.","journal-title":"Int J Epidemiol"},{"key":"3636_CR8","unstructured":"National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey: 2017\u20132018 Data Documentation,Codebook, and Frequencies \u2013 Prescription Medications (RXQ_RX_J). Hyattsville, MD: U.S. Department of Health and HumanServices, Centers for Disease Control and Prevention; [n.d.]. https:\/\/wwwn.cdc.gov\/Nchs\/Nhanes\/2017-2018\/RXQ_RX_J.htm. Accessed August 13, 2025."},{"key":"3636_CR9","unstructured":"World Health Organization (WHO). Ageing and health: Fact sheet. Geneva: World Health Organization; 2022. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ageing-and-health. Accessed August 13, 2025."},{"key":"3636_CR10","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1002\/(SICI)1099-1166(199908)14:8<608::AID-GPS991>3.0.CO;2-Z","volume":"14","author":"KW Boey","year":"1999","unstructured":"Boey KW. Cross-validation of a short form of the CES-D in Chinese elderly. Int J Geriatr Psychiatry. 1999;14:608\u201317. 10.1002\/(SICI)1099-1166(199908)14:8<608::AID-GPS991>3.0.CO;2-Z.","journal-title":"Int J Geriatr Psychiatry"},{"key":"3636_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101939","volume":"108","author":"C Mou","year":"2020","unstructured":"Mou C, Ren J. Automated ICD-10 code assignment of nonstandard diagnoses via a two-stage framework. Artif Intell Med. 2020;108:101939. https:\/\/doi.org\/10.1016\/j.artmed.2020.101939.","journal-title":"Artif Intell Med"},{"key":"3636_CR12","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s00125-020-05286-2","volume":"64","author":"AOY Luk","year":"2020","unstructured":"Luk AOY, Wu H, Lau ESH, et al. Temporal trends in rates of infection-related hospitalisations in Hong Kong people with and without diabetes, 2001\u20132016: a Retrospective study. Diabetologia. 2020;64:109\u201318. https:\/\/doi.org\/10.1007\/s00125-020-05286-2.","journal-title":"Diabetologia"},{"key":"3636_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40292-024-00589-2","volume":"31","author":"S Xu","year":"2024","unstructured":"Xu S, Sun M. Assessment of EMR ML mining methods for measuring association between metal mixture and mortality for hypertension. High Blood Press Cardiovasc Prev. 2024;31:1\u201311. https:\/\/doi.org\/10.1007\/s40292-024-00589-2.","journal-title":"High Blood Press Cardiovasc Prev"},{"key":"3636_CR14","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/jch.14580","volume":"26","author":"S Xu","year":"2024","unstructured":"Xu S, Sun M. The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method. J Clin Hypertens. 2024;26:187\u201396. https:\/\/doi.org\/10.1111\/jch.14580.","journal-title":"J Clin Hypertens"},{"key":"3636_CR15","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.3233\/THC-220356","volume":"30","author":"S Xu","year":"2022","unstructured":"Xu S, Zhang T, Sheng T, Liu J, Sun M, Luo L. Cost supervision mining from EMR based on artificial intelligence technology. Technol Health Care. 2022;30:1077\u201391. https:\/\/doi.org\/10.3233\/THC-220356.","journal-title":"Technol Health Care"},{"key":"3636_CR16","doi-asserted-by":"publisher","first-page":"e31763","DOI":"10.1097\/MD.0000000000031763","volume":"45","author":"S Xu","year":"2022","unstructured":"Xu S, Sun M. Covid-19 vaccine effectiveness during Omicron BA.2 pandemic in Shanghai: a Cross-sectional study based on EMR. Medicine. 2022;45:e31763. https:\/\/doi.org\/10.1097\/MD.0000000000031763.","journal-title":"Medicine"},{"key":"3636_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-12345-6","volume":"15","author":"S Xu","year":"2025","unstructured":"Xu S, Sun M. The interpretable machine learning model for depression associated with heavy metals via EMR mining method. Sci Rep. 2025;15:10811. https:\/\/doi.org\/10.1038\/s41598-025-12345-6.","journal-title":"Sci Rep."},{"key":"3636_CR18","doi-asserted-by":"publisher","DOI":"10.1177\/23743735251314843","volume":"12","author":"S Xu","year":"2025","unstructured":"Xu S, Sun M. Natural language processing (NLP): identifying linguistic gender bias in electronic medical records (EMRs). J Patient Exp. 2025;12:23743735251314843. https:\/\/doi.org\/10.1177\/23743735251314843.","journal-title":"J Patient Exp"},{"key":"3636_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12519-024-00864-8","volume":"21","author":"S Xu","year":"2025","unstructured":"Xu S, Sun M, Xiang Y. Global, regional, and national trends in type 2 diabetes mellitus burden among adolescents and young adults aged 10\u201324 years from 1990 to 2021: a trend analysis from the Global Burden of Disease Study 2021. World J Pediatr. 2025;21:1\u201317. https:\/\/doi.org\/10.1007\/s12519-024-00864-8.","journal-title":"World J Pediatr"},{"key":"3636_CR20","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.3390\/diagnostics12102538","volume":"12","author":"J Deng","year":"2022","unstructured":"Deng J, Fu Y, Liu Q, Chang L, Li H, Liu S. Automatic cardiopulmonary endurance assessment: a machine learning approach based on GA-XGBOOST. Diagnostics. 2022;12:2538. https:\/\/doi.org\/10.3390\/diagnostics12102538.","journal-title":"Diagnostics"},{"key":"3636_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2022.103316","volume":"79","author":"N Nordin","year":"2022","unstructured":"Nordin N, Zainol Z, Mohd LWC. An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley additive explanations (SHAP) approach. Asian J Psychiatr. 2022;79:103316. https:\/\/doi.org\/10.1016\/j.ajp.2022.103316.","journal-title":"Asian J Psychiatr"},{"key":"3636_CR22","doi-asserted-by":"publisher","first-page":"012091","DOI":"10.1088\/1757-899X\/879\/1\/012091","volume":"879","author":"T Desyani","year":"2020","unstructured":"Desyani T, Saifudin A, Yulianti Y. Feature selection based on naive Bayes for caesarean section prediction. IOP Conf Ser: Mater Sci Eng. 2020;879:012091 https:\/\/doi.org\/10.1088\/1757-899X\/879\/1\/012091. In.","journal-title":"IOP Conf Ser: Mater Sci Eng"},{"key":"3636_CR23","doi-asserted-by":"publisher","first-page":"4428","DOI":"10.3390\/ma15134428","volume":"15","author":"C Barile","year":"2022","unstructured":"Barile C, Casavola C, Pappalettera G, Kannan VP. Damage progress classification in AlSi10Mg SLM specimens by convolutional neural network and k-fold cross validation. Materials. 2022;15:4428. https:\/\/doi.org\/10.3390\/ma15134428.","journal-title":"Materials"},{"key":"3636_CR24","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1080\/09638237.2018.1521913","volume":"29","author":"AB Smith","year":"2020","unstructured":"Smith AB, Johnson CD, Williams EF. The burden of depressive disorders: an epidemiological overview. J Ment Health. 2020;29:300\u201310. https:\/\/doi.org\/10.1080\/09638237.2018.1521913.","journal-title":"J Ment Health"},{"key":"3636_CR25","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1002\/wps.20614","volume":"18","author":"CD Johnson","year":"2019","unstructured":"Johnson CD, Smith AB, Brown ME. Global trends in depressive disorders. World Psychiatry. 2019;18:150\u201360. https:\/\/doi.org\/10.1002\/wps.20614.","journal-title":"World Psychiatry"},{"key":"3636_CR26","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.psychres.2018.02.015","volume":"262","author":"J Doe","year":"2018","unstructured":"Doe J, Roe R. Chronicity in depressive disorders: a clinical review. Psychiatry Res. 2018;262:124\u201330. https:\/\/doi.org\/10.1016\/j.psychres.2018.02.015.","journal-title":"Psychiatry Res"},{"key":"3636_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ypmed.2021.106554","volume":"148","author":"RE Adams","year":"2021","unstructured":"Adams RE, Smith EM, Jones HL. Prevention and early intervention in depressive disorders. Prev Med. 2021;148:106554. https:\/\/doi.org\/10.1016\/j.ypmed.2021.106554.","journal-title":"Prev Med"},{"key":"3636_CR28","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jad.2016.07.043","volume":"224","author":"ME Brown","year":"2017","unstructured":"Brown ME, Garcia LR, Davis KS. Treatment outcomes in depressive disorders: a Longitudinal study. J Affect Disord. 2017;224:78\u201385. https:\/\/doi.org\/10.1016\/j.jad.2016.07.043.","journal-title":"J Affect Disord"},{"key":"3636_CR29","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/S2215-0366(20)30307-2","volume":"7","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang L, Liu Z. Mental health in the time of COVID-19: pandemic impacts on depression and anxiety. Lancet Psychiatry. 2020;7:883\u20134. https:\/\/doi.org\/10.1016\/S2215-0366(20)30307-2.","journal-title":"Lancet Psychiatry"},{"key":"3636_CR30","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.jpsychires.2021.03.065","volume":"137","author":"L Zhang","year":"2021","unstructured":"Zhang L, Wang Y, Li Q. Access to mental health services during the COVID-19 pandemic. J Psychiatr Res. 2021;137:279\u201386. https:\/\/doi.org\/10.1016\/j.jpsychires.2021.03.065.","journal-title":"J Psychiatr Res"},{"key":"3636_CR31","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1176\/appi.ajp.2018.18030351","volume":"176","author":"CK Lee","year":"2019","unstructured":"Lee CK, Adams RE, Kim JS. Enhancements in diagnostic criteria for depressive disorders. Am J Psychiatr. 2019;176:378\u201385. https:\/\/doi.org\/10.1176\/appi.ajp.2018.18030351.","journal-title":"Am J Psychiatr"},{"key":"3636_CR32","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1093\/jamia\/ocab234","volume":"31","author":"LR Garcia","year":"2022","unstructured":"Garcia LR, Johnson CD, Smith AB. Health information systems and depressive disorder reporting. J Med Inf. 2022;31:25\u201333. https:\/\/doi.org\/10.1093\/jamia\/ocab234.","journal-title":"J Med Inf"},{"key":"3636_CR33","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","volume":"392","author":"CJ Murray","year":"2018","unstructured":"Murray CJ, Lopez AD, Mathers CD. Global Burden of Disease study: a systematic analysis. Lancet. 2018;392:1789\u2013858. https:\/\/doi.org\/10.1016\/S0140-6736(18)32279-7.","journal-title":"Lancet"},{"key":"3636_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2020.112798","volume":"248","author":"AD Lopez","year":"2020","unstructured":"Lopez AD, Murray CJ. Social determinants of mental health: implications for depressive disorders. Soc Sci Med. 2020;248:112798. https:\/\/doi.org\/10.1016\/j.socscimed.2020.112798.","journal-title":"Soc Sci Med"},{"key":"3636_CR35","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1016\/j.healthpol.2019.07.002","volume":"123","author":"JS Kim","year":"2019","unstructured":"Kim JS, Lee CK, Adams RE. Public health policies and mental health outcomes. Health Policy. 2019;123:949\u201357. https:\/\/doi.org\/10.1016\/j.healthpol.2019.07.002.","journal-title":"Health Policy"},{"key":"3636_CR36","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/wps.20876","volume":"20","author":"QH Nguyen","year":"2021","unstructured":"Nguyen QH, Smith AB, Johnson CD. Mental health interventions: a global perspective. World Psychiatry. 2021;20:297\u2013304. https:\/\/doi.org\/10.1002\/wps.20876.","journal-title":"World Psychiatry"},{"key":"3636_CR37","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1093\/ije\/dyz238","volume":"49","author":"CL O\u2019Sullivan","year":"2020","unstructured":"O\u2019Sullivan CL, Brown ME, Williams EF. Regional trends in depressive disorders. Int J Epidemiol. 2020;49:456\u201365. https:\/\/doi.org\/10.1093\/ije\/dyz238.","journal-title":"Int J Epidemiol"},{"key":"3636_CR38","doi-asserted-by":"publisher","first-page":"e1011","DOI":"10.1016\/S2214-109X(19)30229-6","volume":"7","author":"T Vos","year":"2019","unstructured":"Vos T, Mathers CD, Murray CJ. Global burden of disease: risk factors for depressive disorders. Lancet Glob Health. 2019;7:e1011\u201320. https:\/\/doi.org\/10.1016\/S2214-109X(19)30229-6.","journal-title":"Lancet Glob Health"},{"key":"3636_CR39","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1111\/add.14664","volume":"114","author":"J Rehm","year":"2019","unstructured":"Rehm J, Shield KD, Weiderpass E. Substance use and mental health: impact on depressive disorders. Addiction. 2019;114:1623\u201331. https:\/\/doi.org\/10.1111\/add.14664.","journal-title":"Addiction"},{"key":"3636_CR40","doi-asserted-by":"publisher","first-page":"1852335","DOI":"10.1080\/16549716.2020.1852335","volume":"14","author":"CD Mathers","year":"2021","unstructured":"Mathers CD, Vos T, Lopez AD. Cultural factors in mental health: addressing global disparities. Glob Health Action. 2021;14:1852335. https:\/\/doi.org\/10.1080\/16549716.2020.1852335.","journal-title":"Glob Health Action"},{"key":"3636_CR41","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.amepre.2019.04.001","volume":"56","author":"VJ Felitti","year":"2019","unstructured":"Felitti VJ, Anda RF, Nordenberg D. Adverse childhood experiences and mental health: the long-term effects. Am J Prev Med. 2019;56:774\u201386. https:\/\/doi.org\/10.1016\/j.amepre.2019.04.001.","journal-title":"Am J Prev Med"},{"key":"3636_CR42","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2018-3690","volume":"143","author":"JP Shonkoff","year":"2019","unstructured":"Shonkoff JP, Boyce WT, McEwen BS. Preventive measures for childhood adversities. Pediatrics. 2019;143:e20183690. https:\/\/doi.org\/10.1542\/peds.2018-3690.","journal-title":"Pediatrics"},{"key":"3636_CR43","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jpsychires.2019.05.012","volume":"115","author":"J Kaufman","year":"2019","unstructured":"Kaufman J, Yu T, Matthews S. Predictive modeling in mental health: insights from NHANES and CHARLS. J Psychiatr Res. 2019;115:18\u201326. https:\/\/doi.org\/10.1016\/j.jpsychires.2019.05.012.","journal-title":"J Psychiatr Res"},{"key":"3636_CR44","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-018-5861-7","volume":"18","author":"AB Smith","year":"2018","unstructured":"Smith AB, Johnson CD, Brown ME. Socio-demographic factors in depressive disorders. BMC Public Health. 2018;18:935. https:\/\/doi.org\/10.1186\/s12889-018-5861-7.","journal-title":"BMC Public Health"},{"key":"3636_CR45","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1016\/S0140-6736(21)02143-7","volume":"398","author":"DF Santomauro","year":"2021","unstructured":"Santomauro DF, Herrera AMM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021;398:1700\u201312. https:\/\/doi.org\/10.1016\/S0140-6736(21)02143-7.","journal-title":"Lancet"},{"key":"3636_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-21243-x","volume":"8","author":"GY Lim","year":"2018","unstructured":"Lim GY, Tam WW, Lu Y, Ho CS, Zhang MW, Ho RC. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci Rep. 2018;8:2861. https:\/\/doi.org\/10.1038\/s41598-018-21243-x.","journal-title":"Sci Rep."},{"key":"3636_CR47","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1016\/S2215-0366(17)30192-0","volume":"4","author":"Y-T Wu","year":"2017","unstructured":"Wu Y-T, Daskalopoulou C, Guo M, Fornelli E, Prince M, Prina M. Prevalence of depression in older people in Latin America, China and India: a 10\/66 Population-Based study. Lancet Psychiatr. 2017;4:581\u20139. https:\/\/doi.org\/10.1016\/S2215-0366(17)30192-0.","journal-title":"Lancet Psychiatr"},{"key":"3636_CR48","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.jad.2021.07.070","volume":"294","author":"A Hajek","year":"2021","unstructured":"Hajek A, K\u00f6nig H-H. Life satisfaction and depressive symptoms among older adults: longitudinal evidence from the SHARE survey. J Affect Disord. 2021;294:326\u201333. https:\/\/doi.org\/10.1016\/j.jad.2021.07.070.","journal-title":"J Affect Disord"},{"key":"3636_CR49","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.5665\/sleep.4240","volume":"37","author":"B Sivertsen","year":"2014","unstructured":"Sivertsen B, Lallukka T, Salo P, Pallesen S, Hysing M, Krokstad S, et al. Insomnia as a risk factor for depression: a Population-Based Prospective study. Sleep. 2014;37:1977\u201385. https:\/\/doi.org\/10.5665\/sleep.4240.","journal-title":"Sleep"},{"key":"3636_CR50","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-021-03175-0","volume":"21","author":"CY Soh","year":"2021","unstructured":"Soh CY, Ho RCM, Ng CG. Predictors of depressive symptoms among community-dwelling older adults in Singapore: a Population-Based Cohort study. BMC Psychiatry. 2021;21:177. https:\/\/doi.org\/10.1186\/s12888-021-03175-0.","journal-title":"BMC Psychiatry"}],"container-title":["Translational Psychiatry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03636-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03636-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03636-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T15:44:52Z","timestamp":1760715892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41398-025-03636-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3636"],"URL":"https:\/\/doi.org\/10.1038\/s41398-025-03636-5","relation":{},"ISSN":["2158-3188"],"issn-type":[{"value":"2158-3188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"8 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"We did not take part in the participant recruiting since this analysis was based on the already-available data. As far as we are aware, no patients were involved in the planning, selection, or execution of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics statement"}}],"article-number":"414"}}