{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:34:53Z","timestamp":1790357693607,"version":"4.1.0"},"reference-count":79,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Anxiety and depression are common among hypertensive patients and can lead to significant health complications. This study aimed to use Extreme Gradient Boosting (XGB) machine learning (ML) technique to select associated factors of anxiety and depression symptoms among people with hypertension in rural areas.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methodology<\/jats:title><jats:p>A cross-sectional study was conducted using a multistage cluster random sampling. The anxiety and depression symptoms were evaluated using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scales, respectively. A chi-square test was performed to assess prevalence. XGB model was employed to predict the presence of anxiety and depression symptoms using 13 variables, and the model\u2019s performance was compared with that of the traditional logistic regression (LR) model. Influential variables were explained and ranked using SHapley Additive exPlanations (SHAP) technique.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the 496 rural hypertensive adults, approximately 5.9% and 6.4% experienced the presence of anxiety and depression symptoms, respectively. Anxiety and depression symptoms were more prevalent among higher educated patients (14.0%) and who used tobacco (12.4%), respectively. The XGB model demonstrated improved predictive performance (for anxiety, ROC for XGB: 93.1%; for depression, ROC for XGB: 90.7%) compared to the LR model (for anxiety, ROC for LR: 83.8%; for depression, ROC for XGB: 79.7%) in predicting both outcomes. Marital status, body mass index (BMI), cardiovascular disease (CVD), educational status, family history of hypertension and employment were the influential factors in predicting the presence of anxiety symptoms. Similarly, chewing tobacco, family history of hypertension, marital status, CVD, sex, and educational status are important factors in predicting the presence of anxiety.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In Bangladesh, around 6% rural individuals with hypertension experienced the presence of anxiety and depression symptoms. Educational status, marital status, CVD and family history of hypertension were key factors linked to both outcomes. Future research is needed to validate these findings.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpsyg.2025.1650667","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:26:39Z","timestamp":1757481999000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Factors associated with the presence of anxiety and depression symptoms in rural hypertensive adults in Bangladesh: leveraging extreme gradient booster machine learning algorithm"],"prefix":"10.3389","volume":"16","author":[{"given":"Zarin","family":"Raihana","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manzur","family":"Kader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Zahidul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzana Akhter","family":"Bornee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Nazrul Islam","family":"Mondal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Rocky Khan","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baki","family":"Billah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"853551","DOI":"10.3389\/fpsyt.2022.853551","article-title":"Depression and anxiety among people with hypertension on follow-up in eastern Ethiopia: a multi-center cross-sectional study","volume":"13","author":"Abdisa","year":"2022","journal-title":"Front. 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Rep."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"218","DOI":"10.3389\/fmed.2019.00218","article-title":"Health and well-being in late life: gender differences worldwide","volume":"6","author":"Carmel","year":"2019","journal-title":"Front. Med."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S2215-0366(15)00471-X","article-title":"Cross-trial prediction of treatment outcome in depression: a machine learning approach","volume":"3","author":"Chekroud","year":"2016","journal-title":"Lancet Psychiatry"},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939785","volume-title":"Xgboost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1109\/TENSYMP46218.2019.8971369","volume-title":"Predicting depression in Bangladeshi undergraduates using machine learning","author":"Choudhury","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/S2215-0366(21)00395-3","article-title":"Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u20132019: a systematic analysis for the global burden of disease study 2019","volume":"9","author":"Collaborators","year":"2022","journal-title":"Lancet Psychiatry"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"54","DOI":"10.61838\/kman.aitech.2.4.6","article-title":"A review of cognitive UAVs: AI-driven situation awareness for enhanced operations","volume":"2","author":"Dehghan","year":"2024","journal-title":"AI Tech Behav. Soc. Sci."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s12889-022-14877-0","article-title":"Coping strategies and associated factors among people with physical disabilities for psychological distress in Ethiopia","volume":"23","author":"Desalegn","year":"2023","journal-title":"BMC Public Health"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"85","DOI":"10.5694\/mja16.00526","article-title":"Guideline for the diagnosis and management of hypertension in adults\u20142016","volume":"205","author":"Gabb","year":"2016","journal-title":"Med. J. Aust."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jagp.2020.10.013","article-title":"An idea whose time has come: promoting health equity by preventing the syndemic of depression and medical comorbidity","volume":"29","author":"Gallo","year":"2021","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"472","DOI":"10.3390\/bioengineering12050472","article-title":"Enhanced and interpretable prediction of multiple Cancer types using a stacking ensemble approach with SHAP analysis","volume":"12","author":"Ganie","year":"2025","journal-title":"Bioengineering"},{"key":"ref15","first-page":"627","article-title":"Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation","volume":"4","author":"Hajian-Tilaki","year":"2013","journal-title":"Casp. J. Intern. Med."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/8560835","article-title":"Anxiety and depression among hypertensive outpatients in Afghanistan: a cross-sectional study in Andkhoy City","volume":"2018","author":"Hamrah","year":"2018","journal-title":"Int. J. Hypertens."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"730","DOI":"10.12688\/f1000research.132304.1","article-title":"Prevalence of and factors associated with depression, anxiety and stress among adolescents in Bangladesh during the COVID-19 pandemic: a population-based study","volume":"12","author":"Haque","year":"2023","journal-title":"F1000Res"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e09415","DOI":"10.1016\/j.heliyon.2022.e09415","article-title":"Determinants of anxiety and depression among Bangladeshi adults during COVID-19 lockdown: an online survey","volume":"8","author":"Haque","year":"2022","journal-title":"Heliyon"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1192\/bji.2021.41","article-title":"The current state of mental healthcare in Bangladesh: part 1\u2013an updated country profile","volume":"18","author":"Hasan","year":"2021","journal-title":"BJPsych Int."},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.4135\/9781412985451","volume-title":"Practical sampling","author":"Henry","year":"1990"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e47","DOI":"10.1017\/gmh.2021.49","article-title":"Prevalence and risk factors of the symptoms of depression, anxiety, and stress during the COVID-19 pandemic in Bangladesh: a systematic review and meta-analysis","volume":"8","author":"Hosen","year":"2021","journal-title":"Global Ment. 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Health Prev."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e0289613","DOI":"10.1371\/journal.pone.0289613","article-title":"Predicting the risk of hypertension using machine learning algorithms: a cross sectional study in Ethiopia","volume":"18","author":"Islam","year":"2023","journal-title":"PLoS One"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"e0247898","DOI":"10.1371\/journal.pone.0247898","article-title":"Prevalence of depression, anxiety and associated factors among school going adolescents in Bangladesh: findings from a cross-sectional study","volume":"16","author":"Islam","year":"2021","journal-title":"PLoS One"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1080\/13697137.2016.1240771","article-title":"The prevalence of symptomatic pelvic floor disorders in women in Bangladesh","volume":"19","author":"Islam","year":"2016","journal-title":"Climacteric"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1007\/s11469-020-00242-y","article-title":"Prevalence and factors associated with depression and anxiety among first-year university students in Bangladesh: a cross-sectional study","volume":"20","author":"Islam","year":"2020","journal-title":"Int. J. Ment. Health Addict."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"83","DOI":"10.61838\/kman.hn.1.2.11","article-title":"The role and application of artificial intelligence (AI) in leveraging big data in the healthcare domain","volume":"1","author":"Jabbari","year":"2023","journal-title":"Health Nexus"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1176\/appi.ajp.2020.20030305","article-title":"The critical relationship between anxiety and depression","volume":"177","author":"Kalin","year":"2020","journal-title":"Am. J. Psychiatry"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1038\/mp.2015.198","article-title":"Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports","volume":"21","author":"Kessler","year":"2016","journal-title":"Mol. Psychiatry"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1111\/jch.14363","article-title":"Prevalence, awareness, treatment, and control of hypertension in Bangladesh: findings from National Demographic and health survey, 2017\u20132018","volume":"23","author":"Khan","year":"2021","journal-title":"J. Clin. Hypertens."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1002\/wps.21160","article-title":"The social determinants of mental health and disorder: evidence, prevention and recommendations","volume":"23","author":"Kirkbride","year":"2024","journal-title":"World Psychiatry"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1080\/01621459.1949.10483314","article-title":"A procedure for objective respondent selection within the household","volume":"44","author":"Kish","year":"1949","journal-title":"J. Am. Stat. Assoc."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-4458-8-25","article-title":"Mental health in hypertension: assessing symptoms of anxiety, depression and stress on anti-hypertensive medication adherence","volume":"8","author":"Kretchy","year":"2014","journal-title":"Int. J. Ment. Heal. Syst."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"e028332","DOI":"10.1161\/JAHA.122.028332","article-title":"Association of depression and poor mental health with cardiovascular disease and suboptimal cardiovascular health among young adults in the United States","volume":"12","author":"Kwapong","year":"2023","journal-title":"J. Am. Heart Assoc."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1037\/pha0000436","article-title":"The role of substance use coping in linking depression and alcohol use from late adolescence through early adulthood","volume":"29","author":"Magee","year":"2021","journal-title":"Exp. Clin. Psychopharmacol."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"E191","DOI":"10.1503\/cmaj.110829","article-title":"Optimal cut-off score for diagnosing depression with the patient health questionnaire (PHQ-9): a meta-analysis","volume":"184","author":"Manea","year":"2012","journal-title":"CMAJ"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"e66665","DOI":"10.2196\/66665","article-title":"Identifying adolescent depression and anxiety through real-world data and social determinants of health: machine learning model development and validation","volume":"12","author":"Mardini","year":"2025","journal-title":"JMIR Ment. Health"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1093\/geronb\/56.4.p214","article-title":"Age differences in stress, social resources, and well-being in middle and older age","volume":"56","author":"Martin","year":"2001","journal-title":"J. Gerontol. B Psychol. Sci. Soc. Sci."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.mcpdig.2023.04.003","article-title":"Health inequalities in rural and urban Bangladesh: the implications of digital health","volume":"1","author":"Masuda Akter","year":"2023","journal-title":"Mayo Clin Proc Digital Health"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"100179","DOI":"10.1016\/j.jadr.2021.100179","article-title":"Prevalence and socio-demographic correlates of depression, anxiety, and co-morbidity during COVID-19: a cross-sectional study among public and private university students of Bangladesh","volume":"5","author":"Mehareen","year":"2021","journal-title":"J. Affect. Disord. Rep."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1097\/HJH.0b013e32835080b7","article-title":"Depression increases the risk of hypertension incidence: a meta-analysis of prospective cohort studies","volume":"30","author":"Meng","year":"2012","journal-title":"J. Hypertens."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"503","DOI":"10.3390\/medicina57050503","article-title":"Foundational statistical principles in medical research: sensitivity, specificity, positive predictive value, and negative predictive value","volume":"57","author":"Monaghan","year":"2021","journal-title":"Medicina"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"100058","DOI":"10.1016\/j.crbeha.2021.100058","article-title":"Machine learning approach to predict the depression in job sectors in Bangladesh","volume":"2","author":"Moon","year":"2021","journal-title":"Curr. Res. Behav. Sci."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"e196835-e","DOI":"10.1001\/jamanetworkopen.2019.6835","article-title":"Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention","volume":"2","author":"Mortazavi","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"e038954","DOI":"10.1136\/bmjopen-2020-038954","article-title":"Prevalence and associated factors of depression among adolescent boys and girls in Bangladesh: findings from a nationwide survey","volume":"11","author":"Mridha","year":"2021","journal-title":"BMJ Open"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"275","DOI":"10.3329\/dujbs.v30i2.54652","article-title":"Validation of patient health questionnaire-9 for assessing depression of adults in Bangladesh","volume":"30","author":"Naher","year":"2021","journal-title":"Dhaka Univ. J. Biol. Sci."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1472-6874-11-22","article-title":"Prevalence and associated factors of depressive and anxiety symptoms during pregnancy: a population based study in rural Bangladesh","volume":"11","author":"Nasreen","year":"2011","journal-title":"BMC Womens Health"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"75","DOI":"10.4103\/shb.shb_38_22","article-title":"Comparison of the performance of machine learning-based algorithms for predicting depression and anxiety among university students in Bangladesh: a result of the first wave of the COVID-19 pandemic","volume":"5","author":"Nayan","year":"2022","journal-title":"Asian J. Soc. Health Behav."},{"key":"ref50","volume-title":"National Mental Health Survey 2019","year":"2019"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"e072279","DOI":"10.1136\/bmjopen-2023-072279","article-title":"Patterns, prevalence and risk factors of intimate partner violence and its association with mental health status during COVID-19: a cross-sectional study on early married female adolescents in Khulna district, Bangladesh","volume":"13","author":"Nishat","year":"2023","journal-title":"BMJ Open"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"2274539","DOI":"10.1080\/21642850.2023.2274539","article-title":"Wellbeing and distress in young people with chronic conditions: how do positive psychology variables relate to mental health outcomes?","volume":"11","author":"Parkinson","year":"2023","journal-title":"Health Psychol. Behav. Med."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/s12912-025-02957-6","article-title":"Machine learning-driven identification of key risk factors for predicting depression among nurses","volume":"24","author":"Qi","year":"2025","journal-title":"BMC Nurs."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"1136557","DOI":"10.3389\/fpubh.2023.1136557","article-title":"Depression symptoms, anxiety, and stress among undergraduate entrance admission seeking students in Bangladesh: a cross-sectional study","volume":"11","author":"Rabby","year":"2023","journal-title":"Front. Public Health"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3329\/bjpsy.v34i1.71969","article-title":"Validation of Bangla generalized anxiety disorder 7 scale among general population","volume":"34","author":"Redwan","year":"2020","journal-title":"Bangladesh J. Psychiatry"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41043-021-00276-5","article-title":"Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms","volume":"40","author":"Rois","year":"2021","journal-title":"J. Health Popul. Nutr."},{"key":"ref57","doi-asserted-by":"publisher","first-page":"1035203","DOI":"10.3389\/fcvm.2022.1035203","article-title":"Depressive disorders in older Chinese adults with essential hypertension: a classification tree analysis","volume":"9","author":"Ruan","year":"2022","journal-title":"Front. Cardiovas. Med."},{"key":"ref58","first-page":"10","article-title":"Depression increases the risk for uncontrolled hypertension","volume":"18","author":"Rubio-Guerra","year":"2013","journal-title":"Exp. Clin. Cardiol."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.jclinepi.2009.11.009","article-title":"Use of brier score to assess binary predictions","volume":"63","author":"Rufibach","year":"2010","journal-title":"J. Clin. Epidemiol."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.joep.2009.10.008","article-title":"Socioeconomic status, neighborhood disadvantage, and poverty-related stress: prospective effects on psychological syndromes among diverse low-income families","volume":"32","author":"Santiago","year":"2011","journal-title":"J. Econ. Psychol."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"4765","DOI":"10.48550\/arXiv.1705.07874","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Scott","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1098625","article-title":"Anxiety and depression among hypertensive adults in tertiary care hospitals of Nepal","volume":"2022","author":"Shah","year":"2022","journal-title":"Psychiatry J."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"e064754","DOI":"10.1136\/bmjopen-2022-064754","article-title":"Knowledge, attitudes and practices of COVID-19 in rural Bangladesh: a cross-sectional study","volume":"13","author":"Siddiquea","year":"2023","journal-title":"BMJ Open"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"e0202344","DOI":"10.1371\/journal.pone.0202344","article-title":"Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease","volume":"13","author":"Steele","year":"2018","journal-title":"PLoS One"},{"key":"ref65","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1017\/S2045796016000159","article-title":"Stigma towards people with mental disorders and its components\u2013a perspective from multi-ethnic Singapore","volume":"26","author":"Subramaniam","year":"2017","journal-title":"Epidemiol. Psychiatr. Sci."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"1153101","DOI":"10.3389\/fpsyg.2023.1153101","article-title":"Effects of acne severity and acne-related quality of life on depressive symptoms among adolescents and young adults: a cross-sectional study in Bangladesh","volume":"14","author":"Tasneem","year":"2023","journal-title":"Front. Psychol."},{"key":"ref67","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/s13690-020-00476-1","article-title":"The association of mental disorders with perceived social support, and the role of marital status: results from a national cross-sectional survey","volume":"78","author":"Vaingankar","year":"2020","journal-title":"Arch. Public Health"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the Achilles heel of predictive analytics","volume":"17","author":"Van Calster","year":"2019","journal-title":"BMC Med."},{"key":"ref69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/gmh.2023.72","article-title":"Prevalence of mental disorders in South Asia: a systematic review of reviews","volume":"10","author":"Vidyasagaran","year":"2023","journal-title":"Glob. Men. Health"},{"key":"ref70","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1016\/S0140-6736(20)30925-9","article-title":"Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: a systematic analysis for the global burden of disease study 2019","volume":"396","author":"Vos","year":"2020","journal-title":"Lancet"},{"key":"ref71","doi-asserted-by":"publisher","first-page":"e137","DOI":"10.1016\/S2542-5196(22)00315-1","article-title":"Climate-related shocks and other stressors associated with depression and anxiety in Bangladesh: a nationally representative panel study","volume":"7","author":"Wahid","year":"2023","journal-title":"The Lancet Planet. Health"},{"key":"ref72","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1001\/jamapsychiatry.2014.2502","article-title":"Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis","volume":"72","author":"Walker","year":"2015","journal-title":"JAMA Psychiatry"},{"key":"ref73","unstructured":"Mental disorders\n          \n          \n          World Health Organization\n          \n          2022"},{"key":"ref74","doi-asserted-by":"publisher","first-page":"104625","DOI":"10.5498\/wjp.v15.i4.104625","article-title":"Media consumption patterns and depressive and anxiety symptoms in the Chinese general population during the COVID-19 outbreak","volume":"15","author":"Wu","year":"2025","journal-title":"World J Psychiatry"},{"key":"ref75","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1038\/s41524-023-01000-z","article-title":"Small data machine learning in materials science","volume":"9","author":"Xu","year":"2023","journal-title":"NPJ Comput. Mater."},{"key":"ref76","doi-asserted-by":"publisher","first-page":"1440403","DOI":"10.3389\/fpubh.2024.1440403","article-title":"Unemployment and mental health: a global study of unemployment\u2019s influence on diverse mental disorders","volume":"12","author":"Yang","year":"2024","journal-title":"Front. Public Health"},{"key":"ref77","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1186\/s12877-024-04975-6","article-title":"The relationship between marital status and cognitive impairment in Chinese older adults: the multiple mediating effects of social support and depression","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Geriatr."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"119714","DOI":"10.1016\/j.jad.2025.119714","article-title":"Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: a prospective 10-year cohort study","volume":"389","author":"Zhang","year":"2025","journal-title":"J. Affect. Disord."},{"key":"ref79","doi-asserted-by":"publisher","first-page":"20406223231158561","DOI":"10.1177\/20406223231158561","article-title":"Machine learning for prediction of bleeding in acute myocardial infarction patients after percutaneous coronary intervention","volume":"14","author":"Zhao","year":"2023","journal-title":"Ther. Adv. 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