Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/115923
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dc.contributorDepartment of Language Science and Technology-
dc.creatorWang, J-
dc.creatorLu, X-
dc.creatorNgai, SBC-
dc.creatorXie, L-
dc.creatorLiu, X-
dc.creatorYao, Y-
dc.creatorJin, Y-
dc.date.accessioned2025-11-18T06:47:59Z-
dc.date.available2025-11-18T06:47:59Z-
dc.identifier.urihttp://hdl.handle.net/10397/115923-
dc.language.isoenen_US
dc.publisherAmerican Association for the Advancement of Science (AAAS)en_US
dc.rightsCopyright © 2025 Jingjing Wang et al. Exclusive licensee Peking University Health Science Center. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License(CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Jingjing Wang, Xinran Lu, Sing Bik Cindy Ngai, Lili Xie, Xiaoyun Liu, Yao Yao, Yinzi Jin. Digital Exclusion and Depressive Symptoms among Older People: Findings from Five Aging Cohort Studies across 24 Countries. Health Data Sci. 2025;5:0218 is available at https://doi.org/10.34133/hds.0218.en_US
dc.titleDigital exclusion and depressive symptoms among older people : findings from five aging cohort studies across 24 countriesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume5-
dc.identifier.doi10.34133/hds.0218-
dcterms.abstractBackground: Digital exclusion is a global issue that disproportionately affects older individuals especially inlow- and middle-income nations. However, there is a wide gap in current research regarding the impact of digitalexclusion on the mental health of older adults in both high-income and low- and middle-income countries.-
dcterms.abstractMethods: We analyzed data from 5 longitudinal cohorts: the Health and Retirement Study (HRS), the EnglishLongitudinal Study of Aging (ELSA), the Survey of Health, Ageing and Retirement in Europe (SHARE), the ChinaHealth and Retirement Longitudinal Study (CHARLS), and the Mexican Health and Aging Study (MHAS). Thesecohorts consisted of nationwide samples from 24 countries. Digital exclusion was defined as the self-reportedlack of access to the internet. Depressive symptoms were assessed using comparable scales across all cohorts.We used generalized estimating equation models, fitting a Poisson model, to investigate the association betweenthe digital exclusion and depressive symptoms. We adjusted for the causal directed acyclic graph (DAG) minimalsufficient adjustment set (MSAS), which includes gender, age, retirement status, education, householdwealth, social activities, and weekly contact with their children.-
dcterms.abstractResults: During the study period (2010–2018),122,242 participants underwent up to 5 rounds of follow-up. Digital exclusion varied greatly across countries,ranging from 21.1% in Denmark to 96.9% in China. The crude model revealed a significant associationbetween digital exclusion and depressive symptoms. This association remained statistically significant in theMSAS-adjusted model across all cohorts: HRS [incidence rate ratio (IRR), 1.37; 95% confidence interval (CI),1.28 to 1.47], ELSA (IRR, 1.32; 95% CI, 1.23 to 1.41), SHARE (IRR, 1.30; 95% CI, 1.27 to 1.33), CHARLS (IRR, 1.62;95% CI, 1.38 to 1.91), and MHAS (IRR, 1.31; 95% CI, 1.26 to 1.37); all Ps < 0.001. Notably, this association wasconsistently stronger in individuals living in lower wealth quintile households across all 5 cohorts and amongthose who do not regularly interact with their children, except for ELSA.-
dcterms.abstractConclusions: Digital exclusion is globallywidespread among older adults. Older individuals who are digitally excluded are at a higher risk of developingdepressive symptoms, particularly those with limited communication with their offspring and individuals livingin lower wealth quintile households. Prioritizing the provision of internet access to older populations mayhelp reduce the risks of depression symptoms, especially among vulnerable groups with limited familialsupport and with lower income.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationHealth data science, 2025, v. 5, 0218-
dcterms.isPartOfHealth data science-
dcterms.issued2025-
dc.identifier.eissn2765-8783-
dc.identifier.artn0218-
dc.description.validate202511 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe study was supported by the National Key R&D Program of China (2023YFC3606400 and 2023YFB4603200), Major Project of the National Social Science Fund of China (23&ZD188), and National Natural Science Foundation of China (72374013).en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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