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Using four waves of the China Health and Retirement Longitudinal Study (CHARLS) linked to city-level population change, the study analyzed depressive symptoms in later life. Fixed-effects models show a depressive-symptom gradient favoring non-shrinking contexts, with the highest burden in shrinking rural areas. Piecewise regression identifies a threshold at −12.0% population change where symptoms rise sharply, and provides residence-specific estimates. Results support place-sensitive mental-health strategies targeting shrinking rural prefectures.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/urban-shrinkage-and-depressive-symptoms-among-older-adults-in-china-rural-burden-and-threshold-effects/426114/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/urban-shrinkage-and-depressive-symptoms-among-older-adults-in-china-rural-burden-and-threshold-effects/426114.png","ImageObject",300,407,{"name":92,"@type":93},"Nguyễn Văn Học","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What data and model approach are used to assess depressive symptoms?","Question",{"text":112,"@type":113},"The analysis links four CHARLS waves to city-level population change and uses individual fixed-effects models with time-varying covariates and wave controls.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do depressive symptoms differ across shrinking and non-shrinking urban and rural contexts?",{"text":117,"@type":113},"The findings show a robust gradient: shrinking rural > non-shrinking rural > shrinking urban > non-shrinking urban, with burden concentrated in shrinking rural areas.",{"name":119,"@type":110,"acceptedAnswer":120},"What population-change threshold predicts a sharp rise in depressive symptoms?",{"text":121,"@type":113},"Breakpoint analysis estimates an adjusted overall threshold at −12.0% population change; beyond this level, depressive symptoms increase sharply.",{"name":123,"@type":110,"acceptedAnswer":124},"Why are place-sensitive interventions recommended?",{"text":125,"@type":113},"Because severe demographic decline disproportionately elevates depressive symptoms among rural older adults, strategies should target shrinking rural prefectures rather than rely only on growth-oriented models.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},426114,1790793997,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":147,"read_time":81},1374402739827,"https://ap-avatar.wpscdn.com/avatar/14000c97e7351f1a627?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787885694763230660","Innovation in Aging, 2025, Vol. 9, No. S2 1297  \nUrban shrinkage is rising globally, yet its mental-health implications for older adults—and how they differ by urban–rural context—remain unclear. Using four waves of the China Health and Retirement Longitudinal Study (CHARLS; unbalanced panel of 19,126 adults; 38,366 person-waves) linked to city-level population change, we examined depressive symptoms in later life. City-level data were defined at the prefecture level, encompassing both urban districts and rural counties; “urban/rural” denotes respondents’ residence. Individual fixed-effects models with time-varying covariates (age, gender, income, education, marital status) and wave controls revealed a robust gradient: shrinking rural > non-shrinking rural > shrinking urban > non-shrinking urban, indicating that the depressive-symptom burden is concentrated in shrinking rural contexts rather than uniformly across “shrinking cities.” To probe nonlinearity, we estimated segmented (piecewise linear) regressions of depressive symptoms on the continuous population-change rate, overall and stratified by residence, with the same covariate adjustment. Breakpoint analysis identified an adjusted overall threshold at − 12.0% population change (95% CI: −15.7%, −8.3%), beyond which depressive symptoms rose sharply. In rural areas, the threshold was −10.7%(95% CI:−13.6%, −7.8%); in urban areas, the estimated breakpoint was −5.7%(95% CI: −18.1%, 6.6%) and not statistically distinct. Findings highlight urban shrinkage as a place-based determinant of mental-health inequality in aging, with severe demographic decline disproportionately elevating depressive symptoms among rural older adults. As shrinkage becomes a global reality, strategies for mental health and aging should move beyond growth-oriented models toward targeted, place-sensitive interventions that bolster services, social support, and infrastructure in shrinking rural prefectures.  \nAbstract citation ID: igaf122.4323  \nURBAN SHRINKAGE AND DEPRESSIVE SYMPTOMS  \nAMONG OLDER ADULTS IN CHINA: RURAL BURDEN  \nAND THRESHOLD EFFECTS  \nXinyue Wen1, and Fernando DePaolis2, 1. Middlebury College, Monterey, California, United States, 2. Middlebury Institute of International Studies at Monterey, Monterey, California, United States","cbCailcVv5NllJuJ","https://ap.wps.com/l/cbCailcVv5NllJuJ","pdf",1134927,"English","# Urban shrinkage and mental health inequality\n## Study design and data sources\n## Depressive-symptom gradient by urban–rural context\n## Threshold effects from breakpoint and segmented regression\n## Policy implications for aging mental health","[{\"question\":\"What data and model approach are used to assess depressive symptoms?\",\"answer\":\"The analysis links four CHARLS waves to city-level population change and uses individual fixed-effects models with time-varying covariates and wave controls.\"},{\"question\":\"How do depressive symptoms differ across shrinking and non-shrinking urban and rural contexts?\",\"answer\":\"The findings show a robust gradient: shrinking rural \\u003e non-shrinking rural \\u003e shrinking urban \\u003e non-shrinking urban, with burden concentrated in shrinking rural areas.\"},{\"question\":\"What population-change threshold predicts a sharp rise in depressive symptoms?\",\"answer\":\"Breakpoint analysis estimates an adjusted overall threshold at −12.0% population change; beyond this level, depressive symptoms increase sharply.\"},{\"question\":\"Why are place-sensitive interventions recommended?\",\"answer\":\"Because severe demographic decline disproportionately elevates depressive symptoms among rural older adults, strategies should target shrinking rural prefectures rather than rely only on growth-oriented models.\"}]","Urban Shrinkage and Depressive Symptoms Among Older Adults in China - Rural Burden and Threshold Effects | PDF",1790639401]