{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li J"],"funding":["NIA NIH HHS"],"pagination":["04260"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12434385"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15"],"pubmed_abstract":["<h4>Background</h4>Cardiovascular disease (CVD) is a leading cause of death globally, while the dynamics of CVD risk across different age groups, periods, and birth cohorts remain unclear. This study investigates how age, period, and cohort effects contribute to CVD risk across regions differently.<h4>Methods</h4>We employed a repeated cross-sectional design, analysing data from four large longitudinal surveys in the USA, UK, Europe, and China. A hierarchical age-period-cohort analysis was conducted using Bayesian inference through the integrated nested Laplace approximation to model the effects of age, period, and cohort on CVD risk across these regions. Subgroup analyses were also conducted to examine the moderation effects of social-demographic factors.<h4>Results</h4>CVD risk increases"],"journal":["Journal of global health"],"pubmed_title":["Age-period-cohort analysis of cardiovascular disease trends in middle-aged and older adults: cross-country comparison across HRS, ELSA, SHARE, and CHARLS."],"pmcid":["PMC12434385"],"funding_grant_id":["R03 AG043052","R01 AG030153","RC2 AG036619"],"pubmed_authors":["Xie G","Li J","Lin S","Pei L","Chen G","Pei H"],"additional_accession":[]},"is_claimable":false,"name":"Age-period-cohort analysis of cardiovascular disease trends in middle-aged and older adults: cross-country comparison across HRS, ELSA, SHARE, and CHARLS.","description":"<h4>Background</h4>Cardiovascular disease (CVD) is a leading cause of death globally, while the dynamics of CVD risk across different age groups, periods, and birth cohorts remain unclear. This study investigates how age, period, and cohort effects contribute to CVD risk across regions differently.<h4>Methods</h4>We employed a repeated cross-sectional design, analysing data from four large longitudinal surveys in the USA, UK, Europe, and China. A hierarchical age-period-cohort analysis was conducted using Bayesian inference through the integrated nested Laplace approximation to model the effects of age, period, and cohort on CVD risk across these regions. Subgroup analyses were also conducted to examine the moderation effects of social-demographic factors.<h4>Results</h4>CVD risk increases","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-04-08T19:49:59.739Z","creation":"2026-04-08T14:34:52.574Z"},"accession":"S-EPMC12434385","cross_references":{"pubmed":["40936419"],"doi":["10.7189/jogh.15.04260"]}}