{"title":"Factors Affecting Subjective Life Expectancy: Analysis of Korean Longitudinal Study of Aging","authors":"Jaekyoung Bae, Y. Park, Bo-Kyoung Shine","doi":"10.46308/kmj.2024.00066","DOIUrl":null,"url":null,"abstract":"Subjective life expectancy (SLE) is the predictive value of actual life expectancy. SLE has been notably associated with mortality. The 2006 Korean Longitudinal Study of Aging (KLoSA) representative sample of 10,254 Koreans aged over 45 years to assess the associations between factors of SLE. Descriptive analysis, correlations, and age-adjusted regression analyses were used to examine the relationship between SLE and demographic, socioeconomic, and health factors. We also linked the 2018 KLo-SA death statistics to the 2006 data to evaluate the association between actuarial life expectancy and SLE. We found that chronic illnesses and limitations in activities of daily living affect the life expectancy of individuals. Marriage, gainful employment, and high educational qualifications increase life expectancy. People who exercise expect to live longer, while those who smoke and drink expect to live somewhat shorter lives. Better self-rated health is associated with higher SLE. People who own a house expect to live longer than non-owners, and individuals living in metropolitan cities and urban areas assume a longer life expectancy than those living in rural areas. Participants who died between 2006 and 2018 had previously predicted a lower life expectancy than those who survived until 2018. The results of the study suggest that current health status, health behaviors, socioeconomic status, and actual life expectancy showed significant associations with SLE in the expected directions. These findings imply that we could use SLE as a database of health status, health behavior, and actual life expectancy. This information could help us intervene and improve policies related to SLE.","PeriodicalId":509261,"journal":{"name":"Keimyung Medical Journal","volume":"45 41","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Keimyung Medical Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46308/kmj.2024.00066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Subjective life expectancy (SLE) is the predictive value of actual life expectancy. SLE has been notably associated with mortality. The 2006 Korean Longitudinal Study of Aging (KLoSA) representative sample of 10,254 Koreans aged over 45 years to assess the associations between factors of SLE. Descriptive analysis, correlations, and age-adjusted regression analyses were used to examine the relationship between SLE and demographic, socioeconomic, and health factors. We also linked the 2018 KLo-SA death statistics to the 2006 data to evaluate the association between actuarial life expectancy and SLE. We found that chronic illnesses and limitations in activities of daily living affect the life expectancy of individuals. Marriage, gainful employment, and high educational qualifications increase life expectancy. People who exercise expect to live longer, while those who smoke and drink expect to live somewhat shorter lives. Better self-rated health is associated with higher SLE. People who own a house expect to live longer than non-owners, and individuals living in metropolitan cities and urban areas assume a longer life expectancy than those living in rural areas. Participants who died between 2006 and 2018 had previously predicted a lower life expectancy than those who survived until 2018. The results of the study suggest that current health status, health behaviors, socioeconomic status, and actual life expectancy showed significant associations with SLE in the expected directions. These findings imply that we could use SLE as a database of health status, health behavior, and actual life expectancy. This information could help us intervene and improve policies related to SLE.