{"title":"Geographical variation, demographic and socioeconomic disparities in Active Ageing: The situation in Thailand","authors":"Romnalin Keanjoom , Pichaya Toyoda , Keiko Nakamura","doi":"10.1016/j.puhip.2024.100509","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>Being healthy and active is a goal to achieve a better quality of life as individuals age. This study aimed to explore and validate the Active Ageing (AA) model, and examine geographic variations, and demographic and socioeconomic disparities.</p></div><div><h3>Study design</h3><p>Utilising a cross-sectional secondary data analysis, the analytic unit is older adults aged 60–80 across all provinces in Thailand.</p></div><div><h3>Methods</h3><p>Exploratory Factor Analysis explored the AA structures, and the second-order Confirmatory Factor Analysis validated the model fit. Factor scores were used to identify geographic variation and sociodemographic disparities in AA. The association between geographic, and sociodemographic characteristics, and AA was examined through hierarchical regression analysis.</p></div><div><h3>Results</h3><p>The AA model, comprised of 14 indicators representing three latent factors–physical health, participation, and security–exhibited an optimal fit. Geographic inequality in AA emerged across the country, with specific areas linked to lower AA. An inverse relation between participation and security was observed. Rural residence, younger age, male, being married, and adequate income were associated with better AA. The association between AA and geographic, demographic, and socioeconomic emphasised the positive role of marital and economic status.</p></div><div><h3>Conclusions</h3><p>This study contributes to understanding the social determinants of health by constructing a comprehensive AA model. The findings highlight the geographic variations and demographic and socioeconomic disparities in AA across Thailand. While AA generally declines with age, a better economy may help alleviate these disparities. These findings underscore the need for tailored social and public health policies, avoiding a “one-size-fits-all” approach.</p></div>","PeriodicalId":34141,"journal":{"name":"Public Health in Practice","volume":"7 ","pages":"Article 100509"},"PeriodicalIF":2.2000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666535224000466/pdfft?md5=1c440f48cea4bb633629144e9694956f&pid=1-s2.0-S2666535224000466-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Public Health in Practice","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666535224000466","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives
Being healthy and active is a goal to achieve a better quality of life as individuals age. This study aimed to explore and validate the Active Ageing (AA) model, and examine geographic variations, and demographic and socioeconomic disparities.
Study design
Utilising a cross-sectional secondary data analysis, the analytic unit is older adults aged 60–80 across all provinces in Thailand.
Methods
Exploratory Factor Analysis explored the AA structures, and the second-order Confirmatory Factor Analysis validated the model fit. Factor scores were used to identify geographic variation and sociodemographic disparities in AA. The association between geographic, and sociodemographic characteristics, and AA was examined through hierarchical regression analysis.
Results
The AA model, comprised of 14 indicators representing three latent factors–physical health, participation, and security–exhibited an optimal fit. Geographic inequality in AA emerged across the country, with specific areas linked to lower AA. An inverse relation between participation and security was observed. Rural residence, younger age, male, being married, and adequate income were associated with better AA. The association between AA and geographic, demographic, and socioeconomic emphasised the positive role of marital and economic status.
Conclusions
This study contributes to understanding the social determinants of health by constructing a comprehensive AA model. The findings highlight the geographic variations and demographic and socioeconomic disparities in AA across Thailand. While AA generally declines with age, a better economy may help alleviate these disparities. These findings underscore the need for tailored social and public health policies, avoiding a “one-size-fits-all” approach.
随着年龄的增长,健康和活跃是提高生活质量的一个目标。本研究旨在探索和验证积极老龄化(AA)模型,并研究地域差异以及人口和社会经济差异。研究设计利用横截面二级数据分析,分析单位为泰国各府 60-80 岁的老年人。方法探索性因子分析探索 AA 结构,二阶确认性因子分析验证模型的拟合度。因子得分用于识别 AA 的地域差异和社会人口差异。结果 AA 模型由 14 个指标组成,代表三个潜在因素--身体健康、参与和安全--显示出最佳拟合度。全国范围内出现了 AA 地域不平等现象,特定地区的 AA 水平较低。参与度和安全感之间呈反比关系。居住在农村、年龄较小、男性、已婚和收入充足与较好的 AA 有关。AA 与地理、人口和社会经济之间的关联强调了婚姻和经济状况的积极作用。研究结果凸显了泰国各地 AA 的地域差异以及人口和社会经济差异。虽然 AA 通常会随着年龄的增长而下降,但更好的经济可能有助于缓解这些差异。这些研究结果突出表明,有必要制定有针对性的社会和公共卫生政策,避免 "一刀切 "的做法。