Influence of socioeconomic factors on daily life activities and quality of life of Thai elderly
AbstractBackground: The increasing number of older people is a significant issue in Thailand, resulted in growing demands of health and social welfare services. The study aim was to explore the influence of socioeconomic factors on activities of daily living and quality of life of Thai seniors.
Design and methods: Using randomised cluster sampling, one province was sampled from each of the Central, North, Northeast and South regions, then one subdistrict sampled in each province, and a household survey used to identify the sample of 1678 seniors aged 60 years and over. The Mann-Whitney U-test and binary logistic regression were used to compare and determine the association of socioeconomic variables on quality of life and activities of daily living.
Results: The findings showed that sociodemographic and socioeconomic factors were significantly related to functional capacity of daily living. Education levels were strongly associated with daily life activities, with 3.55 adjusted ORs for respondents with secondary school education. Gender was important, with females comprising 61% of dependent respondents but only 47% of independent respondents. Seniors with low incomes were more likely to be anxious in the past, present and future and less likely to accept death in the late stage, with 1.40 Adjusted ORs (95%CI: 1.02-1.92), and 0.72 (95%CI: 0.53-0.98), respectively. However, they were more likely to engage in social activities.
Conclusions: While socioeconomic factors strongly indicated the functional capacity to live independently, a good quality of life also required other factors leading to happiness and life satisfaction.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2017 Ratana Somrongthong, Sunanta Wongchalee, Chandrika Ramakrishnan, Donnapa Hongthong, Korravarn Yodmai, Nualnong Wongtongkam
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.