Objective: To investigate the self-rated health status of the floating population in a district of Guangzhou.
Methods: Cluster stratified random sampling was applied to survey 219 floating people from a community in Guangzhou, who were assessed with self-rated health status was assessed with Self-Rated Health Measurement Scale.
Results: The scores of the floating population were significantly higher than the normal individuals in physical health sub-scales (P<0.01), while the scores of the floating population were significantly lower in psychological and social health sub-scales (P<0.05). The low score items in the subscale of physical health mainly consisted of fatigue and gastrointestinal symptoms, those in the subscale of psychological health mainly in anxiety, depression and obsession, and those in the subscale of social health mainly in participation in social activities and seeking help from others.
Conclusion: To improve the health status of the floating population, campaigns of health education need to be launched periodically and psychological counseling should be provided for these individuals. Additionally, interactive activities should be increased in their daily social life.
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BMJ Open
January 2025
Economics, University of KwaZulu-Natal - Durban Campus, Durban, KZN, South Africa
Introduction/objective: Previous research on socioeconomic factors influencing health outcomes has often centred on the urban-rural divide, overlooking the nuanced variations within these categories. This study enhances our understanding by moving beyond traditional comparisons to examine the specific impacts of multiple deprivations on self-rated health across traditional authority areas, rural formal areas, urban informal areas and urban formal areas. By doing so, it captures the unique challenges and health implications associated with varying deprivation levels within these diverse contexts.
View Article and Find Full Text PDFCad Saude Publica
January 2025
Universidade Estadual de Campinas, Campinas, Brasil.
This study aims to examine the prevalence of abdominal obesity-dynapenia phenotype, identified by the presence of abdominal obesity and dynapenia, and understand its associated factors with a representative sample of the Brazilian population. Data were collected from the baseline of the Brazilian Longitudinal Study of Aging (ELSI-Brasil) 2015-2016. Abdominal obesity was determined by a waist-to-height ratio ≥ 0.
View Article and Find Full Text PDFGerontologist
January 2025
School of Aging Studies, University of South Florida, Tampa, Florida, USA.
Background And Objectives: To better understand racial/ethnic disparities in hearing aid use, we examined racial differences in discrepancies between subjective hearing ratings and objective hearing tests as a potential source of this disparity.
Research Design And Methods: A cross-sectional assessment was conducted using the data from the Health and Retirement Study (HRS). Our analytic sample included 2,568 participants aged 50 and older: 1,814 non-Hispanic White Americans and 754 non-Hispanic Black Americans.
Prev Med Rep
February 2025
Departments of Medicine, Health, and Society & Sociology, Vanderbilt University, 2201 West End Ave, Nashville, TN 37235, USA.
Introduction: Cigarette smoking is among the largest risk factors for cognitive decline in later life. This study examines the associations between hospitality smoke-free coverage in the US and the prevalence of self-rated cognitive function decline and disparities therein.
Methods: I use the repeated cross-sectional Behavioral Risk Factor Surveillance data collected between 2017 and 2022 from a sample of Americans 45 years and older and estimate logistic regression models predicting self-rated cognitive function decline by calculated smoke-free hospitality coverage in restaurants and bars.
Purpose: To determine the status of depression and its key influencing factors among Chinese older adults in different living situations.
Method: Data of 7,092 older adults were obtained from the China Health and Retirement Longitudinal Survey. This study analyzed key variables influencing depressive symptoms using random forest modeling and logistic regression.
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