Associations between self-perceived quality of life and socio-demographic, psychosocial, and health variables in a group of elderly

• Published in: Cadernos de saúde pública Vol. 29; no. 7; pp. 1437 - 1448
• Main Authors: Oliveira, Sérgio Eduardo Silva de, Von Hohendorff, Jean, Müller, Juliana de Lima, Bandeira, Denise Ruschel, Koller, Sílvia Helena, Fleck, Marcelo Pio de Almeida, Trentini, Clarissa Marceli
• Format: Journal Article
• Language: English
• Published: Brazil Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz 01.07.2013; Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz
• Subjects: Aged; Aged, 80 and over; Anciano; Brazil; Calidad de Vida; Cluster Analysis; Depression - diagnosis; Depression - psychology; Female; Health Services for the Aged; Humans; Male; Middle Aged; Psychiatric Status Rating Scales; PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH; Quality of Life; Salud del Anciano; Self Concept; Socioeconomic Factors; Surveys and Questionnaires; Urban Population; Brazil; Saúde do Idoso; Anciano; Idoso; Calidad de Vida; Qualidade de Vida; Quality of Life; Health of the Elderly; Aged; Salud del Anciano
• ISSN: 0102-311X; 1678-4464; 1678-4464; 0102-311X
• DOI: 10.1590/S0102-311X2013000700017

The objectives of this study were to investigate: (a) multiple relations between socio-demographic, psychosocial, and health variables and quality of life in the elderly and (b) the model's validity through correlation with depressive symptoms. The sample included 339 elderly individuals from 60 to 98 years of age (M = 73.4; SD = 8.3), who answered a socio-demographic questionnaire, WHOQOL-100, and BDI. Cluster analysis of the sample distributed the elders into two groups according to self-perceived quality of life (better versus worse), and logistic regression analysis identified variables that explained better quality of life. Social class, self-rated health status, volunteer work, use of medication, and data collection setting were associated with quality of life (predictive capacity for correct classification 72.3%, specificity 73.6%, and sensitivity 71.1%). The inverse correlation between the model's variables and BDI scores provided evidence of the model's validity. The model can help support public policies aimed at promoting quality of life in the elderly.