Factors associated with common mental disorders: a study based on clusters of women

Abstract

ABSTRACT OBJECTIVE to identify factors associated with common mental disorders (CMD) in a sample of adult women in Southern Brazil. METHODS This population-based study, composed of 1,128 women, investigated socioeconomic, behavioral and health/disease explanatory demographic variables. Five response groups were explored: one group with common mental disorders – cut-off point 6/7 in the Self-Reporting Questionnaire 20 (SRQ-20) – and four others corresponding to the different clusters found using the latent class clustering technique, also from the SRQ-20. These four clusters (low, medium-depressive, medium-digestive and high) were named (denominated) based on the mean scores in the SRQ-20 in each group and on the response patterns of the variables and factorial characteristics. The “low” cluster comprised women with lower SRQ-20 scores and, therefore less likely to present CMD. The “high” cluster, with high mean values in the SRQ-20, was related to higher psychiatric morbidity. We used the Poisson regression technique to compare the findings of the different groups. RESULTS We identified ten variables as factors associated with CMD. Age, education, smoking, physical activity, perception of health and number of medical appointments were the common variables for the cut-off point and cluster-based analyses. Heavy alcohol use was associated only when the sample was evaluated as a cut-off point. Social class, work situation and existence of chronic diseases were associated only when the sample was analyzed by clusters. There was a significant association in the “high” cluster with lower classes (D or E), smoking, physical inactivity, existence of chronic diseases and negative perception of health. CONCLUSION We identified different associated factors according to the response groups considered. New approaches allowing identification of subgroups of individuals with specific characteristics and associated factors may contribute for a more accurate understanding of CMD and provide the basis for health interventions.

Keywords

• Women
• Mental Disorders, epidemiology
• Risk Factors
• Socioeconomic Factors
• Latent Class Analysis