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

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 SelfReporting 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.