title: Measuring the level of social support using latent class analysis.
abstract: Different instruments have been used to measure social support in epidemiological studies of which the most widely used is the Medical Outcomes Study Social Support Scale (SSS-MOS). However, these studies lack measures of the level of social support on health risks. We used latent class analysis (LCA) to distinguish subgroups with different levels of perceived social support and tested the consistency of these subgroups by their associations with the prevalence of Common Mental Disorders (CMD). This is a cross-sectional study of 1013 mothers living in the city of Salvador, Brazil in which psychosocial data were collected through home visits using the SSS-MOS and the Self Reporting Questionnaire-20. For each dimension of social support analysed here, we selected models with two classes using LCA. Multivariate logistic regression models were used to estimate the association between participants' perceived social support and the prevalence of CMD to verify the consistency of the groups defined by LCA. There was a clear difference in the reporting of perceived social support between those classified as high or low using LCA. The probability of perceiving several types of social support was lower in the subgroup classified as low level of social support (13.7-59.8%), and it was much higher in the group classified as high level of social support (84.3-98%). A greater prevalence of CMD was found among mothers with lower levels of social support. LCA seems to be a useful tool to improve measurement of perceived social support by separation into two levels in which the lower level is associated with an increased prevalence of CMD.
authors.affiliation: Instituto de Saúde Coletiva, Universidade Federal da Bahia, Rua Basílio da Gama, s/n°, Campus Universitário, Canela, Salvador, Bahia 40110-040, Brazil. Electronic address: marquesleticia@hotmail.com.
authors.forename: Letícia Marques
authors.initials: LM
authors.lastname: Santos
authors.affiliation: Instituto de Matemática, Universidade Federal da Bahia, Rua Barão de Jeremoabo, s/n, Campus Universitário, Ondina, Salvador, Bahia 40170-115, Brazil. Electronic address: leiladen@ufba.br.
authors.forename: Leila Denise A F
authors.initials: LD
authors.lastname: Amorim
authors.affiliation: Instituto de Saúde Coletiva, Universidade Federal da Bahia, Rua Basílio da Gama, s/n°, Campus Universitário, Canela, Salvador, Bahia 40110-040, Brazil. Electronic address: darci@ufba.br.
authors.forename: Darci Neves
authors.initials: DN
authors.lastname: Santos
authors.affiliation: Instituto de Saúde Coletiva, Universidade Federal da Bahia, Rua Basílio da Gama, s/n°, Campus Universitário, Canela, Salvador, Bahia 40110-040, Brazil. Electronic address: mauricio@ufba.br.
authors.forename: Maurício L
authors.initials: ML
authors.lastname: Barreto
mesh_terms: D000328:Adult; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D012944:Social Support; D011795:Surveys and Questionnaires
keywords: Latent class analysis; Mental health; SSS-MOS; Social support