OBJECTIVES: Previous research shows only limited evidence on the contextual (neighbourhood-based) socioeconomic influences on mental health and depression. We investigated the association between individual and neighbourhood socioeconomic characteristics and depressive symptoms in the Czech Republic. METHODS: Dichotomized CESD score of depressive symptoms was used as the outcome in a random sample of 3534 men and 4082 women aged 45-69 years in the Czech HAPIEE Study. 220 small areas were characterized by the proportion of university educated persons and the proportion of unemployed from the economically active population in the 2001 Census. Multilevel logistic regression was used for the analysis. RESULTS: After controlling for individual-level variables, the effects of area-based characteristics were largely eliminated. The strongest area-based effect was that of the proportion of university educated persons; the ORs for 2(nd), 3(rd) and 4(th) quartile, compared with the 1(st) quartile, were 1.02, 0.93, and 0.82, respectively (p-value for trend 0.06). There were no cross-level interactions between socioeconomic variables. CONCLUSIONS: The effects of neighbourhood characteristics in this study were largely explained by individual socioeconomic variables.
OBJECTIVES: Previous research shows only limited evidence on the contextual (neighbourhood-based) socioeconomic influences on mental health and depression. We investigated the association between individual and neighbourhood socioeconomic characteristics and depressive symptoms in the Czech Republic. METHODS: Dichotomized CESD score of depressive symptoms was used as the outcome in a random sample of 3534 men and 4082 women aged 45-69 years in the Czech HAPIEE Study. 220 small areas were characterized by the proportion of university educated persons and the proportion of unemployed from the economically active population in the 2001 Census. Multilevel logistic regression was used for the analysis. RESULTS: After controlling for individual-level variables, the effects of area-based characteristics were largely eliminated. The strongest area-based effect was that of the proportion of university educated persons; the ORs for 2(nd), 3(rd) and 4(th) quartile, compared with the 1(st) quartile, were 1.02, 0.93, and 0.82, respectively (p-value for trend 0.06). There were no cross-level interactions between socioeconomic variables. CONCLUSIONS: The effects of neighbourhood characteristics in this study were largely explained by individual socioeconomic variables.
Authors: Steven Bell; Annie Britton; Ruzena Kubinova; Sofia Malyutina; Andrzej Pajak; Yuri Nikitin; Martin Bobak Journal: PLoS One Date: 2014-08-13 Impact factor: 3.240
Authors: Aislinne Freeman; Stefanos Tyrovolas; Ai Koyanagi; Somnath Chatterji; Matilde Leonardi; Jose Luis Ayuso-Mateos; Beata Tobiasz-Adamczyk; Seppo Koskinen; Christine Rummel-Kluge; Josep Maria Haro Journal: BMC Public Health Date: 2016-10-19 Impact factor: 3.295