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  • 1.
    Szilcz, Máté
    et al.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Mosquera, Paola A
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Sebastián, Miguel San
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Gustafsson, Per E
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Time trends in absolute and relative socioeconomic inequalities in leisure time physical inactivity in northern Sweden2018In: Scandinavian Journal of Public Health, ISSN 1403-4948, E-ISSN 1651-1905, Vol. 46, no 1, p. 112-123Article in journal (Refereed)
    Abstract [en]

    AIMS: The aim was to investigate the time trends in educational, occupational, and income-related inequalities in leisure time physical inactivity in 2006, 2010, and 2014 in northern Swedish women and men.

    METHODS: This study was based on data obtained from the repeated cross-sectional Health on Equal Terms survey of 2006, 2010, and 2014. The analytical sample consisted of 20,667 (2006), 31,787 (2010), and 21,613 (2014) individuals, aged 16-84. Logistic regressions were used to model the probability of physical inactivity given a set of explanatory variables. Slope index of inequality (SII) and relative index of inequality (RII) were used as summary measures of the social gradient in physical inactivity. The linear trend in inequalities and difference between gender and years were estimated by interaction analyses.

    RESULTS: The year 2010 displayed the highest physical inactivity inequalities for all socioeconomic position indicators, but educational and occupational inequalities decreased in 2014. However, significant positive linear trends were found in absolute and relative income inequalities. Moreover, women had significantly higher RII of education in physical inactivity in 2014 and significantly higher SII and RII of income in physical inactivity in 2010, than did men in the same years.

    CONCLUSIONS: The recent reduction in educational and occupational inequalities following the high inequalities around the time of the great recession in 2010 suggests that the current policies might be fairly effective. However, to eventually alleviate inequities in physical inactivity, the focus of the researchers and policymakers should be directed toward the widening trends of income inequalities in physical inactivity.

  • 2.
    Szilcz, Máté
    et al.
    Umeå University, Faculty of Medicine, Department of Epidemiology and Global Health.
    Mosquera, Paola
    Umeå University, Faculty of Medicine, Department of Epidemiology and Global Health.
    San Sebastian, Miguel
    Umeå University, Faculty of Medicine, Department of Epidemiology and Global Health.
    Gustafsson, Per E
    Umeå University, Faculty of Medicine, Department of Epidemiology and Global Health.
    Income inequalities in leisure time physical inactivity in northern Sweden: a decomposition analysis2019In: Scandinavian Journal of Public Health, ISSN 1403-4948, E-ISSN 1651-1905Article in journal (Refereed)
    Abstract [en]

    AIMS: Increasing income inequalities in leisure time physical inactivity have been reported in the relatively socially equal setting of northern Sweden. The present report seeks to contribute to the literature by exploring the contribution of different factors to the income inequalities in leisure time physical inactivity in northern Sweden.

    METHODS: This study was based on the 2014 Health on Equal Terms survey, distributed in the four northernmost counties of Sweden. The analytical sample consisted of 21,000 respondents aged 16-84. Six thematic groups of explanatory variables were used: demographic variables, socioeconomic factors, material resources, family-, psychosocial conditions and functional limitations. Income inequalities in leisure time physical inactivity were decomposed by Wagstaff-type decomposition analysis.

    RESULTS: Income inequalities in leisure time physical inactivity were found to be explained to a considerable degree by health-related limitations and unfavourable socioeconomic conditions. Material and psychosocial conditions seemed to be of moderate importance, whereas family and demographic characteristics were of minor importance.

    CONCLUSIONS: This study suggests that in order to achieve an economically equal leisure time physical inactivity, policy may need to target the two main barriers of functional limitations and socioeconomic disadvantages.

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