Citation

McEvoy O, Cronin F, Brannigan R, Stanistreet D, Layte R. SSM Popul. Health 2022; 19: e101216.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssmph.2022.101216

PMID

36124255

PMCID

PMC9482142

Abstract

Differentials in physical activity (PA) between social and economic groups has been shown to contribute significantly to social gradients in health and life expectancy, yet relatively little is known about why differentials in PA emerge. This paper uses longitudinal data on a nationally representative sample of 6,216 young people aged between 9 and 18, from Ireland, to measure the role of family, school and neighbourhood level factors in accounting for differentials in PA trajectories between groups of young people, defined by level of maternal education, whilst adjusting for the individual characteristics of the young person (sex, age, personality, body mass index and health-status). Levels of PA fall significantly across the sample between 9 and 18, and the decline in PA is larger for the children of lower educated mothers. We find a clear gradient in PA at each age by maternal education for both males and females. Descriptive analyses found social gradients in the majority of our risk factors. Using multi-level, linear spline regression models to decompose differentials between groups, we find that family-level mechanisms account for the biggest proportion of the differential in PA for both males (50.8%) and females (35.1%). Differences in income across maternal education categories accounted for 24.1% of the differential for males and 14.7% among females, making it the second most effective mechanism in explaining the social patterning of PA. Neighbourhood-level processes resulted in a modest reduction in the same differential, while school level processes had the effect of equalising differences in PA across maternal education groups.

Language: en

Keywords

School; Family; Physical activity; Neighbourhood; Ireland; Social determinants of health; Health inequalities; Longitudinal data