Multiple risk behaviour in adolescence and socio-economic status: findings from a UK birth cohort

Kipping, Ruth; Smith, Michèle C.; Heron, Jon; Hickman, Matthew; Campbell, Rona

doi:10.1093/eurpub/cku078

Cited by 103 publications

(94 citation statements)

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“…We also conducted subgroup analyses to investigate whether results differed when education was measured using paternal, maternal, or parental education; no important differences were found. In England, Kipping et al [28] investigated associations between SES, measured by social class, maternal education, and family income. They found that, after mutual adjustment for other SES variables, family income and maternal education were both inversely associated with TV viewing time [28].…”

Section: Discussionmentioning

confidence: 99%

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Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis

et al. 2016

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BackgroundThe body of evidence on associations between socioeconomic status (SES) and sedentary behaviors in adolescents is growing.ObjectivesThe overall aims of our study were to conduct a systematic review and meta-analysis of this evidence and to assess whether (1) the associations between SES and sedentary behavior are consistent in adolescents from low-middle-income and from high-income countries, (2) the associations vary by domain of sedentary behavior, and (3) the associations vary by SES measure.MethodsWe performed a systematic literature search to identify population-based studies that investigated the association between SES and sedentary behavior in adolescents (aged 10–19 years). Only studies that presented risk estimates were included. We conducted meta-analyses using random effects and univariate meta-regression and calculated pooled effect sizes (ES).ResultsData from 39 studies were included; this provided 106 independent estimates for meta-analyses. Overall, there was an inverse association between SES and sedentary behavior (ES 0.89; 95 % confidence interval [CI] 0.81–0.98). However, the direction of the association varied: in high-income countries, SES was inversely associated with sedentary behavior (ES 0.67; 95 % CI 0.62–0.73), whereas in low-middle-income countries, there was a positive association between SES and sedentary behavior (ES 1.18; 95 % CI 1.04–1.34). In high-income countries, the associations were strongest for screen time (ES 0.68; 95 % CI 0.62–0.74) and television (TV) time (ES 0.58; 95 % CI 0.49–0.69), whereas in low-middle-income countries, the associations were strongest for ‘other’ screen time (i.e., computer, video, study time, but not including TV time) (ES 1.38; 95 % CI 1.07–1.79). All indicators of SES were negatively associated with sedentary behavior in high-income countries, but only resources (income and assets indexes) showed a significant positive association in low-middle-income countries.ConclusionThe associations between SES and sedentary behavior are different in high- and low-middle-income countries, and vary by domain of sedentary behavior. These findings suggest that different approaches may be required when developing intervention strategies for reducing sedentary behavior in adolescents in different parts of the world.Electronic supplementary materialThe online version of this article (doi:10.1007/s40279-016-0555-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…In England, Kipping et al [28] investigated associations between SES, measured by social class, maternal education, and family income. They found that, after mutual adjustment for other SES variables, family income and maternal education were both inversely associated with TV viewing time [28]. …”

Section: Discussionmentioning

confidence: 99%

Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis

et al. 2016

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“…However, the existing studies also yielded controversial results (Adhikari & Summerill, 2000;Alonso et al, 2004;Grinshpoon, Marom, Weizman, & Ponizovsky, 2007;Kipping, Smith, Heron, Hickman, & Campbell, 2015;Legleye, Beck, Khlat, Peretti-Watel, & Chau, 2012;Sandwijk, Cohen, & Langemeijer, 1995).…”

Section: Introductionmentioning

confidence: 99%

Occupational inequalities in psychoactive substance use: A question of conceptualization?

Maron

Kraus

Pogarell³

et al. 2015

Addiction Research & Theory

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Background: Three different conceptualizations of occupational prestige were contrasted by applying social stratification to four exemplarily selected psychoactive substances. Although these conceptualizations partly measure the same construct, it is hypothesized that the gradient of occupational inequality differs depending on the type of conceptualization. Method: Data were taken from the 2012 German Epidemiological Survey of Substance Abuse. The study sample comprised n ¼ 9084 individuals of the general population aged 18-64 years. Use and heavy use of cigarettes, alcohol, cannabis and analgesics were stratified by (a) employment status (six groups: employed, marginally employed, apprenticeship, unemployed, retired and other), (b) occupational status (five groups: low to high) and (c) occupational social class (two groups: blue-collar and white-collar). Absolute and relative differences between occupational groups were calculated. All analyses were stratified by gender. Results: Risk for smoking was increased amongst unemployed males and blue-collar workers. Retired persons, people with low occupational status and female blue-collar workers had a diminished risk for alcohol consumption; apprentices had an increased risk. Amongst males, low occupational status and blue-collar work was associated with episodic heavy drinking. Unemployment and blue-collar work was related to cannabis use. Risk for heavy analgesics use was increased amongst unemployed women, men with low occupational status and male blue-collar workers, respectively. Conclusions: The results suggest that occupational inequality differs depending on the applied conceptualizations of occupational prestige. Consequently, they should not be used interchangeably. ARTICLE HISTORY

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“…The inverse relationship between risk behaviour and socioeconomic status (SES) has been widely reported [8–10]. In response, strategies to improve health behaviour of young people have often targeted low SES groups.…”

Section: Introductionmentioning

confidence: 99%

Examining subgroup effects by socioeconomic status of public health interventions targeting multiple risk behaviour in adolescence

et al. 2018

Self Cite

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BackgroundMultiple risk behaviour (MRB) refers to two or more risk behaviours such as smoking, drinking alcohol, poor diet and unsafe sex. Such behaviours are known to co-occur in adolescence. It is unknown whether MRB interventions are equally effective for young people of low and high socioeconomic status (SES). There is a need to examine these effects to determine whether MRB interventions have the potential to narrow or widen inequalities.MethodsTwo Cochrane systematic reviews that examined interventions to reduce adolescent MRB were screened to identify universal interventions that reported SES. Study authors were contacted, and outcome data stratified by SES and intervention status were requested. Risk behaviour outcomes alcohol use, smoking, drug use, unsafe sex, overweight/obesity, sedentarism, peer violence and dating violence were examined in random effects meta-analyses and subgroup analyses conducted to explore differences between high SES and low SES adolescents.ResultsOf 49 studies reporting universal interventions, only 16 also reported having measured SES. Of these 16 studies, four study authors provided data sufficient for subgroup analysis. There was no evidence of subgroup differences for any of the outcomes. For alcohol use, the direction of effect was the same for both the high SES group (RR 1.26, 95% CI: 0.96, 1.65, p = 0.09) and low SES group (RR 1.14, 95% CI: 0.98, 1.32, p = 0.08). The direction of effect was different for smoking behaviour in favour of the low SES group (RR 0.83, 95% CI: 0.66, 1.03, p = 0.09) versus the high SES group (RR 1.16, 95% CI: 0.82, 1.63, p = 0.39). For drug use, the direction of effect was the same for both the high SES group (RR 1.29, 95% CI: 0.97, 1.73, p = 0.08) and the low SES group (RR 1.28, 95% CI: 0.84, 1.96, p = 0.25).ConclusionsThe majority of studies identified did not report having measured SES. There was no evidence of subgroup difference for all outcomes analysed among the four included studies. There is a need for routine reporting of demographic information within studies so that stronger evidence of effect by SES can be demonstrated and that interventions can be evaluated for their impact on health inequalities.

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Multiple risk behaviour in adolescence and socio-economic status: findings from a UK birth cohort

Cited by 103 publications

References 23 publications

Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis

Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis

Occupational inequalities in psychoactive substance use: A question of conceptualization?

Examining subgroup effects by socioeconomic status of public health interventions targeting multiple risk behaviour in adolescence

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