Inequalities in children's exposure to alcohol outlets in Scotland: a GPS study.

BACKGROUND: Alcohol use is a leading cause of harm in young people and increases the risk of alcohol dependence in adulthood. Alcohol use is also a key driver of rising health inequalities. Quantifying inequalities in exposure to alcohol outlets within the activity spaces of pre-adolescent children-a vulnerable, formative development stage-may help understand alcohol use in later life. METHODS: GPS data were collected from a nationally representative sample of 10-and-11-year-old children (n = 688, 55% female). The proportion of children, and the proportion of each child's GPS, exposed to alcohol outlets was compared across area-level income-deprivation quintiles, along with the relative proportion of exposure occurring within 500 m of each child's home and school. RESULTS: Off-sales alcohol outlets accounted for 47% of children's exposure, which was higher than expected given their availability (31% of alcohol outlets). The proportion of children exposed to alcohol outlets did not differ by area deprivation. However, the proportion of time children were exposed showed stark inequalities. Children living in the most deprived areas were almost five times more likely to be exposed to off-sales alcohol outlets than children in the least deprived areas (OR 4.83, 3.04-7.66; P < 0.001), and almost three times more likely to be exposed to on-sales alcohol outlets (OR 2.86, 1.11-7.43; P = 0.03). Children in deprived areas experienced 31% of their exposure to off-sales outlets within 500 m of their homes compared to 7% for children from less deprived areas. Children from all areas received 22-32% of their exposure within 500 m of schools, but the proportion of this from off-sales outlets increased with area deprivation. CONCLUSIONS: Children have little control over what they are exposed to, so policies that reduce inequities in alcohol availability should be prioritised to ensure that all children have the opportunity to lead healthy lives.

GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative.

Natural environments, such as parks, woodlands and lakes, have positive impacts on health and wellbeing. Urban Green and Blue Spaces (UGBS), and the activities that take place in them, can significantly influence the health outcomes of all communities, and reduce health inequalities. Improving access and quality of UGBS needs understanding of the range of systems (e.g. planning, transport, environment, community) in which UGBS are located. UGBS offers an ideal exemplar for testing systems innovations as it reflects place-based and whole society processes , with potential to reduce non-communicable disease (NCD) risk and associated social inequalities in health. UGBS can impact multiple behavioural and environmental aetiological pathways. However, the systems which desire, design, develop, and deliver UGBS are fragmented and siloed, with ineffective mechanisms for data generation, knowledge exchange and mobilisation. Further, UGBS need to be co-designed with and by those whose health could benefit most from them, so they are appropriate, accessible, valued and used well. This paper describes a major new prevention research programme and partnership, GroundsWell, which aims to transform UGBS-related systems by improving how we plan, design, evaluate and manage UGBS so that it benefits all communities, especially those who are in poorest health. We use a broad definition of health to include physical, mental, social wellbeing and quality of life. Our objectives are to transform systems so that UGBS are planned, developed, implemented, maintained and evaluated with our communities and data systems to enhance health and reduce inequalities. GroundsWell will use interdisciplinary, problem-solving approaches to accelerate and optimise community collaborations among citizens, users, implementers, policymakers and researchers to impact research, policy, practice and active citizenship. GroundsWell will be shaped and developed in three pioneer cities (Belfast, Edinburgh, Liverpool) and their regional contexts, with embedded translational mechanisms to ensure that outputs and impact have UK-wide and international application.

Neighbourhood natural space and the narrowing of socioeconomic inequality in children's social, emotional, and behavioural wellbeing.

INTRODUCTION: The natural environment may benefit children's social, emotional and behavioural wellbeing, whilst offering a lever to narrow socioeconomic health inequalities. We investigated whether immediate neighbourhood natural space and private gardens were related to children's wellbeing outcomes and whether these relationships were moderated by household income. METHODS: A nationally representative sample of 774 children (55% female, 10/11 years old) from the Studying Physical Activity in Children's Environments across Scotland study. Social, emotional and behavioural difficulty scores (Strengths and Difficulties Questionnaire) represented wellbeing outcomes. Percentage of total natural space and private gardens within 100m of the child's residence was quantified using Ordnance Survey's MasterMap Topography Layer®. Linear regression, including interaction terms, explored the two main research questions. RESULTS: A 10% increase in residential natural space was associated with a 0.08 reduction (-0.15, -0.01; 95%CI) in Emotional Problem scores and a 0.09 improvement (0.02, 0.16; 95%CI) in Prosocial Behaviour scores. Household income moderated the associations between % natural space and private gardens on Prosocial Behaviour scores: for natural space, there was a positive relationship for those in the lowest income quintile (0.25 (0.09, 0.41; 95%CI)) and a null relationship for those in the highest quintile (-0.07 (-0.16, 0.02; 95%CI)). For private garden space, there was a positive relationship for those in the highest quintile (0.15 (0.05, 0.26; 95%CI)) and negative relationship with those in the lowest quintile (-0.30 (-0.50, -0.07, 95%CI)). CONCLUSION: The natural environment could be a lever to benefit those from less advantaged backgrounds, particularly the development of prosocial behaviours.

Theory driven analysis of social class and health outcomes using UK nationally representative longitudinal data.

BACKGROUND: Social class is frequently used as a means of ranking the population to expose inequalities in health, but less often as a means of understanding the social processes of causation. We explored how effectively different social class mechanisms could be measured by longitudinal cohort data and whether those measures were able to explain health outcomes. METHODS: Using a theoretically informed approach, we sought to map variables within the National Child Development Study (NCDS) to five different social class mechanisms: social background and early life circumstances; habitus and distinction; exploitation and domination; location within market relations; and power relations. Associations between the SF-36 physical, emotional and general health outcomes at age 50 years and the social class measures within NCDS were then assessed through separate multiple linear regression models. R2 values were used to quantify the proportion of variance in outcomes explained by the independent variables. RESULTS: We were able to map the NCDS variables to the each of the social class mechanisms except 'Power relations'. However, the success of the mapping varied across mechanisms. Furthermore, although relevant associations between exposures and outcomes were observed, the mapped NCDS variables explained little of the variation in health outcomes: for example, for physical functioning, the R2 values ranged from 0.04 to 0.10 across the four mechanisms we could map. CONCLUSIONS: This study has demonstrated both the potential and the limitations of available cohort studies in measuring aspects of social class theory. The relatively small amount of variation explained in the outcome variables in this study suggests that these are imperfect measures of the different social class mechanisms. However, the study lays an important foundation for further research to understand the complex interactions, at various life stages, between different aspects of social class and subsequent health outcomes.

The relationship between living in urban and rural areas of Scotland and children's physical activity and sedentary levels: a country-wide cross-sectional analysis.

BACKGROUND: Living in urban or rural environments may influence children's levels of physical activity and sedentary behaviours. We know little about variations in device-measured physical activity and sedentary levels of urban and rural children using nationally representative samples, or if these differences are moderated by socioeconomic factors or seasonal variation. Moreover, little is known about the influence of 'walkability' in the UK context. A greater understanding of these can better inform intervention strategies or policy initiatives at the population level. METHODS: Country-wide cross-sectional study in Scotland in which 774 children (427 girls, 357 boys), aged 10/11 years, wore an accelerometer on one occasion for at least four weekdays and one weekend day. Mean total physical activity, time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA), per day were extracted for weekdays, weekend days, and all days combined. Regression analyses explored associations between physical activity outcomes, urban/rural residence, and a modified walkability index (dwelling density and intersection density); with interactions fitted for household equivalised income and season of data collection. Sensitivity analyses assessed variation in findings by socioeconomic factors and urbanicity. RESULTS: Rural children spent an average of 14 min less sedentary (95% CI of difference: 2.23, 26.32) and 13 min more in light intensity activity (95% CI of difference, 2.81, 24.09) per day than those from urban settlements. No urban-rural differences were found for time spent in MVPA or in total levels of activity. Our walkability index was not associated with any outcome measure. We found no interactions with household equivalised income, but there were urban/rural differences in seasonal variation; urban children engaged in higher levels of MVPA in the spring months (difference: 10 mins, p = 0.06, n.s) and significantly lower levels in winter (difference: 8.7 mins, p = 0.036). CONCLUSIONS: Extrapolated across one-year, rural children would accumulate approximately 79 h (or just over 3 days) less sedentary time than urban children, replacing this for light intensity activity. With both outcomes having known implications for health, this finding is particularly important. Future work should prioritise exploring the patterns and context in which these differences occur to allow for more targeted intervention/policy strategies.

Theorising social class and its application to the study of health inequalities.

The literature on health inequalities often uses measures of socio-economic position pragmatically to rank the population to describe inequalities in health rather than to understand social and economic relationships between groups. Theoretical considerations about the meaning of different measures, the social processes they describe, and how these might link to health are often limited. This paper builds upon Wright's synthesis of social class theories to propose a new integrated model for understanding social class as applied to health. This model incorporates several social class mechanisms: social background and early years' circumstances; Bourdieu's habitus and distinction; social closure and opportunity hoarding; Marxist conflict over production (domination and exploitation); and Weberian conflict over distribution. The importance of discrimination and prejudice in determining the opportunities for groups is also explicitly recognised, as is the relationship with health behaviours. In linking the different social class processes we have created an integrated theory of how and why social class causes inequalities in health. Further work is required to test this approach, to promote greater understanding of researchers of the social processes underlying different measures, and to understand how better and more comprehensive data on the range of social class processes these might be collected in the future.

Comparison of two methods to assess physical activity prevalence in children: an observational study using a nationally representative sample of Scottish children aged 10-11 years.

OBJECTIVES: To describe the objectively measured levels of physical activity (PA) and sedentary time in a nationally representative sample of 10-11-year-old children, and compare adherence estimates to the UK PA guidelines using two approaches to assessing prevalence. DESIGN: Nationally representative longitudinal cohort study. SETTING: Scotland wide in partnership with the Growing up in Scotland (GUS) study. Data collection took place between May 2015 and May 2016. PARTICIPANTS: The parents of 2402 GUS children were approached and 2162 consented to contact. Consenting children (n=1096) wore accelerometers for 8 consecutive days and 774 participants (427 girls, 357 boys) met inclusion criteria. PRIMARY AND SECONDARY OUTCOME MEASURES: Total PA (counts per minute, cpm); time spent sedentary and in moderate-to-vigorous PA (MVPA); proportion of children with ≥60 min MVPA on each day of wear (daily approach); proportion of children with ≥60 min of MVPA on average across days of wear (average approach)-presented across boys and girls, index of multiple deprivation and season. RESULTS: Mean PA level was 648 cpm (95% CI, 627 to 670). Children spent 7.5 hours (7.4-7.6) sedentary/day and 72.6 min (70.0-75.3) in MVPA/day. 11% (daily) and 68% (average) of children achieved the recommended levels of PA (P<0.05 for difference); a greater proportion of boys met the guidelines (12.5% vs 9.8%, NS; 75.9% vs 59.5%, P<0.001); guideline prevalence exhibited seasonal variation. No significant socioeconomic patterning existed across any outcome measure. CONCLUSIONS: PA estimates are significantly influenced by the analytical method used to assess prevalence. This could have a substantial impact on the evaluation of interventions, policy objectives and public health investment. Existing guidelines, which focus on daily PA only may not further our understandings about the underlying construct itself. Gender differences exist within this age-group, suggesting greater investment, with particular consideration of seasonality, is needed for targeted intervention work in younger children.