4.8 SOBRE PUEBLOS E INDÍGENAS EN EL DERECHO INTERNACIONAL Algunos instrumentos jurídicos creados entre los Estados pueden contener

1G.1 Spend your health to gain your wealth or spend your wealth to regain your health? The relationship between financial expectations and health investment decisions

Matthew Little, Heather Brown, Jing Shen and John Wildman, Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK

Avoidable health inequalities are partly the result of socio-economic inequalities. A common example is the positive correlation between income and health. The

mechanisms behind this relationship are less well understood. One possible mechanism which has not previously been explored is possible links between expected future wealth and current health. This paper uses data from the British Household Panel SurveyWaves 1-18 (1991-2008) to identify relationships between subjective financial expectations and health investment decisions. Financial expectations are measured using the survey question on whether participants expected their financial position for the coming year to get 1) better; 2) worse; or 3) remain about the same. Health is measured using health investment decisions specifically the number of GP visits in the last year and whether the individual currently smokes. The analysis employs a random effects generalised least squares and fixed effects framework to test for potential endogeneity bias stemming from omitted variable bias. The results indicate the expectation responses are correlated with both current smoking status and the number of GP visits. An expected worse financial situation next year is associated with an increased likelihood to visit a GP on multiple occasions for men and women as well as a reduced likelihood to smoke for women. The results are robust after controlling for marital status, employment status, education, household size and income, age, gender, geographical region and time effects. The findings show financial expectations influence health investment decisions. This may partly explain countercyclical patterns observed in macroeconomic data between health and wealth.

1G.2 Income-related inequalities in adiposity in the United Kingdom: evidence from multiple adiposity measures

Apostolos Davillas and Michaela Benzeval, ISER, University of Essex, Colchester, UK

Obesity is a key public health problem; rapidly increasing in prevalence and being a significant predictor of subsequent poor health. Socio-economic inequalities in adiposity are, therefore, of particular interest themselves but also because they may contribute to broader health inequalities. Focusing on income, the aim of this paper is to better understand inequalities using concentration indexes (CI) and decomposition techniques. Applying data from Understanding SocietyWave 2 we estimate CI for body mass index (BMI), body composition (percentage body fat, %BF) and central obesity (waist

circumference, WC). CI are then decomposed into the contribution of each of the adiposity determinants to the total inequalities, in order to identify the underlying factors shaping the income-related inequalities in adiposity. In males, we found no income-related inequalities using BMI. However, disentangling fat from lean-mass we show significant pro-rich inequalities (fat-mass is more concentrated among the poor). %BF and WC are associated with similar pro-rich inequalities (CI: -0.017 and -0.013 respectively; P<0.05). Results for females indicate the presence of pro-rich inequalities irrespective of the adiposity measure (CI: ranged between -0.020 and -0.031; P<0.01). Decomposition analysis revealed that the pro-rich inequalities are due to correlation between income and other determinants of adiposity. We found that educational attainment, subjective financial status and material deprivation measures are significant determinants of adiposity and make the most prominent influence on explained these inequalities. This suggests that policy efforts should to focus on upstream rather than proximal causes of inequalities.

1G.3 Social inequality and health: what role for sleep?

Robert Meadows and Sara Arber, Sociology, Surrey University, Guildford, UK

There is now a growing body of literature which suggests that sleep quality/quantity

mediates the relationship between socio-economic status (SES) and health. This research is limited in two respects. First, social inequality is multidimensional and different dimensions of social position have different pathways to ill-health. There is a need to investigate whether the mediating role of sleep varies depending on what aspect of social inequality is being investigated. Second, sleep may play a complex role as a mediator. On the one hand, sleep can be seen as a health behaviour. On the other hand, sleep can be situated as a physiological response through which other pathways operate (i.e. job strain).

This paper uses data from Wave 4 of Understanding Society(n=43138). A series of path models are developed; each of which explore pathways from multiple measures of social position to self-reported ill-health. Model 1 includes ‘sleep problems’ as a potential

mediator. Model 2 adds further risk and protective factors parallel to sleep problems. Model 3 positions sleep problems as both a parallel mediator and part of the causal chain through which other risk and protective factors may operate.

All models will be discussed. Results from model 1, for example, highlight how those in the lowest occupational group report poorer health (direct effect = 0.189). Those experiencing material deprivation also experience poor health (0.1). In both instances, the indirect effect through sleep is significant (Sobel test statistic = 4.86 and 5.19 respectively).

1G.4 Inferring health milieu geographies from Understanding Societyand the

UK Census

Jens Kandt, Geography, University College London, London, UK

This paper presents theory, methods and results of combining the substantive depth of

Understanding Societywith the pervasiveness of 2011 Census neighbourhood statistics to estimate the local prevalence of so-called health behaviours in the context of lifestyle milieus. Drawing on Bourdieu’s theory of social practice, Wave 2 and 3 Understanding Societyrespondents were clustered based on a range of behavioural and attitudinal

variables. Nine milieus were found, which differed strongly in their social and demographic profiles. In addition, the milieus diverged significantly on various age-standardised

Understanding Societyhealth variables; this divergence cannot be solely attributed to socio-economic position. There rather appear to be specific health pathways at work with differential impacts. Deterministic spatial microsimulation was used to infer the milieus’ geographic distributions. The technique matches demographics of survey respondents with those of small areas and has so far produced distinct and plausible spatial distributions of milieus in London. The findings suggest that a combined approach of sample segmentation and microsimulation offers opportunities to render subjective information of social surveys amenable to applied spatial statistics.

The work is ongoing and will be extended to other UK cities with the additional objective to account for potential place effects in milieu specificity and associated socio-demographics and geographies. Integrating Understanding Societyand population-wide datasets may be a promising way forward not only in inferring spatial distributions of survey responses but also in adding domain-relevant subjective orientations to neighbourhood statistics and supporting targeted, area-based policy interventions.