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Given previous evidence that not all Scotland's higher mortality compared to England & Wales (E&W) can be explained by deprivation, the aim was to enhance understanding of this excess by analysing changes in deprivation and mortality in Scotland and E&W between 1981 and 2011. Mortality was compared by means of direct standardisation and log-linear Poisson regression models, adjusting for age, sex and deprivation. Different measures of deprivation were employed, calculated at different spatial scales. Results show that Scotland became less deprived compared to E&W between 1981 and 2011. However, the Scottish excess (the difference in mortality rates relative to E&W after adjustment for deprivation) increased from 4% higher (c.1981) to 10% higher in 2010-12. The latter figure equates to c. 5000 extra deaths per year. The increase was driven by higher mortality from cancer, suicide, alcohol related causes and drugs-related poisonings. The size and increase in Scottish excess mortality are major concerns. Investigations into its underlying causes continue, the findings of which will be relevant to other populations, given that similar excesses have been observed elsewhere in Britain., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Migrant and ethnic minority groups are often assumed to have poor health relative to the majority population. Few countries have the capacity to study a key indicator, mortality, by ethnicity and country of birth. We hypothesized at least 10% differences in mortality by ethnic group in Scotland that would not be wholly attenuated by adjustment for socio-economic factors or country of birth.Methods and Findings: We linked the Scottish 2001 Census to mortality data (2001-2013) in 4.62 million people (91% of estimated population), calculating age-adjusted mortality rate ratios (RRs; multiplied by 100 as percentages) with 95% confidence intervals (CIs) for 13 ethnic groups, with the White Scottish group as reference (ethnic group classification follows the Scottish 2001 Census). The Scottish Index of Multiple Deprivation, education status, and household tenure were socio-economic status (SES) confounding variables and born in the UK or Republic of Ireland (UK/RoI) an interacting and confounding variable. Smoking and diabetes data were from a primary care sub-sample (about 53,000 people). Males and females in most minority groups had lower age-adjusted mortality RRs than the White Scottish group. The 95% CIs provided good evidence that the RR was more than 10% lower in the following ethnic groups: Other White British (72.3 [95% CI 64.2, 81.3] in males and 75.2 [68.0, 83.2] in females); Other White (80.8 [72.8, 89.8] in males and 76.2 [68.6, 84.7] in females); Indian (62.6 [51.6, 76.0] in males and 60.7 [50.4, 73.1] in females); Pakistani (66.1 [57.4, 76.2] in males and 73.8 [63.7, 85.5] in females); Bangladeshi males (50.7 [32.5, 79.1]); Caribbean females (57.5 [38.5, 85.9]); and Chinese (52.2 [43.7, 62.5] in males and 65.8 [55.3, 78.2] in females). The differences were diminished but not eliminated after adjusting for UK/RoI birth and SES variables. A mortality advantage was evident in all 12 minority groups for those born abroad, but in only 6/12 male groups and 5/12 female groups of those born in the UK/RoI. In the primary care sub-sample, after adjustment for age, UK/RoI born, SES, smoking, and diabetes, the RR was not lower in Indian males (114.7 [95% CI 78.3, 167.9]) and Pakistani females (103.9 [73.9, 145.9]) than in White Scottish males and females, respectively. The main limitations were the inability to include deaths abroad and the small number of deaths in some ethnic minority groups, especially for people born in the UK/RoI.Conclusions: There was relatively low mortality for many ethnic minority groups compared to the White Scottish majority. The mortality advantage was less clear in UK/RoI-born minority group offspring than in immigrants. These differences need explaining, and health-related behaviours seem important. Similar analyses are required internationally to fulfil agreed goals for monitoring, understanding, and improving health in ethnically diverse societies and to apply to health policy, especially on health inequalities and inequities. [ABSTRACT FROM AUTHOR]

Background Many theories have been proposed to explain the high levels of ‘excess' mortality (i.e. higher mortality over and above that explained by differences in socio-economic circumstances) shown in Scotland—and, especially, in its largest city, Glasgow—compared with elsewhere in the UK. One such proposal relates to differences in optimism, given previously reported evidence of the health benefits of an optimistic outlook. Methods A representative survey of Glasgow, Liverpool and Manchester was undertaken in 2011. Optimism was measured by the Life Orientation Test (Revised) (LOT-R), and compared between the cities by means of multiple linear regression models, adjusting for any differences in sample characteristics. Results Unadjusted analyses showed LOT-R scores to be similar in Glasgow and Liverpool (mean score (SD): 14.7 (4.0) for both), but lower in Manchester (13.9 (3.8)). This was consistent in analyses by age, gender and social class. Multiple regression confirmed the city results: compared with Glasgow, optimism was either similar (Liverpool: adjusted difference in mean score: 20.16 (95% CI 20.45 to 0.13)) or lower (Manchester: 20.85 (21.14 to 20.56)). Conclusions The reasons for high levels of Scottish ‘excess' mortality remain unclear. However, differences in psychological outlook such as optimism appear to be an unlikely explanation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]