Neighbourhood deprivation and biomarkers of health in Britain: the mediating role of the physical environment

Background: Neighborhood deprivation has been consistently linked to poor individual health outcomes; however, studies exploring the mechanisms involved in this association are scarce. The objective of this study was to investigate whether objective measures of the physical environment mediate the association between neighborhood socioeconomic deprivation and biomarkers of health in Britain. Methods: We linked individual-level biomarker data from Understanding Society: The UK Household Longitudinal Survey (2010–2012) to neighborhood-level data from different governmental sources. Our outcome variables were forced expiratory volume in 1 s (FEV1%; n=16,347), systolic blood pressure (SBP; n=16,846), body mass index (BMI; n=19,417), and levels of C-reactive protein (CRP; n=11,825). Our measure of neighborhood socioeconomic deprivation was the Carstairs index, and the neighborhood-level mediators were levels of air pollutants (sulphur dioxide [SO2], particulate matter [PM10], nitrogen dioxide [NO2], and carbon monoxide [CO]), green space, and proximity to waste and industrial facilities. We fitted a multilevel mediation model following a multilevel structural equation framework in MPlus v7.4, adjusting for age, gender, and income. Results: Residents of poor neighborhoods and those exposed to higher pollution and less green space had worse health outcomes. However, only SO2 exposure significantly and partially mediated the association between neighborhood socioeconomic deprivation and SBP, BMI, and CRP. Conclusion: Reducing air pollution exposure and increasing access to green space may improve population health but may not decrease health inequalities in Britain

Mixing modes and measurement methods in longitudinal studies

Across the world longitudinal studies are facing falling response rates, at the same time cost imperatives are bringing into question the feasibility of large scale regular face-to-face data collection. While, the rapid development of communications technology and associated cultural changes is assumed to mean that study participants will increasingly expect to be able to answer surveys when and how it suits them. All of these factors are driving longitudinal studies to combine different modes of data collection both to increase response and to reduce costs. Mixing modes of data collection either across individuals at one point in time or within individuals over time, presents longitudinal researchers with a range of methodological challenges in both data collection and analysis. Within CLOSER, and beyond, studies are investigating different aspects of the implications of mixed mode data collection, and giving data users varying degrees of support and advice about issues that should be of concern. Drawing on evidence from across CLOSER’s longitudinal studies, this report reviews the latest evidence gathered on the effect of mixing modes and measurement methods on response, measurement issues and survey costs. The review also focuses on the implications for analysis of measures collected in different ways either across individuals at the same point in time or within individuals over time. Building on these reviews, we identify what further research is required in relation to both the design and analysis of mixed mode data collection. The contents of this report is based on a CLOSER workshop held in November 2016 (http://www.closer.ac.uk/event/mixing-modes-measurement-methods-longitudinal-studies/). The workshop and report were funded by a CLOSER Innovation grant awarded to Michaela Benzeval and Annette Jäckle (University of Essex), and Kate Tilling and Dr Andy Skinner (University of Bristol) and are part of a series of three reports (see Jäckle, Gaia, & Benzeval, 2017; Stone & Skinner, 2017)

Associations of Successful Aging With Socioeconomic Position Across the Life-Course: The West of Scotland Twenty-07 Prospective Cohort Study

Objective: The aim of this study is to investigate how socioeconomic position (SEP) is associated with multidimensional measures of successful aging (SA), and how this varies and accumulates across the life-course. Method: Using data from 1,733 Scottish men and women from two cohorts aged around 57 and 76, respectively, we explored associations of SA, based on the Rowe?Kahn model, with 10 measures of SEP measured in childhood and, distally and proximally, in adulthood. Results: Individual SEP associations with SA score were generally consistent across different indicators and life stages: Respondents with the most versus least favorable SEP had two additional positive SA dimensions. There was also a strong association between SA and cumulative SEP based on all 10 measures combined; respondents with the most versus least favorable lifelong SEP had four additional positive SA dimensions. Conclusion: SEP advantages/disadvantages act and accumulate across the life-course, resulting in widening socioeconomic inequalities in SA in later life

To What Extent Do Financial Strain and Labour Force Status Explain Social Class Inequalities in Self-Rated Health? Analysis of 20 Countries in the European Social Survey

Introduction: Nordic countries do not have the smallest health inequalities despite egalitarian social policies. A possible explanation for this is that drivers of class differences in health such as financial strain and labour force status remain socially patterned in Nordic countries. Methods: Our analyses used data for working age (25?59) men (n=48,249) and women (n=52,654) for 20 countries from five rounds (2002?2010) of the European Social Survey. The outcome was self-rated health in 5 categories. Stratified by gender we used fixed effects linear regression models and marginal standardisation to instigate how countries varied in the degree to which class inequalities were attenuated by financial strain and labour force status. Results and Discussion: Before adjustment, Nordic countries had large inequalities in self-rated health relative to other European countries. For example the regression coefficient for the difference in health between working class and professional men living in Norway was 0.34 (95% CI 0.26 to 0.42), while the comparable figure for Spain was 0.15 (95% CI 0.08 to 0.22). Adjusting for financial strain and labour force status led to attenuation of health inequalities in all countries. However, unlike some countries such as Spain, where after adjustment the regression coefficient for working class men was only 0.02 (95% CI 2 0.05 to 0.10), health inequalities persisted after adjustment for Nordic countries. For Norway the adjusted coefficient was 0.17 (95% CI 0.10 to 0.25). Results for women and men were similar. However, in comparison to men, class inequalities tended to be stronger for women and more persistent after adjustment. Conclusions: Adjusting for financial security and labour force status attenuates a high proportion of health inequalities in some counties, particularly Southern European countries, but attenuation in Nordic countries was modest and did not improve their relative position

Capacitor performance limitations in high power converter applications

Background: Over the last 80 years the association between social class and obesity has changed. In the 1930s obesity rates were low and wealthy people tended to have a higher risk of obesity than poor people. However, rising affluence and industrialisation has lead to both rising rates of obesity and an obesogenic environment in which socioeconomically disadvantaged people have the highest risk of obesity. This study investigates the magnitude of these changes by modelling trajectories of adiposity by social class and cohort using the Twenty-07 study. Methods: The Twenty-07 study contains three cohorts of people (n = 4510), born in Glasgow in the 1930s, 1950s and 1970s. Two measures of adiposity, BMI and Waist to Height Ratio (WHtR), were recorded at baseline in 1987/8 when study participants were aged 15, 35 or 55, and again on 4 further occasions over 20 years. Parental social class (manual/non-manual) was collected at baseline. For each gender, we apply multilevel models to identify trajectories of adiposity by cohort and social class. Results: The trajectories indicated that adiposity increased with age and rates of increase varied by cohort, social class and gender. For any given age the youngest cohort had the fastest rate of increase and the highest predicted adiposity. For example, at age 35 for non-manual men, BMI was 24.2 (95% CI 23.7, 24.8) for the 1950s cohort and 27.2 (26.8, 27.5) for the 1970s cohort. By the end of the study respondents in more recent cohorts had BMI values approximately equivalent to those of people aged 20 years older in an earlier cohort. Cohort variation was much greater than socioeconomic variation. The smallest cohort difference in BMI was 2.10 (0.94, 3.26), a comparison of the 1950 and 1930s cohorts for non-manual men at age 55. In contrast, the largest social class difference in BMI, a comparison of manual and non manual women at age 64, was only 1.18 (0.37, 1.98). Socioeconomic inequalities tended to be smaller for men than women, particularly for the 1930s cohort where there was no evidence of a socioeconomic gradient for men unlike for women. The main difference between WHtR and BMI was that increases in WHtR accelerated with age whilst increases in BMI slowed with age. Conclusion: Increases in adiposity for younger cohorts across all socioeconomic groups dwarf any socioeconomic inequalities in adiposity. This highlights the damaging impact for the whole population of living in an obesogenic environment

Ageing, social class and common mental disorders: longitudinal evidence from three cohorts in the West of Scotland

Background: Understanding how common mental disorders such as anxiety and depression vary with socio-economic circumstances as people age can help to identify key intervention points. However, much research treats these conditions as a single disorder when they differ significantly in terms of their disease burden. This paper examines the socio-economic pattern of anxiety and depression separately and longitudinally to develop a better understanding of their disease burden for key social groups at different ages. Method: The Twenty-07 Study has followed 4510 respondents from three cohorts in the West of Scotland for 20 years and 3846 respondents had valid data for these analyses. Hierarchical repeated-measures models were used to investigate the relationship between age, social class and the prevalence of anxiety and depression over time measured as scores of 8 or more out of 21 on the relevant subscale of the Hospital Anxiety and Depression Scale (HADS). Results: Social class differences in anxiety and depression widened with age. For anxiety there was a nonlinear decrease in prevalence with age, decreasing more slowly for those from manual classes compared to non-manual, whereas for depression there was a non-linear increase in prevalence with age, increasing more quickly for those from manual classes compared to non-manual. This relationship is robust to cohort, period and attrition effects. Conclusions: The more burdensome disorder of depression occurs more frequently at ages where socio-economic inequalities in mental health are greatest, representing a ‘double jeopardy’ for older people from a manual class

How does money influence health?

Why do people in poverty tend to have poorer health? This study looks at hundreds of theories to consider how income influences health. There is a graded association between money and health ? increased income equates to better health. But the reasons are debated. Researchers have reviewed theories from 272 wide-ranging papers, most of which examined the complex interactions between people?s income and their health throughout their lives. Key points This research identifies four main ways money affects people?s wellbeing: Material: Money buys goods and services that improve health. The more money families have, the better the goods they can buy. Psychosocial: Managing on a low income is stressful. Comparing oneself to others and feeling at the bottom of the social ladder can be distressing, which can lead to biochemical changes in the body, eventually causing ill health. Behavioural: For various reasons, people on low incomes are more likely to adopt unhealthy behaviours ? smoking and drinking, for example ? while those on higher incomes are more able to afford healthier lifestyles. Reverse causation (poor health leads to low income): Health may affect income by preventing people from taking paid employment. Childhood health may also affect educational outcomes, limiting job opportunities and potential earnings. The research is part of our programme of work on poverty in the UK

Age modification of the relationship between C-reactive protein and fatigue: findings from Understanding Society (UKHLS)

Background: Systemic inflammation may play a role in the development of idiopathic fatigue, that is, fatigue not explained by infections or diagnosed chronic illness, but this relationship has never been investigated in community studies including the entire adult age span. We examine the association of the inflammatory marker C-reactive protein (CRP) and fatigue assessed annually in a 3-year outcome period for UK adults aged 16–98. Methods: Multilevel models were used to track fatigue 7, 19, and 31 months after CRP measurement, in 10 606 UK individuals. Models accounted for baseline fatigue, demographics, health conditions diagnosed at baseline and during follow-up, adiposity, and psychological distress. Sensitivity analyses considered factors including smoking, sub-clinical disease (blood pressure, anaemia, glycated haemoglobin), medications, ethnicity, and alcohol consumption. Results: Fatigue and CRP increased with age, and women had higher values than men. CRP was associated with future self-reported fatigue, but only for the oldest participants. Thus, in those aged 61–98 years, high CRP ( > 3 mg/L) independently predicted greater fatigue 7, 19, and 31 months after CRP measurement [odds ratio for new-onset fatigue after 7 months: 1.88, 95% confidence interval (CI) 1.21–2.92; 19 months: 2.25, CI 1.46–3.49; 31 months: 1.65, CI 1.07–2.54]. No significant longitudinal associations were seen for younger participants. Conclusions: Our findings support previously described CRP–fatigue associations in older individuals. However, there are clear age modifications in these associations, which may reflect a contribution of unmeasured sub-clinical disease of limited relevance to younger individuals. Further work is necessary to clarify intervening processes linking CRP and fatigue in older individuals