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)

The income-health gradient: Evidence from self-reported health and biomarkers using longitudinal data on income

This paper adds to the literature on the income-health gradient by exploring the association of short- and long-term income with a wide set of self-reported health measures and objective nurse-administered and blood-based biomarkers as well as employing estimation techniques that allow for analysis 'beyond the mean' and accounting for unobserved heterogeneity. The income-health gradients are greater in magnitude in case of long-run rather than cross- sectional income measures. Unconditional quantile regressions reveal that the differences between the long-run and the short-run income gradients are more evident towards the right tails of the distributions, where both higher risk of illnesses and steeper income gradients are observed. A two-step estimator, involving a fixed-effects income model at the first stage, shows that the individual-specific selection effects have a systematic impact in the long-run income gradients in self-reported health but not in biomarkers, highlighting the importance of reporting error in self-reported health

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

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

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

Socioeconomic position across the lifecourse & allostatic load: data from the West of Scotland Twenty-07 cohort study

Background: We examined how socioeconomic position (SEP) across the lifecourse (three critical periods, social mobility and accumulated over time) is associated with allostatic load (a measure of cumulative physiological burden). Methods. Data are from the West of Scotland Twenty-07 Study, with respondents aged 35 (n = 740), 55 (n = 817) and 75 (n = 483). SEP measures representing childhood, the transition to adulthood and adulthood SEP were used. Allostatic load was produced by summing nine binary biomarker scores (1 = in the highest-risk quartile). Linear regressions were used for each of the lifecourse models; with model fits compared using partial F-tests. Results: For those aged 35 and 55, higher SEP was associated with lower allostatic load (no association in the 75-year-olds). The accumulation model (more time spent with higher SEP) had the best model fit in those aged 35 (b = -0.50, 95%CI = -0.68, -0.32, P = 0.002) and 55 (b = -0.31, 95%CI = -0.49, -0.12, P < 0.001). However, the relative contributions of each life-stage differed, with adulthood SEP less strongly associated with allostatic load. Conclusions: Long-term, accumulated higher SEP has been shown to be associated with lower allostatic load (less physiological burden). However, the transition to adulthood may represent a particularly sensitive period for SEP to impact on allostatic load. © 2014 Robertson et al.; licensee BioMed Central Ltd

Is telomere length socially patterned? Evidence from the West of Scotland Twenty-07 study

Lower socioeconomic status (SES) is strongly associated with an increased risk of morbidity and premature mortality, but it is not known if the same is true for telomere length, a marker often used to assess biological ageing. The West of Scotland Twenty-07 Study was used to investigate this and consists of three cohorts aged approximately 35 (N = 775), 55 (N = 866) and 75 years (N = 544) at the time of telomere length measurement. Four sets of measurements of SES were investigated: those collected contemporaneously with telomere length assessment, educational markers, SES in childhood and SES over the preceding twenty years. We found mixed evidence for an association between SES and telomere length. In 35-year-olds, many of the education and childhood SES measures were associated with telomere length, i.e. those in poorer circumstances had shorter telomeres, as was intergenerational social mobility, but not accumulated disadvantage. A crude estimate showed that, at the same chronological age, social renters, for example, were nine years (biologically) older than home owners. No consistent associations were apparent in those aged 55 or 75. There is evidence of an association between SES and telomere length, but only in younger adults and most strongly using education and childhood SES measures. These results may reflect that childhood is a sensitive period for telomere attrition. The cohort differences are possibly the result of survival bias suppressing the SES-telomere association; cohort effects with regard different experiences of SES; or telomere possibly being a less effective marker of biological ageing at older ages

What do older people do when sitting and why? Implications for decreasing sedentary behaviour

Background and Objectives: Sitting less can reduce older adults’ risk of ill health and disability. Effective sedentary behavior interventions require greater understanding of what older adults do when sitting (and not sitting), and why. This study compares the types, context, and role of sitting activities in the daily lives of older men and women who sit more or less than average. Research Design and Methods: Semistructured interviews with 44 older men and women of different ages, socioeconomic status, and objectively measured sedentary behavior were analyzed using social practice theory to explore the multifactorial, inter-relational influences on their sedentary behavior. Thematic frameworks facilitated between-group comparisons. Results: Older adults described many different leisure time, household, transport, and occupational sitting and non-sitting activities. Leisure-time sitting in the home (e.g., watching TV) was most common, but many non-sitting activities, including “pottering” doing household chores, also took place at home. Other people and access to leisure facilities were associated with lower sedentary behavior. The distinction between being busy/not busy was more important to most participants than sitting/not sitting, and informed their judgments about high-value “purposeful” (social, cognitively active, restorative) sitting and low-value “passive” sitting. Declining physical function contributed to temporal sitting patterns that did not vary much from day-to-day. Discussion and Implications: Sitting is associated with cognitive, social, and/or restorative benefits, embedded within older adults’ daily routines, and therefore difficult to change. Useful strategies include supporting older adults to engage with other people and local facilities outside the home, and break up periods of passive sitting at home