The Role of Socioeconomic Status in the Association of Lung Function and Air Pollution-A Pooled Analysis of Three Adult ESCAPE Cohorts.

Ambient air pollution is a leading environmental risk factor and its broad spectrum of adverse health effects includes a decrease in lung function. Socioeconomic status (SES) is known to be associated with both air pollution exposure and respiratory function. This study assesses the role of SES either as confounder or effect modifier of the association between ambient air pollution and lung function. Cross-sectional data from three European multicenter adult cohorts were pooled to assess factors associated with lung function, including annual means of home outdoor NO2. Pre-bronchodilator lung function was measured according to the ATS-criteria. Multiple mixed linear models with random intercepts for study areas were used. Three different factors (education, occupation and neighborhood unemployment rate) were considered to represent SES. NO2 exposure was negatively associated with lung function. Occupation and neighborhood unemployment rates were not associated with lung function. However, the inclusion of the SES-variable education improved the models and the air pollution-lung function associations got slightly stronger. NO2 associations with lung function were not substantially modified by SES-variables. In this multicenter European study we could show that SES plays a role as a confounder in the association of ambient NO2 exposure with lung function.

Socioeconomic position during pregnancy and DNA methylation signatures at three stages across early life: epigenome-wide association studies in the ALSPAC birth cohort.

BACKGROUND: Socioeconomic experiences are recognized determinants of health, and recent work has shown that social disadvantages in early life may induce sustained biological changes at molecular level that are detectable later in life. However, the dynamics and persistence of biological embedding of socioeconomic position (SEP) remains vastly unexplored. METHODS: Using the data from the ALSPAC birth cohort, we performed epigenome-wide association studies of DNA methylation changes at three life stages (birth, n = 914; childhood at mean age 7.5 years, n = 973; and adolescence at mean age 15.5 years, n = 974), measured using the Illumina HumanMethylation450 Beadchip, in relation to pregnancy SEP indicators (maternal and paternal education and occupation). RESULTS: Across the four early life SEP metrics investigated, only maternal education was associated with methylation levels at birth, and four CpGs mapped to SULF1, GLB1L2 and RPUSD1 genes were identified [false discovery rate (FDR)-corrected P-value <0.05]. No epigenetic signature was found associated with maternal education in child samples, but methylation levels at 20 CpG loci were found significantly associated with maternal education in adolescence. Although no overlap was found between the differentially methylated CpG sites at different ages, we identified two CpG sites at birth and during adolescence which are 219 bp apart in the SULF1 gene that encodes an heparan sulphatase involved in modulation of signalling pathways. Using data from an independent birth cohort, the ENVIRONAGE cohort, we were not able to replicate these findings. CONCLUSIONS: Taken together, our results suggest that parental SEP, and particularly maternal education, may influence the offspring's methylome at birth and adolescence.

Relationship between mediation analysis and the structured life course approach.

Many questions in life course epidemiology involve mediation and/or interaction because of the long latency period between exposures and outcomes. In this paper, we explore how mediation analysis (based on counterfactual theory and implemented using conventional regression approaches) links with a structured approach to selecting life course hypotheses. Using theory and simulated data, we show how the alternative life course hypotheses assessed in the structured life course approach correspond to different combinations of mediation and interaction parameters. For example, an early life critical period model corresponds to a direct effect of the early life exposure, but no indirect effect via the mediator and no interaction between the early life exposure and the mediator. We also compare these methods using an illustrative real-data example using data on parental occupational social class (early life exposure), own adult occupational social class (mediator) and physical capability (outcome).

Childhood wheezing, asthma, allergy, atopy, and lung function: different socioeconomic patterns for different phenotypes.

Identifying preventable exposures that lead to asthma and associated allergies has proved challenging, partly because of the difficulty in differentiating phenotypes that define homogeneous disease groups. Understanding the socioeconomic patterns of disease phenotypes can help distinguish which exposures are preventable. In the present study, we identified disease phenotypes that are susceptible to socioeconomic variation, and we determined which life-course exposures were associated with these inequalities in a contemporary birth cohort. Participants included children from the Avon Longitudinal Study of Parents and Children, a population-based birth cohort in England, who were born in 1991 and 1992 and attended the clinic at 7-8 years of age (n = 6,378). Disease phenotypes included asthma, atopy, wheezing, altered lung function, and bronchial reactivity phenotypes. Combining atopy with a diagnosis of asthma from a doctor captured the greatest socioeconomic variation, including opposing patterns between phenotype groups: Children with a low socioeconomic position (SEP) had more asthma alone (adjusted multinomial odds ratio = 1.50, 95% confidence interval: 1.21, 1.87) but less atopy alone (adjusted multinomial odds ratio = 0.80, 95% confidence interval: 0.66, 0.98) than did children with high SEP. Adjustment for maternal exposure to tobacco smoke during pregnancy and childhood exposure to tobacco smoke reduced the odds of asthma alone in children with a low SEP. Current inequalities among children who have asthma but not atopy can be prevented by eliminating exposure to tobacco smoke. Other disease phenotypes were not socially patterned or had SEP patterns that were not related to smoke exposure.

High life in the sky? Mortality by floor of residence in Switzerland.

Living in high-rise buildings could influence the health of residents. Previous studies focused on structural features of high-rise buildings or characteristics of their neighbourhoods, ignoring differences within buildings in socio-economic position or health outcomes. We examined mortality by floor of residence in the Swiss National Cohort, a longitudinal study based on the linkage of December 2000 census with mortality and emigration records 2001-2008. Analyses were based on 1.5 million people living in buildings with four or more floors and 142,390 deaths recorded during 11.4 million person-years of follow-up. Cox models were adjusted for age, sex, civil status, nationality, language, religion, education, professional status, type of household and crowding. The rent per m² increased with higher floors and the number of persons per room decreased. Mortality rates decreased with increasing floors: hazard ratios comparing the ground floor with the eighth floor and above were 1.22 [95% confidence interval (CI) 1.15-1.28] for all causes, 1.40 (95% CI 1.11-1.77) for respiratory diseases, 1.35 (95% CI 1.22-1.49) for cardiovascular diseases and 1.22 (95% CI 0.99-1.50) for lung cancer, but 0.41 (95% CI 0.17-0.98) for suicide by jumping from a high place. There was no association with suicide by any means (hazard ratio 0.81; 95% CI 0.57-1.15). We conclude that in Switzerland all-cause and cause-specific mortality varies across floors of residence among people living in high-rise buildings. Gradients in mortality suggest that floor of residence captures residual socioeconomic stratification and is likely to be mediated by behavioural (e.g. physical activity), and environmental exposures, and access to a method of suicide.

Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities.

BACKGROUND: Although cohort members tend to be healthy and affluent compared with the whole population, some studies indicate this does not bias certain exposure-outcome associations. It is less clear whether this holds when socioeconomic position (SEP) is the exposure of interest. METHODS: As an illustrative example, we use data from the Avon Longitudinal Study of Parents and Children. We calculate estimates of maternal education inequalities in outcomes for which data are available on almost the whole cohort (birth weight and length, breastfeeding, preterm birth, maternal obesity, smoking during pregnancy, educational attainment). These are calculated for the full cohort (n~12,000) and in restricted subsamples defined by continued participation at age 10 years (n∼7,000) and age 15 years (n∼5,000). RESULTS: Loss to follow-up was related both to SEP and outcomes. For each outcome, loss to follow-up was associated with underestimation of inequality, which increased as participation rates decreased (eg, mean birth-weight difference between highest and lowest SEP was 116 g [95% confidence interval = 78 to 153] in the full sample and 93 g [45 to 141] and 62 g [5 to 119] in those attending at ages 10 and 15 years, respectively). CONCLUSIONS: Considerable attrition from cohort studies may result in biased estimates of socioeconomic inequalities, and the degree of bias may worsen as participation rates decrease. However, even with considerable attrition (>50%), qualitative conclusions about the direction and approximate magnitude of inequalities did not change among most of our examples. The appropriate analysis approaches to alleviate bias depend on the missingness mechanism.

Associations between socioeconomic position and asthma: findings from a historical cohort.

Understanding the association between asthma and socioeconomic position (SEP) is key to identify preventable exposures to prevent inequalities and lessen overall disease burden. We aim to assess the variation in asthma across SEP groups in a historical cohort before the rise in asthma prevalence. Male students participating in a health survey at Glasgow University from 1948 to 1968 (n = 11,274) completed medical history of bronchitis, asthma, hay fever, eczema/urticaria, and reported father's occupation. A subsample responded to postal follow-up in adulthood (n = 4,101) that collected data on respiratory diseases, early life and adult SEP. Lower father's occupational class was associated with higher odds of asthma only (asthma without eczema/urticaria or hay fever) (trend adjusted multinomial odds ratio (aMOR) = 1.23, 95 % CI 1.03-1.47) but with lower odds of asthma with atopy (asthma with eczema/urticaria or hay fever) (trend aMOR = 0.66, 95 % CI 0.52-0.83) and atopy alone (trend aMOR = 0.84, 95 % CI 0.75-0.93). Household amenities (<3), in early life was associated with higher odds of adult-onset asthma (onset > 30 years) (OR = 1.48, 95 % CI 1.07-2.05) though this association attenuated after adjusting for age. Adult SEP (household crowding, occupation, income and car ownership) was not associated with adult-onset asthma. Lower father's occupational class in early life was associated with higher odds of asthma alone but lower odds of asthma with atopy in a cohort that preceded the 1960s rise in asthma prevalence. Different environmental exposures and/or disease awareness may explain this opposed socioeconomic patterning, but it is important to highlight that such patterning was already present before rises in the prevalence of asthma and atopy.

A Swiss neighbourhood index of socioeconomic position: development and association with mortality.

BACKGROUND: Area-based measures of socioeconomic position (SEP) suitable for epidemiological research are lacking in Switzerland. The authors developed the Swiss neighbourhood index of SEP (Swiss-SEP). METHODS: Neighbourhoods of 50 households with overlapping boundaries were defined using Census 2000 and road network data. Median rent per square metre, proportion households headed by a person with primary education or less, proportion headed by a person in manual or unskilled occupation and the mean number of persons per room were analysed in principle component analysis. The authors compared the index with independent income data and examined associations with mortality from 2001 to 2008. RESULTS: 1.27 million overlapping neighbourhoods were defined. Education, occupation and housing variables had loadings of 0.578, 0.570 and 0.362, respectively, and median rent had a loading of -0.459. Mean yearly equivalised income of households increased from SFr42 000 to SFr72 000 between deciles of neighbourhoods with lowest and highest SEP. Comparing deciles of neighbourhoods with lowest to highest SEP, the age- and sex-adjusted HR was 1.38 (95% CI 1.36 to 1.41) for all-cause mortality, 1.83 (95% CI 1.71 to 1.95) for lung cancer, 1.48 (95% CI 1.44 to 1.51) for cardiovascular diseases, 2.42 (95% CI 1.94 to 3.01) for traffic accidents, 0.93 (95% CI 0.85 to 1.02) for breast cancer and 0.86 (95% CI 0.78 to 0.95) for suicide. CONCLUSIONS: Developed using a novel approach to define neighbourhoods, the Swiss-SEP index was strongly associated with household income and some causes of death. It will be useful for clinical- and population-based studies, where individual-level socioeconomic data are often missing, and to investigate the effects on health of the socioeconomic characteristics of a place.

Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper.

Much has been written about the measurement of socio-economic position (SEP) in high-income countries (HIC). Less has been written for an epidemiology, health systems and public health audience about the measurement of SEP in low- and middle-income countries (LMIC). The social stratification processes in many LMIC-and therefore the appropriate measurement tools-differ considerably from those in HIC. Many measures of SEP have been utilized in epidemiological studies; the aspects of SEP captured by these measures and the pathways through which they may affect health are likely to be slightly different but overlapping. No single measure of SEP will be ideal for all studies and contexts; the strengths and limitations of a given indicator are likely to vary according to the specific research question. Understanding the general properties of different indicators, however, is essential for all those involved in the design or interpretation of epidemiological studies. In this article, we describe the measures of SEP used in LMIC. We concentrate on measures of individual or household-level SEP rather than area-based or ecological measures such as gross domestic product. We describe each indicator in terms of its theoretical basis, interpretation, measurement, strengths and limitations. We also provide brief comparisons between LMIC and HIC for each measure.

Social inequalities in height: persisting differences today depend upon height of the parents.

BACKGROUND: Substantial increases in height have occurred concurrently with economic development in most populations during the last century. In high-income countries, environmental exposures that can limit genetic growth potential appear to have lessened, and variation in height by socioeconomic position may have diminished. The objective of this study is to investigate inequalities in height in a cohort of children born in the early 1990s in England, and to evaluate which factors might explain any identified inequalities. METHODS AND FINDINGS: 12,830 children from The Avon Longitudinal Study of Parents and Children (ALSPAC), a population based cohort from birth to about 11.5 years of age, were used in this analysis. Gender- and age-specific z-scores of height at different ages were used as outcome variables. Multilevel models were used to take into account the repeated measures of height and to analyze gender- and age-specific relative changes in height from birth to 11.5 years. Maternal education was the main exposure variable used to examine socioeconomic inequalities. The roles of parental and family characteristics in explaining any observed differences between maternal education and child height were investigated. Children whose mothers had the highest education compared to those with none or a basic level of education, were 0.39 cm longer at birth (95% CI: 0.30 to 0.48). These differences persisted and at 11.5 years the height difference was 1.4 cm (95% CI: 1.07 to 1.74). Several other factors were related to offspring height, but few changed the relationship with maternal education. The one exception was mid-parental height, which fully accounted for the maternal educational differences in offspring height. CONCLUSIONS: In a cohort of children born in the 1990s, mothers with higher education gave birth to taller boys and girls. Although height differences were small they persisted throughout childhood. Maternal and paternal height fully explained these differences.

Socioeconomic differences in childhood growth trajectories: at what age do height inequalities emerge?

BACKGROUND: Socioeconomic differentials in adult height are frequently observed, but the age at which these inequalities emerge and the patterns they follow through childhood are unknown. SUBJECTS AND METHODS: Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), height trajectories from birth to 10 years (N=12366) were modelled. Individual trajectories were estimated using mixed-effects models. Differences in trajectories by socioeconomic position (SEP) were investigated. RESULTS: There was a clear gradient in birth length across categories of maternal education; average birth length in boys was 0.41 cm lower in the lowest maternal education category compared with the highest, which is 0.9% of the average birth length for the highest SEP category (equivalent results for girls 0.65 cm, 1.3%). Socioeconomic differences in childhood growth were small, and only resulted in minimal widening of the height inequality with increasing age. By the age of 10 years, the mean difference between children in the lowest and highest maternal education categories was 1.4 cm for boys and 1.7 cm for girls; similar proportionate differences to those seen at birth (1.0% for boys and 1.2% for girls). Patterns were the same when father's education or household occupational social class were used to measure SEP. CONCLUSIONS: The socioeconomic differential in height during childhood in this cohort of children born in the UK in the 1990s arises largely through inequalities in birth length, with small increases in the inequality from differences in growth in later childhood.

Socioeconomic disparities in trajectories of adiposity across childhood.

BACKGROUND: Socioeconomic inequalities in obesity are consistently observed in high-income countries. The development of such inequalities across childhood; however, has not been studied using longitudinal data. METHODS: Using data from the Avon Longitudinal Study of Parents and Children (participants were born 1991/2 in South-West England), we modelled trajectories of ponderal index (PI) (N=12 246) from birth to 2 years and body mass index (BMI) (N=11 380) from 2 to 10 years. Individual trajectories were estimated using mixed-effects models, and differences in trajectories by socioeconomic position (measured by maternal education) were investigated. RESULTS: There was little socioeconomic patterning of PI from birth to 2 years. Socioeconomic differences in BMI began to emerge by 4 years old, and widened with increasing age. Amongst girls there was a clear gradient across all categories of maternal education by age 8, with daughters of more educated women being less adipose. Amongst boys, sons of degree-educated women had lower BMI but there was little difference between the lower maternal education categories. By 10 years old the mean BMI difference between the highest and lowest maternal education category was 0.38 kg/m(2) for boys and 0.89 kg/m(2) for girls. The results imply that interventions to prevent inequalities in childhood obesity should begin in pre-school years.

Measuring socioeconomic position in health research.

OBJECTIVE: In this article we review different measures of socioeconomic position (SEP) and their uses in health-related research. AREAS OF AGREEMENT: Socioeconomic circumstances influence health. AREAS OF CONTROVERSY: Generally, poorer socioeconomic circumstances lead to poorer health. This has generated a search for generic mechanisms that could explain such a general association. However, we propose that there is a greater variation in the association between SEP and health than is generally acknowledged when specific health outcomes are investigated. We propose that studying these variations provide a better understanding of the aetiological mechanisms relating specific diseases with specific exposures. AREAS TO DEVELOP RESEARCH: Using different indicators of SEP in health research can better capture these variations and is important when evaluating the full contribution of confounding by socioeconomic conditions. We propose that using an array of SEP indicators within a life course framework also offers considerable opportunity to explore causal pathways in disease aetiology.

Indicators of socioeconomic position (part 2).

This is the second part of a glossary on indicators of socioeconomic position used in health research (the first part was published in the January issue of the journal).

Indicators of socioeconomic position (part 1).

This glossary presents a comprehensive list of indicators of socioeconomic position used in health research. A description of what they intend to measure is given together with how data are elicited and the advantages and limitation of the indicators. The glossary is divided into two parts for journal publication but the intention is that it should be used as one piece. The second part highlights a life course approach and will be published in the next issue of the journal.

Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood.

PURPOSE: Adverse socioeconomic circumstances in childhood may confer a greater risk for adult cardiovascular disease (CVD). The purpose of this review is to systematically evaluate evidence for an association between socioeconomic circumstances during childhood and specific CVD subtypes, independent of adult socioeconomic conditions. METHODS: We systematically retrieved individual-level studies of morbidity and mortality from CVD and specific CVD subtypes linked to early life influences, including coronary heart disease (CHD), ischemic and hemorrhagic stroke, peripheral vascular disease, markers of atherosclerosis (carotid intima-media thickness and stenosis), and rheumatic heart disease. Indicators of socioeconomic position in childhood varied, although most studies relied on father's occupation. RESULTS: We located 40 studies (24 prospective, 11 case-control, and 5 cross-sectional) reported in 50 publications. Thirty-one studies (19 prospective, 7 case-control, and all 5 cross-sectional) found a robust inverse association between childhood circumstances and CVD risk, although findings sometimes varied among specific outcomes, socioeconomic measures, and sex. Case-control studies reported mixed results. The association was stronger for stroke and, in particular, hemorrhagic stroke, than for CHD. Childhood socioeconomic conditions remained important predictors of CVD, even in younger cohorts. CONCLUSION: Childhood and adulthood socioeconomic circumstances are important determinants of CVD risk. The specific contribution of childhood and adulthood characteristics varies across different CVD subtypes. Disease-specific mechanisms are likely to explain the childhood origins of these adult health inequalities.