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The importance of age, sex and place in understanding socioeconomic inequalities in allostatic load: Evidence from the Scottish Health Survey (2008-2011).

ABSTRACT: Given the broad spectrum of health and wellbeing outcomes that are patterned by socioeconomic position (SEP), it has been suggested that there may be common biological pathways linking SEP and health. Allostatic load is one such pathway, which aims to measure cumulative burden/dysregulation across multiple physiological systems. This study aimed to determine the contextual and demographic factors (age, sex and place) that may be important in better understanding the links between lower SEP and higher allostatic load.Data were from a nationally representative sample of adults (18+): the Scottish Health Survey (2008-2011). Higher SEP ('1') was defined as having 'Higher'-level, secondary school qualifications versus having lower level or no qualifications ('0'). For allostatic load, a range of 10 biomarkers across the cardiovascular, metabolic and immune systems were used. Respondents were scored "1" for each biomarker that fell into the highest quartile of risk. Linear regressions were run in STATA, including SEP, age (continuous and as a 7-category variable), sex (male/female), urbanity (a 5-category variable ranging from primary cities to remote rural areas) and geographical location (based on 10 area-level healthboards). Interactions between SEP and each predictor, as well as stratified analyses, were tested.Lower SEP was associated with higher allostatic load even after adjusting for age, sex and place (b = -0.631, 95 % CI -0.795, -0.389, p < 0.001). There was no significant effect moderation between SEP and age, sex or place. Stratified analysis did show that the inequality identified in the baseline models widened with age, becoming significant at ages 35-44, before narrowing at older ages (75+). There was no difference by sex, but more mixed findings with regards place (urbanity or geographical location), with a mix of significant and non-significant results by SEP that did not appear to follow any pattern.Inequalities in allostatic load by educational attainment, as a measure of SEP, are consistent with age, sex and place. However, these stratified analyses showed that these inequalities did widen with age, before narrowing in later life, matching the patterns seen with other objective and subjective health measures. However, effect moderation analysis did not support evidence of a statistically significant interaction between age and SEP. Context remains an important feature in understanding and potentially addressing inequalities, although may be less of an issue in terms of physiological burden.

SUBMITTER: Robertson T

PROVIDER: S-EPMC4746832 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Project description:Background and objectivesLow socioeconomic status confers unfavorable health, but the degree and mechanisms by which life course socioeconomic status affects kidney health is unclear.Design, setting, participants, & measurementsWe examined the association between cumulative lifetime socioeconomic status and CKD in black Americans in the Jackson Heart Study. We used conditional process analysis to evaluate allostatic load as a potential mediator of this relation. Cumulative lifetime socioeconomic status was an age-standardized z-score, which has 1-SD units by definition, and derived from self-reported childhood socioeconomic status, education, and income at baseline. Allostatic load encompassed 11 baseline biomarkers subsuming neuroendocrine, metabolic, autonomic, and immune physiologic systems. CKD outcomes included prevalent CKD at baseline and eGFR decline and incident CKD over follow-up.ResultsAmong 3421 participants at baseline (mean age 55 years [SD 13]; 63% female), cumulative lifetime socioeconomic status ranged from -3.3 to 2.3, and 673 (20%) had prevalent CKD. After multivariable adjustment, lower cumulative lifetime socioeconomic status was associated with greater prevalence of CKD both directly (odds ratio [OR], 1.18; 95% confidence interval [95% CI], 1.04 to 1.33 per 1 SD and OR, 1.45; 95% CI, 1.15 to 1.83 in lowest versus highest tertile) and via higher allostatic load (OR, 1.09; 95% CI, 1.06 to 1.12 per 1 SD and OR, 1.17; 95% CI, 1.11 to 1.24 in lowest versus highest tertile). After a median follow-up of 8 years (interquartile range, 7-8 years), mean annual eGFR decline was 1 ml/min per 1.73 m2 (SD 2), and 254 out of 2043 (12%) participants developed incident CKD. Lower cumulative lifetime socioeconomic status was only indirectly associated with greater CKD incidence (OR, 1.04; 95% CI, 1.01 to 1.07 per 1 SD and OR, 1.08; 95% CI, 1.02 to 1.14 in lowest versus highest tertile) and modestly faster annual eGFR decline, in milliliters per minute (OR, 0.01; 95% CI, 0.00 to 0.02 per 1 SD and OR, 0.02; 95% CI, 0.00 to 0.04 in lowest versus highest tertile), via higher baseline allostatic load.ConclusionsLower cumulative lifetime socioeconomic status was substantially associated with CKD prevalence but modestly with CKD incidence and eGFR decline via baseline allostatic load.

Project description:OBJECTIVES:To investigate potential geographical and socioeconomic patterning of allostatic load (AL) in China. DESIGN:Multilevel longitudinal study of the 2010 Chronic Disease Risk Factor Surveillance linked to the National Death Surveillance up to 31 December 2015. SETTING:All 31 provinces in China, not including Hong Kong, Macao or Taiwan. PARTICIPANTS:96 466 ≥ 18 years old (women=54.3%). EXPOSURES:Person-level educational attainment and mean years of education in counties. OUTCOME:AL was measured using clinical guidelines for nine biomarkers: body mass index; waist circumference; systolic blood pressure; diastolic blood pressure; fasting blood glucose; total cholesterol; triglycerides; high-density lipoprotein cholesterol; low-density lipoprotein cholesterol. RESULTS:Multilevel logistic regressions adjusted for sex, age, marital status, person-level education, county mean years of education and urban/rural reported ORs of 1.22 (95% CI 1.08 to 1.38) for 5-year all-cause mortality (n=3284) and 1.20 (1.04-1.37) for deaths from non-communicable diseases (n=2891) among people in AL quintile 5 (high) compared with quintile 1 (low). The median rate ratio estimated from unadjusted multilevel negative binomial regression showed AL clustered geographically (province=1.14; county=1.12; town=1.11; village=1.14). After adjusting for aforementioned confounders, AL remained higher with age (rate ratio 1.02, 95% CI 1.02 to 1.02), higher in women compared with men (1.17, 1.15 to 1.19), lower among singletons (0.83, 0.81 to 0.85) and widowers (0.96, 0.94 to 0.98). AL was lower among people with university-level compared with no education (0.92, 0.89 to 0.96), but higher in counties with higher mean education years (1.03, 1.01 to 1.05). A two-way interaction suggested AL was higher (1.04, 1.02 to 1.06) among those with university-level compared with no education within counties with higher mean years of education. Similar results were observed for alternative constructions of AL using 75th and 80th percentile cut-points. CONCLUSIONS:AL in China is patterned geographically. The degree of association between AL and person-level education seems to be dependent on area-level education, which may be a proxy for other contextual factors that warrant investigation.

Project description:BackgroundSmoking inequalities in Turkey were previously demonstrated in an early stage of the smoking epidemic model. This paper aimed to assess the trends for socioeconomic inequalities in smoking in Turkey over the years in the context of the smoking epidemic model using data from the Global Adult Tobacco Survey (GATS) Turkey 2008-2012-2016.MethodsCross-sectional data were analyzed to calculate the association of smoking with, wealth, education, occupation and place of residence using age-standardized prevalence rates, odds ratios, relative index of inequality (RII) and slope index of inequality (SII). The analysis was performed separately for age groups (younger: 20-39 years/older: 40 and above years) and sex.ResultsYounger women with higher wealth and older women with higher wealth and education smoked more. For both age groups, smoking was increased for working class and urban women. Relative wealth inequalities in smoking narrowed and then showed a reversal for younger women (RII2008 = 3.37; 95% CI:1.64-3.40; RII2012 = 2.19; 95% CI:1.48-3.24; RII2016 = 0.80; 95% CI:0.58-1.10, p-for trend < 0.0001). Relative educational inequalities in smoking for older women also showed a narrowing (RII2008 = 21.45; 95% CI:11.74-39.19; RII2012 = 15.25; 95% CI:9.10-25.55; and RII2016 = 5.48; 95% CI:3.86-7.78, p-for trend < 0.0001). For older women, a similar narrowing was observed for wealth (RII2008 = 3.94; 95% CI:2.38-6.53; RII2012 = 2.79; 95% CI:1.80-4.32; and RII2016 = 1.34; 95% CI:0.94-1.91, p-for trend = 0.0001). The only significant trend for absolute inequalities was for younger women by wealth. This trend showed a narrowing and then a reversal (SII2008 = 0.14; 95% CI:0.09-1.20; SII2012 = 0.12; 95% CI:0.06-0.18; and SII2016 = -0.05; 95% CI:-0.12-0.02, p-for trend = 0.0001). Unlike women, smoking in men showed inverse associations for wealth and education, although not statistically confirmed for all years. Smoking was increased in working classes and unemployed men in 2012 and 2016. Inequalities did not show a trend in relative and absolute terms for men.ConclusionsFor smoking inequalities in Turkey, a transition to the next stage was observed, although the previously defined Southern European pattern also existed. Low socioeconomic women deserve special attention as well as stressors at work and drivers of smoking at urban settings.

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The importance of age, sex and place in understanding socioeconomic inequalities in allostatic load: Evidence from the Scottish Health Survey (2008-2011).

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