Determinants of social inequalities in stroke incidence across Europe: a collaborative analysis of 126 635 individuals from 48 cohort studies

Abstract

BackgroundKnowledge on the origins of the social gradient in stroke incidence in different populations is limited. This study aims to estimate the burden of educational class inequalities in stroke incidence and to assess the contribution of risk factors in determining these inequalities across Europe.Materials and methodsThe MORGAM (MOnica Risk, Genetics, Archiving and Monograph) Study comprises 48 cohorts recruited mostly in the 1980s and 1990s in four European regions using standardised procedures for baseline risk factor assessment and fatal and non-fatal stroke ascertainment and adjudication during follow-up. Among the 126 635 middle-aged participants, initially free of cardiovascular diseases, generating 3788 first stroke events during a median follow-up of 10 years, we estimated differences in stroke rates and HRs for the least versus the most educated individuals.ResultsCompared with their most educated counterparts, the overall age-adjusted excess hazard for stroke was 1.54 (95% CI 1.25 to 1.91) and 1.41 (95% CI 1.16 to 1.71) in least educated men and women, respectively, with little heterogeneity across populations. Educational class inequalities accounted for 86–413 and 78–156 additional stroke events per 100 000 person-years in the least compared with most educated men and women, respectively. The additional events were equivalent to 47%–130% and 40%–89% of the average incidence rates. Inequalities in risk factors accounted for 45%–70% of the social gap in incidence in the Nordic countries, the UK and Lithuania-Kaunas (men), but for no more than 17% in Central and South Europe. The major contributors were cigarette smoking, alcohol intake and body mass index.ConclusionsSocial inequalities in stroke incidence contribute substantially to the disease rates in Europe. Healthier lifestyles in the most disadvantaged individuals should have a prominent impact in reducing both inequalities and the stroke burden.

Decomposing the educational gradient in allostatic load across European populations. What matters the most: differentials in exposure or in susceptibility?

“…1 In many of the same populations, low education is also associated with higher stroke incidence, though with some geographic heterogeneity, especially in women. 2 The extent to which clinical, biological and behavioural risk factors play a role in explaining these associations is less known. Results from observational and Mendelian randomisation studies converge in attributing to differential exposure (DE) to risk factors, up to half of the educational class inequalities in disease rates.…”

“…Results from observational and Mendelian randomisation studies converge in attributing to differential exposure (DE) to risk factors, up to half of the educational class inequalities in disease rates. 1–3 In addition to DE, recent literature hypothesises that the effects of risk factors on cardiovascular disease onset may differ across social strata, a mechanism named ‘differential susceptibility’ (DS). 4 A few studies have estimated the role of DS in social disparities in cardiovascular diseases, adopting different methodological approaches including additive interaction, 5 6 mediation 7 and the Oaxaca-Blinder decomposition.…”

Social determinants of health associated with the development of sepsis in adults: a scoping review protocol

“…Social factors have been found to be associated with the incidence of multiple health conditions, such as cardiovascular disease 11 and stroke. 12 Furthermore, they have been found to impact breast cancer stage at diagnosis and subsequent survival. 13 Given that social determinants of health have been implicated in the progression and development of many health conditions, it stands to reason that they may be involved in the development of sepsis.…”

NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) and the Risk of Stroke

“…We selected possible confounding variables for regression models based on previous analyses from the same populations. 16 Additional adjustment was also made for baseline estimated glomerular filtration rate or for coronary heart disease, atrial fibrillation or heart failure as time-dependent variables, as these events occurred during follow-up (Model 3). From this latter analysis, the MATISS Study and MONICA Brianza Study were excluded because data on atrial fibrillation or heart failure were not available.…”

“…Full details of baseline data have been provided elsewhere. 15,16 Cohort descriptions are provided in Table I in the online-only Data Supplement. For each cohort the following harmonized variables were available at baseline: age, sex, body mass index, systolic and diastolic blood pressure, antihypertensive medication, smoking status, total and HDL (high-density lipoprotein) cholesterol and estimated glomerular filtration rate calculated by using of Chronic Kidney Disease Epidemiology Collaboration formula, history of diabetes mellitus, myocardial infarction, atrial fibrillation, and heart failure.…”