Do repeated risk factor measurements influence the impact of education on cardiovascular mortality?

Ariansen, Inger; Graff-Iversen, Sidsel; Stigum, Hein; Strand, Bjørn Heine; Wills, Andrew K.; Næss, Øyvind

doi:10.1136/heartjnl-2014-307380

Cited by 12 publications

(9 citation statements)

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“…It may be argued that using multiple measurements of the explanatory variables would be better as it allows to account for possible changes over time in inequalities in these variables. Recent studies have shown that the contribution of behavioural factors to socioeconomic inequalities in mortality was (slightly) larger when multiple measurements over time of these factors were included [ 44 – 46 ]. Using the same GLOBE data, Oude Groeniger and colleagues also found a larger contribution of behavioural factors to educational inequalities in mortality when multiple measurements were used, but a smaller contribution of material factors [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

What’s the difference? A gender perspective on understanding educational inequalities in all-cause and cause-specific mortality

et al. 2018

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BackgroundMaterial and behavioural factors play an important role in explaining educational inequalities in mortality, but gender differences in these contributions have received little attention thus far. We examined the contribution of a range of possible mediators to relative educational inequalities in mortality for men and women separately.MethodsBaseline data (1991) of men and women aged 25 to 74 years participating in the prospective Dutch GLOBE study were linked to almost 23 years of mortality follow-up from Dutch registry data (6099 men and 6935 women). Cox proportional hazard models were used to calculate hazard ratios with 95% confidence intervals, and to investigate the contribution of material (financial difficulties, housing tenure, health insurance), employment-related (type of employment, occupational class of the breadwinner), behavioural (alcohol consumption, smoking, leisure and sports physical activity, body mass index) and family-related factors (marital status, living arrangement, number of children) to educational inequalities in all-cause and cause-specific mortality, i.e. mortality from cancer, cardiovascular disease, other diseases and external causes.ResultsEducational gradients in mortality were found for both men and women. All factors together explained 62% of educational inequalities in mortality for lowest educated men, and 71% for lowest educated women. Yet, type of employment contributed substantially more to the explanation of educational inequalities in all-cause mortality for men (29%) than for women (− 7%), whereas the breadwinner’s occupational class contributed more for women (41%) than for men (7%). Material factors and employment-related factors contributed more to inequalities in mortality from cardiovascular disease for men than for women, but they explained more of the inequalities in cancer mortality for women than for men.ConclusionsGender differences in the contribution of employment-related factors to the explanation of educational inequalities in all-cause mortality were found, but not of material, behavioural or family-related factors. A full understanding of educational inequalities in mortality benefits from a gender perspective, particularly when considering employment-related factors.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5940-5) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

What’s the difference? A gender perspective on understanding educational inequalities in all-cause and cause-specific mortality

et al. 2018

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“…Educational attainment was considered as a confounding factor, associated with both smoking and anthropometric measures [25, 26], and was assessed by self-report according to five levels. For stratified analysis we defined three educational levels: basic (until 10 years), secondary (1–4 years after basic), and tertiary (more than 4 years completed education after basic).…”

Section: Methodsmentioning

confidence: 99%

Associations of tobacco smoking with body mass distribution; a population-based study of 65,875 men and women in midlife

et al. 2019

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BackgroundStudies indicate an effect of smoking toward abdominal obesity, but few assess hip and waist circumferences (HC and WC) independently. The present study aimed to assess the associations of smoking status and volume smoked with HC and WC and their ratio in a population with low prevalence of obesity together with high prevalence of smoking.MethodsWe used cross-sectional survey data from 11 of a total 19 Norwegian counties examined in 1997–99 including 65,875 men and women aged 39–44 years. Analysis of associations were adjusted for confounding by socioeconomic position, health indicators, and additionally for BMI.ResultsCompared with never-smokers, when adjusting for confounders and in addition for BMI, mean HC remained lower while mean WC and waist-hip-ratio (WHR) were higher in current smokers. The finding of a lower HC and higher WHR level among smokers was consistent by sex and in strata by levels of education and physical activity, while the finding of higher WC by smoking was less consistent. Among current smokers, BMI-adjusted mean HC decreased whereas WC and WHR increased by volume smoked. Compared with current smokers, former smokers had higher BMI-adjusted HC, lower WHR and among women WC was lower.ConclusionsThe main finding in this study was the consistent negative associations of smoking with HC. In line with the hypothesis that lower percentage gluteofemoral fat is linked with higher cardiovascular risk, our results suggest that smoking impacts cardiovascular risk through mechanisms that reduce the capacity of fat storage in the lower body region.

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“…The inferior cardiovascular health observed among indigenous peoples relative to non-indigenous peoples in North America may be due to lower socio-economic status and poorer access to quality health care among the former [ 4 ]. A publication from Norway found that differences in smoking habits, systolic blood pressure, serum cholesterol and body mass index explained 72% and 56% of the absolute and relative educational gradients, respectively, in CVD mortality [ 31 ]. Assuming that the educational gradient in Sami and non-Sami populations is the same, the most plausible explanation for the similar levels of risk factors and risk of AMI or CS observed in our study may be that Sami and non-Sami populations in Norway differ little with regard to education levels ( Tables I and II ).…”

Section: Discussionmentioning

confidence: 99%

Distribution of risk factors for cardiovascular disease and the estimated 10-year risk of acute myocardial infarction or cerebral stroke in Sami and non-Sami populations: The SAMINOR 2 Clinical Survey

Siri

Br̈aaten

Jacobsen

et al. 2018

Scand J Public Health

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Objective: This study aimed to assess and compare the distribution of cardiovascular risk factors and the estimated 10-year risk of fatal or non-fatal acute myocardial infarction (AMI) or cerebral stroke (CS) among the Sami and non-Sami populations of Northern Norway. Methods: The SAMINOR 2 Clinical Survey is a cross-sectional survey conducted in 10 municipalities in the counties of Finnmark, Troms and Nordland in rural Northern Norway in 2012–2014. All inhabitants aged 40–79 years were invited to participate, and 6004 (48.2%) accepted. The NORRISK 2 model was used to estimate the 10-year risk of fatal or non-fatal AMI or CS. Sex and age were included in the model, as well as the following risk factors for cardiovascular disease (CVD): serum total cholesterol, serum high-density lipoprotein cholesterol, systolic blood pressure, smoking habits and anti-hypertensive treatment. Results: Only minor ethnic differences were observed between Sami and non-Sami populations in a number of individual risk factors for CVDs. Overall, the NORRISK 2 model revealed no ethnic differences in the 10-year risk of AMI or CS. Conclusions: There were no differences in 10-year risk of AMI or CS between the Sami and non-Sami populations in 10 selected municipalities in Northern Norway.

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Do repeated risk factor measurements influence the impact of education on cardiovascular mortality?

Abstract: In this cohort, repeated measurements of risk factors seemed to explain more of the educational gradient in CVD mortality. This suggests that a substantial part of the excess CVD mortality among those with lower education might be explained by conventional risk factors.

Cited by 12 publications

References 28 publications

What’s the difference? A gender perspective on understanding educational inequalities in all-cause and cause-specific mortality

What’s the difference? A gender perspective on understanding educational inequalities in all-cause and cause-specific mortality

Associations of tobacco smoking with body mass distribution; a population-based study of 65,875 men and women in midlife

Distribution of risk factors for cardiovascular disease and the estimated 10-year risk of acute myocardial infarction or cerebral stroke in Sami and non-Sami populations: The SAMINOR 2 Clinical Survey

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