Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

Hughes, John R.; Kabir, Zubair; Kee, Frank; Bennett, Kathleen

doi:10.1136/bmjopen-2016-013442

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…This included inequalities between those who received and did not receive health insurance subsidy from the government of Geneva, which provides further support for the link between socioeconomic disadvantage and diabetes prevalence. Furthermore, our findings revealed that between 2005-2010 and 2015-2017, socioeconomic inequalities, in relative and absolute terms, have increased over time across different education and income groups, which are consistent with findings showing an increase in socioeconomic inequalities over time in England, 15 Ireland and Northern Ireland, 16 Scotland, 17 and Spain, 18 as well as in the USA. 3 strengths and limitations To our knowledge, this is the first study to show trends in prevalence of diabetes according to a series of sociodemographic and cardiovascular risk factors in a Swiss population.…”

Section: Discussionsupporting

confidence: 90%

Thirteen-year trends in the prevalence of diabetes according to socioeconomic condition and cardiovascular risk factors in a Swiss population

Mestral

Stringhini

Guessous

et al. 2020

BMJ Open Diab Res Care

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IntroductionTo estimate the prevalence of and trends in diabetes according to sociodemographic indicators and cardiovascular risk factors in a Swiss population.Research design and methodsAnnual cross-sectional study of adults residing in the state of Geneva. We included 9886 participants (51% women; mean age (SD) of 48.9 (13.4) years). Diagnosed diabetes was self-reported; undiagnosed diabetes was defined as having fasting plasma glucose level of ≥7 mmol/L and no previous diagnosis; total diabetes as the sum of diagnosed and undiagnosed diabetes. To assess trends, we grouped survey years into three time periods: 2005–2010, 2011–2014, and 2015–2017. To assess inequalities, we constructed the relative index of inequality (RII) and the slope index of inequality (SII) for education, income, and health insurance subsidy (state program based on socioeconomic disadvantage).ResultsIn total, 683 diabetes cases were identified. In 2015–2017, total diabetes prevalence was 11.8% (8.6%–14.9%) among lowest income participants, and 4.7% (3.4%–5.9%) among highest income participants (p<0.01). Similar findings were observed for education. Among participants with full health insurance subsidy, diabetes prevalence was 19.4% (12.1%–26.8%), and 6.1% (5.3%–7.0%) among those without (p<0.01). High diabetes prevalence was observed among participants who were men, older, overweight or obese, hypertensive, and hypercholesterolemic. Among participants with diabetes, 74.0% (63.5%–84.4%) in the lowest income group were diagnosed, compared with 90.2% (81.9%–98.4%) in the highest income group (p=0.04). Over the 13-year period, widening relative and absolute inequalities in total diabetes prevalence were observed for education and income. The education-RII (95% CI) increased from 1.51 (95% CI 1.01 to 2.32) in 2005–2010 to 2.54 (95% CI 1.58 to 4.07) in 2015–2017 (p=0.01), and the education-SII (95% CI) from 0.04 (95% CI 0.01 to 0.08) to 0.08 (95% CI 0.04 to 0.10; p<0.01). The income-RII increased from 2.35 (95% CI 1.44 to 3.84) to 3.91 (95% CI 2.24 to 6.85; p<0.01), and the income-SII from 0.08 (95% CI 0.04 to 0.12) to 0.011 (95% CI 0.07 to 0.14; p=0.01). Inequalities by health insurance subsidy were large (RII 3.56 (95% CI 1.90 to 6.66) and SII 0.10 (95% CI 0.05 to 0.15)) but stable across the study period.ConclusionAmong adults living in Geneva, Switzerland, substantial differences were observed in diabetes prevalence across socioeconomic and cardiovascular risk groups over a 13-year period, and relative and absolute socioeconomic inequalities appeared to have increased.

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Section: Discussionsupporting

confidence: 90%

Thirteen-year trends in the prevalence of diabetes according to socioeconomic condition and cardiovascular risk factors in a Swiss population

Mestral

Stringhini

Guessous

et al. 2020

BMJ Open Diab Res Care

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“…Another study among white women found that educational inequalities, relative and absolute, increased between 1986 and 2006 for heart disease and cerebrovascular disease [74]. Furthermore, several studies have found widening educational inequalities in CVRFs over time, particularly smoking, obesity and diabetes in England [75], Northern Ireland and Ireland [76], Norway [77], and South Korea [78]. Trends in the educational patterning of CVD and CVRFs have yet to be examined in LMICs.…”

Section: Review Of the Evidence Education And Cvdmentioning

confidence: 99%

Socioeconomic Status and Cardiovascular Disease: an Update

2017

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In high-income countries (HICs), the evidence continues to expand, with meta-analyses of large longitudinal cohort studies consistently confirming the inverse association between SES and several CVD and CVRFs. The evidence remains limited in low-income and middle-income countries (LMICs), where most of the evidence originates from cross-sectional studies of varying quality and external validity; the available evidence indicates that the association between SES and CVD and CVRFs depends on the socioeconomic development context and the stage in the demographic, epidemiological, and nutrition transition of the population. The recent evidence confirms that SES is strongly inversely associated with CVD and CVRFs in HICs. However, there remains a need for more research to better understand the way socioeconomic circumstances become embodied in early life and throughout the life course to affect cardiovascular risk in adult and later life. In LMICs, the evidence remains scarce; thus, there is an urgent need for large longitudinal studies to disaggregate CVD and CVRFs by socioeconomic indicators, particularly as these countries already suffer the greatest burden of CVD.

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“…There were eight longitudinal studies (follow-ups were five ( Camões et al, 2010 ), 10 ( Chung and Kim, 2020 ), 13 (von Hippel & Lynch, 2014) , 14 ( Coogan et al, 2012 ), 23 ( Salsberry and Reagan, 2009 ), 29 (Cohen, Rehkopf, Deardorff, & Abrams, 2013b) , 33 ( Salonen et al, 2009 ) and 36 years ( Kim, 2016 )). Six studies reported results of multiple countries ( Devaux & Sassi, 2013 ; Drewnowski et al, 2005 ; Hughes et al, 2017 ; Klumbiene et al, 2004 ; Roskam et al, 2010 ; Sarlio-Lähteenkorva et al, 2006 ). Another six studies, all performed in the USA, reported on multiple ethnicities ( Beltrán-Sánchez et al, 2016 ; Cohen, Rehkopf, Deardorff, & Abrams, 2013b ; Ng et al, 2011 ; Qobadi and Payton, 2017 ; Salsberry and Reagan, 2009 ; Zhang and Wang, 2004 ).…”

Section: Resultsmentioning

confidence: 99%

Do associations between education and obesity vary depending on the measure of obesity used? A systematic literature review and meta-analysis

Witkam

Gwinnutt

Humphreys

et al. 2021

SSM - Population Health

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Background Consistent evidence suggests a relationship between lower educational attainment and total obesity defined using body mass index (BMI); however, a comparison of the relationships between educational attainment and total obesity (BMI ≥30 kg/m 2 ) and central obesity (waist circumference (WC) > 102 cm for men and WC > 88 cm for women) has yet to be carried out. This systematic literature review (SLR) and meta-analyses aimed to understand whether i) the associations between education and obesity are different depending on the measures of obesity used (BMI and WC), and ii) to explore whether these relationships differ by gender and region. Methods Medline, Embase and Web of Science were searched to identify studies investigating the associations between education and total and central obesity among adults in the general population of countries in the Organisation for Economic Co-operation and Development (OECD). Meta-analyses and meta-regression were performed in a subset of comparable studies (n=36 studies; 724,992 participants). Results 86 eligible studies (78 cross-sectional and eight longitudinal) were identified. Among women, most studies reported an association between a lower education and total and central obesity. Among men, there was a weaker association between lower education and central than total obesity (OR central vs total obesity in men 0.79 (95% CI 0.60, 1.03)). The association between lower education and obesity was stronger in women compared with men (OR women vs men 1.66 (95% CI 1.32, 2.08)). The relationship between lower education and obesity was less strong in women from Northern than Southern Europe (OR Northern vs Southern Europe in women 0.37 (95% CI 0.27, 0.51)), but not among men. Conclusions Associations between education and obesity differ depending on whether total or central obesity is used among men, but not in women. These associations are stronger among women than men, particularly in Southern European countries.

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Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

Cited by 6 publications

References 32 publications

Thirteen-year trends in the prevalence of diabetes according to socioeconomic condition and cardiovascular risk factors in a Swiss population

Thirteen-year trends in the prevalence of diabetes according to socioeconomic condition and cardiovascular risk factors in a Swiss population

Socioeconomic Status and Cardiovascular Disease: an Update

Do associations between education and obesity vary depending on the measure of obesity used? A systematic literature review and meta-analysis

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