Income Inequality and Obesity Prevalence Among Oecd Countries

Su, Dejun; Esqueda, Omar A.; Li, Lifeng L.; Pagán, José A.

doi:10.1017/s002193201100071x

Cited by 39 publications

(37 citation statements)

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“…While income inequality was treated as a potential confounding variable in this study, and was not the specific focus of any hypothesis, there has been sufficient research looking at income inequality as a putative cause of health inequality that the results are worthy of consideration here [26, 27, 31, 35, 36]. After controlling for national income, household income, and other individual level factors, national income inequality was not significantly associated with BMI.…”

Section: Discussionmentioning

confidence: 99%

“…A positive correlation between national income and BMI exists, with the prevalence of obesity being greater in developed countries than less developed countries, and obesity rates increasing as per capita incomes increases [29, 30]. However, some previous studies showed no association of BMI and national income, but the majority of these studies were based only on high-income countries [31]. …”

Section: Discussionmentioning

confidence: 99%

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Effect of national wealth on BMI: An analysis of 206,266 individuals in 70 low-, middle- and high-income countries

Masood

Reidpath

2017

PLoS ONE

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BackgroundThis study explores the relationship between BMI and national-wealth and the cross-level interaction effect of national-wealth and individual household-wealth using multilevel analysis.MethodsData from the World Health Survey conducted in 2002–2004, across 70 low-, middle- and high-income countries was used. Participants aged 18 years and over were selected using multistage, stratified cluster sampling. BMI was used as outcome variable. The potential determinants of individual-level BMI were participants’ sex, age, marital-status, education, occupation, household-wealth and location(rural/urban) at the individual-level. The country-level factors used were average national income (GNI-PPP) and income inequality (Gini-index). A two-level random-intercepts and fixed-slopes model structure with individuals nested within countries was fitted, treating BMI as a continuous outcome.ResultsThe weighted mean BMI and standard-error of the 206,266 people from 70-countries was 23.90 (4.84). All the low-income countries were below the 25.0 mean BMI level and most of the high-income countries were above. All wealthier quintiles of household-wealth had higher scores in BMI than lowest quintile. Each USD10000 increase in GNI-PPP was associated with a 0.4 unit increase in BMI. The Gini-index was not associated with BMI. All these variables explained 28.1% of country-level, 4.9% of individual-level and 7.7% of total variance in BMI. The cross-level interaction effect between GNI-PPP and household-wealth was significant. BMI increased as the GNI-PPP increased in first four quintiles of household-wealth. However, the BMI of the wealthiest people decreased as the GNI-PPP increased.ConclusionBoth individual-level and country-level factors made an independent contribution to the BMI of the people. Household-wealth and national-income had significant interaction effects.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effect of national wealth on BMI: An analysis of 206,266 individuals in 70 low-, middle- and high-income countries

Masood

Reidpath

2017

PLoS ONE

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“…There is evidence to suggest that there may be stronger social pressure against obesity in women compared with men, and the pressure is stronger among women at higher socioeconomic status than among women at low socioeconomic status [33,39]. Although enough research on attitudes toward body weight has been conducted on young women, available reports suggest that body image dissatisfaction and weight preoccupation remain high in aging women [40].…”

Section: Discussionmentioning

confidence: 99%

Socioeconomic inequalities and body mass index in Västerbotten County, Sweden: a longitudinal study of life course influences over two decades

Padyab

Norberg

2014

International Journal for Equity in Health

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IntroductionLife course socioeconomic inequalities in heart disease, stroke and all-cause mortality are well studied in Sweden. However, few studies have sought to explain the mechanism for such associations mainly due to lack of longitudinal data with multiple measures of socioeconomic status (SES) across the life course. Given the population health concern about how socioeconomic inequality is related to poorer health, we aim to tackle obesity as one of the prime suspects that could explain the association between SES inequality and cardiovascular disease and consequently premature death. The aim of this study is to test which life course model best describes the association between socioeconomic disadvantage and obesity among 60 year old inhabitants of Västerbotten County in Northern Sweden.MethodsA birth cohort consisting of 3340 individuals born between 1930 and 1932 was studied. Body mass index (BMI) at the age of 60 and information on socioeconomic status at three stages of life (ages 40, 50, and 60 years) was collected. Independent samples t-test was used to compare BMI between advantaged and disadvantaged groups and one-way ANOVA was used to compare BMI among eight SES trajectories. We applied a structured modeling approach to examine three different hypothesized life course SES models (accumulation, critical period, and social mobility) in relation to BMI.ResultsWe found sex differences in the way that late adulthood socioeconomic disadvantage is associated with BMI among inhabitants of Northern Sweden. Our study suggests that social adversity in all stages of late adulthood is a particularly important indicator for addressing the social gradients in BMI among women in Northern Sweden and that unhealthy behaviors in terms of smoking and physical inactivity are insufficient to explain the relationships between social and lifestyle inequalities and BMI.ConclusionIn order for local authorities to develop informed preventive efforts, we suggest further research to identify modifiable risk factors across the life course which could explain this health inequality.

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“…59 There are also methodological issues regarding confounding variables, particularly associations between measures of SES and ethnicity. However, in the most extensively studied, if not necessarily representative, country 60 (USA), the association between one measure of SES (education) and BMI has weakened over the period 1970-2000. 61 It may be that SES now plays a more minor role compared to the national level of affluence in the most developed countries.…”

Section: Genetic Variants Contributing To Common Obesity Are Expressementioning

confidence: 99%

Future management of human obesity: understanding the meaning of genetic susceptibility

Jenkins¹,

Campbell²

2014

AGG

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Gene-environment interactions are central to the expression of obesity. The condition is strongly heritable (ie, genetic), and most of the variation in obesity levels between countries and between individuals can be explained by the effects of obesogenic environments on individual genetic susceptibilities. The nature of the obesogenic environmental influences is not clear in detail, but they correlate closely with measures of affluence. The causes of variation in genetic susceptibility are also not clearly defined, but their general nature has become clearer. The failure of genome-wide association studies or large linkage studies to identify or replicate causative genetic variants, together with the segregation of obesity-related traits in families, implicates a heterogenetic mechanism in which rare, dominantly or additively expressed genetic variants are responsible for most of common obesity. The search for rare causative variants continues with some successes, but those identified contribute very little to the overall burden and, assuming heterogenetics, there are many more to find. The time when genomic risk factors provide more information than do currently available markers, such as family history, is a long way off. Genomic studies to date have contributed little, if anything, to the prevention and treatment of common obesity and its associated disorders. This contrasts with the obvious and immediate potential implications of the well-established overall genetic basis of obesity, which have not yet been exploited in the clinical or public health arenas. Genomic studies, which have helped to define the genetic basis of common obesity mainly by exclusion, will in the future play an increasingly important role in understanding and managing obesity, but only with parallel studies of the physiological, behavioral, and economic influences. Keywords: obesity phenotypes, obesogenic environment, genomics, pathophysiology, treatment, prevention Background and aimsAccording to the World Health Organization projections, by 2015 about 2.3 billion adults in the world will be overweight and over 700 million will be obese, and economic costing predicts that obesity-related expenditure in USA will approach US$100 billion per annum.1 It is now widely, if not universally, accepted that the rising national and global prevalence of overweight and obesity since the 1980s can be understood as the effects of increasingly obesogenic environments (OEs), which are correlated with measures of affluence and food availability, 2 in genetically predisposed individuals. 3,4 Obesity is assessed by the body mass index (BMI, kg/m 2 ) in most epidemiological and genetic studies, often together with measures of central fat distribution such as waist circumference because of adverse metabolic and health associations. However, neither BMI nor other anthropometric measurements are by themselves sufficiently accurate

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Income Inequality and Obesity Prevalence Among Oecd Countries

Cited by 39 publications

References 39 publications

Effect of national wealth on BMI: An analysis of 206,266 individuals in 70 low-, middle- and high-income countries

Effect of national wealth on BMI: An analysis of 206,266 individuals in 70 low-, middle- and high-income countries

Socioeconomic inequalities and body mass index in Västerbotten County, Sweden: a longitudinal study of life course influences over two decades

Future management of human obesity: understanding the meaning of genetic susceptibility

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