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The burden of obesity: better ways of measuring

Fat crop

This article was first published in Society Now (Winter 2016)

The prevalence of obesity is a burgeoning concern worldwide. Obesity is associated with increased mortality and morbidity risks and so places a significant burden on healthcare systems worldwide. A growing number of countries and the World Health Organization have recently established policies and strategies to tackle obesity levels. UK governments have identified tackling obesity as a key priority. Recent evidence has shown that the UK not only has one of the highest obesity prevalence rates in Western Europe and the 8th highest among all OECD member countries but is one of the countries with the highest obesity growth rates in the past three decades. Socio-economic inequalities in obesity are therefore of particular interest.

Previous studies that explored socio-economic inequalities in adiposity were limited, mainly using conventional self-reported obesity measures, such as the Body Mass Index (BMI). Typically, BMI is defined as body weight (in kg) over the square of height (in meters). These studies suggested that more socio-economically disadvantaged females experienced higher obesity levels, while this is not true for males. But these studies have a number of limitations. First, BMI is a noisy adiposity measure, which does not distinguish fat from lean body mass. Moreover, previous reports may have been biased because they rely on self-reported body weight and height values (self-report questionnaires).

A new study I co-authored, published in Social Science and Medicine, used alternative measures of obesity to unpack the link between social inequalities and adiposity. This research looked at over 13,000 adult participants in a specific health study within Understanding Society (the UK Household Longitudinal Study) and compared income-related inequalities with both the conventional BMI and a number of alternative adiposity-measures such as waist circumference, absolute and relative measures of body fat. These body fat measures allowed distinguishing between the fat- and lean-mass components of BMI, while waist circumference captured central adiposity. Findings indicate that the absence of income-related obesity inequalities for males in the existing reports may be attributed to their focus on BMI-based measures. This is because similar income-related inequalities are found for both males and females when authors consider alternative BMI measures such as central adiposity (waist circumference) and body fat.

Capitalising on the richness of the Understanding Society dataset, we went beyond the conventional BMI measures to use body composition and central adiposity measures, thus distinguishing between the fat- and lean-mass components of BMI. Moreover, in contrast to many of the previous studies, we used nurse-administered adiposity measures that were not subject to self-reporting. We suggested and found that previous measures focusing solely on BMI may have missed the link between income and obesity for men.

Our findings have important implications for the measurement of socio-economic inequalities in adiposity and indicate that central adiposity and body composition measures should both be included in health policy agendas.

In this study, some further analysis explored the factors that lie behind the observed income-related inequalities in adiposity. Our findings also highlight the importance of schooling, as differences in education between obese and non-obese individuals explain a large part of the observed income-related inequalities in adiposity. Over and above the role played by schooling, our study also shows that the association between income and obesity may be partially driven by psycho-social mechanisms that link individuals’ perceptions of their financial conditions (such as feeling unable to manage on their income or perceived material deprivation) to adiposity.

Image credit: Anthony Cullen