The charade of socioeconomic body-mass index determinants
Authors: Aluisio J D Barros
Journal: The Lancet Global Health (2018)

One of the greatest current public health issues is the increasing proportion of people with overweight and obesity. A global analysis of body-mass index (BMI) in children, adolescents, and adults showed a practically linear upward trend for both women and men.1NCD Risk Factor Collaboration (NCD-RisC)Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults.Lancet. 2017; 390: 2627-2642Summary Full Text Full Text PDF PubMed Scopus (3822) Google Scholar The trend is not homogeneous across regions, with no increase detectable in some settings, such as eastern Europe, and steep increases in others, such as Latin America and central Asia. This increase in overweight and obesity is clearly linked to an increase in cardiovascular disease2Yatsuya H Li Y Hilawe EH et al.Global trend in overweight and obesity and its association with cardiovascular disease incidence.Circ J. 2014; 78: 2807-2818Crossref PubMed Scopus (152) Google Scholar and mortality;3Lim SS Vos T Flaxman AD et al.A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010.Lancet. 2012; 380: 2224-2260Summary Full Text Full Text PDF PubMed Scopus (8582) Google Scholar therefore, understanding its drivers is essential. In their Article on how socioeconomic factors explain variation in BMI, published in The Lancet Global Health, Rockli Kim and colleagues4Kim R Kawachi I Coull BA Subramanian S V Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data.Lancet Glob Health. 2018; 6: e777-e786Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar start by raising the issue that processes leading to changes in population averages might be different from determinants of individual cases. This is an interesting idea that was previously explored for growth faltering among children.5Roth DE Krishna A Leung M Shi J Bassani DG Barros AJD Early childhood linear growth faltering in low-income and middle-income countries as a whole-population condition: analysis of 179 Demographic and Health Surveys from 64 countries (1993–2015).Lancet Glob Heal. 2017; 5: e1249-e1257Summary Full Text Full Text PDF PubMed Scopus (53) Google Scholar In their study,5Roth DE Krishna A Leung M Shi J Bassani DG Barros AJD Early childhood linear growth faltering in low-income and middle-income countries as a whole-population condition: analysis of 179 Demographic and Health Surveys from 64 countries (1993–2015).Lancet Glob Heal. 2017; 5: e1249-e1257Summary Full Text Full Text PDF PubMed Scopus (53) Google Scholar Roth and colleagues recognised that the changes observed in the height-for-age distribution in the first 3 years of life could not be explained solely by individual determinants (eg, wealth). Multilevel statistical models, popularised with the ML3 and MLwiN packages by Harvey Goldstein and his team in the 1990s,6Goldstein H Multilevel statistical models.2nd edn. Edward Arnold, London1995Google Scholar made the study of contextual determinants and different levels of variability much easier. But, as was learned with a study of day care centre characteristics as determinants of childhood illness,7Barros AJ Ross DA Fonseca W V Williams LA Moreira-Filho DC Preventing acute respiratory infections and diarrhoea in child care centres.Acta Paediatr. 1999; 88: 1113-1118Crossref PubMed Google Scholar a big problem with contextual determinants is that the number of groups in most studies is very small compared with the number of individuals, hence these studies have low power to assess the associations of interest. Additionally, the variation of the outcome is concentrated at the individual level, so that little variation is left to be explained at the group level. Therefore, it was no surprise that Kim and colleagues found that most of the BMI variation (after or before adjustment for covariates) was at the individual level.4Kim R Kawachi I Coull BA Subramanian S V Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data.Lancet Glob Health. 2018; 6: e777-e786Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar A more interesting finding is that the socioeconomic status variables explain very little of the individual-level variability and much more (in relative terms) of the cluster-level variability. The authors conclude that these determinants are not the main drivers of individual-level differences in BMI. But why would individual characteristics like wealth and education not determine BMI at the individual level and achieve that at the group level? The pattern of the predictor variation might have a role in the explanation. To explore the predictor variation, I calculated the intraclass correlation (ICC) for the continuous wealth score (the percentage of between-cluster variation relative to the total variation) for the 58 countries used in the analysis. The ICC and countries' per-capita gross domestic product were negatively correlated—ie, the poorer the country, the more homogeneous the individuals were within clusters and the more heterogeneous the clusters were between themselves. In many countries, 70% or more of the total variation was between clusters. Next, I calculated the correlation between the proportion of BMI variance explained by socioeconomic status variables at the cluster level (as reported in the Article4Kim R Kawachi I Coull BA Subramanian S V Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data.Lancet Glob Health. 2018; 6: e777-e786Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar) and the ICC, and found a correlation of 0·33 (p=0·010). In countries with low wealth ICC (richer countries), the socioeconomic status variables explained less than 20% of between-cluster BMI variance (figure). As the ICC increases, the percentage of between-cluster BMI variability increases, on average—ie, when there is more wealth heterogeneity between clusters, the socioeconomic status variables explain a larger percentage of BMI between-cluster variance. This finding might, in part at least, explain what was observed in Kim and colleagues study. The pooled analysis presented in the Article requires the assumption that in each country the effect of age and each of the socioeconomic status variables are the same. This assumption is not the case, and it is most visible when the direction of the association between BMI and wealth and education is examined. In half of the 26 studies reviewed by Dinsa and colleagues,8Dinsa GD Goryakin Y Fumagalli E Suhrcke M Obesity and socioeconomic status in developing countries: a systematic review.Obes Rev. 2012; 13: 1067-1079Crossref PubMed Scopus (460) Google Scholar BMI increased with education, whereas the association was reversed in the other studies. These authors also showed that the direction of the association was related to country income level, with BMI increasing with socioeconomic status in poorer countries, and decreasing with socioeconomic status in richer ones. Therefore, the pooled analysis might present a poor fit for the effect of some socioeconomic status variables. The country-specific analyses are more robust in this sense than the pooled analysis, and thus more interpretable. However, important lessons are to be learned here. In the study by Kim and colleagues,4Kim R Kawachi I Coull BA Subramanian S V Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data.Lancet Glob Health. 2018; 6: e777-e786Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar most of the variability in BMI could not be explained with the socioeconomic status variables. Theoretically, it should have been possible to explain a large part of such variability, since the socioeconomic status variables were distal determinants, and intermediate ones, such as lifestyle, are strongly related to socioeconomic status. Additionally, the range of percentages of variability explained across countries was wide, at both the individual and cluster level. Taken together, these results highlight the difficulties in identifying determinants of BMI amenable to interventions, and the need for solutions that are setting specific. I declare no competing interests. Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel dataOur findings on variance decomposition in BMI and explanation by socioeconomic factors at population and individual levels indicate that inferential questions that target within and between populations are importantly inter-related and should be considered simultaneously. Full-Text PDF Open Access