ArticleEuropean journal of epidemiology2018
Patterning of individual heterogeneity in body mass index: evidence from 57 low- and middle-income countries.
Article in European journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Assessment of heterogeneous Head Start treatment effects on cognitive and social-emotional outcomes.Scientific reports · 2022Trial
- Testing the Geoffrey Rose's assumption of uniform population change for body mass index distribution in India.The Lancet regional health. Southeast Asia · 2025Article
- Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.Behavior genetics · 2022Article
- Patterning of individual variability in neurocognitive health among South African women exposed to childhood maltreatment.Scientific reports · 2021Article
- Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016.Journal of epidemiology · 2020Article
- The Relative Contributions of Socioeconomic and Genetic Factors to Variations in Body Mass Index Among Young Adults.American journal of epidemiology · 2020Article
- Heterogeneity in adult anthropometry by socioeconomic factors: Indian National Family Health Survey 2006 and 2016.European journal of clinical nutrition · 2020Article
- Objectives, design and main findings until 2020 from the Rotterdam Study.European journal of epidemiology · 2020Article
- The obesity transition: stages of the global epidemic.The lancet. Diabetes & endocrinology · 2019Review
- Data-intensive Undergraduate Research Project Informs to Advance Healthcare Analytics.... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium · 2018Article
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Authors and funding
4 authors.
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Abstract
Modeling variation at population level has become increasingly valued, but no clear application exists for modeling differential variation in health between individuals within a given population. We applied Goldstein's method (in: Everrit, Howell (eds) Encyclopedia of statistics in behavioral science, Wiley, Hoboken, 2005) to model individual heterogeneity in body mass index (BMI) as a function of basic sociodemographic characteristics, each independently and jointly. Our analytic sample consisted of 643,315 non-pregnant women aged 15-49 years pooled from the latest Demographic Health Surveys (rounds V, VI, or VII; years 2005-2014) across 57 low- and middle-income countries. Individual variability in BMI ranged from 9.8 (95% CI: 9.8, 9.9) for the youngest to 23.2 (95% CI: 22.9, 23.5) for the oldest age group; 14.2 (95% CI: 14.1, 14.3) for those with no formal education to 19.7 (95% CI: 19.5, 19.9) for those who have completed higher education; and 13.6 (95% CI: 13.5, 13.7) for the poorest quintile to 20.1 (95% CI: 20.0, 20.2) for the wealthiest quintile group. Moreover, variability in BMI by age was also different for different socioeconomic groups. Empirically testing the fundamental assumption of constant variance and identifying groups with systematically large differentials in health experiences have important implications for reducing health disparity.
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