Evidence map›Paper›PMID 31774821›Full record

ArticlePLoS medicine2019

The overweight and obesity transition from the wealthy to the poor in low- and middle-income countries: A survey of household data from 103 countries.

Tara Templin, Tiago Cravo Oliveira Hashiguchi, Blake Thomson, Joseph Dieleman, Eran Bendavid

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In one paragraph

Article in PLoS medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
108citing papers in PubMed, 4 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

108 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Identifying Social Media-Based Interactions That Help Adults to Adhere to Weight Loss Goals: A Systematic Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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48 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tara TemplinDepartment of Health Research and Policy, Stanford University School of Medicine, Stanford, California, United States of America.ORCID 0000-0003-4984-6472
Tiago Cravo Oliveira HashiguchiOrganisation for Economic Co-operation and Development ELS/HD, Paris, France.
Blake ThomsonNuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Joseph DielemanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0001-7976-7412
Eran BendavidCenter for Population Health Sciences, Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, California, United States of America.ORCID 0000-0002-8364-4711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn high-income countries, obesity prevalence (body mass index greater than or equal to 30 kg/m2) is highest among the poor, while overweight (body mass index greater than or equal to 25 kg/m2) is prevalent across all wealth groups. In contrast, in low-income countries, the prevalence of overweight and obesity is higher among wealthier individuals than among poorer individuals. We characterize the transition of overweight and obesity from wealthier to poorer populations as countries develop, and project the burden of overweight and obesity among the poor for 103 countries. METHODS AND

findingsOur sample used 182 Demographic and Health Surveys and World Health Surveys (n = 2.24 million respondents) from 1995 to 2016. We created a standard wealth index using household assets common among all surveys and linked national wealth by country and year identifiers. We then estimated the changing probability of overweight and obesity across every wealth decile as countries' per capita gross domestic product (GDP) rises using logistic and linear fixed-effect regression models. We found that obesity rates among the wealthiest decile were relatively stable with increasing national wealth, and the changing gradient was largely due to increasing obesity prevalence among poorer populations (3.5% [95% uncertainty interval: 0.0%-8.3%] to 14.3% [9.7%-19.0%]). Overweight prevalence among the richest (45.0% [35.6%-54.4%]) and the poorest (45.5% [35.9%-55.0%]) were roughly equal in high-income settings. At $8,000 GDP per capita, the adjusted probability of being obese was no longer highest in the richest decile, and the same was true of overweight at $10,000. Above $25,000, individuals in the richest decile were less likely than those in the poorest decile to be obese, and the same was true of overweight at $50,000. We then projected overweight and obesity rates by wealth decile to 2040 for all countries to quantify the expected rise in prevalence in the relatively poor. Our projections indicated that, if past trends continued, the number of people who are poor and overweight will increase in our study countries by a median 84.4% (range 3.54%-383.4%), most prominently in low-income countries. The main limitations of this study included the inclusion of cross-sectional, self-reported data, possible reverse causality of overweight and obesity on wealth, and the lack of physical activity and food price data.

conclusionsOur findings indicate that as countries develop economically, overweight prevalence increased substantially among the poorest and stayed mostly unchanged among the wealthiest. The relative poor in upper- and lower-middle income countries may have the greatest burden, indicating important planning and targeting needs for national health programs.

Indexed as

AdolescentAdultBody Mass IndexCross-Sectional StudiesDeveloping CountriesFamily CharacteristicsFemaleGlobal HealthHealth SurveysHumansIncomeMaleMiddle AgedObesityOverweightPoverty

Identifiers

PMID31774821
PMCPMC6880978

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.