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 aff001; Tiago Cravo Oliveira Hashiguchi aff002; Blake Thomson aff003; Joseph Dieleman aff004; Eran Bendavid aff005
Působiště autorů: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, United States of America aff001; Organisation for Economic Co-operation and Development ELS/HD, Paris, France aff002; Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom aff003; Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America aff004; Center for Population Health Sciences, Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, California, United States of America aff005
Vyšlo v časopise: 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. PLoS Med 16(11): e32767. doi:10.1371/journal.pmed.1002968
Kategorie: Research Article
In 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 findings
Our 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.
Our 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.
Body Mass Index – Economic analysis – Economic development – Food consumption – Health surveys – Obesity – Public and occupational health – Surveys
1. NCD 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 Dec 16;390(10113):2627–42. doi: 10.1016/S0140-6736(17)32129-3 29029897
2. Roberto CA, Swinburn B, Hawkes C, Huang TT, Costa SA, Ashe M, Zwicker L, Cawley JH, Brownell KD. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet. 2015 Jun 13;385(9985):2400–9. doi: 10.1016/S0140-6736(14)61744-X 25703111
3. World Health Organization. Obesity: preventing and managing the global epidemic. World Health Organization; 2000.
4. World Health Organization. A comprehensive global monitoring framework including indicators and a set of voluntary global targets for the prevention and control of noncommunicable diseases. Geneva: World Health Organization. 2012.
5. Chang AY, Riumallo-Herl C, Salomon JA, Resch SC, Brenzel L, Verguet S. Estimating the distribution of morbidity and mortality of childhood diarrhea, measles, and pneumonia by wealth group in low-and middle-income countries. BMC medicine. 2018 Dec;16(1):102. doi: 10.1186/s12916-018-1074-y 29970074
6. Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: a systematic review. Obesity reviews. 2012 Nov;13(11):1067–79. doi: 10.1111/j.1467-789X.2012.01017.x 22764734
7. Deuchert E, Cabus S, Tafreschi D. A short note on economic development and socioeconomic inequality in female body weight. Health economics. 2014 Jul;23(7):861–9. doi: 10.1002/hec.2968 23873750
8. Goryakin Y, Lobstein T, James WP, Suhrcke M. The impact of economic, political and social globalization on overweight and obesity in the 56 low and middle income countries. Social Science & Medicine. 2015 May 1;133:67–76.
9. Bollyky TJ, Templin T, Cohen M, Dieleman JL. Lower-income countries that face the most rapid shift in noncommunicable disease burden are also the least prepared. Health Affairs. 2017 Nov 1;36(11):1866–75. doi: 10.1377/hlthaff.2017.0708 29137514
10. Malik VS, Willett WC, Hu FB. Global obesity: trends, risk factors and policy implications. Nature Reviews Endocrinology. 2013 Jan;9(1):13. doi: 10.1038/nrendo.2012.199 23165161
11. Goryakin Y, Suhrcke M. Economic development, urbanization, technological change and overweight: What do we learn from 244 Demographic and Health Surveys?. Economics & Human Biology. 2014 Jul 1;14:109–27.
12. Popkin BM, Slining MM. New dynamics in global obesity facing low‐and middle‐income countries. Obesity Reviews. 2013 Nov;14:11–20. doi: 10.1111/obr.12102 24102717
13. Hawkes C. Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Globalization and health. 2006 Dec;2(1):4.
14. Drewnowski A, Specter SE. Poverty and obesity: the role of energy density and energy costs. The American journal of clinical nutrition. 2004 Jan 1;79(1):6–16. doi: 10.1093/ajcn/79.1.6 14684391
15. ICF International. Demographic and Health Surveys (various) [Datasets]. Funded by USAID. Rockville, Maryland: ICF; 1995–2016.
16. Üstün TB, Chatterji S, Mechbal A, Murray CJ. The World Health Surveys. Health systems performance assessment: debates, methods and empiricism. Geneva. World Health Organization. 2003;797.
17. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. Bmj. 2000 May 6;320(7244):1240. doi: 10.1136/bmj.320.7244.1240 10797032
18. Danubio ME, Miranda G, Vinciguerra MG, Vecchi E, Rufo F. Comparison of self-reported and measured height and weight: Implications for obesity research among young adults. Economics & Human Biology. 2008 Mar 1;6(1):181–90.
19. James SL, Gubbins P, Murray CJ, Gakidou E. Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015. Population health metrics. 2012 Dec;10(1):12. doi: 10.1186/1478-7954-10-12 22846561
20. United Nations Population Division. World population prospects: the 2017 revision. New York: United Nations; 2017.
21. Anjana RM, Deepa M, Pradeepa R, Mahanta J, Narain K, Das HK, Adhikari P, Rao PV, Saboo B, Kumar A, Bhansali A. Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR–INDIAB population-based cross-sectional study. The lancet Diabetes & endocrinology. 2017 Aug 1;5(8):585–96.
22. Dagenais GR, Gerstein HC, Zhang X, McQueen M, Lear S, Lopez-Jaramillo P, Mohan V, Mony P, Gupta R, Kutty VR, Kumar R. Variations in diabetes prevalence in low-, middle-, and high-income countries: results from the prospective urban and rural epidemiological study. Diabetes care. 2016 May 1;39(5):780–7. doi: 10.2337/dc15-2338 26965719
23. Cooksey-Stowers K, Schwartz MB, Brownell KD. Food swamps predict obesity rates better than food deserts in the United States. International journal of environmental research and public health. 2017 Nov 14;14(11):1366.
24. Darmon N, Drewnowski A. Does social class predict diet quality?. The American journal of clinical nutrition. 2008 May 1;87(5):1107–17. doi: 10.1093/ajcn/87.5.1107 18469226
25. Afshin A, Peñalvo JL, Del Gobbo L, Silva J, Michaelson M, O'Flaherty M, Capewell S, Spiegelman D, Danaei G, Mozaffarian D. The prospective impact of food pricing on improving dietary consumption: a systematic review and meta-analysis. PLoS ONE. 2017;12(3):e0172277. doi: 10.1371/journal.pone.0172277 28249003
26. Colchero MA, Rivera-Dommarco J, Popkin BM, Ng SW. In Mexico, evidence of sustained consumer response two years after implementing a sugar-sweetened beverage tax. Health Affairs. 2017 Feb 22;36(3):564–71. doi: 10.1377/hlthaff.2016.1231 28228484
27. Baker P, Gill T, Friel S, Carey G, Kay A. Generating political priority for regulatory interventions targeting obesity prevention: an Australian case study. Social science & medicine. 2017 Mar 1;177:141–9.
28. Gomes CS, Matozinhos FP, Mendes LL, Pessoa MC, Velasquez-Melendez G. Physical and social environment are associated to leisure time physical activity in adults of a brazilian city: a cross-sectional study. PLoS ONE. 2016;11(2):e0150017. doi: 10.1371/journal.pone.0150017 26915091
29. Zagorsky JL. Is obesity as dangerous to your wealth as to your health?. Research on Aging. 2004 Jan;26(1):130–52.
30. Okop KJ, Mukumbang FC, Mathole T, Levitt N, Puoane T. Perceptions of body size, obesity threat and the willingness to lose weight among black South African adults: a qualitative study. BMC public health. 2016 Dec;16(1):365
31. Rguibi M, Belahsen R. Body size preferences and sociocultural influences on attitudes towards obesity among Moroccan Sahraoui women. Body Image. 2006 Dec 1;3(4):395–400. doi: 10.1016/j.bodyim.2006.07.007 18089243
32. Ford ND, Patel SA, Narayan KV. Obesity in low-and middle-income countries: burden, drivers, and emerging challenges. Annual review of public health. 2017 Mar 20;38:145–64. doi: 10.1146/annurev-publhealth-031816-044604 28068485
33. The Organisation for Economic Co-operation and Development. Obesity Update 2017. The Organisation for Economic Co-operation and Development. 2017 [cited 2019 Jun 17]. Available from: https://www.oecd.org/health/obesity-update.htm
Článek vyšel v časopise
2019 Číslo 11
- Příznivý vliv Armolipidu Plus na hladinu cholesterolu a zánětlivé parametry u pacientů s chronickým subklinickým zánětem
- Berberin: přírodní hypolipidemikum se slibnými výsledky
- Nutraceutikum Armolipid Plus podle klinických důkazů zlepšuje lipidový profil − metaanalýza
- Armolipid Plus – doplněk stravy s potvrzeným účinkem na dyslipidemii
- Vliv kombinace nutraceutik na remodelaci levé komory srdeční u osob s metabolickým syndromem
Nejčtenější v tomto čísle
- Prescription of benzodiazepines, z-drugs, and gabapentinoids and mortality risk in people receiving opioid agonist treatment: Observational study based on the UK Clinical Practice Research Datalink and Office for National Statistics death records
- Oxygen systems to improve clinical care and outcomes for children and neonates: A stepped-wedge cluster-randomised trial in Nigeria
- Frequency of cannabis and illicit opioid use among people who use drugs and report chronic pain: A longitudinal analysis
- Supervised injection facility use and all-cause mortality among people who inject drugs in Vancouver, Canada: A cohort study
Zvyšte si kvalifikaci online z pohodlí domova
Deprese u dětí a adolescentůnový kurz