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Change in the Body Mass Index Distribution for Women: Analysis of Surveys from 37 Low- and Middle-Income Countries


Background:
There are well-documented global increases in mean body mass index (BMI) and prevalence of overweight (BMI≥25.0 kg/m2) and obese (BMI≥30.0 kg/m2). Previous analyses, however, have failed to report whether this weight gain is shared equally across the population. We examined the change in BMI across all segments of the BMI distribution in a wide range of countries, and assessed whether the BMI distribution is changing between cross-sectional surveys conducted at different time points.

Methods and Findings:
We used nationally representative surveys of women between 1991–2008, in 37 low- and middle-income countries from the Demographic Health Surveys ([DHS] n = 732,784). There were a total of 96 country-survey cycles, and the number of survey cycles per country varied between two (21/37) and five (1/37). Using multilevel regression models, between countries and within countries over survey cycles, the change in mean BMI was used to predict the standard deviation of BMI, the prevalence of underweight, overweight, and obese. Changes in median BMI were used to predict the 5th and 95th percentile of the BMI distribution. Quantile-quantile plots were used to examine the change in the BMI distribution between surveys conducted at different times within countries. At the population level, increasing mean BMI is related to increasing standard deviation of BMI, with the BMI at the 95th percentile rising at approximately 2.5 times the rate of the 5th percentile. Similarly, there is an approximately 60% excess increase in prevalence of overweight and 40% excess in obese, relative to the decline in prevalence of underweight. Quantile-quantile plots demonstrate a consistent pattern of unequal weight gain across percentiles of the BMI distribution as mean BMI increases, with increased weight gain at high percentiles of the BMI distribution and little change at low percentiles. Major limitations of these results are that repeated population surveys cannot examine weight gain within an individual over time, most of the countries only had data from two surveys and the study sample only contains women in low- and middle-income countries, potentially limiting generalizability of findings.

Conclusions:
Mean changes in BMI, or in single parameters such as percent overweight, do not capture the divergence in the degree of weight gain occurring between BMI at low and high percentiles. Population weight gain is occurring disproportionately among groups with already high baseline BMI levels. Studies that characterize population change should examine patterns of change across the entire distribution and not just average trends or single parameters.



Please see later in the article for the Editors' Summary


Vyšlo v časopise: Change in the Body Mass Index Distribution for Women: Analysis of Surveys from 37 Low- and Middle-Income Countries. PLoS Med 10(1): e32767. doi:10.1371/journal.pmed.1001367
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001367

Souhrn

Background:
There are well-documented global increases in mean body mass index (BMI) and prevalence of overweight (BMI≥25.0 kg/m2) and obese (BMI≥30.0 kg/m2). Previous analyses, however, have failed to report whether this weight gain is shared equally across the population. We examined the change in BMI across all segments of the BMI distribution in a wide range of countries, and assessed whether the BMI distribution is changing between cross-sectional surveys conducted at different time points.

Methods and Findings:
We used nationally representative surveys of women between 1991–2008, in 37 low- and middle-income countries from the Demographic Health Surveys ([DHS] n = 732,784). There were a total of 96 country-survey cycles, and the number of survey cycles per country varied between two (21/37) and five (1/37). Using multilevel regression models, between countries and within countries over survey cycles, the change in mean BMI was used to predict the standard deviation of BMI, the prevalence of underweight, overweight, and obese. Changes in median BMI were used to predict the 5th and 95th percentile of the BMI distribution. Quantile-quantile plots were used to examine the change in the BMI distribution between surveys conducted at different times within countries. At the population level, increasing mean BMI is related to increasing standard deviation of BMI, with the BMI at the 95th percentile rising at approximately 2.5 times the rate of the 5th percentile. Similarly, there is an approximately 60% excess increase in prevalence of overweight and 40% excess in obese, relative to the decline in prevalence of underweight. Quantile-quantile plots demonstrate a consistent pattern of unequal weight gain across percentiles of the BMI distribution as mean BMI increases, with increased weight gain at high percentiles of the BMI distribution and little change at low percentiles. Major limitations of these results are that repeated population surveys cannot examine weight gain within an individual over time, most of the countries only had data from two surveys and the study sample only contains women in low- and middle-income countries, potentially limiting generalizability of findings.

Conclusions:
Mean changes in BMI, or in single parameters such as percent overweight, do not capture the divergence in the degree of weight gain occurring between BMI at low and high percentiles. Population weight gain is occurring disproportionately among groups with already high baseline BMI levels. Studies that characterize population change should examine patterns of change across the entire distribution and not just average trends or single parameters.



Please see later in the article for the Editors' Summary


Zdroje

1. FinucaneMM, StevensGA, CowanMJ, DanaeiG, LinJK, et al. (2011) National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet 377: 557–567.

2. Jones-SmithJC, Gordon-LarsenP, SiddiqiA, PopkinBM (2011) Cross-national comparisons of time trends in overweight inequality by socioeconomic status among women using repeated cross-sectional surveys from 37 developing countries, 1989–2007. Am J Epidemiol 173: 667–675.

3. Rose G, Khaw K-T, Marmot M (2008) Rose's strategy of preventive medicine: the complete original text. New York: Oxford University Press.

4. RoseG, DayS (1990) The population mean predicts the number of deviant individuals. BMJ 301: 1031–1034.

5. FreedmanDS, SrinivasanSR, ValdezRA, WilliamsonDF, BerensonGS (1997) Secular increases in relative weight and adiposity among children over two decades: the Bogalusa Heart Study. Pediatrics 99: 420–426.

6. BjørnelvS, LydersenS, MykletunA, HolmenTL (2007) Changes in BMI-distribution from 1966–69 to 1995–97 in adolescents. The Young-HUNT study, Norway. BMC Public Health 7: 279.

7. FreedmanDS, KhanLK, SerdulaMK, GaluskaDA, DietzWH (2002) Trends and correlates of class 3 obesity in the United States from 1990 through 2000. JAMA 288: 1758–1761.

8. FlegalKM, TroianoRP (2000) Changes in the distribution of body mass index of adults and children in the US population. Int J Obes (Lond) 24: 807–818.

9. FlegalKM, CarrollMD, KitBK, OgdenCL (2012) Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA 307: 491–497.

10. SubramanianSV, PerkinsJM, KhanKT (2009) Do burdens of underweight and overweight coexist among lower socioeconomic groups in India? Am J Clin Nutr 90: 369–376.

11. SubramanianSV, FinlayJE, NeumanM (2011) Global trends in body-mass index. Lancet 377: 1915–1916.

12. SubramanianSV, PerkinsJM, ÖzaltinE, Davey SmithG (2011) Weight of nations: a socioeconomic analysis of women in low- to middle-income countries. Am J Clin Nutr 93: 413–421.

13. MEASURE DHS (2011) Quality information to plan, monitor and improve population, health, and nutrition programs (n.d.) MEASURE DHS | Quality information to plan, monitor and improve population, health, and nutrition programs. measuredhscom. Available: http://measuredhs.com/. Accessed 19 November 2011.

14. Rutstein SO, Rojas G (2007) Guide to DHS statistics. Available: http://www.measuredhs.com/pubs/pdf/DHSG1/Guide_DHS_Statistics.pdf. Accessed 19 November 2011.

15. CorsiDJ, NeumanM, FinlayJE, SubramanianSV (2012) Demographic and health surveys: a profile. Int J Epidemiol doi:10.1093/ije/dys184Epub ahead of print.

16. United Nations (2005) Household sample surveys in developing and transition countries - studies in methods series, F number 96. New York: United Nations.

17. Macro International Inc. (1996) Sampling manual. Calverton (Maryland): Macro International Inc.

18. CorsiDJ, FinlayJE, SubramanianSV (2011) Global burden of double malnutrition: has anyone seen it? PLoS ONE 6: e25120 doi:10.1371/journal.pone.0025120.

19. Goldstein H (2003) Multilevel statistical models. London: Arnold.

20. Subramanian SV, Jones K, Duncan C (2003) Multilevel methods for public health researchers. Kawachi I, editor. Neighborhoods and health. New York: Oxford University Press.

21. RoseG (1991) Ancel Keys lecture. Circulation 84: 1405–1409.

22. WilkMB, GnanadesikanR (1968) Probability plotting methods for the analysis of data. Biometrika 55: 1–17.

23. ShettyPS, JamesWP (1994) Body mass index. A measure of chronic energy deficiency in adults. FAO Food Nutr Pap 56: 1–57.

24. World Health Organization (1999) Obesity: preventing and managing the global epidemic. Report of a WHO consultation. Geneva: WHO. 254 pp.

25. Berrington de GonzalezA, HartgeP, CerhanJR, FlintAJ, HannanL, et al. (2010) Body-mass index and mortality among 1.46 million white adults. New Engl J Med 363: 2211–2219.

26. Prospective Studies Collaboration (2009) WhitlockG, LewingtonS, SherlikerP, ClarkeR, et al. (2009) Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 373: 1083–1096.

27. GuD, HeJ, DuanX, ReynoldsK, WuX, et al. (2006) Body weight and mortality among men and women in China. JAMA 295: 776–783.

28. ZhengW, McLerranDF, RollandB, ZhangX, InoueM, et al. (2011) Association between body-mass index and risk of death in more than 1 million Asians. New Engl J Med 364: 719–729.

29. NeumanM, FinlayJE, Davey SmithG, SubramanianSV (2011) The poor stay thinner: stable socioeconomic gradients in BMI among women in lower- and middle-income countries. Am J Clin Nutr 94: 1348–1357.

30. SwinburnB, EggerG (2004) The runaway weight gain train: too many accelerators, not enough brakes. BMJ 329: 736–739.

31. Jones-SmithJC, Gordon-LarsenP, SiddiqiA, PopkinBM (2011) Is the burden of overweight shifting to the poor across the globe? Time trends among women in 39 low- and middle-income countries (1991–2008). Int J Obes Relat Metab Disord

32. MendezMA, MonteiroCA, PopkinBM (2005) Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr 81: 714–721.

33. DanaeiG, FinucaneMM, LuY, SinghGM, CowanMJ, et al. (2011) National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet 378: 31–40.

34. FarzadfarF, FinucaneMM, DanaeiG, PelizzariPM, CowanMJ, et al. (2011) National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3·0 million participants. Lancet 377: 578–586.

35. DanaeiG, FinucaneMM, LinJK, SinghGM, PaciorekCJ, et al. (2011) National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5·4 million participants. Lancet 377: 568–577.

36. YusufS, HawkenS, OunpuuS, BautistaL, FranzosiMG, et al. (2005) Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 366: 1640–1649.

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