Child; Child, Preschool; Demography; Developing Countries/statistics & numerical data; Female; Geographic Mapping; Humans; Income/statistics & numerical data; Infant; Infant, Newborn; Male; Malnutrition/epidemiology; Nutritional Status; Overweight/epidemiology; Pediatric Obesity/epidemiology; Poverty/statistics & numerical data; Prevalence; Social Class; Wasting Syndrome/epidemiology; Developing Countries; Income; Malnutrition; Overweight; Pediatric Obesity; Poverty; Wasting Syndrome; Biochemistry, Genetics and Molecular Biology (all); General Biochemistry, Genetics and Molecular Biology; General Medicine
Abstract :
[en] A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1-70.8) million) to 6.4% (58.3 (47.6-70.7) million), but is predicted to remain above the World Health Organization's Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8-38.5) million) in 2000 to 6.0% (55.5 (44.8-67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic.
Disciplines :
Public health, health care sciences & services
Author, co-author :
LBD Double Burden of Malnutrition Collaborators
Khosravi, Mohammadhossein ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Language :
English
Title :
Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.
This work was primarily supported by grant OPP1132415 from the Bill & Melinda Gates Foundation awarded to S.I.H. The corresponding author had full access to data in the study and had final responsibility for the decision to submit the study for publication.This study was funded by the Bill & Melinda Gates Foundation. Co-authors employed by the Bill & Melinda Gates Foundation provided feedback on initial maps and drafts of this manuscript. Otherwise, the funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the final report or the decision to publish. The corresponding author had full access to all data in the study and had final responsibility for the decision to submit for publication. Dr Uddin reports personal fees from Deakin University Institute for Physical Activity and Nutrition, Australia, outside the submitted work. Dr Lorkowski reports personal fees from Akcea Therapeutics, Amedes MVZ für Laboratoriumsdiagnostik und Mikrobiologie, AMGEN, Berlin-Chemie, Boehringer Ingelheim Pharma, Daiichi Sankyo, MSD Sharp & Dohme, Novo Nordisk, Sanofi-Aventis, Synlab, Unilever and Upfield, as well as nonfinancial support from Preventicus outside the submitted work. Prof. Postma reports grants and personal fees from various pharmaceutical industries, all outside the submitted work. Prof. Postma holds stocks in Ingress Health and Pharmacoeconomics Advice Groningen and is advisor to Asc Academics, all pharmacoeconomic consultancy companies. Dr Remuzzi reports personal fees and nonfinancial support from Alnylam, personal fees and nonfinancial support from Boehringer Ingelheim, personal fees and nonfinancial support from Handock Inc., personal fees and nonfinancial support from Inception Sciences Canada and personal fees and nonfinancial support from Achillion, outside the submitted work. Dr. Jakovljevic reports grants from Ministry of Education Science and Technological Development of the Republic of Serbia outside the submitted work. Dr Flohr reports grants from European Union IMI grant scheme (Horizon 2020) outside the submitted work. Dr Jozwiak reports personal fees from ALAB LABORATORIA,
Ng, M. et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the global burden of disease study 2013. Lancet 384, 766–781 (2014).
Black, R. E. et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).
Black, R. E. et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371, 243–260 (2008).
World Health Organization. Double burden of malnutrition http://www.who.int/nutrition/double-burden-malnutrition/en/ (2018).
de Onis, M., Blössner, M. & Borghi, E. Global prevalence and trends of overweight and obesity among preschool children. Am. J. Clin. Nutr. 92, 1257–1264 (2010).
Popkin, B. M., Corvalan, C. & Grummer-Strawn, L. M. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet 395, 65–74 (2020).
UNICEF, WHO & The World Bank. Joint child malnutrition estimates - levels and trends (2017 edition) https://www.who.int/nutgrowthdb/estimates2016/en/ (2017)
Hawkes, C., Demaio, A. R. & Branca, F. Double-duty actions for ending malnutrition within a decade. Lancet Glob. Health 5, e745–e746 (2017).
Cole, T. J., Bellizzi, M. C., Flegal, K. M. & Dietz, W. H. Establishing a standard definition for child overweight and obesity worldwide: international survey. Brit. Med. J. 320, 1240 (2000).
World Health Organization. Training course on child growth assessment http://www.who.int/childgrowth/training/en/ (2008).
de Onis, M. et al. Prevalence thresholds for wasting, overweight and stunting in children under 5 years. Public Health Nutr. 22, 175–179 (2019).
United Nations. Goal 2: sustainable development knowledge platform https://sustainabledevelopment.un.org/sdg2.
World Health Organization. Global nutrition targets 2025: childhood overweight policy brief http://www.who.int/nutrition/publications/globaltargets2025_policybrief_overweight/en/ (2014).
World Health Organization. Global nutrition targets 2025: wasting policy brief http://www.who.int/nutrition/publications/globaltargets2025_policybrief_wasting/en/ (2014).
Development Initatives. 2018 Global nutrition report: shining a light to spur action on nutrition https://globalnutritionreport.org/reports/global-nutrition-report-2018/ (2018).
United Nations Children’s Fund, World Health Organization & The World Bank. Levels and trends in child malnutrition: key findings of the 2018 edition https://www.who.int/nutgrowthdb/2018-jme-brochure.pdf?ua=1 (2018).
World Health Organization. Data: nutrition - joint child malnutrition estimates (2018 edition) http://apps.who.int/gho/tableau-public/tpc-frame.jsp?id=402 (2018)
Tzioumis, E., Kay, M. C., Bentley, M. E. & Adair, L. S. Prevalence and trends in the childhood dual burden of malnutrition in low- and middle-income countries, 1990–2012. Public Health Nutr. 19, 1375–1388 (2016).
Abarca-Gómez, L. et al. Worldwide trends in body-mass index, underweight, overweight and obesity from 1975 to 2016: a pooled analysis of 2,416 population-based measurement studies in 128.9 million children, adolescents and adults. Lancet 390, 2627–2642 (2017).
Humbwavali, J. B., Giugliani, C., Silva, I. C. Mda & Duncan, B. B. Temporal trends in the nutritional status of women and children under five years of age in sub-Saharan African countries: ecological study. Sao Paulo Med. J. Rev. Paul. Med. 136, 454–463 (2018).
Kinyoki, D. K. et al. Mapping child growth failure across low- and middle-income countries. Nature 577, 231–234 (2020).
Diggle, P. & Ribeiro, P. J. Model-based Geostatistics (Springer, 2007); 10.1007/978-0-387-48536-2
FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2019. Safeguarding Against Economic Slowdowns and Downturns (FAO, 2019).
Wells, J. C. et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet 395, 75–88 (2020).
Bixby, H. et al. Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 569, 260–264 (2019).
Food and Agriculture Organization of the United Nations. The double burden of malnutrition. Case studies from six developing countries. FAO Food Nutr. Pap. 84, 1–334 (2006).
Monteiro, C. A., Levy, R. B., Claro, R. M., de Castro, I. R. R. & Cannon, G. Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil. Public Health Nutr. 14, 5–13 (2010).
Wang, Y. & Chen, H.-J. in Handbook of Anthropometry (ed. Preedy, V. R.) 29–48 (Springer, 2012).
WHO. Physical status: the use and interpretation of anthropometry: report of a WHO Expert Committee http://apps.who.int/iris/bitstream/handle/10665/37003/WHO_TRS_854.pdf;jsessionid=2FE8F4F177D025F6433656F4F7577F3F?sequence=1 (1995).
Neufeld, L. M. & Osemdar, S. J. M. World nutrition situation: global, regional and country trends in underweight and stunting as indicators of nutrition and health of populations. Internat. Nutr. 78, 11–19 (2013).
CDC. Causes and consequences of childhood obesity https://www.cdc.gov/obesity/childhood/causes.html (2016).
Ong, K. K. L., Ahmed, M. L., Emmett, P. M., Preece, M. A. & Dunger, D. B. Association between postnatal catch-up growth and obesity in childhood: prospective cohort study. Brit. Med. J. 320, 967 (2000).
Global Panel on Agriculture and Food Systems for Nutrition. The cost of malnutrition: why policy action is urgent https://glopan.org/sites/default/files/pictures/CostOfMalnutrition.pdf (2016).
Scaling Up Nutrition Civil Society Network. SUN Movement Strategy & Roadmap 2016–2020. http://docs.scalingupnutrition.org/wp-content/uploads/2016/09/SR_20160901_ENG_web_pages.pdf (2016).
Hawkes, C. et al. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. Lancet 395, 142–155 (2020).
World Health Organization. United Nations decade of action on nutrition 2016–2025: towards country-specific SMART commitments for action on nutrition http://www.fao.org/3/a-i6130e.pdf (2016).
Nugent, R. et al. Economic effects of the double burden of malnutrition. Lancet 395, 156–165 (2020).
Global Administrative Areas. GADM database of global administrative areas http://www.gadm.org (2018).
Land Processes Distributed Active Archive Center. Combined MODIS 5.1. MCD12Q1 | LP DAAC: NASA Land Data Products and Services. https://lpdaac.usgs.gov/products/mcd12q1v006/ (2017).
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).
World Wildlife Fund. Global lakes and wetlands database, level 3 https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database (2004).
Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 170004 (2017).
GeoNetwork. The Global Administrative Unit Layers (GAUL) http://www.fao.org/geonetwork/srv/en/main.home (2015).
Murray, C. J. et al. GBD 2010: design, definitions and metrics. Lancet 380, 2063–2066 (2012).
Dicker, D. et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the global burden of disease study 2017. Lancet 392, 1684–1735 (2018).
Stevens, G. A. et al. Guidelines for accurate and transparent health estimates reporting: the GATHER statement. PLoS Med. 13, e1002056 (2016).
USAID. Demographic and health surveys (DHS) http://dhsprogram.com/.
World Bank Group. Living standards measurement survey (LSMS) http://go.worldbank.org/UK1ETMHBN0.
World Bank Group. Core welfare indicators questionnaire survey (CWIQ) http://ghdx.healthdata.org/series/core-welfare-indicators-questionnaire-survey-cwiq.
Lumley, T. in Complex Surveys: A Guide to Analysis Using R. (Wiley, 2010).
Lumley, T. Analysis of complex survey samples. J. Stat. Softw. 9, 1–19 (2004).
FAO. The global administrative unit layers (GAUL): technical aspects http://www.fao.org/geonetwork/srv/en/main.home.
Global Administrative Areas (GADM). GADM database of global administrative areas http://www.gadm.org.
WHO Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr. Suppl, 450, 76–85 (2006).
Indrayan, A. Demystifying LMS and BCPE methods of centile estimation for growth and other health parameters. Indian Pediatr. 51, 37–43 (2014).
WHO Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr 95, 76–85 (2006).
Bhatt, S. et al. Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization. J. R. Soc. Interface 14, 20170520 (2017).
Fienberg, S. E. The Analysis of Cross-Classified Categorical Data (Springer, 2007); 10.1007/978-0-387-72825-4
Ananth, C. V. & Kleinbaum, D. G. Regression models for ordinal responses: a review of methods and applications. Int. J. Epidemiol. 26, 1323–1333 (1997).
Mosser, J. F. et al. Mapping diphtheria-pertussis-tetanus vaccine coverage in Africa, 2000–2016: a spatial and temporal modelling study. Lancet 393, 1843–1855 (2019).
Stein, M. L. Interpolation of Spatial Data (Springer, 1999).
Diggle, Peter J. & Ribeiro, Paulo J. Model-based Geostatistics (Springer, 2007); 10.1007/978-0-387-48536-2.
Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009).
Martins, T. G., Simpson, D., Lindgren, F. & Rue, H. Bayesian computing with INLA: new features. Comput. Stat. Data Anal. 67, 68–83 (2013).
Lindgren, F., Rue, H. & Lindström, J. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R. Stat. Soc. Ser. B Stat. Methodol.73, 423–498 (2011).
Golding, N. et al. Mapping under-5 and neonatal mortality in Africa, 2000–15: a baseline analysis for the sustainable development goals. Lancet 390, 2171–2182 (2017).
Osgood-Zimmerman, A. et al. Mapping child growth failure in Africa between 2000 and 2015. Nature 555, 41–47 (2018).
Alegana, V. A. et al. Fine resolution mapping of population age-structures for health and development applications. J. R. Soc. Interface 12, 20150073–20150073 (2015).
Patil, A. P., Gething, P. W., Piel, F. B. & Hay, S. I. Bayesian geostatistics in health cartography: the perspective of malaria. Trends Parasitol. 27, 246–253 (2011).
Gething, P. W., Patil, A. P. & Hay, S. I. Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation. PLoS Comput. Biol. 6, e1000724 (2010).
Scharlemann, J. P. W. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3, e1408 (2008).
Blangiardo, M. & Cameletti, M. in Spatial and Spatio‐temporal Bayesian Models with R‐INLA 235–258 (John Wiley & Sons, 2015).
Cameletti, M., Lindgren, F., Simpson, D. & Rue, H. Spatio-temporal modeling of particulate matter concentration through the SPDE approach. AStA Adv. Stat. Anal. 97, 109–131 (2013).
Blangiardo, M., Cameletti, M., Baio, G. & Rue, Havard Spatial and spatio-temporal models with R-INLA. Spat. Spatio-Temporal Epidemiol. 7, 39–55 (2013).
