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. 2020 May;26(5):750-759.
doi: 10.1038/s41591-020-0807-6. Epub 2020 Apr 20.

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

LBD Double Burden of Malnutrition Collaborators
Collaborators

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

LBD Double Burden of Malnutrition Collaborators. Nat Med. 2020 May.

Erratum in

Abstract

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.

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Conflict of interest statement

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, nonfinancial support from SERVIER, nonfinancial support from MICROLIFE, personal fees from TEVA POLSKA, nonfinancial support from SUPERPHARM and nonfinancial support from MEDICOVER, outside the submitted work. W. Mendoza is currently Program Analyst Population and Development at the United Nations Population Fund-UNFPA Country Office in Peru, which does not necessarily endorse this study. Prof. Saxena reports grants from NIHR School for Public Health Research, grants from NIHR Applied Research Collaboration and grants from The Daily Mile Foundation supported by INEOS, outside the submitted work. Dr Dunachie reports grants from The Fleming Fund at UK Department of Health and Social Care, during the conduct of the study. Dr Mozaffarian reports research funding from the National Institutes of Health and the Gates Foundation; personal fees from GOED, Nutrition Impact, Bunge, Indigo Agriculture, Motif FoodWorks, Amarin, Acasti Pharma, Cleveland Clinic Foundation, America’s Test Kitchen and Danone; scientific advisory board, Brightseed, DayTwo, Elysium Health and Filtricine; and chapter royalties from UpToDate; all outside the submitted work. Dr J. Singh reports personal fees from Crealta/Horizon, Medisys, Fidia, UBM LLC, Medscape, WebMD, Clinical Care Options, Clearview Healthcare Partners, Putnam Associates, Spherix, the National Institutes of Health and the American College of Rheumatology, stock options in Amarin Pharmaceuticals and Viking Pharmaceuticals and participates in the steering committee of OMERACT, an international organization that develops measures for clinical trials receives arm’s length funding from 12 pharmaceutical companies and is also on the speaker’s bureau of Simply Speaking.

Figures

Fig. 1
Fig. 1. Prevalence of overweight children under 5 in LMICs (2000–2017).
a,b, Prevalence of overweight among children under 5 at 5 × 5-km resolution in 2000 (a) and 2017 (b). c, Overlapping population-weighted lowest and highest 10% of grid cells and AROC in overweight from 2000 to 2017. d, Overlapping population-weighted quartiles of overweight and relative 95% uncertainty in 2017. Maps reflect administrative boundaries, land cover, lakes and population; gray colored areas have grid cells classified as ‘barren or sparsely vegetated’ and had fewer than ten people per 1 × 1-km grid cell in 2017 or were not included in this analysis–. Maps were generated using ArcGIS Desktop 10.6.
Fig. 2
Fig. 2. Number of overweight children under 5 in LMICs (2000–2017) and progress toward 2025.
a,b, Number of children under 5 affected by overweight at a 5 × 5-km resolution (a) and by first administrative units (b). c, Annualized decrease (AD) in overweight prevalence from 2000 to 2017. d, Grid cell-level predicted overweight prevalence in 2025 based on AD achieved from 2000 to 2017 and projected from 2017. Maps reflect administrative boundaries, land cover, lakes and population; gray colored areas have grid cells classified as ‘barren or sparsely vegetated’ and had fewer than ten people per 1 × 1-km grid cell in 2017 or were not included in this analysis–. Maps were generated using ArcGIS Desktop 10.6.
Fig. 3
Fig. 3. Prevalence of wasted children under 5 in LMICs (2000–2017).
a–c, Prevalence of moderate and severe wasting among children under 5 at a 5 × 5-km resolution in 2000 (a) and 2017 (b). c, Overlapping population-weighted lowest and highest 10% of grid cells and AROC in wasting from 2000 to 2017. d, Overlapping population-weighted quartiles of wasting and relative 95% uncertainty in 2017. Maps reflect administrative boundaries, land cover, lakes and population; gray colored areas have grid cells classified as ‘barren or sparsely vegetated’ and had fewer than ten people per 1 × 1-km grid cell in 2017 or were not included in this analysis–. Maps were generated using ArcGIS Desktop 10.6.
Fig. 4
Fig. 4. Number of wasted children under 5 in LMICs (2000–2017) and progress toward 2025.
a,b, Number of children under 5 affected by wasting at the 5 × 5-km resolution (a) and by first administrative units (b). c, AD in wasting prevalence from 2000 to 2017. d, Grid cell-level predicted stunting prevalence in 2025 based on AD achieved from 2000 to 2017 and projected from 2017. Maps reflect administrative boundaries, land cover, lakes and population; gray colored areas have grid cells classified as ‘barren or sparsely vegetated’ and had fewer than ten people per 1 × 1-km grid cell in 2017 or were not included in this analysis–. Maps were generated using ArcGIS Desktop 10.6.
Fig. 5
Fig. 5. Overlapping population-weighted quartiles of overweight and wasting prevalence in children under 5 across LMICs in 2017 and 2025.
a–d, Prevalence of moderate-to-severe overweight (OVR) and wasting (MSW) among children under 5 years of age in 2017 at the first administrative unit (a) and at a 5 × 5-km resolution (b). c,d, Estimated prevalence of moderate to severe OVR and MSW among children under 5 years of age in 2025 at the first administrative unit (c) and at a 5 × 5-km resolution (d). Quartile cutoffs were 0–5%, ≥5–10%, ≥10–15% and ≥15%. Maps reflect administrative boundaries, land cover, lakes and population; gray colored areas have grid cells classified as ‘barren or sparsely vegetated’ and had fewer than ten people per 1 × 1-km grid cell in 2017 or were not included in these analyses–. Maps were generated using ArcGIS Desktop 10.6.
Extended Data Fig. 1
Extended Data Fig. 1. Prevalence of under-5 childhood overweight in LMICs in 2017 at administrative levels 0, 1, 2, and at 5 × 5-km resolution.
Prevalence of overweight among children under 5 at administrative level 0 (national-level estimates) (a), first administrative unit (b), second administrative unit (c), and at the 5 × 5-km resolution (d). Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as “barren or sparsely vegetated” and had fewer than ten people per 1 × 1-km grid cell–, or were not included in this analysis. Maps were generated using ArcGIS Desktop 10.6.
Extended Data Fig. 2
Extended Data Fig. 2. Prevalence of under-5 child wasting in LMICs at administrative levels 0, 1, 2, and at 5 × 5-km resolution in 2017.
Prevalence of wasting among children under 5 at administrative level 0 (national-level estimates) (a), first administrative unit (b), second administrative unit (c), and at the 5 × 5-km resolution (d). Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as “barren or sparsely vegetated” and had fewer than ten people per 1 × 1-km grid cell–, or were not included in this analysis. Maps were generated using ArcGIS Desktop 10.6.
Extended Data Fig. 3
Extended Data Fig. 3. Modelling regions.
Modelling regions were based on geographic and socio-demographic index (SDI) regions from the Global Burden of Disease, defined as: Andean South America, Central America and the Caribbean, Central sub-Saharan Africa (SSA), East Asia, Eastern SSA, Middle East, North Africa, Oceania, Southeast Asia, South Asia, South SSA, Central Asia, Tropical South America, and Western SSA. Regions in grey (Stage 3) were not included in our models due to high-middle and high SDI. Map was generated using ArcGIS Desktop 10.6.
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