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

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 20...

Full description

Autores:
Kinyoki, Damaris K.
Ross, Jennifer M.
Lazzar-Atwood, Alice
Munro, Sandra B.
Schaeffer, Lauren E.
Abbasalizad-Farhangi, Mahdieh
Abbasi, Masoumeh
Abbastabar, Hedayat
Abdelalim, Ahmed
Abdoli, Amir
Alvis-Guzman, Nelson
LBD Double Burden of Malnutrition Collaborators
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7224
Acceso en línea:
https://hdl.handle.net/11323/7224
https://doi.org/10.1038/s41591-020-0807-6
https://repositorio.cuc.edu.co/
Palabra clave:
Malnutrition
Obesity
Risk factors
Signs and symptoms
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_aa8dfb2c90d572ac1abbb8e5fbed1dce
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7224
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
title Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
spellingShingle Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
Malnutrition
Obesity
Risk factors
Signs and symptoms
title_short Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
title_full Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
title_fullStr Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
title_full_unstemmed Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
title_sort Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
dc.creator.fl_str_mv Kinyoki, Damaris K.
Ross, Jennifer M.
Lazzar-Atwood, Alice
Munro, Sandra B.
Schaeffer, Lauren E.
Abbasalizad-Farhangi, Mahdieh
Abbasi, Masoumeh
Abbastabar, Hedayat
Abdelalim, Ahmed
Abdoli, Amir
Alvis-Guzman, Nelson
LBD Double Burden of Malnutrition Collaborators
dc.contributor.author.spa.fl_str_mv Kinyoki, Damaris K.
Ross, Jennifer M.
Lazzar-Atwood, Alice
Munro, Sandra B.
Schaeffer, Lauren E.
Abbasalizad-Farhangi, Mahdieh
Abbasi, Masoumeh
Abbastabar, Hedayat
Abdelalim, Ahmed
Abdoli, Amir
Alvis-Guzman, Nelson
LBD Double Burden of Malnutrition Collaborators
dc.subject.spa.fl_str_mv Malnutrition
Obesity
Risk factors
Signs and symptoms
topic Malnutrition
Obesity
Risk factors
Signs and symptoms
description 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.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-09T16:45:57Z
dc.date.available.none.fl_str_mv 2020-11-09T16:45:57Z
dc.date.issued.none.fl_str_mv 2020-04
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 1546-170X
1078-8956
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7224
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1038/s41591-020-0807-6
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1546-170X
1078-8956
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7224
https://doi.org/10.1038/s41591-020-0807-6
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. 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).
2. Black, R. E. et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).
3. Black, R. E. et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371, 243–260 (2008).
4. World Health Organization. Double burden of malnutrition http://www.who.int/nutrition/double-burden-malnutrition/en/ (2018).
5. 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).
6.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).
7. UNICEF, WHO & The World Bank. Joint child malnutrition estimates - levels and trends (2017 edition) https://www.who.int/nutgrowthdb/estimates2016/en/ (2017)
8. Hawkes, C., Demaio, A. R. & Branca, F. Double-duty actions for ending malnutrition within a decade. Lancet Glob. Health 5, e745–e746 (2017).
9. 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).
10. World Health Organization. Training course on child growth assessment http://www.who.int/childgrowth/training/en/ (2008).
11. de Onis, M. et al. Prevalence thresholds for wasting, overweight and stunting in children under 5 years. Public Health Nutr. 22, 175–179 (2019).
12. United Nations. Goal 2: sustainable development knowledge platform https://sustainabledevelopment.un.org/sdg2.
13. World Health Organization. Global nutrition targets 2025: childhood overweight policy brief http://www.who.int/nutrition/publications/globaltargets2025_policybrief_overweight/en/ (2014).
14. World Health Organization. Global nutrition targets 2025: wasting policy brief http://www.who.int/nutrition/publications/globaltargets2025_policybrief_wasting/en/ (2014).
15. Development Initatives. 2018 Global nutrition report: shining a light to spur action on nutrition https://globalnutritionreport.org/reports/global-nutrition-report-2018/ (2018).
16. 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).
17. World Health Organization. Data: nutrition - joint child malnutrition estimates (2018 edition) http://apps.who.int/gho/tableau-public/tpc-frame.jsp?id=402 (2018)
18. 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).
19. 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).
20. 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).
21. Kinyoki, D. K. et al. Mapping child growth failure across low- and middle-income countries. Nature 577, 231–234 (2020).
22. Diggle, P. & Ribeiro, P. J. Model-based Geostatistics (Springer, 2007); https://doi.org/10.1007/978-0-387-48536-2
23. FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2019. Safeguarding Against Economic Slowdowns and Downturns (FAO, 2019).
24. Wells, J. C. et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet 395, 75–88 (2020).
25. 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).
26. 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).
27. 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).
28. Wang, Y. & Chen, H.-J. in Handbook of Anthropometry (ed. Preedy, V. R.) 29–48 (Springer, 2012).
29. 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).
30. 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).
31. CDC. Causes and consequences of childhood obesity https://www.cdc.gov/obesity/childhood/causes.html (2016).
32. 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).
33. 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).
34. 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).
35. Scaling Up Nutrition (SUN) Movement: Annual Progress Report 2016. https://scalingupnutrition.org/wp-content/uploads/2016/11/SUN_Report_20161129_web_All.pdf (2016).
36. Hawkes, C. et al. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. Lancet 395, 142–155 (2020).
37. 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).
38. Nugent, R. et al. Economic effects of the double burden of malnutrition. Lancet 395, 156–165 (2020).
39. Global Administrative Areas. GADM database of global administrative areas http://www.gadm.org (2018).
40. 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).
41. Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).
42. World Wildlife Fund. Global lakes and wetlands database, level 3 https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database (2004).
43. Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 170004 (2017).
44. WorldPop. WorldPop dataset http://www.worldpop.org.uk/data/get_data/ (2017).
45. GeoNetwork. The Global Administrative Unit Layers (GAUL) http://www.fao.org/geonetwork/srv/en/main.home (2015).
46. Murray, C. J. et al. GBD 2010: design, definitions and metrics. Lancet 380, 2063–2066 (2012).
47. 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).
48. Stevens, G. A. et al. Guidelines for accurate and transparent health estimates reporting: the GATHER statement. PLoS Med. 13, e1002056 (2016).
49. USAID. Demographic and health surveys (DHS) http://dhsprogram.com/
50. UNICEF. Multiple indicator cluster surveys (MICS) http://mics.unicef.org.
51. World Bank Group. Living standards measurement survey (LSMS) http://go.worldbank.org/UK1ETMHBN0.
52. World Bank Group. Core welfare indicators questionnaire survey (CWIQ) http://ghdx.healthdata.org/series/core-welfare-indicators-questionnaire-survey-cwiq.
53. Lumley, T. in Complex Surveys: A Guide to Analysis Using R. (Wiley, 2010).
54. Lumley, T. Analysis of complex survey samples. J. Stat. Softw. 9, 1–19 (2004).
55. FAO. The global administrative unit layers (GAUL): technical aspects http://www.fao.org/geonetwork/srv/en/main.home.
56. Global Administrative Areas (GADM). GADM database of global administrative areas http://www.gadm.org.
57. WHO Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr. Suppl, 450, 76–85 (2006).
58. Indrayan, A. Demystifying LMS and BCPE methods of centile estimation for growth and other health parameters. Indian Pediatr. 51, 37–43 (2014).
59. WHO Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr 95, 76–85 (2006).
60. Bhatt, S. et al. Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization. J. R. Soc. Interface 14, 20170520 (2017).
61. Fienberg, S. E. The Analysis of Cross-Classified Categorical Data (Springer, 2007); https://doi.org/10.1007/978-0-387-72825-4
62. Ananth, C. V. & Kleinbaum, D. G. Regression models for ordinal responses: a review of methods and applications. Int. J. Epidemiol. 26, 1323–1333 (1997).
63. 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).
64. Stein, M. L. Interpolation of Spatial Data (Springer, 1999).
65. Diggle, Peter J. & Ribeiro, Paulo J. Model-based Geostatistics (Springer, 2007); https://doi.org/10.1007/978-0-387-48536-2.
66. 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).
67. Martins, T. G., Simpson, D., Lindgren, F. & Rue, H. Bayesian computing with INLA: new features. Comput. Stat. Data Anal. 67, 68–83 (2013).
68. 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).
69. 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).
70. Osgood-Zimmerman, A. et al. Mapping child growth failure in Africa between 2000 and 2015. Nature 555, 41–47 (2018).
71. 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).
72. 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).
73. 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).
74. 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).
75. Blangiardo, M. & Cameletti, M. in Spatial and Spatio‐temporal Bayesian Models with R‐INLA 235–258 (John Wiley & Sons, 2015).
76. 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).
77. Blangiardo, M., Cameletti, M., Baio, G. & Rue, Havard Spatial and spatio-temporal models with R-INLA. Spat. Spatio-Temporal Epidemiol. 7, 39–55 (2013).
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Nature Medicine
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.nature.com/articles/s41591-020-0807-6
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/54e4dec8-6744-4190-9d48-3d93ac97522e/download
https://repositorio.cuc.edu.co/bitstreams/1500ee66-51a9-40c6-8cb8-b5d7e95047ae/download
https://repositorio.cuc.edu.co/bitstreams/8b9c083e-c00a-4a2b-8f36-e4c1d8f90e28/download
https://repositorio.cuc.edu.co/bitstreams/ffba4e9b-8382-41b6-a06a-5e16dbc8a441/download
https://repositorio.cuc.edu.co/bitstreams/cafb8965-ae25-4510-bf53-154f7d305eef/download
bitstream.checksum.fl_str_mv 6c4dbf9f956bd86d715fd675195d6498
42fd4ad1e89814f5e4a476b409eb708c
e30e9215131d99561d40d6b0abbe9bad
ae3bf0685bac85eb1fe3c05fd63004c4
0bdc0d0a63608d9bf2f5e1666273669b
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760800197509120
spelling Kinyoki, Damaris K.Ross, Jennifer M.Lazzar-Atwood, AliceMunro, Sandra B.Schaeffer, Lauren E.Abbasalizad-Farhangi, MahdiehAbbasi, MasoumehAbbastabar, HedayatAbdelalim, AhmedAbdoli, AmirAlvis-Guzman, NelsonLBD Double Burden of Malnutrition Collaborators2020-11-09T16:45:57Z2020-11-09T16:45:57Z2020-041546-170X1078-8956https://hdl.handle.net/11323/7224https://doi.org/10.1038/s41591-020-0807-6Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Kinyoki, Damaris K.Ross, Jennifer M.Lazzar-Atwood, AliceMunro, Sandra B.Schaeffer, Lauren E.Abbasalizad-Farhangi, MahdiehAbbasi, MasoumehAbbastabar, HedayatAbdelalim, AhmedAbdoli, AmirAlvis-Guzman, NelsonLBD Double Burden of Malnutrition Collaboratorsapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Nature Medicinehttps://www.nature.com/articles/s41591-020-0807-6MalnutritionObesityRisk factorsSigns and symptomsMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017Artículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. 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).2. Black, R. E. et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).3. Black, R. E. et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371, 243–260 (2008).4. World Health Organization. Double burden of malnutrition http://www.who.int/nutrition/double-burden-malnutrition/en/ (2018).5. 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).6.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).7. UNICEF, WHO & The World Bank. Joint child malnutrition estimates - levels and trends (2017 edition) https://www.who.int/nutgrowthdb/estimates2016/en/ (2017)8. Hawkes, C., Demaio, A. R. & Branca, F. Double-duty actions for ending malnutrition within a decade. Lancet Glob. Health 5, e745–e746 (2017).9. 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).10. World Health Organization. Training course on child growth assessment http://www.who.int/childgrowth/training/en/ (2008).11. de Onis, M. et al. Prevalence thresholds for wasting, overweight and stunting in children under 5 years. Public Health Nutr. 22, 175–179 (2019).12. United Nations. Goal 2: sustainable development knowledge platform https://sustainabledevelopment.un.org/sdg2.13. World Health Organization. Global nutrition targets 2025: childhood overweight policy brief http://www.who.int/nutrition/publications/globaltargets2025_policybrief_overweight/en/ (2014).14. World Health Organization. Global nutrition targets 2025: wasting policy brief http://www.who.int/nutrition/publications/globaltargets2025_policybrief_wasting/en/ (2014).15. Development Initatives. 2018 Global nutrition report: shining a light to spur action on nutrition https://globalnutritionreport.org/reports/global-nutrition-report-2018/ (2018).16. 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).17. World Health Organization. Data: nutrition - joint child malnutrition estimates (2018 edition) http://apps.who.int/gho/tableau-public/tpc-frame.jsp?id=402 (2018)18. 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).19. 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).20. 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).21. Kinyoki, D. K. et al. Mapping child growth failure across low- and middle-income countries. Nature 577, 231–234 (2020).22. Diggle, P. & Ribeiro, P. J. Model-based Geostatistics (Springer, 2007); https://doi.org/10.1007/978-0-387-48536-223. FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2019. Safeguarding Against Economic Slowdowns and Downturns (FAO, 2019).24. Wells, J. C. et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet 395, 75–88 (2020).25. 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).26. 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).27. 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).28. Wang, Y. & Chen, H.-J. in Handbook of Anthropometry (ed. Preedy, V. R.) 29–48 (Springer, 2012).29. 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).30. 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).31. CDC. Causes and consequences of childhood obesity https://www.cdc.gov/obesity/childhood/causes.html (2016).32. 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).33. 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).34. 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).35. Scaling Up Nutrition (SUN) Movement: Annual Progress Report 2016. https://scalingupnutrition.org/wp-content/uploads/2016/11/SUN_Report_20161129_web_All.pdf (2016).36. Hawkes, C. et al. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. Lancet 395, 142–155 (2020).37. 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).38. Nugent, R. et al. Economic effects of the double burden of malnutrition. Lancet 395, 156–165 (2020).39. Global Administrative Areas. GADM database of global administrative areas http://www.gadm.org (2018).40. 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).41. Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).42. World Wildlife Fund. Global lakes and wetlands database, level 3 https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database (2004).43. Tatem, A. J. WorldPop, open data for spatial demography. Sci. Data 4, 170004 (2017).44. WorldPop. WorldPop dataset http://www.worldpop.org.uk/data/get_data/ (2017).45. GeoNetwork. The Global Administrative Unit Layers (GAUL) http://www.fao.org/geonetwork/srv/en/main.home (2015).46. Murray, C. J. et al. GBD 2010: design, definitions and metrics. Lancet 380, 2063–2066 (2012).47. 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).48. Stevens, G. A. et al. Guidelines for accurate and transparent health estimates reporting: the GATHER statement. PLoS Med. 13, e1002056 (2016).49. USAID. Demographic and health surveys (DHS) http://dhsprogram.com/50. UNICEF. Multiple indicator cluster surveys (MICS) http://mics.unicef.org.51. World Bank Group. Living standards measurement survey (LSMS) http://go.worldbank.org/UK1ETMHBN0.52. World Bank Group. Core welfare indicators questionnaire survey (CWIQ) http://ghdx.healthdata.org/series/core-welfare-indicators-questionnaire-survey-cwiq.53. Lumley, T. in Complex Surveys: A Guide to Analysis Using R. (Wiley, 2010).54. Lumley, T. Analysis of complex survey samples. J. Stat. Softw. 9, 1–19 (2004).55. FAO. The global administrative unit layers (GAUL): technical aspects http://www.fao.org/geonetwork/srv/en/main.home.56. Global Administrative Areas (GADM). GADM database of global administrative areas http://www.gadm.org.57. WHO Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr. Suppl, 450, 76–85 (2006).58. Indrayan, A. Demystifying LMS and BCPE methods of centile estimation for growth and other health parameters. Indian Pediatr. 51, 37–43 (2014).59. WHO Multicentre Growth Reference Study Group. WHO child growth standards based on length/height, weight and age. Acta Paediatr 95, 76–85 (2006).60. Bhatt, S. et al. Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization. J. R. Soc. Interface 14, 20170520 (2017).61. Fienberg, S. E. The Analysis of Cross-Classified Categorical Data (Springer, 2007); https://doi.org/10.1007/978-0-387-72825-462. Ananth, C. V. & Kleinbaum, D. G. Regression models for ordinal responses: a review of methods and applications. Int. J. Epidemiol. 26, 1323–1333 (1997).63. 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).64. Stein, M. L. Interpolation of Spatial Data (Springer, 1999).65. Diggle, Peter J. & Ribeiro, Paulo J. Model-based Geostatistics (Springer, 2007); https://doi.org/10.1007/978-0-387-48536-2.66. 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).67. Martins, T. G., Simpson, D., Lindgren, F. & Rue, H. Bayesian computing with INLA: new features. Comput. Stat. Data Anal. 67, 68–83 (2013).68. 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).69. 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).70. Osgood-Zimmerman, A. et al. Mapping child growth failure in Africa between 2000 and 2015. Nature 555, 41–47 (2018).71. 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).72. 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).73. 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).74. 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).75. Blangiardo, M. & Cameletti, M. in Spatial and Spatio‐temporal Bayesian Models with R‐INLA 235–258 (John Wiley & Sons, 2015).76. 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).77. Blangiardo, M., Cameletti, M., Baio, G. & Rue, Havard Spatial and spatio-temporal models with R-INLA. Spat. Spatio-Temporal Epidemiol. 7, 39–55 (2013).PublicationORIGINALMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.pdfMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.pdfapplication/pdf24026447https://repositorio.cuc.edu.co/bitstreams/54e4dec8-6744-4190-9d48-3d93ac97522e/download6c4dbf9f956bd86d715fd675195d6498MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/1500ee66-51a9-40c6-8cb8-b5d7e95047ae/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/8b9c083e-c00a-4a2b-8f36-e4c1d8f90e28/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.pdf.jpgMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.pdf.jpgimage/jpeg91959https://repositorio.cuc.edu.co/bitstreams/ffba4e9b-8382-41b6-a06a-5e16dbc8a441/downloadae3bf0685bac85eb1fe3c05fd63004c4MD54TEXTMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.pdf.txtMapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017.pdf.txttext/plain193084https://repositorio.cuc.edu.co/bitstreams/cafb8965-ae25-4510-bf53-154f7d305eef/download0bdc0d0a63608d9bf2f5e1666273669bMD5511323/7224oai:repositorio.cuc.edu.co:11323/72242024-09-17 12:45:46.459http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares