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Modelling stunting in LiST: the effect of applying smoothing to linear growth data

BACKGROUND: The Lives Saved Tool (LiST) is a widely used resource for evidence-based decision-making regarding health program scale-up in low- and middle-income countries. LiST estimates the impact of specified changes in intervention coverage on mortality and stunting among children under 5 years o...

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Autores principales: Cousens, Simon, Perin, Jamie, Christian, Parul, Wu, Lee Shu-Fune, Soofi, Sajid, Bhutta, Zulfiqar, Lanata, Claudio, Guerrant, Richard L., Lima, Aldo A. M., Mølbak, Kåre, Valentiner-Branth, Palle, Checkley, William, Gilman, Robert H., Sack, R. Bradley, Black, Robert E., Humphrey, Jean, Walker, Neff
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688407/
https://www.ncbi.nlm.nih.gov/pubmed/29143649
http://dx.doi.org/10.1186/s12889-017-4744-3
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author Cousens, Simon
Perin, Jamie
Christian, Parul
Wu, Lee Shu-Fune
Soofi, Sajid
Bhutta, Zulfiqar
Lanata, Claudio
Guerrant, Richard L.
Lima, Aldo A. M.
Mølbak, Kåre
Valentiner-Branth, Palle
Checkley, William
Gilman, Robert H.
Sack, R. Bradley
Black, Robert E.
Humphrey, Jean
Walker, Neff
author_facet Cousens, Simon
Perin, Jamie
Christian, Parul
Wu, Lee Shu-Fune
Soofi, Sajid
Bhutta, Zulfiqar
Lanata, Claudio
Guerrant, Richard L.
Lima, Aldo A. M.
Mølbak, Kåre
Valentiner-Branth, Palle
Checkley, William
Gilman, Robert H.
Sack, R. Bradley
Black, Robert E.
Humphrey, Jean
Walker, Neff
author_sort Cousens, Simon
collection PubMed
description BACKGROUND: The Lives Saved Tool (LiST) is a widely used resource for evidence-based decision-making regarding health program scale-up in low- and middle-income countries. LiST estimates the impact of specified changes in intervention coverage on mortality and stunting among children under 5 years of age. We aimed to improve the estimates of the parameters in LiST that determine the rate at which the effects of interventions to prevent stunting attenuate as children get older. METHODS: We identified datasets with serial measurements of children’s lengths or heights and used random effects models and restricted cubic splines to model the growth trajectories of children with at least six serial length/height measurements. We applied WHO growth standards to both measured and modelled (smoothed) lengths/heights to determine children’s stunting status at multiple ages (1, 6, 12, 24 months). We then calculated the odds ratios for the association of stunting at one age point with stunting at the next (“stunting-to-stunting ORs”) using both measured and smoothed data points. We ran analyses in LiST to compare the impact on intervention effect attenuation of using smoothed rather than measured stunting-to-stunting ORs. RESULTS: A total of 21,786 children with 178,786 length/height measurements between them contributed to our analysis. The odds of stunting at a given age were strongly related to whether a child is stunted at an earlier age, using both measured and smoothed lengths/heights, although the relationship was stronger for smoothed than measured lengths/heights. Using smoothed lengths/heights, we estimated that children stunted at 1 month have 45 times the odds of being stunted at 6 months, with corresponding odds ratios of 362 for the period 6 to 12 months and 175 for the period 12 to 24 months. Using the odds ratios derived from the smoothed data in LiST resulted in a somewhat slower attenuation of intervention effects over time, but substantial attenuation was still observed in the LiST outputs. For example, in Mali the effect of effectively eliminating SGA births reduced prevalence of stunting at age 59 months from 44.4% to 43.7% when using odds ratios derived from measured lengths/heights and from 44.4% to 41.9% when using odds ratios derived from smoothed lengths/heights. CONCLUSIONS: Smoothing of children’s measured lengths/heights increased the strength of the association between stunting at a given age and stunting at an earlier age. Using odds ratios based on smoothed lengths/heights in LiST resulted in a small reduction in the attenuation of intervention effects with age and thus some increase in the estimated benefits, and may better reflect the true benefits of early nutritional interventions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-017-4744-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-56884072017-11-21 Modelling stunting in LiST: the effect of applying smoothing to linear growth data Cousens, Simon Perin, Jamie Christian, Parul Wu, Lee Shu-Fune Soofi, Sajid Bhutta, Zulfiqar Lanata, Claudio Guerrant, Richard L. Lima, Aldo A. M. Mølbak, Kåre Valentiner-Branth, Palle Checkley, William Gilman, Robert H. Sack, R. Bradley Black, Robert E. Humphrey, Jean Walker, Neff BMC Public Health Research BACKGROUND: The Lives Saved Tool (LiST) is a widely used resource for evidence-based decision-making regarding health program scale-up in low- and middle-income countries. LiST estimates the impact of specified changes in intervention coverage on mortality and stunting among children under 5 years of age. We aimed to improve the estimates of the parameters in LiST that determine the rate at which the effects of interventions to prevent stunting attenuate as children get older. METHODS: We identified datasets with serial measurements of children’s lengths or heights and used random effects models and restricted cubic splines to model the growth trajectories of children with at least six serial length/height measurements. We applied WHO growth standards to both measured and modelled (smoothed) lengths/heights to determine children’s stunting status at multiple ages (1, 6, 12, 24 months). We then calculated the odds ratios for the association of stunting at one age point with stunting at the next (“stunting-to-stunting ORs”) using both measured and smoothed data points. We ran analyses in LiST to compare the impact on intervention effect attenuation of using smoothed rather than measured stunting-to-stunting ORs. RESULTS: A total of 21,786 children with 178,786 length/height measurements between them contributed to our analysis. The odds of stunting at a given age were strongly related to whether a child is stunted at an earlier age, using both measured and smoothed lengths/heights, although the relationship was stronger for smoothed than measured lengths/heights. Using smoothed lengths/heights, we estimated that children stunted at 1 month have 45 times the odds of being stunted at 6 months, with corresponding odds ratios of 362 for the period 6 to 12 months and 175 for the period 12 to 24 months. Using the odds ratios derived from the smoothed data in LiST resulted in a somewhat slower attenuation of intervention effects over time, but substantial attenuation was still observed in the LiST outputs. For example, in Mali the effect of effectively eliminating SGA births reduced prevalence of stunting at age 59 months from 44.4% to 43.7% when using odds ratios derived from measured lengths/heights and from 44.4% to 41.9% when using odds ratios derived from smoothed lengths/heights. CONCLUSIONS: Smoothing of children’s measured lengths/heights increased the strength of the association between stunting at a given age and stunting at an earlier age. Using odds ratios based on smoothed lengths/heights in LiST resulted in a small reduction in the attenuation of intervention effects with age and thus some increase in the estimated benefits, and may better reflect the true benefits of early nutritional interventions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-017-4744-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-07 /pmc/articles/PMC5688407/ /pubmed/29143649 http://dx.doi.org/10.1186/s12889-017-4744-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Cousens, Simon
Perin, Jamie
Christian, Parul
Wu, Lee Shu-Fune
Soofi, Sajid
Bhutta, Zulfiqar
Lanata, Claudio
Guerrant, Richard L.
Lima, Aldo A. M.
Mølbak, Kåre
Valentiner-Branth, Palle
Checkley, William
Gilman, Robert H.
Sack, R. Bradley
Black, Robert E.
Humphrey, Jean
Walker, Neff
Modelling stunting in LiST: the effect of applying smoothing to linear growth data
title Modelling stunting in LiST: the effect of applying smoothing to linear growth data
title_full Modelling stunting in LiST: the effect of applying smoothing to linear growth data
title_fullStr Modelling stunting in LiST: the effect of applying smoothing to linear growth data
title_full_unstemmed Modelling stunting in LiST: the effect of applying smoothing to linear growth data
title_short Modelling stunting in LiST: the effect of applying smoothing to linear growth data
title_sort modelling stunting in list: the effect of applying smoothing to linear growth data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688407/
https://www.ncbi.nlm.nih.gov/pubmed/29143649
http://dx.doi.org/10.1186/s12889-017-4744-3
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