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, 577 (7789), 231-234

Mapping Child Growth Failure Across Low- And Middle-Income Countries

Collaborators

Mapping Child Growth Failure Across Low- And Middle-Income Countries

Local Burden of Disease Child Growth Failure Collaborators. Nature.

Abstract

Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0-59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3-5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization's median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications.

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 the data in the study and had final responsibility for the decision to submit for publication.

Figures

Fig. 1
Fig. 1. Prevalence of stunting in children under five in LMICs (2000–2017) and progress towards 2025.
a, b, Prevalence of stunting in children under five at the 5 × 5-km resolution in 2000 (a) and 2017 (b). c, Overlapping population-weighted tenth and ninetieth percentiles (lowest and highest) of 5 × 5-km grid cells and AROC in stunting, 2000–2017. d, Overlapping population-weighted quartiles of stunting prevalence and relative 95% uncertainty in 2017. e, f, Number of children under five who were stunted, at the 5 × 5-km (e) and first-administrative-unit (f) levels. g, 2000–2017 annualized decrease in stunting prevalence relative to rates needed during 2017–2025 to meet the WHO GNT. h, Grid-cell-level predicted stunting prevalence in 2025. Maps were produced using ArcGIS Desktop 10.6. Interactive visualization tools are available at https://vizhub.healthdata.org/lbd/cgf.
Fig. 2
Fig. 2. Prevalence of wasting in children under five in LMICs (2000–2017) and progress towards 2025.
a, b, Prevalence of child wasting in children under five at the 5 × 5-km resolution in 2000 (a) and 2017 (b). c, Overlapping population-weighted tenth and ninetieth percentiles (lowest and highest) of 5 × 5-km grid cells and AROC in wasting, 2000–2017. d, Overlapping population-weighted quartiles of wasting prevalence and relative 95% uncertainty in 2017. e, f, Number of children under five affected by wasting, at the 5 × 5-km (e) and first-administrative-unit (f) levels. g, 2000–2017 annualized decrease in wasting prevalence relative to rates needed during 2017–2025 to meet the WHO GNT. h, Grid-cell-level predicted wasting prevalence in 2025. Maps were produced using ArcGIS Desktop 10.6. Interactive visualization tools are available at https://vizhub.healthdata.org/lbd/cgf.
Extended Data Fig. 1
Extended Data Fig. 1. Prevalence of stunting in children under five in LMICs at administrative levels 0, 1, 2, and at 5 × 5-km resolution in 2017.
Administrative level 0 are national-level estimates; administrative level 1 are first administrative-level (for example, states or provinces) estimates; administrative level 2 are second administrative-level (for example, districts or departments) estimates. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells had fewer than ten people per 1 × 1-km grid cell and were classified as ‘barren or sparsely vegetated’,,,,–, or were not included in these analyses. Maps were produced using ArcGIS Desktop 10.6.
Extended Data Fig. 2
Extended Data Fig. 2. Geographical inequality in the prevalence of child stunting across 105 countries.
The bars represent the range of stunting prevalence in children under five in the second administrative-level units in each country. Bars indicating the range in 2017 are coloured according to the regions defined by the Global Burden of Disease (GBD). Grey bars indicate the range in 2000. The graph was produced using R project v.3.5.1.
Extended Data Fig. 3
Extended Data Fig. 3. Prevalence of wasting in children under five in LMICs at administrative levels 0, 1, 2, and at 5 × 5-km resolution in 2017.
Administrative levels are as described in Extended Data Fig. 1. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells had fewer than ten people per 1 × 1-km grid cell and were classified as ‘barren or sparsely vegetated’,,,,–, or were not included in these analyses. Maps were produced using ArcGIS Desktop 10.6.
Extended Data Fig. 4
Extended Data Fig. 4. Geographical inequality in prevalence of child wasting across 105 countries.
The bars represent the range of wasting prevalence in children under five in the second administrative-level units in each country. Bars indicating the range in 2017 are coloured according to their GBD-defined regions. Grey bars indicate the range in 2000. The graph was produced using R project v.3.5.1.
Extended Data Fig. 5
Extended Data Fig. 5. Prevalence of underweight in children under five in LMICs (2000–2017) and progress towards 2025.
a, b, Prevalence of underweight in children under five at the 5 × 5-km resolution in 2000 (a) and 2017 (b). c, Overlapping population-weighted tenth and ninetieth percentiles (lowest and highest) of 5 × 5-km grid cells and AROC in underweight, 2000–2017. d, Overlapping population-weighted quartiles of underweight prevalence and relative 95% uncertainty in 2017. e, f, Number of underweight children under five, at the 5 × 5-km (e) and first-administrative-unit (f) levels. g, 2000–2017 annualized decrease in underweight prevalence relative to rates needed during 2017–2025 to meet WHO GNT. h, Grid-cell-level predicted underweight prevalence in 2025. Maps were produced using ArcGIS Desktop 10.6. Interactive visualization tools are available at https://vizhub.healthdata.org/lbd/cgf.
Extended Data Fig. 6
Extended Data Fig. 6. Geographical inequality in prevalence of child underweight across 105 countries.
The bars represent the range of underweight prevalence in the second administrative-level units in each country. Bars indicating the range in 2017 are coloured according to their GBD-defined regions. Grey bars indicate the range in 2000. The graph was produced using R project v.3.5.1.
Extended Data Fig. 7
Extended Data Fig. 7. Probability that WHO GNT had been achieved in 2017 at the first administrative and 5 × 5-km grid-cell levels for stunting, wasting, and underweight.
af, Probability of WHO GNT achievement in 2017 at the first administrative and 5 × 5-km levels for stunting (a, d), wasting (b, e), and underweight (c, f). Dark-blue and dark-red grid cells indicate >95% and <5% probability, respectively, of having met the WHO GNT in 2017. Given that there was no WHO GNT established for underweight, we based the underweight target on WHO GNT for stunting, as the conditions are similarly widespread and prevalent. Maps were produced using ArcGIS Desktop 10.6.
Extended Data Fig. 8
Extended Data Fig. 8. Probability of meeting WHO GNT in 2025 at the first administrative and 5 × 5-km grid-cell levels for stunting, wasting, and underweight.
af, Probability of WHO GNT achievement in 2025 at the first administrative and 5 × 5-km levels for stunting (a, d), wasting (b, e), and underweight (c, f). Dark-blue and dark-red grid cells indicate >95% and <5% probability, respectively, of meeting WHO GNT in 2025. Given that there was no WHO GNT established for underweight, we based the underweight target on WHO GNT for stunting as the conditions are similarly widespread and prevalent. Maps were produced using ArcGIS Desktop 10.6.
Extended Data Fig. 9
Extended Data Fig. 9. Flowchart of CGF prevalence modelling process.
The process used to produce CGF prevalence estimates in LMICs involved three main parts. In the data-processing steps (green), data were identified, extracted, and prepared for use in the models. In the modelling phase (red), we used these data and covariates in stacked generalization ensemble models and spatiotemporal Gaussian process models for each CGF indicator. In post-processing (blue), we calibrated the prevalence estimates to match 2017 GBD study estimates and aggregated the estimates to the first- and second-administrative-level units in each country.
Extended Data Fig. 10
Extended Data Fig. 10. Modelling regions.
Modelling regions were based on geographical and SDI regions from the GBD study, defined as: Andean South America, Central America and the Caribbean, central SSA, East Asia, eastern SSA, Middle East, North Africa, Oceania, Southeast Asia, South Asia, southern SSA, Central Asia, Tropical South America, and western SSA. ‘High income country’ refers to regions not included in our models owing to high-middle or a high SDI. The map was produced using ArcGIS Desktop 10.6.

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