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International Journal for Equity in Health
Published in: International Journal for Equity in Health 1/2020

Open Access 01-12-2020 | Anemia | Research

Measuring socioeconomic gaps in nutrition and early child development in Bolivia

Authors: Pablo Celhay, Sebastian Martinez, Cecilia Vidal

Published in: International Journal for Equity in Health | Issue 1/2020

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Abstract

Background

A large body of evidence shows that socioeconomic status (SES) is strongly associated to children’s early development, health and nutrition. Few studies have looked at within sample differences across multiple measures of child nutrition and development. This paper examines SES gaps in child nutritional status and development in Bolivia using a representative sample of children 0–59 months old and a rich set of outcomes, including micronutrient deficiencies, anthropometic measures, and gross motor and communicative development.

Methods

We construct direct and proxy measures of living standards based on household expenditures and on ownership of assets combined with access to services and dwelling characteristics. The data for this study come from a nationally representative household survey in Bolivia that contains information on health, nutrition, and child development tests. We used a regression framework to assess the adjusted associations between child development outcomes and socioeconomic status, after controlling for other demographic factors that might affect child’s development. The SES gap in child development was estimated by OLS. To explore when the development gaps between children in different socioeconomic groups start and how they change for children at different ages, we analyze the differences in outcomes between the poorest (Q1) and richest (Q5) quintiles by child’s age by estimating kernel weighted local polynomial regressions of standardized scores for all child development indicators.

Results

There are large and statistically significant differences in all anthropometrics z-scores between children in Q5 and children in Q1: height for age (0.95 SD), weight for age (0.70 SD), and weight for height (0.21 SD). When we divide the sample into children at the bottom and top consumption quintiles the results show that 68.6% of children in the poorest quintile are anemic. While this percentage falls to 40.9% for children in the richest quintile, it remains high compared to other countries in the region. The prevalence of vitamin A deficiency is 29.9% for children in the richest quintile and almost 10 percentage points higher for those at the bottom quintile (39.0%); the prevalence of Iron deficiency for children in the top and bottom quintiles is 16.4% and 23.8%, respectively. Compared to the most deprived quintile, children in the wealthiest quintile are less likely to have iron deficiency, anemia, to be stunted, and to have a risk of delays in gross motor and communicative development. At age three, most of these gaps have increased substantially. Our findings are robust to the choice of socioeconomic measurement and highlight the need for targeted policies to reduce developmental gaps.

Conclusion

These findings highlight the need for targeted public policies that invest in multiple dimensions of child development as early as possible, including health, nutrition and cognitive and verbal stimulation. From a policy perspective, the large socioeconomic gaps in nutrition outcomes documented here reinforce the need to strengthen efforts that tackle the multiple causes of malnutrition for the poorest.
Appendix
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Footnotes
1
According to Grantham-McGregor et al. [2], the degree of loss of developmental potential is the discrepancy between children’s developmental levels and what they would have achieved in a more nurturing environment with adequate stimulation and nutrition.
 
2
Such as involvement in violent behavior (physical fight, gun use, gang membership).
 
3
Following the framework described in Walker et al. [11], these factors can be grouped in biological risks (such as undernutrition, micronutrient deficiencies and disease) and psychosocial risks (parenting and contextual factors) that affect children’s development through changes in brain function and behavior.
 
4
The WHO Global Health Observatory country estimate for 2016 is 46.9% [23.3 – 70.0], the second highest rate in the region, only after Haiti and more than double the regional average of 22.7% (https://​apps.​who.​int/​gho/​data/​node.​main.​ANEMIACHILDREN?​lang=​en).
 
5
The survey was conducted to evaluate the effect of two national social programs: the Bono Juana Azurduy and the Zero Malnutrition Program.
 
6
In a first stage, 424 clusters were selected (300 rural and 124 urban); in a second stage, 20 eligible households were randomly selected within each cluster.
 
7
Z-score = (observed value - median value of the reference population) / standard deviation value of reference population.
 
8
The WHO reference growth standards were established in 2006 and represent the growth patterns of healthy children from different regions of the world.
 
9
For children between 6 and 59 months of age we used standard WHO cutoff values: mild anemia, Hb 10.0 - 10.9 g/dL; moderate anemia, Hb 7.0 - 9.9 g/dL; severe anemia, Hb <7.0 g/dL. Because there is no standard cutoff to define anemia among infants 3 to 5 months, we used a cutoff of 10.5 g/dl following Chandyo et.al. (2016).
 
10
DBS were collected at the end of the interview after caregiver’s consent. Samples were identified and stored in hermetic plastic containers with drying agents to accelerate the dehydration process and to control humidity during transportation. Once dry they were packed using Ziploc bags. The DBS method had several advantages in field work. First, its simplicity and micro volume of blood required makes it appropriate for its use in small children, with very low health and biological risks. Second, the technique does not require specialized health personnel, intensive training or equipment. Third, DBS are stable to normal outside temperature for relatively long periods, which allows sufficient time to transport samples from rural areas and then to laboratories. In addition, it does not require special transportation conditions (such as cold chain), except humidity control and excess heath, which can both damage samples. Blood samples for DBS were collected separately from samples to determine hemoglobin concentrations.
 
11
For analysis, all samples were flown to a specialized laboratory at the University of Giessen, Germany.
 
12
From the subsample of 2,000 children 6-23 months selected for the DBS study, the ESNUT was able to collect 1,701 valid blood samples (the rest had either quality problems or were not available for analysis). In addition, 46 samples had to be discarded due to identification issues (incorrect household or child id).
 
13
Age groups were: 3.6-6.5 months; 6.6 to 9.5 months; 9.6-11.5 months; 11.6-13.5 months; 13.6-15.5 months; 15.6-17.5 months; 17.6-19.5 months; 19.6-22.5 months; 22.6-24.5 months; 24.6-30.5 months; 30.6-36.5 months.
 
14
The ASQ-2 is a developmental screening tool for children between 4 and 60 months, comprising 19 age-specific questionnaires designed to screen infants and young children for developmental delays during the first 5 years of life (Ages & Stages Questionnaires, Second Edition. A Parent-Completed, Child Monitoring System, by Diane Bricker and Jane Squires. Copyright © 1999 by Paul H. Brookes Publishing Co.).
 
15
Following Rutstein and Johnson [27], the quintile cutoffs are based on total household population, instead of the households themselves. Quintiles were constructed using a weighted frequency distribution of households where weights were the product of the number of de jure household members and the sampling weight of the household. Consequently, a tabulation of the unweighted sample will not produce five quintile groups of equal size, but the weighted sample will.
 
16
We test whether the coefficients for the quintile indicators are jointly zero.
 
17
Note, however, that Rubio-Codina et al (2016) internal validity assessment is based on the ASQ-3.
 
18
The ESNUT did not collect information about the use value of durables to construct a complete measure of household consumption.
 
19
Principal components analysis uses statistical methods to determine the weights of items in the index. Individual items are weighted to maximize the variability of the new composite variable.
 
20
Principal components were computed using the Stata® command factor with the pcf option specified.
 
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Metadata
Title
Measuring socioeconomic gaps in nutrition and early child development in Bolivia
Authors
Pablo Celhay
Sebastian Martinez
Cecilia Vidal
Publication date
01-12-2020
Publisher
BioMed Central
Published in
International Journal for Equity in Health / Issue 1/2020
Electronic ISSN: 1475-9276
DOI
https://doi.org/10.1186/s12939-020-01197-1

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