Decomposing the educational inequalities in the factors associated with severe acute malnutrition among under-five children in Low- and Middle-Income Countries

DOI: https://doi.org/10.21203/rs.2.16244/v5

Abstract

Background: Low- and Middle-Income Countries (LMIC) have remained plagued with the burden of severe acute malnutrition (SAM). The decomposition of the educational inequalities in SAM across individual, neighbourhood and national level characteristics in LMIC have not been explored. This study aims to decompose educational-related inequalities in the development of SAM among under-five children in LMIC and identify the risk factors that contribute to the inequalities. Methods: We pooled successive secondary data from the Demographic and Health Survey conducted between 2010 and 2018 in 51 LMIC. We analysed data of 532,680 under-five children nested within 55,823 neighbourhoods. Severe acute malnutrition was the outcome variable while the literacy status of mothers was the main exposure variable. The explanatory variables cut across the individual-, household- and neighbourhood-level factors of the mother-child pair. Oaxaca-Blinder decomposition method was used at p=0.05. Results: Mothers with no formal education ranged from 0.1 % in Armenia and Kyrgyz to as much as 86.1 % in Niger. The overall prevalence of SAM in the group of children whose mothers had no education was 5.8 % compared with 4.2 % among those whose mothers were educated, this varied within each country. Fourteen countries (Cameroon(p<0.001), Chad(p<0.001), Comoro(p=0.047), Burkina Faso(p<0.001), Ethiopia(p<0.001), India(p<0.001), Kenya(p<0.001), Mozambique(p=0.012), Namibia(p=0.001), Nigeria(p<0.001), Pakistan(p<0.001), Senegal(p=0.003), Togo(p=0.013), and Timor Leste(p<0.001) had statistically significant pro-illiterate inequality while no country showed statistically significant pro-literate inequality. We found significant differences in SAM prevalence across child’s age(p<0.001), child’s sex(p<0.001), maternal age(p=0.001), household wealth quintile(p=0.001), mother’s access to media(p=0.001), birth weight(p<0.001) and neighbourhood socioeconomic status disadvantage(p<0.001). On the average, neighbourhood socioeconomic status disadvantage, location of residence were the most important factors in most countries. Other contributors to the explanation of educational inequalities are birth weight, maternal age and toilet type. Conclusions: SAM is prevalent in most LMIC with wide educational inequalities explained by individual, household and community-level factors. Promotion of women education should be strengthened as better education among women will close the gaps and reduce the burden of SAM generally. We recommend further studies of other determinate causes of inequalities in severe acute malnutrition in LMIC.

Background

Malnutrition among under-five children (U5C) remains both a social and public health burden[1,2] especially in the Low- and Middle-Income Countries (LMIC). The World Health Organisation (WHO) maintains that malnutrition is responsible, directly or indirectly, for 35% of deaths among U5C [3], among which is Severe Acute Malnutrition (SAM). SAM is the most extreme and visible form of undernutrition among U5C. Under-five children with SAM usually “have very low weight for their height and severe muscle wasting”[4]. The likelihood that a child with SAM will eventually die is very high [4,5]. Besides, “children with severe acute malnutrition are nine times more likely to die than well-nourished children” [4]. The UNICEF (United Nations International Children’s Emergency Fund) reported that SAM affected more than 16 million children globally in 2016 [4]. Although this figure is staggering, it is likely to have been underestimated [5].

To reduce the burden of SAM, there is a need to implement multi-sectoral evidence-based interventions which will enhance child and maternal health [3] in the long run.   However, the development of the appropriate strategies, programmes and policies on the reduction of SAM, is hinged on the availability of information that can enhance child health interventions. While the literature is replete with the factors predisposing children to SAM and other poor nutrition outcomes, decomposition of these factors on key variables significant to poor nutrition is scarce in the literature. The identified factors are largely individual and household factors such as food insecurity, inadequate care and feeding, unhealthy environment, poor access to education, child’s age and sex, and mothers’ employment status and income [1,6–12].

There is a paucity of data on SAM in LMIC, especially on its decomposition by maternal educational differences, which has limited the understanding of the depth of the challenges of SAM for evidence-based interventions. This is despite UNICEF’s recommendation that complex social and political efforts are crucial to ending SAM [4].  The role(s) of educational inequalities in the distribution of SAM in the LMIC and factors associated with the inequalities have not received sufficient attention. A recent Ghanaian study found a high level of influence of educational inequalities on all factors associated with malnutrition in the study [13]. Amongst others, the study showed that the nutritional status of children from educated mothers are generally better than among those from uneducated mothers and there are factors that influence these differentials.

Inequalities in maternal education remain a key barrier to the occurrence of SAM among U5C[9,11,12,14–17]. However, the underlying causes of educational inequalities in the development of SAM among U5C remain poorly operationalized, studied and understood. There is, therefore, a need to understand what influences the wide gap in the development of SAM among children from educated and uneducated mothers. In order to understand what explains the education-related inequalities in the development of SAM among U5C and adapt the relevant strategies for interventions, we assessed the level of educational inequalities in LMIC and examined the factors associated with these inequalities in the development of SAM among U5C in LMIC. We were motivated to account for the causes and extent to which educational inequalities in the development of SAM among U5C vary in the LMIC beyond compositional characteristics. A good understanding of the gaps in the development of SAM among U5C in the LMIC would guide interventions for improving child nutrition.

Methods

Study design and data

The nationally representative cross-sectional data obtained from successive Demographic and Health Surveys (DHS) conducted in LMIC was used for this study. We extracted data from 51 most recent successive DHS conducted between 2010 and 2018 which were available as of March 2019 and these included under-five children (U5C) anthropometry data. Typically, the DHS uses a multi-stage, stratified sampling design with households as the sampling unit [18,19]. Country-specific sampling methodologies are also available at dhsprogram.com and in report forms [20–22]. Within each sampled household, women and men meeting the eligibility criteria were interviewed. Sampling weights were calculated to account for unequal selection probabilities including non-response whose application makes survey findings represent the full target populations. All the DHS questionnaires are standardized and implemented across countries with similar interviewer training, supervision, and implementation protocols. In this study, we used the DHS children recode data. The data covered the health experiences of under-five children born to sampled women within five years preceding the survey date. The anthropometry measurements were taken using standard procedures.

Dependent variable

The dependent variable in this study is severe acute malnutrition defined as “a very low weight for height score (WHZ) below -3 z-scores of the median WHO growth standards, by visible severe wasting, or by the presence of nutritional oedema”[3]. The z-scores are composite scores computed using the weight and height of the children. We generated z-scores using the WHO-approved methodologies [23] and categorized children with z-scores <-3 standard deviation as having SAM (Yes = 1) and as No = 0 if otherwise.

Main determinant variable

Maternal education was used as a proxy for literacy in this study. Literacy a key skill and an important measure of a population’s level of education. Literacy is the ability to both read and write a short, simple statement about one's own life[24]. We, therefore, categorized education as no formal education (illiterate) and educated (at least completed primary education - Literate).

Independent variables

Individual-level factors: sex of the children (male versus female), children age in years (under 1 year and 12-59 months), maternal age (15 to 24, 25 to 34, 35 to 49), occupation (working or not working), access to media (at least one of radio, television, or newspaper), sources of drinking water (improved or unimproved), toilet type (improved or unimproved), weight at birth (average+, small, and very small), ability to pay for health care, health insurance coverage, birth interval (firstborn, <36 months, and >36 months) and birth order (1, 2, 3, and 4+). We used the DHS wealth index as a proxy indicator for socioeconomic status (SES). The methods used in computing DHS wealth index have been described previously[25].

Neighbourhood-level factors

In this study, the term “neighbourhood” was used to describe clustering within the same geographical living environment. Neighbourhoods were based on sharing a common primary sample unit (PSU) within the DHS data [18,19]. Operationally, we define “neighbourhood” as clusters and “neighbours” as a member of the same cluster. The PSUs were identified using the most recent census in each country where DHS was carried out. We considered neighbourhood socioeconomic disadvantage as a community-level variable in this study. Neighbourhood socioeconomic disadvantage was operationalized with a principal component comprised of the proportion of respondents with no education (illiterate), unemployed, rural resident, and living below the poverty level.

Statistical analyses

In this study, we carried out analytical analyses comprising descriptive statistics and multivariate analysis. Univariable and bivariable analysis were used to describe the study population. Descriptive statistics was used to show the distribution of respondents by country and key variables.  Estimates were expressed as percentages and confidence intervals. Secondly, we computed the risk difference in the development of SAM between U5C whose mothers were literate and the others that were not literate. A risk difference (RD) greater than 0 suggests that SAM are prevalent among children born to uneducated mothers (pro-illiterate inequality). Conversely, a negative RD indicates that SAM is prevalent among children born to educated mothers (pro-educated inequality).

Thirdly, in the multivariabe analysis, the logistic regression method using the pooled cross-sectional data from the 51 LMIC was used to carry out a Blinder-Oaxaca decomposition analysis. The Blinder-Oaxaca decomposition[26,27] was a counterfactual methodology with an assumption that children born to uneducated mothers had the same characteristics as their educated counterparts. Our choice of the Blinder-Oaxaca method is hinged on the fact that it allows for the decomposition of the differences in an outcome variable between 2 groups into 2 components so that the gaps can be seen more clearly. The first component of the decomposition is the “explained” portion of that gap that captures differences in the distributions of the measurable characteristics (also known as the “compositional” or “endowments”) of these groups. This method enabled the quantification of how much of the gap between the “advantaged” and the “disadvantaged” groups is attributable to differences in specific measurable characteristics. The second component is the “unexplained” part (also referred to as the structural component) which captures the gap due to the differences in the regression coefficients and the unmeasured variables between the two groups being compared.

Results

Sample characteristics

We analysed data of 532,680 under-five children nested within 55,823 neighbourhoods from 51 LMIC who participated in the DHS between 2010 and 2018. The regions of the world, countries, year of data collection, numbers of neighbourhoods, number of under-five children, percentage of the uneducated mothers and the weighted prevalence of SAM among children of uneducated and educated mothers are listed in Table 1. The proportions of mothers with no formal education ranged from 0.1 % in Armenia and Kyrgyz to 86.1 % in Niger and a median of 20.1 % in Haiti.

Prevalence of SAM by countries and maternal education

We found wide variations in the prevalence of SAM among children of educated and uneducated mothers across the 51 LMIC studied (Table 1 and Figure 1). The overall SAM prevalence was 4.7% with a median prevalence of 1.8% ranging from 0.1 % in Guatemala to 9.9 % in Timor-Leste as shown in Table 1. The prevalence of SAM among children of uneducated mothers ranged from 0.0 % in Lesotho, Zimbabwe, Kyrgyz, Armenia and Guatemala to 12.7 % in Timor-Leste, while it ranged from 0.1 % in Peru and Guatemala to 9.4% in Timor-Leste among children of the educated mothers. Mantel Haenszel test of homogeneity of odds ratio was used to test statistical significance with literacy level as an effect modifier. We found significant pro-illiterate inequalities in fourteen countries: Cameroon (p<0.001), Chad (p<0.001), Comoro (p=0.047), Burkina Faso (p<0.001), Ethiopia (p<0.001), India (p<0.001), Kenya (p<0.001), Mozambique (p=0.012), Namibia (p=0.001), Nigeria (p<0.001), Pakistan (p<0.001), Senegal  (p=0.003), Togo (p=0.013), and Timor Leste (p<0.001) but no country has pro-educated inequalities as shown in Table 1.

Prevalence of SAM by children characteristics and maternal education

Table 2 presents the descriptive statistics for the characteristics of the pooled sample of children across the 51 LMIC. About 51 % of the children were males while only 20% were infants. About 53% of the mothers were aged 25 to 34 years old and about 31% had no formal education. Nearly one-third of the mothers were not working at the time of the survey. The overall prevalence of SAM in the group of children whose mothers had no education was 5.8 % compared with 4.2 % among those whose mothers were educated. The Mantel Haenszel test of homogeneity of odds ratio used to test statistical significance with literacy level as an effect modifier showed that all the characteristics considered were independently significant.  For instance child’s age (p<0.001), child’s sex (p<0.001), maternal age (p=0.001), household wealth quintile (p=0.001), mother’s access to media (p=0.001), birth weight (p<0.001) and neighbourhood socioeconomic status disadvantage (p<0.001) had significant differences in SAM prevalence viz-a-viz mothers’ literacy (Table 2). Infants, male children and mothers at extreme age intervals; 15 to 24 and 34 to 49 had overall higher SAM prevalence. For wealth index, births of women from lowest wealth quintile had the highest rate of SAM within the “uneducated” group compared with those from richest wealth quintile (6.8 % vs 3.4%) but the margins were closer within the “educated” group. 

Magnitude and variations in educational inequality in SAM

Figures 1 and 2 show the risk differences (a measure of inequality) between children of uneducated and educated women across the 51 countries studied. Among the 51 countries included in this analysis, 14 countries showed statistically significant pro-illiterate inequality (i.e. SAM is more prevalent among children from uneducated mothers).  None of the countries showed statistically significant pro-literacy (i.e. when SAM is more prevalent among children from educated mothers) while 37 countries showed no statistically significant inequality. As illustrated by Fig. 1, in Eastern Africa, the educational difference was largest for Ethiopia (20.55 per 1000 children) and lowest for Malawi (− 0.50). In Western Africa, the largest educational difference was in Nigeria (48.22) and lowest for Cote d’Ivoire (-6.41). In the Caribbean, the difference was largest for Timor Leste (32.60) and lowest for Myanmar (-0.96). Burundi and Senegal with 2.5 % weight each had the largest contribution to the pooled result. In the pooled analysis, Nigeria still had the highest pro-illiterate inequality (48.22) and followed by Namibia (44.75) as shown in Figure 2. Overall, there was significant pro-illiterate in the total pooled sample of children in this study. The risk difference was 7.18 (95% Confidence Interval (CI): 3-12) per 1000 children among children of uneducated mothers compared with those of educated mothers as shown in the random effects in Figure 1. The random effect shows the overall risk difference among all children born to educated and uneducated mothers irrespective of their countries. In Figure 2, we used the colours blue, yellow and red to indicate statistically significant pro-illiterate inequality, no significant inequality and statistically significant pro-literate inequality respectively.

Two of the nine countries in Eastern Africa inequality, 2 of the countries in Middle Africa, none in Northern Africa, and only Namibia in Southern Africa showed statistically significant pro-illiterate. In Western Africa, 2 of the 13 countries while only 2 of the five countries in Southern Asia showed statistically significant pro-illiterate inequality compared with only one country among the four countries studied in the Caribbean.

Relationship between prevalence of SAM and magnitude of the educational inequality

Figure 3 shows the relationship between the prevalence of SAM and the magnitude of inequality for all the 51 countries in this study. The 51 countries were categorized into 4 distinct categories: (1) High severe acute malnutrition and high pro-illiterate inequality such as Timor-Leste and Nigeria; (2) High severe acute malnutrition and high pro-literate inequality was not found in any country; (3) Low severe acute malnutrition and high pro-illiterate inequality such as Namibia and Kenya; and (4) Low severe acute malnutrition and high pro-literate inequality was not found in any country. In Figure 3, colours cyan, orange and red were used to depict statistically significant pro-illiterate inequality, no significant inequality and statistically significant pro-literate inequality respectively.

Decomposition of educational inequality in the prevalence of SAM

Figure 4 shows the detailed decomposition of the part of the inequality that was caused by compositional effects of the determinants of SAM among under-five children. There were variations in the important factors responsible for the educational inequalities across the countries. The “explained” (compositional component) and the “unexplained” (structural component) portions of the educational inequalities are depicted by red and blue colours respectively; the lighter the red colour the lower the percentage contribution of the “explained” portion and the lighter the blue colour, the lower the percentage contribution of the “unexplained” portion. 

On the average, neighbourhood socioeconomic status disadvantage and, location of residence were the most important factors in most countries. In Senegal, the largest contributions to the educational inequality in the prevalence of SAM was by neighbourhood socioeconomic disadvantage, followed by the location of residence, wealth index and access to media. Wealth index and media access narrowed the inequality in the development of SAM between children from educated and uneducated mothers. In Togo, location of residence had the largest contribution to the educational inequality followed by neighbourhood socioeconomic status disadvantage and followed by media access. Marital status, child age and sex, birth weight and mother’s employment status did not show any significant contribution to educational inequality in the development of SAM in any of the 13 countries.

Discussion

The goal of this study was to use the DHS data to analyse and decompose educational inequalities in the development of SAM across 51 low- and middle-income countries. This study was carried out with the purpose of improving our knowledge of the compositional and structural factors associated with educational inequalities in the development of SAM in the countries. The study is premised on the fact that SAM has continued to be a major public health challenge. We found wide variations in the prevalence of SAM among children of illiterate and literate mothers. Our results showed significant education-related differences that could be explained by structural and compositional factors nested both at the neighbourhoods and the country levels. We also found wide inter-country differences viz-a-viz literacy level in the prevalence of SAM. The inter-country variations could be ascribed to the prevalent differences in individual country socioeconomic characteristics, policies, strategies and intervention on child nutrition. Our findings are corroborated by some previous research which found similar differentials in the prevalence of SAM.

In particular, the analysis in this study shows the unequal distribution in the prevalence of SAM between the children of the educated and uneducated mothers, suggesting the presence of educational inequalities. In 13 of the 51 countries, SAM was significantly prevalent among children born to uneducated mothers (pro-illiterate inequality) but pro-literate inequality, although higher in 16 countries, was insignificant in any of the countries. Among the countries with statistically significant pro-illiterate inequalities, the risk difference used as the measure of inequality in our study showed that 8 to 48 per 1000 of children born to uneducated mothers will develop SAM compared with children from educated mothers.

Overall, there was significant pro-illiterate inequality among the total pooled sample of children in this study with 7 more children of every 1000 children of uneducated mothers developing SAM compared with children born to educated mothers. Educational attainment of caregivers is an important factor in the determination of whether a child develops SAM or not. Our finding is in consonance with previous studies which reported that children whose mothers were not educated were associated with a poor range of nutritional outcomes such as stunting, wasting and malnutrition [7,12,16,28–31]. This finding has several implications; first, there is a need for LMIC to develop child nutrition public health policies, interventions and programmes that particularly inform and train uneducated mothers on the need to provide their children with adequate nutrition.

Also, there is a need to increase the knowledge of mothers and households in general so that they can have a higher capacity to afford good nutrition for their children. In addition, governments may wish to subsidize children foods as a means of relieving a huge household burden of getting food for their wards. Nonetheless, such public health interventions should be all-encompassing. It should include health education and promotion, adequate communication, seminars, political will and the involvement of the community and religious leaders on the need for children to have good nutrition. This is consistent with a UNICEF report that prevention and long term solutions to the burden of SAM will involve “dismantling unequal power structures, improving equitable access to health services and nutritious foods, promoting breastfeeding and optimal infant and young child feeding practices, improving water and sanitation, and planning for cyclic food shortages and emergencies” [4].

It is very evident from our analysis that compositional effects of the additional explanatory variables explored were mainly responsible for the majority of the inequality in SAM between the uneducated and educated mothers in Chad, Timor-Leste and Mozambique. While in Togo, and Kenya, structural effects of the determinants were responsible for most of the inequality in SAM.

The decomposition analysis has shown that compositional factors such as neighbourhood SES, location of residence, wealth index and access to media were the most important contributors to education-related inequalities across the countries. Obviously, to attain a meaningful reduction in educational inequalities in SAM, there is a need to look outside the box and properly understand the connection among the structure, composition and context in which the children live. A wholesome approach should be used to address the challenges of educational inequalities in child health in general and in SAM in particular. This finding underscores the advantage of enhancing both the compositional and structural characteristics if educational-related inequalities in SAM are to be reduced. Earlier reports on child malnutrition have clearly indicated the nuances of individual, community and country-level factors associated with child nutrition [2,4,8,10].

We find interesting results in our attempt to map the relationships between the prevalence of SAM and educational inequality. Countries such as Namibia and Kenya had low SAM prevalence and high pro-illiterate inequality while countries such as Timor-Leste and Nigeria had a high SAM prevalence and high pro-illiterate inequality. These variations can be explained by access to media, household wealth status, country-level policies and programmes for child nutrition, famine, war, internal displacement, political and economic instability. It is quite understandable that we did not find significant pro-literate inequality in any of the countries studied. An educated mother should engage in good nutritional practices for her wards.

Our findings on the effect of neighbourhood SES on the likelihood that children of an educated mother have SAM are consistent with the literature on compositional and structure effects. These studies showed that residents in high socioeconomic areas have a higher likelihood of more positive outcomes than persons who reside in socioeconomically disadvantaged areas [32,33]. It is therefore important that the countries with high SAM and high pro-illiterate inequalities in SAM rework their child nutrition policies by taking a cue from countries with a low SAM and low pro-educated inequalities. For instance, researchers and health programmers in such countries may wish to explore the differentials in child health and nutrition in Nigeria and Kenya. Why is SAM higher in Nigeria than in Kenya despite the fact that both countries have pro-illiteracy inequalities?

Study Limitations and Strengths

We have used household wealth status as a proxy for household income as the DHS survey questionnaire does not contain data on household income. Hence, our findings may not be generalizable in settings where direct measurement of income is available. While multilevel analysis is an efficient method to understand disparities and to monitor health care indicators, Blinder-Oaxaca decomposition analysis does not clearly allow causal interpretation of the results but gives robust evidence of inequalities after controlling for the exposure variable. There may be a need for further studies to examine the association of structural and compositional factors with educational-inequalities in the prevalence of SAM. Nonetheless, our study has major strengths. As shown in Figure 5, we quantified the magnitude of the explained and unexplained factors associated with our outcome measure. The study covered 51 LMIC using the DHS data is reputed for accuracy and comparability across countries.

Conclusions

We identified that SAM is prevalent in most LMIC with wide educational variations. The occurrence of SAM was explained by the individual, household and community-level factor. The overall significance of our exposure variable in explaining the difference in SAM prevalence is a pointer that education of the whole population, especially the girl child who is a potential mother, is very important to child health. The advantages of education in human endeavour cannot be overemphasized. The low- and middle-income countries must improve their tactics in child nutrition with the goal of eradication of severe acute malnutrition which would eventually reduce child morbidity, opportunistic infections and mortality. To address the educational inequalities in SAM, an urgent child nutrition intervention is a must in the low- and middle- income countries, especially in those identified as having pro-illiterate inequalities as better education among all women will close the gaps and reduce the burden of SAM generally. We recommend further studies of other determinate causes of inequalities in severe acute malnutrition in low- and middle-income countries.

List Of Abbreviations

CI                    Confidence Interval

DHS                Demographic and Health Survey

IRB                 Institutional Review Board

LMIC              Low- And Middle-Income Countries

PSU                 Primary Sample Unit

RD                  Risk Difference

SAM               Severe Acute Malnutrition

SES                 Socioeconomic Status

U5C                Under-Five Children

UNICEF         United Nations International Children’s Emergency Fund

WHO              World Health Organization

WHZ               Weight for Height Score

Declarations

Ethics approval and consent to participate

This study was based on the analysis of existing survey data. The Institutional Review Board (IRB) of ICF Macro at Fairfax, Virginia in the USA reviewed and approved the MEASURE Demographic and Health Surveys Project Phase III. The 2010-2018 DHS’s are categorized under that approval. The IRB of ICF Macro complied with the United States Department of Health and Human Services requirements for the “Protection of Human Subjects” (45 CFR 46). See the Supplementary Material for samples of the approvals. All study participants gave written informed consent before participation and all information was collected without identifiers and kept confidentially. The full details of the ethical approvals can be found at http://dhsprogram.com.

Consent for publication

All authors agreed to the publication

Availability of data and materials

The data supporting this article is available at http://dhsprogram.com. The data is publicly available but permission to use the data is required. The authors obtained permission from the data owners to use the data.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors receive no funding for this study. However, the Consortium for Advanced Research and Training in Africa (CARTA) provided logistic supports to AFF in the course of writing this paper.

Authors Contributions

AFF and OU conceived and designed the study and analysed the data; AFF retrieved and merged the data, drew the Figures and wrote the results; AFF, OU, and NBK carried out the literature search, data interpretation, and writing of the manuscript. All authors read and consented to the final version of the manuscript.

Acknowledgements

The authors are grateful to ICF Macro, USA, for granting the authors the permission to use the data.

Authors Information

AFF is a Senior Lecturer and Medical Statistician at the Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Nigeria and a Visiting Academic under CARTA Fellowship at the Division of Health Sciences, Populations, Evidence and Technologies Group, Warwick Medical School, University of Warwick, United Kingdom. NBK is a Professor of Biostatistics at the Department of Mathematics, Physics & Electrical Engineering (MPEE), Northumbria University, UK. OU is an Associate Professor of Public Health at the Division of Health Sciences, Populations, Evidence and Technologies Group, University of Warwick, United Kingdom.

References

  1. Khan S, Zaheer S, Safdar NF. Determinants of stunting, underweight and wasting among children &lt; 5 years of age: evidence from 2012-2013 Pakistan demographic and health survey. BMC Public Health [Internet]. 2019 [cited 2019 Jun 21];19:358. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30935382
  2. Akombi B, Agho K, Hall J, Wali N, Renzaho A, Merom D. Stunting, Wasting and Underweight in Sub-Saharan Africa: A Systematic Review. Int. J. Environ. Res. Public Health [Internet]. 2017 [cited 2019 Jun 21];14:863. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28788108
  3. WHO. Severe acute malnutrition [Internet]. Geneva, Switzerland; 2019. Available from: https://www.who.int/nutrition/topics/severe_malnutrition/en/
  4. UNICEF. Severe acute malnutrition [Internet]. Nutrition. 2015 [cited 2019 Jun 29]. p. 1. Available from: https://www.unicef.org/nutrition/index_sam.html
  5. Bulti A, Briend A, Dale NM, De Wagt A, Chiwile F, Chitekwe S, et al. Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria. Arch. Public Heal. [Internet]. BioMed Central; 2017 [cited 2019 Jun 29];75:66. Available from: http://archpublichealth.biomedcentral.com/articles/10.1186/s13690-017-0234-4
  6. Titaley CR, Ariawan I, Hapsari D, Muasyaroh A, Dibley MJ. Determinants of the Stunting of Children Under Two Years Old in Indonesia: A Multilevel Analysis of the 2013 Indonesia Basic Health Survey. Nutrients [Internet]. 2019 [cited 2019 Jun 21];11:1106. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31109058
  7. Fantay GK, Mekonnen HW, Haftom TA, Oumer SA, Afework MB. Determinants of stunting among under-five children in Ethiopia: a multilevel mixed-effects analysis of 2016 Ethiopian demographic and health survey data. BMC Pediatr. [Internet]. 2019 [cited 2019 Jun 21];19:176. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31153381
  8. Motedayen M, Dousti M, Sayehmiri F, Pourmahmoudi AA. An Investigation of the Prevalence and Causes of Malnutrition in Iran: a Review Article and Meta-analysis. Clin. Nutr. Res. [Internet]. 2019 [cited 2019 Jun 21];8:101. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31089464
  9. Binns P, Myatt M. Does treatment of short or stunted children aged 6-59 months for severe acute malnutrition using ready to use therapeutic food make them overweight? Data from Malawi. Arch. Public Health [Internet]. BioMed Central; 2018 [cited 2019 Jun 28];76:78. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30559964
  10. Harimbola DR, Mizumoto K. Individual and Household Risk Factors for Severe Acute Malnutrition among Under-Five Children in the Analamanga Region, Madagascar. Int. J. MCH AIDS [Internet]. Global Health and Education Projects, Inc.; 2018 [cited 2019 Jun 28];7:217–25. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30631640
  11. Kshatriya GK, Acharya SK. Gender Disparities in the Prevalence of Undernutrition and the Higher Risk among the Young Women of Indian Tribes. PLoS One [Internet]. Public Library of Science; 2016 [cited 2019 Jun 28];11:e0158308. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27379521
  12. Alemayehu M, Tinsae F, Haileslassie K, Seid O, Gebregziabher G, Yebyo H. Undernutrition status and associated factors in under-5 children, in Tigray, Northern Ethiopia. Nutrition [Internet]. Elsevier; 2015 [cited 2019 Jun 28];31:964–70. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0899900715000817?via%3Dihub
  13. Novignon J, Aboagye E, Agyemang OS, Aryeetey G. Socioeconomic-related inequalities in child malnutrition: evidence from the Ghana multiple indicator cluster survey. Health Econ. Rev. [Internet]. BioMed Central; 2015 [cited 2019 Jun 29];5:34. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26603158
  14. Khan T, Khan R, Raza MA. Gender analysis of malnutrition: a case study of school-going children in Bahawalpur. Asian Dev. Policy Rev. [Internet]. 2015 [cited 2019 Jun 28];3:29–48. Available from: http://www.aessweb.com/journals/5008
  15. Jawaregowda KS, Angadi M. Gender differences in nutritional status among under five children in rural areas of Bijapur district, Karnataka, India. Int. J. Community Med. Public Heal. [Internet]. 2015 [cited 2019 Jun 28];2:506–9. Available from: http://www.ijcmph.com
  16. Eshete H, Abebe Y, Loha E, Gebru T, Tesheme T. Nutritional status and effect of maternal employment among children aged 6–59 months in Wolayta Sodo Town, Southern Ethiopia: a cross-sectional study. Ethiop. J. Health Sci. [Internet]. 2017 [cited 2019 Jun 28];27:155. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28579711
  17. Nhampossa T, Sigaúque B, Machevo S, Macete E, Alonso P, Bassat Q, et al. Severe malnutrition among children under the age of 5 years admitted to a rural district hospital in southern Mozambique. Public Health Nutr. [Internet]. 2013 [cited 2019 Jun 28];16:1565–74. Available from: https://www.cambridge.org/core/product/identifier/S1368980013001080/type/journal_article
  18. Croft TN, Marshall AMJ, Allen CK. Guide to DHS Statistics [Internet]. 2018. Available from: https://dhsprogram.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_DHS-7.pdf
  19. ICF International. Demographic and Health Survey: Sampling and Household Listing Manual [Internet]. Calverton; 2012. Available from: https://www.dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf
  20. ICF International Inc. Uganda Demographic and Health Survey 2011. Kampala, Uganda: UBOS and Calverton, Maryland:; 2012.
  21. National Bureau of Statistics Tanzania and ICF - Macro. Tanzania Demographic and Health Survey 2010. Natl. Bur. Stat. Dar es Salaam, Tanzania ICF Macro Calverton, Maryland, USA. 2011.
  22. National Population Commission (Nigeria) and ICF International. Nigeria Demographic and Health Survey 2013. Abuja, Nigeria; 2014.
  23. WHO. WHO AnthroPlus for personal computers Manual: Software for assessing growth of the world’s children and adolescents [Internet]. Geneva, Switzerland; 2009. Available from: http://www.who.int/growthref/tools/en/
  24. Roser M, Ortiz-Ospina E. Global Rise of Education [Internet]. Ourworldindata.org. 2019 [cited 2019 Jun 29]. p. 1. Available from: https://ourworldindata.org/global-rise-of-education
  25. Vyass S, Kumaranayake L. Constructing Socioeconomic Status Indexes: How to Use Principal Component Analysis. Health Policy Plan. 2006;21:459–68.
  26. Oaxaca R. Male-female wage differentials in urban labor markets. Intl Econ. Rev. 1973;14:693–709.
  27. Blinder AS. Wage discrimination: Reduced form and structural estimates. J Hum Resour. 1973;8:436–55.
  28. Mawa R, Lawoko S. Malnutrition Among Children Under Five Years in Uganda. Am. J. Heal. Res. 2018;6:56–66.
  29. Man SL., Guo Y. Research on the social determinants of malnutrition among children under the age of 5 in China. Beijing Da Xue Xue Bao. [Internet]. 2016;48:418–23. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27318901
  30. Abdulahi A, Shab-Bidar S, Rezaei S, Djafarian K. Nutritional status of under five children in Ethiopia: a systematic review and meta-analysis. Ethiop. J. Health Sci. [Internet]. 2017 [cited 2019 Jun 21];27:175. Available from: https://www.ajol.info/index.php/ejhs/article/view/153152
  31. Meshram II, Arlappa N, Balakrishna N, Mallikharjuna Rao K, Laxmaiah A, Brahmam GNV. Trends in the prevalence of undernutrition, nutrient and food intake and predictors of undernutrition among under five year tribal children in India. Asia Pac. J. Clin. Nutr. [Internet]. 2012 [cited 2019 Jun 21];21:568–76. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23017315
  32. Uthman OA, Sambala EZ, Adamu AA, Ndwandwe D, Wiyeh AB, Olukade T, et al. Does it really matter where you live? A multilevel analysis of factors associated with missed opportunities for vaccination in sub-Saharan Africa. Hum. Vaccin. Immunother. [Internet]. Taylor & Francis; 2018 [cited 2019 Jun 22];14:2397–404. Available from: https://www.tandfonline.com/doi/full/10.1080/21645515.2018.1504524
  33. Fagbamigbe AF, Bamgboye EA, Yusuf BO, Akinyemi JO, Issa BK, Ngige E, et al. The Nigeria wealth distribution and health seeking behaviour : evidence from the 2012 national HIV / AIDS and reproductive health survey. Health Econ. Rev. 2015;5:e1–10.

Tables

Table 1: Description of Demographic and Health Surveys data by countries and SAM prevalence among under-five children in LMIC, 2010-2018

Country

Year of Survey

Number of Under-5 Children

Weighted SAM prevalence (%) 

Weighted  Uneducated (%)

*Weighted SAM (%) Uneducated

Weighted SAM (%) Educated

All

 

532,680

           4.7 

31.1

5.8

4.2

Eastern Africa

67,418

1.5

29.4

2.5

1.1

Burundi

2016

6,052

           0.9 

47.5

0.9

0.9

Comoro

2012

2,387

           3.9 

47.8

*4.9

*2.9

Ethiopia

2016

8,919

           3.0 

65.8

*3.5

*2.0

Kenya

2014

18,656

           1.0 

11.9

*2.3

*0.8

Malawi

2016

5,178

           0.6 

13.3

0.5

0.6

Mozambique

2011

9,313

           2.1 

37.6

*2.6

*1.9

Rwanda

2015

3,538

           0.6 

14.4

0.9

0.6

Tanzania

2016

8,962

           1.3 

21.5

1.5

1.2

Uganda

2016

4,413

           1.4 

11.2

2.0

1.3

Middle Africa

37,136

 2.5

32.4

4.1

1.8

Angola

2016

6,407

           1.0 

28.9

1.4

0.9

Cameroon

2010

5,033

           1.9 

26.2

*4.3

*1.0

Chad

2015

9,826

           4.3 

65.3

*5.2

*2.3

Congo

2012

4,475

           1.6 

7.0

2.8

1.5

DRC

2014

8,059

           2.7 

19.3

2.7

2.7

Gabon

2012

3,336

           1.2 

6.9

1.6

1.1

Northern Africa

13,682

3.8

17.9

4.3

3.7

Egypt

2014

13,682

           3.8 

17.9

4.3

3.7

Southern Africa

20,273

1.7

7.2

2.3

1.6

Lesotho

2016

1,312

           0.7 

0.9

0.0

0.7

Namibia

2013

1,558

           2.2 

6.8

*7.9

*1.7

South Africa

2016

1,082

           0.5 

2.1

3.1

0.5

Zambia

2014

11,407

           2.1 

11.2

2.0

2.1

Zimbabwe

2015

4,914

           1.1 

1.2

0.0

1.1

Western Africa

85,462

4.7

60.8

5.4

3.7

Benin

2018

12,033

           1.1 

65.7

1.2

0.9

Burkina Faso

2010

6,532

           5.8 

83.8

6.1

4.5

Cote d’Ivoire

2012

3,200

           1.8 

64.8

1.7

2.0

Gambia

2013

3,098

           4.7 

59.6

4.9

4.4

Ghana

2014

2,720

           0.7 

28.8

0.9

0.7

Guinea

2012

3,085

           3.7 

78.7

4.1

2.4

Liberia

2013

3,171

           2.2 

42.5

2.1

2.3

4.5

Mali

2013

4,306

           5.1 

82.9

5.2

4.5

Niger

2012

4,771

           6.2 

86.1

6.2

6.2

Nigeria

2013

24,505

           8.8 

46.4

*11.9

*6.2

Senegal

2017

10,787

           1.5 

61.6

*1.9

*1.0

Sierra Leone

2013

4,069

           3.8 

69.8

3.6

4.3

Togo

2014

3,185

           1.6 

40.6

*2.2

*1.1

Central Asia

9,883

1.5

1.7

1.0

1.6

Kyrgyz

2012

4,016

           1.1 

0.1

0.0

1.1

Tajikistan

2017

5,867

           1.8 

2.7

1.0

1.8

South-Eastern Asia

4,324

2.4

13.2

2.9

2.4

Cambodia

2014

4,324

           2.4 

13.2

2.9

2.4

Southern Asia

240,849

7.1

29.4

7.8

6.8

Bangladesh

2014

6,965

           3.1 

16.3

3.0

3.1

India

2016

225,002

           7.4 

29.7

*8.1

*7.1

Maldives

2016

2,362

           2.0 

1.2

0.0

2.0

Nepal

2016

2,369

           1.9 

34.5

1.7

2.0

Pakistan

2018

4,151

           2.3 

48.6

*2.6

*2.1

Western Asia

1561

1.5

0.1

0.0

1.5

Armenia

2016

1561

            1.5

0.1

0.0

1.5

Central America

21,717

0.2

12.6

0.1

0.2

Guatemala

2012

11,744

           0.1 

18.6

0.0

0.1

Honduras

2016

9,973

           0.3 

4.9

0.4

0.3

South America

9,213

0.1

3.1

0.3

0.1

0.1

 

Peru

2012

9,213

           0.1 

3.1

0.3

2.

0.1

 

South Europe

2,462

0.5

1.1

2.7

0.5

Albania

2018

2,462

           0.5 

1.1

2.7

0.5

Caribbean


18,700

3.9

17.7

6.7

3.3

Dominica

2013

3,187

           0.6 

2.2

1.2

0.6

Haiti

2016

5,598

           0.9 

20.1

1.2

0.8

Myanmar

2016

4,197

           1.4 

16.6

1.4

1.4

Timor-Leste

2016

5,718

           9.9 

24.4

*13.4

*8.8

*Significant at 0.05 in Mantel Haenszel test of homogeneity of the odds ratio 

 


 

 

Table 2: Summary of pooled sample characteristics of the studied children in 51 LMIC 

Characteristics

Weighted n

Weighted %

Weighted (%) Uneducated (%)

SAM (%) Uneducated 

SAM (%) Educated

Individual Level

 

 

 

 

 

Age

 

 

 

 

 

<12 Months

103,379

20.0

29.0

*9.0

*6.7

12 - 59 Months

413,718

80.0

31.7

5.1

3.5

Sex



 

 

 

Female

252,541

48.8

31.5

*5.4

*3.8

Male

264,556

51.2

30.8

6.3

4.5

Maternal Age



 

 

 

15-24

160,133

31.0

22.4

*6.7

*4.8

25-34

273,802

52.9

31.8

5.8

4.1

35-49

83,162

16.1

45.7

5.1

2.7

Wealth Index



 

 

 

Poorest

122,991

23.8

54.5

*6.8

*4.3

Poorer

112,755

21.8

37.0

5.7

4.4

Middle

104,194

20.1

26.4

5.3

4.2

Richer

96,896

18.7

18.3

4.4

4.2

Richest

80,261

15.5

8.8

3.4

3.8

Employment



 

 

 

Yes

366,033

70.8

31.7

*5.9

*4.6

No

151,064

29.2

31.1

5.5

3.2

Access To Media



 

 

 

No

188,357

36.5

55.8

*6.1

*4.3

Yes

328,311

63.5

17.0

5.3

4.1

Drinking-Water Sources



 

 

 

Unimproved

95,544

19.2

43.9

*5.4

*3.1

Improved

402,688

80.8

28.7

5.9

4.3

Toilet Type



 

 

 

Unimproved

248,331

49.9

45.3

*6.0

*4.4

Improved

249,753

50.1

18.1

5.2

3.9

Marital Status



 

 

 

Never Married

12,199

2.4

10.0

*3.5

*1.7

Currently Married

484,949

93.8

32.0

5.9

4.3

Formerly Married

19,946

3.9

23.5

4.1

1.8

Weight At Birth



 

 

 

Average+

423,017

85.4

30.4

*5.7

*4.2

Small

52,939

10.7

33.5

6.0

4.4

Very Small

19,624

4.0

43.7

7.7

5.4

Birth Interval



 

 

 

1st

157,067

30.4

17.0

*6.3

*4.5

<36

193,030

37.4

39.9

5.8

4.4

36+

165,780

32.1

34.5

5.6

3.5

Birth Order



 

 

 

1

157,065

30.4

17.0

*6.3

*4.5

2

134,436

26.0

23.3

5.9

4.6

3

83,134

16.1

34.7

6.0

3.9

4

142,462

27.6

52.0

5.5

3.1

Have money for health care

 

 

 

 

 

Not Problem

101,954

20.5

21.2

*7.0

*6.2

Problem

395,445

79.5

33.2

5.8

3.7

Has Health Insurance

 

 

 

 

 

No

409,359

87.3

32.8

*6.1

*4.5

Yes

59,643

12.7

16.1

6.3

3.9

Community SES Quintiles

 

 

 

 

 

1 (Highest)

117,186

20.2

9.6

*4.5

*4.2

2

101,302

20.0

17.8

4.8

4.2

3

103,795

20.1

28.9

5.0

3.9

4

100,611

20.0

42.6

6.0

4.2

5 (Lowest)

94,203

19.7

62.4

6.7

4.2

Total 

532,680

100.0

31.1

*5.8

*4.2

*Significant at 0.05 in Mantel Haenszel test of homogeneity of the odds ratio