The Open Public Health Journal




ISSN: 1874-9445 ― Volume 13, 2020
RESEARCH ARTICLE

Determinants of Malnutrition in Under-five Children in Angola, Malawi and Senegal



Chris Khulu1, *, Shaun Ramroop1
1 School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville 3209, Durban, South Africa

Abstract

Introduction:

Malnutrition is one of the leading causes of under-five mortality globally. With the estimated target of reducing mortality in this age group by 2030, understanding and determining the factors contributing to child mortality are critical.

Methods:

The current study used Demographic Health Survey (DHS) data from Angola (2016), Malawi (2016) and Senegal (2016). The DHS data for under-five children from these three countries were then combined in this study to create a pooled sample. This method allows for a comparison and generalization of the results across countries and has also been used in previous studies. The dependent variables (severely nourished, moderately nourished and nourished) were developed by using calculated Weight-for-age Z-scores (WAZ) from DHS data. The exploratory analysis was conducted by performing a gamma measure and chi-square test of independence to evaluate the association between malnutrition status and covariates.

Results & Discussion:

Based on the generalized linear mixed model, the type of residence, sex of the child, age of the child, mother’s level of education, birth interval, wealth index and the birth order are correlated to malnutrition in Angola, Malawi and Senegal. Children who are from rural communities, poor households, with a mother having attained primary education, are female and are between the age of 24 and 59 months are associated with malnutrition. The results of the study suggest that children from these three countries who reside with mothers who have attained only primary education are at the highest risk of being affected by malnutrition.

Conclusion:

The results show the necessity of collaboration among the three countries in order to achieve the Sustainable Development Goal (SDG) target.

Keywords: Malnutrition, Weight-for-age Z-score, Gamma measure, Chi-square test, Proportional odds model, DHS.


Article Information


Identifiers and Pagination:

Year: 2020
Volume: 13
First Page: 55
Last Page: 61
Publisher Id: TOPHJ-13-55
DOI: 10.2174/1874944502013010055

Article History:

Received Date: 27/09/2019
Revision Received Date: 11/12/2019
Acceptance Date: 14/1/2020
Electronic publication date: 20/03/2020
Collection year: 2020

© 2020 Khulu and Ramroop.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville 3209, Durban, South Africa, Tel: 0319104742;
E-mail: chriskhulu@gmail.com






1. INTRODUCTION

Malnutrition is defined as a lack of proper nutrition. This condition is observed when an individual does not receive a sufficient amount of vitamins, minerals and other nutrients [1Makuka RM, Mofu M. The status of hunger and malnutrition inZambia: A review of methods and indicators. Indaba Agricultural Policy Research Institute Indaba Agricultural Policy Research Institute (IAPRI) 2016. Available from: http://www.renapri.org/wp-content/ uploads/2017/01/IAPRI_TP5.pdf]. Natural disasters such as earthquakes, droughts, floods, landslides, cyclones, tsunamis, hurricanes and tornados expose both children and adults to malnutrition [2Pradhan PM, Dhital R, Subhani H. Nutrition interventions for children aged less than 5 years following natural disasters: a systematic review. BMJ Open 2016; 6(9)e011238
[http://dx.doi.org/10.1136/bmjopen-2016-011238] [PMID: 27650759]
]. Developing countries are highly affected by malnutrition and children are more prone to suffering from this condition than adults [3Amare D, Negesse A, Tsegaye B, Assefa B, Ayenie B. Prevalence of undernutrition and its associated factors among children below five years of age in Bure Town, West Gojjam Zone, Amhara National State, Northwest Ethiopia. Hindawi 2016; 2016: 1-8.
[http://dx.doi.org/10.1155/2016/7145708]
, 4Habyarimana F, Zewotir T, Ramroop S. A proportional odds model with complex sampling design to identify key determinants of malnutrition of children under five years in Rwanda. MCSER 2014; 5: 1642-8.
[http://dx.doi.org/10.5901/mjss.2014.v5n23p1642]
].

Under-five child malnutrition is commonly measured using anthropometric indicators: 1) height-for-age (stunting); 2) weight-for-height (wasting); and 3) weight-for-age (underweight) [4Habyarimana F, Zewotir T, Ramroop S. A proportional odds model with complex sampling design to identify key determinants of malnutrition of children under five years in Rwanda. MCSER 2014; 5: 1642-8.
[http://dx.doi.org/10.5901/mjss.2014.v5n23p1642]
-6Kandala NB, Madungu TP, Emina JBO, Nzita KPD, Cappuccio FP. Malnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter? BMC Public Health 2011; 11: 261.
[http://dx.doi.org/10.1186/1471-2458-11-261] [PMID: 21518428]
]. Worldwide, 248 million children under five years of age are affected by malnutrition [7World Health Organization and World Bank Group. Levels and trends in child malnutrition 2017. Available from: https:// www.who.int/nutgrowthdb/jme_brochoure2017.pdf]. The prevalence of stunted children in this age group is estimated to be 23% globally. This form of malnutrition showed an approximate 10% decline from 2000 to 2016. Southern Africa, middle Africa, eastern Africa and western Africa have a greater

prevalence than the average global prevalence (28%, 33%, 37%, and 31%, respectively) [7World Health Organization and World Bank Group. Levels and trends in child malnutrition 2017. Available from: https:// www.who.int/nutgrowthdb/jme_brochoure2017.pdf]. There was an approximate 7% decline of stunting in under-five children in Africa from 2000 to 2016 [7World Health Organization and World Bank Group. Levels and trends in child malnutrition 2017. Available from: https:// www.who.int/nutgrowthdb/jme_brochoure2017.pdf].

The prevalence of wasting for children under five years of age is estimated to be 8% globally. Northern Africa and western Africa are the two African regions with greater local prevalence than the average global prevalence. An estimated 41 million under-five children are overweight (6% prevalence). There was a one percent climb in this form of malnutrition from 2000 to 2016. Southern and northern Africa are the two African regions with greater prevalence than the average global prevalence [7World Health Organization and World Bank Group. Levels and trends in child malnutrition 2017. Available from: https:// www.who.int/nutgrowthdb/jme_brochoure2017.pdf].

Malnutrition contributes to the highest percentage of under-five mortality causes [8Nkurunziza S, Meessen B. Van geertruyde J, Korachais C. Determinants of stunting and severe stunting among Burundian children aged 6–23 months: Evidence from a national cross-sectional household survey, 2014. BMC Pediatr 2017; 17: 1-14.
[http://dx.doi.org/10.1186/s12887-017-0929-2]
-11Nagahori CRN, Kinjo Y, Tchuani JPRN. Malnutrition among vaccinated children aged 0-5 years in Batouri, Republic of Cameroon Journal of general and family medicine 2017; 30: 365-71.]. Statistics released by UNICEF (2018) reveal that the under-five mortality rate has declined by more than half since 1990. However, sub-Saharan Africa still has the highest rate. The projections released by UNICEF (2018) indicate that under-five mortality in Africa in 2030 will be 54 deaths per 1 000 live births. One of the Sustainable Development Goal (SDG) targets is to reduce under-five mortality to 25 deaths per 1 000 live births by 2030 [12World Health Organization, World Bank Group and United Nation. Levels and trends in child mortality 2017.]. Based on the projections, Africa will represent more than half of the SDG target by 2030. Mayotte, Réunion, Seychelles and Egypt are the only African countries that currently have under-five mortality rates below the SDG 2030 target.

Angola is situated on the west coast of Africa with a population of approximately 25 million people where the 2016 under-five mortality was 82.5 deaths per 1 000 live births. Senegal, a country populated with 15 million, had under-five mortality of 47.1 deaths per 1 000 live births. Malawi’s under-five mortality rate was estimated to be 55.1 deaths per 1000 live births with a population of 18 million.

Angola, Malawi and Senegal are all expected to reduce their under-five mortality rate by at least half of their current rate. This requires serious interventions and targeting significant causes of under-five mortality. Hence, the aim of this paper is to assess malnutrition, identify its determinants and propose significant injections to meet the 2030 SDG target in these three countries. This study is significant in underpinning the improvement in collaboration among these three countries and recommends that their governments invest necessary resources in order to meet the SDG target. Furthermore, this strategy may be regarded as one of the many cost-effective ones in which these countries can share resources.

2. MATERIALS AND METHODS

2.1. Data Source

This study employed nationally representative data from three African countries: the Angola Demographic and Health Survey (ADHS, 2016), the Malawi Demographic and Health Survey (MDHS, 2016) and the Senegal Demographic and Health Survey (SDHS, 2016). There was no ethical approval required, since the study employed the secondary data obtained from Micro International. However, a written request was submitted to DHS Micro for approval.

2.1.1. ADHS, 2016

ADHS, 2016 was implemented jointly by the Angola National Institute of Statistics (INE), the Ministry of Health (MINSA) and the Ministry of Planning and Territory Development (MPDT). The objective of the survey, which was collected from October 2015 through March 2016, was to provide insightful information with regard to the demographic and health situation of women, men and children, which included fertility levels, marriage, sexual activity, fertility preferences, family planning methods, childhood and maternal mortality, maternal and child health, breast feeding practices, nutrition, malaria, HIV/AIDS, domestic violence and child well-being.

A sample of 16 109 households was selected, from which 14 379 women aged 15-49 years and 5 684 men aged 15-54 years participated. The response rate for women and men was 96% and 94%, respectively. The sample design was able to provide estimates at the national, provincial and place-of-residence levels.

2.1.2. MDHS, 2016

The 2015–2016 MDHS data were stratified and selected in two stages. In the first stage, 850 standard enumeration areas (SEAs), including 173 SEAs in urban areas and 677 in rural areas, were selected with the probability proportional to the SEA size and with independent selection in each sampling stratum. In the second stage, a fixed number of 30 households per urban cluster and 33 per rural cluster were selected with an equal probability systematic selection from the newly created household listing. A total sample of 27 516 households was selected, of which 26 564 were occupied and 26 361 were successfully interviewed. This yielded a response rate of 99%.

2.1.3. SDHS, 2016

The National Agency of Statistics and Demographic (ANSD) implemented SDHS, 2016 in collaboration with the Ministry of Health and Social Action (MSAS). One of the objectives of the survey was to respond to ongoing data needs for planning, monitoring and evaluating health and safety population programs.

A total sample of 4 437 households was selected and 8 865 women aged 15-49 years and 3 527 men aged 15-59 years were interviewed successfully. A total of 5, 722 under-five children were measured and weighed to determine their nutritional status, 5, 239 children aged 6-59 months were tested for anemia and 5, 237 were tested with the exam microscope for malaria parasitaemia. The sample was designed to provide results at national, provincial and place of residence levels.

2.1.4. Pooled sample

The DHS data of children under five years old from Angola, Malawi and Senegal were combined in this study to create a pooled sample. This method allows a comparison and generalization of the results across countries. The same method was also used in other studies [13Takele K, Zewotir T, Ndanguza D. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health 2019; 19(1): 626.
[http://dx.doi.org/10.1186/s12889-019-6984-x] [PMID: 31118013]
-15Neuman M, Kawachi I, Gortmaker S, Subramanian SV. Urban-rural differences in BMI in low- and middle-income countries: the role of socioeconomic status. Am J Clin Nutr 2013; 97(2): 428-36.
[http://dx.doi.org/10.3945/ajcn.112.045997] [PMID: 23283503]
].

2.2. Variables

2.2.1. Response Variable

As per the recommendation of the WHO, weight-for-age Z-score (WAZ) nutritional status of children was divided into three ordinal categories: Severe Malnutrition (<-3.0 WAZ), Moderate Malnutrition (-3.0 to -2.0 WAZ) and Nourished (>-2.0 WAZ) [16Habyarimana F, Zewotir T, Ramroop S. Key determinants of malnutrition of children under five years of age in Rwanda: Simultaneous measurement of three anthropometric indices. African Population Sudies 2016; 30: 2328-40.
[http://dx.doi.org/10.11564/30-2-836]
].

2.2.2. Explanatory Variables

The study employed the WHO’s Stunting Framework in choosing a set of socioeconomic and demographic variables related to child malnutrition, and these variables were considered as covariates in the development of the proportional odds model. The WHO’s Stunting Framework builds upon the traditional UNICEF framework, which focuses on the causes of malnutrition [8Nkurunziza S, Meessen B. Van geertruyde J, Korachais C. Determinants of stunting and severe stunting among Burundian children aged 6–23 months: Evidence from a national cross-sectional household survey, 2014. BMC Pediatr 2017; 17: 1-14.
[http://dx.doi.org/10.1186/s12887-017-0929-2]
, 17Wirth JP, Rohner F, Petry N, et al. Assessment of the WHO Stunting Framework using Ethiopia as a case study. Matern Child Nutr 2017; 13(2): 1-16.
[http://dx.doi.org/10.1111/mcn.12310] [PMID: 27126511]
]. The variables selected for this study were mother’s level of education (primary, secondary or higher), type of resident (rural or urban), household size (0-5, 6-10, 11-15 or 15), child’s age in months (<12, 12-23, 24-35, 36-47 or 48-59), sex of child (male or female), wealth index (poor, middle or not poor), birth interval in months (<24, 24-47 or >47) and birth order (2-3, 4-5 or >5).

2.3. Statistical Analysis

2.3.1. Test of association

The association between explanatory and response variables was examined by performing bivariate and multinomial analysis. Chi-square and gamma measures were employed to measure the strength of association. If the covariates were in the ordinal scale, the gamma measure was used to determine the strength of association, whereas, if the covariates were on a nominal scale, the chi-square was used to determine the strength of association. The estimator of gamma (γˆ) is given by [18Talukder A. Factors associated with malnutrition among under five children: Illustration using Bangladesh demographic and health survey, 2014 data. Children (Basel) 2017; 4(10): 1-8.
[http://dx.doi.org/10.3390/children4100088] [PMID: 29048355]
]:

(1)

where C is the total number of concordant pairs

and D is the total number of discordant pairs. Chi-square has the form,

(2)

where O represents the observed frequency and E is the expected frequency under the null hypothesis. E is calculated as follows:

(3)

The association measure uses the fact that the statistic, gamˆma, follows a normal distribution with mean = γ and standard error (SE) calculated from the delta method. Chi-square uses the fact that it follows a chi-square distribution with (r-1)(c-1) degrees of freedom (df), where r is the number of categories of the covariates and c is the number of categories of the response variable.

2.3.2. Generalized Linear Mixed Model

A generalized linear mixed model (GLMM) is a statistical model that extends the class of a generalized linear model by incorporating random effects that follow a distribution. A GLMM is useful in accommodating the over-dispersion often observed among outcomes that normally have binomial or Poisson distribution, and for modelling the dependence among outcome variables in longitudinal or repeated measure designs. A GLMM can be developed for non-linear distributed responses and will allow a non-linear link between the mean of the response and the predictor. Its broader application is useful in various disciplines, including analysis of clustered data with longitudinal data or repeated measures [19Feddag ML, Mesbah M. Approximate estimation in Generalized Linear Mixed Models with applications to the Rasch Model. Comput Math Appl 2006; 51: 269-78.
[http://dx.doi.org/10.1016/j.camwa.2005.11.012]
]. We consider the form of the GLMM:

(4)

where η is normal, distributed with mean 0,

Z is an N x q dimension model matrix for the random effect, β is the p x 1 dimension vector of fixed effect parameters, and X is an N x p dimension design matrix that includes covariates for the fixed effect.

3. RESULTS

3.1. Exploratory Data Analysis

The results of cross-tabulation for the pooled sample of children under five years of age are summarised in Table 1. The p-value of the covariates was obtained from chi-square test and gamma measure. The results from Table 1 indicate that type of residence, sex of the child, child’s age, mother’s level of education, birth interval, wealth index and birth order are significantly associated with malnutrition at a 5% level of significance. There is a high prevalence of under-five children residing in urban areas suffering from malnutrition when compared to under-five children residing in rural areas (24.4%, p-value = 0.000). Male children are more likely to suffer from malnutrition when compared to female children (17.9%, p-value = 0.000). Moreover, it is observed that a high proportion of under-five children that have malnutrition are among the age group 36–47 months (18%, p-value = 0.000). Children under five years of age with illiterate mothers are more vulnerable to malnutrition than those with literate mothers (20.5%, p-value = 0.000). Furthermore, children with short birth spacing are more exposed to malnutrition when compared to those with long birth spacing (20.2% and 22.1%, respectively).

In addition, under-five children from poor households are more likely to suffer from malnutrition, when compared to those from households considered middle and not poor according to the wealth index (37.2%, p-value = 0.000). Similarly, children of a birth order of 4-5 and greater than 5 are more likely to suffer from malnutrition, when compared to children of birth order of 2-3 (18.9% and 18.9%, respectively, p-value = 0.000).

3.2. GLMM Application

Model selection was attained, firstly, by including all the covariates and interaction terms in the model. Only the statistically significant covariates were retained for the final model. There are seven covariates included in the final model, as shown below:

(5)

where β0, β1, ..., β7 are the unknown parameter coefficients of fixed effects, bi and βij are country and cluster level random effects, respectively.

Table 2 shows the information criteria for the comparison of the two random intercept models. The results reveal that the AIC of one random intercept model is lower than that of two random intercept models (2663.7). These findings indicate that the one random intercept model is a parsimonious one.

The final model incorporated one random intercept (country), excluding cluster random effects. Table 3 presents odds ratio estimates associated with the type of residence, sex of the child, child’s age, mother’s level of education, birth interval, wealth index and birth order. The results indicate that children residing in rural areas are more likely to be affected by malnutrition (OR = 1.034, p-value = 0.820), when compared to children residing in urban areas. This finding is confirmation of the lack of service delivery in rural areas and the inaccessibility of health services for rural communities. Female children are more likely to be exposed to malnutrition (OR = 1.042, p-value= 0.705) when compared to male children.

Table 1
Distribution of childhood malnutrition and its selected covariates.




Table 2
Information criteria for the comparison of two random intercept models


Table 3
Parameter estimates and odds ratios.


With reference to younger children (<12 months), the likelihood for them to be affected by malnutrition is higher for children between the age of 24 and 35 months (OR = 1.031, p-value = 0.895), 36 and 47 months (OR = 1.104, p-value = 0.662) and 48 to 59 months (OR = 1.229, p-value = 0.336).

Results further reveal that children residing with mothers who have attained secondary education are 36% less likely to be affected by malnutrition when compared to children residing with mothers who have attained only primary education. Similarly, children from households with mothers who have attained higher education are 37% less likely to be affected by malnutrition, when compared to children residing with mothers who have attained only primary education. These findings indicate that improving mothers’ education levels will reduce the prevalence of malnutrition among under-five children in Angola, Malawi and Senegal.

With reference to short birth spacing (<24 months), children with long birth spacing are more likely to be affected by malnutrition. The results indicated that children of birth spacing of 24-47 months (OR = 1.089, p-value = 0.613) and >47 months (OR = 1.419, p-value = 0.056) are more likely to be affected by malnutrition, when compared to children with short birth spacing (<24 months).

Moreover, children from households with mothers who have attained at least secondary education are less likely to be affected by malnutrition. Children from households that are not poor are 36% less likely to be exposed to malnutrition, when compared to children from poor households. Children from households considered middle according to the wealth index are 22% less likely to be exposed to malnutrition, when compared to children from poor households.

In addition, it is observed that children of the birth order 4-5 (OR = 0.992, p-value = 0.553) and >5 (OR = 0.680, p-value = 0.004) are less likely to be affected by malnutrition, when compared to children of the birth order 2–3.

4. DISCUSSION

In this study, the GLMM was employed to identify the key determinants of malnutrition for children under five years of age in Angola, Malawi and Senegal. The study used 2016 ADHS, MDHS and SDHS data. The data from the three countries were then combined to create a pooled sample. A similar method was used in other studies [13Takele K, Zewotir T, Ndanguza D. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health 2019; 19(1): 626.
[http://dx.doi.org/10.1186/s12889-019-6984-x] [PMID: 31118013]
-15Neuman M, Kawachi I, Gortmaker S, Subramanian SV. Urban-rural differences in BMI in low- and middle-income countries: the role of socioeconomic status. Am J Clin Nutr 2013; 97(2): 428-36.
[http://dx.doi.org/10.3945/ajcn.112.045997] [PMID: 23283503]
].

Descriptive analysis was performed in SPSS and GLMM was performed in R version 3.5.2. The analysis confirmed the type of residence, sex of the child, age of the child, mother’s level of education, birth interval, wealth index and birth order as statistically significant risk factors of malnutrition in Angola, Malawi and Senegal.

The results showed that children residing in rural areas of these countries are at a higher risk of being affected by malnutrition than those residing in urban areas. Therefore, the governments of these countries need to focus on how to bring equity to rural communities to eradicate child malnutrition.

Based on demographic factors associated with malnutrition, the analysis showed that female children in Angola, Malawi and Senegal are at a higher risk of being affected by malnutrition when compared to their male counterparts. Results also reveal that an increase in children’s age is positively associated with being exposed to malnutrition. Children aged 24-35 months are at a higher risk of being affected. These findings have indicated the need for the health institutes of Angola, Malawi and Senegal to monitor the well-being of children of all ages, not only younger children, as malnutrition affects older children as well. A mother’s level of education is associated with malnutrition. The higher the mother’s level of education the more the child’s risk of being affected by malnutrition is reduced. Angola, Malawi and Senegal need to strengthen the educational system and develop strategies for channelling educational services to designated populations. The results indicated that children with mothers having attained at least secondary level education are less likely to be affected by malnutrition. These results are in agreement with [13Takele K, Zewotir T, Ndanguza D. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health 2019; 19(1): 626.
[http://dx.doi.org/10.1186/s12889-019-6984-x] [PMID: 31118013]
].

The household wealth index is another determinant found to be associated with malnutrition. Improving household wealth reduces the risk of child exposure to malnutrition. This might be the result of affording access to health services and food security. This finding is similar to the results of the studies that were conducted in Burkina Faso, Bangladesh and Ethiopia [18Talukder A. Factors associated with malnutrition among under five children: Illustration using Bangladesh demographic and health survey, 2014 data. Children (Basel) 2017; 4(10): 1-8.
[http://dx.doi.org/10.3390/children4100088] [PMID: 29048355]
, 20Poda GG, Hsu CY, Chao JC. Factors associated with malnutrition among children <5 years old in Burkina Faso: evidence from the Demographic and Health Surveys IV 2010. Int J Qual Health Care 2017; 29(7): 901-8.
[http://dx.doi.org/10.1093/intqhc/mzx129] [PMID: 29045661]
, 21Abuka TJ, Tsegaw D. Determinants for acute malnutrition among under-five children at public health facilities in Gedeo Zone, Ethiopia: A Case-Control Study. Pediatr Ther 2017; 7: 317-28.
[http://dx.doi.org/10.4172/2161-0665.1000317]
]. Hence, this risk factor can be observed as likely to affect all developing countries, globally.

5. CONCLUSION

Based on the GLMM, this paper identified the determinants of malnutrition for children less than five years old in Angola, Malawi and Senegal. The results showed that type of residence, sex of the child, age of the child, mother’s level of education, birth interval, wealth index and birth order are the correlates of malnutrition in these three countries. Children from rural communities, from poor households with a mother having attained only primary education, who are female and who are between 24-59 months of age are associated with malnutrition problems. These results of the study suggest that children residing with mothers who have only attained primary education are at a higher risk of being affected by malnutrition.

The results have shown the necessity for collaboration among the three countries in order to achieve the SGD target. The African continent is a developing one with limited resources for equity in communities and for meeting their needs. Therefore, it is recommended that Angola, Malawi and Senegal work jointly to eradicate child malnutrition.

Limitations of the study include modelling common covariates. This logical explanation resulted in excluding variables that are found in the literature to be significant in defining the mortality of under-five children.

Future studies should focus on incorporating mothers’ dietary explanatory variable(s) before and after giving birth. This would ensure a better understanding of under-five malnutritional causes. It is also suggested that a similar study should be carried out including more than three countries. This would address the distribution of services aimed at reducing under-five mortality across the African continent and encourage better collaboration among African countries.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

written request was submitted to DHS: Micro Angola, Malawi and Senegal for approval.

HUMAN AND ANIMAL RIGHTS

Not applicable.

CONSENT FOR PUBLICATION

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The data supporting the findings of the article are available in the DHS program at http://dhsprogram.com.

FUNDING

None.

CONFLICT OF INTEREST

The author declares no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

The author would like to thank all the relevant contributors for the collection of the data from Angola, Malawi and Senegal.

REFERENCES

[1] Makuka RM, Mofu M. The status of hunger and malnutrition inZambia: A review of methods and indicators. Indaba Agricultural Policy Research Institute Indaba Agricultural Policy Research Institute (IAPRI) 2016. Available from: http://www.renapri.org/wp-content/ uploads/2017/01/IAPRI_TP5.pdf
[2] Pradhan PM, Dhital R, Subhani H. Nutrition interventions for children aged less than 5 years following natural disasters: a systematic review. BMJ Open 2016; 6(9)e011238
[http://dx.doi.org/10.1136/bmjopen-2016-011238] [PMID: 27650759]
[3] Amare D, Negesse A, Tsegaye B, Assefa B, Ayenie B. Prevalence of undernutrition and its associated factors among children below five years of age in Bure Town, West Gojjam Zone, Amhara National State, Northwest Ethiopia. Hindawi 2016; 2016: 1-8.
[http://dx.doi.org/10.1155/2016/7145708]
[4] Habyarimana F, Zewotir T, Ramroop S. A proportional odds model with complex sampling design to identify key determinants of malnutrition of children under five years in Rwanda. MCSER 2014; 5: 1642-8.
[http://dx.doi.org/10.5901/mjss.2014.v5n23p1642]
[5] Akombi BJ, Agho KE, Merom D, Renzaho AM, Hall JJ. Child malnutrition in sub-Saharan Africa: A meta-analysis of demographic and health surveys (2006-2016). PLoS One 2017; 12(5)e0177338
[http://dx.doi.org/10.1371/journal.pone.0177338] [PMID: 28494007]
[6] Kandala NB, Madungu TP, Emina JBO, Nzita KPD, Cappuccio FP. Malnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter? BMC Public Health 2011; 11: 261.
[http://dx.doi.org/10.1186/1471-2458-11-261] [PMID: 21518428]
[7] World Health Organization and World Bank Group. Levels and trends in child malnutrition 2017. Available from: https:// www.who.int/nutgrowthdb/jme_brochoure2017.pdf
[8] Nkurunziza S, Meessen B. Van geertruyde J, Korachais C. Determinants of stunting and severe stunting among Burundian children aged 6–23 months: Evidence from a national cross-sectional household survey, 2014. BMC Pediatr 2017; 17: 1-14.
[http://dx.doi.org/10.1186/s12887-017-0929-2]
[9] Kapungwe AK. Quality of child health care and under five mortality in Zambia: A case study of two districts in Luapula Province. Max Planck Institute 2015; 12: 301-22.
[10] Das AC. Childhood mortality and child nutrition status of Bangladesh: A review on demographic and heath survey. Journal of current and advanced medical research 2015; 2: 42-6.
[11] Nagahori CRN, Kinjo Y, Tchuani JPRN. Malnutrition among vaccinated children aged 0-5 years in Batouri, Republic of Cameroon Journal of general and family medicine 2017; 30: 365-71.
[12] World Health Organization, World Bank Group and United Nation. Levels and trends in child mortality 2017.
[13] Takele K, Zewotir T, Ndanguza D. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health 2019; 19(1): 626.
[http://dx.doi.org/10.1186/s12889-019-6984-x] [PMID: 31118013]
[14] Subramanian SV, Perkins JM, Özaltin E, Davey Smith G. Weight of nations: a socioeconomic analysis of women in low- to middle-income countries. Am J Clin Nutr 2011; 93(2): 413-21.
[http://dx.doi.org/10.3945/ajcn.110.004820] [PMID: 21068343]
[15] Neuman M, Kawachi I, Gortmaker S, Subramanian SV. Urban-rural differences in BMI in low- and middle-income countries: the role of socioeconomic status. Am J Clin Nutr 2013; 97(2): 428-36.
[http://dx.doi.org/10.3945/ajcn.112.045997] [PMID: 23283503]
[16] Habyarimana F, Zewotir T, Ramroop S. Key determinants of malnutrition of children under five years of age in Rwanda: Simultaneous measurement of three anthropometric indices. African Population Sudies 2016; 30: 2328-40.
[http://dx.doi.org/10.11564/30-2-836]
[17] Wirth JP, Rohner F, Petry N, et al. Assessment of the WHO Stunting Framework using Ethiopia as a case study. Matern Child Nutr 2017; 13(2): 1-16.
[http://dx.doi.org/10.1111/mcn.12310] [PMID: 27126511]
[18] Talukder A. Factors associated with malnutrition among under five children: Illustration using Bangladesh demographic and health survey, 2014 data. Children (Basel) 2017; 4(10): 1-8.
[http://dx.doi.org/10.3390/children4100088] [PMID: 29048355]
[19] Feddag ML, Mesbah M. Approximate estimation in Generalized Linear Mixed Models with applications to the Rasch Model. Comput Math Appl 2006; 51: 269-78.
[http://dx.doi.org/10.1016/j.camwa.2005.11.012]
[20] Poda GG, Hsu CY, Chao JC. Factors associated with malnutrition among children <5 years old in Burkina Faso: evidence from the Demographic and Health Surveys IV 2010. Int J Qual Health Care 2017; 29(7): 901-8.
[http://dx.doi.org/10.1093/intqhc/mzx129] [PMID: 29045661]
[21] Abuka TJ, Tsegaw D. Determinants for acute malnutrition among under-five children at public health facilities in Gedeo Zone, Ethiopia: A Case-Control Study. Pediatr Ther 2017; 7: 317-28.
[http://dx.doi.org/10.4172/2161-0665.1000317]
Track Your Manuscript:


Endorsements



"Open access will revolutionize 21st century knowledge work and accelerate the diffusion of ideas and evidence that support just in time learning and the evolution of thinking in a number of disciplines."


Daniel Pesut
(Indiana University School of Nursing, USA)

"It is important that students and researchers from all over the world can have easy access to relevant, high-standard and timely scientific information. This is exactly what Open Access Journals provide and this is the reason why I support this endeavor."


Jacques Descotes
(Centre Antipoison-Centre de Pharmacovigilance, France)

"Publishing research articles is the key for future scientific progress. Open Access publishing is therefore of utmost importance for wider dissemination of information, and will help serving the best interest of the scientific community."


Patrice Talaga
(UCB S.A., Belgium)

"Open access journals are a novel concept in the medical literature. They offer accessible information to a wide variety of individuals, including physicians, medical students, clinical investigators, and the general public. They are an outstanding source of medical and scientific information."


Jeffrey M. Weinberg
(St. Luke's-Roosevelt Hospital Center, USA)

"Open access journals are extremely useful for graduate students, investigators and all other interested persons to read important scientific articles and subscribe scientific journals. Indeed, the research articles span a wide range of area and of high quality. This is specially a must for researchers belonging to institutions with limited library facility and funding to subscribe scientific journals."


Debomoy K. Lahiri
(Indiana University School of Medicine, USA)

"Open access journals represent a major break-through in publishing. They provide easy access to the latest research on a wide variety of issues. Relevant and timely articles are made available in a fraction of the time taken by more conventional publishers. Articles are of uniformly high quality and written by the world's leading authorities."


Robert Looney
(Naval Postgraduate School, USA)

"Open access journals have transformed the way scientific data is published and disseminated: particularly, whilst ensuring a high quality standard and transparency in the editorial process, they have increased the access to the scientific literature by those researchers that have limited library support or that are working on small budgets."


Richard Reithinger
(Westat, USA)

"Not only do open access journals greatly improve the access to high quality information for scientists in the developing world, it also provides extra exposure for our papers."


J. Ferwerda
(University of Oxford, UK)

"Open Access 'Chemistry' Journals allow the dissemination of knowledge at your finger tips without paying for the scientific content."


Sean L. Kitson
(Almac Sciences, Northern Ireland)

"In principle, all scientific journals should have open access, as should be science itself. Open access journals are very helpful for students, researchers and the general public including people from institutions which do not have library or cannot afford to subscribe scientific journals. The articles are high standard and cover a wide area."


Hubert Wolterbeek
(Delft University of Technology, The Netherlands)

"The widest possible diffusion of information is critical for the advancement of science. In this perspective, open access journals are instrumental in fostering researches and achievements."


Alessandro Laviano
(Sapienza - University of Rome, Italy)

"Open access journals are very useful for all scientists as they can have quick information in the different fields of science."


Philippe Hernigou
(Paris University, France)

"There are many scientists who can not afford the rather expensive subscriptions to scientific journals. Open access journals offer a good alternative for free access to good quality scientific information."


Fidel Toldrá
(Instituto de Agroquimica y Tecnologia de Alimentos, Spain)

"Open access journals have become a fundamental tool for students, researchers, patients and the general public. Many people from institutions which do not have library or cannot afford to subscribe scientific journals benefit of them on a daily basis. The articles are among the best and cover most scientific areas."


M. Bendandi
(University Clinic of Navarre, Spain)

"These journals provide researchers with a platform for rapid, open access scientific communication. The articles are of high quality and broad scope."


Peter Chiba
(University of Vienna, Austria)

"Open access journals are probably one of the most important contributions to promote and diffuse science worldwide."


Jaime Sampaio
(University of Trás-os-Montes e Alto Douro, Portugal)

"Open access journals make up a new and rather revolutionary way to scientific publication. This option opens several quite interesting possibilities to disseminate openly and freely new knowledge and even to facilitate interpersonal communication among scientists."


Eduardo A. Castro
(INIFTA, Argentina)

"Open access journals are freely available online throughout the world, for you to read, download, copy, distribute, and use. The articles published in the open access journals are high quality and cover a wide range of fields."


Kenji Hashimoto
(Chiba University, Japan)

"Open Access journals offer an innovative and efficient way of publication for academics and professionals in a wide range of disciplines. The papers published are of high quality after rigorous peer review and they are Indexed in: major international databases. I read Open Access journals to keep abreast of the recent development in my field of study."


Daniel Shek
(Chinese University of Hong Kong, Hong Kong)

"It is a modern trend for publishers to establish open access journals. Researchers, faculty members, and students will be greatly benefited by the new journals of Bentham Science Publishers Ltd. in this category."


Jih Ru Hwu
(National Central University, Taiwan)


Browse Contents




Webmaster Contact: info@benthamopen.net
Copyright © 2020 Bentham Open