Trends and Determinants of Underweight among Children under Five Years in Ethiopia: Further Analysis with Ethiopian Demographic and Health Survey 2005-2016 – Multivariate Decomposition Analysis

Journal of Nutrition and Metabolism, Volume 2022, Year 2022

Interpretation

Background. Underweight is one of the paramount major worldwide health problems, and it traces a big number of populations from infancy to old age. This study aimed to analyze the trends and predictors of change in underweight among children under five years in Ethiopia. Method. The data for this study were accessed from three Ethiopian Demographic and Health Survey data sets 2005, 2011, and 2016. The trend was examined separately for the periods 2005-2011, 2005-2016, and 2011-2016. Multivariate decomposition analysis of change in underweight was employed to answer the major research question of this study. The technique employed the output from the logistic regression model to parcel out the observed difference in underweight into components, and STATA 14 was utilized for data management and analysis. Result. Perceiving the overall trend, the rate of underweight was decreased from 38% in 2005 to 24% in 2016. The decomposition analysis results revealed that, about 12.60% of declines in underweight have been explained by the difference in population characteristics or endowments (E) over the study period. The size of the child at birth, husband’s education, women’s education, and household wealth index contributed significantly to the compositional decline in underweight. Conclusion. The magnitude of underweight among children under five years indicates a remarkable decline over the last ten years in Ethiopia. In this study, two-twelfth of the overall decrease in underweight among children under five years over the decade was due to the difference in characteristics between 2005 and 2016. Continuing to educate the population and boost the population’s economy is needed on the government side in Ethiopia. This study was based on a secondary analysis of cross-sectional population data from Ethiopia Demographic Health Surveys (EDHS) 2005, 2011, and 2016 to investigate trends and the factors associated with underweight among children under five years in Ethiopia. In addition, in Ethiopia, four consecutive surveys were conducted in the cross-sectional years of 2000, 2005, 2011, and 2016. Similar to other demographic and health surveys, the principal objective of the Ethiopian Demographic and Health Survey (EDHS) was to offer current and consistent data on fertility and family planning behavior, child mortality, adult and maternal mortality, children’s nutritional status, and use of maternal and child health services, as well as data, which were collected on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviors and condom use. The sampling frame used for the 2016 EDHS was the Ethiopia Population and Housing Census (EPHC), which was conducted in 2007 by the Ethiopia Central Statistical Agency. The census frame is a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. An EA is a geographical area covering on average 181 households. The sampling frame contains information about the EA location, type of residence (urban or rural), and an estimated number of residential households. Except for EAs in the six zones of the Somali region, each EA has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the EA that help identify the EA. In Somali, a cartographic frame was used in three zones where sketch maps delineating the EA geographic boundaries were available for each EA; in the remaining six zones, satellite image maps were used to provide a map for each EA. The outcome variable for this study was underweight measured based on WHO guidelines, children under five years with weight-for-age Z-score of less than two. Weight-for-age is a composite index of height-for-age and weight-for-height that accounts for both acute and chronic undernutrition. Children whose weight-for-age Z-score is below minus two standard deviations (−2 SD) from the median of the reference population are classified as underweight, while weight-for-age Z-score is above minus two standard deviations (−2 SD) considered as normal weight. Children whose weight-for-age Z-score is below minus three standard deviations (−3 SD) from the median are considered severely underweight. The explanatory variables of interest in this study were as follows: child’s age (months), child’s sex, living area (urban/rural), mother’s education level, and household socioeconomic status, place of delivery, antenatal care service during pregnancy, birth order, duration of breastfeeding, size of child at birth, BMI of women’s, occupational status, vaccination status, and religion. This study employed a trend analysis of underweight among children under five years and decomposition of changes in underweight. The trend in underweight was analyzed using descriptive analyses, stratified by region, urban-rural residence, and selected sociodemographic characteristics. The trend was examined separately for the periods 2005–2011, 2005−2016, and 2011−2016. Data from EDHS 2005, 2011, and 2016 were appended together after extracting important variables for trend and decomposition analysis. Multivariate decomposition analysis of change in underweight was employed to answer the major research question of this study. The purpose of the decomposition analysis was to identify the sources of changes in underweight in the last decade. Both changes in population composition and population behavior related to underweight are important. This method is used for several purposes in demography, economics, and other fields. The present analysis focused on how underweight responds to changes in children’s characteristics at adult age and how these factors form differences across surveys conducted at different times. Both the difference in composition (Endowments) of the population and the difference in the effect of characteristics (coefficients) between the surveys are important to know the factors contributing to the decrease in underweight over the last ten years. The multivariate decomposition analysis for nonlinear response utilizes the output from the logistic regression model (Binary outcome) to parcel out the observed difference in underweight into components. The difference can be attributed to compositional changes between surveys (i.e., the difference in characteristics) and changes in the effects of selected explanatory variables (i.e., the difference in the coefficients due to changes in population behavior). Logit-based decomposition analysis technique was used to identify factors contributing to the change in underweight rate over the last decades. The observed difference in underweight between different surveys is additively decomposed into a characteristic (or endowment) component and a coefficient (or effect of characteristics) component. STATA 14 was utilized for data management and analysis, and STATA command with mvdcmp package was employed throughout the process of analysis. All calculations presented in this manuscript were weighted for sampling probabilities and nonresponse using the weighted factor included in the EDHS data. In the process of testing statistical significance or associations, with 95% confidence interval calculations), complex sampling procedures were considered. The detailed sampling procedure was presented in the full EDHS report [23, 29, 30]. For linear relations, the dependent variable is a function of a linear combination of predictors and regression coefficients, where Y = F (X β), where Y denotes the N × 1 dependent variable, X is an N × K matrix of independent variables, and β is a K × 1 vector of coefficients, where A and B represent EDHS 2016 and 2005, respectively. The mean difference in Y between groups A and B can be decomposed as For our logistic regression, the logit or log-odds of underweight are taken as The E component refers to the part of the differential owing to differences in endowments or characteristics. The C component refers to that part of the differential attributable to differences in the coefficients of effect [31].

AI Digest

Study Justification:

– Underweight among children under five years is a significant global health issue.
– Analyzing the trends and predictors of underweight in Ethiopia can provide valuable insights for addressing this problem.
– The study utilizes data from three Ethiopian Demographic and Health Surveys to provide a comprehensive analysis.

Highlights:

– The rate of underweight among children under five years in Ethiopia decreased from 38% in 2005 to 24% in 2016.
– Multivariate decomposition analysis revealed that changes in population characteristics, such as child size at birth, husband’s education, women’s education, and household wealth index, contributed significantly to the decline in underweight.
– The study highlights the importance of continued education and economic development in addressing underweight in Ethiopia.

Recommendations:

– The government should prioritize efforts to educate the population and improve the economy to further reduce underweight among children under five years.
– Policies and programs should focus on improving child size at birth, increasing educational opportunities for both men and women, and addressing household wealth disparities.

Key Role Players:

– Government agencies responsible for education and economic development
– Ministry of Health
– Non-governmental organizations working on child nutrition and development

Cost Items for Planning Recommendations:

– Educational programs and campaigns
– Economic development initiatives
– Healthcare infrastructure and services
– Nutritional support programs for children under five years
– Monitoring and evaluation systems for tracking progress
Please note that the cost items provided are general categories and not actual cost estimates. The specific budget requirements would depend on the scale and scope of the recommended interventions.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on secondary analysis of cross-sectional population data from Ethiopia Demographic Health Surveys (EDHS) 2005, 2011, and 2016. The study utilized multivariate decomposition analysis to examine the trends and predictors of underweight among children under five years in Ethiopia. The abstract provides a clear description of the methodology and findings. However, to improve the strength of the evidence, the abstract could include information on the sample size, sampling methodology, and any limitations of the study. Additionally, providing more details on the statistical analysis techniques used and the significance of the findings would enhance the overall quality of the evidence.

Abstract

This study was based on a secondary analysis of cross-sectional population data from Ethiopia Demographic Health Surveys (EDHS) 2005, 2011, and 2016 to investigate trends and the factors associated with underweight among children under five years in Ethiopia. In addition, in Ethiopia, four consecutive surveys were conducted in the cross-sectional years of 2000, 2005, 2011, and 2016. Similar to other demographic and health surveys, the principal objective of the Ethiopian Demographic and Health Survey (EDHS) was to offer current and consistent data on fertility and family planning behavior, child mortality, adult and maternal mortality, children’s nutritional status, and use of maternal and child health services, as well as data, which were collected on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviors and condom use. The sampling frame used for the 2016 EDHS was the Ethiopia Population and Housing Census (EPHC), which was conducted in 2007 by the Ethiopia Central Statistical Agency. The census frame is a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. An EA is a geographical area covering on average 181 households. The sampling frame contains information about the EA location, type of residence (urban or rural), and an estimated number of residential households. Except for EAs in the six zones of the Somali region, each EA has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the EA that help identify the EA. In Somali, a cartographic frame was used in three zones where sketch maps delineating the EA geographic boundaries were available for each EA; in the remaining six zones, satellite image maps were used to provide a map for each EA. The outcome variable for this study was underweight measured based on WHO guidelines, children under five years with weight-for-age Z-score of less than two. Weight-for-age is a composite index of height-for-age and weight-for-height that accounts for both acute and chronic undernutrition. Children whose weight-for-age Z-score is below minus two standard deviations (−2 SD) from the median of the reference population are classified as underweight, while weight-for-age Z-score is above minus two standard deviations (−2 SD) considered as normal weight. Children whose weight-for-age Z-score is below minus three standard deviations (−3 SD) from the median are considered severely underweight. The explanatory variables of interest in this study were as follows: child’s age (months), child’s sex, living area (urban/rural), mother’s education level, and household socioeconomic status, place of delivery, antenatal care service during pregnancy, birth order, duration of breastfeeding, size of child at birth, BMI of women’s, occupational status, vaccination status, and religion. This study employed a trend analysis of underweight among children under five years and decomposition of changes in underweight. The trend in underweight was analyzed using descriptive analyses, stratified by region, urban-rural residence, and selected sociodemographic characteristics. The trend was examined separately for the periods 2005–2011, 2005−2016, and 2011−2016. Data from EDHS 2005, 2011, and 2016 were appended together after extracting important variables for trend and decomposition analysis. Multivariate decomposition analysis of change in underweight was employed to answer the major research question of this study. The purpose of the decomposition analysis was to identify the sources of changes in underweight in the last decade. Both changes in population composition and population behavior related to underweight are important. This method is used for several purposes in demography, economics, and other fields. The present analysis focused on how underweight responds to changes in children’s characteristics at adult age and how these factors form differences across surveys conducted at different times. Both the difference in composition (Endowments) of the population and the difference in the effect of characteristics (coefficients) between the surveys are important to know the factors contributing to the decrease in underweight over the last ten years. The multivariate decomposition analysis for nonlinear response utilizes the output from the logistic regression model (Binary outcome) to parcel out the observed difference in underweight into components. The difference can be attributed to compositional changes between surveys (i.e., the difference in characteristics) and changes in the effects of selected explanatory variables (i.e., the difference in the coefficients due to changes in population behavior). Logit-based decomposition analysis technique was used to identify factors contributing to the change in underweight rate over the last decades. The observed difference in underweight between different surveys is additively decomposed into a characteristic (or endowment) component and a coefficient (or effect of characteristics) component. STATA 14 was utilized for data management and analysis, and STATA command with mvdcmp package was employed throughout the process of analysis. All calculations presented in this manuscript were weighted for sampling probabilities and nonresponse using the weighted factor included in the EDHS data. In the process of testing statistical significance or associations, with 95% confidence interval calculations), complex sampling procedures were considered. The detailed sampling procedure was presented in the full EDHS report [23, 29, 30]. For linear relations, the dependent variable is a function of a linear combination of predictors and regression coefficients, where Y = F (X β), where Y denotes the N × 1 dependent variable, X is an N × K matrix of independent variables, and β is a K × 1 vector of coefficients, where A and B represent EDHS 2016 and 2005, respectively. The mean difference in Y between groups A and B can be decomposed as For our logistic regression, the logit or log-odds of underweight are taken as The E component refers to the part of the differential owing to differences in endowments or characteristics. The C component refers to that part of the differential attributable to differences in the coefficients of effect [31].

Materials

N/A

Innovations

Based on the provided information, it seems that the study focused on analyzing the trends and predictors of underweight among children under five years in Ethiopia. The study utilized data from the Ethiopian Demographic and Health Surveys (EDHS) conducted in 2005, 2011, and 2016. The analysis employed multivariate decomposition analysis to understand the factors contributing to the decline in underweight over the years.

In terms of innovations to improve access to maternal health, the study did not specifically address this topic. However, based on the findings and the context of maternal and child health in Ethiopia, some potential recommendations could include:

1. Strengthening maternal and child healthcare services: Enhance the availability and accessibility of quality healthcare services for pregnant women and children, including antenatal care, skilled birth attendance, postnatal care, and immunization.

2. Improving maternal nutrition: Implement programs and interventions that promote proper nutrition for pregnant women, including access to balanced diets, nutritional supplements, and education on healthy eating habits.

3. Enhancing maternal education: Invest in education programs for women, particularly in rural areas, to improve their knowledge and awareness of maternal and child health, nutrition, and hygiene practices.

4. Addressing socioeconomic factors: Implement strategies to reduce poverty and improve household socioeconomic status, as these factors can influence access to healthcare services and adequate nutrition for pregnant women and children.

5. Strengthening community engagement: Promote community-based initiatives that raise awareness about maternal and child health, encourage community participation in healthcare decision-making, and support the development of local solutions to address specific challenges.

6. Improving data collection and analysis: Enhance the capacity for data collection and analysis to monitor maternal and child health indicators, identify gaps, and inform evidence-based decision-making and policy development.

It is important to note that these recommendations are based on the general context of maternal and child health in Ethiopia and may need to be tailored to specific local needs and circumstances.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health based on the study’s findings is to focus on the following areas:

1. Education: Continuing to educate the population, particularly women, on maternal and child health is crucial. The study found that both women’s and husband’s education contributed significantly to the decline in underweight among children under five years. Investing in education programs that provide information on proper nutrition during pregnancy and early childhood can help improve maternal and child health outcomes.

2. Economic empowerment: Boosting the population’s economy is another important aspect to consider. The study revealed that household wealth index was a significant factor in the decline of underweight. Improving economic conditions can lead to better access to nutritious food, healthcare services, and overall well-being for mothers and children.

3. Antenatal care: Encouraging and ensuring access to antenatal care services during pregnancy is crucial for improving maternal and child health. The study did not specifically mention the impact of antenatal care, but it is a well-established factor in reducing maternal and child mortality and improving health outcomes.

4. Nutritional support: Providing adequate nutrition support to pregnant women and young children is essential. The size of the child at birth was found to contribute significantly to the decline in underweight. Implementing programs that focus on improving nutrition during pregnancy and early childhood can help prevent underweight and promote healthy growth.

Overall, a comprehensive approach that includes education, economic empowerment, access to antenatal care, and nutritional support can contribute to improving access to maternal health and reducing underweight among children under five years in Ethiopia.

AI Innovations Methodology

Based on the provided information, it seems that the study focused on analyzing the trends and determinants of underweight among children under five years in Ethiopia using data from the Ethiopian Demographic and Health Survey (EDHS) conducted in 2005, 2011, and 2016. The study utilized a multivariate decomposition analysis to understand the factors contributing to the changes in underweight rates over the years.

To improve access to maternal health, here are some potential recommendations:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, particularly in rural areas, can improve access to maternal health services. This includes building and equipping health centers, hospitals, and maternity clinics, as well as ensuring the availability of essential medical supplies and equipment.

2. Increasing the number of skilled healthcare providers: Training and deploying more skilled healthcare providers, such as midwives and obstetricians, can enhance the quality of maternal healthcare services and ensure that women receive appropriate care during pregnancy, childbirth, and postpartum.

3. Promoting community-based interventions: Implementing community-based programs that focus on maternal health education, awareness, and support can help improve access to maternal healthcare. These programs can involve training community health workers, conducting health campaigns, and providing information on antenatal care, safe delivery practices, and postpartum care.

4. Enhancing transportation and communication: Improving transportation infrastructure, especially in remote areas, can facilitate access to maternal health services. This can involve providing ambulances or transportation vouchers for pregnant women to reach healthcare facilities. Additionally, ensuring reliable communication systems can enable timely access to emergency obstetric care and facilitate coordination between healthcare providers.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define indicators: Identify specific indicators that reflect access to maternal health, such as the number of antenatal care visits, institutional delivery rates, or postpartum care utilization.

2. Data collection: Gather relevant data on the selected indicators from various sources, including surveys, health facility records, and population data.

3. Baseline assessment: Analyze the current situation by examining the baseline values of the selected indicators. This provides a starting point for comparison and helps establish the impact of the recommendations.

4. Scenario development: Develop different scenarios based on the recommendations mentioned earlier. For each scenario, determine the expected changes in the selected indicators. This can be done through expert opinions, literature review, or modeling techniques.

5. Impact assessment: Apply the scenarios to the baseline data and simulate the impact of the recommendations on the selected indicators. This can involve statistical analysis, modeling, or simulation techniques to estimate the potential changes in access to maternal health.

6. Evaluation and interpretation: Assess the results of the impact assessment and interpret the findings. Compare the different scenarios to identify the most effective recommendations for improving access to maternal health.

7. Policy implications: Based on the evaluation, provide recommendations and insights for policymakers and stakeholders to guide decision-making and prioritize interventions that can have the most significant impact on improving access to maternal health.

It is important to note that the methodology for simulating the impact may vary depending on the available data, resources, and specific context.

Authors & Co-Authors

Statistics:

Authors: 2

Identifiers:

DOI: 10.1155/2022/6663756

ISSN: 20900724

Research Areas:

Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female