Determinants of undernutrition among young children in Ethiopia

Scientific Reports, Volume 12, No. 1, Year 2022

Interpretation

Ethiopia is one of the countries in sub-Saharan Africa with the highest burden of childhood undernutrition. Despite the high burden of this scourge, little is known about the magnitude and contributing determinants to anthropometric failure among children aged 0–23 months, a period regarded as the best window of opportunity for interventions against undernutrition. This study examined factors associated with undernutrition (stunting, wasting, and underweight) among Ethiopian children aged 0–23 months. This study used a total weighted sample of 2146 children aged 0–23 months from the 2019 Ethiopian Mini Demographic and Health Survey. The data were cleaned and weighted using STATA version 14.0. Height-for-age (HFA), weight-for-height (WFH), and weight-for-age (WFA) z-scores < − 2 SD were calculated and classified as stunted, wasting, and underweight, respectively. Multilevel mixed-effects logistic regression models adjusted for cluster and survey weights were used. Adjusted odds ratio (AOR) and 95% confidence interval (CI) were estimated. Statistical significance was declared at p < 0.05. The overall weighted prevalence of stunting, wasting, and underweight respectively were 27.21% [95% CI (25.32–29.18)], 7.80% [95% CI (6.71–9.03)], and 16.44% [95% CI (14.90–18.09)] among children aged 0–23 months in Ethiopia. Female children were less likely to be associated with stunting [AOR: 0.68, 95% CI (0.54–0.86)], wasting [AOR: 0.70, 95% CI (0.51, 0.98)], and underweight [AOR: 0.64, 95% CI (0.49, 0.83)] than their male counterparts. Conversely, older children aged 12–17 months [AOR: 2.22, 95% CI (1.52, 3.23)] and 18–23 months [AOR: 4.16, 95% CI (2.75, 6.27)] were significantly at an increased odds of becoming stunted. Similarly, the likelihood of being underweight was higher in older age groups: 6–11 months [AOR: 1.74, 95% CI (1.15, 2.63)], 12–17 months [AOR: 2.13, 95% CI (1.40, 3.24)], and 18–23 months [AOR: 4.08, 95% CI (2.58, 6.44)] compared with the children younger than 6 months. Lower wealth quintile was one of the other significant determinants of stunting and underweight. The study’s findings indicated that the most consistent significant risk factors for undernutrition among children aged 0–23 months are: male sex, older age groups and lower wealth quintile. These findings emphasize the importance of strengthening nutrition-specific and sensitive interventions that address the immediate and underlying drivers of childhood undernutrition in early life, as well as targeting low-income households with male children, in order for Ethiopia to meet the Sustainable Development Goals (SDGs) 1,2 and 3 by 2030. The current study used the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS). The EMDHS) used a cross-sectional design and is the latest survey addressing childhood health issues in Ethiopia. Ethiopia is located in the horn of Africa. Its geographical coordinates are 9.145° N latitude and 40.4897° East longitude. The country covers an area of 1.1 million square Kilometers. Administratively, Ethiopia is divided into eleven geographical regions and two administrative cities (namely, Addis Ababa, Afar, Amhara, Benishangul-Gumuz, Dire Dawa, Gambella, Harari, Oromia, Somali, Southern Nations and Nationalities and People [SNNP], Tigray, Sidama, and South West Ethiopia Peoples' Region). In this analysis, Sidama and South West Ethiopia Peoples' regions were under South Nations and Nationalities Peoples region. Oromia, Amhara, and SNNP are highly populous states that account for 37·9%, 21·6%, and 21·3% of the country’s population, respectively54. The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is the second Mini Demographic and Health Survey conducted in Ethiopia. Data collection took place from 21 March 2019 to 28 June 201913. The data was based on the nationally representative sample that provided estimates at the national and regional levels and for urban and rural areas. The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The 2019 EMDHS sample was stratified and selected in two stages. In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected proportionally, considering the EA size. In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. In all selected households, height and weight measurements were collected from children age 0–59 months13. The 2019 EMDHS gathered anthropometric data from children under five (n = 5,279 for stunting, n = 5,408 for wasting, and n = 5,338 for underweight)13. For the analysis of the current study, a total of 2,146 children aged 0–23 months who had valid and complete anthropometric measurements were included. In the anthropometry questionnaire, height and weight measurements were recorded for eligible children aged 0–59 months in all interviewed households. Weight measurements were obtained using lightweight, electronic SECA 874 scales with a digital screen and the mother and child function. Height measurements were performed using measuring boards. Children younger than age 24 months were measured lying down (recumbent) on the board. Health professionals were trained to measure children’s height and weight. Training on child height measurement included standardization exercises13. Stunting, wasting, and underweight were taken separately as dependent variables with binary categories. All anthropometric failure outcomes were constructed based on the 2006 World Health Organization (WHO) child growth standards1. Stunting was defined as a height-for-age z score less than − 2 standard deviations (SDs) of the median, wasting as a weight-for-height z score less than − 2 SDs, and underweight as a weight-for-age z score of less than − 2 SDs1. Individual, household and community-level factors were considered as the potential determinants variables. The individual-level factors included were child sex, child age, number of under-five children, birth order, birth interval, dietary diversity score, meal frequency, breastfeeding status, received vitamin A in last 6 months, maternal age (years), maternal education, antenatal care, place of delivery. Household-level factors include household wealth, household size, household toilet facility, and household’s source of drinking water. Community-level factors include place of residence and region. Minimum dietary diversity is a proxy for adequate micronutrient density of foods. Minimum dietary diversity defined as the proportion of children age 6–23 months who received a minimum of five out of eight food groups during the previous day. The five groups should come from a list of eight food groups: breast milk; grains, roots, and tubers; legumes and nuts; dairy products (milk, yogurt, and cheese); flesh foods (meat, fish, poultry, and liver/organ meat); eggs; vitamin A-rich fruits and vegetables; and other fruits and vegetables. Minimum meal frequency was defined as proportion of children age 6–23 months who received solid, semisolid, or soft food (including milk feeds for non-breastfed children) the minimum number of times or more during the previous day. Minimum meal frequency is a proxy for meeting energy requirements. Breastfed children aged 6–8 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods at least twice a day. Breastfed children aged 6–23 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods at least three times a day. Non-breastfed children aged 6–23 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods or milk feeds at least four times a day and if at least one of the feeds is a solid, semisolid, or soft food55. Toilet facility was categorized as “Improved", "Unimproved" or "Open defecation. Facilities would be considered improved if any of the following occurred: flush/pour flush toilets to piped sewer systems, septic tanks, and pit latrines; ventilated improved pit (VIP) latrines; pit latrines with slabs; and composting toilets. Unimproved facilities included: flush or pour-flush to elsewhere; pit latrine without a slab or open pit; bucket, hanging toilet or hanging latrine. Other facilities, including households with no facility or use of bush/field, were considered as open defecation56. Source of drinking water was categorized as “Improved”, or “Unimproved”. Improved sources of drinking water included piped water, public taps, standpipes, tube wells, boreholes, protected dug wells and springs, and rainwater. Other sources of drinking water were regarded as unimproved56. The principal components statistical procedure was used to determine the weights for the wealth index based on the number and kinds of consumer goods they own, ranging from a television to a bicycle or car, and housing characteristics such as source of drinking water, toilet facilities, and flooring materials. This index was divided into quintiles categories, and the bottom 40% of household wealth index factor score were classified as the poorest households, the next 20% as the middle-class households and the top 40% as the rich households, as used in the past studies6. The EMDHS, 2019 household member recode (PR) file, a nationally representative large-scale dataset, served as the data source for this analysis. To gain access to the EMDHS-2019 dataset, we used the study title and significance to download the data from the Measure DHS website at www.measuredhs.com after receiving permission for registration. This was followed by the extraction of a wide range of information about potential individual and community level factors. Analyses were performed using STATA version 14.0 Statistical software (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). Given the complexity of the two-stage sampling design of EMDHS, sample weighting was used to account for stratification and clustering for precision. For the complex sample design, it is necessary to consider three types of information: (i) the primary sampling unit or cluster variable, (ii) the stratification variable, and (iii) the weight variable to ensure that the estimates are representative at the national level. Data cleaning was performed prior to the analysis to ensure that our findings were consistent with the number of young children categorized in the final Ethiopia Mini-DHS 2019 report and according to the three anthropometric indices of nutritional status: height-for-age, weight-for-height, and weight-for-age. Descriptive analysis was conducted to describe the prevalence of undernutrition according to the independent variables. Multilevel mixed effect models were particularly suitable for our analysis where data for participants were organized at more than one level due to the hierarchical nature of DHS data (i.e., nested data). With such cluster data, children within a cluster may be more similar to each other than children in the rest of the cluster. As a result, we applied a multilevel logistic regression model to assess the association between determinants and undernutrition (stunting, wasting, and underweight). All possible covariates with a p-value of less than 0.25 in the multilevel bi-variable analysis were included in the multilevel multivariable logistic regression models. To avoid or minimize statistical error, multicollinearity was tested. In this study, the variance inflation factor was less than 10, so there was no multicollinearity collinearity between independent variables. For the analysis, four models were used. The primary model (empty or null model) was fitted without explanatory variables. The second model (individual level factors), third model (community-level factors), and fourth model (final or full model) were adjusted for individual and community-level factors simultaneously. Adjusted odds ratio (AOR), along with their corresponding 95% confidence intervals (CIs), were used to estimate the strength and direction of the association between the determinants and outcome variables (stunting, wasting, and underweight). Statistical significance was defined as a p‐value of less than 0.05. For model comparison, we used the log-likelihood ratio (LL) and the Deviance Information Criterion (DIC). The DIC was used as a measure of how well our different models fitted the data with a lower value on DIC indicating a better fit of the model. Moreover, Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used as diagnostics to determine the goodness of fit. After the values for each model of AIC and BIC were compared, the lowest one was thought to be a better explanatory model. Intra-cluster correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variance (PCV) statistics were calculated to measure the variation between clusters57,58. ICC is the measure of variation attributed to contextual neighborhood factors (such as residential level factors) and is often used to operationalize the concept of contextual phenomena59. MOR is used to measure unexplained cluster heterogeneity and it is the median value of the odds ratio between the clusters at low and high risk of outcome variables. PCV is used to measure the total variation of each outcome explained by the final model57,59. The data were downloaded and analyzed after the purpose of the analysis was communicated and approved by MEASURE DHS. After the approval, we downloaded this study data from http://www.dhsprogram.com. Ethics approval and participants consent were not required, as this is publicly available data. The data were obtained via online registration to measure the DHS program and downloaded after the purpose of the analysis was communicated and approved. An approval letter for the use of the EDHS data set was gained from MEASURE DHS. All methods were carried out in accordance with relevant guidelines and regulations.

AI Digest

Study Justification:

– Ethiopia has a high burden of childhood undernutrition, but there is limited knowledge about the factors contributing to undernutrition among children aged 0-23 months.
– This study aims to fill this knowledge gap by examining the determinants of undernutrition (stunting, wasting, and underweight) among Ethiopian children in this age group.
– Understanding these determinants is crucial for developing effective interventions to address undernutrition and achieve the Sustainable Development Goals.

Study Highlights:

– The study used data from the 2019 Ethiopian Mini Demographic and Health Survey, which is a nationally representative survey.
– The prevalence of stunting, wasting, and underweight among children aged 0-23 months in Ethiopia was found to be 27.21%, 7.80%, and 16.44% respectively.
– Female children were less likely to be stunted, wasted, and underweight compared to male children.
– Older children (12-23 months) were at increased odds of becoming stunted and underweight compared to younger children.
– Lower wealth quintile was also a significant determinant of stunting and underweight.

Recommendations for Lay Reader:

– Strengthen nutrition-specific and sensitive interventions targeting children aged 0-23 months to address undernutrition.
– Focus on addressing the immediate and underlying drivers of childhood undernutrition in early life.
– Target low-income households, particularly those with male children, as they are at higher risk of undernutrition.

Recommendations for Policy Maker:

– Allocate resources to strengthen nutrition-specific and sensitive interventions for children aged 0-23 months.
– Implement policies and programs that address the immediate and underlying drivers of childhood undernutrition.
– Target low-income households, especially those with male children, with interventions to reduce undernutrition.

Key Role Players:

– Ministry of Health: Responsible for developing and implementing nutrition-specific and sensitive interventions.
– Non-governmental organizations (NGOs): Involved in implementing nutrition programs and providing support to low-income households.
– Community health workers: Play a crucial role in delivering nutrition interventions and providing education to families.
– Health facilities: Provide healthcare services and support for undernourished children.

Cost Items for Planning Recommendations:

– Funding for nutrition programs and interventions.
– Training and capacity building for healthcare providers and community health workers.
– Development and distribution of educational materials for families.
– Monitoring and evaluation of interventions.
– Research and data collection to monitor progress and inform future interventions.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used a nationally representative sample and conducted multilevel mixed-effects logistic regression models to analyze the data. The adjusted odds ratios and 95% confidence intervals were estimated. The study also provided weighted prevalence rates for stunting, wasting, and underweight among children aged 0-23 months in Ethiopia. However, the abstract could be improved by providing more information on the sampling methodology, such as the sampling frame and the selection process of the households. Additionally, it would be helpful to include information on the limitations of the study and potential sources of bias.

Abstract

The current study used the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS). The EMDHS) used a cross-sectional design and is the latest survey addressing childhood health issues in Ethiopia. Ethiopia is located in the horn of Africa. Its geographical coordinates are 9.145° N latitude and 40.4897° East longitude. The country covers an area of 1.1 million square Kilometers. Administratively, Ethiopia is divided into eleven geographical regions and two administrative cities (namely, Addis Ababa, Afar, Amhara, Benishangul-Gumuz, Dire Dawa, Gambella, Harari, Oromia, Somali, Southern Nations and Nationalities and People [SNNP], Tigray, Sidama, and South West Ethiopia Peoples' Region). In this analysis, Sidama and South West Ethiopia Peoples' regions were under South Nations and Nationalities Peoples region. Oromia, Amhara, and SNNP are highly populous states that account for 37·9%, 21·6%, and 21·3% of the country’s population, respectively54. The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is the second Mini Demographic and Health Survey conducted in Ethiopia. Data collection took place from 21 March 2019 to 28 June 201913. The data was based on the nationally representative sample that provided estimates at the national and regional levels and for urban and rural areas. The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The 2019 EMDHS sample was stratified and selected in two stages. In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected proportionally, considering the EA size. In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. In all selected households, height and weight measurements were collected from children age 0–59 months13. The 2019 EMDHS gathered anthropometric data from children under five (n = 5,279 for stunting, n = 5,408 for wasting, and n = 5,338 for underweight)13. For the analysis of the current study, a total of 2,146 children aged 0–23 months who had valid and complete anthropometric measurements were included. In the anthropometry questionnaire, height and weight measurements were recorded for eligible children aged 0–59 months in all interviewed households. Weight measurements were obtained using lightweight, electronic SECA 874 scales with a digital screen and the mother and child function. Height measurements were performed using measuring boards. Children younger than age 24 months were measured lying down (recumbent) on the board. Health professionals were trained to measure children’s height and weight. Training on child height measurement included standardization exercises13. Stunting, wasting, and underweight were taken separately as dependent variables with binary categories. All anthropometric failure outcomes were constructed based on the 2006 World Health Organization (WHO) child growth standards1. Stunting was defined as a height-for-age z score less than − 2 standard deviations (SDs) of the median, wasting as a weight-for-height z score less than − 2 SDs, and underweight as a weight-for-age z score of less than − 2 SDs1. Individual, household and community-level factors were considered as the potential determinants variables. The individual-level factors included were child sex, child age, number of under-five children, birth order, birth interval, dietary diversity score, meal frequency, breastfeeding status, received vitamin A in last 6 months, maternal age (years), maternal education, antenatal care, place of delivery. Household-level factors include household wealth, household size, household toilet facility, and household’s source of drinking water. Community-level factors include place of residence and region. Minimum dietary diversity is a proxy for adequate micronutrient density of foods. Minimum dietary diversity defined as the proportion of children age 6–23 months who received a minimum of five out of eight food groups during the previous day. The five groups should come from a list of eight food groups: breast milk; grains, roots, and tubers; legumes and nuts; dairy products (milk, yogurt, and cheese); flesh foods (meat, fish, poultry, and liver/organ meat); eggs; vitamin A-rich fruits and vegetables; and other fruits and vegetables. Minimum meal frequency was defined as proportion of children age 6–23 months who received solid, semisolid, or soft food (including milk feeds for non-breastfed children) the minimum number of times or more during the previous day. Minimum meal frequency is a proxy for meeting energy requirements. Breastfed children aged 6–8 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods at least twice a day. Breastfed children aged 6–23 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods at least three times a day. Non-breastfed children aged 6–23 months are considered to be fed with a minimum meal frequency if they receive solid, semisolid, or soft foods or milk feeds at least four times a day and if at least one of the feeds is a solid, semisolid, or soft food55. Toilet facility was categorized as “Improved", "Unimproved" or "Open defecation. Facilities would be considered improved if any of the following occurred: flush/pour flush toilets to piped sewer systems, septic tanks, and pit latrines; ventilated improved pit (VIP) latrines; pit latrines with slabs; and composting toilets. Unimproved facilities included: flush or pour-flush to elsewhere; pit latrine without a slab or open pit; bucket, hanging toilet or hanging latrine. Other facilities, including households with no facility or use of bush/field, were considered as open defecation56. Source of drinking water was categorized as “Improved”, or “Unimproved”. Improved sources of drinking water included piped water, public taps, standpipes, tube wells, boreholes, protected dug wells and springs, and rainwater. Other sources of drinking water were regarded as unimproved56. The principal components statistical procedure was used to determine the weights for the wealth index based on the number and kinds of consumer goods they own, ranging from a television to a bicycle or car, and housing characteristics such as source of drinking water, toilet facilities, and flooring materials. This index was divided into quintiles categories, and the bottom 40% of household wealth index factor score were classified as the poorest households, the next 20% as the middle-class households and the top 40% as the rich households, as used in the past studies6. The EMDHS, 2019 household member recode (PR) file, a nationally representative large-scale dataset, served as the data source for this analysis. To gain access to the EMDHS-2019 dataset, we used the study title and significance to download the data from the Measure DHS website at www.measuredhs.com after receiving permission for registration. This was followed by the extraction of a wide range of information about potential individual and community level factors. Analyses were performed using STATA version 14.0 Statistical software (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). Given the complexity of the two-stage sampling design of EMDHS, sample weighting was used to account for stratification and clustering for precision. For the complex sample design, it is necessary to consider three types of information: (i) the primary sampling unit or cluster variable, (ii) the stratification variable, and (iii) the weight variable to ensure that the estimates are representative at the national level. Data cleaning was performed prior to the analysis to ensure that our findings were consistent with the number of young children categorized in the final Ethiopia Mini-DHS 2019 report and according to the three anthropometric indices of nutritional status: height-for-age, weight-for-height, and weight-for-age. Descriptive analysis was conducted to describe the prevalence of undernutrition according to the independent variables. Multilevel mixed effect models were particularly suitable for our analysis where data for participants were organized at more than one level due to the hierarchical nature of DHS data (i.e., nested data). With such cluster data, children within a cluster may be more similar to each other than children in the rest of the cluster. As a result, we applied a multilevel logistic regression model to assess the association between determinants and undernutrition (stunting, wasting, and underweight). All possible covariates with a p-value of less than 0.25 in the multilevel bi-variable analysis were included in the multilevel multivariable logistic regression models. To avoid or minimize statistical error, multicollinearity was tested. In this study, the variance inflation factor was less than 10, so there was no multicollinearity collinearity between independent variables. For the analysis, four models were used. The primary model (empty or null model) was fitted without explanatory variables. The second model (individual level factors), third model (community-level factors), and fourth model (final or full model) were adjusted for individual and community-level factors simultaneously. Adjusted odds ratio (AOR), along with their corresponding 95% confidence intervals (CIs), were used to estimate the strength and direction of the association between the determinants and outcome variables (stunting, wasting, and underweight). Statistical significance was defined as a p‐value of less than 0.05. For model comparison, we used the log-likelihood ratio (LL) and the Deviance Information Criterion (DIC). The DIC was used as a measure of how well our different models fitted the data with a lower value on DIC indicating a better fit of the model. Moreover, Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used as diagnostics to determine the goodness of fit. After the values for each model of AIC and BIC were compared, the lowest one was thought to be a better explanatory model. Intra-cluster correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variance (PCV) statistics were calculated to measure the variation between clusters57,58. ICC is the measure of variation attributed to contextual neighborhood factors (such as residential level factors) and is often used to operationalize the concept of contextual phenomena59. MOR is used to measure unexplained cluster heterogeneity and it is the median value of the odds ratio between the clusters at low and high risk of outcome variables. PCV is used to measure the total variation of each outcome explained by the final model57,59. The data were downloaded and analyzed after the purpose of the analysis was communicated and approved by MEASURE DHS. After the approval, we downloaded this study data from http://www.dhsprogram.com. Ethics approval and participants consent were not required, as this is publicly available data. The data were obtained via online registration to measure the DHS program and downloaded after the purpose of the analysis was communicated and approved. An approval letter for the use of the EDHS data set was gained from MEASURE DHS. All methods were carried out in accordance with relevant guidelines and regulations.

Materials

https://efashare.b-cdn.net/materials/2826e2121d88e96b513298666e5182acc916731b231e87c362b63d5dbd7e98f3/41598_2022_25160_MOESM1_ESM.docx

Innovations

Based on the provided information, it seems that you are looking for innovations to improve access to maternal health in Ethiopia. However, the information you provided is a detailed description of a study on determinants of undernutrition among young children in Ethiopia. It does not directly address innovations for improving access to maternal health.

To provide relevant recommendations, it would be helpful to have more specific information about the areas of maternal health access that you are interested in improving. This could include aspects such as prenatal care, skilled birth attendance, postnatal care, access to healthcare facilities, transportation, or any other specific areas of concern.

Once you provide more specific information, I will be able to provide you with appropriate recommendations and innovations to improve access to maternal health in Ethiopia.

AI Innovations Description

The study titled “Determinants of undernutrition among young children in Ethiopia” provides valuable insights into the factors associated with undernutrition (stunting, wasting, and underweight) among Ethiopian children aged 0-23 months. The study used data from the 2019 Ethiopian Mini Demographic and Health Survey, which included a nationally representative sample of 2,146 children.

The study found that the overall prevalence of stunting, wasting, and underweight among children aged 0-23 months in Ethiopia was 27.21%, 7.80%, and 16.44% respectively. Several factors were identified as significant determinants of undernutrition:

1. Gender: Female children were less likely to be associated with stunting, wasting, and underweight compared to their male counterparts.

2. Age: Older children aged 12-17 months and 18-23 months were significantly at an increased odds of becoming stunted and underweight compared to children younger than 6 months.

3. Wealth quintile: Lower wealth quintile was a significant determinant of stunting and underweight.

Based on these findings, it is recommended to strengthen nutrition-specific and sensitive interventions that address the immediate and underlying drivers of childhood undernutrition in early life. Specifically, targeting low-income households with male children could be an effective strategy. These interventions should focus on improving access to nutritious food, promoting breastfeeding, and providing adequate healthcare and education for mothers.

By implementing these recommendations, Ethiopia can work towards achieving the Sustainable Development Goals (SDGs) 1, 2, and 3, which aim to end poverty, ensure food security, and promote good health and well-being for all.

AI Innovations Methodology

Based on the provided information, the study focuses on determinants of undernutrition among young children in Ethiopia. To improve access to maternal health, the following innovations and recommendations can be considered:

1. Strengthening nutrition-specific interventions: Implementing targeted interventions that address the immediate causes of undernutrition, such as promoting exclusive breastfeeding, improving dietary diversity, and providing essential nutrients like vitamin A, can help improve maternal and child health outcomes.

2. Enhancing nutrition-sensitive interventions: Addressing the underlying causes of undernutrition, such as poverty, lack of education, and limited access to clean water and sanitation, can have a significant impact on maternal and child health. Implementing programs that focus on improving household income, education, and access to clean water and sanitation can contribute to better nutrition outcomes.

3. Integrating maternal and child health services: Integrating maternal and child health services can improve access to comprehensive care for both mothers and children. This can include providing antenatal care, postnatal care, family planning services, and immunizations in a coordinated and integrated manner.

4. Empowering women and communities: Empowering women and communities through education and awareness programs can help improve maternal and child health outcomes. Promoting gender equality, women’s education, and women’s participation in decision-making processes can contribute to better health outcomes for both mothers and children.

To simulate the impact of these recommendations on improving access to maternal health, a methodology can be developed as follows:

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as antenatal care coverage, skilled birth attendance, postnatal care utilization, and maternal mortality rates.

2. Collect baseline data: Gather baseline data on the selected indicators from reliable sources, such as national surveys, health facility records, and population-based studies. Ensure that the data is representative and covers the target population.

3. Develop a simulation model: Create a simulation model that incorporates the identified innovations and recommendations. The model should consider the potential impact of each recommendation on the selected indicators. This can be done using statistical modeling techniques, such as regression analysis or mathematical modeling.

4. Input data and parameters: Input the baseline data and relevant parameters into the simulation model. This includes information on the current status of access to maternal health, as well as the potential impact of the recommended interventions.

5. Run simulations: Run the simulation model to estimate the potential impact of the recommended interventions on the selected indicators. This can be done by adjusting the relevant parameters based on the expected effects of the interventions.

6. Analyze results: Analyze the simulation results to assess the potential impact of the recommended interventions on improving access to maternal health. This can include comparing the baseline data with the simulated outcomes to determine the magnitude of change.

7. Validate and refine the model: Validate the simulation model by comparing the simulated outcomes with real-world data, if available. Refine the model as needed to improve its accuracy and reliability.

8. Communicate findings: Present the findings of the simulation analysis in a clear and concise manner. Highlight the potential benefits of the recommended interventions in improving access to maternal health and inform policy and decision-making processes.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of innovations and recommendations on improving access to maternal health. This can guide the development and implementation of effective strategies to address maternal and child health challenges in Ethiopia.

Authors & Co-Authors

Statistics:

Citations: 1

Authors: 15

Identifiers:

DOI: 10.1038/s41598-022-25160-y

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female