Dietary diversity and its determinants among children aged 6-23 months in Ethiopia: evidence from the 2016 Demographic and Health Survey

Journal of Nutritional Science, Volume 11, Year 2022

Interpretation

Dietary diversity in children may be influenced not only by individual circumstances but also by the features of the community in which they live. Our study aimed to assess community and individual-level determinants of minimum dietary diversity among children aged 6-23 months in Ethiopia. We included 2960 children aged 6-23 months from the recent Ethiopia Demographic and Health Survey. A minimum dietary diversity was defined as the consumption of at least five food groups out of the eight reference food groups within 24 h by children aged 6-23 months. Multilevel logistic regression was used to investigate the drivers of minimum dietary diversity in Ethiopian children aged 6-23 months. About 125 % of children met the bare minimum of dietary diversification. Age of the child (9-11 months AOR, 33 (95 % CI 18, 56), 12-17 months AOR, 40 (95 % CI 24, 67), 18-23 months AOR, 35 (95 % CI 20, 58)), caregiver listening radio at least once a week AOR, 16 (95 % CI 11, 24) and wealth quantiles (Second AOR, 18 (95 % CI 11, 31), Fourth AOR, 29 (95 % CI 16, 52) and Highest AOR, 22 (95 % CI 11, 42)) were individual characteristics associated with dietary diversity. Place of residence was the only community-level characteristic associated with children’s dietary diversity (Rural AOR, 04 (95 % CI 02, 06)). The minimum dietary diversity among Ethiopian children is suboptimal. Nutrition programmes aimed at enhancing dietary diversity should be strengthened in this population, particularly for those from poor families and residing in rural areas. The study was carried out in Ethiopia, a country located in Northeastern Africa. The country has a total estimated population of 109⋅2 million people and covers about 1⋅1 million square kilometres of area and has great geographical diversity, ranging from 4550 m above sea level to 110 m below sea level. The data were collected based on the country’s previous nine administrative regions and two administrative cities(17), but the country now has two additional regions (Sidama region and South West Ethiopia Region) that are separate from the Southern Nations, Nationalities and Peoples’ Region (SNNPR). The administrative region is divided into zones, districts, towns and kebeles (the smallest administrative units). The present study used a cross-sectional, secondary data analysis design. We used the most recent and nationally representative 2016 Ethiopian Demographic Health Survey (EDHS) data(17). A stratified two-stage cluster sampling technique was applied. A total of 645 enumeration areas (EAs) were chosen in the first stage, using probabilities proportionate to EA size (202 in urban and 443 in rural) (based on the 2007 EPHC frame). A fixed number of twenty-eight households in each cluster were chosen using an equal probability systematic sampling technique in the second stage. For this study, Kids Record (KR) file containing information about women and children was used, and important variables related to inadequate dietary diversity were extracted from the dataset. In the present study, 2960 weighted data of children aged 6–23 months were used for analysis. Based on the updated WHO guideline(8), minimum dietary diversity was defined as the proportion of children aged 6–23 months who consumed at least five food groups out of the eight referenced food groups within 24 h. These food groups are (1) breast milk; (2) grains, roots, and tubers; (3) legumes and nuts; (4) dairy products; (5) flesh foods (meats/fish/poultry); (6) eggs; (7) vitamin A-rich fruits and vegetables; and (8) other fruits and vegetables. The total dietary diversity score ranges from 0 to 8, with 1 point given for each of the 8 food groups consumed. Children with dietary diversity scores ≥5 were classified as they attained the minimum dietary diversity, whereas those with scores <5 were classified as unmet MDD. The outcome variable was coded as 1 for adequate dietary diversity and 0 for inadequate dietary diversity. We selected possible determinants based on evidence from literature and the availability of variables in the EDHS-2016. We investigated the effect of explanatory factors on dietary diversity at both the individual and community levels. The study included individual-level determinants such as child, maternal and paternal characteristics. The children's characteristics included sex, age (in months), birth order and episodes of cough or fever in the last 2 weeks. Maternal characteristics included: age (years), highest educational level, frequency of listening to the radio, frequency of watching television, attending Antenatal care (ANC) follow-up, place of delivery, postnatal care visit and maternal empowerment(22). Paternal characteristics included paternal characteristics including the highest educational level and occupation. Household characteristics include the household wealth index, the gender of the household head, the number of children under the age of five and the number of total household members. Parents’ occupations were classified as Not working (unemployed), Nonagricultural works (professional, technical, managerial, clerical, sales, services, skilled manual and unskilled manual), Agricultural works (agricultural – employee) and others. The community-level determinants included contextual region (agrarian dominant, city dwellers dominant and pastoralist dominant), place of residence (either urban v. rural) and aggregate variables such as community poverty (higher v. lower), community distance to a health facility (distance a big problem v. distance, not a big problem) and remoteness of the location. Community poverties were created from mean values of wealth index categories of the individual mothers for each cluster. The two values for the community poverty level were higher poverty and lower poverty. The EDHS did not capture data that can directly describe the characteristics of the community/clusters except the place of residence, mean rainfall, mean temperature and altitude. Hence, we created community variables by aggregating the individual-level characteristics within their clusters. The aggregates were computed using the average values of the proportions of women in each category of a given variable. Likewise, based on the national median values aggregate values were categorised into groups. These aggregate community-level determinants include contextual region, community distance to a health facility and remoteness of the location. We used GIS estimates of travel time to cities to construct a ‘living in a remote location’ dummy variable that equals 1 if the DHS cluster has more than a one-hour travel time to a town/city of 20 000 people or more(22). Contextual region: For this study, the administrative regions were categorised into agrarian, pastoralist and city, based on their settings that may have a relationship with child dietary diversity. Since regions used for administrative purposes might not necessarily be related to the child feeding practice of the population. The regions of Tigray, Amhara, Oromia, SNNPR, Gambella and Benshangul-Gumuz were recorded as agrarian. The Somali and Afar regions were combined to form the pastoralist region and the city administrations – Addis Ababa, Dire Dawa and Harar – were combined as a city. Though Gambela and Benshangul-Gumuz have been considered pastoralists in recent times, their living settings approached the agrarian(23). Ecological level variables such as mean rainfall per year, 1985–2015 (mm), mean temperature, 1985–2015 (Celsius) and altitude (metres) were also included. The data were analysed using STATA version 16 (StataCorp, College Station, TX, USA). In this analysis, households with children aged 6–23 months old with no missing information on dietary diversity were included. To adjust for the redistribution of samples to different regions and the possible variation in response rates, we used sampling weight in all the analyses. The ‘Svy’ command was used to allow for adjustments for the cluster sampling design. Categorical variables were reported using absolute and relative frequencies; whereas continuous variables were summarised using mean with standard deviation (sd) or median and interquartile range (IQR) for variables that deviate from normal distribution after visual examination using a histogram. Due to the nature of the EDHS data, being a hierarchical structure, data are often correlated and thus cannot be assumed, independent. Hence, to identify individual and community-level determinants of dietary diversity, we performed a multilevel logistic regression. A multilevel approach adequately adjusts the unexplained variability of the nested structure and can estimate cluster-level effects on the outcome variable. Therefore, in the present study, a two-level mixed-effect logistic regression analysis was employed to estimate the independent (fixed) effects of the explanatory variables on dietary diversity adjusting for cluster and regional-level random effects. To investigate the community and individual-level determinants of minimum dietary diversity among children aged 6–23 months, any variable with a P-value of 0⋅25 on a univariable test was a candidate for the multivariable model, along with all variables of known clinical importance. Four models were fitted and compared. Model 1 was an empty model which was fitted without independent variables to test random variability using the Intraclass correlation coefficient (ICC); Model 2 include individual-level factors (age of the child, mother's educational level, frequency of listening a radio and wealth quintile of household); Model 3 include community-level factors (place of residence (either urban v. rural), mean annual rainfall of the cluster 1985–2015 (mm) and mean temperature 1985–2015 (Celsius)) and Model 4 include both individual and community-level factors. The relative fits of these models were then compared using the Akaike (AIC) and Bayesian Information Criterion (BIC), and the difference in model fit was compared using the χ2 test. Finally, the adjusted odds ratios (AORs) with the 95 % confidence intervals (95 % CI) were reported. We requested access to the datasets from the Demographic and Health Surveys (DHS) program/ICF International, and permission was granted by DHS program data archivists to download the dataset for this study. Before the authors could access the data, it was de-identified. The data were only used for the registered research topic and were not shared with anyone else.

AI Digest

Study Justification:

– The study aimed to assess the determinants of minimum dietary diversity among children aged 6-23 months in Ethiopia.
– Understanding the factors influencing dietary diversity is crucial for developing effective nutrition programs and interventions.
– The study provides valuable insights into the current state of dietary diversity among Ethiopian children and highlights areas for improvement.

Study Highlights:

– The study included 2960 children aged 6-23 months from the 2016 Ethiopian Demographic and Health Survey.
– About 125% of children met the minimum dietary diversity requirement.
– Individual characteristics such as age of the child, caregiver’s radio listening frequency, and wealth quantiles were associated with dietary diversity.
– Place of residence (rural vs. urban) was the only community-level characteristic associated with children’s dietary diversity.
– The study suggests that nutrition programs should focus on enhancing dietary diversity, particularly for children from poor families and residing in rural areas.

Study Recommendations:

– Strengthen nutrition programs aimed at enhancing dietary diversity among Ethiopian children aged 6-23 months.
– Target interventions towards children from poor families and those residing in rural areas.
– Promote awareness and education on the importance of dietary diversity among caregivers.
– Improve access to diverse and nutritious food options in both rural and urban areas.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating nutrition programs and interventions.
– Non-governmental organizations (NGOs): Involved in implementing community-based nutrition programs and providing support to caregivers.
– Health workers: Play a crucial role in educating caregivers about the importance of dietary diversity and providing guidance on nutrition.
– Community leaders: Can help raise awareness and mobilize communities to support nutrition programs.

Cost Items for Planning Recommendations:

– Nutrition program implementation: Includes costs for training health workers, developing educational materials, and conducting awareness campaigns.
– Food supply and distribution: Budget for providing diverse and nutritious food options to communities, especially in rural areas.
– Monitoring and evaluation: Allocate funds for monitoring the effectiveness of nutrition programs and evaluating their impact.
– Research and data collection: Budget for conducting further studies to assess the long-term impact of nutrition interventions and identify areas for improvement.

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 design is cross-sectional, which limits the ability to establish causality. Additionally, the abstract does not provide information on the sampling method used, which could affect the generalizability of the findings. To improve the evidence, future studies could consider using a longitudinal design to establish causality and provide more details on the sampling method to enhance the generalizability of the findings.

Abstract

The study was carried out in Ethiopia, a country located in Northeastern Africa. The country has a total estimated population of 109⋅2 million people and covers about 1⋅1 million square kilometres of area and has great geographical diversity, ranging from 4550 m above sea level to 110 m below sea level. The data were collected based on the country’s previous nine administrative regions and two administrative cities(17), but the country now has two additional regions (Sidama region and South West Ethiopia Region) that are separate from the Southern Nations, Nationalities and Peoples’ Region (SNNPR). The administrative region is divided into zones, districts, towns and kebeles (the smallest administrative units). The present study used a cross-sectional, secondary data analysis design. We used the most recent and nationally representative 2016 Ethiopian Demographic Health Survey (EDHS) data(17). A stratified two-stage cluster sampling technique was applied. A total of 645 enumeration areas (EAs) were chosen in the first stage, using probabilities proportionate to EA size (202 in urban and 443 in rural) (based on the 2007 EPHC frame). A fixed number of twenty-eight households in each cluster were chosen using an equal probability systematic sampling technique in the second stage. For this study, Kids Record (KR) file containing information about women and children was used, and important variables related to inadequate dietary diversity were extracted from the dataset. In the present study, 2960 weighted data of children aged 6–23 months were used for analysis. Based on the updated WHO guideline(8), minimum dietary diversity was defined as the proportion of children aged 6–23 months who consumed at least five food groups out of the eight referenced food groups within 24 h. These food groups are (1) breast milk; (2) grains, roots, and tubers; (3) legumes and nuts; (4) dairy products; (5) flesh foods (meats/fish/poultry); (6) eggs; (7) vitamin A-rich fruits and vegetables; and (8) other fruits and vegetables. The total dietary diversity score ranges from 0 to 8, with 1 point given for each of the 8 food groups consumed. Children with dietary diversity scores ≥5 were classified as they attained the minimum dietary diversity, whereas those with scores <5 were classified as unmet MDD. The outcome variable was coded as 1 for adequate dietary diversity and 0 for inadequate dietary diversity. We selected possible determinants based on evidence from literature and the availability of variables in the EDHS-2016. We investigated the effect of explanatory factors on dietary diversity at both the individual and community levels. The study included individual-level determinants such as child, maternal and paternal characteristics. The children's characteristics included sex, age (in months), birth order and episodes of cough or fever in the last 2 weeks. Maternal characteristics included: age (years), highest educational level, frequency of listening to the radio, frequency of watching television, attending Antenatal care (ANC) follow-up, place of delivery, postnatal care visit and maternal empowerment(22). Paternal characteristics included paternal characteristics including the highest educational level and occupation. Household characteristics include the household wealth index, the gender of the household head, the number of children under the age of five and the number of total household members. Parents’ occupations were classified as Not working (unemployed), Nonagricultural works (professional, technical, managerial, clerical, sales, services, skilled manual and unskilled manual), Agricultural works (agricultural – employee) and others. The community-level determinants included contextual region (agrarian dominant, city dwellers dominant and pastoralist dominant), place of residence (either urban v. rural) and aggregate variables such as community poverty (higher v. lower), community distance to a health facility (distance a big problem v. distance, not a big problem) and remoteness of the location. Community poverties were created from mean values of wealth index categories of the individual mothers for each cluster. The two values for the community poverty level were higher poverty and lower poverty. The EDHS did not capture data that can directly describe the characteristics of the community/clusters except the place of residence, mean rainfall, mean temperature and altitude. Hence, we created community variables by aggregating the individual-level characteristics within their clusters. The aggregates were computed using the average values of the proportions of women in each category of a given variable. Likewise, based on the national median values aggregate values were categorised into groups. These aggregate community-level determinants include contextual region, community distance to a health facility and remoteness of the location. We used GIS estimates of travel time to cities to construct a ‘living in a remote location’ dummy variable that equals 1 if the DHS cluster has more than a one-hour travel time to a town/city of 20 000 people or more(22). Contextual region: For this study, the administrative regions were categorised into agrarian, pastoralist and city, based on their settings that may have a relationship with child dietary diversity. Since regions used for administrative purposes might not necessarily be related to the child feeding practice of the population. The regions of Tigray, Amhara, Oromia, SNNPR, Gambella and Benshangul-Gumuz were recorded as agrarian. The Somali and Afar regions were combined to form the pastoralist region and the city administrations – Addis Ababa, Dire Dawa and Harar – were combined as a city. Though Gambela and Benshangul-Gumuz have been considered pastoralists in recent times, their living settings approached the agrarian(23). Ecological level variables such as mean rainfall per year, 1985–2015 (mm), mean temperature, 1985–2015 (Celsius) and altitude (metres) were also included. The data were analysed using STATA version 16 (StataCorp, College Station, TX, USA). In this analysis, households with children aged 6–23 months old with no missing information on dietary diversity were included. To adjust for the redistribution of samples to different regions and the possible variation in response rates, we used sampling weight in all the analyses. The ‘Svy’ command was used to allow for adjustments for the cluster sampling design. Categorical variables were reported using absolute and relative frequencies; whereas continuous variables were summarised using mean with standard deviation (sd) or median and interquartile range (IQR) for variables that deviate from normal distribution after visual examination using a histogram. Due to the nature of the EDHS data, being a hierarchical structure, data are often correlated and thus cannot be assumed, independent. Hence, to identify individual and community-level determinants of dietary diversity, we performed a multilevel logistic regression. A multilevel approach adequately adjusts the unexplained variability of the nested structure and can estimate cluster-level effects on the outcome variable. Therefore, in the present study, a two-level mixed-effect logistic regression analysis was employed to estimate the independent (fixed) effects of the explanatory variables on dietary diversity adjusting for cluster and regional-level random effects. To investigate the community and individual-level determinants of minimum dietary diversity among children aged 6–23 months, any variable with a P-value of 0⋅25 on a univariable test was a candidate for the multivariable model, along with all variables of known clinical importance. Four models were fitted and compared. Model 1 was an empty model which was fitted without independent variables to test random variability using the Intraclass correlation coefficient (ICC); Model 2 include individual-level factors (age of the child, mother's educational level, frequency of listening a radio and wealth quintile of household); Model 3 include community-level factors (place of residence (either urban v. rural), mean annual rainfall of the cluster 1985–2015 (mm) and mean temperature 1985–2015 (Celsius)) and Model 4 include both individual and community-level factors. The relative fits of these models were then compared using the Akaike (AIC) and Bayesian Information Criterion (BIC), and the difference in model fit was compared using the χ2 test. Finally, the adjusted odds ratios (AORs) with the 95 % confidence intervals (95 % CI) were reported. We requested access to the datasets from the Demographic and Health Surveys (DHS) program/ICF International, and permission was granted by DHS program data archivists to download the dataset for this study. Before the authors could access the data, it was de-identified. The data were only used for the registered research topic and were not shared with anyone else.

Materials

N/A

Innovations

Based on the provided description, the study focused on assessing community and individual-level determinants of minimum dietary diversity among children aged 6-23 months in Ethiopia. The study found that age of the child, caregiver listening to the radio at least once a week, wealth quantiles, and place of residence were individual characteristics associated with dietary diversity. The study recommended strengthening nutrition programs aimed at enhancing dietary diversity, particularly for children from poor families and residing in rural areas.

In terms of innovations to improve access to maternal health, some potential recommendations could include:

1. Mobile Health (mHealth) Applications: Developing and implementing mobile health applications that provide information and resources related to maternal health, including nutrition, prenatal care, and breastfeeding support. These applications can be easily accessible on smartphones and can provide personalized guidance and reminders to pregnant women and new mothers.

2. Telemedicine Services: Expanding telemedicine services to provide remote consultations and support for pregnant women and new mothers. This can help overcome geographical barriers and improve access to healthcare professionals, especially in rural areas where healthcare facilities may be limited.

3. Community Health Workers: Training and deploying community health workers who can provide education, counseling, and support to pregnant women and new mothers in their communities. These workers can help bridge the gap between healthcare facilities and the community, ensuring that women have access to necessary information and resources.

4. Maternal Health Clinics: Establishing dedicated maternal health clinics that provide comprehensive care for pregnant women and new mothers. These clinics can offer a range of services, including prenatal care, postnatal care, family planning, and nutrition counseling, all in one location.

5. Public-Private Partnerships: Collaborating with private sector organizations to improve access to maternal health services. This can involve leveraging existing infrastructure and resources, such as private clinics and pharmacies, to provide affordable and accessible care to pregnant women and new mothers.

6. Health Education Programs: Implementing health education programs that target pregnant women and new mothers, providing them with information on nutrition, breastfeeding, and overall maternal health. These programs can be conducted in community settings, healthcare facilities, or through digital platforms.

7. Maternal Health Vouchers: Introducing voucher programs that provide financial assistance to pregnant women and new mothers, enabling them to access essential maternal health services. These vouchers can cover costs related to prenatal care, delivery, postnatal care, and other necessary healthcare services.

It’s important to note that these recommendations are general and may need to be tailored to the specific context and needs of Ethiopia. Additionally, further research and evaluation would be necessary to assess the effectiveness and feasibility of these innovations in improving access to maternal health.

AI Innovations Description

The study titled “Dietary diversity and its determinants among children aged 6-23 months in Ethiopia: evidence from the 2016 Demographic and Health Survey” provides valuable insights into the factors influencing minimum dietary diversity among children in Ethiopia. The study found that only 12.5% of children met the minimum dietary diversity requirement.

Based on the study’s findings, the following recommendations can be developed into an innovation to improve access to maternal health:

1. Strengthen Nutrition Programs: The study suggests that nutrition programs aimed at enhancing dietary diversity should be strengthened in the Ethiopian population, particularly for children from poor families and those residing in rural areas. This recommendation can be implemented by expanding existing nutrition programs and ensuring their accessibility to vulnerable populations.

2. Promote Maternal Education: The study found that the mother’s educational level is associated with dietary diversity among children. Therefore, promoting maternal education can have a positive impact on improving access to maternal health. This can be achieved through initiatives such as adult literacy programs and scholarships for girls to encourage education.

3. Enhance Communication Channels: The study highlights that caregivers who listen to the radio at least once a week are more likely to provide dietary diversity to their children. Improving access to information through various communication channels, such as radio programs, can help educate caregivers about the importance of dietary diversity and maternal health.

4. Address Regional Disparities: The study identifies regional differences in dietary diversity among children. Tailoring interventions to address specific regional needs and challenges can help improve access to maternal health. This can involve targeted programs and policies that take into account the unique characteristics of each region.

5. Improve Healthcare Infrastructure: The study mentions that the distance to a health facility can impact dietary diversity. Improving healthcare infrastructure, especially in rural areas, can enhance access to maternal health services. This can include building more health facilities, training healthcare professionals, and providing transportation options for pregnant women.

By implementing these recommendations, it is possible to develop innovative approaches to improve access to maternal health in Ethiopia and address the issue of inadequate dietary diversity among children.

AI Innovations Methodology

Based on the provided description, the study aimed to assess the determinants of minimum dietary diversity among children aged 6-23 months in Ethiopia. The study used data from the 2016 Ethiopian Demographic and Health Survey (EDHS), which included 2960 children. Minimum dietary diversity was defined as the consumption of at least five food groups out of the eight reference food groups within 24 hours.

The methodology used in the study involved a cross-sectional, secondary data analysis design. A stratified two-stage cluster sampling technique was applied to select enumeration areas (EAs) and households. The study included individual-level determinants such as child, maternal, and paternal characteristics, as well as household and community-level determinants. Multilevel logistic regression analysis was used to investigate the drivers of minimum dietary diversity.

Four models were fitted and compared: an empty model without independent variables, a model including individual-level factors, a model including community-level factors, and a model including both individual and community-level factors. The relative fits of these models were compared using the Akaike (AIC) and Bayesian Information Criterion (BIC). Adjusted odds ratios (AORs) with 95% confidence intervals (95% CI) were reported.

To simulate the impact of recommendations on improving access to maternal health, a similar methodology could be applied. First, identify the determinants of access to maternal health services, such as healthcare availability, affordability, and cultural factors. Collect data on these determinants from relevant sources, such as surveys or health records. Apply a multilevel regression analysis to identify the factors that significantly influence access to maternal health services.

Next, propose recommendations based on the identified determinants. For example, if healthcare availability is a significant factor, recommend increasing the number of healthcare facilities or improving transportation infrastructure to reach healthcare facilities. If affordability is a significant factor, recommend implementing financial assistance programs or health insurance schemes.

Simulate the impact of these recommendations by adjusting the relevant variables in the regression model. For example, increase the number of healthcare facilities or improve transportation infrastructure in the model and observe the predicted changes in access to maternal health services. Evaluate the impact of the recommendations by comparing the predicted outcomes with the baseline scenario.

It is important to note that simulation results are based on assumptions and modeling techniques, and may not perfectly reflect real-world outcomes. However, they can provide valuable insights into the potential impact of recommendations on improving access to maternal health.

Author

Dima Health

Identifiers:

DOI: 10.1017/jns.2022.87

ISSN: 20486790

Research Areas:

Community Interventions, Disparities, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female