Residential Food Environment, Household Wealth and Maternal Education Association to Preschoolers’ Consumption of Plant-Based Vitamin A-Rich Foods: The EAT Addis Survey in Addis Ababa

Nutrients, Volume 14, No. 2, Year 2022

Interpretation

Vitamin A deficiency is common among preschoolers in low-income settings and a serious public health concern due to its association to increased morbidity and mortality. The limited consumption of vitamin A-rich food is contributing to the problem. Many factors may influence children’s diet, including residential food environment, household wealth, and maternal education. However, very few studies in low-income settings have examined the relationship of these factors to children’s diet together. This study aimed to assess the importance of residential food availability of three plant-based groups of vitamin A-rich foods, household wealth, and maternal education for preschoolers’ consumption of plant-based vitamin A-rich foods in Addis Ababa. A multistage sampling procedure was used to enroll 5467 households with under-five children and 233 residential food environments with 2568 vendors. Data were analyzed using a multilevel binary logistic regression model. Overall, 36% (95% CI: 34.26, 36.95) of the study children reportedly consumed at least one plant-based vitamin A-rich food group in the 24-h dietary recall period. The odds of consuming any plant-based vitamin A-rich food were significantly higher among children whose mothers had a higher education level (AOR: 2.55; 95% CI: 2.01, 3.25), those living in the highest wealth quintile households (AOR: 2.37; 95% CI: 1.92, 2.93), and in residentials where vitamin A-rich fruits were available (AOR: 1.20; 95% CI: 1.02, 1.41). Further research in residential food environment is necessary to understand the purchasing habits, affordability, and desirability of plant-based vitamin A-rich foods to widen strategic options to improve its consumption among preschoolers in low-income and low-education communities. Addis Ababa is the largest city in Ethiopia, and one of the fastest-growing cities in the African continent [29], with an estimated population of 3.6 million, 10.6% of which are estimated to be children under five years of age [30]. The population highly heterogeneous with regard to economic status [31], with an unemployment rate of 31.4%, and 18.9% of the population living below poverty line. The city is also the largest urban recipient of migrants [32,33]. Together, the services (63%) and industry (36%) sectors share almost all economic structure of the city [33]. The food environment in Addis Ababa is diverse, with vendors ranging from micro-vendors (locally known as Gulit) to formal supermarkets [34]. At the time of data collection, Addis Ababa was administratively divided into 10 sub-cities and 117 woredas (the smallest formal administrative unit), with each sub-city comprising of 10–15 woredas. This study utilizes data collected in the EAT Addis survey which collected data from households and vendors in residential areas in all woredas of Addis Ababa. Data collection was conducted in two rounds to account for seasonality, capturing July to August 2017, reflecting a wet season, and January to February 2018, reflecting the post-harvest period. We used a multistage sampling procedure. Each of the 117 woredas in Addis Ababa were included in the survey. Each woreda was divided into five clusters to simulate the enumeration procedure in the Ethiopian Demographic and Health Surveys [30]. One cluster from each woreda was selected for inclusion using a simple random sampling procedure. To identify eligible households with children under five years of age in the cluster, we used a systematic random sampling procedure visiting every third household from a random starting position until we reached a total of 60 households per cluster. These households were assessed for eligibility based on presence of at least one child under five years of age. In cases where an eligible household had more than one eligible child, one child was randomly selected to be the index child from whom dietary data was to be collected. The mother or caretaker of the index child was invited to participate in the survey and was also the main respondent at the household data collection. Mothers or caregivers not at home following three recruitment visits were declared unavailable. For the purpose of this study, household with children age below six months were not included. To identify a residential food environment representing the cluster, one household per cluster was randomly selected to serve as an index household, around which all vendors within a five-minute walk in all direction were surveyed. In total, 14 type of vendors such as kiosk, micro vendor, bakery, fruit/vegetable shop, four mill, butcher, cooperative shop, ET-Fruit, street food vendor, mini market, dairy shop, mobile micro-vendor, livestock market, and fish market were included in the survey. Vendors which were not open at the time of the visit were declared unavailable. Instruments were developed to collect data from households and vendors. These instruments were drafted in English and subsequently translated into Amharic. All data collectors and supervisors had extensive previous field research experience and received training on the data collection tools, interview procedures, and ethical conduct pertaining to the study [35]. Pilot testing refined the development of our survey tools and procedures, including the use of tablets with Open Data Kit (ODK) software. Primary data was sent directly to a protected data server at Addis Continental Institute of Public Health. An overall aim in the EAT Addis survey was to evaluate the association between food availability in residential food environments and food consumption of the household and pre-school children. To facilitate comparison, we developed a common metric to assess food availability and food consumption. A frequently used type of indicator is diet diversity, and indicators have been developed for use in different population groups including children [36,37,38]. We developed a set of food groups which could be used to derive diet diversity indicators both for the household and the preschoolers, as well as to be used to define availability in the residential area. For this particular study we used collected information on three plant-based vitamin A-rich food groups: vitamin A-rich fruits, dark green leafy vegetables, and vitamin A-rich vegetables and roots. The household survey featured data collection on a number of aspects, but of relevance for this study is mainly infant and young child feeding, household wealth, maternal education, and distance between households and index household. Children aged 6–59 months were included in this study. The caregiver of the index child was asked to recall the child’s food consumption during 24 h recall period. In addition to the initial recall by the caretaker a photo gallery of locally available items was used to augment the listing of food groups to ensure a common understanding among our enumerators and participants. For the purpose of this study, three photos with common items representative of the food groups were used: vitamin A-rich vegetables (pumpkin, carrot, red bell pepper), vitamin A-rich fruits (mango, papaya), and dark green leafy vegetables (amaranth, cassava leaves, Ethiopian kale, cabbage, Swiss chard, broccoli). Consumption was categorized as yes or no to each of the three food groups. The household wealth index was developed by principal component analysis and categorized into wealth quintiles [39]. This was calculated based on household assets, household characteristics, access to utilities, and infrastructure variables. Maternal education was assessed based on the reported highest level of grade completed by mothers at the time of the survey, and then categorized according to the Ethiopian educational system—never attend school/not finished first grade, grade 1–4, grade 5–8, grade 9–12, and college-educated [40]. Food security was assessed by use of household food insecurity access scale [41]. Distance between households and index household were calculated based on geographical positioning system (GPS coordinates) of each household during the data collection. The Euclidian distance was calculated based on the geographical location points (latitude/longitude) using a trigonometric approach (distance formula) [42]. A survey of all food vendors within a five-minute walking radius from the index household was conducted. The data collection tool for the residential food environment survey consists of the vendor characteristics, vendor properties, and food availability. The survey tool was pilot tested in three randomly selected residential food environments to assess the feasibility and the relevance of the terminology and typologies. The same food groups and photo gallery of locally available food items used in the household survey was also used in the survey of vendors in the residential area. Of relevance for this study, each vendor was categorized as selling or not selling any items from each of the three vitamin A food rich food groups, such as vitamin A-rich fruits, dark green leafy vegetables, and vitamin A-rich vegetables and roots. For each food group, the residential availability was defined as the presence of at least one vendor in the area selling any item from that particular group [43]. We used STATA 14 software for data analysis. Frequencies and percentages were calculated to report descriptive statistics. The predictor of plant-based vitamin A-rich food consumption was assessed using a multilevel binary logistic regression analysis with meaningful nested hierarchy at household and residential food environment level [44]. The intra-cluster correlation coefficient (ICC) in an empty model showed variability in child consumption of plant-based vitamin A-rich food attributed to differences in residential food environment, also referred as between-cluster variability. Initially, each independent variable was evaluated individually to generate unadjusted effect estimates. After this, three multivariable multilevel logistic regression models were fitted. Model I included residential food environment variables (level 2 variables), comprised of availability of vitamin A-rich vegetables and roots, dark green leafy vegetables, and vitamin A-rich fruits. Model II included household and individual variables (level 1 variables), comprised of household and individual status. Thus, Model I adjusted for residential wealth in tertial and household distance to the indexed household, and Model II adjusted for maternal age, child age, marital status, and number of under-five children [45]. Model III included both level 2 and level 1 variables together to adjust for maternal age, child age, marital status, number of under-five children, household distance to the indexed household, and residential wealth tertial. The observed associations were expressed as unadjusted and adjusted odds ratio with 95% confidence intervals. This study was approved by the Ethical Review Board of Addis Continental Institute of Public Health (Ref No. ACIPH/IRB/004/2015), and the Ethical Review Board at University of Gondar (R.No.-V/P/RCS/05/355/2019). No identifying information was available for this study.

AI Digest

Study Justification:

– Vitamin A deficiency is a serious public health concern among preschoolers in low-income settings, leading to increased morbidity and mortality.
– Limited consumption of vitamin A-rich foods contributes to the problem.
– Factors such as residential food environment, household wealth, and maternal education may influence children’s diet.
– However, few studies have examined the relationship of these factors to children’s diet in low-income settings.

Highlights:

– The study aimed to assess the importance of residential food availability, household wealth, and maternal education for preschoolers’ consumption of plant-based vitamin A-rich foods in Addis Ababa.
– A multistage sampling procedure was used to enroll 5467 households with under-five children and 233 residential food environments with 2568 vendors.
– Overall, 36% of the study children reportedly consumed at least one plant-based vitamin A-rich food group in the 24-hour dietary recall period.
– The odds of consuming any plant-based vitamin A-rich food were significantly higher among children whose mothers had a higher education level, those living in the highest wealth quintile households, and in residentials where vitamin A-rich fruits were available.

Recommendations:

– Further research in residential food environment is necessary to understand the purchasing habits, affordability, and desirability of plant-based vitamin A-rich foods.
– Strategic options should be developed to improve the consumption of plant-based vitamin A-rich foods among preschoolers in low-income and low-education communities.

Key Role Players:

– Researchers and research institutions
– Government agencies and policymakers
– Non-governmental organizations (NGOs) working in nutrition and public health
– Community leaders and local organizations
– Health professionals and educators

Cost Items for Planning Recommendations:

– Research funding for further studies on residential food environment and plant-based vitamin A-rich foods
– Budget for data collection, analysis, and interpretation
– Resources for developing and implementing strategic options to improve consumption
– Funding for nutrition education and awareness campaigns
– Support for community-based interventions and programs
– Monitoring and evaluation costs to assess the impact of interventions

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study utilized a multistage sampling procedure and a multilevel binary logistic regression model to analyze the data. The odds ratios and confidence intervals were reported for the factors influencing preschoolers’ consumption of plant-based vitamin A-rich foods. However, the abstract does not provide information on potential limitations of the study or suggestions for improving the evidence. To improve the strength of the evidence, future studies could consider including a larger sample size, conducting longitudinal research, and exploring additional factors that may influence children’s diet.

Abstract

Addis Ababa is the largest city in Ethiopia, and one of the fastest-growing cities in the African continent [29], with an estimated population of 3.6 million, 10.6% of which are estimated to be children under five years of age [30]. The population highly heterogeneous with regard to economic status [31], with an unemployment rate of 31.4%, and 18.9% of the population living below poverty line. The city is also the largest urban recipient of migrants [32,33]. Together, the services (63%) and industry (36%) sectors share almost all economic structure of the city [33]. The food environment in Addis Ababa is diverse, with vendors ranging from micro-vendors (locally known as Gulit) to formal supermarkets [34]. At the time of data collection, Addis Ababa was administratively divided into 10 sub-cities and 117 woredas (the smallest formal administrative unit), with each sub-city comprising of 10–15 woredas. This study utilizes data collected in the EAT Addis survey which collected data from households and vendors in residential areas in all woredas of Addis Ababa. Data collection was conducted in two rounds to account for seasonality, capturing July to August 2017, reflecting a wet season, and January to February 2018, reflecting the post-harvest period. We used a multistage sampling procedure. Each of the 117 woredas in Addis Ababa were included in the survey. Each woreda was divided into five clusters to simulate the enumeration procedure in the Ethiopian Demographic and Health Surveys [30]. One cluster from each woreda was selected for inclusion using a simple random sampling procedure. To identify eligible households with children under five years of age in the cluster, we used a systematic random sampling procedure visiting every third household from a random starting position until we reached a total of 60 households per cluster. These households were assessed for eligibility based on presence of at least one child under five years of age. In cases where an eligible household had more than one eligible child, one child was randomly selected to be the index child from whom dietary data was to be collected. The mother or caretaker of the index child was invited to participate in the survey and was also the main respondent at the household data collection. Mothers or caregivers not at home following three recruitment visits were declared unavailable. For the purpose of this study, household with children age below six months were not included. To identify a residential food environment representing the cluster, one household per cluster was randomly selected to serve as an index household, around which all vendors within a five-minute walk in all direction were surveyed. In total, 14 type of vendors such as kiosk, micro vendor, bakery, fruit/vegetable shop, four mill, butcher, cooperative shop, ET-Fruit, street food vendor, mini market, dairy shop, mobile micro-vendor, livestock market, and fish market were included in the survey. Vendors which were not open at the time of the visit were declared unavailable. Instruments were developed to collect data from households and vendors. These instruments were drafted in English and subsequently translated into Amharic. All data collectors and supervisors had extensive previous field research experience and received training on the data collection tools, interview procedures, and ethical conduct pertaining to the study [35]. Pilot testing refined the development of our survey tools and procedures, including the use of tablets with Open Data Kit (ODK) software. Primary data was sent directly to a protected data server at Addis Continental Institute of Public Health. An overall aim in the EAT Addis survey was to evaluate the association between food availability in residential food environments and food consumption of the household and pre-school children. To facilitate comparison, we developed a common metric to assess food availability and food consumption. A frequently used type of indicator is diet diversity, and indicators have been developed for use in different population groups including children [36,37,38]. We developed a set of food groups which could be used to derive diet diversity indicators both for the household and the preschoolers, as well as to be used to define availability in the residential area. For this particular study we used collected information on three plant-based vitamin A-rich food groups: vitamin A-rich fruits, dark green leafy vegetables, and vitamin A-rich vegetables and roots. The household survey featured data collection on a number of aspects, but of relevance for this study is mainly infant and young child feeding, household wealth, maternal education, and distance between households and index household. Children aged 6–59 months were included in this study. The caregiver of the index child was asked to recall the child’s food consumption during 24 h recall period. In addition to the initial recall by the caretaker a photo gallery of locally available items was used to augment the listing of food groups to ensure a common understanding among our enumerators and participants. For the purpose of this study, three photos with common items representative of the food groups were used: vitamin A-rich vegetables (pumpkin, carrot, red bell pepper), vitamin A-rich fruits (mango, papaya), and dark green leafy vegetables (amaranth, cassava leaves, Ethiopian kale, cabbage, Swiss chard, broccoli). Consumption was categorized as yes or no to each of the three food groups. The household wealth index was developed by principal component analysis and categorized into wealth quintiles [39]. This was calculated based on household assets, household characteristics, access to utilities, and infrastructure variables. Maternal education was assessed based on the reported highest level of grade completed by mothers at the time of the survey, and then categorized according to the Ethiopian educational system—never attend school/not finished first grade, grade 1–4, grade 5–8, grade 9–12, and college-educated [40]. Food security was assessed by use of household food insecurity access scale [41]. Distance between households and index household were calculated based on geographical positioning system (GPS coordinates) of each household during the data collection. The Euclidian distance was calculated based on the geographical location points (latitude/longitude) using a trigonometric approach (distance formula) [42]. A survey of all food vendors within a five-minute walking radius from the index household was conducted. The data collection tool for the residential food environment survey consists of the vendor characteristics, vendor properties, and food availability. The survey tool was pilot tested in three randomly selected residential food environments to assess the feasibility and the relevance of the terminology and typologies. The same food groups and photo gallery of locally available food items used in the household survey was also used in the survey of vendors in the residential area. Of relevance for this study, each vendor was categorized as selling or not selling any items from each of the three vitamin A food rich food groups, such as vitamin A-rich fruits, dark green leafy vegetables, and vitamin A-rich vegetables and roots. For each food group, the residential availability was defined as the presence of at least one vendor in the area selling any item from that particular group [43]. We used STATA 14 software for data analysis. Frequencies and percentages were calculated to report descriptive statistics. The predictor of plant-based vitamin A-rich food consumption was assessed using a multilevel binary logistic regression analysis with meaningful nested hierarchy at household and residential food environment level [44]. The intra-cluster correlation coefficient (ICC) in an empty model showed variability in child consumption of plant-based vitamin A-rich food attributed to differences in residential food environment, also referred as between-cluster variability. Initially, each independent variable was evaluated individually to generate unadjusted effect estimates. After this, three multivariable multilevel logistic regression models were fitted. Model I included residential food environment variables (level 2 variables), comprised of availability of vitamin A-rich vegetables and roots, dark green leafy vegetables, and vitamin A-rich fruits. Model II included household and individual variables (level 1 variables), comprised of household and individual status. Thus, Model I adjusted for residential wealth in tertial and household distance to the indexed household, and Model II adjusted for maternal age, child age, marital status, and number of under-five children [45]. Model III included both level 2 and level 1 variables together to adjust for maternal age, child age, marital status, number of under-five children, household distance to the indexed household, and residential wealth tertial. The observed associations were expressed as unadjusted and adjusted odds ratio with 95% confidence intervals. This study was approved by the Ethical Review Board of Addis Continental Institute of Public Health (Ref No. ACIPH/IRB/004/2015), and the Ethical Review Board at University of Gondar (R.No.-V/P/RCS/05/355/2019). No identifying information was available for this study.

Materials

N/A

Innovations

Based on the provided description, here are some potential innovations that could improve access to maternal health:

1. Mobile Health Clinics: Implementing mobile health clinics that can travel to different areas of Addis Ababa, especially low-income and low-education communities, to provide maternal health services. This would increase accessibility for women who may have difficulty accessing traditional healthcare facilities.

2. Maternal Education Programs: Developing and implementing educational programs specifically targeted at mothers in low-income and low-education communities. These programs could provide information on maternal health, nutrition, and the importance of consuming vitamin A-rich foods during pregnancy and breastfeeding.

3. Community Health Workers: Training and deploying community health workers in low-income and low-education communities to provide education, support, and referrals for maternal health services. These community health workers could also help identify and address barriers to accessing vitamin A-rich foods.

4. Subsidized Nutritional Supplements: Introducing subsidized or low-cost nutritional supplements that are rich in vitamin A for pregnant and breastfeeding women. This would help ensure that women in low-income communities have access to the necessary nutrients for their own health and the health of their children.

5. Public-Private Partnerships: Collaborating with private companies and organizations to improve access to maternal health services and vitamin A-rich foods. This could involve partnerships to distribute nutritional supplements, provide educational materials, or support community health initiatives.

It’s important to note that these recommendations are based on the information provided and may need to be tailored to the specific context and needs of Addis Ababa.

AI Innovations Description

The study titled “Residential Food Environment, Household Wealth and Maternal Education Association to Preschoolers’ Consumption of Plant-Based Vitamin A-Rich Foods: The EAT Addis Survey in Addis Ababa” explores the factors that influence the consumption of plant-based vitamin A-rich foods among preschoolers in Addis Ababa, Ethiopia. The study aims to improve access to maternal health by addressing the issue of vitamin A deficiency, which is common among preschoolers in low-income settings and is associated with increased morbidity and mortality.

The study found that the odds of preschoolers consuming plant-based vitamin A-rich foods were significantly higher when their mothers had a higher education level, when they lived in households with higher wealth quintiles, and when vitamin A-rich fruits were available in their residential food environments. These findings suggest that improving maternal education, household wealth, and the availability of vitamin A-rich foods in residential areas can contribute to improving access to maternal health.

To develop this recommendation into an innovation to improve access to maternal health, several strategies can be considered:

1. Education and awareness programs: Implement programs that focus on educating mothers about the importance of a balanced diet, including the consumption of plant-based vitamin A-rich foods. These programs can be conducted through community health centers, schools, and other community-based organizations.

2. Economic empowerment: Develop initiatives that aim to improve household wealth and economic opportunities for women. This can include providing vocational training, microfinance support, and entrepreneurship programs to empower women to generate income and improve their families’ access to nutritious foods.

3. Agricultural interventions: Promote the cultivation and availability of vitamin A-rich fruits and vegetables in residential areas. This can be done through community gardens, urban farming initiatives, and partnerships with local farmers to ensure a steady supply of these foods.

4. Food environment interventions: Improve the availability and accessibility of vitamin A-rich foods in residential areas by working with local vendors and markets. This can involve providing incentives for vendors to stock and promote these foods, as well as implementing regulations and policies that support the availability of nutritious foods.

5. Behavior change communication: Develop targeted communication campaigns that aim to change behaviors and attitudes towards the consumption of plant-based vitamin A-rich foods. This can include using various media channels, such as radio, television, and social media, to disseminate information and promote healthy eating habits.

By implementing these strategies, it is possible to create an innovative approach that addresses the factors identified in the study and improves access to maternal health by increasing the consumption of plant-based vitamin A-rich foods among preschoolers in low-income and low-education communities.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase maternal education: Promote and provide opportunities for women to receive higher education, as the study found that children whose mothers had a higher education level were more likely to consume plant-based vitamin A-rich foods. This can be achieved through scholarships, vocational training programs, and awareness campaigns.

2. Improve household wealth: Implement policies and programs that aim to reduce poverty and improve household wealth, as the study found that children living in the highest wealth quintile households were more likely to consume plant-based vitamin A-rich foods. This can be done through income generation programs, microfinance initiatives, and social welfare programs.

3. Enhance residential food availability: Focus on increasing the availability of vitamin A-rich fruits, dark green leafy vegetables, and vitamin A-rich vegetables and roots in residential areas. This can be achieved through initiatives such as promoting urban agriculture, supporting local farmers, and establishing community gardens.

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

1. Define indicators: Identify specific indicators that can measure the impact of the recommendations on access to maternal health. For example, indicators could include the percentage of pregnant women receiving prenatal care, the percentage of women with access to skilled birth attendants, and the percentage of women receiving postnatal care.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population. This can be done through surveys, interviews, and analysis of existing data sources.

3. Implement interventions: Implement the recommended interventions, such as promoting maternal education, improving household wealth, and enhancing residential food availability. Monitor the implementation process to ensure that interventions are being carried out effectively.

4. Collect post-intervention data: After a certain period of time, collect data on the impact of the interventions. This can be done through follow-up surveys, interviews, and analysis of relevant data sources. Compare the post-intervention data with the baseline data to assess the changes in access to maternal health services.

5. Analyze and evaluate: Analyze the data collected to determine the impact of the interventions on access to maternal health. Use statistical methods, such as regression analysis, to assess the relationship between the interventions and the outcome indicators. Evaluate the effectiveness of the interventions and identify areas for improvement.

6. Adjust and refine: Based on the findings of the analysis and evaluation, make adjustments and refinements to the interventions as needed. This could involve scaling up successful interventions, modifying strategies that were less effective, and addressing any barriers or challenges that were identified during the evaluation process.

7. Monitor and sustain: Continuously monitor the progress of the interventions and sustain the efforts to improve access to maternal health. Regularly collect data and evaluate the impact of the interventions to ensure that improvements are being sustained over time.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and make evidence-based decisions for future interventions.

Author

Dima Health

Identifiers:

DOI: 10.3390/nu14020296

Research Areas:

Disparities, Food Security, 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