Micronutrient intake status and associated factors among children aged 6–23 months in the emerging regions of Ethiopia: A multilevel analysis of the 2016 Ethiopia demographic and health survey

PLoS ONE, Volume 16, No. 10 October, Year 2021

Interpretation

Background Micronutrient (MN) deficiency among children is recognised as a major public health problem in Ethiopia. The scarcity of MNs in Ethiopia, particularly in pastoral communities, might be severe due to poor diets mitigated by poor healthcare access, drought, and poverty. To reduce MNs deficiency, foods rich in vitamin A (VA) and iron were promoted and programs like multiple micronutrient powder (MNP), iron and vitamin A supplements (VAS) and or deworming have been implemented. Nationally for children aged 6–23 months, consumption of four or more food groups from diet rich in iron and VA within the previous 24 hours, MNP and iron supplementation within seven days, and VAS and >75% of deworming within the last 6 months is recommend; however, empirical evidence is scarce. Therefore, this study aimed to assess the recommended MN intake status of children aged 6–23 months in the emerging regions of Ethiopia. Methods Data from the Ethiopia Demographic and Health Survey 2016 were used. A two-stage stratified sampling technique was used to identify 1009 children aged 6–23 months. MN intake status was assessed using six options: food rich in VA or iron consumed within the previous 24 hours, MNP or iron supplementation with the previous seven days, VAS or deworming within six months. A multilevel mixed-effect logistic regression analysis was computed, and a p-value of < 0.05 and Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were used to identify the individual and community-level factors. Results In this analysis, 37.3% (95% CI: 34.3–40.3) of children aged 6–23 months had not received any to the recommended MNs sources. The recommended MNs resulted; VAS (47.2%), iron supplementation (6.0%), diet rich in VA (27.7%), diet rich in iron (15.6%), MNP (7.5%), and deworming (7.1%). Antenatal care visit (AOR: 1.9, 95% CI: 1.4–2.8), work in the agriculture (AOR: 2.2, 95% CI: 1.3–3.8) and children aged 13 to 23 months (AOR: 1.7, 95% CI: 1.2–2.4) were the individual-level factors and also Benishangul (AOR: 2.2, 95% CI: 1.3–4.9) and Gambella regions (AOR: 1.9, 95% CI: 1.0–3.4) were the community-level factors that increased micronutrient intake whereas residence in rural (AOR: 0.4, 95% CI: 0.1–0.9) was the community-level factors that decrease micronutrient intake. Conclusions Micronutrient intake among children aged 6–23 months in the pastoral community was low when compared to the national recommendation. After adjusting for individual and community level factors, women’s occupational status, child’s age, antenatal visits for recent pregnancy, residence and region were significantly associated with the MN intake status among children aged 6–23 months. The study used the EDHS 2016 data, a nationally representative household survey data collected every five years. It has been implemented by the Central Statistical Agency (CSA) [23] with the primary objective of providing up-to-date estimates of key demographic and health indicators. Administratively, Ethiopia is divided into nine regions (Tigray, Afar, Amhara, Oromia, Benishangul-Gumuz, Gambela, South Nation Nationalities and Peoples’ Region (SNNPR), Harari and Somali) and two administrative cities (Addis Ababa and Dire-Dawa) (Fig 1). These regions are again categorised as developed and emerging regions. The emerging regions are Afar, Somali, Benishangul, and Gambela, where scattered pastoralists predominantly live. Inadequate infrastructure, inaccessibility of health services, drought, poverty and absence of clear and detailed regulations are the common characteristics in emerging regions [36,37]. The developed regions are Amhara, Oromia, Tigray, SNNPR and Harari regions and the city administrations characterised by a relatively denser population and better infrastructure, and access to health and education services. The sampling frame for the 2016 EDHS used the 2007 Ethiopian population and housing census, which was conducted by the CSA of Ethiopia. The census used a complete list of 84,915 enumeration areas (EAs), which contains the location, type of residence, and the estimated number of residential households. The 2016 EDHS sample was stratified in two stages, and samples of EAs were selected independently from each stratum. The regions were stratified into urban and rural areas. At each lower administrative level, implicit stratification and proportional allocation were achieved within each sampling stratum before sample selection at different levels. In the first stage, 645 EAs were selected with probability proportional to the EA size, and each sampling stratum was selected from the given samples. The total residential households in the EA were the EA size, and a household listing operation was implemented. Then, the resulting lists of households were used as the sampling frame for selecting households in the second stage. Twenty-eight households from each cluster were selected with an equal probability in the second stage, a systematic selection from the newly created household listing. The survey interviewer interviewed only pre-selected households. No replacements or changes of the pre-selected households were allowed in the implementing stages to prevent bias. In this study, the 2016 EDHS childhood datasets of the four emerging regional states: Afar, Benishangul, Gambella and Somali, were used for analysis. All women aged 15–49 years who are the usual members of the selected households were eligible for the survey. Children aged 6–23 months were the source population and included 1009 mothers/caregivers and their recent children aged 6–23 months in the analysis. In contrast, the second and third child within the last five years (for those who have more than a child), children living with other than their mothers/caregivers were excluded from the analysis (Fig 2). For mothers/caregivers with twins, only one was selected by convenience. Potential individual and community level independent variables were also selected, and further analysis was done. The dependent variable of the study was MN intake status among children aged 6–23 months, which was determined by respondents’ reports and assessment of intake status. So, there were six options: food rich in VA or iron in the last 24 hours, MNP or iron supplement consumed within the previous seven days, VAS or deworming within the previous six months [19–21,38]. Accordingly, if the respondent reported that the child had eaten’ at least one of the minimum recommended MNs, we considered it "Yes"; if the children received none of the minimum recommended MNs, it was considered as "No". Foods rich in VA were measured by the seven food groups’ consumption within the preceding 24 hours. These food groups were I. Eggs, ii. Meat (beef, pork, lamb, chicken), iii. pumpkin, carrots, and squash, iv. any dark green leafy vegetables, v. mangoes, papayas, and others with VA fruits, vi. Liver, heart, and other organs and vii. Fish or shellfish. Accordingly, if the respondent reported that the child had eaten’ at least one of these, we considered “yes”; otherwise “no” VA rich food. Foods rich in iron were measured by the four iron-rich food groups’ consumption within the past 24 hours. These groups were i. eggs, ii. meat (beef, pork, lamb, chicken), iii. Liver, heart, and other organs, and iv. Fish or shellfish. Thus, if the respondent reported that the child had eaten’ at least one of these, we considered “yes”; otherwise “no” iron-rich food. Multiple MN powders were assessed by asking the respondents whether their child had received micronutrient powders in the previous seven days. Iron supplementation was assessed by asking the respondents whether their child had iron supplementation defined as iron pills, sprinkles with iron, or iron syrup in the previous seven days. VAS and deworming were assessed for those 6–23 months of children whether they received for the last six months or not by reviewing the integrated child health card, which consists of immunisation and growth monitoring history and also from the mother’s verbal response. The obstetric characteristics of women included current pregnancy status and use of maternal health services (ANC, institutional delivery and PNC). The child characteristics include birth weight, and current age. Birth weight was categorised as small, average or large. The household wealth index was calculated as an index based on consumer goods such as television, bicycle, or car. Household characteristics such as the material used for floor and roof and toilet facilities were also considered in calculating the household wealth index. The household wealth index was computed using principal component analysis and ranked into poor, middle, and rich. Simultaneously, the community-level variables were residence, region, community-level wealth quantile, community-level media exposure, and distance to the nearest health facility. Community-level wealth quantile was assessed using the asset index based on data from the entire country sample on separate scores prepared for rural and urban households, and combined to produce an index for all households as the community level and ranked into five (poorest, poorer, middle, richer, and richest). In other words, the community level wealth quantile was used to measure the community level poverty and it is a relative measure of how wealth is distributed within the population from the quantiles were calculated. Community media exposure was assessed as “yes” if they have access to all three media (newsletter, radio, and television) at least once a week, otherwise “no” if they did not have any media exposure. Distance to the health facility was assessed by the question “distance to the nearest health facility is a problem?” and the responses were categorised as “big problem” or “not a problem” [39]. The data were cleaned, re-coded and analysed using STATA (StataCorp, College Station, TX) version 14. Descriptive statistics were presented using tables and narration to describe the magnitude of MN intake status by sociodemographic, maternal obstetric and child characteristics. A multilevel analysis was conducted after checking the eligibility. The model eligibility was assessed by calculating the Intra-class Correlation Coefficient (ICC) and a model with ICC greater than 10% for multilevel analysis. In this study, the ICC was 27.3%. Since the data were hierarchical (individuals were nested within communities), a two-level mixed-effects logistic regression model was fitted to estimate both the individual and community level variables (fixed and random effect) on MN intake status, and the log of the probability of MN intake was modelled using the formula as follows [40]: Where i is an individual level unit and j is a community-level unit; X and Z refer to individual and community-level variables, respectively; πij is the probability of MN intake for the ith child in the jth community; the β’s are the fixed coefficients. Whereas β0 is the intercept; the effect on the probability of MN intake in the absence of influence of predictors, and uj showed the community’s effect (random effect) on MN intake for the jth community and eij showed random errors at the individual levels. By assuming each community had different intercepts (β0 + Uj) and fixed coefficient (β1,2), the clustered data nature and the within and between community variations were considered. Bivariable and multivariable analyses were computed. In the bivariable logistic regression analysis, a p-value of less than 0.2 was used to fit three models (models for the individual, community, and individual and community levels). Then, in the final model (fixed effect), a p-value of less than 0.05 and an Adjusted Odds Ratio (AOR) with a 95% confidence interval (CI) were used to estimate the association of individual and community level factors with MN intake status. The measures of variation (random-effects) between clusters were reported using ICC and proportional change in variance (PCV). The ICC refers to the ratio of cluster variance to total variance, and it tells us the proportion of the total variance in the outcome variable that is accounted at the cluster level. The loglikelihood test was used to estimate the goodness of fit of the final adjusted model compared to the preceding models. A model with the smallest value of loglikelihood is better; accordingly, model three (a model for both individual and community-level variables) had the lowest value. The ethical approval and permission to access the data were obtained from the MEASURE DHS (available from https://www.dhsprogram.com/Data/: accessed on April 06, 2020) after a brief study concept was submitted.

AI Digest

Study Justification:

The study aimed to assess the micronutrient intake status of children aged 6-23 months in the emerging regions of Ethiopia. Micronutrient deficiency is a major public health problem in Ethiopia, particularly in pastoral communities, due to poor diets, limited healthcare access, drought, and poverty. This study aimed to provide empirical evidence on the recommended intake of micronutrients and identify associated factors.

Study Highlights:

– The study used data from the 2016 Ethiopia Demographic and Health Survey, which is a nationally representative household survey conducted every five years.
– The study focused on the emerging regions of Ethiopia, including Afar, Benishangul, Gambela, and Somali, where pastoralists predominantly live.
– The study assessed the intake status of six recommended micronutrients: vitamin A, iron, multiple micronutrient powder, iron supplementation, vitamin A supplementation, and deworming.
– The study found that 37.3% of children aged 6-23 months had not received any of the recommended micronutrients.
– Factors associated with higher micronutrient intake included antenatal care visits, working in agriculture, and children aged 13-23 months.
– Factors associated with lower micronutrient intake included residence in rural areas.

Recommendations for Lay Reader:

– The study found that a significant number of children in the emerging regions of Ethiopia are not receiving the recommended micronutrients.
– It is important to improve access to healthcare services, particularly antenatal care, to ensure better nutrition for children.
– Programs promoting micronutrient-rich foods, such as vitamin A and iron, should be strengthened in these regions.
– Efforts should be made to address the factors contributing to lower micronutrient intake, such as rural residence and poverty.

Recommendations for Policy Maker:

– The study highlights the need for targeted interventions to address micronutrient deficiency among children in the emerging regions of Ethiopia.
– Policies should focus on improving healthcare access, particularly antenatal care, to ensure better nutrition for children.
– Programs promoting the consumption of vitamin A and iron-rich foods should be strengthened, with a particular emphasis on reaching rural communities.
– Efforts should be made to address poverty and improve infrastructure in the emerging regions to improve overall nutrition outcomes.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating interventions to address micronutrient deficiency.
– Non-governmental Organizations (NGOs): Involved in implementing nutrition programs and providing support to communities.
– Community Health Workers: Play a crucial role in delivering nutrition education and interventions at the community level.
– Health Facilities: Provide antenatal care services and distribute micronutrient supplements.
– Agricultural Extension Workers: Involved in promoting agricultural practices that enhance micronutrient-rich food production.

Cost Items for Planning Recommendations:

– Health facility infrastructure and equipment: Budget for improving healthcare facilities and ensuring the availability of necessary equipment for antenatal care and micronutrient supplementation.
– Training and capacity building: Allocate funds for training healthcare providers, community health workers, and agricultural extension workers on nutrition interventions.
– Nutrition education materials: Budget for the development and distribution of educational materials to raise awareness about the importance of micronutrient-rich foods.
– Program implementation and monitoring: Allocate funds for the implementation and monitoring of nutrition programs, including the distribution of supplements and the assessment of impact.
– Research and evaluation: Set aside funds for further research and evaluation to assess the effectiveness of interventions and inform future policy decisions.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study used nationally representative data from the Ethiopia Demographic and Health Survey 2016, which enhances the generalizability of the findings. The study also employed a multilevel mixed-effect logistic regression analysis to identify individual and community-level factors associated with micronutrient intake status among children aged 6-23 months. However, the abstract could be improved by providing more specific details about the sample size, data collection methods, and statistical analysis techniques. Additionally, it would be helpful to include a summary of the main findings and implications for public health interventions.

Abstract

The study used the EDHS 2016 data, a nationally representative household survey data collected every five years. It has been implemented by the Central Statistical Agency (CSA) [23] with the primary objective of providing up-to-date estimates of key demographic and health indicators. Administratively, Ethiopia is divided into nine regions (Tigray, Afar, Amhara, Oromia, Benishangul-Gumuz, Gambela, South Nation Nationalities and Peoples’ Region (SNNPR), Harari and Somali) and two administrative cities (Addis Ababa and Dire-Dawa) (Fig 1). These regions are again categorised as developed and emerging regions. The emerging regions are Afar, Somali, Benishangul, and Gambela, where scattered pastoralists predominantly live. Inadequate infrastructure, inaccessibility of health services, drought, poverty and absence of clear and detailed regulations are the common characteristics in emerging regions [36,37]. The developed regions are Amhara, Oromia, Tigray, SNNPR and Harari regions and the city administrations characterised by a relatively denser population and better infrastructure, and access to health and education services. The sampling frame for the 2016 EDHS used the 2007 Ethiopian population and housing census, which was conducted by the CSA of Ethiopia. The census used a complete list of 84,915 enumeration areas (EAs), which contains the location, type of residence, and the estimated number of residential households. The 2016 EDHS sample was stratified in two stages, and samples of EAs were selected independently from each stratum. The regions were stratified into urban and rural areas. At each lower administrative level, implicit stratification and proportional allocation were achieved within each sampling stratum before sample selection at different levels. In the first stage, 645 EAs were selected with probability proportional to the EA size, and each sampling stratum was selected from the given samples. The total residential households in the EA were the EA size, and a household listing operation was implemented. Then, the resulting lists of households were used as the sampling frame for selecting households in the second stage. Twenty-eight households from each cluster were selected with an equal probability in the second stage, a systematic selection from the newly created household listing. The survey interviewer interviewed only pre-selected households. No replacements or changes of the pre-selected households were allowed in the implementing stages to prevent bias. In this study, the 2016 EDHS childhood datasets of the four emerging regional states: Afar, Benishangul, Gambella and Somali, were used for analysis. All women aged 15–49 years who are the usual members of the selected households were eligible for the survey. Children aged 6–23 months were the source population and included 1009 mothers/caregivers and their recent children aged 6–23 months in the analysis. In contrast, the second and third child within the last five years (for those who have more than a child), children living with other than their mothers/caregivers were excluded from the analysis (Fig 2). For mothers/caregivers with twins, only one was selected by convenience. Potential individual and community level independent variables were also selected, and further analysis was done. The dependent variable of the study was MN intake status among children aged 6–23 months, which was determined by respondents’ reports and assessment of intake status. So, there were six options: food rich in VA or iron in the last 24 hours, MNP or iron supplement consumed within the previous seven days, VAS or deworming within the previous six months [19–21,38]. Accordingly, if the respondent reported that the child had eaten’ at least one of the minimum recommended MNs, we considered it "Yes"; if the children received none of the minimum recommended MNs, it was considered as "No". Foods rich in VA were measured by the seven food groups’ consumption within the preceding 24 hours. These food groups were I. Eggs, ii. Meat (beef, pork, lamb, chicken), iii. pumpkin, carrots, and squash, iv. any dark green leafy vegetables, v. mangoes, papayas, and others with VA fruits, vi. Liver, heart, and other organs and vii. Fish or shellfish. Accordingly, if the respondent reported that the child had eaten’ at least one of these, we considered “yes”; otherwise “no” VA rich food. Foods rich in iron were measured by the four iron-rich food groups’ consumption within the past 24 hours. These groups were i. eggs, ii. meat (beef, pork, lamb, chicken), iii. Liver, heart, and other organs, and iv. Fish or shellfish. Thus, if the respondent reported that the child had eaten’ at least one of these, we considered “yes”; otherwise “no” iron-rich food. Multiple MN powders were assessed by asking the respondents whether their child had received micronutrient powders in the previous seven days. Iron supplementation was assessed by asking the respondents whether their child had iron supplementation defined as iron pills, sprinkles with iron, or iron syrup in the previous seven days. VAS and deworming were assessed for those 6–23 months of children whether they received for the last six months or not by reviewing the integrated child health card, which consists of immunisation and growth monitoring history and also from the mother’s verbal response. The obstetric characteristics of women included current pregnancy status and use of maternal health services (ANC, institutional delivery and PNC). The child characteristics include birth weight, and current age. Birth weight was categorised as small, average or large. The household wealth index was calculated as an index based on consumer goods such as television, bicycle, or car. Household characteristics such as the material used for floor and roof and toilet facilities were also considered in calculating the household wealth index. The household wealth index was computed using principal component analysis and ranked into poor, middle, and rich. Simultaneously, the community-level variables were residence, region, community-level wealth quantile, community-level media exposure, and distance to the nearest health facility. Community-level wealth quantile was assessed using the asset index based on data from the entire country sample on separate scores prepared for rural and urban households, and combined to produce an index for all households as the community level and ranked into five (poorest, poorer, middle, richer, and richest). In other words, the community level wealth quantile was used to measure the community level poverty and it is a relative measure of how wealth is distributed within the population from the quantiles were calculated. Community media exposure was assessed as “yes” if they have access to all three media (newsletter, radio, and television) at least once a week, otherwise “no” if they did not have any media exposure. Distance to the health facility was assessed by the question “distance to the nearest health facility is a problem?” and the responses were categorised as “big problem” or “not a problem” [39]. The data were cleaned, re-coded and analysed using STATA (StataCorp, College Station, TX) version 14. Descriptive statistics were presented using tables and narration to describe the magnitude of MN intake status by sociodemographic, maternal obstetric and child characteristics. A multilevel analysis was conducted after checking the eligibility. The model eligibility was assessed by calculating the Intra-class Correlation Coefficient (ICC) and a model with ICC greater than 10% for multilevel analysis. In this study, the ICC was 27.3%. Since the data were hierarchical (individuals were nested within communities), a two-level mixed-effects logistic regression model was fitted to estimate both the individual and community level variables (fixed and random effect) on MN intake status, and the log of the probability of MN intake was modelled using the formula as follows [40]: Where i is an individual level unit and j is a community-level unit; X and Z refer to individual and community-level variables, respectively; πij is the probability of MN intake for the ith child in the jth community; the β’s are the fixed coefficients. Whereas β0 is the intercept; the effect on the probability of MN intake in the absence of influence of predictors, and uj showed the community’s effect (random effect) on MN intake for the jth community and eij showed random errors at the individual levels. By assuming each community had different intercepts (β0 + Uj) and fixed coefficient (β1,2), the clustered data nature and the within and between community variations were considered. Bivariable and multivariable analyses were computed. In the bivariable logistic regression analysis, a p-value of less than 0.2 was used to fit three models (models for the individual, community, and individual and community levels). Then, in the final model (fixed effect), a p-value of less than 0.05 and an Adjusted Odds Ratio (AOR) with a 95% confidence interval (CI) were used to estimate the association of individual and community level factors with MN intake status. The measures of variation (random-effects) between clusters were reported using ICC and proportional change in variance (PCV). The ICC refers to the ratio of cluster variance to total variance, and it tells us the proportion of the total variance in the outcome variable that is accounted at the cluster level. The loglikelihood test was used to estimate the goodness of fit of the final adjusted model compared to the preceding models. A model with the smallest value of loglikelihood is better; accordingly, model three (a model for both individual and community-level variables) had the lowest value. The ethical approval and permission to access the data were obtained from the MEASURE DHS (available from https://www.dhsprogram.com/Data/: accessed on April 06, 2020) after a brief study concept was submitted.

Materials

Innovations

The study titled “Micronutrient intake status and associated factors among children aged 6–23 months in the emerging regions of Ethiopia: A multilevel analysis of the 2016 Ethiopia demographic and health survey” provides valuable insights into the nutritional status of children in Ethiopia and identifies factors associated with micronutrient intake. Based on the study findings, here are some potential recommendations for innovations to improve access to maternal health:

1. Strengthening Antenatal Care (ANC) Services: Enhance the quality and coverage of ANC services to ensure that pregnant women receive adequate information and support regarding proper nutrition during pregnancy. This can include regular monitoring of maternal nutrition status, provision of dietary counseling, and distribution of prenatal supplements.

2. Community-Based Nutrition Programs: Implement community-based programs that focus on promoting optimal nutrition practices for pregnant women and lactating mothers. These programs can include nutrition education sessions, cooking demonstrations, and the distribution of nutrient-rich foods or supplements.

3. Mobile Health (mHealth) Interventions: Utilize mobile technology to deliver targeted health messages and reminders to pregnant women and new mothers. This can include SMS or voice messages providing information on proper nutrition, reminders for ANC visits, and alerts for taking prenatal supplements.

4. Integration of Nutrition Services: Integrate nutrition services with existing maternal and child health programs to ensure a comprehensive approach to addressing maternal and child nutrition. This can involve training healthcare providers on nutrition counseling and incorporating nutrition assessments and interventions into routine healthcare visits.

5. Strengthening Health Infrastructure: Improve access to healthcare facilities in rural and remote areas by investing in the development of health infrastructure. This can include building or upgrading health centers, ensuring the availability of essential equipment and supplies, and recruiting and training healthcare professionals to provide quality maternal and child health services.

6. Empowering Women: Promote women’s empowerment through education and economic opportunities, as these factors have been shown to positively influence maternal nutrition. This can include initiatives to improve girls’ education, vocational training programs for women, and income-generating activities that empower women economically.

It is important to note that these recommendations are based on the study’s findings and should be tailored to the specific context and needs of the target population.

AI Innovations Description

The study titled “Micronutrient intake status and associated factors among children aged 6–23 months in the emerging regions of Ethiopia: A multilevel analysis of the 2016 Ethiopia demographic and health survey” aimed to assess the recommended micronutrient (MN) intake status of children aged 6–23 months in the emerging regions of Ethiopia.

The study used data from the Ethiopia Demographic and Health Survey (EDHS) 2016, which is a nationally representative household survey conducted every five years. The survey collected data on key demographic and health indicators. The study focused on the emerging regions of Ethiopia, namely Afar, Benishangul, Gambella, and Somali, where pastoral communities predominantly live.

The study found that the micronutrient intake among children aged 6–23 months in the pastoral community was low compared to the national recommendation. Only 37.3% of children had received the recommended MN sources. The recommended MNs included vitamin A supplementation (47.2%), iron supplementation (6.0%), consumption of food rich in vitamin A (27.7%), consumption of food rich in iron (15.6%), multiple micronutrient powder (7.5%), and deworming (7.1%).

Several individual and community-level factors were found to be associated with the MN intake status. Individual-level factors that increased micronutrient intake included antenatal care visits, working in agriculture, and children aged 13 to 23 months. Community-level factors that increased micronutrient intake were Benishangul and Gambella regions. On the other hand, residing in a rural area decreased micronutrient intake.

To improve access to maternal health and address the issue of micronutrient deficiency among children, the following recommendations can be considered:

1. Strengthen antenatal care services: Encourage pregnant women to attend regular antenatal care visits, as it was found to be associated with increased micronutrient intake. Antenatal care visits provide an opportunity to educate women about the importance of proper nutrition during pregnancy and early childhood.

2. Improve agricultural practices: Since working in agriculture was associated with increased micronutrient intake, promoting agricultural practices that enhance the availability and accessibility of nutritious foods can be beneficial. This can include promoting the cultivation of vitamin A-rich fruits and vegetables and raising awareness about the importance of a diverse and balanced diet.

3. Enhance community-level interventions: Given that certain regions had higher micronutrient intake, it is important to identify and replicate successful community-level interventions in other regions. This can involve implementing programs that provide access to vitamin A and iron supplementation, multiple micronutrient powders, and deworming.

4. Improve access to healthcare facilities: Addressing the issue of poor healthcare access in rural areas is crucial. Efforts should be made to improve infrastructure, increase the number of healthcare facilities, and ensure that they are adequately equipped to provide maternal and child health services.

5. Promote nutrition education: Conduct nutrition education programs targeting mothers and caregivers to raise awareness about the importance of a balanced diet and the consumption of foods rich in essential micronutrients. This can be done through community-based initiatives, health centers, and schools.

By implementing these recommendations, it is possible to develop innovative approaches that can improve access to maternal health and address the issue of micronutrient deficiency among children in Ethiopia’s emerging regions.

AI Innovations Methodology

The study titled “Micronutrient intake status and associated factors among children aged 6–23 months in the emerging regions of Ethiopia: A multilevel analysis of the 2016 Ethiopia demographic and health survey” aims to assess the recommended micronutrient (MN) intake status of children aged 6–23 months in the emerging regions of Ethiopia.

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

1. Strengthening healthcare infrastructure: Improve the availability and accessibility of healthcare facilities, especially in rural and remote areas. This can involve building new health centers, upgrading existing facilities, and ensuring the availability of essential equipment and supplies.

2. Enhancing antenatal care services: Increase the coverage and quality of antenatal care services, including regular check-ups, screenings, and health education for pregnant women. This can help identify and address potential health issues early on and promote healthy behaviors during pregnancy.

3. Promoting community-based interventions: Implement community-based programs that focus on maternal health, such as training community health workers to provide basic healthcare services, education, and support to pregnant women and new mothers. This can help bridge the gap between healthcare facilities and remote communities.

4. Improving transportation and referral systems: Develop efficient transportation systems and referral networks to ensure timely access to emergency obstetric care for pregnant women in need. This can involve providing ambulances, improving road infrastructure, and establishing clear protocols for transferring patients between healthcare facilities.

5. Increasing awareness and education: Conduct awareness campaigns and educational programs to promote maternal health practices, including the importance of proper nutrition, prenatal care, and skilled birth attendance. This can help empower women and their families to make informed decisions regarding their health.

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

1. Define the indicators: Identify specific indicators that reflect access to maternal health, such as the number of antenatal care visits, the percentage of births attended by skilled health personnel, or the availability of emergency obstetric care.

2. Collect baseline data: Gather data on the current status of these indicators in the target population or region. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on the selected indicators. This model should take into account various factors, such as population size, geographical distribution, healthcare infrastructure, and resource allocation.

4. Input the intervention scenarios: Define different scenarios based on the recommendations mentioned earlier. For each scenario, specify the expected changes in the indicators, such as an increase in the number of antenatal care visits or a decrease in maternal mortality rates.

5. Run the simulations: Use the simulation model to calculate the projected changes in the indicators for each intervention scenario. This can be done by adjusting the relevant parameters in the model and running the simulations multiple times to account for uncertainty.

6. Analyze the results: Compare the projected changes in the indicators across different intervention scenarios. Assess the potential impact of each recommendation on improving access to maternal health and identify the most effective strategies.

7. Validate the results: Validate the simulation results by comparing them with real-world data or expert opinions. This can help ensure the accuracy and reliability of the simulation model.

8. Communicate the findings: Present the simulation results in a clear and understandable manner, highlighting the potential benefits of the recommendations for improving access to maternal health. This can help inform policymakers, healthcare providers, and other stakeholders about the potential impact of these interventions.

By following this methodology, policymakers and stakeholders can make informed decisions and allocate resources effectively to improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 4

Identifiers:

DOI: 10.1371/journal.pone.0258954

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female