Validation of the CIMI-ethiopia program and seasonal variation in maternal nutrient intake in enset (False banana) growing areas of Southern Ethiopia

listen audio

Study Justification:
– Tools for analyzing nutrient intakes in Southern Ethiopia are lacking
– CIMI-Ethiopia program developed to address this problem
– Validation of CIMI-Ethiopia needed to ensure accuracy
– Assessment of nutrient intakes in different seasons important for understanding variations
Study Highlights:
– CIMI-Ethiopia showed high accuracy in calculating nutrient intakes
– Protein, pantothenic acid, and vitamin B1 had very high accuracy
– Iron, zinc, magnesium, vitamin B12, vitamin B6, and energy had good accuracy
– Calcium, niacin, and vitamin A had moderate accuracy
– Nutrient intakes were highly correlated between CIMI-Ethiopia and NS
– Lean wet season showed a decline in iron, zinc, magnesium, protein, and energy intakes
– Calcium and vitamin A intake increased in the lean wet season
Study Recommendations:
– CIMI-Ethiopia is a valid tool for estimating nutrient intakes in Southern Ethiopia
– Fortification and supplementation programs should be considered to combat maternal malnutrition in rural Southern Ethiopia
Key Role Players:
– Researchers and scientists
– Health professionals and nutritionists
– Policy makers and government officials
– Non-governmental organizations (NGOs)
– Community leaders and local authorities
Cost Items for Planning Recommendations:
– Research and data collection expenses
– Training and capacity building for data collectors
– Software and technology for nutrient analysis
– Communication and dissemination of findings
– Implementation of fortification and supplementation programs
– Monitoring and evaluation of program effectiveness

Background: Tools for the rapid and accurate analysis of nutrient intakes from diets of individuals in Southern Ethiopia are lacking. The Calculator of Inadequate Micronutrient Intake program for Ethiopia (CIMI-Ethiopia) has been developed to overcome this problem. CIMI-Ethiopia also computes protein and energy intakes from the diet. The objectives of the current study were to validate CIMI-Ethiopia for the dietary pattern of Southern Ethiopia, and assess the nutrient intakes in postharvest dry and lean wet seasons. Methods: 24-h dietary recall (24HR) data was collected from 578 women of a reproductive age in postharvest dry and lean wet seasons in 2017. For analysis, 24HR data was entered into NutriSurvey (NS), which was the reference nutrition software, and then into CIMI-Ethiopia. For validation, the mean and standard deviation (SD) of the difference between CIMI-Ethiopia and NS were computed. The percentage of participants with an inadequate intake was calculated. The correlation between CIMI-Ethiopia and NS results was determined. The nutrient intakes in postharvest dry and lean seasons were compared. Results: Among the nutrients, pantothenic acid, vitamin B1, and protein showed a very high accuracy in CIMI-Ethiopia calculation (|difference (D)| < 5.0% of the NS result). Nutrients with a good accuracy (|D| = 5%–15%) were iron, zinc, magnesium, vitamin B12, vitamin B6, and energy. The accuracy for calcium, niacin, and vitamin A was moderate (|D| = 15%–30%). The intakes calculated by CIMI-Ethiopia and NS of iron, zinc, magnesium, calcium, B-complex vitamins, vitamin A, protein, and energy were highly correlated (r = 0.85–0.97, p < 0.001). NS analysis identified a significant reduction in the mean intake of iron; zinc; magnesium; pantothenic acid; vitamin B1, B12, and D; protein; and energy in the lean wet season; however, calcium and vitamin A intake increased. Conclusions: It has been found that CIMI-Ethiopia is a valid tool for estimating nutrient intakes at an individual level in Southern Ethiopia. The study demonstrated a decline in intakes of iron; zinc; magnesium; pantothenic acid; vitamin B1, B12, and D; protein; and energy in the lean wet season. This result provides some hint for fortification and supplementation programs that aim to combat maternal malnutrition in rural Southern Ethiopia.

This study was conducted in Shebedino and Hula Districts from Sidama Zone in the Southern Nations Nationalities and Peoples Region (SNNPR). The regional capital city Hawassa is located nearly 278 km south of the national capital Addis Ababa. Sidama Zone has a total population of nearly 3.1 million [25]. Enset, maize, and coffee are among the major crops in Sidama Zone. Enset is a crucial drought-resistant crop with significant cultural value in the zone [26]. Shebedino and Hula are two of nineteen districts and two city administrations of the zone [27]. Nearly 99% of the households in the five kebeles (kebele is the smallest administrative unit of a district) sampled from the two districts produced enset in the l2 months before the survey [28]. This is a longitudinal study in which food and nutrient intake data were collected in postharvest dry (January to first week of February 2017) and lean wet seasons (June 2017). All women of a reproductive age with 24–59-month-old children were eligible for this study. In order to reduce those lost to follow-up, temporarily resident mothers were not included in the study. Besides, women were excluded from the survey when reported as being severely sick to avoid the effect of illness on the dietary intake due to a reduced appetite. For this particular analysis, a basic sample size of 319 was computed using a correlation coefficient (r) with the following formula [29], assuming a two-tailed test: α (type I error rate), 0.050; β (type II error rate), 0.050; and r (expected correlation coefficient), 0.20. Here, N = sample size; α = Zα = 1.960; β = Zβ = 1.645; and C = 0.5 * ln[(1 + r)/(1 − r)] = 0.203. On top of this, the design effect of 1.5 was considered, and 10% of the basic sample size was added to compensate for those lost to follow-up, resulting in a total of 510. However, a total of 625 mothers with 24–59-month-old children were enrolled at the beginning in order to obtain a sufficient sample size for additional analysis [28]. Nonetheless, 578 completed the study, while the rest left for different reasons: death (1), delivery (4), and sickness or moving to other places (42). A two-stage sampling technique was employed. Firstly, Shebedino and Hula Districts were selected from the Sidama Zone. Secondly, Fura, Howolso, Dila Gumbe, Worare, and Chirone Kebeles were randomly selected using probability proportional to size (PPS) from the two districts. Then, the sample size was distributed to each kebele using PPS. The samples were randomly selected based on the sampling frame prepared from a house-to-house listing of households with 24–59-month-old children [28]. Ethical clearance was received from the Institutional Review Board of Hawassa University, Ethiopia, and Ethik-Kommission, Landesäztekammer Baden-Württemberg, Germany (F-2016-127). Permission was obtained from health administrative offices of the study districts to access the study population and collect data. Informed written consent was received from mothers. Information was kept confidential with pseudonymous codes. Data collectors with previous experience were recruited. The principal investigator trained the data collection staff for one week. The same data collection team collected the lean season data after additional project-specific training. The pre-test of survey tools was carried out on five percent of the total sample size to check for appropriateness. Data collection was supervised by the principal investigator. A structured questionnaire was first prepared in English, and then translated to Amharic. The purpose of this questionnaire was to collect the sociodemographic data. A standard 24HR protocol was used to collect the dietary intake in the 24 h preceding the survey. To assess the usual dietary nutrient intake, the dietary consumption data was collected on days other than fasting and holidays. In order to facilitate mothers’ recall, food models and food charts were used. The amounts of foods and beverages consumed were estimated in terms of local units: coffee cup, ladle, soup spoon, water glass, and tea spoon. Printed pictures aided to identify portion sizes, for instance large or medium or small, for countable food items like potato. Data collectors probed the mothers to remember all food items consumed in the 24 h preceding the survey. A compact scale CS2000 (Ohaus Corporation, Parsippany, NJ, USA) was used to convert the weight equivalents of the local units into grams of foods consumed. Before data entry, the uniformity of the nutrient compositions of foods in CIMI-Ethiopia and NS were assured by adopting the compositions configured in the prior one. Following this, the dietary consumption data collected with 24HR was entered into NS 2007, which is a reference program used to compute nutrient intakes. Then, nutrient data was exported from NS into Microsoft Excel 2016, and subsequently into Statistical Package for Social Sciences (SPSS) ver. 20 (IBM Corporation, Armonk, NY, USA) for analysis. Secondly, the same dietary intake data from 24HR was entered into CIMI-Ethiopia. The nutrient intake data calculated by CIMI-Ethiopia was automatically transferred to a server when WIFI was available. Later, the data was downloaded from the server and imported into SPSS. Thirdly, the sociodemographic data was entered directly into SPSS. Then, the data sets were compiled together and checked for completeness before analysis. One participant was excluded from this analysis, as the amount of the food consumed was missing, though types of foods were recorded. Data normality was checked with the Kolmogorov–Smirnov test. For validation, the mean and standard deviation (SD), and median with 25th and 75th values of intakes, were calculated for iron, zinc, calcium, magnesium, vitamin A, vitamin B1, niacin, pantothenic acid, vitamin B6, vitamin B12, vitamin D, protein, and energy from CIMI-Ethiopia and NS. The Pearson’s test was used to determine the correlation between the nutrient results from CIMI-Ethiopia and NS. Furthermore, a Bland–Altman plot was used to determine how well the data fit. Besides, the percentage of participants below the threshold of an inadequate intake was computed. The nutrient intake less than two-thirds of the FAO/WHO RNI [22,23,24] was taken as a cutoff point for the threshold of an inadequate intake [14,30,31]. To determine the level of accuracy, the mean and SD of the difference between the intake computed by CIMI-Ethiopia minus the intake calculated by NS were computed. Accordingly, an absolute difference, i.e., |D (difference)| 30% as a low accuracy [17]. In order to determine the seasonal differences of nutrient intakes, the Wilcoxon signed-rank test was used to compare non-normally distributed mean nutrient intakes between postharvest dry and lean wet seasons computed by NS. The cutoff point for statistical significance was a p-value ≤ 0.05.

The study titled “Validation of the CIMI-Ethiopia program and seasonal variation in maternal nutrient intake in enset (False banana) growing areas of Southern Ethiopia” focuses on improving access to maternal health by developing a tool called CIMI-Ethiopia that accurately analyzes nutrient intakes from diets in Southern Ethiopia. The study aims to validate CIMI-Ethiopia for the dietary patterns in Southern Ethiopia and assess nutrient intakes in different seasons.

The study found that CIMI-Ethiopia accurately calculated nutrient intakes for various nutrients such as pantothenic acid, vitamin B1, and protein. Nutrients like iron, zinc, magnesium, vitamin B12, vitamin B6, and energy showed good accuracy, while calcium, niacin, and vitamin A had moderate accuracy. The nutrient intakes calculated by CIMI-Ethiopia were highly correlated with the results from the reference nutrition software.

Additionally, the study observed a decline in the intake of iron, zinc, magnesium, pantothenic acid, vitamin B1, B12, and D, protein, and energy during the lean wet season. This information can be useful for fortification and supplementation programs aimed at combating maternal malnutrition in rural Southern Ethiopia.

The study was conducted in Shebedino and Hula Districts in the Southern Nations Nationalities and Peoples Region (SNNPR) of Ethiopia. The sample size included 578 women of reproductive age with 24-59-month-old children. Data on dietary intake was collected using a 24-hour dietary recall method and analyzed using NutriSurvey (NS) and CIMI-Ethiopia.

Ethical clearance was obtained, and informed written consent was received from the participants. Data collection was supervised, and data was entered into NS and CIMI-Ethiopia for analysis. The mean and standard deviation of nutrient intakes were calculated, and the correlation between CIMI-Ethiopia and NS results was determined. The Wilcoxon signed-rank test was used to compare nutrient intakes between different seasons.

Overall, the study provides valuable insights into nutrient intakes and seasonal variations in maternal health in Southern Ethiopia, and the CIMI-Ethiopia tool can contribute to improving access to maternal health by accurately assessing nutrient intakes.
AI Innovations Description
The study titled “Validation of the CIMI-Ethiopia program and seasonal variation in maternal nutrient intake in enset (False banana) growing areas of Southern Ethiopia” provides valuable insights into improving access to maternal health in rural Southern Ethiopia. The study focuses on validating the Calculator of Inadequate Micronutrient Intake program for Ethiopia (CIMI-Ethiopia) and assessing nutrient intakes in different seasons.

The study found that CIMI-Ethiopia is a valid tool for estimating nutrient intakes at an individual level in Southern Ethiopia. It demonstrated a decline in the intake of important nutrients such as iron, zinc, magnesium, pantothenic acid, vitamin B1, B12, and D, protein, and energy during the lean wet season. This information can be used to inform fortification and supplementation programs aimed at combating maternal malnutrition in rural Southern Ethiopia.

To improve access to maternal health based on these findings, the following recommendations can be considered:

1. Strengthening the use of CIMI-Ethiopia: The study validates CIMI-Ethiopia as a reliable tool for estimating nutrient intakes. To improve access to maternal health, healthcare providers and policymakers can promote the use of CIMI-Ethiopia in assessing and monitoring the nutritional status of pregnant women and providing appropriate interventions.

2. Seasonal interventions: The study highlights the seasonal variation in nutrient intakes, with a decline during the lean wet season. To address this, targeted interventions can be implemented during the lean wet season to ensure pregnant women have access to adequate nutrition. This can include promoting the consumption of nutrient-rich foods, providing nutritional supplements, and implementing fortification programs.

3. Community-based education and awareness: To improve access to maternal health, community-based education and awareness programs can be implemented to educate pregnant women and their families about the importance of proper nutrition during pregnancy. These programs can provide information on locally available nutrient-rich foods, dietary diversity, and the benefits of consuming a balanced diet.

4. Integration of nutrition services: To improve access to maternal health, nutrition services can be integrated into existing maternal health programs. This can include incorporating nutrition assessments, counseling, and interventions into antenatal care visits, as well as training healthcare providers on nutrition counseling and support.

5. Collaboration and partnerships: To effectively improve access to maternal health, collaboration and partnerships between government agencies, non-governmental organizations, healthcare providers, and community-based organizations are essential. These collaborations can help ensure the availability and accessibility of nutritious foods, as well as the implementation of targeted interventions and programs.

By implementing these recommendations, it is possible to develop innovative approaches to improve access to maternal health in rural Southern Ethiopia, ultimately reducing maternal malnutrition and improving maternal and child health outcomes.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthening Community Health Workers (CHWs): CHWs can play a crucial role in improving access to maternal health by providing education, counseling, and basic healthcare services to pregnant women and new mothers in remote areas. Training and equipping CHWs with necessary tools and resources can help bridge the gap between healthcare facilities and communities.

2. Mobile Health (mHealth) Solutions: Utilizing mobile technology can improve access to maternal health services, especially in areas with limited healthcare infrastructure. mHealth solutions can include mobile apps, SMS reminders, and telemedicine consultations, allowing pregnant women to receive timely information, reminders for antenatal care visits, and access to healthcare professionals remotely.

3. Transportation Support: Lack of transportation is a significant barrier to accessing maternal health services in rural areas. Providing transportation support, such as ambulances or community transport systems, can ensure that pregnant women can reach healthcare facilities in a timely manner, especially during emergencies.

4. Community-Based Maternal Health Programs: Implementing community-based programs that focus on maternal health can help raise awareness, provide education, and offer essential services within the community itself. These programs can involve community health workers, local leaders, and women’s groups to address specific maternal health needs and challenges.

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 key indicators that measure access to maternal health, such as the number of antenatal care visits, institutional deliveries, postnatal care visits, and maternal mortality rates.

2. Baseline data collection: Collect baseline data on the selected indicators from the target population or region. This data will serve as a reference point for comparison after implementing the recommendations.

3. Implement the recommendations: Introduce the recommended interventions, such as strengthening CHWs, implementing mHealth solutions, providing transportation support, and community-based maternal health programs.

4. Data collection post-implementation: Collect data on the same indicators after implementing the recommendations. Ensure that the data collection methods are consistent with the baseline data collection.

5. Data analysis: Analyze the post-implementation data and compare it with the baseline data to assess the impact of the recommendations. Look for changes in the selected indicators to determine if access to maternal health has improved.

6. Evaluation and interpretation: Evaluate the results and interpret the findings to understand the effectiveness of the recommendations. Identify any challenges or limitations encountered during the implementation process.

7. Recommendations and adjustments: Based on the evaluation, make recommendations for further improvements and adjustments to the interventions. This could involve scaling up successful interventions, addressing identified challenges, or modifying strategies to better meet the needs of the target population.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of these recommendations on improving access to maternal health and make informed decisions for future interventions.

Partagez ceci :
Facebook
Twitter
LinkedIn
WhatsApp
Email