Dietary diversity and anthropometric status of mother–child pairs from enset (False banana) staple areas: A panel evidence from southern Ethiopia

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Study Justification:
– The study aimed to investigate the dietary diversity and anthropometric status of mother-child pairs in Southern Ethiopia, specifically in areas where enset (false banana) foods are a major staple.
– The study aimed to fill the gap in knowledge regarding the seasonal trend of dietary diversity and anthropometric status in these areas.
– The study aimed to identify factors associated with anthropometric status and provide recommendations for improving nutritional status.
Highlights:
– Over 94% of the mothers did not meet the minimum diet diversity score in both postharvest dry and lean wet seasons.
– Meal frequency and pulses/legumes intake significantly declined in the lean wet season, while dark green leaves consumption increased.
– Meat, poultry, and fish consumption dropped to almost zero in the lean wet season.
– Weight-for-age and weight-for-height of children significantly declined in the lean wet season.
– Maternal mid upper arm circumference, body weight, and body mass index dropped in the lean wet season.
– Factors such as being pregnant and a lactating mother, poverty, and the ability to make decisions independently predicted maternal undernutrition.
– Maternal undernutrition and education were associated with child underweight.
Recommendations:
– Continuous nutrition intervention is needed to improve dietary diversity in mother-child pairs.
– Policy targeting should focus on improving the nutritional status of mothers and children in rural Southern Ethiopia.
Key Role Players:
– Researchers and scientists specializing in nutrition and public health.
– Local government officials and policymakers.
– Non-governmental organizations (NGOs) working in the field of nutrition and community development.
– Community health workers and volunteers.
– Health professionals, including doctors, nurses, and nutritionists.
Cost Items for Planning Recommendations:
– Research and data collection expenses, including personnel salaries, transportation, and equipment.
– Nutrition intervention programs, including the development and implementation of educational materials, training workshops, and community outreach activities.
– Monitoring and evaluation costs to assess the effectiveness of the interventions.
– Collaboration and coordination expenses with local government agencies, NGOs, and community organizations.
– Infrastructure and logistics costs, such as establishing nutrition centers, storage facilities, and transportation networks.
– Communication and awareness campaigns to disseminate information and promote behavior change.
– Capacity building and training programs for healthcare providers and community health workers.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study conducted a community-based panel study with a sample size of 578 mother-child pairs. Data was collected in both postharvest dry and lean wet seasons, allowing for a comparison of dietary diversity and anthropometric status. Statistical tests were used to analyze the data and identify risk factors for malnutrition. However, the abstract does not provide information on the representativeness of the sample or the generalizability of the findings. To improve the strength of the evidence, future studies could consider increasing the sample size and including a more diverse population. Additionally, providing information on the sampling method and addressing potential biases would enhance the validity of the results.

Background: A sizable cross-sectional studies demonstrated a low dietary diversity in Southern Ethiopia. However, its seasonal trend has not been well studied in areas where nutrient-poor enset (false banana (Ensete ventricosum)) foods are major staple. Moreover, there is scarcity of information on seasonal nature of anthropometric status of mother–child pairs (MCP) from the same areas in Southern Ethiopia. Therefore, the present study aimed to investigate the dietary diversity and anthropometric status of MCP in postharvest dry and lean wet seasons and identify factors associated with anthropometric status. Methods: The dietary intake and anthropometric data were collected from 578 households (578 mothers and 578 children) January–June 2017. The study compared data of the two seasons using McNemar’s test for dichotomous, Wilcoxon signed-rank test for non-normally distributed, and paired samples t-test for normally distributed continuous data. Logistic regression was conducted to identify risk factors for malnutrition. In addition, Spearman’s Rho test was used to determine correlations between maternal and child variables. Results: Over 94% of the mothers did not fulfil the minimum diet diversity score in both seasons. The meal frequency and pulses/legumes intake significantly declined in lean wet season; however, dark green leaves consumption increased. Meat, poultry, and fish consumption dropped to almost zero in the lean wet season. The dietary diversity and anthropometric status of the MCP were correlated. Weight-for-age (WAZ) and weight-for-height (WHZ) of children significantly declined in the lean wet season. In the same way, maternal mid upper arm circumference (MUAC), body weight, and body mass index (BMI) dropped (p < 0.001) in this season. Being pregnant and a lactating mother, poverty, and the ability to make decisions independently predicted maternal undernutrition (low MUAC). On the other hand, maternal undernutrition and education were associated with child underweight. Conclusions: The results demonstrated that the dietary diversity of MCP is low in both postharvest dry and lean wet seasons. This suggests the need for continuous nutrition intervention to improve the dietary diversity. In addition, the anthropometric status of MCP declines in lean wet season. This may provide some clue for policy targeting on improving nutritional status of mothers and children in rural Southern Ethiopia.

This study was conducted in Shebedino and Hula Districts from Sidama Zone in Southern Nations Nationalities and Peoples Region (SNNPR). The SNNPR contains 56 ethnic groups reflecting extreme diversity with unique social and cultural identities [37]. The region is known for enset production and enset food products are amongst its typical traditional foods and staples. Sidama is one among the 15 zones in this region. According to 2017 population projection, Sidama Zone has a total population of nearly 3.1 million [38]. Coffee, enset, and maize are amongst the major crops in this zone. This is a community-based panel study. Dietary diversity and anthropometric status of MCPs were assessed. Data collection was conducted in postharvest dry season (January to first week of February 2017) and lean wet season (June 2017). Reproductive age mothers and 24–59-month-old children were eligible for this study. In order to reduce the loss to follow-up, temporary-resident mothers and children were excluded from this study. Furthermore, the mothers and children who were severely sick during the survey periods were excluded as sickness alters the dietary intake pattern. For this study, a sample size of 492 was calculated using a single population proportion formula. In the calculation, 0.05 degree of precision, 1.5 effect design, and 0.27 expected prevalence of BMI < 18.5 kg/m2 were considered [31]. Finally, 10% of basic sample size was added to compensate for the loss to follow-up. To obtain a sufficient sample size for additional analysis, a total of 625 MCP were enrolled in postharvest dry season. A two-stage sampling technique was employed. Firstly, two enset growing districts (Shebedino and Hula) were selected from Sidama Zone. Secondly, five kebeles (kebele is the smallest administrative unit of district) were randomly selected using probability proportional to size (PPS). Then, the sample size was distributed to each kebele using PPS. Accordingly, 239 MCP was sampled from Hula District (Worare 155 MCP and Chirone 84 MCP) and 386 MCP from Shebedino District (Fura 126 MCP, Howolso 124 MCP, and Dila Gumbe 136 MCP). The MCP was randomly selected based on the sampling frame prepared from house-to-house listing of households with 24–59-month-old children. From the initially registered total, 578 MCP completed the study. However, 47 left the study due to delivery, migration, death, and severe illness during the lean wet season data collection. For body weight measurement, portable digital scale (Seca 770, Hanover, Germany) was used. Body weight was measured with light wear to minimize weight effect of over coats. Mothers’ height was measured with dissembling plastic height measuring board with a sliding head bar. A Shorr sliding length board was used to measure standing height of children. The study participants were requested to take off their shoes to minimize its effect on height measurement. Mid upper arm circumference (MUAC) of mothers was measured using MUAC tape. Height and MUAC were measured to the nearest 0.1 cm and weight to the nearest 0.01 kg. Measurements were taken in duplicate and averaged. When the variation was above maximum tolerable difference (0.5 kg in weight, 1.0 cm in height, and 0.5 cm in MUAC), a third measurement took place. In order to assess the seasonal trends of anthropometric status, z-scores of weight-for-age (WAZ) and weight-for-height (WHZ) were computed for children. Next, BMI of the mothers was calculated as weight in kilograms divided by the square of their height in meters [39]. To identify factors associated with anthropometric status of the MCP, the children with WAZ < −2 SD were categorized as underweight while the ones with WAZ ≥ −2 SD as normal [39]. As the BMI was not appropriate for maternal categorization due to the weight gain of pregnant women, MUAC was used. Accordingly, the mothers were categorized as undernourished when MUAC < 22 cm for non-pregnant/non-lactating, and normal when MUAC ≥ 22 cm [40]. Pregnant and lactating mothers with MUAC < 23 cm were classified undernourished, and normal when MUAC ≥ 23 cm [40,41]. Household’s wealth status was assessed collecting data on variables related to ownership of valuable assets, type of living house, and possession of improved water and sanitation facilities. For principal component analysis (PCA), 18 variables (types of roof and floor, number of sleeping rooms, availability of window, availability of separate kitchen, owning cow/ox, sheep, goat, transport animals (horse/mule and/or donkey), source of cooking fuel, cleaning drinking water, saving account, radio, mobile, chair, bed, land size, and electricity) were used in this study. The categorical variables were recoded into dichotomous indicators (0, bad; 1, improved). Then, PCA was analyzed to produce common factor scores. The first factor, which explained 66.30% of the variation, was taken to characterize the households’ wealth status. Finally, the households were ranked as five wealth quintiles (highest, second, middle, fourth, and lowest) [32]. The household food insecurity access scale (HFIAS) status indicator questionnaire was used to determine the degree of food insecurity (access) in the households. The HFIAS questionnaire contained nine main questions. Each main question was followed by a sub-question on frequency of occurrence of a condition in the past four weeks (1, rarely; 2, sometimes; 3, often). Based on the HFIAS indicators, the households were categorized into four groups (food secure, mild, moderately, and severely food insecure) [42]. In order to capture the usual intake, dietary consumption data were collected using 24 h dietary recalls in days other than fasting and holidays. In addition, mothers and children who were severely sick at the time of the survey were excluded to avoid effect of illness on the usual dietary pattern. Food models, food charts, and local units were used to facilitate mothers’ recall. Mothers’ DDS was constructed based on 10 food groups (grains, white roots, and tubers; pulses (beans, peas, and lentils); nuts and seeds; dairy; meat, poultry and fish; eggs; dark green leafy vegetables; other vitamin A-rich fruits and vegetables; other vegetables; and other fruits) [3]. Children’s DDS was created based on 7 food groups (grains, roots, and tubers; legumes and nuts; dairy products (milk, yogurt, cheese); flesh foods (meat, fish, poultry, and liver/organ meats); eggs; vitamin-A rich fruits and vegetables; and other fruits and vegetables) [6]. Food groups were assigned to 1 if any food item within the group consumed, otherwise 0, if not eaten in the last 24 h. Negligible food items with intake less than 15 g/day were not considered in the current study. A cutoff point of ≥5 was used to determine minimum dietary diversity score (MDDS-W) for mothers [3]. On the other hand, a cutoff point of ≥4 was used to compute minimum dietary diversity score of children (MDDS-C) [6]. A structured and semi-structured questionnaire was prepared first in English, then translated to Amharic. Enumerators with educational background of health, nutrition, and agriculture who have experience of data collection were recruited. Data collectors were trained by the principal investigator for one week. Similar enumerators collected the lean wet season data after a refreshing training. The data collection instruments were pre-tested on 5% of the sample size in other kebele for the necessary adjustment. Anthropometric measurements were taken by the nutritionists from Hawassa University. Data collection was supervised by the principal investigator. Before data entry, data templates were prepared, and variables were coded. Following, data were entered into SPSS version 20 (IBM Corporation, Armonk, NY, USA) for analysis. Children’s WHZ and WAZ were analyzed using WHO Anthro version 3.2.2 (World Health Organization, Geneva, Switzerland). Data normality was checked with Kolmogorov–Smirnov test. To compare the postharvest dry and lean wet seasons, Wilcoxon signed-rank test was used for non-normally distributed data (DDS, animal source food consumption (ASF), meal frequency, enset food intake, MUAC, and BMI). Additionally, a paired samples t-test and McNemar’s test were employed to compare normally distributed continuous (WAZ and WHZ) and dichotomous data (food group consumption), respectively. Logistic regression was conducted to identify association between the outcomes (maternal undernutrition and child underweight) and independent (socioeconomic and dietary intake related) variables. Univariate logistic regression was conducted to identify independent variables, which were candidates for multivariate multiple logistic regression. The independent variables with p-value < 0.25 were entered into the final logistic regression model to adjust for confounders. Multi-collinearity of the variables was checked with the variance inflation factor and standard error with respective cutoff points of <10 and <2. The Hosmer and Lemeshow test was used to check for goodness of fit for the final model. Crude and adjusted odds ratios were reported with 95% confidence interval to show the strength of association between the outcomes and predictors. The cutoff point for statistical significance test was a p-value of 0.05. Ethical clearance was received from Institutional Review Board of Hawassa University, Ethiopia; and Ethik-Kommission, Landesäztekammer Baden-Württemberg, Germany (F-2016-127). Supplementary permission was obtained from health administrative offices of the study districts. Written informed consent was received from mothers. Information was kept confidential by providing pseudonymous codes.

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

1. Nutrition intervention programs: Continuous nutrition intervention programs could be implemented to improve dietary diversity among mothers and children. These programs could focus on promoting the consumption of a variety of nutrient-rich foods, including fruits, vegetables, grains, legumes, and animal-source foods.

2. Seasonal nutrition education: Since the study found a decline in dietary diversity and anthropometric status during the lean wet season, targeted nutrition education could be provided to mothers and caregivers during this time. This education could emphasize the importance of maintaining a diverse and nutritious diet even when certain foods are less available.

3. Maternal and child health clinics: Establishing or strengthening maternal and child health clinics in the study area could improve access to healthcare services. These clinics could provide regular check-ups, nutritional counseling, and support for pregnant women and mothers with young children.

4. Community-based interventions: Engaging the community in maternal health interventions could be beneficial. This could involve training community health workers or volunteers to provide education and support to mothers and caregivers, as well as facilitating access to healthcare services.

5. Income generation activities: Implementing income generation activities could help alleviate poverty, which was identified as a risk factor for maternal undernutrition. These activities could include skills training, microfinance initiatives, or support for small-scale agricultural or business ventures.

6. Women’s empowerment programs: Empowering women to make decisions independently could contribute to improving maternal nutrition. Programs that focus on women’s education, leadership skills, and decision-making abilities could be implemented to address this issue.

7. Improved access to clean water and sanitation facilities: Ensuring access to clean water and sanitation facilities is crucial for maintaining good health. Efforts should be made to improve access to safe drinking water and sanitation facilities in the study area.

8. Strengthening healthcare infrastructure: Investing in healthcare infrastructure, including hospitals, clinics, and health centers, could improve access to maternal healthcare services. This could involve improving facilities, equipment, and staffing levels to provide quality care for pregnant women and mothers.

It is important to note that these recommendations are based on the information provided in the study and may need to be adapted to suit the specific context and needs of the study area.
AI Innovations Description
The study mentioned in the description focuses on the dietary diversity and anthropometric status of mother-child pairs in Southern Ethiopia, specifically in the Shebedino and Hula Districts of the Sidama Zone. The study found that the dietary diversity of the mother-child pairs was low in both the postharvest dry and lean wet seasons. Additionally, the anthropometric status of the mother-child pairs declined in the lean wet season.

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

1. Continuous Nutrition Intervention: There is a need for continuous nutrition intervention programs that aim to improve dietary diversity among mother-child pairs in Southern Ethiopia. These programs can provide education and resources to promote the consumption of a variety of nutrient-rich foods.

2. Seasonal Nutrition Programs: Since the study found that the anthropometric status of mother-child pairs declined in the lean wet season, it is important to implement seasonal nutrition programs that specifically target this period. These programs can focus on providing additional support and resources during the lean wet season to ensure adequate nutrition for mothers and children.

3. Maternal Health Education: It is crucial to provide education and awareness programs to pregnant and lactating mothers about the importance of maintaining a healthy diet during pregnancy and breastfeeding. These programs can emphasize the need for a diverse and balanced diet to ensure optimal maternal and child health.

4. Empowering Women: The study identified that the ability to make decisions independently was a predictor of maternal undernutrition. Therefore, it is important to empower women in decision-making processes related to their own health and the health of their children. This can be done through community-based programs that promote women’s rights and provide support for their active participation in decision-making.

5. Addressing Poverty: Poverty was identified as a risk factor for maternal undernutrition. To improve access to maternal health, it is essential to address poverty and provide economic opportunities for women in rural Southern Ethiopia. This can be achieved through initiatives that promote income generation, access to resources, and economic empowerment for women.

By implementing these recommendations, it is possible to develop innovative approaches that improve access to maternal health and address the challenges identified in the study.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Nutrition intervention programs: Implement continuous nutrition intervention programs to improve dietary diversity among mothers and children in rural Southern Ethiopia. These programs can focus on promoting the consumption of a variety of food groups, including grains, pulses, nuts, dairy, meat, poultry, fish, and fruits and vegetables.

2. Seasonal nutrition strategies: Develop strategies to address the seasonal decline in dietary diversity and anthropometric status of mother-child pairs during the lean wet season. These strategies can include promoting the consumption of locally available nutrient-rich foods, such as dark green leafy vegetables, during this season.

3. Maternal education and empowerment: Enhance maternal education and empowerment to improve maternal and child nutritional status. This can involve providing education on nutrition, health, and hygiene practices, as well as promoting women’s decision-making power within households.

4. Targeted interventions for vulnerable groups: Implement targeted interventions for vulnerable groups, such as pregnant and lactating mothers, to address their specific nutritional needs. These interventions can include providing additional support, resources, and education to ensure adequate nutrient intake during pregnancy and lactation.

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

1. Define indicators: Identify key indicators to measure the impact of the recommendations, such as dietary diversity scores, anthropometric measurements (e.g., weight-for-age, weight-for-height), and maternal and child nutritional status.

2. Data collection: Collect baseline data on the selected indicators before implementing the recommendations. This can involve conducting surveys, interviews, and anthropometric measurements among a representative sample of mother-child pairs in the target population.

3. Intervention implementation: Implement the recommended interventions, such as nutrition intervention programs, seasonal nutrition strategies, and maternal education and empowerment initiatives. Ensure that these interventions are implemented consistently and effectively.

4. Post-intervention data collection: After a certain period of time, collect post-intervention data on the selected indicators using the same methods as the baseline data collection. This will allow for a comparison between the pre- and post-intervention data.

5. Data analysis: Analyze the collected data to assess the impact of the recommendations on improving access to maternal health. This can involve comparing the baseline and post-intervention data using statistical tests, such as paired samples t-tests, McNemar’s test, and logistic regression analysis.

6. Interpretation of results: Interpret the results of the data analysis to determine the effectiveness of the recommendations in improving access to maternal health. This can involve assessing changes in dietary diversity scores, anthropometric measurements, and maternal and child nutritional status.

7. Recommendations and future actions: Based on the findings, make recommendations for further improvements and future actions. This can include refining the interventions, scaling up successful strategies, and addressing any remaining gaps or challenges in improving access to maternal health.

It is important to note that this methodology is a general framework and can be adapted and customized based on the specific context and objectives of the study.

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