Variations between post- and pre-harvest seasons in stunting, wasting, and infant and young child feeding (IYCF) practices among children 6-23 months of age in lowland and midland agro-ecological zones of rural Ethiopia

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Study Justification:
– Food availability and access are strongly affected by seasonality in Ethiopia.
– Little data on seasonal variation in Infant and Young Child Feeding (IYCF) practices and malnutrition among children 6-23 months old in different agro-ecological zones of rural Ethiopia.
– Understanding the variations between post- and pre-harvest seasons in stunting, wasting, and IYCF practices can help inform interventions to reduce child malnutrition.
Study Highlights:
– Child stunting and underweight increased from post-harvest to pre-harvest seasons, with the biggest increase in the midland zone.
– Wasting decreased from post-harvest to pre-harvest seasons, with the biggest decline in the lowland zone.
– Minimum meal frequency, minimum acceptable diet, and poor dietary diversity increased considerably in the pre-harvest season in the lowland zone.
– Feeding practices and maternal age were predictors of wasting, while women’s dietary diversity and children’s age were predictors of child dietary diversity in both seasons.
Study Recommendations:
– Health information strategies focused on both IYCF practices and dietary diversity of mothers could be a sensible approach to reduce the burden of child malnutrition in rural Ethiopia.
– Interventions should be targeted towards improving feeding practices, especially during the pre-harvest season.
– Additional research is needed to further understand the factors contributing to seasonal variation in malnutrition and IYCF practices.
Key Role Players:
– Researchers and data collectors
– Supervisors and principal investigators
– Health professionals and nurses
– Policy makers and government officials
– Community leaders and volunteers
Cost Items for Planning Recommendations:
– Research and data collection expenses
– Training and capacity building for researchers and data collectors
– Supervision and coordination costs
– Communication and dissemination of findings
– Implementation of health information strategies
– Monitoring and evaluation of interventions
– Community engagement and awareness campaigns

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study provides data on seasonal variation in Infant and Young Child Feeding (IYCF) practices and malnutrition among children in different agro-ecological zones of rural Ethiopia. The study includes a community-based longitudinal design and collects data from a sufficient sample size. However, the study does not mention the specific methods used for data collection and analysis, which could affect the reliability of the findings. To improve the strength of the evidence, the authors should provide more details on the methods used, including the sampling strategy, data collection tools, and statistical analysis techniques.

Introduction: Food availability and access are strongly affected by seasonality in Ethiopia. However, there are little data on seasonal variation in Infant and Young Child Feeding (IYCF) practices and malnutrition among 6-23 months old children in different agro-ecological zones of rural Ethiopia. Methods: Socio-demographic, anthropometry and IYCF indicators were assessed in post-and pre-harvest seasons among children aged 6-23 months of age randomly selected from rural villages of lowland and midland agro-ecological zones. Results: Child stunting and underweight increased from prevalence of 39.8% and 26.9% in post-harvest to 46.0% and 31.8% in pre-harvest seasons, respectively. The biggest increase in prevalence of stunting and underweight between post- and pre-harvest seasons was noted in the midland zone. Wasting decreased from 11.6% post-harvest to 8.5% pre-harvest, with the biggest decline recorded in the lowland zone. Minimum meal frequency, minimum acceptable diet and poor dietary diversity increased considerably in pre-harvest compared to post-harvest season in the lowland zone. Feeding practices and maternal age were predictors of wasting, while women’s dietary diversity and children age was predictor of child dietary diversity in both seasons. Conclusion: There is seasonal variation in malnutrition and IYCF practices among children 6-23 months of age with more pronounced effect in midland agro-ecological zone. A major contributing factor for child malnutrition may be poor feeding practices. Health information strategies focused on both IYCF practices and dietary diversity of mothers could be a sensible approach to reduce the burden of child malnutrition in rural Ethiopia.

This study was conducted in the Babile, Enderta and Hintalowajirat districts of Ethiopia from January to February 2014 and July to August 2014. Babile District (Woreda), which is 560 km away from Addis Ababa in the eastern part of Ethiopia, represents lowland agro-ecological area. The altitude of Babile Woreda ranges from 950 to 2000 meters above sea level and data were collected from 1000-1500 meters above sea level. The major agricultural product for consumption is sorghum and oil seeds; and groundnuts are used as cash crop. Khat (Catha edulis) is also a major cash crop in this region. Hintalo Wajirat and Enderta districts (683 km and 773 km away from Addis Ababa in the northern part of Ethiopia, respectively) represent midland agro-ecological areas. Data were collected from altitudes of greater than 2000 meters above sea level where the majority produce cereals (Teff and barley) and are involved in animal husbandry. A community based longitudinal study was conducted in eight kebeles (smallest administrative unit of Ethiopia) randomly selected from each geographical area from January to February 2014 (post-harvest season which is dry) and from July to August 2014 (pre-harvest season which is rainy). Two hundred sixteen mother/child pairs were included in the study and the ages of children were 6-23 months old during post-harvest, of which 206 were surveyed again during pre-harvest season. Mothers with children 6-23 months of age were selected randomly from a registration list available in each kebele and used by researchers to verify maternal and child age. The number mother/child pairs selected in each kebele was proportional to population size in each kebele. Dietary diversity was calculated in a standardized way using a tool developed by FANTA [17, 18]. A simple questionnaire allowed all foods eaten during the 24 previous hours to be noted. Each woman involved in the study was asked to recall all the communal dishes she had eaten and given to her child in the compound during this period. Information collected allowed us to calculate a Dietary Diversity Score (DDS) using seven food groups for infants [17]: cereals/roots/tubers; pulses/nuts; vitamin A rich fruits/vegetables; other vegetables and fruits; flesh foods; eggs; milk/dairy products. A nine food group classification was used for women. Women who consumed five or more than five food groups were considered as adequate while four or greater than four food groups was considered as adequate for children. The recall was randomly made on weekdays or on weekend days, since weekends do not have any special significance with respect to dietary intake in the context of our study. We took care to not include atypical days (local feasts or celebrations) in the recall. Minimum meal frequency and minimum acceptable diet were defined and calculated according to WHO guidelines [19]. The anthropometric measurements for mothers and children were performed using the standardized procedures recommended by WHO [19]. The study participants were weighed to the nearest 100 g on electronic scales (SECA, Germany) with a weighing capacity of 0 to 140 kg with minimal (light) clothing and removed their shoes and hats during the measurement. Children were weighed together with the mother of the child, and the child’s weight was calculated by subtracting the respective mother’s weight, and this was recorded on the form during the fieldwork and confirmed later on by supervisors. Their length/height was measured to the nearest one centimetre with locally made portable devices (SECA 2006 sliding board). The BMI was calculated by dividing weight by height in meters squared [weight/height2 (kg/m2)]. The mid-upper arm circumference (MUAC) of the left arm was measured to the nearest mm with a non-stretch measuring tape (MUAC 12.5 measuring tape/PAC-50). Data collectors were nurses holding diploma level or above qualifications. They were recruited and trained intensively on the data collection procedures, the context of specific questions across the questionnaire and anthropometric measurement procedures to be used. The questionnaire was prepared first in English then translated to Tigrigna and Afan Oromo languages as both agro-ecological zones have their own local languages. The process of data collection was overseen by supervisors and principal investigators. A pre-test survey was conducted on 5% of the total sample size in another rural area which has similar characteristics. Problems identified during the pre-test survey were corrected before the start of the actual survey. Two different measurements were taken by separate data collectors for height and weight of every study subject. In case of variation among the data collectors, the principal investigator took the measurement again for validation. Finally, the principal investigator was responsible for co-ordination and supervision of the overall data collection process. All children were apparently healthy during data collection and children with apparent sign of fever, diarrhoea, or any acute illness were excluded from the survey. Dependent variables were IYCF and nutritional status (malnutrition). The independent variables were the socio-demographic and household level characteristics of the family, health status of mothers and children, breastfeeding, housing, water and sanitation, health services utilization and cultural/social characteristics related to feeding style. Frequency of complementary feeding, dietary diversity and child illness and health seeking behaviour of the family were also assessed. The data were double entered by separate data clerks into EPI Data version 3.1. Data cleaning and editing were undertaken before analyses. For analyses, data were transferred to SPSS (v 16.0) and Stata (v 11). Frequency, mean and standard deviation were computed for the variables of interest. Normality was checked graphically using different plots (P-P and/or Q-Q-plot). Assumptions including normality, homoscedasticity and linearity were checked. The WHO Anthro 2005 and ENA software of the WHO were used for calculating the Z-scores (WAZ, WLZ, LAZ, and MUACZ) and cut-off points of -2 standard deviations were used to define undernutrition. IYCF practices and women’s dietary diversity were assessed based on the UNICEF guidelines [20]. Multivariable linear regressions were applied to isolate independent effects of predictors of weight-for-height z-score, weight-for-length z-score and infant dietary diversity score and paired t-test was used to determine if significant differences existed between post- and pre-harvest seasons.

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

1. Mobile Health (mHealth) Applications: Develop mobile applications that provide information and guidance on maternal health, including nutrition, breastfeeding, and child feeding practices. These apps can be easily accessible to mothers in rural areas, providing them with valuable resources and support.

2. Community Health Workers: Train and deploy community health workers in rural areas to provide education and support to mothers regarding maternal health, nutrition, and child feeding practices. These workers can conduct home visits, organize community workshops, and provide personalized guidance to mothers.

3. Telemedicine: Establish telemedicine services that allow mothers in remote areas to consult with healthcare professionals through video calls or phone calls. This can help address barriers to accessing healthcare services, especially for mothers who are unable to travel long distances to reach healthcare facilities.

4. Nutritional Support Programs: Implement programs that provide nutritional support to pregnant women and mothers, especially during the pre-harvest season when food availability may be limited. This can include distributing nutrient-rich food supplements or implementing community gardens to improve access to diverse and nutritious food options.

5. Maternal Health Education Campaigns: Conduct targeted education campaigns to raise awareness about the importance of maternal health and nutrition. These campaigns can use various mediums such as radio, posters, and community gatherings to reach a wide audience and promote positive health behaviors.

6. Integration of Maternal Health Services: Integrate maternal health services with existing healthcare facilities, such as primary healthcare centers or antenatal clinics. This can ensure that mothers have access to comprehensive care, including prenatal check-ups, nutrition counseling, and postnatal support.

7. Public-Private Partnerships: Foster collaborations between government agencies, non-profit organizations, and private sector entities to improve access to maternal health services. This can involve leveraging private sector resources and expertise to expand healthcare infrastructure and service delivery in rural areas.

8. Maternal Health Vouchers: Implement voucher programs that provide financial assistance to mothers for accessing maternal health services, including prenatal care, delivery, and postnatal care. These vouchers can help reduce financial barriers and increase utilization of essential maternal health services.

9. Mobile Clinics: Set up mobile clinics that travel to remote areas, providing maternal health services, including prenatal check-ups, vaccinations, and health education. These clinics can reach underserved populations and ensure that mothers receive timely and quality care.

10. Empowerment and Capacity Building: Focus on empowering women and building their capacity to make informed decisions about their own health and the health of their children. This can involve providing training on maternal health, nutrition, and child feeding practices, as well as promoting women’s rights and gender equality.

It is important to note that the specific implementation and effectiveness of these innovations would require further research and evaluation.
AI Innovations Description
The study mentioned focuses on variations in stunting, wasting, and infant and young child feeding (IYCF) practices among children aged 6-23 months in lowland and midland agro-ecological zones of rural Ethiopia during post- and pre-harvest seasons. The study found that child stunting and underweight increased during the pre-harvest season, with the biggest increase observed in the midland zone. Wasting decreased during the pre-harvest season, particularly in the lowland zone. Minimum meal frequency, minimum acceptable diet, and poor dietary diversity increased in the pre-harvest season in the lowland zone. Feeding practices and maternal age were predictors of wasting, while women’s dietary diversity and children’s age were predictors of child dietary diversity in both seasons.

Based on these findings, a recommendation to improve access to maternal health and address the issue of child malnutrition in rural Ethiopia could be to implement health information strategies that focus on improving IYCF practices and dietary diversity of mothers. These strategies could include providing education and support to mothers on proper feeding practices for infants and young children, promoting the consumption of diverse and nutritious foods, and ensuring access to adequate healthcare services for mothers and children. By addressing these factors, it is possible to reduce the burden of child malnutrition and improve maternal and child health outcomes in rural Ethiopia.
AI Innovations Methodology
Based on the information provided, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and education: Develop and implement targeted educational programs to raise awareness about the importance of maternal health and the impact of seasonal variations on nutrition and feeding practices. This can include community workshops, health campaigns, and information dissemination through local health centers.

2. Strengthen healthcare infrastructure: Improve access to healthcare facilities in rural areas by increasing the number of health centers and trained healthcare professionals. This can involve building new facilities, upgrading existing ones, and providing training and support for healthcare workers.

3. Enhance nutrition support: Implement programs that focus on improving nutrition during both the post- and pre-harvest seasons. This can include providing nutritional supplements, promoting breastfeeding and proper infant feeding practices, and ensuring access to diverse and nutritious food options.

4. Mobile health interventions: Utilize mobile technology to deliver maternal health information and services to remote areas. This can include mobile apps, text messaging services, and telemedicine consultations to provide guidance and support to pregnant women and new mothers.

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

1. Define the indicators: Identify specific indicators that will be used to measure the impact of the recommendations. This can include metrics such as maternal mortality rates, infant and child mortality rates, nutritional status of children, and access to healthcare services.

2. Collect baseline data: Gather data on the current state of maternal health and access to healthcare services in the target areas. This can involve surveys, interviews, and data collection from healthcare facilities and local authorities.

3. Develop a simulation model: Create a simulation model that incorporates the identified indicators and factors that influence access to maternal health. This can be a mathematical model or a computer-based simulation that takes into account variables such as population demographics, healthcare infrastructure, and seasonal variations.

4. Input the recommendations: Introduce the recommended interventions into the simulation model and assess their potential impact on the identified indicators. This can involve adjusting variables such as the number of healthcare facilities, the level of awareness and education, and the availability of nutrition support.

5. Run simulations: Conduct multiple simulations using different scenarios and assumptions to evaluate the potential outcomes of the recommendations. This can help identify the most effective interventions and their potential impact on improving access to maternal health.

6. Analyze results: Analyze the simulation results to determine the potential benefits and limitations of the recommended interventions. This can involve comparing different scenarios, identifying key factors that influence the outcomes, and assessing the feasibility and cost-effectiveness of the interventions.

7. Refine and implement: Based on the simulation results, refine the recommendations and develop an implementation plan. This can involve prioritizing interventions, securing funding and resources, and collaborating with relevant stakeholders to ensure successful implementation.

It is important to note that the methodology described above is a general framework and can be adapted and customized based on the specific context and resources available for the study.

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