Comparison of nutritional status and associated factors of lactating women between lowland and highland communities of District Raya, Alamata, Southern Tigiray, Ethiopia

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Study Justification:
– The study aimed to compare the nutritional status of lactating women in lowland and highland communities in District Raya Alamata, Southern Tigiray, Ethiopia.
– The prevalence of under-nutrition is relatively high in Ethiopian lowlands, but under-nutrition is also prevalent in the highlands of Tigiray.
– Understanding the factors associated with under-nutrition in different regions can help inform targeted interventions to improve the nutritional status of lactating women.
Study Highlights:
– The study found that the prevalence of chronic energy deficiency (CED) among lactating mothers was 17.5% in the lowland communities and 24.6% in the highland communities.
– Factors associated with CED differed between the lowland and highland communities. For lowland lactating women, factors such as age, husband occupation, vitamin A intake, and consumption of extra food during lactation were associated with CED. For highland lactating women, factors such as parity, number of meals per day, and household consumption of iodized salt were associated with CED.
– The study highlights the importance of nutrition interventions, such as nutrition security programs, to address under-nutrition in the study area. It also emphasizes the need to promote nutrition diversification and increase dietary diversity among lactating women.
Recommendations for Lay Reader and Policy Maker:
– Implement nutrition security programs to address under-nutrition in both lowland and highland communities.
– Promote nutrition diversification and increase dietary diversity among lactating women.
– Improve access to vitamin A supplementation and encourage consumption of extra food during lactation in lowland communities.
– Focus on improving parity, increasing the number of meals per day, and promoting household consumption of iodized salt in highland communities.
Key Role Players:
– Local government authorities and policymakers
– Health professionals and nutritionists
– Community health workers
– Non-governmental organizations (NGOs) working in the field of nutrition
– Women’s groups and community leaders
Cost Items for Planning Recommendations:
– Development and implementation of nutrition security programs
– Training and capacity building for health professionals and community health workers
– Provision of vitamin A supplements and extra food for lactating women in lowland communities
– Education and awareness campaigns on nutrition diversification and dietary diversity
– Monitoring and evaluation of nutrition interventions
– Research and data collection to assess the impact of interventions on nutritional status

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is a community-based comparative cross-sectional study, which allows for comparison between lowland and highland communities. The sample size was determined using population estimation formulas and the data was collected using anthropometric measurements and a structured questionnaire. Bivariate and multivariable logistic regression analyses were conducted to determine the association between explanatory variables and chronic energy deficiency. The study found significant factors associated with chronic energy deficiency in both lowland and highland lactating women. However, there are some limitations that could be addressed to improve the strength of the evidence. First, the study design is cross-sectional, which limits the ability to establish causality. A longitudinal study design would provide stronger evidence. Second, the study period was relatively short, which may not capture seasonal variations in nutritional status. Extending the study period could provide a more comprehensive understanding of the factors influencing nutritional status. Finally, the abstract does not provide information on the representativeness of the sample and the generalizability of the findings to other populations. Including this information would enhance the applicability of the study results.

Background: The Ethiopian regions have a relatively higher prevalence of under-nutrition are found in the lowlands of the country, with the exception of the highlands of Tigiray, where under-nutrition is also prevalent. The intention of this study was to compare anthropometric nutritional status and associated factors of lactating women between lowland and highland communities of district Raya Alamata, Southern Tigiray, Ethiopia. Methods: A community based comparative cross-sectional study design was conducted from January 27-March 7, 2014. Sample size was determined by two population estimation formula. The total calculated sample size was 456. A stratified sampling technique was used to stratify the study area to highland and lowland. Study participants were selected by simple random sampling technique. Data were collected using anthropometric measurements and structured questionnaire. The raw data were entered and analyzed using SPSS version 20.0. Bivariate and multivariable Logistic regression was done to determine the association between explanatory variable with chronic energy deficiency (CED) using body mass index (BMI), by computing odds ratio at 95% confidence level. A P – value <0.05 was considered as statistically significant. Result: The prevalence of CED of lactating mothers from lowland and highland was 17.5% and 24.6% respectively. After multivariable logistic regression: age, husband occupation, taking vitamin A immediately after delivery or within the first 8 weeks after delivery and consumption of extra food during lactation time were factors associated with chronic energy deficiency for lowland lactating women whereas parity, number of meals per day and household consumption of iodized salt were factors associated with chronic energy deficiency for highland lactating women. Conclusion: CED in both comparative studies were a serious public health problem. As it is known food security does not mean nutritionally secured, Therefore, the need to develop nutrition intervention such as nutrition security programs to address under-nutrition in the study area is significant, as it was found food secured participants were slightly vulnerable than food insecure. The dietary diversity score of the participants were very low so that encourage the community about nutrition diversification is substantial for adequate nutrient intake.

The study was conducted in district Raya Alamata. It is one of the districts in the Tigiray Region of Ethiopia. It is located 600 km north of Addis Ababa and about 180 km south of the Tigiray Regional capital Mekelle. Altitude in the area ranges from1178 to 3148 m. 75% of the district is lowland (1500 m above sea level or below) and only 25% is found in intermediate highlands (between 1500 and 3148 m above sea level). Shortage of rainfall is a major constraint of agricultural production in the district. Based on the 2007 national census conducted by the Central Statistical Agency of Ethiopia (CSA), this district had a total population of 85,403, of whom 42,483 were men and 42,920 women. The study period was from January 27–March 7, 2014. The study design was community based comparative cross sectional survey. The Sample size was determined using two population estimation formula, that is were P = (P1 + P2)/2, pooled estimation of P1 and P2, Z is the value of standard normal distribution which is 1.96, P1 and P2 is estimated prevalence of chronic energy deficiency that is 31% and 19.1% in the lowland and highland respectively [14]. Assumptions were 95% confidence level, 5% marginal error, Power (1-β): 80%, Non-response rate: 10%. Therefore, the total calculated sample size was 464. Stratified sampling technique was used to stratify the study area in to lowland and highland. The district has a total of fifteen lowest administrative levels (Kebeles). Among these lowest administrative levels, ten of them were lowlands and five of them were highlands. Since the purpose of this study is to compare the two groups, thus three lowest administrative levels (kebeles) from highland and lowland were selected. The total calculated sample size 464 was shared equally into two; 232 for the lowland and 232 for the highland participants. The two divided sample sizes were distributed to each selected lowest administrative levels (kebeles) using proportional allocation to size (PAS). Finally participants were selected by simple random sampling technique. Structured questionnaire was prepared from related literatures, and the questionnaire was translated to local language Tigrigna. It was administered to the participants by health professionals who are fluent speakers of the local language. The dietary diversity of the lactating women was collected using women dietary diversity score (WDDS). It is a simple count of food groups that an individual has consumed over the preceding 24 h. It is calculated by summing the number of food groups consumed by the individual respondent over the 24-h recall period. WDDS uses the following nine food groups: starchy staples, dark green leafy vegetables, vitamin A-rich vegetables and fruits, other fruits and vegetables, organ meat, flesh meat and fish, eggs, dairy, and legumes and nuts [15]. Study participants were asked whether or not they had eaten each food group over the last 24 h. The cut off point for the micronutrient adequacy of women’s diet is consumption of at least five of ten food groups [16]. Household food insecurity of study participants was collected using household food insecurity access scale (HFIAS). It is the measure of the degree of food insecurity in the household in the past 4 weeks (30 days). It consists of two types of related questions. The first question type is called an occurrence question. There are nine occurrence questions that ask whether a specific condition associated with the experience of food insecurity ever occurred during the previous 4 weeks (30 days). Each severity question is followed by a frequency of occurrence question, which asks how often a reported condition occurred during the previous 4 weeks. There are three response options representing a range of frequencies (1 = rarely, 2 = sometimes, 3 = often). HFIAS score is calculated for each household by summing each frequency of occurrence question. The maximum score for a household is 27 (the household response to all nine frequency of occurrence questions was “often”, coded with response code of 3); the minimum score is 0 (the household responded “no” to all occurrence questions, the higher the score, the more food insecurity the household experienced. The lower the score, the less food insecurity a household experienced [17]. Then the households were classified as most food secure scores of 0–11; medium food secure = 12–16; and least food secure = 17 or more [18]. Weights of the lactating women were measured to the nearest 0.1 kg with weight measuring scale (Prestige Model) and heights were measured to the nearest 0.1 cm using a wooden height-measuring board with a sliding head bar. During anthropometric measurement Calibrated equipment and standardized techniques [19] was used to take anthropometric (body) measurements on the lactating women. The measurements were taken with the women wearing light clothing and no shoes to minimize error. Weighing scales were checked before and after each measurement for their accuracy by an object with known weight. Pre-test was carried out 5% of the sample size. During data collection data collectors were strictly follow standard measuring procedure to measure height and weight. Questionnaires were checked for their completeness every day after data collection. For data collectors regular supervisions and follow up were carried by supervisors and principal investigator. The diagnostic criteria for chronic energy deficiency were based on BMI which was calculated as weight in kilograms divided by the square of height in meters (kg/m2). It was classified according to WHO classification, BMI 30 kg/m2) were not considered having chronic energy deficiency. The raw data were coded, entered, cleaned and analyzed using SPSS version 20. The 95% confidence level was used in significance analysis. The association between each explanatory variable with dependent variable was examined through bivariate analysis, by computing odds ratio at 95% confidence level. Variables from bivariate analysis were selected and transferred to multivariable logistic regression by using preset p-value of <0.25 [20]. To identify factors associated with outcome variables, multiple logistic regressions at 95% confidence level was used. A p-value  5 was used as cutoff point [20]. The final model was then tested for its goodness of fit by Hosmer and Lemeshow p-value and a p-value >0.05 was best fit. Maternal chronic energy deficiency was measured using maternal BMI which was calculated as weight/height2 (kg/m2); BMI < 18•5 kg/m2 was considered to be underweight, while ≥18.5 kg/m2 was considered as normal weight. For the purpose of analysis CED was taken as a dichotomous measure based on body mass index cutoff <18.5 kg/m2 and above. Since the interest is in identifying women at risk of underweight, the dependent variables were coded as 1 if the woman was underweight (BMI < 18.5 kg/m2) and coded as 0 if not.

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide information and resources related to maternal health, including nutrition, prenatal care, and breastfeeding. These apps can be easily accessible to women in low-resource settings, providing them with valuable guidance and support.

2. Telemedicine: Establish telemedicine programs that allow pregnant women in remote areas to consult with healthcare professionals and receive prenatal care remotely. This can help overcome geographical barriers and ensure that women have access to necessary healthcare services.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas, improving access to maternal health services.

4. Nutritional Interventions: Implement targeted nutritional interventions to address under-nutrition among pregnant women. This can include providing fortified foods, supplements, and education on proper nutrition during pregnancy.

5. Health Education Programs: Develop and implement comprehensive health education programs that focus on maternal health, including topics such as prenatal care, nutrition, breastfeeding, and postpartum care. These programs can be delivered through community workshops, radio broadcasts, and other accessible platforms.

6. Transportation Support: Improve transportation infrastructure and provide transportation support for pregnant women to access healthcare facilities. This can include providing transportation vouchers, establishing community transportation systems, or partnering with local transportation providers.

7. Maternal Health Clinics: Establish dedicated maternal health clinics in underserved areas to provide comprehensive prenatal care, delivery services, and postpartum care. These clinics can be staffed with skilled healthcare professionals and equipped with necessary medical equipment.

8. Public-Private Partnerships: Foster collaborations between government agencies, non-profit organizations, and private sector entities to improve access to maternal health services. These partnerships can leverage resources, expertise, and funding to implement innovative solutions and reach more women in need.

It is important to note that the specific implementation of these innovations would require careful planning, coordination, and evaluation to ensure their effectiveness and sustainability in improving access to maternal health.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health in the study area would be to develop and implement nutrition intervention programs. These programs should focus on addressing under-nutrition among lactating women in both lowland and highland communities of District Raya Alamata, Southern Tigiray, Ethiopia.

The study found that chronic energy deficiency (CED) was prevalent among lactating mothers in both lowland and highland communities. Factors such as age, husband occupation, vitamin A intake, consumption of extra food during lactation, parity, number of meals per day, and household consumption of iodized salt were associated with CED in the respective communities.

To address this public health problem, nutrition security programs should be developed to ensure adequate nutrient intake among lactating women. These programs should aim to improve food security and promote nutrition diversification. Encouraging the consumption of a diverse range of food groups, including starchy staples, dark green leafy vegetables, vitamin A-rich vegetables and fruits, other fruits and vegetables, organ meat, flesh meat and fish, eggs, dairy, and legumes and nuts, can help improve the dietary diversity and micronutrient adequacy of lactating women.

Additionally, efforts should be made to increase awareness and education about nutrition among the community. This can include providing information on the importance of a balanced diet, the benefits of consuming a variety of foods, and the role of nutrition in maternal and child health.

By implementing these recommendations, it is expected that access to maternal health will be improved by addressing the nutritional needs of lactating women in the study area.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Increase awareness and education: Implement programs to educate pregnant women and their families about the importance of maternal health, proper nutrition, and the risks associated with under-nutrition. This can be done through community health workers, health clinics, and educational campaigns.

2. Improve access to prenatal care: Ensure that pregnant women have access to regular prenatal check-ups, screenings, and necessary medical interventions. This can be achieved by expanding healthcare facilities, increasing the number of healthcare providers, and providing transportation services for women in remote areas.

3. Enhance nutrition support: Develop nutrition intervention programs that focus on improving the dietary diversity and quality of meals for pregnant women. This can include providing nutritional supplements, promoting the consumption of locally available nutrient-rich foods, and educating women on proper meal planning.

4. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, equipment, and resources in areas with high maternal health needs. This includes ensuring the availability of skilled healthcare providers, essential medications, and emergency obstetric care services.

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

1. Define the target population: Identify the specific group of pregnant women or lactating mothers who will be the focus of the simulation.

2. Collect baseline data: Gather information on the current state of access to maternal health services, including factors such as the number of healthcare facilities, healthcare providers, and the prevalence of under-nutrition.

3. Define indicators: Determine the key indicators that will be used to measure the impact of the recommendations. This could include indicators such as the percentage of pregnant women receiving prenatal care, the prevalence of under-nutrition, and the number of maternal deaths.

4. Develop a simulation model: Create a mathematical or computational model that simulates the impact of the recommendations on the defined indicators. This model should take into account factors such as population size, geographical distribution, and resource availability.

5. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. This can involve adjusting different variables, such as the number of healthcare facilities or the coverage of nutrition intervention programs, to see how they affect the outcomes.

6. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This can include comparing different scenarios and identifying the most effective strategies.

7. Refine and validate the model: Continuously refine and validate the simulation model based on real-world data and feedback from experts in the field. This will ensure that the model accurately represents the complex dynamics of improving access to maternal health.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations and make informed decisions to improve access to maternal health.

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