What factors are associated with maternal undernutrition in eastern zone of Tigray, Ethiopia? Evidence for nutritional well-being of lactating mothers

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Study Justification:
– Maternal undernutrition is a significant health problem in Ethiopia.
– The study aims to identify the level of maternal undernutrition and its associated factors in a specific region of Ethiopia.
– The findings will provide valuable information for developing interventions to improve maternal nutrition and well-being.
Study Highlights:
– The overall prevalence of maternal undernutrition based on mid-upper-arm circumference (MUAC) measurement was 38%.
– Recent occurrence of household morbidity and history of adult mortality from chronic diseases were associated with increased risk of maternal undernutrition.
– Good maternal health-seeking practices and production of diverse food crops were associated with a lower risk of maternal undernutrition.
– Housing and environmental factors also played a role, with higher scores associated with a lower risk of maternal undernutrition.
Study Recommendations:
– Efforts to address maternal undernutrition should consider the influence of adult mortality from chronic diseases.
– Integrated intervention programs that combine nutrition-sensitive development programs with nutrition-specific sectoral services are needed.
Key Role Players:
– Researchers and academics in the field of maternal and child health.
– Health policymakers and government officials.
– Non-governmental organizations (NGOs) working on maternal and child health.
– Community health workers and healthcare providers.
– Local community leaders and organizations.
Cost Items for Planning Recommendations:
– Research and data collection expenses.
– Training and capacity building for healthcare providers and community health workers.
– Development and implementation of integrated intervention programs.
– Monitoring and evaluation of intervention programs.
– Communication and awareness campaigns.
– Infrastructure and equipment for healthcare facilities.
– Collaboration and coordination with relevant stakeholders.
– Administrative and operational costs.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is rated 8 because it provides detailed information about the study design, methods, and results. The study used a large sample size and employed statistical models to estimate the effects of various factors on maternal undernutrition. The prevalence of maternal undernutrition and its associated factors were clearly reported. The abstract also mentions the need for integrated interventions and provides information about the data collection process. However, the abstract could be improved by including information about the limitations of the study and suggestions for future research.

Background: Maternal undernutrition is a pervasive health problem among Ethiopian mothers. This study aims at identifying the level of maternal undernutrition and its associated factors in Kilte Awaleo-Health and Demographic Surveillance Site (KA-HDSS), Tigray region, Ethiopia. Methods: Nutritional status of 2260 lactating mothers was evaluated using the mid-upper-arm circumference (MUAC). Data from the vital events and verbal autopsy databases were linked to the survey and baseline recensus data to investigate the association of adult mortality from chronic causes of death (CoD) on maternal undernutrition. We employed a generalized log-binomial model to estimate the independent effects of the fitted covariates. Results: The overall prevalence of maternal undernutrition based on MUAC < 23 cm was 38% (95% CI: 36.1, 40.1%). Recent occurrence of household morbidity (adjusted prevalence ratio (adjPR) = 1.49; 95%CI: 1.22, 1.81) was associated with increased risk of maternal undernutrition. In addition, there was a 28% higher risk (adjPR = 1.28; 95%CI: 0.98, 1.67) of maternal undernutrition for those mothers who lived in households with history of adult mortality from chronic diseases. Especially, its association with severe maternal undernutrition was strong (adjusted OR = 3.27; 95%CI: 1.48, 7.22). In contrast, good maternal health-seeking practice (adjPR = 0.86; 95%CI: 0.77, 0.96) and production of diverse food crops (adjPR = 0.72; 95%CI: 0.64, 0.81) were associated with a lower risk of maternal undernutrition. Relative to mothers with low scores of housing and environmental factors index (HAEFI), those with medium and higher scores of HAEFI had 0.81 (adjPR = 0.81; 95%CI: 0.69, 0.95) and 0.82 (adjPR = 0.82; 95%CI: 0.72, 0.95) times lower risk of maternal undernutrition, respectively. Conclusions: Efforts to ameliorate maternal undernutrition need to consider the influence of the rising epidemiology of adult mortality from chronic diseases. Our data clearly indicate the need for channeling the integrated intervention power of nutrition-sensitive development programs with that of nutrition-specific sectoral services.

Due to poor quality or absence of complete civil registration systems (CVRS), evidence-based health policy formulation and decision-making is challenging in most sub-Saharan Africa [30]. To date, complete and quality CVRS system has not been available in Ethiopia. However, there are other health data sources despite their limitations. In addition to the periodic and routine data sources (censuses, demographic and health survey, routine health information reports), selecting geographically defined populations, and generating longitudinal vital events (birth, migration, and death), causes of death (CoD), and marital status information could also be an alternative systematic approach. Such population-based and ongoing longitudinal health and demographic projects are known as health and demographic surveillance sites (HDSS), and are linked through The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) network [30, 31]. Pooling data from multiple HDSS, in a given country, may help to narrow the chronic data gaps in low and middle income countries [31], although this approach cannot be a replacement for a fully functional and quality CVRS. In Ethiopia, there are six HDSS projects home-based at their respective local university and networked by the support of Ethiopian Public Health Association (EPHA) and Center for Diseases Control (CDC-ETHIOPIA). Health and demographic data are pooled from these six sites and disseminated for public use on annual basis. KA-HDSS, which is located 835 km north of Addis Ababa in the Eastern Zone of Tigray, was established in 2009 with a total baseline population of 66,438 residents. The current study is based on data extracted from the KA-HDSS longitudinal data, and the nutrition and baseline recensus survey data, which was collected from July to December in 2015. The nutrition survey was conducted as a baseline for the installment of longitudinal nutrition project to the already existing main surveillance project. Our study population included all the 2260 breast-feeding mothers of KA-HDSS, who were at least 18 years old. The data collectors were high school completed permanent employees of the surveillance project (KA-HDSS). They were chosen to ensure the overall sustainability of the project and its collateral contribution to the community. They were rigorously trained, and experienced on the standard data collection tools and procedures, including on how to consent the study participants properly. Before the start of the data collection, the data collectors introduced themselves to the study participants, and gave information on the purpose of the study. The data collection started after consent from the participants was given. Furthermore, the interviewers were residents of the surveillance population, with better knowledge about the culture of their community, thus facilitating the proper implementation of the consent procedures. Face-to-face interviews were conducted together with direct observations and measurements. KA-HDSS data collection supervisors led by the field manager (from the field KA-HDSS office) and academic research members (from the KA-HDSS central home office at Mekelle University, College of Health Sciences) strictly monitored the data collection process. Maternal nutritional status was assessed using the mid-upper-arm circumference (MUAC) measurement. The midpoint between the tip of the shoulder and the tip of the elbow of the left arm was measured using a flexible, inelastic MUAC tape. Circumferential measurements were taken after making sure that the tape had a proper tension, not too loose or too tight, around the midpoint and all values were recorded to the nearest 0.1 cm. Lactating mothers with a MUAC below 23 cm were classified as undernourished, and those with less than 21 cm as severely undernourished [32, 33]. Studies have reported that MUAC is a reliably efficient alternative indicator for body mass index for evaluating adult nutritional status, particularly in developing countries where large logistical mobilization is needed to accurately measure the height and weight in population-based surveys [34, 35]. Its positive association with infant breast milk intake, breast milk volume and quality, its measurement simplicity and ability to predict mortality, particularly among the older adults, are additional advantages [35–38]. Detailed lists of agricultural asset ownership, livestock and food crops produced, were counted and converted to monetary terms and quintile classified. The lowest two quintiles were treated as “poor” and the remaining three upper quintiles as “not poor”. Maternal health practice index was constructed from two maternal health service utilization indicators: ever use of modern family planning methods and delivering at health facilities. If the score was 2 (mother used both health services), then it was operationalized as “good”. If the score was  10%), which is the case in this study [47–50]. In addition, its easiness of interpretation makes prevalence ratio preferable to the odds ratio [50]. Univariable log-binomial analyses were performed to observe the association of each independent variable with maternal undernutrition. In the next step, all the variables with a P-value of less than 0.25 in the univariable analysis were taken to the multivariable log-binomial model. Adjusted prevalence ratios and their 95% confidence intervals (95% CI) were reported at P-value of less than 0.05. In addition, we also assessed the factors associated with severe maternal undernutrition. In this case, since the prevalence of severe maternal undernutrition was less than 10%, we used multivariable binary logistic regression model to identify the potential associated factors. Similar to the above approach, variable selection was made at P-value of less than 0.25 in the univariable binary logistic regression and adjusted odds ratio (AOR) with 95% CI were produced from the multivariable model at P-value of less than 0.05. However, to encourage the interpretation of the associations from the perspective of practical (public health) significance, rather than focusing only on statistical significance, we decided to skip the P-values and instead report only the measures of association and their corresponding 95% CI. Categorical characteristics of the study participants are presented using frequencies and proportions, while the continuous characteristics are summarized using means with standard deviations (SD) and median with interquartile ranges (IQR), depending on their distribution. All analyses were performed in Stata 15.1 (StataCorp, LLC, College Station, TX).

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

1. Mobile Health (mHealth) Solutions: Develop mobile applications or text messaging services to provide pregnant women and new mothers with information on nutrition, healthcare services, and reminders for prenatal and postnatal appointments.

2. Telemedicine: Implement telemedicine programs that allow pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls, reducing the need for travel and improving access to medical advice.

3. Community Health Workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women and new mothers in underserved areas.

4. Maternal Health Vouchers: Introduce voucher programs that provide pregnant women with subsidized or free access to prenatal care, delivery services, and postnatal care.

5. Maternal Health Clinics: Establish dedicated maternal health clinics in areas with high maternal mortality rates, equipped with skilled healthcare professionals and necessary medical equipment to provide comprehensive care to pregnant women.

6. Transportation Support: Develop transportation initiatives, such as providing free or subsidized transportation services, to ensure that pregnant women can easily access healthcare facilities for prenatal and postnatal care.

7. Maternal Health Education Programs: Implement community-based education programs that focus on improving maternal nutrition, promoting breastfeeding, and raising awareness about the importance of prenatal and postnatal care.

8. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve infrastructure, healthcare services, and access to maternal health resources.

9. Maternal Health Financing: Develop innovative financing mechanisms, such as microinsurance or community-based health financing schemes, to ensure that pregnant women have financial protection and can afford necessary healthcare services.

10. Data Collection and Analysis: Strengthen the collection and analysis of maternal health data to identify gaps in access and quality of care, and inform evidence-based policies and interventions.

These innovations can help address the challenges of maternal undernutrition and improve access to maternal health services in the eastern zone of Tigray, Ethiopia.
AI Innovations Description
The study mentioned in the description aims to identify the level of maternal undernutrition and its associated factors in the Kilte Awaleo-Health and Demographic Surveillance Site (KA-HDSS) in Tigray region, Ethiopia. The study collected data from 2260 lactating mothers and evaluated their nutritional status using the mid-upper-arm circumference (MUAC) measurement. The study found that the overall prevalence of maternal undernutrition, based on MUAC < 23 cm, was 38%. Factors associated with increased risk of maternal undernutrition included recent occurrence of household morbidity and history of adult mortality from chronic diseases. On the other hand, good maternal health-seeking practice and production of diverse food crops were associated with a lower risk of maternal undernutrition. The study also highlighted the importance of nutrition-sensitive development programs and nutrition-specific sectoral services in addressing maternal undernutrition. The study used data from the Kilte Awaleo-Health and Demographic Surveillance Site (KA-HDSS), which is part of the International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) network. The study suggests that pooling data from multiple HDSS sites in a country can help narrow data gaps in low and middle-income countries. The study concludes that efforts to improve maternal undernutrition should consider the influence of adult mortality from chronic diseases and the integration of nutrition-sensitive development programs with nutrition-specific sectoral services.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Investing in healthcare facilities, including hospitals, clinics, and health centers, in the eastern zone of Tigray, Ethiopia, can improve access to maternal health services. This includes ensuring the availability of skilled healthcare providers, essential medical equipment, and necessary medications.

2. Enhancing community-based healthcare services: Implementing community-based healthcare programs, such as mobile clinics and community health workers, can bring maternal health services closer to the population. These programs can provide prenatal care, postnatal care, family planning services, and health education to pregnant women and lactating mothers.

3. Promoting nutrition-sensitive interventions: Interventions that focus on improving nutrition during pregnancy and lactation can help address maternal undernutrition. This can include promoting a diverse and balanced diet, providing nutritional supplements, and educating women on the importance of proper nutrition for maternal and child health.

4. Increasing awareness and education: Conducting awareness campaigns and educational programs can help raise awareness about the importance of maternal health and encourage women to seek timely and appropriate healthcare services. This can include educating women on the signs of complications during pregnancy and childbirth and promoting the utilization of antenatal and postnatal care services.

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 that reflect access to maternal health services, such as the percentage of pregnant women receiving antenatal care, the percentage of births attended by skilled healthcare providers, and the percentage of women receiving postnatal care.

2. Collect baseline data: Gather baseline data on the selected indicators from the study area. This can be done through surveys, interviews, or existing health information systems.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, healthcare infrastructure, community engagement, and resource availability.

4. Input data and parameters: Input the baseline data and relevant parameters into the simulation model. This includes information on the current state of maternal health services, the proposed interventions, and their expected outcomes.

5. Run simulations: Run multiple simulations using different scenarios and assumptions to assess the potential impact of the recommendations on improving access to maternal health. This can involve adjusting parameters such as the coverage of healthcare services, the effectiveness of community-based interventions, and the level of awareness and education.

6. Analyze results: Analyze the simulation results to determine the projected changes in the selected indicators. This can include comparing the baseline data with the simulated outcomes to assess the potential improvements in access to maternal health services.

7. Validate and refine the model: Validate the simulation model by comparing the simulated outcomes with real-world data, if available. Refine the model based on feedback and additional data to improve its accuracy and reliability.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, including policymakers, healthcare providers, and community members. Use the results to advocate for the implementation of the recommended interventions and to inform decision-making processes.

By following this methodology, policymakers and stakeholders can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions to prioritize and implement the most effective strategies.

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