Household Food Insecurity, Underweight Status, and Associated Characteristics among Women of Reproductive Age Group in Assayita District, Afar Regional State, Ethiopia

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Study Justification:
– Poor nutritional status of women is a serious problem in Ethiopia, particularly in rural areas.
– Afar region has a high prevalence of undernourishment and food insecurity.
– Household food insecurity in Afar region has not been adequately studied.
– Understanding the prevalence and associated factors of household food insecurity and underweight status among reproductive age women is important for addressing these issues.
Highlights:
– Prevalence of household food insecurity in Assayita district was 70.4%, with 26.1% experiencing mild food insecurity, 30.2% experiencing moderate food insecurity, and 14.1% experiencing severe food insecurity.
– Prevalence of underweight among reproductive age women was 41.1%, with 34.5% being mildly underweight, 3.9% being moderately underweight, and 2.7% being severely underweight.
– Factors associated with household food insecurity and maternal underweight included age, parity, and having more than two children below five years of age.
Recommendations:
– Implement interventions to address household food insecurity and improve nutritional status among reproductive age women in Assayita district.
– Focus on addressing the factors associated with household food insecurity, such as age, parity, and number of children below five years of age.
– Develop and implement programs that promote dietary diversity and improve access to nutritious foods.
– Strengthen healthcare services and education to address maternal underweight and its associated factors.
Key Role Players:
– Ministry of Health: Responsible for coordinating and implementing interventions to address household food insecurity and maternal underweight.
– Regional Health Bureau: Provides support and resources for implementing interventions at the regional level.
– District Health Office: Responsible for implementing interventions at the district level and coordinating with local communities.
– Non-governmental Organizations (NGOs): Collaborate with government agencies to provide resources, expertise, and support for interventions.
– Community Leaders: Engage and mobilize local communities to participate in interventions and promote behavior change.
Cost Items for Planning Recommendations:
– Training and capacity building for healthcare workers and community leaders.
– Development and distribution of educational materials on nutrition and household food security.
– Implementation of nutrition programs, including provision of nutritious foods and supplements.
– Monitoring and evaluation of interventions to assess their effectiveness.
– Coordination and collaboration between government agencies, NGOs, and community organizations.
– Research and data collection to monitor progress and inform future interventions.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study design is community-based cross-sectional, which provides valuable information about the prevalence of household food insecurity and underweight status among women in Assayita district, Afar Regional State, Ethiopia. The sample size of 549 households is relatively large, increasing the reliability of the findings. The study also includes multivariate regression models to measure associations between various factors. However, the study was conducted in 2015, and it would be beneficial to have more recent data. Additionally, the abstract lacks information about the data collection methods, such as the training of data collectors and the measures taken to ensure data quality. To improve the evidence, future studies could consider conducting a longitudinal study to track changes over time and provide more robust evidence. It would also be helpful to include more details about the data collection process and quality assurance measures.

Background. Poor nutritional status of women has been a serious problem in Ethiopia. Rural women are more likely to be undernourished than urban women. Afar region is the most likely to be undernourished (43.5%). Despite the humanitarian and food aid, food insecurity and maternal underweight are very high in the region. Household food insecurity is not adequately studied in Afar region. The aim of this study was to assess the prevalence of household food insecurity and underweight status and its association among reproductive age women. Method. The study was conducted in Assayita district in June 2015. Community-based cross-sectional study design was used among nonpregnant women. Household data was collected using structured questionnaire. Multistage cluster sampling procedure was applied. Two pastoral and two agropastoral Kebeles have been selected by simple random sampling. Systematic random sampling was used to select respondents. The total sample size was 549 households. Household Food Insecurity Access Scale (HFIAS) and anthropometric data were used to determine food insecurity and underweight, respectively. Multivariate regression models were used to measure associations. Results. Prevalence of HFIAS was 70.4 with a mean of 7.0 (3.6 ± SD); 26.1%, 30.20%, and 14.1% were mild, moderate, and severe food insecurity, respectively. Underweight prevalence (BMI 2 children below five years of age were statistically associated with household food insecurity and maternal underweight. Conclusion. Household food insecurity and maternal underweight were very high. Age, parity, and having ≥2 children below five years of age were associated with household food insecurity. Maternal underweight was associated with maternal age, marital status, parity, number of children below 5 years, household food insecurity, and vocation of the respondents.

The study was conducted in June 2015 in Assayita zone, Afar regional state, which is located 650 km away from Addis Ababa, the capital of Ethiopia. Based on the 2007 Census Result of the Central Statistical Agency of Ethiopia (CSAE), the total population of Afar region was 1,411,092, consisting of 786,338 men and 624,754 women. Rural inhabitants constitute 1,222,369 (86.6%) of the total population. 67.3% of inhabitants fall into the lowest wealth quintile; adult literacy for men is 27% and it is 15.6% for women [18]. Assayita is one of the largest districts which has thirteen Kebeles; of which two are urban, six are pastoral, and five are agropastoral Kebeles. Total population of the district was 47,210. Of the total population, 31,162 (66%) live in rural areas and the rest, 16,048 (34%), live in urban areas [18]. The district has four clinics, three health posts, and one health center [19]. A community-based cross-sectional study design was applied and the source population was all households with reproductive age women, while study population was households of randomly selected agropastoral and pastoral community (Figure 1). Households with at least one reproductive age woman were included. However, if more than one eligible woman was available in one household, the one who is responsible for family care and/or is head was considered for this study. Percentage of households in each category of food security for agropastoral and pastoral households, Assayita district, June 2015 (n = 490). Sample size was computed using single population proportion formula assuming a marginal error of 5% and 95% confidence interval. During sample size determination, prevalence of undernourished women and national prevalence for food insecurity were taken into consideration and the prevalence that yields the maximum sample size was taken as final sample. Besides this, 35% undernourished prevalence rate from national food insecurity survey [17] with 5% nonresponse rate and design effect of 1.5 gives us a maximum sample size of 549. The sample was distributed across the selected Kebeles proportional to their household size. With regard to sampling procedures, first multistage stratified sampling procedure was deployed to get a representative data. Two pastoral and two agropastoral Kebeles were selected using simple random sampling. Systematic random sampling was used to identify respondents and probability proportionate to size (PPS) technique was applied. Data was collected through interviews and anthropometric measurements. During interview, structured questionnaire consisted of socioeconomic and demographic characteristics and frequency of 24-hour dietary recall and household food insecurity measurements were used. The questionnaire was initially prepared in English and then translated into Amharic. Six experienced data collectors who had Diploma certificate in health and were able to speak the local language fluently collected the data. Meanwhile, two supervisors from the district health office were involved in supervising the overall data collection process. Household Food Insecurity Access Scale (HFIAS) was used to create a continuous numeric food insecurity “score,” which can then be compared to established cut-points to categorize the level of food insecurity experienced by the household. Nine-item questionnaire with three domains of food insecurity, anxiety/uncertainty about the household food supply, insufficient quality of food (including variety and food preferences), and insufficient food intake and its physical consequences, was used. The participants’ responses indicate a frequency of occurrence of the following: never, rarely (1 to 2 times), sometimes (3 to 10 times), and often (>10 times) for each of the questions over the previous 30 days. This was then used to calculate HFIAS scores. HFIAS scores range from 0 to 27, with a higher score indicating greater food insecurity [20]. The last three questions of the HFIAS were used to calculate the Household Hunger Scale (HHS). The three questions inquired about whether participants “had no food in the house,” “went to sleep hungry,” or “lacked food for 24 hrs.” The household score recodes the responses to each frequency-of-occurrence question from three frequency categories (“rarely,” “sometimes,” and “often”) into two frequency categories (“rarely or sometimes” and “often”). Each household will have score between 0 and 6. These values are then used to generate the household indicators which in turn are categorized into little to no hunger (0-1) in the household, moderate hunger (2-3) in the household, and severe hunger (4–6) in the household [21]. Data on household dietary diversity was collected using a 24-hour recall method and information was entered into the Household Dietary Diversity Score (HDDS) sheet. The HDDS captures dietary diversity in a normal 24-hour period by the household as a whole and not a single member. Food consumed outside the home which was not prepared in the home was not included. A set of 12 food groups were used to guide the scoring as per the food items consumed, with 1 being the minimum score and 12 being the maximum score [22]. To determine the impact of household food insecurity on nutritional status of reproductive age women’s weight, height measurements were taken from all study subjects. Weight was measured to the nearest 0.5 kg using a weight measurements scale. Height was measured to the nearest centimeters also using tap meter; the scales were calibrated after each session of measurements. Malnutrition in women was assessed using the body mass index (BMI), which is defined as a woman’s weight in kilograms divided by the square of her height in meters (BMI = kg/m2). A BMI below 18.5 among nonpregnant, nonlactating women indicates chronic energy deficiency or undernutrition. When BMI is above 25, women are considered overweight [6]. Underweight prevalence (BMI < 18.5 kg/m2) was further categorized by WHO standards for mild (BMI: 18.5–17 kg/m2), moderate (BMI: 16.99–16.00 kg/m2), and severe (BMI: < 16 kg/m2) underweight [23]. Household food insecurity status and underweight status among women of reproductive age were considered as dependent variables, whereas sociodemographic characters, height, weight, and BMI were our independent variables. To ensure quality of data, structured questionnaire was employed to attain the required information after getting written and verbal consent from the respondents. The data collectors and supervisors were trained on objectives of study sampling procedures, techniques of interviews, and data handling. The questionnaire was pretested in a community similar to the study population and the necessary modification was made. The supervisors and principal investigator were closely following the day-to-day data collection process and ensured completeness and consistency of questionnaire administered each day. Statistical software was used to analyze data. The data was entered using Epi Info version 7 and analysis was done using Statistical Package for Social Sciences (SPPS version 21). Descriptive statistics were tabulated to describe the characteristics of households in each level of food security, as well as the nutritional outcomes associated with food security. For variables expressed as percentages or proportions, chi-square test was used to assess differences between food security classifications. Multiple binary logistic regression models were used to quantify the association between household food security and nutritional outcomes among reproductive age women. Ethical clearance was obtained from ethical review committee of College of Health Sciences, Addis Ababa University. An official letter was also obtained from Afar Regional Health Bureau and district health office. Similarly, written consent was obtained from interviewee before proceeding to data collection. All information that was obtained from the individual was treated confidentially. In this study, underweight was defined as BMI < 18.5 kg/m2; normal weight was defined as BMI ≥ 18.5 and <25 kg/m2 [23]. Food-secure household was defined as the household that experiences none of the food insecurity (access) conditions or just experiences worry but rarely. Mildly food-insecure household is defined as the household that sometimes or often worries about not having enough food and/or is unable to eat preferred foods and/or eats a more monotonous diet than desired and/or some foods considered undesirable but only rarely but it does not cut back on quantity and does not experience any of three most severe conditions. Moderately food-insecure household is considered so if it sacrifices quality more frequently by eating a monotonous diet or undesirable foods sometimes or often and/or has started to cut back on quantity by reducing the size of meals or number of meals rarely or sometimes. But it does not experience any of the three most severe conditions. Severely food-insecure household is the household that is forced to cut back on meal size or number of meals often and/or experiences any of the three most severe conditions (running out of food, going to bed hungry, or going a whole day and night without eating), even as infrequently as rarely this is assumed as. Dietary diversity is the number of different foods or food groups consumed over 24-hour period.

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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 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 rural areas, where access to healthcare facilities may be limited.

2. Telemedicine: Implement telemedicine programs that allow pregnant women to consult with healthcare professionals remotely. This can help overcome geographical barriers and provide access to medical advice and support, especially in areas with limited healthcare infrastructure.

3. Community Health Workers: Train and deploy community health workers who can provide education and support to pregnant women in their communities. These workers can conduct regular check-ups, provide basic prenatal care, and refer women to healthcare facilities when necessary.

4. Nutritional Support Programs: Establish programs that provide nutritional support to pregnant women, especially in areas with high rates of food insecurity. This can include providing food vouchers, fortified food supplements, or community gardens to improve access to nutritious food.

5. Maternal Health Education Campaigns: Launch targeted education campaigns to raise awareness about the importance of maternal health and the available resources and services. These campaigns can be conducted through various channels, such as radio, television, community meetings, and posters.

6. Transportation Solutions: Develop transportation solutions, such as mobile clinics or ambulance services, to ensure that pregnant women can easily access healthcare facilities for prenatal care, delivery, and postnatal care.

7. Public-Private Partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers, pharmacies, and technology companies to expand access and resources.

8. Maternal Health Financing: Explore innovative financing models, such as microinsurance or community-based health financing, to make maternal health services more affordable and accessible to women in low-income communities.

9. Maternal Health Monitoring Systems: Implement robust monitoring systems to track maternal health indicators and identify areas of improvement. This can help policymakers and healthcare providers make informed decisions and allocate resources effectively.

10. Maternal Health Task Forces: Establish task forces or committees dedicated to improving maternal health outcomes. These groups can bring together stakeholders from various sectors, including healthcare, government, NGOs, and community organizations, to collaborate on strategies and initiatives.

It is important to note that the specific context and needs of the community should be considered when implementing these innovations.
AI Innovations Description
Based on the study conducted in Assayita district, Afar Regional State, Ethiopia, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening Food Security Programs: Implement targeted interventions to address household food insecurity in the region. This can include providing nutritional support, such as food assistance programs, to vulnerable households. Additionally, promoting sustainable agricultural practices and improving access to markets can help increase food production and availability.

2. Maternal Nutrition Education: Develop and implement educational programs that focus on improving maternal nutrition. This can include providing information on the importance of a balanced diet, promoting dietary diversity, and addressing cultural beliefs and practices that may hinder optimal nutrition during pregnancy.

3. Healthcare Facility Support: Enhance the capacity of healthcare facilities in the region to provide comprehensive maternal health services. This can involve training healthcare providers on maternal nutrition and integrating nutrition counseling into routine antenatal care visits. Additionally, ensuring the availability of essential maternal health supplies and equipment is crucial.

4. Community Engagement: Engage local communities in promoting maternal health and nutrition. This can be done through community-based education programs, support groups for pregnant women, and involving community leaders in advocating for improved access to maternal health services.

5. Monitoring and Evaluation: Establish a robust monitoring and evaluation system to track the progress and impact of interventions aimed at improving access to maternal health. This can help identify areas of improvement and ensure that resources are allocated effectively.

By implementing these recommendations, it is possible to develop innovative approaches to improve access to maternal health in the Assayita district and potentially replicate these strategies in other regions facing similar challenges.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Increase availability and accessibility of nutritious food: Implement programs that focus on improving agricultural practices, promoting sustainable food production, and increasing access to diverse and nutrient-rich foods. This can be achieved through initiatives such as community gardens, agricultural training, and support for small-scale farmers.

2. Enhance maternal nutrition education: Develop educational programs that target women of reproductive age and provide them with information on the importance of proper nutrition during pregnancy and lactation. This can include workshops, counseling sessions, and the distribution of educational materials.

3. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, particularly in rural areas, to ensure that pregnant women have access to quality prenatal care, skilled birth attendants, and emergency obstetric services. This can involve building new healthcare facilities, training healthcare workers, and providing necessary medical equipment and supplies.

4. Increase community engagement: Engage local communities in the planning and implementation of maternal health programs. This can be done through community meetings, involvement of community leaders, and the establishment of community health committees. By involving the community, programs can be tailored to meet the specific needs and cultural preferences of the population.

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

1. Baseline data collection: Collect data on the current status of maternal health, including indicators such as maternal mortality rates, access to prenatal care, and nutritional status of pregnant women. This data will serve as a baseline for comparison.

2. Intervention design: Develop a detailed plan for implementing the recommended interventions, including specific targets, timelines, and resources required. This plan should be based on evidence-based practices and take into account the local context and resources available.

3. Simulation modeling: Use simulation modeling techniques to estimate the potential impact of the interventions on maternal health outcomes. This can involve creating mathematical models that simulate the effects of the interventions on various indicators, such as the number of prenatal care visits, the percentage of underweight women, and the reduction in maternal mortality rates.

4. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation results. This involves testing the model under different scenarios and assumptions to determine the range of potential outcomes.

5. Evaluation and monitoring: Implement the interventions and continuously monitor their progress and impact. Collect data on key indicators at regular intervals and compare them to the baseline data to assess the effectiveness of the interventions.

6. Adjustments and improvements: Based on the evaluation results, make adjustments and improvements to the interventions as needed. This can involve scaling up successful interventions, addressing any challenges or barriers identified during the implementation, and refining the simulation model based on new data and insights.

By following this methodology, policymakers and healthcare providers can gain valuable insights into the potential impact of different interventions on improving access to maternal health and make informed decisions on resource allocation and program implementation.

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