Undernutrition among pregnant women in rural communities in southern ethiopia

International Journal of Women's Health, Volume 13, Year 2021

Ukuhunyushwa

Background: Maternal undernutrition rates in Ethiopia are among the highest in the world. In addition, a huge inequity exists within the country, with pregnant women in rural communities being at increased risk. This study assessed the prevalence of undernutrition and its associated factors among pregnant women in a rural community in southern Ethiopia. Methods: A community-based cross-sectional study was conducted among 376 randomly selected pregnant women. Data were collected through face-to-face interview followed by mid–upper arm circumference measurement. Household food insecurity and minimum dietary diversity for women were assessed. Data were entered into EpiData 3.1 and exported to SPSS 20 for analysis. Logistic regression models were fitted to check associations between independent variables and undernutrition. Statistical significance was set at p<0.05. Results: The prevalence of undernutrition was 41.2% (95% CI 36.3%–46.3%). Unintended pregnancy (AOR 2.06, 95% CI 1.27–3.36) and not participating in Wome's Health Development Army meetings (AOR 3.64, 95% CI 1.51–8.77) were independent predictors of undernutrition. However, minimum dietary diversity for women of five or more food groups (AOR 0.24, 95% CI 0.07–0.82), having at least one antenatal care visit (AOR 0.46, 95% CI 0.27–0.78), age at first pregnancy ≥20 years (AOR 0.39, 95% CI 0.21–0.76), and being from food-secure households (AOR 0.26, 95% CI 0.16–0.43) were independent protective factors against undernutrition. Conclusion: Undernutrition among pregnant women was highly prevalent in the study area. Interventions aiming to reduce undernutrition should focus on discouraging teenage and unintended pregnancy, reducing household food insecurity, and promoting antenatal care visits and encouraging consumption of diversified diets by women. Strengthening the existing network of the Women’s Health Development Army seems to be very important. The study was conducted in Goro Dola District, Guji Zone, Oromia Regional State, southern Ethiopia from June 15 to 30, 2020. The district is located 595 km to the south of Addis Ababa. It has three urban and 18 rural rural kebeles (the smallest administrative unit in Ethiopia), and an estimated population of 83,243, of which 2,889 were pregnant. A majority of the residents are sedentary farmers, whose basic livelihood is livestock. This was a community-based cross-sectional study. All pregnant women in any trimester residing in the district were the source population, while all pregnant women in any trimester who were registered on pregnancy screening–registration books of the health posts in the selected kebeles of the district during the study period were the study population. The primary outcome of the study was to identify the prevalence of undernutrition and factors associated with it. The sample size was calculated by using a single population–proportion formula with assumptions of undernutrition prevalence among pregnant women of 31.8% from a previous study in the Ethiopian Central Rift Valley,16 5% margin of error, 95% confidence level, and 15% nonresponse rate. As such, the sample size calculated was 383. After stratifying kebeles in the district into urban (n=3) and rural (n=18), we allocated the sample size proportionally (proportional to the number of pregnant women in each kebele) to each stratum. The identity of each woman was obtained from the pregnancy screening–registration book of health posts and was used as a sampling frame. Women were then selected by simple random sampling using computer-generated random numbers. Data were collected through face-to-face interviews and anthropometric measurements by going from home to home. Data on sociodemographic, reproductive, medical, behavioral, and health-care factors were collected using a structured questionnaire developed from the literature.17–19 Data on minimum dietary diversity for women (MDDW) were collected using the standard FAO 2016 tool.20 The MDDW is a composite indicator used to reflect dietary micronutrient adequacy. It is computed using ten food groups (grains, white roots, tubers and plantains, other vegetables, dairy foods, pulses, dark-green leafy vegetables, other vitamin A–rich fruit and vegetables, eggs, other fruit, meat, poultry, and fish, nuts, and seeds) for comparison. Household food insecurity was assessed with the standard Food and Nutrition Technical Assistance 2007 tool.21 Mid–upper arm circumference (MUAC) was measured on women’s nondominant hand (arm) at the midpoint between the olecranon process and acromion process.Unstretchable MUAC tape with the correct tension (not too loose/tight) was used, and values were recorded to the nearest 0.1 cm. We took measurements twice, and average values were used for analysis. Eight nurses collected the data, and four health officers supervised the field data–collection process. The questionnaire was prepared in English and translated into the local language (Afaan Oromoo), then translated back to English by two experts with good command of both languages. Two days’ training was given to data collectors and field supervisors on the objectives of the study, contents of the questionnaire, interview techniques, and confidentiality and rights of respondents. To minimize intra- and interobserver variability in measurements, relative technical error of measurement was calculated during the training among ten pregnant women. In addition, a pretest was conducted on 19 pregnant women residing in the kebeles of a nearby district. The data-collection process was supervised by SZ and field supervisors. Data were cleaned, coded, and entered into EpiData 3.1, then exported to SPSS 20 for analysis. The outcome variable, undernutrition, was categorized and coded as 1 for “yes” if MUAC were <23 cm and 0 for “no” for MUAC ≥23 cm. Descriptive analysis — simple frequencies, means, and ranges — were calculated and presented in the form of statements and tables. A household wealth index was constructed using principal-component analysis by considering locally available household assets and then categorizing as poor, medium, and rich. A household food-insecurity access score was calculated for each household by summing up the frequency of occurrence of the nine food insecurity–related conditions thatmeasure household food insecurity in the previous 4 weeks. The nine items were recorded as 0 for “no” to each occurrence and 1 for “yes” responses. It was then categorized as food-secure when all items had been answered “no” and food in-secure otherwise. MDDW, a dichotomous indicator of whether or not the women had eaten five or more of ten food groups in the last 24 hours, was categorized as “MDDW not met” if fewer than five and “MDDW met” otherwise. Bivariate analyses were done to assess the association between each independent variable and the outcome variable with CORs and 95% CIs. Then, variables with p<0.25 were considered as candidates for the multivariate model. Multicollinearity among the independent variables was checked using the variance-inflation factor. Model fitness was checked with Hosmer–Lemeshow goodness-of-fit test. AORs and 95% CI were used to estimate strength of association. Statistical significance was set at p<0.05.

I-AI Digest

Study Justification:

– Maternal undernutrition rates in Ethiopia are among the highest in the world.
– Pregnant women in rural communities in Ethiopia are at increased risk of undernutrition.
– Understanding the prevalence and factors associated with undernutrition among pregnant women in rural communities is crucial for developing effective interventions.

Study Highlights:

– The study was conducted in Goro Dola District, Guji Zone, Oromia Regional State, southern Ethiopia.
– A community-based cross-sectional study design was used.
– Data were collected through face-to-face interviews and anthropometric measurements.
– The prevalence of undernutrition among pregnant women in the study area was 41.2%.
– Factors associated with undernutrition included unintended pregnancy, lack of participation in Women’s Health Development Army meetings, and household food insecurity.
– Protective factors against undernutrition included consuming a diversified diet, receiving antenatal care, having the first pregnancy at age 20 or older, and belonging to food-secure households.
– The study recommends interventions that focus on discouraging teenage and unintended pregnancy, reducing household food insecurity, promoting antenatal care visits, and encouraging consumption of diversified diets by women.
– Strengthening the existing network of the Women’s Health Development Army is also recommended.

Recommendations for Lay Reader and Policy Maker:

– Implement interventions to reduce undernutrition among pregnant women in rural communities.
– Focus on discouraging teenage and unintended pregnancy.
– Address household food insecurity through targeted interventions.
– Promote antenatal care visits among pregnant women.
– Encourage pregnant women to consume diversified diets.
– Strengthen the Women’s Health Development Army network.

Key Role Players:

– Health professionals (doctors, nurses, midwives) for providing antenatal care and nutrition counseling.
– Community health workers for implementing interventions and educating pregnant women.
– Local government officials for policy development and resource allocation.
– Non-governmental organizations for providing support and resources.

Cost Items for Planning Recommendations:

– Training programs for health professionals and community health workers.
– Development and distribution of educational materials.
– Implementation of nutrition interventions.
– Monitoring and evaluation of interventions.
– Coordination and collaboration between stakeholders.
– Research and data collection for monitoring progress and evaluating the effectiveness of interventions.

Amandla Obufakazi

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is rated 8 because it provides a clear description of the study design, sample size calculation, data collection methods, and statistical analysis. The prevalence of undernutrition and its associated factors are reported with confidence intervals and odds ratios. The study provides actionable steps to improve undernutrition, such as discouraging teenage and unintended pregnancy, reducing household food insecurity, promoting antenatal care visits, and encouraging consumption of diversified diets by women. However, the abstract could be improved by providing more information on the representativeness of the sample and potential limitations of the study.

Abstract

The study was conducted in Goro Dola District, Guji Zone, Oromia Regional State, southern Ethiopia from June 15 to 30, 2020. The district is located 595 km to the south of Addis Ababa. It has three urban and 18 rural rural kebeles (the smallest administrative unit in Ethiopia), and an estimated population of 83,243, of which 2,889 were pregnant. A majority of the residents are sedentary farmers, whose basic livelihood is livestock. This was a community-based cross-sectional study. All pregnant women in any trimester residing in the district were the source population, while all pregnant women in any trimester who were registered on pregnancy screening–registration books of the health posts in the selected kebeles of the district during the study period were the study population. The primary outcome of the study was to identify the prevalence of undernutrition and factors associated with it. The sample size was calculated by using a single population–proportion formula with assumptions of undernutrition prevalence among pregnant women of 31.8% from a previous study in the Ethiopian Central Rift Valley,16 5% margin of error, 95% confidence level, and 15% nonresponse rate. As such, the sample size calculated was 383. After stratifying kebeles in the district into urban (n=3) and rural (n=18), we allocated the sample size proportionally (proportional to the number of pregnant women in each kebele) to each stratum. The identity of each woman was obtained from the pregnancy screening–registration book of health posts and was used as a sampling frame. Women were then selected by simple random sampling using computer-generated random numbers. Data were collected through face-to-face interviews and anthropometric measurements by going from home to home. Data on sociodemographic, reproductive, medical, behavioral, and health-care factors were collected using a structured questionnaire developed from the literature.17–19 Data on minimum dietary diversity for women (MDDW) were collected using the standard FAO 2016 tool.20 The MDDW is a composite indicator used to reflect dietary micronutrient adequacy. It is computed using ten food groups (grains, white roots, tubers and plantains, other vegetables, dairy foods, pulses, dark-green leafy vegetables, other vitamin A–rich fruit and vegetables, eggs, other fruit, meat, poultry, and fish, nuts, and seeds) for comparison. Household food insecurity was assessed with the standard Food and Nutrition Technical Assistance 2007 tool.21 Mid–upper arm circumference (MUAC) was measured on women’s nondominant hand (arm) at the midpoint between the olecranon process and acromion process.Unstretchable MUAC tape with the correct tension (not too loose/tight) was used, and values were recorded to the nearest 0.1 cm. We took measurements twice, and average values were used for analysis. Eight nurses collected the data, and four health officers supervised the field data–collection process. The questionnaire was prepared in English and translated into the local language (Afaan Oromoo), then translated back to English by two experts with good command of both languages. Two days’ training was given to data collectors and field supervisors on the objectives of the study, contents of the questionnaire, interview techniques, and confidentiality and rights of respondents. To minimize intra- and interobserver variability in measurements, relative technical error of measurement was calculated during the training among ten pregnant women. In addition, a pretest was conducted on 19 pregnant women residing in the kebeles of a nearby district. The data-collection process was supervised by SZ and field supervisors. Data were cleaned, coded, and entered into EpiData 3.1, then exported to SPSS 20 for analysis. The outcome variable, undernutrition, was categorized and coded as 1 for “yes” if MUAC were <23 cm and 0 for “no” for MUAC ≥23 cm. Descriptive analysis — simple frequencies, means, and ranges — were calculated and presented in the form of statements and tables. A household wealth index was constructed using principal-component analysis by considering locally available household assets and then categorizing as poor, medium, and rich. A household food-insecurity access score was calculated for each household by summing up the frequency of occurrence of the nine food insecurity–related conditions thatmeasure household food insecurity in the previous 4 weeks. The nine items were recorded as 0 for “no” to each occurrence and 1 for “yes” responses. It was then categorized as food-secure when all items had been answered “no” and food in-secure otherwise. MDDW, a dichotomous indicator of whether or not the women had eaten five or more of ten food groups in the last 24 hours, was categorized as “MDDW not met” if fewer than five and “MDDW met” otherwise. Bivariate analyses were done to assess the association between each independent variable and the outcome variable with CORs and 95% CIs. Then, variables with p<0.25 were considered as candidates for the multivariate model. Multicollinearity among the independent variables was checked using the variance-inflation factor. Model fitness was checked with Hosmer–Lemeshow goodness-of-fit test. AORs and 95% CI were used to estimate strength of association. Statistical significance was set at p<0.05.

Izinto zokwakha

N/A

Emisha

The study titled “Undernutrition among pregnant women in rural communities in southern Ethiopia” provides valuable insights into the prevalence and factors associated with undernutrition among pregnant women in a specific region. The study highlights several recommendations that can be developed into innovations to improve access to maternal health. These recommendations include:

1. Strengthening the Women’s Health Development Army: Enhancing and expanding the existing network of the Women’s Health Development Army can improve access to maternal health. This can be achieved by providing additional training and resources to army members, increasing their reach to remote rural communities, and ensuring regular meetings and engagement with pregnant women to provide education and support on nutrition and antenatal care.

2. Promoting antenatal care visits: Increasing the uptake of antenatal care services can improve access to maternal health. Community-based outreach programs, mobile clinics, or telemedicine initiatives can be implemented to provide antenatal care services to pregnant women in remote areas who may face challenges in accessing healthcare facilities.

3. Reducing household food insecurity: Implementing interventions to reduce household food insecurity can address one of the factors associated with undernutrition. This can include promoting sustainable agriculture practices, providing agricultural training and resources to farmers, improving access to markets for farmers to sell their produce, and implementing social safety net programs to support vulnerable households.

4. Encouraging consumption of diversified diets: Promoting and facilitating the consumption of diversified diets by pregnant women can improve their nutritional status. This can be achieved through nutrition education programs, community gardens, and initiatives to improve access to a variety of nutritious foods, including fruits, vegetables, grains, and protein sources.

By implementing these recommendations as innovative interventions, access to maternal health can be improved, leading to a reduction in undernutrition among pregnant women in rural communities in southern Ethiopia.

AI Innovations Description

Based on the study titled “Undernutrition among pregnant women in rural communities in southern Ethiopia,” the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening the Women’s Health Development Army: The study found that not participating in Women’s Health Development Army meetings was a predictor of undernutrition among pregnant women. Therefore, an innovation could involve enhancing and expanding the existing network of the Women’s Health Development Army. This could include providing additional training and resources to the army members, increasing their reach to remote rural communities, and ensuring regular meetings and engagement with pregnant women to provide education and support on nutrition and antenatal care.

2. Promoting antenatal care visits: The study identified that having at least one antenatal care visit was a protective factor against undernutrition. To improve access to maternal health, an innovation could focus on increasing the uptake of antenatal care services. This could involve community-based outreach programs, mobile clinics, or telemedicine initiatives to provide antenatal care services to pregnant women in remote areas who may face challenges in accessing healthcare facilities.

3. Reducing household food insecurity: The study found that being from food-secure households was a protective factor against undernutrition. To address this, an innovation could involve implementing interventions to reduce household food insecurity. This could include promoting sustainable agriculture practices, providing agricultural training and resources to farmers, improving access to markets for farmers to sell their produce, and implementing social safety net programs to support vulnerable households.

4. Encouraging consumption of diversified diets: The study highlighted that meeting the minimum dietary diversity for women (MDDW) was a protective factor against undernutrition. An innovation could focus on promoting and facilitating the consumption of diversified diets by pregnant women. This could involve nutrition education programs, community gardens, and initiatives to improve access to a variety of nutritious foods, including fruits, vegetables, grains, and protein sources.

AI Innovations Methodology

To simulate the impact of the main recommendations on improving access to maternal health, the following methodology can be used:

1. Strengthening the Women’s Health Development Army: The simulation can involve expanding the network of the Women’s Health Development Army by increasing the number of trained members and their reach to remote rural communities. The simulation can estimate the number of additional army members needed, the resources required for training and support, and the frequency of meetings with pregnant women. The impact can be measured by tracking the increase in attendance at Women’s Health Development Army meetings and assessing the improvement in knowledge and practices related to nutrition and antenatal care among pregnant women.

2. Promoting antenatal care visits: The simulation can focus on implementing community-based outreach programs, mobile clinics, or telemedicine initiatives to provide antenatal care services to pregnant women in remote areas. The simulation can estimate the number of additional healthcare providers needed, the resources required for setting up mobile clinics or telemedicine infrastructure, and the expected increase in antenatal care visits. The impact can be measured by tracking the number of antenatal care visits made by pregnant women in remote areas and assessing the improvement in maternal health outcomes.

3. Reducing household food insecurity: The simulation can involve implementing interventions to reduce household food insecurity, such as promoting sustainable agriculture practices, providing agricultural training and resources to farmers, improving access to markets, and implementing social safety net programs. The simulation can estimate the resources required for implementing these interventions and the expected reduction in household food insecurity. The impact can be measured by tracking changes in household food insecurity levels and assessing the improvement in maternal nutrition outcomes.

4. Encouraging consumption of diversified diets: The simulation can focus on promoting and facilitating the consumption of diversified diets by pregnant women. This can involve nutrition education programs, community gardens, and initiatives to improve access to a variety of nutritious foods. The simulation can estimate the resources required for implementing these interventions and the expected increase in dietary diversity among pregnant women. The impact can be measured by tracking changes in dietary diversity and assessing the improvement in maternal nutrition outcomes.

The simulation can use data from the study, such as the prevalence of undernutrition and the factors associated with it, to estimate the potential impact of the recommendations on improving access to maternal health. By comparing the simulated outcomes with the baseline data, the effectiveness of the recommendations can be evaluated, and adjustments can be made to optimize the interventions.

Umbhali

Dima Health

Statistics:

Citations: 8

Identifiers:

DOI: 10.2147/IJWH.S285132

Research Areas:

Community Interventions, Disparities, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health

Study Countries:

Ethiopia

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: