Prevalence of Undernutrition and Its Associated Factors Among Lactating Women in the Shebedino District, Sidama Regional State, Ethiopia

  • Inquiry (United States), Volume 59, Year 2022

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Study Justification:
– Ensuring the nutritional status of lactating women is crucial to prevent maternal morbidity and mortality in poor countries like Ethiopia.
– The prevalence of undernutrition among lactating women in the Shebedino district, Sidama Regional State, Ethiopia is unknown.
– This study aimed to assess the prevalence of undernutrition and its associated factors among lactating women in the study area.
Study Highlights:
– A community-based cross-sectional study was conducted among 612 randomly selected lactating women.
– Data were collected through interviews and physical measurements.
– The prevalence of undernutrition among lactating women in the Shebedino district was found to be 25.9%.
– Factors positively associated with undernutrition included having a polygamous husband, belonging to households with less than 5 members, having a history of abortion in the last 6 months, and poor or medium household wealth status.
Study Recommendations:
– Attention should be given to the economic status of lactating women to improve their nutritional status.
– Family planning services should be provided to prevent unintended pregnancies and reduce the risk of undernutrition.
– Efforts should be made to prevent abortions and provide appropriate care for women who have experienced abortions.
– The practice of polygamy should be addressed, as it is associated with undernutrition among lactating women.
Key Role Players:
– Ministry of Health: Responsible for implementing policies and programs related to maternal and child health, including nutrition.
– Regional Health Bureau: Provides oversight and support to health facilities in the Sidama Regional State.
– District Health Office: Implements health programs and services at the district level, including nutrition interventions.
– Health Centers: Provide primary healthcare services, including antenatal care and nutrition counseling.
– Community Health Agents: Assist in data collection and community outreach activities.
Cost Items for Planning Recommendations:
– Training: Budget for training data collectors and supervisors on survey procedures and data collection tools.
– Data Collection: Allocate funds for transportation, communication, and logistics related to data collection.
– Analysis: Budget for software licenses and personnel to analyze the collected data.
– Program Implementation: Allocate resources for implementing interventions related to family planning, economic empowerment, and abortion prevention.
– Monitoring and Evaluation: Set aside funds for monitoring and evaluating the impact of interventions on the nutritional status of lactating women.
Please note that the provided cost items are general categories and not actual cost estimates. The actual cost will depend on the specific context and implementation plan.

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 undernutrition among lactating women in a specific district in Ethiopia. The sample size is adequate, and data collection methods are described. However, the abstract lacks information on the representativeness of the sample and the response rate. Additionally, the abstract does not provide information on the statistical methods used for analysis. To improve the strength of the evidence, the abstract should include details on the representativeness of the sample and the response rate. It should also mention the specific statistical methods used for analysis, such as descriptive statistics, bivariable logistic regression, and multivariable logistic regression. This would provide more transparency and allow readers to assess the validity of the findings.

Background: Ensuring the nutritional status of lactating women is crucial to prevent maternal morbidity and mortality in poor countries like Ethiopia. Hence, this study aimed to assess the prevalence of undernutrition and its associated factors among lactating women in Shebedino district, Sidama Regional State, Ethiopia. Methods: A community-based cross-sectional study was conducted among randomly selected 612 lactating women from February to March 2020. Data were collected by using an interviewer-administered, structured, and pretested questionnaire. Also, physical measurements (weight, height, and body mass index) were measured by using standardized and calibrated instruments. Data entered into Epi data version 3.1 and exported to SPSS version 23 for further analysis. Descriptive statistics, bivariable, and multivariable logistic regression analysis were done. A P-value of ≤.05 was used to consider the statistical significance. Result: The prevalence of undernutrition was 25.9% (95% CI: 22.5, 29.5). Having polygamous husband (AOR = 3.47, 95% CI: 1.13, 10.68), belonged to households with less than 5 members (AOR = 1.81, 95% CI: 1.16, 2.83), abortion history in the last 6 months (AOR = 3.09, 95% CI: 1.73, 5.51), poor household wealth status (AOR = 3.85, 95% CI: 1.89, 7.81), and medium wealth status (AOR = 2.07, 95% CI: 1.06, 4.03) were factors positively associated with undernutrition. Conclusion: Undernutrition among lactating women was high in the study area. Attention should be given to the economic status of the women, family planning services, abortion prevention, and habits of marrying more than 1 wife (polygamy).

This study was conducted in the Shebedino district which is located 27 km from Hawassa and 302 km from Addis Ababa, the capital of Sidama Regional state and Ethiopia, respectively. According to the Ethiopian Central Statistical Agency report, the total population of the district was 192,359. Among them, 51% are females. The district consists of 26 kebeles. It has a total of annually estimated 6656 (3.46%) lactating mothers. There are 6 health centers, 5 private clinics, and twenty-three health posts. 16 A community-based cross-sectional study design was conducted from February to March 2020. The source population for this study was all lactating women in the Shebedino district. All lactating mothers in randomly selected kebeles who fulfilled the eligibility criteria were the study population. Those mothers who had up to 24 months of the child, and lived in the study area at least for 6 months were included. However, those mothers who were seriously ill and unable to be interviewed during the data collection period were excluded. For the first objective, the sample size was calculated by using single population proportion formula based on the following assumptions: prevalence of undernutrition among lactating mothers (P = 40.6%) taken from the previous study, 23 10% of non-response rate, and design effect of 1.5, the final sample size was 612. From a total number of 26 kebeles (the smallest administrative unit in Ethiopia) found in the Shebedino district, 14 kebeles were selected by a lottery method. The lists of eligible households were obtained from pregnant women registration book at health posts in the selected kebeles. Then, a calculated sample size was proportionally allocated based on the number of eligible mothers obtained from each kebele. Community health agents were assigned with data collectors to access the eligible households. Finally, the study participants were selected by simple random sampling technique. Data were collected by using an interviewer-administered, pretested, and structured questionnaire. The questionnaire had different sections: socio-demographic characteristics of the respondents, items related to dietary practice assessment, and anthropometric measurements. Minimum dietary diversity score was obtained by collecting 24-hours dietary recalls as consumed/not consumed from different food groups. The score was calculated by using 10 food groups as the summation of consumed food. Anthropometric measurements (height, weight, and BMI) were measured by using standardized and calibrated instruments. Weight was measured to the nearest .1 kg on a battery-powered digital scale (Seca770, Hanover Germany), and height was measured to the nearest .1 cm using a wooden height-measuring board with a sliding head bar following standard anthropometric techniques. After checking for its completeness and consistencies, data were entered into Epi Data version 3.1 and exported to the Statistical Package for Social Science (SPSS) version 23 software for further analysis. Descriptive analysis was done for each predictor variable. A cross-tabulation was performed to see the distribution of predictors with the outcome variable. Bivariable logistic regression analysis was done for each independent variable with the outcome variable. Variables with a P-value of ≤.25 were entered into multivariable logistic regression analysis. The wealth index was constructed by using locally available tools related to ownership of selected household’s durable assets, domestic animals, and productive assets. Scores are derived by using principal component analysis. Wealth quintiles were compiled by assigning the household score to each usual household member, ranking by total score. The component with Eigenvalues greater than 1 was retained to construct the wealth index, and grouped into 3 socio-economic statuses as poor, medium, and rich. To check multicollinearity effect, variance inflation factor less than 10 and tolerance test greater than .1 was considered. Adjusted odds ratio (AOR) with a 95% confidence interval (CI) was calculated. A P-value ≤.05 was used to consider statistically significant variables. Finally, the results were described by texts and tables. All data collectors and supervisors were trained for 2 consecutive days on the general purpose of the survey and procedures. The tool was translated into local language (Sidaamu Afoo) and back to English by language experts to check its consistency. Instruments were calibrated before taking anthropometric measurements. A pretest was conducted on 5% of the sample outside of the study area. Collected data were checked for its completeness on daily manner, and all necessary modifications and measurements taken accordingly. In this study, underweight was the primary outcome variable of interest, defined as body mass index (BMI< 18.5  kg/m2). 2 In the final model (logistic regression analysis), we only considered underweight women and those with normal BMI and excluded those who were overweight and obese. The independent variables were socio-demographic factors (age, marital status, occupational status, level of education, household’s wealth index, and family size), obstetric and health care related factors (antenatal care, place of delivery, history of abortion, and mode of delivery), anthropometric measurements (weight, height, and BMI), and environmental factors (source of drinking water, availability of latrine, and waste disposal system). Undernutrition: According to this study, it is a nutritional status of lactating women (underweight) when BMI <18.5 kg/m2. Body mass index (BMI): Calculated as weight in kilograms divided by square of the height in meter.

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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 and implement mobile applications that provide information and resources on maternal health, including nutrition, antenatal care, and family planning. These apps can be easily accessible to lactating women, providing them with personalized guidance and reminders.

2. Telemedicine Services: Establish telemedicine services that allow lactating women in remote areas to consult with healthcare professionals through video calls or phone calls. This can help overcome geographical barriers and provide timely medical advice and support.

3. Community Health Workers: Train and deploy community health workers who can visit lactating women in their homes, provide health education, monitor their nutritional status, and refer them to healthcare facilities when necessary. These workers can act as a bridge between the community and healthcare system, improving access to maternal health services.

4. Nutritional Support Programs: Implement targeted nutritional support programs for lactating women, focusing on improving their dietary diversity and access to micronutrient-rich foods. This can be done through community-based interventions, such as kitchen gardens or food supplementation programs.

5. Maternal Health Vouchers: Introduce voucher programs that provide financial assistance to lactating women for accessing maternal health services, including antenatal care, delivery, and postnatal care. These vouchers can help reduce financial barriers and increase utilization of essential maternal health services.

6. Public-Private Partnerships: Foster collaborations between public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare facilities and resources to expand service coverage and ensure quality care for lactating women.

7. Health Information Systems: Strengthen health information systems to collect, analyze, and utilize data on maternal health. This can help identify gaps in service delivery, monitor progress, and inform evidence-based decision-making for improving access to maternal health services.

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

1. Strengthen family planning services: The study found that having a polygamous husband was positively associated with undernutrition among lactating women. Promoting family planning services can help women have control over their reproductive health and spacing of pregnancies, which can contribute to better maternal nutrition.

2. Focus on abortion prevention: The study identified a history of abortion in the last 6 months as a factor positively associated with undernutrition. Implementing comprehensive reproductive health programs that include access to safe and legal abortion services, as well as post-abortion care, can help prevent complications and improve maternal health.

3. Improve economic status: The study found that poor household wealth status was positively associated with undernutrition. Implementing interventions that address poverty, such as income-generating activities, microfinance programs, and social protection schemes, can help improve the economic status of lactating women and their families, leading to better access to nutritious food and healthcare.

4. Raise awareness about the risks of polygamy: The study highlighted the association between polygamy and undernutrition among lactating women. Raising awareness about the potential negative impacts of polygamy on maternal health and promoting gender equality can help address this issue.

5. Enhance healthcare infrastructure: The study area had a limited number of health centers and clinics. Investing in the expansion and improvement of healthcare infrastructure, including increasing the number of health facilities and trained healthcare providers, can improve access to maternal health services, including antenatal care and skilled delivery assistance.

6. Promote hygiene and sanitation practices: The study did not directly assess the impact of hygiene and sanitation on maternal health, but environmental factors such as the availability of latrines and waste disposal systems were included. Promoting hygiene and sanitation practices, including access to clean water and proper waste management, can contribute to better maternal health outcomes.

By implementing these recommendations, innovative approaches can be developed to improve access to maternal health in the Shebedino district and similar settings. These approaches can include community-based interventions, policy changes, and collaborations between healthcare providers, community organizations, and government agencies.
AI Innovations Methodology
To improve access to maternal health, here are some potential recommendations:

1. Strengthening healthcare infrastructure: Investing in the development and improvement of healthcare facilities, including health centers, clinics, and health posts, can help ensure that maternal health services are easily accessible to women in remote areas.

2. Mobile health clinics: Implementing mobile health clinics can bring healthcare services directly to communities, especially in hard-to-reach areas. These clinics can provide prenatal care, postnatal care, and other essential maternal health services.

3. Community health workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and communities. These workers can provide education, counseling, and basic healthcare services to pregnant women and new mothers, improving access to maternal health information and care.

4. Telemedicine: Utilizing telemedicine technologies can enable pregnant women to access healthcare services remotely. Through video consultations and remote monitoring, healthcare providers can offer guidance, monitor progress, and address concerns without the need for in-person visits.

5. Transportation support: Lack of transportation can be a significant barrier to accessing maternal health services. Providing transportation support, such as ambulances or transportation vouchers, can help ensure that pregnant women can reach healthcare facilities in a timely manner.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the number of prenatal care visits, the percentage of women delivering in healthcare facilities, or the time taken to reach a healthcare facility.

2. Data collection: Gather data on the current status of these indicators in the target area. This can be done through surveys, interviews, or existing data sources.

3. Baseline assessment: Calculate the baseline values for the selected indicators to establish a starting point for comparison.

4. Introduce the recommendations: Simulate the implementation of the recommended interventions by adjusting the relevant indicators based on the expected impact. For example, increase the number of prenatal care visits or the percentage of women delivering in healthcare facilities.

5. Impact assessment: Compare the baseline values with the adjusted values to determine the impact of the recommendations on improving access to maternal health. This can be done through statistical analysis or modeling techniques.

6. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the results by varying the assumptions or parameters used in the simulation.

7. Interpretation and reporting: Analyze the results and provide a clear interpretation of the findings. Present the findings in a comprehensive report that highlights the potential impact of the recommendations on improving access to maternal health.

It is important to note that the methodology may vary depending on the specific context and available data.


Identifiers:
DOI: 10.1177/00469580221087883
ISSN: 00469580
Research Areas:
Community Interventions, Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants
Study Countries:
Ethiopia
Study Design:
Case-Control Study, Cross Sectional Study, Grounded Theory
Study Approach:
Quantitative
Participants Gender:
Female
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