Prevalence of Undernutrition and Associated Factors among Lactating Mothers of Angecha District, Kembata Tembaro Zone, Southern Ethiopia

Journal of Nutrition and Metabolism, Volume 2021, Year 2021

Interpretation

Background. Major reasons for malnutrition, particularly among those who live in low- and middle-income countries, are physiological vulnerability and inadequate intake. The objective of the study was to assess the prevalence of undernutrition and associated factors among lactating mothers of Angecha District, Southern Ethiopia. Methods. A community-based cross-sectional study was conducted among randomly selected lactating mothers in Angecha District from March to April 2017. A pretested structured questionnaire was used to assess the prevalence of undernutrition and associated factors among lactating mothers. Undernutrition was defined as the body mass index of less than 18.5 kg/m2. A multivariable logistic regression model was fitted, and the adjusted odds ratio (AOR) at a p value less than 0.05 was used to determine a statistically significant association between predictors and outcome variables. Result. The prevalence of undernutrition among lactating mothers was 21.2% (95% CI: 17.52, 25.46). The odds of undernutrition were higher among lactating mothers in the younger age group (AOR 4.12 (95% CI: 1.25-13.63), compared to 36-49 years group), dietary diversity less than five food groups (AOR 2.4, 95% CI: 1.35-4.36), and not attending antenatal care (ANC) (AOR 2.90 (95% CI: 1.43-5.86), compared to those who attended ANC for 4 or more times). The odds of undernutrition among lactating women from 3rd quantile wealth index households reduced by nearly half (AOR 0.47, 95% CI: 0.23-0.98) compared to lactating mothers from 1st quantile wealth index households. Conclusion. Nearly one in every five lactating mothers was undernourished. Age, dietary diversity score, ANC visit, and wealth index were found to be the associated factors of undernutrition. Therefore, due attention should have to be given to increase the use of ANC. The study was conducted in Angecha District, Kembata Tembaro Zone, Southern Ethiopia. The administrative center of the district is Angecha town, which is located 255 km to the South of Addis Ababa, the capital city of Ethiopia. According to the Central Statistical Agency population projection for Ethiopia, in 2016, the total population of the district was 94,978 [18] and the district health office report showed that there are 20 health posts and 5 health centers. A community-based cross-sectional study was conducted from March to April 2017. The inclusion criteria for the study population were lactating mothers whose child’s age was less than 2 years and who lived in the study area for more than 6 months. Based on the review of the health posts’ record, 2,825 eligible lactating women were identified. To determine the sample size of the study, single population proportion formula was used with the assumption of 95% confidence level, 5% margin of error, 25% estimated prevalence of undernutrition among lactating mothers [19], and 10% nonresponse rate. Then the total sample size was 414. To use a simple random sampling technique, a sampling frame was prepared by reviewing the health posts’ family folders and records from all 20 kebeles of the district. To follow a simple random sampling technique, a computer-generated random number was used to select the mothers. Data was collected through a home-to-home visit using a structured and pretested interviewer-administered paper-based questionnaire and measurements. Socioeconomic and demographic characteristics, antenatal care (ANC) utilization, gravidity, maternal and child feeding practice, environmental health condition of the household, household food security, and wealth status were the components of the questionnaire. Household food insecurity access scale (HFIAS) was used to assess the food security status of the households, which was developed by the Food and Nutrition Technical Assistance [20]. The mother’s dietary diversity was measured by the recall of all food consumed by the mother during the previous 24 hours, which is according to the Food and Agricultural Organization’s (FAO’s) guideline for measuring household and individual dietary diversity [21]. Nutrition knowledge was measured using questions that are developed for assessing the practical nutrition knowledge of the lactating mothers. Wealth index questions were obtained from the Ethiopia Demographic and Health Survey, which was based on the household ownership of the productive asset and household characteristics [6]. In addition, the weight and height of the mothers were measured. The weight of the mothers was measured to the nearest 0.1 kg on a battery-powered digital scale. The height of the mothers was measured to the nearest 0.1 cm using a wooden height-measuring board with a sliding head bar following standard anthropometric techniques [22]. The questionnaire was initially developed in English and translated into Kambatic (local) language. Residents who completed high school and were fluent in speaking and writing of Kambatic language were recruited for data collection, and nurses were recruited for supervision. The training was given for two days by the investigator. The questionnaire was retranslated back to English by an individual who was blind to the original English version for checking consistency. Pretest of the questionnaire was employed prior to the actual data collection period on 5% of the sample size, and modification was made based on the finding. The functionality of digital weight scales was checked using known weight every morning before data collection began, and the data collectors were assured that the scale reading is exactly at zero before every weight measurement. Supervision was done by the investigator and supervisors, and they checked the collected data for completeness, accuracy, and consistency throughout the data collection period. The independent variables include sociodemographic characteristics of the mother and head of the household, family size, source of water for the household, dietary diversity of the mother, frequency of breastfeeding, age of introduction of complementary food for the child and its frequency, cultural avoided food, and nutritional knowledge of the mother. After generating the number of diversified food types consumed by the mothers using FAO’s manual [21], the dietary diversity score was calculated and the mean score (4.5 food groups) was used to classify low and high diversity. Nutrition knowledge index categories were generated based on the proportion of questions correctly responded to by the three groups. In addition, household food insecure level and wealth index were also included. The HFIAS category was calculated, and households’ food security status was categorized into four based on the Food and Nutrition Technical Assistance manual [20]. Wealth index was generated using a principal component factor analysis based on the household ownership of the productive asset and household characteristics and categorized into three quantiles [6]. The dependent variable is body mass index (BMI) which was categorized as undernutrition for those with less than 18.5 kg/m2. All the questionnaires were checked visually, coded, and entered into Epi info version 3.5.4 and imported to SPSS Version 20.0 software package for analysis. To assess the presence of an association between dependent and independent variables, bivariate analysis was done. Variables with a p value of less than 0.20 were entered into multiple logistic regression model to identify the independent predictors of undernutrition of lactating mothers. The presence of an association between independent and dependent variables using multiple logistic regression model was assessed by using an odds ratio of a p value of less than 0.05. For the assessment of multicollinearity, variable inflation factors were used. The fitness of the model was tested by Hosmer–Lemeshow goodness of fit test and the test revealed a p value of 0.289. Ethical approval for the study was obtained from Institutional Review Board of Arba Minch University (ሕጤሳኮ/4284/54; Date 11/02/2009 EC). Permission to conduct the study was obtained from Angecha District Health Office. Verbal informed consent was obtained from each study participant before the interviews and measurement. The privacy of the study participants was maintained by interviewing the mother alone. The interview was conducted by the data collector alone and sometimes with the presence of the supervisor.

AI Digest

Study Justification:

The study aimed to assess the prevalence of undernutrition and associated factors among lactating mothers in Angecha District, Southern Ethiopia. This is important because malnutrition is a significant issue, particularly in low- and middle-income countries. Understanding the prevalence and factors associated with undernutrition among lactating mothers can help inform interventions and policies to improve maternal and child health.

Highlights:

– The prevalence of undernutrition among lactating mothers in Angecha District was found to be 21.2%.
– Factors associated with undernutrition included younger age, low dietary diversity, and not attending antenatal care.
– Lactating women from households with higher wealth index had lower odds of undernutrition.
– The study highlights the importance of increasing the use of antenatal care and improving dietary diversity to address undernutrition among lactating mothers.

Recommendations:

Based on the findings, the following recommendations are suggested:

1. Increase access to and utilization of antenatal care services for pregnant women to improve maternal nutrition and health.
2. Promote dietary diversity among lactating mothers by providing education and support on balanced and nutritious diets.
3. Implement interventions to improve the socioeconomic status of households, particularly those in the lower wealth index quantiles, to reduce the risk of undernutrition among lactating mothers.

Key Role Players:

1. Ministry of Health: Responsible for developing and implementing policies and programs related to maternal and child health, including nutrition.
2. District Health Office: Involved in planning and implementing local health programs and interventions, including those targeting undernutrition among lactating mothers.
3. Health Extension Workers: Frontline healthcare providers who can deliver nutrition education and support to lactating mothers at the community level.
4. Non-Governmental Organizations (NGOs): Organizations working in the field of nutrition and maternal health can provide technical expertise, resources, and support for interventions.

Cost Items for Planning Recommendations:

1. Training and Capacity Building: Budget for training healthcare providers, including health extension workers, on maternal nutrition and counseling techniques.
2. Education and Awareness Campaigns: Allocate funds for developing and implementing nutrition education materials and campaigns targeting lactating mothers and their families.
3. Antenatal Care Services: Ensure adequate resources for the provision of quality antenatal care services, including regular check-ups, nutritional counseling, and supplementation if needed.
4. Food and Nutrition Programs: Allocate funds for the implementation of programs that promote dietary diversity and improve access to nutritious foods for lactating mothers.
5. Monitoring and Evaluation: Set aside a budget for monitoring and evaluating the effectiveness of interventions, including data collection, analysis, and reporting.
Please note that the cost items provided are general categories and may vary depending on the specific context and resources available.

Strength of Evidence

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 cross-sectional study, which provides valuable information on the prevalence of undernutrition and associated factors among lactating mothers. The sample size calculation and random sampling technique enhance the reliability of the findings. The use of a structured questionnaire and measurements adds to the validity of the data collected. The statistical analysis, including multivariable logistic regression, helps identify significant associations. However, the study’s limitations include the reliance on self-reported data, potential recall bias, and the inability to establish causality due to the cross-sectional design. To improve the evidence, future studies could consider a longitudinal design to establish temporal relationships and minimize bias. Additionally, incorporating objective measures of dietary intake and physical activity can enhance the accuracy of the findings.

Abstract

The study was conducted in Angecha District, Kembata Tembaro Zone, Southern Ethiopia. The administrative center of the district is Angecha town, which is located 255 km to the South of Addis Ababa, the capital city of Ethiopia. According to the Central Statistical Agency population projection for Ethiopia, in 2016, the total population of the district was 94,978 [18] and the district health office report showed that there are 20 health posts and 5 health centers. A community-based cross-sectional study was conducted from March to April 2017. The inclusion criteria for the study population were lactating mothers whose child’s age was less than 2 years and who lived in the study area for more than 6 months. Based on the review of the health posts’ record, 2,825 eligible lactating women were identified. To determine the sample size of the study, single population proportion formula was used with the assumption of 95% confidence level, 5% margin of error, 25% estimated prevalence of undernutrition among lactating mothers [19], and 10% nonresponse rate. Then the total sample size was 414. To use a simple random sampling technique, a sampling frame was prepared by reviewing the health posts’ family folders and records from all 20 kebeles of the district. To follow a simple random sampling technique, a computer-generated random number was used to select the mothers. Data was collected through a home-to-home visit using a structured and pretested interviewer-administered paper-based questionnaire and measurements. Socioeconomic and demographic characteristics, antenatal care (ANC) utilization, gravidity, maternal and child feeding practice, environmental health condition of the household, household food security, and wealth status were the components of the questionnaire. Household food insecurity access scale (HFIAS) was used to assess the food security status of the households, which was developed by the Food and Nutrition Technical Assistance [20]. The mother’s dietary diversity was measured by the recall of all food consumed by the mother during the previous 24 hours, which is according to the Food and Agricultural Organization’s (FAO’s) guideline for measuring household and individual dietary diversity [21]. Nutrition knowledge was measured using questions that are developed for assessing the practical nutrition knowledge of the lactating mothers. Wealth index questions were obtained from the Ethiopia Demographic and Health Survey, which was based on the household ownership of the productive asset and household characteristics [6]. In addition, the weight and height of the mothers were measured. The weight of the mothers was measured to the nearest 0.1 kg on a battery-powered digital scale. The height of the mothers was measured to the nearest 0.1 cm using a wooden height-measuring board with a sliding head bar following standard anthropometric techniques [22]. The questionnaire was initially developed in English and translated into Kambatic (local) language. Residents who completed high school and were fluent in speaking and writing of Kambatic language were recruited for data collection, and nurses were recruited for supervision. The training was given for two days by the investigator. The questionnaire was retranslated back to English by an individual who was blind to the original English version for checking consistency. Pretest of the questionnaire was employed prior to the actual data collection period on 5% of the sample size, and modification was made based on the finding. The functionality of digital weight scales was checked using known weight every morning before data collection began, and the data collectors were assured that the scale reading is exactly at zero before every weight measurement. Supervision was done by the investigator and supervisors, and they checked the collected data for completeness, accuracy, and consistency throughout the data collection period. The independent variables include sociodemographic characteristics of the mother and head of the household, family size, source of water for the household, dietary diversity of the mother, frequency of breastfeeding, age of introduction of complementary food for the child and its frequency, cultural avoided food, and nutritional knowledge of the mother. After generating the number of diversified food types consumed by the mothers using FAO’s manual [21], the dietary diversity score was calculated and the mean score (4.5 food groups) was used to classify low and high diversity. Nutrition knowledge index categories were generated based on the proportion of questions correctly responded to by the three groups. In addition, household food insecure level and wealth index were also included. The HFIAS category was calculated, and households’ food security status was categorized into four based on the Food and Nutrition Technical Assistance manual [20]. Wealth index was generated using a principal component factor analysis based on the household ownership of the productive asset and household characteristics and categorized into three quantiles [6]. The dependent variable is body mass index (BMI) which was categorized as undernutrition for those with less than 18.5 kg/m2. All the questionnaires were checked visually, coded, and entered into Epi info version 3.5.4 and imported to SPSS Version 20.0 software package for analysis. To assess the presence of an association between dependent and independent variables, bivariate analysis was done. Variables with a p value of less than 0.20 were entered into multiple logistic regression model to identify the independent predictors of undernutrition of lactating mothers. The presence of an association between independent and dependent variables using multiple logistic regression model was assessed by using an odds ratio of a p value of less than 0.05. For the assessment of multicollinearity, variable inflation factors were used. The fitness of the model was tested by Hosmer–Lemeshow goodness of fit test and the test revealed a p value of 0.289. Ethical approval for the study was obtained from Institutional Review Board of Arba Minch University (ሕጤሳኮ/4284/54; Date 11/02/2009 EC). Permission to conduct the study was obtained from Angecha District Health Office. Verbal informed consent was obtained from each study participant before the interviews and measurement. The privacy of the study participants was maintained by interviewing the mother alone. The interview was conducted by the data collector alone and sometimes with the presence of the supervisor.

Materials

N/A

Innovations

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, antenatal care, breastfeeding, and child feeding practices. These apps can be easily accessible to lactating mothers, providing them with valuable information and guidance.

2. Telemedicine: Establish telemedicine services that allow lactating mothers in remote or underserved areas to consult with healthcare professionals remotely. This can help address the lack of access to healthcare facilities and specialists, ensuring that mothers receive timely and appropriate care.

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

4. Maternal Health Vouchers: Implement voucher programs that provide financial assistance to lactating mothers, enabling them to access essential maternal health services such as antenatal care, postnatal care, and skilled birth attendance. These vouchers can help reduce financial barriers and increase utilization of maternal health services.

5. Maternal Health Clinics: Establish dedicated maternal health clinics that provide comprehensive care for lactating mothers, including antenatal care, postnatal care, family planning, and nutrition counseling. These clinics can serve as one-stop centers for maternal health services, ensuring that mothers receive holistic care.

6. Health Education Campaigns: Conduct targeted health education campaigns to raise awareness about the importance of maternal health and nutrition. These campaigns can include community workshops, radio programs, and informational materials to educate lactating mothers and their families about best practices for maternal health.

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 and facilities to expand service coverage and reduce wait times for lactating mothers.

8. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, counseling, and referrals to lactating mothers. These hotlines can be available 24/7, ensuring that mothers have access to immediate support and guidance.

9. Transportation Support: Develop transportation support programs that provide transportation vouchers or services to lactating mothers who face challenges in accessing healthcare facilities. This can help overcome geographical barriers and ensure that mothers can reach healthcare facilities in a timely manner.

10. Maternal Health Monitoring Systems: Implement digital health solutions that enable the monitoring and tracking of maternal health indicators, such as weight, blood pressure, and nutritional status. These systems can help healthcare providers identify high-risk cases and provide targeted interventions to improve maternal health outcomes.

It is important to note that the implementation of these innovations should be context-specific and tailored to the local healthcare system and resources available in Angecha District, Southern Ethiopia.

AI Innovations Description

Based on the study conducted in Angecha District, Southern Ethiopia, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Increase awareness and utilization of antenatal care (ANC): The study found that not attending ANC was associated with higher odds of undernutrition among lactating mothers. Therefore, efforts should be made to increase the use of ANC services by providing education and awareness campaigns about the importance of ANC visits for maternal and child health.

Innovation: Develop a mobile application or SMS-based system that sends reminders and educational messages to pregnant women and lactating mothers about the importance of ANC visits. The system can also provide information on the nearest health facilities offering ANC services and their contact details.

2. Improve dietary diversity: The study found that lactating mothers with dietary diversity less than five food groups had higher odds of undernutrition. Therefore, interventions should focus on improving access to a variety of nutritious foods for lactating mothers.

Innovation: Establish community-based nutrition programs that promote the cultivation and consumption of diverse food crops. This can include training and support for small-scale farming, community gardens, and nutrition education sessions on the importance of a balanced diet for lactating mothers.

3. Address socioeconomic factors: The study found that wealth index was associated with undernutrition among lactating mothers, with those from lower wealth index households having higher odds of undernutrition. Therefore, efforts should be made to address socioeconomic factors that contribute to undernutrition.

Innovation: Implement income-generating activities and livelihood support programs for lactating mothers from low-income households. This can include vocational training, microfinance initiatives, and support for small businesses, which can help improve their economic status and access to nutritious food.

Overall, these recommendations can be used to develop innovative approaches that address the specific factors identified in the study and improve access to maternal health in Angecha District, Southern Ethiopia.

AI Innovations Methodology

Based on the provided study, here are some potential recommendations for improving access to maternal health:

1. Increase awareness and utilization of antenatal care (ANC): The study found that not attending ANC was associated with higher odds of undernutrition among lactating mothers. Therefore, efforts should be made to increase awareness about the importance of ANC visits and encourage pregnant women to attend regular check-ups.

2. Improve dietary diversity: The study found that lactating mothers with dietary diversity less than five food groups had higher odds of undernutrition. Promoting a diverse and balanced diet that includes foods from different food groups can help improve the nutritional status of lactating mothers.

3. Address socioeconomic factors: The study found that lactating women from households with higher wealth index had lower odds of undernutrition. Addressing socioeconomic factors such as poverty and inequality can contribute to improving access to maternal health by ensuring that women have the resources and opportunities to access nutritious food and healthcare 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 population group that will be the focus of the simulation, such as lactating mothers in a particular region or community.

2. Collect baseline data: Gather relevant data on the current status of access to maternal health, including indicators such as ANC attendance rates, dietary diversity, and prevalence of undernutrition among lactating mothers.

3. Define the intervention scenarios: Develop different scenarios that represent the potential impact of the recommendations. For example, one scenario could assume an increase in ANC attendance rates, another scenario could assume an improvement in dietary diversity, and so on.

4. Model the impact: Use statistical modeling techniques to estimate the potential impact of each scenario on access to maternal health. This could involve analyzing the association between the recommended interventions and the outcome variables (e.g., undernutrition prevalence) using regression models.

5. Simulate the outcomes: Apply the estimated impact of each scenario to the baseline data to simulate the potential outcomes. This could involve calculating the expected changes in ANC attendance rates, dietary diversity, and undernutrition prevalence based on the modeled associations.

6. Evaluate the results: Assess the simulated outcomes to determine the potential effectiveness of the recommendations in improving access to maternal health. Compare the outcomes of different scenarios to identify the most promising interventions.

7. Refine and iterate: Based on the evaluation results, refine the recommendations and simulation methodology as needed. Iterate the process to further explore and refine potential interventions for improving access to maternal health.

It’s important to note that the specific methodology for simulating the impact may vary depending on the available data, resources, and context. It’s recommended to consult with experts in the field of maternal health and utilize appropriate statistical and modeling techniques to ensure the accuracy and validity of the simulation results.

Author

Dima Health

Statistics:

Citations: 4

Identifiers:

DOI: 10.1155/2021/6691982

ISSN: 20900724

Research Areas:

Community Interventions, Disability, Disparities, Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female