Undernutrition and its associated factors among pregnant mothers in Gondar town, Northwest Ethiopia

PLoS ONE, Volume 14, No. 4, Year 2019

Ukuhunyushwa

Background Regardless of significant gains and signs of progress in the last decades, maternal undernutrition remains a major public health concern in Ethiopia. Supporting the progress of interventions being taken in the country with evidence might be important to keep the sustainability of the government effort. We aimed at determining the extent of undernutrition and its associated factors among pregnant mothers in Gondar town, Northwest Ethiopia. Method A community-based cross-sectional study was conducted by including 940 selected pregnant mothers through a cluster sampling. A face-to-face interview was administered to pregnant mothers at a household level. We collected data using an Online Data collection kit (ODK) and the collected data was directly downloaded from the Google Cloud platform and finally imported to Stata 14 for further analysis. A multivariable logistic regression model was fitted to identify factors associated with undernutrition. A crude and adjusted odds ratio with their 95% confidence interval was calculated to declare the association and its significance. Model fitness was assured through the Hosmer and Lemeshow goodness of fit test and model classification accuracy. Result 14.4% (95%CI: 12.3-16.7) of pregnant mothers were undernourished. After adjusting for the main covariates; as the age of the pregnant mothers increases the odds of being undernourished decreases by 10% (AOR: 0.90; 95%CI: 0.87-0.95) and having a poor marital condition (AOR: 2.18; 95%CI: 1.03-4.59) increased the odds of undernutrition. The risk of undernutrition was also decreased by 43% among those pregnant mothers who consumed coffee sometimes (AOR: 0.57; 95%CI: 0.36-0.89) as compared to daily consumers. Conclusion A significant proportion of pregnant mother were undernourished. Integration of nutritional interventions with maternity health services would be highly important to improve the nutritional status of the mothers. It is also important to counsel pregnant mothers about a consequence of frequent coffee drinking during their pregnancy. We conducted a cross-sectional study from June 15 to July 30, 2018, on pregnant mothers in their second and third trimester of pregnancy who are living in Gondar Town. Gondar town is located in the Northern part of Amhara regional state at a distance of 747 km away from Addis Ababa and 170 km from Bahir Dar (the regional capital city). Gondar town has a total population of 333,103 and an expected number of pregnant women in the town is estimated to be 11,225 in which at least 8,913 of them are living in urban kebeles (clusters) in 2017/2018. Pregnant women who were living in a randomly selected urban kebeles were considered as the study population. A cluster sampling was used to reach the study participants. On the first stage, five urban clusters from 12 urban clusters were selected by lottery method and on the second stage, a house to house census of pregnant mothers found in the six selected clusters was conducted. The required sample size was determined in Epi Info 7 by using a single and double population proportion formula. A parity variable from a study conducted to determine undernutrition in Gondar referral hospital [8] was used to determine a sample size for our study assuming 80% power, 95% confidence level, an odds ratio of 2.25, proportion of undernutrition in those women’s with no previous birth as 11.62%, proportion of undernutrition among pregnant mothers with >4 births as 22.8%, cluster effect of 2, and a 10% non-response rate. Our double population proportion formula yielded the higher sample size of 858. However, because of the nature of the cluster sampling, 940 pregnant mothers were actually found and included in the study. An interviewer-administered Amharic version of the questionnaire was used to collect the required information from the study participants. The online data collection kit (ODK) application was used to collect and manage data to improve its quality. The prepared questionnaire was designed on the excel spreadsheet, converted to XLSForm online, and checked for its validity using Enketo. The validated form was downloaded and uploaded on a Lenovo tab 7 ODK application. The data storage place for the project was created on the Google cloud platform. The data collectors sent the collected data to the online created data storage system and the principal investigator directly downloaded the data from the system. Maternal nutrition was assessed using a Mid Upper Arm Circumference (MUAC) and categorized as undernutrition (MUAC < 22) and normal (MUAC > = 22) [8, 16]. Maternal depression and anxiety were measured by using an Edinburgh Postnatal Depression Scale (EPDS) revised for Ethiopian context [17]. A mother was considered as depressed if she had a total measurement scale of > = 12. Anxiety was measured by using the third, fourth, and fifth scale on EPDS and a mother with a total scale of > = 6 was classified as having anxiety symptoms [18]. Social support was assessed by using the Oslo Social Support Scale (OSSS-3) and pregnant mothers who scored nine and above were labeled as having “Good” social support and those scored below nine were labeled as having “Poor” social support [19]. Husband support was assessed by a question “My husband helps me a lot” with the options: “Always (5)”, “Most of the time (4), “Some of the time (3)”, “Rarely (2)”, and “Never (1)”. Coffee consumption was assessed based on the number of days the mother consumed coffee in a week. Those who have been consuming coffee one to three days per week were considered as consuming coffee sometimes, those reported consuming coffee every day as daily drinkers, and those have not been drunk before as non-drinkers. A marital condition was also assessed based on mother’s perspective regarding their marital situation in a day to day life and if the marital situation is loving and easy going without conflict and disagreement was considered as “Bad” and if not it was considered as “Good”. Physical activity of the mother was assessed by a question “Have you practice physical activity such as brisk walking, dancing, gardening, and usual housework for at least three hours/week” and their answer was documented as “Yes” and “No” [20]. Food access for the last three months was assessed by a single question: “In the last three months, have you ever worried that your household would not have enough food?” a standard question which has been used for assessing the level of food inaccessibility in a household. The data collection tool was first prepared in English, translated into Amharic and back-translated to English to check for its consistency before administration. Nine trained BSc. nurses were recruited, trained, and collected the data through a house to house survey. The principal investigator and one additional recruited field supervisor supervised the overall data collection activity. A data that was collected online was downloaded from the Google Cloud Platform and imported to the Stata 14 for further analysis. Data were checked and re-checked for completeness before importing and further cleaning was done by running frequencies. Mean, median, proportion/percentage, interquartile range, standard deviations, and exploratory analysis were conducted to understand the nature of the data. Preliminary findings were presented using tables. A bi-variable and multivariable logistic regression model was fitted to identify factors associated with undernutrition. Adjusted odds ratio with its 95% confidence interval was computed to test for statistical significance. Model adequacy was checked using the Hosmer and Lemeshow goodness of fit test (p-value = 0.59) and a classification accuracy (85.6%). The University of Gondar Institutional Review Board ethics committee approved this study. A support letter was obtained from the University of Gondar Research and Community Service to the Gondar town health office and respective districts. Participants of the study were informed about the purpose, objectives and their right to participate or not participate in the study. Privacy and confidentiality of the study participant were ensured by not using a personal identifier. Written informed consent was obtained from the study participants in order to be part of the study. Pregnant mothers who were seriously ill during a house to house data collection time were referred to Gondar University Specialized Hospital and those found severely malnourished were also counseled about proper nutrition.

I-AI Digest

Study Justification:

– Maternal undernutrition is a significant public health concern in Ethiopia.
– Supporting government efforts with evidence-based interventions is crucial for sustainability.
– This study aimed to determine the extent of undernutrition and its associated factors among pregnant mothers in Gondar town, Northwest Ethiopia.

Highlights:

– 14.4% of pregnant mothers in Gondar town were found to be undernourished.
– Factors associated with undernutrition included older age, poor marital condition, and daily coffee consumption.
– Pregnant mothers who consumed coffee sometimes had a lower risk of undernutrition.
– Integration of nutritional interventions with maternity health services is recommended to improve the nutritional status of mothers.
– Pregnant mothers should be counseled about the consequences of frequent coffee drinking during pregnancy.

Recommendations:

– Implement nutritional interventions as part of maternity health services.
– Provide counseling to pregnant mothers about the risks of frequent coffee consumption during pregnancy.

Key Role Players:

– Government health departments
– Maternity health service providers
– Nutritionists and dieticians
– Community health workers
– Non-governmental organizations (NGOs) working in maternal and child health

Cost Items for Planning Recommendations:

– Training and capacity building for health professionals
– Development and distribution of educational materials
– Integration of nutritional interventions into existing health services
– Monitoring and evaluation of intervention programs
– Awareness campaigns and community outreach activities
– Research and data collection on the effectiveness of interventions

Amandla Obufakazi

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 about the prevalence and associated factors of undernutrition among pregnant mothers in Gondar town, Northwest Ethiopia. The sample size of 940 pregnant mothers is relatively large, increasing the generalizability of the findings. The study also used a multivariable logistic regression model to identify factors associated with undernutrition, which strengthens the analysis. However, the evidence could be further improved by considering the following actionable steps: 1) Conducting a longitudinal study to establish causal relationships between the identified factors and undernutrition; 2) Including a control group of pregnant mothers without undernutrition for comparison; 3) Using objective measures of undernutrition, such as body mass index or biochemical markers, in addition to the Mid Upper Arm Circumference (MUAC) measurement; 4) Collecting data on dietary intake and nutritional status of the mothers to better understand the underlying causes of undernutrition; 5) Ensuring the representativeness of the sample by using a more rigorous sampling method, such as stratified random sampling, to select participants from different socioeconomic backgrounds.

Abstract

We conducted a cross-sectional study from June 15 to July 30, 2018, on pregnant mothers in their second and third trimester of pregnancy who are living in Gondar Town. Gondar town is located in the Northern part of Amhara regional state at a distance of 747 km away from Addis Ababa and 170 km from Bahir Dar (the regional capital city). Gondar town has a total population of 333,103 and an expected number of pregnant women in the town is estimated to be 11,225 in which at least 8,913 of them are living in urban kebeles (clusters) in 2017/2018. Pregnant women who were living in a randomly selected urban kebeles were considered as the study population. A cluster sampling was used to reach the study participants. On the first stage, five urban clusters from 12 urban clusters were selected by lottery method and on the second stage, a house to house census of pregnant mothers found in the six selected clusters was conducted. The required sample size was determined in Epi Info 7 by using a single and double population proportion formula. A parity variable from a study conducted to determine undernutrition in Gondar referral hospital [8] was used to determine a sample size for our study assuming 80% power, 95% confidence level, an odds ratio of 2.25, proportion of undernutrition in those women’s with no previous birth as 11.62%, proportion of undernutrition among pregnant mothers with >4 births as 22.8%, cluster effect of 2, and a 10% non-response rate. Our double population proportion formula yielded the higher sample size of 858. However, because of the nature of the cluster sampling, 940 pregnant mothers were actually found and included in the study. An interviewer-administered Amharic version of the questionnaire was used to collect the required information from the study participants. The online data collection kit (ODK) application was used to collect and manage data to improve its quality. The prepared questionnaire was designed on the excel spreadsheet, converted to XLSForm online, and checked for its validity using Enketo. The validated form was downloaded and uploaded on a Lenovo tab 7 ODK application. The data storage place for the project was created on the Google cloud platform. The data collectors sent the collected data to the online created data storage system and the principal investigator directly downloaded the data from the system. Maternal nutrition was assessed using a Mid Upper Arm Circumference (MUAC) and categorized as undernutrition (MUAC = 22) [8, 16]. Maternal depression and anxiety were measured by using an Edinburgh Postnatal Depression Scale (EPDS) revised for Ethiopian context [17]. A mother was considered as depressed if she had a total measurement scale of > = 12. Anxiety was measured by using the third, fourth, and fifth scale on EPDS and a mother with a total scale of > = 6 was classified as having anxiety symptoms [18]. Social support was assessed by using the Oslo Social Support Scale (OSSS-3) and pregnant mothers who scored nine and above were labeled as having “Good” social support and those scored below nine were labeled as having “Poor” social support [19]. Husband support was assessed by a question “My husband helps me a lot” with the options: “Always (5)”, “Most of the time (4), “Some of the time (3)”, “Rarely (2)”, and “Never (1)”. Coffee consumption was assessed based on the number of days the mother consumed coffee in a week. Those who have been consuming coffee one to three days per week were considered as consuming coffee sometimes, those reported consuming coffee every day as daily drinkers, and those have not been drunk before as non-drinkers. A marital condition was also assessed based on mother’s perspective regarding their marital situation in a day to day life and if the marital situation is loving and easy going without conflict and disagreement was considered as “Bad” and if not it was considered as “Good”. Physical activity of the mother was assessed by a question “Have you practice physical activity such as brisk walking, dancing, gardening, and usual housework for at least three hours/week” and their answer was documented as “Yes” and “No” [20]. Food access for the last three months was assessed by a single question: “In the last three months, have you ever worried that your household would not have enough food?” a standard question which has been used for assessing the level of food inaccessibility in a household. The data collection tool was first prepared in English, translated into Amharic and back-translated to English to check for its consistency before administration. Nine trained BSc. nurses were recruited, trained, and collected the data through a house to house survey. The principal investigator and one additional recruited field supervisor supervised the overall data collection activity. A data that was collected online was downloaded from the Google Cloud Platform and imported to the Stata 14 for further analysis. Data were checked and re-checked for completeness before importing and further cleaning was done by running frequencies. Mean, median, proportion/percentage, interquartile range, standard deviations, and exploratory analysis were conducted to understand the nature of the data. Preliminary findings were presented using tables. A bi-variable and multivariable logistic regression model was fitted to identify factors associated with undernutrition. Adjusted odds ratio with its 95% confidence interval was computed to test for statistical significance. Model adequacy was checked using the Hosmer and Lemeshow goodness of fit test (p-value = 0.59) and a classification accuracy (85.6%). The University of Gondar Institutional Review Board ethics committee approved this study. A support letter was obtained from the University of Gondar Research and Community Service to the Gondar town health office and respective districts. Participants of the study were informed about the purpose, objectives and their right to participate or not participate in the study. Privacy and confidentiality of the study participant were ensured by not using a personal identifier. Written informed consent was obtained from the study participants in order to be part of the study. Pregnant mothers who were seriously ill during a house to house data collection time were referred to Gondar University Specialized Hospital and those found severely malnourished were also counseled about proper nutrition.

Izinto zokwakha

Emisha

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant mothers with access to important health information, such as nutrition guidelines, prenatal care schedules, and reminders for medication or appointments. These apps can also include features for tracking maternal health indicators, such as weight gain and blood pressure.

2. Telemedicine Services: Establish telemedicine services that allow pregnant mothers in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This can help address the lack of access to specialized maternal health services in certain regions.

3. Community Health Workers: Train and deploy community health workers who can provide education, support, and basic healthcare services to pregnant mothers in their own communities. These workers can help bridge the gap between healthcare facilities and pregnant mothers, particularly in rural areas.

4. Nutritional Support Programs: Implement programs that provide pregnant mothers with access to nutritious food and supplements, especially for those who are undernourished. This can be done through partnerships with local farmers, food banks, or government initiatives.

5. Maternal Health Vouchers: Introduce voucher programs that provide pregnant mothers with financial assistance to access essential maternal health services, such as prenatal care, delivery, and postnatal care. These vouchers can be distributed to vulnerable populations to ensure equitable access to healthcare.

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 social media campaigns to reach a wide audience.

7. Integration of Services: Improve coordination and integration between maternal health services and other related sectors, such as nutrition, mental health, and social support. This can ensure that pregnant mothers receive comprehensive care that addresses their various needs.

8. Public-Private Partnerships: Foster partnerships between the public and private sectors to improve access to maternal health services. This can involve collaborations with private healthcare providers, pharmaceutical companies, and technology companies to leverage their resources and expertise.

9. Transportation Support: Address transportation barriers by providing pregnant mothers with transportation vouchers or arranging community transportation services to help them reach healthcare facilities for prenatal visits and deliveries.

10. Maternal Health Hotlines: Establish toll-free hotlines staffed by trained healthcare professionals who can provide information, counseling, and referrals to pregnant mothers. This can be particularly helpful for those who have questions or concerns but are unable to visit a healthcare facility.

These innovations can help improve access to maternal health services, address undernutrition, and support the overall well-being of pregnant mothers in Gondar town, Northwest Ethiopia.

AI Innovations Description

Based on the description provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Integration of Nutritional Interventions with Maternity Health Services: To improve the nutritional status of pregnant mothers, it is important to integrate nutritional interventions with existing maternity health services. This can include providing education and counseling on proper nutrition during pregnancy, promoting healthy eating habits, and ensuring access to nutritious food options.

2. Increased Focus on Undernutrition Screening: Implementing routine undernutrition screening during prenatal visits can help identify pregnant mothers who are at risk of undernutrition. This can be done through the use of tools such as the Mid Upper Arm Circumference (MUAC) measurement. Early identification of undernutrition can lead to timely interventions and support.

3. Mental Health Support: Addressing maternal depression and anxiety is crucial for improving maternal health outcomes. Integrating mental health support services within maternal health programs can help identify and provide appropriate care for pregnant mothers experiencing mental health challenges.

4. Social Support Programs: Enhancing social support for pregnant mothers can contribute to improved maternal health. Implementing programs that promote community engagement, peer support, and involvement of family members can help create a supportive environment for pregnant mothers.

5. Targeted Interventions for Vulnerable Groups: Identifying and targeting vulnerable groups, such as pregnant mothers with poor marital conditions, can help address specific challenges they may face. Tailored interventions can be developed to provide additional support and resources to these groups.

6. Health Education and Counseling: Providing comprehensive health education and counseling sessions to pregnant mothers can empower them with knowledge and skills to make informed decisions about their health and nutrition. This can include topics such as the consequences of frequent coffee drinking during pregnancy and the importance of a balanced diet.

By implementing these recommendations, access to maternal health can be improved, leading to better health outcomes for pregnant mothers and their babies.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Integration of nutritional interventions: Integrate nutritional interventions with maternity health services to address undernutrition among pregnant mothers. This can include providing access to nutrient-rich foods, promoting healthy eating habits, and offering nutritional counseling.

2. Increase awareness about the consequences of frequent coffee drinking: Educate pregnant mothers about the potential negative effects of frequent coffee consumption during pregnancy. This can be done through health education campaigns, antenatal care visits, and community outreach programs.

3. Improve social support: Enhance social support systems for pregnant mothers, as good social support has been shown to have a positive impact on maternal health. This can involve engaging family members, community leaders, and support groups to provide emotional, practical, and financial support to pregnant mothers.

4. Address marital conditions: Address poor marital conditions that increase the risk of undernutrition among pregnant mothers. This can be done through couples counseling, promoting healthy relationships, and providing resources for conflict resolution.

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 can measure the impact of the recommendations, such as the percentage of pregnant mothers receiving integrated nutritional interventions, the change in coffee consumption patterns, the improvement in social support scores, and the reduction in poor marital conditions.

2. Collect baseline data: Gather baseline data on the current status of access to maternal health, including undernutrition rates, coffee consumption patterns, social support levels, and marital conditions among pregnant mothers in the target population.

3. Implement the recommendations: Implement the recommended interventions, such as integrating nutritional interventions, conducting awareness campaigns, improving social support systems, and addressing marital conditions.

4. Monitor and evaluate: Continuously monitor the implementation of the recommendations and collect data on the indicators identified in step 1. This can be done through surveys, interviews, and data collection tools.

5. Analyze the data: Analyze the collected data to assess the impact of the recommendations on improving access to maternal health. This can involve comparing the baseline data with the post-intervention data and calculating the changes in the indicators.

6. Interpret the results: Interpret the results of the analysis to determine the effectiveness of the recommendations in improving access to maternal health. Identify any significant changes, trends, or patterns observed in the data.

7. Adjust and refine: Based on the findings, make any necessary adjustments or refinements to the recommendations and interventions to further improve access to maternal health.

8. Communicate the findings: Share the results of the impact assessment with relevant stakeholders, including policymakers, healthcare providers, and community members. Use the findings to advocate for further support and resources to sustain and scale up the successful interventions.

By following this methodology, it is possible to simulate the impact of the recommendations on improving access to maternal health and make informed decisions to address the challenges identified in the study.

Ababhali & Co-Ababhali

Statistics:

Citations: 25

Authors: 2

Identifiers:

DOI: 10.1371/journal.pone.0215305

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Exploratory Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: