Undernutrition and its associated factors among pregnant women at the public hospitals of Bench-Sheko and Kaffa zone, southwest Ethiopia

Heliyon, Volume 8, No. 5, Year 2022

Interpretação

Background: Undernutrition in pregnant women, expressed as low mid-upper arm circumference, is responsible for maternal mortality and morbidity, adverse birth outcomes, subsequent childhood malnutrition, and mortality. As a result, the purpose of this study was to determine the prevalence of maternal undernutrition and associated factors during pregnancy in public hospitals in the Bench-Sheko and Kaffa zones of southwest Ethiopia. Methods: A facility-based cross-sectional study design was employed among 566 women who received antenatal care from March–May 2021 at the public hospitals of the Bench-Sheko and Kaffa zones, Southwest Ethiopia. A systematic random sampling technique was used to select the research unit. Undernutrition was measured by mid-upper arm circumference. The data were entered into Epi- Data version 3.1 and then exported to Statistical Package for Social Science (SPSS) version 21 software for analysis. Multivariate logistic regression models were constructed using variables with a P-value <0.25 in bivariate logistic regression analysis. Finally, in multivariate logistic regression analysis, the variable with a (P-value < 0.05) is considered statistically significant. Results: A total of 566 pregnant women participated in our study with a response rate of 98.3%. The overall prevalence of undernutrition among pregnant women was 42.4% (95% CI: 38.3, 46.5). In multivariate logistic regression, the age of mothers between 16-24 years old (AOR = 3.9, 95% CI: 1.60, 9.70), household food insecurity (AOR = 1.81, 95% CI: 1.04, 3.15), and poor dietary knowledge (AOR = 3.25, 95% CI: 1.94, 5.47) were the factors significantly associated with undernutrition among pregnant women. Conclusion: According to this study finding, the prevalence of undernutrition was very much high in the study area, which was significantly associated with the age groups of 16–24 years older women, poor dietary knowledge, and household food insecurity. Therefore, the strategies and programs targeted towards reducing and preventing undernutrition among pregnant mothers should be made at all levels to improve their nutritional status, and also health information, nutrition counseling, and food assistant should be provided. This research was done in three public hospitals in the Bench-Sheko and Kaffa zones of SNNPR, Ethiopia. The administrative center of the Bench-Sheko zone is Mizan-Aman town, which is situated 562 km from Addis Ababa, Ethiopia's capital city. According to zonal annual reports of 2019, the total population of the zone was 653,270, of whom 324,542 were men and 328,728 women. There is one general hospital in the Bench-Sheko zone with a 3-month average number of 540 antenatal care (ANC) attendants before the data collection period. Bonga town, 468 km from Addis Ababa, serves as the administrative center of the Kaffa zone. In 2017, the zone's overall population was predicted to be 1,171,133, with 578,151 men (49.4 %) and 592,982 women (50.6 %). There is one general hospital and one primary hospital in the zone. with the 3- months an average number of 490 and 401 ANC attendants before the data collection period in each hospital respectively. The study was conducted from March to May 2021. A hospital -based cross-sectional study design was used. All women in the Bench-Sheko and Kaffa zones, Southwest Ethiopia, received ANC at the public hospitals. All women who were systematically selected during ANC follow-up. All pregnant women attended ANC at public hospitals. The pregnant women who were sick or mentally unstable. In this study, the sample size was calculated using a single population proportion formula with the following assumptions in mind: The prevalence of undernutrition (P) among pregnant women from the Silte zone study finding, 21.8% [14], 5% marginal error(d), 95% confidence level (Zα/2 = 1.96), none responses rate of 10% and the design effect of 2. As a result, the sample size was calculated as follows: n= ((1.96)2 ∗0.218 (1-0.218))/(0.05)2 = ∼ 262. Thus, a minimum number of 262 pregnant women were the required number for the study. Then when we considered the design effect of 2 (262∗2) it became 524. Finally, adding a 10% none-response rate (524 ∗10%), 524 + 52 = 576 of sample size were used. First, each public hospital of the Bench-Sheko and Kaffa zones received a proportionate share of the entire sample size based on their average number of clients attending ANC before the data collection period. Next, a systematic random sampling technique was used to select the study units by using the list of pregnant mothers attending ANC as a sampling frame, and the sampling interval (Kth) was calculated by using the formula of k = N/n. Finally, every Kth person (roughly 2), as they registered, was included in the study until the desired sample size was attained from each hospital. Undernutrition of pregnant women. Age, marital status, religion, family size, occupation, education, income level, television (TV)/radio, mobile, household food insecurity, meals frequency, skipping meals, eating a snack, eating additional meals, excessive workload, residency, number of live birth, number of pregnancies, pregnancy interval, number of ANC visits, trimester, history of illness, nutrition information, source of nutrition information, dietary knowledge and attitude of pregnant women were independent variables. Structured and semi-structured questionnaires administered by Midwives and Nurses were used to collect the data. The data on socio-demographic and economic, obstetric and pregnancy-related factors, household food insecurity, dietary knowledge, dietary attitude, a dietary related habit of pregnant women like, eating habit of snacks, skipping of meals, meals frequency, eating additional meals, and nutritional status of pregnant women were assessed. The general content validity of the questionnaires was checked by relevant professionals against the conceptual framework of the study and its reliability was checked by using a test-retest method and the questions with less than 0.7 Pearson coefficient values were avoided from the questionnaire. The household food insecurity level was measured with standardized and validated tools of Household Food Insecurity Access Scale (HFIAS)that was developed mainly by Food and Nutrition Technical Assistance (FANTA), and classified the households as food secured or not [15, 16]. The tool consists of nine questions that represent the severity of food insecurity in general (access). Nine "frequency-of-occurrence" questions inquire about changes in households' diets or food consumption patterns over the previous 30 days due to limited food resources. Participants were assigned a score between 0 and 27 based on their responses to the nine questions and their frequency of occurrence over the preceding 30 days. A higher HFIAS score indicates more inadequate access to food and greater household food insecurity, while a score of 0 indicated secure access to food. Ten open-ended questions adapted from a previous study were used to assess dietary knowledge, which tried to evaluate the nutritional knowledge of pregnant women's on the nutritional aspects of pregnancy [17]. Its reliability in this study was a Cronbach Alpha of 0.92. The items measuring nutritional knowledge were scored on a dichotomous scale as 0 = does not know and 1 = knows. Each correct answer was coded as 1 and each incorrect answer was coded as 0. Then the total score was obtained by summation of each score. Finally, nutritional knowledge level was categorized as knowledgeable if she correctly answered greater than or equal to 70% of the total nutrition knowledge questions and not knowledgeable If respondents scored <70% [18, 19]. The attitude of pregnant women toward nutrition during pregnancy was assessed by using nine questions. The reliability of the attitude questions was checked and showed a Cronbach Alpha of 0.84. The pregnant woman was given one mark if the answer were a favorable attitude toward nutrition during pregnancy and zero scores if the response were an unfavorable attitude [18, 20]. Following the summation of the scores, the respondent was classified as having a favorable attitude if their attitude score was greater than or equal to the median of the scores, and as having an unfavorable attitude if their attitude score was less than or equal to the median of the scores [18]. The circumference of the middle upper arm (MUAC) was measured with MUAC tape that was non-elastic and non-stretchable. First, we removed any clothing that might cover the pregnant mother's left arm then calculated the midpoint of the pregnant mother's left upper arm by first locating the tip of the pregnant mother's shoulder, bending the pregnant mother's elbow to make a right angle, and inspected the tension of the tape on the pregnant's arm. We also made sure that the tape has proper tension and was not too tight or loose. When the tape was in the correct position on the arm with the correct tension, read and called out the measurement to the nearest 0.1cm, and the average value was taken after measuring twice. A range <23 cm was used as a cut-off point for undernutrition and while a range of ≥23 cm was for normal nutritional status. This study was conducted according to the Declaration of Helsinki. First, ethical approval was obtained from Mizan-Tepi University Institutional Research Ethics and Review Committee to conduct this study. A formal letter was sent to the Bech-Sheko and Kaffa zone health bureau administrators, as well as the selected hospitals, prior to the study. Before any data was collected, the study's goal, benefits, confidentiality, and risks were explained to the participants, and all respondents signed a written informed consent form. The respondents have agreed to maintain their anonymity, and the information they provide will be used solely for the purposes of the study. After ensuring that all data were complete and consistent internally, they were coded and entered into the Epi Data 3.1 computer software package. For further analysis, the data was exported to the Statistical Package for Social Science (SPSS) version 21 software. Undernutrition was classified and coded as 1 for "yes" if the MUAC was 23 cm and 0 for "no" if the MUAC was 23 cm [[14], [21], [22], [23], [24], [25]]. The household food insecurity access score was calculated for each household by summing up the nine food insecurity frequencies in the previous 30 days. The nine items were recorded as 0 for "no" to each occurrence and 1 for "yes" response, and then it was categorized as food secure when all items had been answered "no" and food insecure for "yes". For the descriptive statistics analyses, percentage, frequency, mean and standard deviation were calculated. We used bivariate logistic regression to examine the relationship between the dependent and independent variables. Multivariate logistic regression models were constructed using variables with a P-value <0.25 in bivariate logistic regression analysis to control for all possible confounders and identify factors that are independently associated with the undernutrition of pregnant women. To determine the strength and direction of association between dependent and independent variables, the Crude Odd Ratio (COR) and Adjusted Odd Ratio (AOR) with a 95% Confidence Interval (CI) were calculated. Finally, in multivariate logistic regression analysis, the variable with a (P-value < 0.05) is considered statistically significant. Standard error (SE) was used to test for multicollinearity between independent variables, and SE values greater than 2 were excluded from the analysis. The Hosmer-Lemeshow test was used to determine the model's fitness for goodness of fit, and the model was considered fitted if the Hosmer-Lemeshow P-value was greater than 0.05. A pretest was conducted on 5% of the total study population. The final version of the questionnaire prepared in English was translated into the local language of the respondents and then translated back to English. Two days of training were given for collectors and supervisors on the instruments, data collection method, how to take anthropometric measurements, ethical issues, and the purpose of the study. The intra and inter-observer variability of the data collector's relative technical error of measurement (%TEM) was calculated during training among ten pregnant women to minimize random anthropometric measurement error. The accepted relative technical measurement errors for intra-observers were less than 1.5%, while inter-observers were less than 2%. During training and pretesting, the accuracy of data collectors' anthropometric measurements was standardized with their trainer. Data collectors have measured anthropometric measurements twice and then the average value was taken. Double data entry was done to compare two data cells and resolve whenever there was some difference.

AI Digest

Study Justification:

– Undernutrition in pregnant women is a significant public health issue that can lead to maternal mortality, adverse birth outcomes, childhood malnutrition, and mortality.
– This study aimed to determine the prevalence of undernutrition and associated factors among pregnant women in public hospitals in the Bench-Sheko and Kaffa zones of southwest Ethiopia.
– The findings of this study can provide valuable information for policymakers and healthcare providers to develop strategies and programs to reduce and prevent undernutrition among pregnant women.

Study Highlights:

– The study included 566 pregnant women who received antenatal care at public hospitals in the Bench-Sheko and Kaffa zones.
– The overall prevalence of undernutrition among pregnant women was found to be 42.4%.
– Factors significantly associated with undernutrition included the age of mothers between 16-24 years old, household food insecurity, and poor dietary knowledge.
– These findings highlight the need for targeted interventions to improve the nutritional status of pregnant women in the study area.

Study Recommendations for Lay Readers:

– Strategies and programs should be implemented at all levels to reduce and prevent undernutrition among pregnant women.
– Health information, nutrition counseling, and food assistance should be provided to pregnant women.
– Pregnant women should be educated about proper dietary practices and the importance of a balanced diet during pregnancy.
– Efforts should be made to address household food insecurity and improve access to nutritious food for pregnant women.

Study Recommendations for Policy Makers:

– Develop and implement policies to address undernutrition among pregnant women, with a focus on the identified risk factors such as young maternal age and household food insecurity.
– Allocate resources to provide health information, nutrition counseling, and food assistance to pregnant women in public hospitals.
– Strengthen the healthcare system to ensure adequate antenatal care services and support for pregnant women.
– Collaborate with relevant stakeholders, including health professionals, community organizations, and NGOs, to implement comprehensive interventions to improve the nutritional status of pregnant women.

Key Role Players:

– Ministry of Health: Responsible for developing policies and guidelines related to maternal nutrition and coordinating efforts to address undernutrition among pregnant women.
– Health Bureau: Provides oversight and support to public hospitals in implementing interventions to improve maternal nutrition.
– Public Hospitals: Deliver antenatal care services and implement nutrition counseling and food assistance programs for pregnant women.
– Health Professionals: Including midwives, nurses, and nutritionists, play a crucial role in providing education, counseling, and support to pregnant women.
– Community Organizations and NGOs: Can contribute to awareness campaigns, community-based interventions, and advocacy efforts to address undernutrition among pregnant women.

Cost Items for Planning Recommendations:

– Training: Budget for training healthcare professionals on maternal nutrition, counseling techniques, and food assistance programs.
– Health Information Materials: Allocate funds for the development and distribution of educational materials on proper dietary practices during pregnancy.
– Food Assistance: Budget for providing nutritious food to pregnant women, either through direct provision or vouchers.
– Monitoring and Evaluation: Allocate resources for monitoring and evaluating the effectiveness of interventions and making necessary adjustments.
– Collaboration and Coordination: Budget for meetings, workshops, and coordination activities with key stakeholders involved in addressing undernutrition among pregnant women.
Please note that the cost items provided are general suggestions and may vary based on the specific context and resources available in the study area.

Força da prova

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 facility-based cross-sectional study, which provides valuable information on the prevalence of undernutrition among pregnant women in the Bench-Sheko and Kaffa zones of southwest Ethiopia. The sample size calculation and data collection methods are clearly described. The statistical analysis includes multivariate logistic regression to identify factors associated with undernutrition. However, there are some areas for improvement. First, the abstract does not mention the specific criteria used to define undernutrition, which could affect the interpretation of the results. Second, the abstract does not provide information on the validity and reliability of the data collection tools used, which is important for assessing the quality of the data. Finally, the abstract does not mention any limitations of the study, such as potential biases or confounding factors. To improve the evidence, the authors should provide clear definitions of undernutrition, report the validity and reliability of the data collection tools, and discuss any limitations of the study.

Resumo

This research was done in three public hospitals in the Bench-Sheko and Kaffa zones of SNNPR, Ethiopia. The administrative center of the Bench-Sheko zone is Mizan-Aman town, which is situated 562 km from Addis Ababa, Ethiopia's capital city. According to zonal annual reports of 2019, the total population of the zone was 653,270, of whom 324,542 were men and 328,728 women. There is one general hospital in the Bench-Sheko zone with a 3-month average number of 540 antenatal care (ANC) attendants before the data collection period. Bonga town, 468 km from Addis Ababa, serves as the administrative center of the Kaffa zone. In 2017, the zone's overall population was predicted to be 1,171,133, with 578,151 men (49.4 %) and 592,982 women (50.6 %). There is one general hospital and one primary hospital in the zone. with the 3- months an average number of 490 and 401 ANC attendants before the data collection period in each hospital respectively. The study was conducted from March to May 2021. A hospital -based cross-sectional study design was used. All women in the Bench-Sheko and Kaffa zones, Southwest Ethiopia, received ANC at the public hospitals. All women who were systematically selected during ANC follow-up. All pregnant women attended ANC at public hospitals. The pregnant women who were sick or mentally unstable. In this study, the sample size was calculated using a single population proportion formula with the following assumptions in mind: The prevalence of undernutrition (P) among pregnant women from the Silte zone study finding, 21.8% [14], 5% marginal error(d), 95% confidence level (Zα/2 = 1.96), none responses rate of 10% and the design effect of 2. As a result, the sample size was calculated as follows: n= ((1.96)2 ∗0.218 (1-0.218))/(0.05)2 = ∼ 262. Thus, a minimum number of 262 pregnant women were the required number for the study. Then when we considered the design effect of 2 (262∗2) it became 524. Finally, adding a 10% none-response rate (524 ∗10%), 524 + 52 = 576 of sample size were used. First, each public hospital of the Bench-Sheko and Kaffa zones received a proportionate share of the entire sample size based on their average number of clients attending ANC before the data collection period. Next, a systematic random sampling technique was used to select the study units by using the list of pregnant mothers attending ANC as a sampling frame, and the sampling interval (Kth) was calculated by using the formula of k = N/n. Finally, every Kth person (roughly 2), as they registered, was included in the study until the desired sample size was attained from each hospital. Undernutrition of pregnant women. Age, marital status, religion, family size, occupation, education, income level, television (TV)/radio, mobile, household food insecurity, meals frequency, skipping meals, eating a snack, eating additional meals, excessive workload, residency, number of live birth, number of pregnancies, pregnancy interval, number of ANC visits, trimester, history of illness, nutrition information, source of nutrition information, dietary knowledge and attitude of pregnant women were independent variables. Structured and semi-structured questionnaires administered by Midwives and Nurses were used to collect the data. The data on socio-demographic and economic, obstetric and pregnancy-related factors, household food insecurity, dietary knowledge, dietary attitude, a dietary related habit of pregnant women like, eating habit of snacks, skipping of meals, meals frequency, eating additional meals, and nutritional status of pregnant women were assessed. The general content validity of the questionnaires was checked by relevant professionals against the conceptual framework of the study and its reliability was checked by using a test-retest method and the questions with less than 0.7 Pearson coefficient values were avoided from the questionnaire. The household food insecurity level was measured with standardized and validated tools of Household Food Insecurity Access Scale (HFIAS)that was developed mainly by Food and Nutrition Technical Assistance (FANTA), and classified the households as food secured or not [15, 16]. The tool consists of nine questions that represent the severity of food insecurity in general (access). Nine "frequency-of-occurrence" questions inquire about changes in households' diets or food consumption patterns over the previous 30 days due to limited food resources. Participants were assigned a score between 0 and 27 based on their responses to the nine questions and their frequency of occurrence over the preceding 30 days. A higher HFIAS score indicates more inadequate access to food and greater household food insecurity, while a score of 0 indicated secure access to food. Ten open-ended questions adapted from a previous study were used to assess dietary knowledge, which tried to evaluate the nutritional knowledge of pregnant women's on the nutritional aspects of pregnancy [17]. Its reliability in this study was a Cronbach Alpha of 0.92. The items measuring nutritional knowledge were scored on a dichotomous scale as 0 = does not know and 1 = knows. Each correct answer was coded as 1 and each incorrect answer was coded as 0. Then the total score was obtained by summation of each score. Finally, nutritional knowledge level was categorized as knowledgeable if she correctly answered greater than or equal to 70% of the total nutrition knowledge questions and not knowledgeable If respondents scored <70% [18, 19]. The attitude of pregnant women toward nutrition during pregnancy was assessed by using nine questions. The reliability of the attitude questions was checked and showed a Cronbach Alpha of 0.84. The pregnant woman was given one mark if the answer were a favorable attitude toward nutrition during pregnancy and zero scores if the response were an unfavorable attitude [18, 20]. Following the summation of the scores, the respondent was classified as having a favorable attitude if their attitude score was greater than or equal to the median of the scores, and as having an unfavorable attitude if their attitude score was less than or equal to the median of the scores [18]. The circumference of the middle upper arm (MUAC) was measured with MUAC tape that was non-elastic and non-stretchable. First, we removed any clothing that might cover the pregnant mother's left arm then calculated the midpoint of the pregnant mother's left upper arm by first locating the tip of the pregnant mother's shoulder, bending the pregnant mother's elbow to make a right angle, and inspected the tension of the tape on the pregnant's arm. We also made sure that the tape has proper tension and was not too tight or loose. When the tape was in the correct position on the arm with the correct tension, read and called out the measurement to the nearest 0.1cm, and the average value was taken after measuring twice. A range <23 cm was used as a cut-off point for undernutrition and while a range of ≥23 cm was for normal nutritional status. This study was conducted according to the Declaration of Helsinki. First, ethical approval was obtained from Mizan-Tepi University Institutional Research Ethics and Review Committee to conduct this study. A formal letter was sent to the Bech-Sheko and Kaffa zone health bureau administrators, as well as the selected hospitals, prior to the study. Before any data was collected, the study's goal, benefits, confidentiality, and risks were explained to the participants, and all respondents signed a written informed consent form. The respondents have agreed to maintain their anonymity, and the information they provide will be used solely for the purposes of the study. After ensuring that all data were complete and consistent internally, they were coded and entered into the Epi Data 3.1 computer software package. For further analysis, the data was exported to the Statistical Package for Social Science (SPSS) version 21 software. Undernutrition was classified and coded as 1 for "yes" if the MUAC was 23 cm and 0 for "no" if the MUAC was 23 cm [[14], [21], [22], [23], [24], [25]]. The household food insecurity access score was calculated for each household by summing up the nine food insecurity frequencies in the previous 30 days. The nine items were recorded as 0 for "no" to each occurrence and 1 for "yes" response, and then it was categorized as food secure when all items had been answered "no" and food insecure for "yes". For the descriptive statistics analyses, percentage, frequency, mean and standard deviation were calculated. We used bivariate logistic regression to examine the relationship between the dependent and independent variables. Multivariate logistic regression models were constructed using variables with a P-value <0.25 in bivariate logistic regression analysis to control for all possible confounders and identify factors that are independently associated with the undernutrition of pregnant women. To determine the strength and direction of association between dependent and independent variables, the Crude Odd Ratio (COR) and Adjusted Odd Ratio (AOR) with a 95% Confidence Interval (CI) were calculated. Finally, in multivariate logistic regression analysis, the variable with a (P-value < 0.05) is considered statistically significant. Standard error (SE) was used to test for multicollinearity between independent variables, and SE values greater than 2 were excluded from the analysis. The Hosmer-Lemeshow test was used to determine the model's fitness for goodness of fit, and the model was considered fitted if the Hosmer-Lemeshow P-value was greater than 0.05. A pretest was conducted on 5% of the total study population. The final version of the questionnaire prepared in English was translated into the local language of the respondents and then translated back to English. Two days of training were given for collectors and supervisors on the instruments, data collection method, how to take anthropometric measurements, ethical issues, and the purpose of the study. The intra and inter-observer variability of the data collector's relative technical error of measurement (%TEM) was calculated during training among ten pregnant women to minimize random anthropometric measurement error. The accepted relative technical measurement errors for intra-observers were less than 1.5%, while inter-observers were less than 2%. During training and pretesting, the accuracy of data collectors' anthropometric measurements was standardized with their trainer. Data collectors have measured anthropometric measurements twice and then the average value was taken. Double data entry was done to compare two data cells and resolve whenever there was some difference.

Materiais

N/A

Inovações

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

1. Mobile Health (mHealth) Interventions: Develop and implement mobile phone-based interventions to provide pregnant women with access to health information, nutrition counseling, and reminders for antenatal care visits. This can help improve their knowledge and awareness of maternal health issues.

2. Community Health Workers (CHWs): Train and deploy community health workers to provide education and support to pregnant women in remote areas. CHWs can conduct regular home visits, provide counseling on nutrition and healthy practices, and facilitate referrals to healthcare facilities when necessary.

3. Telemedicine: Establish telemedicine services to enable pregnant women in remote areas to consult with healthcare professionals through video or phone calls. This can help address the shortage of healthcare providers in underserved areas and ensure timely access to prenatal care.

4. Maternal Health Vouchers: Implement a voucher system that provides pregnant women with subsidized or free access to essential maternal health services, including antenatal care, delivery, and postnatal care. This can help reduce financial barriers and increase utilization of maternal health services.

5. Strengthening Health Infrastructure: Invest in improving the infrastructure of public hospitals and health centers in underserved areas. This includes ensuring the availability of skilled healthcare providers, essential medical equipment, and necessary supplies for maternal health services.

6. Community-Based Nutrition Programs: Implement community-based nutrition programs that focus on improving the dietary knowledge and practices of pregnant women. This can include nutrition education sessions, cooking demonstrations, and the establishment of community gardens to promote access to nutritious foods.

7. Public-Private Partnerships: Foster collaborations between public healthcare facilities and private sector organizations to improve access to maternal health services. This can involve leveraging private sector resources and expertise to enhance the quality and availability of care.

8. Transportation Support: Provide transportation support for pregnant women in remote areas to overcome geographical barriers and ensure timely access to healthcare facilities. This can include the provision of ambulances or the establishment of transportation networks for pregnant women.

9. Maternal Health Awareness Campaigns: Conduct targeted awareness campaigns to educate pregnant women and their families about the importance of maternal health and the available services. This can help reduce cultural and social barriers that may prevent women from seeking care.

10. Data-Driven Decision Making: Establish robust data collection and monitoring systems to track maternal health indicators and identify areas for improvement. This can inform evidence-based decision making and resource allocation to address the specific needs of pregnant women in the study area.

It is important to note that the specific recommendations for improving access to maternal health should be tailored to the local context and consider the resources and infrastructure available in the study area.

AI Innovations Description

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

1. Strengthen nutrition education and counseling: Develop and implement comprehensive nutrition education programs targeting pregnant women in the Bench-Sheko and Kaffa zones. These programs should focus on improving dietary knowledge and promoting healthy eating habits during pregnancy. Nutrition counseling sessions can be conducted during antenatal care visits to provide personalized guidance and support.

2. Enhance household food security: Address the issue of household food insecurity by implementing interventions that improve access to nutritious food for pregnant women. This can include initiatives such as income-generating activities, agricultural support, and social safety nets to ensure that pregnant women have access to an adequate and diverse diet.

3. Improve antenatal care services: Strengthen the quality and coverage of antenatal care services in the public hospitals of the Bench-Sheko and Kaffa zones. This can be achieved by training healthcare providers on maternal nutrition, implementing standardized protocols for nutrition assessment and counseling, and ensuring the availability of essential resources and equipment for antenatal care.

4. Community engagement and awareness: Engage the community in promoting maternal health and nutrition. Conduct awareness campaigns to educate community members, including husbands, family members, and community leaders, about the importance of maternal nutrition and the role they can play in supporting pregnant women. This can help create a supportive environment for pregnant women and encourage positive health-seeking behaviors.

5. Collaboration and coordination: Foster collaboration and coordination among relevant stakeholders, including healthcare providers, policymakers, community organizations, and NGOs. This can help ensure a comprehensive and integrated approach to improving access to maternal health, with a focus on nutrition.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in undernutrition among pregnant women in the Bench-Sheko and Kaffa zones of southwest Ethiopia.

AI Innovations Methodology

Based on the research conducted in the Bench-Sheko and Kaffa zones of southwest Ethiopia, there are several potential recommendations that can be considered to improve access to maternal health and address the issue of undernutrition among pregnant women. Some possible innovations include:

1. Nutrition Education Programs: Implementing comprehensive nutrition education programs that target pregnant women and their families. These programs can provide information on the importance of a balanced diet, micronutrient supplementation, and healthy eating habits during pregnancy.

2. Food Assistance Programs: Establishing food assistance programs specifically designed for pregnant women in the study area. These programs can provide nutritious food packages or vouchers to ensure access to a diverse range of foods that meet the nutritional needs of pregnant women.

3. Mobile Health (mHealth) Interventions: Utilizing mobile health technologies to deliver health information and reminders to pregnant women. This can include text messages or mobile applications that provide guidance on nutrition, antenatal care visits, and other important aspects of maternal health.

4. Community-Based Interventions: Implementing community-based interventions that involve local community health workers or volunteers. These individuals can provide support, education, and counseling to pregnant women in their communities, ensuring that they have access to the necessary resources and information.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Baseline Data Collection: Collect data on the current status of maternal health, including undernutrition rates, access to antenatal care, and knowledge of maternal health practices in the study area.

2. Intervention Design: Develop detailed plans for each recommended intervention, including the target population, implementation strategies, and expected outcomes. This could involve collaboration with local health authorities, community leaders, and relevant stakeholders.

3. Simulation Modeling: Use simulation modeling techniques to estimate the potential impact of the interventions on improving access to maternal health. This could involve creating a mathematical model that incorporates various factors such as population size, intervention coverage, and expected changes in health outcomes.

4. Data Analysis: Analyze the simulation results to assess the potential effectiveness of the interventions. This could include evaluating changes in undernutrition rates, antenatal care utilization, and other relevant indicators of maternal health.

5. Sensitivity Analysis: Conduct sensitivity analysis to explore the robustness of the simulation results. This could involve testing different assumptions and scenarios to understand the potential variability in outcomes.

6. Policy Recommendations: Based on the simulation findings, develop policy recommendations for implementing the most effective interventions to improve access to maternal health. These recommendations should consider the feasibility, cost-effectiveness, and sustainability of the interventions.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions and make informed decisions to improve access to maternal health in the study area.

Autores & Coautores

Statistics:

Citations: 1

Authors: 3

Identifiers:

DOI: 10.1016/j.heliyon.2022.e09380

ISSN: 24058440

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female

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