Micronutrient intake inadequacy and its associated factors among lactating women in Bahir Dar city, Northwest Ethiopia, 2021

PLoS ONE, Volume 17, No. 7 July, Year 2022

Interpretation

Background Inadequate intake of micronutrients in lactating women was prevalent worldwide. In particular, to our knowledge, there has been little report concerning Ethiopian lactating women regarding their micronutrient intake. Our objective was to assess micronutrient intake inadequacy and its associated factors among lactating women in Bahir Dar city, Northwest Ethiopia, 2021. Methods Community-based cross-sectional study was conducted from February 15 to March 05, 2021. Four hundred thirteen respondents were selected through systematic random sampling. Data were collected by interviewer-administered semi-structured questionnaire and a single multiphasic 24 hours dietary recall was used to assess dietary assessment. Data entry and analysis were carried out using EpiData and SPSS respectively. The ESHA food processor, Ethiopian food composition table, and world food composition table have used the calculation of nutrient values of the selected micronutrient. The nutrient intakes were assessed by Nutrient Adequacy Ratio (NAR) and Mean Adequacy Ratio (MAR). Multivariable binary logistic regression analysis was done to identify the factors of overall micronutrient intake inadequacy. Result The overall prevalence of micronutrient intake inadequacy across 12 nutrients was 39.9% [95% CI (34.9, 45.0)]. The inadequate intake of vitamin A was 98.2%. Similarly, the inadequate intake of B vitamins ranges from 13.4% to 68.5%. The insufficient intakes of calcium, iron, and zinc were 70.9%, 0%, and 4.7%, respectively. Around 36 and 91.6% of the respondents had inadequate intake of selenium and sodium, respectively. On multivariable logistic regression analysis; Being divorced was 2.7 times more likely to have overall micronutrient intake inadequacy than being married [AOR = 2.71, 95% CI (1.01, 7.33)]. The odds of overall micronutrient intake inadequacy were 2.6 higher in merchants than in housewives [AOR = 2.63, 95% CI (1.40, 4.93)]. Lactating women who had poor nutritional knowledge were 2.7 times more likely to have overall micronutrient intake inadequacy than those who had good nutritional knowledge [AOR = 2.71, 95% CI (1.47, 4.99)]. Conclusion and recommendation Overall, the micronutrient intake in lactating women was lower than the recommended levels. Therefore; educating lactating women about appropriate dietary intake is essential. The study was conducted in Bahir Dar city, Northwest Ethiopia, which is the capital city of the Amhara region and it is 565 km far from Addis Ababa, the capital city of Ethiopia. A community-based cross-sectional study design was conducted from February 15 to March 05, 2021. All lactating women who were living in Bahir Dar city and lactating women who were living in the selected kebeles of Bahir Dar city were considered as source and study population respectively. Lactating women who were between the ages of 19–49 years old, after 45 days postpartum, breastfeeding their infants during the data collection period, and who had been living for six months and above in Bahir Dar city were included from the study. Lactating women who were celebrating festivals (e.g. marriage, birth dates, and Christianity) in the last 24hrs were excluded from the study. The calculated sample size was 413 after adding a 10% non-response rate and using the epi info software by considering the assumption of confidence limit (5%), and the overall prevalence of micronutrient intake inadequacy(42.2%) among lactating women in Samre Woreda, South Eastern Zone of Tigray, Ethiopia [23]. There are 26 Kebeles in Bahir Dar city. Of them, 8 kebeles were selected randomly by the lottery method. According to the Bahir Dar city administration office report of 2020, there were a total of 1059 lactating women in Bahir Dar city administration at the time of the study(Bahir Dar city administration office. 2020 annual report. unpublished) [24]. In the selected kebeles; 2022 study participants were found. Of 2022 lactating women, 413 study participants were selected by systematic random sampling method. The value of ‘K’ is calculated from N/n 2022/413 = 4; Where N = study population, n = sample size. The study participants were proportionally allocated. Poor knowledge: The respondent answers less than 50% of knowledge questions(0–11) [25], Medium knowledge: The respondent answers 50% to 80% of knowledge questions(12–19) [25], Good knowledge: The respondent answers more than 80% of knowledge questions(≥20) [25]. Household Food Insecurity Accesses Scale (HFIAS): Can be scored and classified as; food secure, and food insecure [26]. Adequate Intake: The micronutrient intake is equal to or greater than the RDA/RNI/Adequate intake level (AI). Nutrient Adequacy Ratio (NAR): The actual micronutrient intake per day for a particular micronutrient divided by the RDA of that micronutrient. Mean Adequacy Ratio (MAR): The summation of the Nutrient Adequacy Ratio (NAR) of all micronutrients included in the study, divided by the total number of micronutrients. Recommended Dietary Allowances/Reference Nutrient Intake (RNI): this is the daily intake, which meets the nutrient requirements of almost all (97.5 percent) lactating women [27]. Portion Size: The amount of a food item consumed at a time. Kebele: The smallest unit of administration in the government structure [28, 29]. The data were collected by six trained Public Health Officers and two BSc Nurses and supervised by two Public Health Officers. Socio-demographic and economic factors, knowledge-related factors, and health-related factors were gathered using a standardized structured questionnaire which was prepared after reading various literature, and the dietary data were assessed by the Food and Agriculture Organization of the United Nations (FAO) Standardized tool [30]. Food insecurity was measured by the Household Food Insecurity Access Scale (HFIAS) which consists of nine occurrence questions that represent a generally increasing level of severity of food insecurity (access), and nine “frequency-of-occurrence” questions that were asked as a follow-up to each occurrence question to determine how often the condition occurred during the previous 4 weeks (last one month) [26]. The wealth index of the households was assessed based on household assets. Information on the wealth index was based on data collected in the household questionnaire. Each household asset for which information was assigned a weight or factor score generated through principal components analysis. These standardized scores are then used to create the breakpoints that define five groups of wealth quintiles poorest, poor, middle, rich, and richest [31]. Knowledge of the respondent about the requirement of additional meals during lactation, the importance of iron-folic acid supplementation, nutrient intake benefits, and its food sources were assessed. Overall 10 knowledge assessing yes/no and multiple response questions with a total score of 24 were used [32]. A single multiple-pass 24 hours recall was used in the community. Women were asked to name all foods and beverages eaten during the previous day (24 hours), including everything consumed outside the home as well as the cooking method. Initially, a survey was done among 21 lactating women and supermarkets, to identify the common food items and to take photographs of apparatuses that were typically used in the households. For each apparatus, a code was given for actual data collection. After coding the photographs of apparatuses, the actual data collection was started. The respondents were asked which apparatuses were used from the photo banner. Some food items like orange, mango, banana, and lemon were recorded in number, and size as large, medium, and small. For mixed dish foods, the respondents were asked to list all the food types and the ingredients (Fig 1). The data collection tool (Questionnaire) was prepared in English and translated into the local language (Amharic) and translated back into English to check its consistency. The data quality was maintained and assisted by a pretest, close supervision, and training of data collectors. In addition to this, the data quality was assured through checkups in data completeness at the field carefully every day, used photograph banners of household apparatuses for portion size estimation, and the digital food weighing scale was calibrated to zero during the standardization of the portion sizes of consumed food. The data were entered and analyzed using EpiData version.3.0, and using IBM SPSS Statistics for Windows version 24.0 respectively. After the data were checked by Kolmogrove Smirnov and Shapiro Wilk test of normality, mean and standard deviation (mean, SD) were used to present normally distributed variables (p≥0.05), while the median and interquartile range was used to present skewed distribution (p<0.05). Both descriptive and binary logistic regression analysis was done. The micronutrient value of each food item per 100 grams was obtained from the Ethiopian Food Composition Table (EFCT) [33, 34] and the World Food Composition Table [35, 36]. The World Food Composition Table (Kenya and Tanzania) was used to extract the micronutrient values of certain food items, and incomplete data on micronutrient values, which were not included in the Ethiopian Food Composition Table [35, 36]. In order to create a micronutrient database and to calculate the actual intake of the micronutrient; each food item that was consumed by respondents and the corresponding micronutrient values of the studied micronutrient must enter into the ESHA Food Processor Version 8.1 software. Finally, the calculated micronutrient values of each respondent by ESHA Food Processor software were transferred to excel and then to SPSS for analysis. These studied micronutrients are selected based on their potential to decrease breast milk concentrations with low maternal intakes [37, 38], or the possible consequences arising from deficiencies for the health of both mothers and their infants [17, 39]. The estimated prevalence of inadequate intake of micronutrients (vitamin C, iron, niacin, riboflavin, thiamine, vitamin B6, vitamin B12 vitamin A, folate zinc, calcium selenium, and sodium) was calculated by NAR and the overall prevalence of nutrient intake was calculated by MAR. NAR of micronutrients and MAR of all micronutrients included in the study for lactating women were calculated by using the following formula [40]: This reflected what percentage of the recommended intake was consumed by lactating women in the last 24 hours. The RDA/AI of nutrients was taken from the Institute of Medicine the national academies press of America [41]. NAR was truncated at 1 so that a nutrient with a high NAR could not compensate for a nutrient with low NAR. Both bivariable and multivariable binary logistic regression analyses were employed to identify the factors affecting overall micronutrient intake inadequacy among lactating women. Variables observed in bivariable binary logistic regressions (p≤0.25) were subsequently included in the multivariable binary logistic regression analysis to identify their independent effect. In multivariable binary logistic regression models, the Hosmer-Lemeshow goodness of fit test was performed for model fitness and a p-value >0.05 is a good fit. Variables with P-value less than 0.05 on multivariable binary logistic regression analysis were considered statistically significant factors. The strength of association between a dependent variable and independent variables was expressed by the Adjusted Odds Ratio (AOR). Then the final result was presented by texts, tables, and graphs. Ethical clearance (informed written consent) was obtained from the Ethical Review Board of Bahir Dar University, College of Medicine and Health Science, School of Public Health. Informed written permission was also obtained from the concerned authority of Bahir Dar city municipality administrative and the local government representative bodies of the selected kebeles. Oral consent was also secured from each lactating woman during data collection.

AI Digest

Study Justification:

– Inadequate intake of micronutrients in lactating women is a prevalent issue worldwide.
– There is a lack of information regarding micronutrient intake among Ethiopian lactating women.
– This study aimed to assess micronutrient intake inadequacy and its associated factors among lactating women in Bahir Dar city, Northwest Ethiopia.

Study Highlights:

– A community-based cross-sectional study was conducted from February 15 to March 05, 2021.
– 413 lactating women were selected through systematic random sampling.
– Data were collected using a semi-structured questionnaire and a single multiphasic 24-hour dietary recall.
– Nutrient intakes were assessed using Nutrient Adequacy Ratio (NAR) and Mean Adequacy Ratio (MAR).
– The overall prevalence of micronutrient intake inadequacy across 12 nutrients was 39.9%.
– The inadequate intake of vitamin A was 98.2%, and B vitamins ranged from 13.4% to 68.5%.
– The insufficient intakes of calcium, iron, and zinc were 70.9%, 0%, and 4.7%, respectively.
– Factors associated with overall micronutrient intake inadequacy included marital status, occupation, and nutritional knowledge.

Recommendations:

– Educating lactating women about appropriate dietary intake is essential to improve micronutrient intake.
– Strategies should be developed to increase awareness and knowledge about the importance of micronutrients during lactation.
– Interventions should be implemented to address the specific micronutrient deficiencies identified in the study.

Key Role Players:

– Researchers and public health professionals
– Health policymakers and government officials
– Healthcare providers and nutritionists
– Community leaders and women’s associations

Cost Items for Planning Recommendations:

– Development and implementation of educational materials and programs
– Training and capacity building for healthcare providers and nutritionists
– Awareness campaigns and community outreach activities
– Monitoring and evaluation of interventions
– Research and data collection on micronutrient intake
– Collaboration and coordination between stakeholders

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a community-based cross-sectional study conducted in Bahir Dar city, Northwest Ethiopia. The study used a sample size of 413 lactating women and collected data through a semi-structured questionnaire and a single multiphasic 24-hour dietary recall. Nutrient intakes were assessed using Nutrient Adequacy Ratio (NAR) and Mean Adequacy Ratio (MAR). The study found that the overall prevalence of micronutrient intake inadequacy was 39.9%, with vitamin A being the most inadequate nutrient. The study also identified factors associated with overall micronutrient intake inadequacy, such as marital status, occupation, and nutritional knowledge. The study provides valuable insights into the micronutrient intake of lactating women in Ethiopia and highlights the need for education on appropriate dietary intake. However, the evidence is limited to a specific population and may not be generalizable to other regions or countries. To improve the evidence, future studies could consider a larger sample size and include a more diverse population to enhance the external validity of the findings.

Abstract

The study was conducted in Bahir Dar city, Northwest Ethiopia, which is the capital city of the Amhara region and it is 565 km far from Addis Ababa, the capital city of Ethiopia. A community-based cross-sectional study design was conducted from February 15 to March 05, 2021. All lactating women who were living in Bahir Dar city and lactating women who were living in the selected kebeles of Bahir Dar city were considered as source and study population respectively. Lactating women who were between the ages of 19–49 years old, after 45 days postpartum, breastfeeding their infants during the data collection period, and who had been living for six months and above in Bahir Dar city were included from the study. Lactating women who were celebrating festivals (e.g. marriage, birth dates, and Christianity) in the last 24hrs were excluded from the study. The calculated sample size was 413 after adding a 10% non-response rate and using the epi info software by considering the assumption of confidence limit (5%), and the overall prevalence of micronutrient intake inadequacy(42.2%) among lactating women in Samre Woreda, South Eastern Zone of Tigray, Ethiopia [23]. There are 26 Kebeles in Bahir Dar city. Of them, 8 kebeles were selected randomly by the lottery method. According to the Bahir Dar city administration office report of 2020, there were a total of 1059 lactating women in Bahir Dar city administration at the time of the study(Bahir Dar city administration office. 2020 annual report. unpublished) [24]. In the selected kebeles; 2022 study participants were found. Of 2022 lactating women, 413 study participants were selected by systematic random sampling method. The value of ‘K’ is calculated from N/n 2022/413 = 4; Where N = study population, n = sample size. The study participants were proportionally allocated. Poor knowledge: The respondent answers less than 50% of knowledge questions(0–11) [25], Medium knowledge: The respondent answers 50% to 80% of knowledge questions(12–19) [25], Good knowledge: The respondent answers more than 80% of knowledge questions(≥20) [25]. Household Food Insecurity Accesses Scale (HFIAS): Can be scored and classified as; food secure, and food insecure [26]. Adequate Intake: The micronutrient intake is equal to or greater than the RDA/RNI/Adequate intake level (AI). Nutrient Adequacy Ratio (NAR): The actual micronutrient intake per day for a particular micronutrient divided by the RDA of that micronutrient. Mean Adequacy Ratio (MAR): The summation of the Nutrient Adequacy Ratio (NAR) of all micronutrients included in the study, divided by the total number of micronutrients. Recommended Dietary Allowances/Reference Nutrient Intake (RNI): this is the daily intake, which meets the nutrient requirements of almost all (97.5 percent) lactating women [27]. Portion Size: The amount of a food item consumed at a time. Kebele: The smallest unit of administration in the government structure [28, 29]. The data were collected by six trained Public Health Officers and two BSc Nurses and supervised by two Public Health Officers. Socio-demographic and economic factors, knowledge-related factors, and health-related factors were gathered using a standardized structured questionnaire which was prepared after reading various literature, and the dietary data were assessed by the Food and Agriculture Organization of the United Nations (FAO) Standardized tool [30]. Food insecurity was measured by the Household Food Insecurity Access Scale (HFIAS) which consists of nine occurrence questions that represent a generally increasing level of severity of food insecurity (access), and nine “frequency-of-occurrence” questions that were asked as a follow-up to each occurrence question to determine how often the condition occurred during the previous 4 weeks (last one month) [26]. The wealth index of the households was assessed based on household assets. Information on the wealth index was based on data collected in the household questionnaire. Each household asset for which information was assigned a weight or factor score generated through principal components analysis. These standardized scores are then used to create the breakpoints that define five groups of wealth quintiles poorest, poor, middle, rich, and richest [31]. Knowledge of the respondent about the requirement of additional meals during lactation, the importance of iron-folic acid supplementation, nutrient intake benefits, and its food sources were assessed. Overall 10 knowledge assessing yes/no and multiple response questions with a total score of 24 were used [32]. A single multiple-pass 24 hours recall was used in the community. Women were asked to name all foods and beverages eaten during the previous day (24 hours), including everything consumed outside the home as well as the cooking method. Initially, a survey was done among 21 lactating women and supermarkets, to identify the common food items and to take photographs of apparatuses that were typically used in the households. For each apparatus, a code was given for actual data collection. After coding the photographs of apparatuses, the actual data collection was started. The respondents were asked which apparatuses were used from the photo banner. Some food items like orange, mango, banana, and lemon were recorded in number, and size as large, medium, and small. For mixed dish foods, the respondents were asked to list all the food types and the ingredients (Fig 1). The data collection tool (Questionnaire) was prepared in English and translated into the local language (Amharic) and translated back into English to check its consistency. The data quality was maintained and assisted by a pretest, close supervision, and training of data collectors. In addition to this, the data quality was assured through checkups in data completeness at the field carefully every day, used photograph banners of household apparatuses for portion size estimation, and the digital food weighing scale was calibrated to zero during the standardization of the portion sizes of consumed food. The data were entered and analyzed using EpiData version.3.0, and using IBM SPSS Statistics for Windows version 24.0 respectively. After the data were checked by Kolmogrove Smirnov and Shapiro Wilk test of normality, mean and standard deviation (mean, SD) were used to present normally distributed variables (p≥0.05), while the median and interquartile range was used to present skewed distribution (p0.05 is a good fit. Variables with P-value less than 0.05 on multivariable binary logistic regression analysis were considered statistically significant factors. The strength of association between a dependent variable and independent variables was expressed by the Adjusted Odds Ratio (AOR). Then the final result was presented by texts, tables, and graphs. Ethical clearance (informed written consent) was obtained from the Ethical Review Board of Bahir Dar University, College of Medicine and Health Science, School of Public Health. Informed written permission was also obtained from the concerned authority of Bahir Dar city municipality administrative and the local government representative bodies of the selected kebeles. Oral consent was also secured from each lactating woman during data collection.

Materials

N/A

Innovations

Based on the information provided, 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 guidance on proper nutrition during lactation. These apps can also send reminders for taking supplements and provide access to virtual consultations with healthcare professionals.

2. Community Health Workers: Train and deploy community health workers to educate lactating women on the importance of proper nutrition and provide guidance on meal planning. These workers can also conduct regular home visits to monitor the nutritional status of lactating women and provide support.

3. Micronutrient Supplementation Programs: Establish programs that provide free or subsidized micronutrient supplements to lactating women. This can help address the high prevalence of inadequate intake of vitamins and minerals, such as vitamin A, B vitamins, calcium, iron, zinc, selenium, and sodium.

4. Nutrition Education Programs: Implement comprehensive nutrition education programs that target lactating women and their families. These programs can include workshops, cooking demonstrations, and educational materials that promote the consumption of nutrient-rich foods and highlight the importance of a balanced diet.

5. Integration of Maternal Health Services: Integrate maternal health services, including nutrition counseling and supplementation, into existing healthcare facilities. This can ensure that lactating women have easy access to these services during routine check-ups or visits for other healthcare needs.

6. Public-Private Partnerships: Foster collaborations between the government, private sector, and non-profit organizations to improve access to maternal health services. This can involve initiatives such as public-private partnerships to distribute affordable or free nutrient-rich foods, supplements, and educational materials.

7. Telemedicine and Teleconsultation: Utilize telemedicine and teleconsultation platforms to provide remote access to healthcare professionals for lactating women in remote or underserved areas. This can help overcome geographical barriers and ensure timely access to expert advice and support.

8. Maternal Health Awareness Campaigns: Launch targeted awareness campaigns to educate the general public about the importance of maternal health and proper nutrition during lactation. These campaigns can use various media channels, including radio, television, social media, and community events, to reach a wide audience.

9. Maternity Support Groups: Establish support groups for lactating women where they can share experiences, receive emotional support, and learn from each other. These groups can be facilitated by healthcare professionals or trained peer counselors.

10. Policy and Advocacy: Advocate for policy changes and increased funding to prioritize maternal health and nutrition programs. This can involve engaging with policymakers, raising awareness among key stakeholders, and advocating for the inclusion of maternal health in national health agendas.

It is important to note that the implementation of these innovations should be context-specific and tailored to the local needs and resources available in Bahir Dar city, Northwest Ethiopia.

AI Innovations Description

Based on the study conducted in Bahir Dar city, Northwest Ethiopia, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Nutrition Education and Counseling: Develop and implement targeted nutrition education and counseling programs for lactating women in Bahir Dar city. These programs should focus on raising awareness about the importance of adequate micronutrient intake during lactation and provide information on the food sources of essential nutrients.

2. Community-based Interventions: Establish community-based interventions that promote healthy eating habits and provide support for lactating women. This can include organizing cooking demonstrations, nutrition workshops, and support groups where women can share their experiences and learn from each other.

3. Collaboration with Healthcare Providers: Strengthen collaboration between healthcare providers and lactating women to ensure that they receive accurate and up-to-date information on maternal nutrition. This can be done through regular check-ups, where healthcare providers can assess the nutritional status of lactating women and provide personalized recommendations.

4. Food Fortification: Explore the possibility of fortifying commonly consumed foods with essential micronutrients. This can be done in collaboration with food manufacturers and local authorities to ensure that lactating women have access to nutrient-rich foods.

5. Policy Development: Advocate for the development and implementation of policies that support improved access to maternal health services. This can include policies that prioritize maternal nutrition and provide financial support for lactating women to access nutritious foods.

6. Research and Monitoring: Conduct further research to assess the impact of interventions on improving access to maternal health and monitor the progress of these interventions. This will help identify areas for improvement and ensure that interventions are effective in addressing the micronutrient intake inadequacy among lactating women.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to better health outcomes for lactating women and their infants in Bahir Dar city, Northwest Ethiopia.

AI Innovations Methodology

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

1. Increase awareness and education: Implement educational programs to educate lactating women about the importance of proper nutrition and the benefits of adequate micronutrient intake. This can be done through community health centers, antenatal and postnatal care visits, and community outreach programs.

2. Improve availability and affordability of nutrient-rich foods: Enhance access to a variety of nutrient-rich foods, such as fruits, vegetables, whole grains, lean proteins, and dairy products. This can be achieved by promoting local agricultural production, supporting farmers’ markets, and implementing subsidies or vouchers for nutritious foods.

3. Strengthen healthcare infrastructure: Invest in healthcare facilities and services, particularly in rural areas, to ensure that lactating women have access to quality antenatal and postnatal care. This includes providing adequate staffing, equipment, and resources for maternal health services.

4. Enhance social support systems: Establish support networks for lactating women, including peer support groups and community-based initiatives. These networks can provide emotional support, share knowledge and experiences, and promote healthy behaviors.

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

1. Define the indicators: Determine the key indicators that will be used to measure the impact of the recommendations. These could include the prevalence of micronutrient intake inadequacy, the percentage of lactating women with adequate nutrient intake, and the overall health outcomes of lactating women and their infants.

2. Collect baseline data: Conduct a survey or data collection process to gather baseline information on the current status of maternal health, including the prevalence of micronutrient intake inadequacy and other relevant factors. This can be done through interviews, questionnaires, and dietary assessments.

3. Develop a simulation model: Create a simulation model that incorporates the recommended interventions and their potential impact on improving access to maternal health. This model should consider factors such as population size, geographical distribution, socio-economic status, and existing healthcare infrastructure.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations. This can be done by adjusting the parameters of the model, such as the coverage and effectiveness of the interventions, and observing the resulting changes in the indicators.

5. Analyze and interpret results: Analyze the simulation results to assess the potential impact of the recommendations on improving access to maternal health. This can involve comparing the baseline data with the simulated outcomes and identifying any significant changes or improvements.

6. Refine and validate the model: Continuously refine and validate the simulation model based on new data and feedback. This can help improve the accuracy and reliability of the model and ensure that it reflects the real-world context.

7. Communicate findings and make recommendations: Present the findings of the simulation study in a clear and concise manner, highlighting the potential benefits of the recommended interventions. Use the results to inform policy and decision-making processes, and make recommendations for further action.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. Therefore, it is recommended to consult with experts in the field of maternal health and simulation modeling to develop a robust and accurate methodology.

Author

Dima Health

Identifiers:

DOI: 10.1371/journal.pone.0271984

Research Areas:

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

Study Countries:

Ethiopia, Kenya, Multi-Countries, Tanzania

Study Design:

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

Study Approach:

Quantitative

Participants Gender:

Female