Spatial variation of child stunting and maternal malnutrition after controlling for known risk factors in a drought-prone rural community in Southern Ethiopia

Annals of Global Health, Volume 87, No. 1, Year 2021

Interpretation

Background: Globally, understanding spatial analysis of malnutrition is increasingly recognized. However, our knowledge on spatial clustering of malnutrition after controlling for known risk factors of malnutrition such as wealth status, food insecurity, altitude and maternal characteristics is limited from Ethiopia. Previous studies from southern Ethiopia have shown seasonal patterns of malnutrition, yet they did not evaluate spatial clustering of malnutrition. Objective: The aim of this study was to assess whether child stunting and maternal malnutrition were spatially clustered in drought-prone areas after controlling for previously known risk factors of malnutrition. Methods: We used a community-based cohort study design for a one-year study period. We used SaTScan software to identify high rates of child stunting and maternal malnutrition clustering. The outcome based was the presence or absence of stunting and maternal malnutrition ([BMI] <18.5 kg/m2). We controlled for previously known predictors of child stunting and maternal malnutrition to evaluate the presence of clustering. We did a logistic regression model with declaring data to be time-series using Stata version 15 for further evaluation of the predictors of spatial clustering. Results: The crude analysis of SaTScan showed that there were areas (clusters) with a higher risk of stunting and maternal malnutrition than in the underlying at risk populations. Stunted children within an identified spatial cluster were more likely to be from poor households, had younger and illiterate mothers, and often the mothers were farmers and housewives. Children identified within the most likely clusters were 1.6 times more at risk of stunting in the unadjusted analysis. Similarly, mothers within the clusters were 2.4 times more at risk of malnutrition in the unadjusted analysis. However, after adjusting for known risk factors such as wealth status, household food insecurity, altitude, maternal age, maternal education, and maternal occupation with SaTScan analysis, we show that child stunting and maternal malnutrition were not spatially clustered. Conclusion: The observed spatial clustering of child stunting and maternal malnutrition before controlling for known risk factors for child stunting and maternal malnutrition could be due to non-random distribution of risk factors such as poverty and maternal characteristics. Moreover, our results indicated the need for geographically targeted nutritional interventions in a drought-prone area. This study was conducted in Boricha, which is geographically located at 6º 46’N and 38º 04’E to 7º 01’N and 38º 24’E in southern Ethiopia. Boricha district is a drought prone area, approximately located 34 km south of Hawassa, the capital city of Southern Nations Nationalities of Peoples Region (SNNPR). Sidama is the largest ethnic group in the district and more than 90% of the population lives in rural areas. Protestant Christianity is the main religion in the district and most of people make their living directly from subsistence farming and livestock rearing. Based on the 2007 national census, approximately 315,000 people lived in the district in 2017. The district has 39 rural kebeles (smallest administrative units), each with an average population of 1,000 to 6,000 people. In 2017, there was one government hospital, five public health centres, 39 health posts in the district. Each kebele has at least one health post staffed by two health extension workers who report to the health centre. As Boricha is the drought prone area, malnutrition is the major health problem. Highest peak of acute malnutrition cases occurs between March and June during Belg rainy season, and the lowest occurrence of acute malnutrition occurs between September and December, following rains in March and June [11]. Moreover, the absence of rivers and far to reach to underground water serve as potential malnutrition sites. Thus, study area is known for drought prone and included in Safety Net Programme. This study is a part of a community-based cohort study conducted to evaluate child and maternal malnutrition at the community level. During 2017, all households were visited four times. The aim of our study was to measure the patterns and determinants of child stunting and child wasting, determine the risk factors of maternal malnutrition, and evaluate spatial clustering of child and maternal malnutrition. The main outcomes of the study were the spatial clustering of child stunting and maternal malnutrition. The main exposure variables were wealth, altitude, household food insecurity access, maternal education, maternal occupation, and maternal age. The aforementioned covariates were related to child and maternal malnutrition according to their significance level in the previous studies conducted in Boricha district and they are assumed to be non-randomly distributed geographically [11,12]. Thus, we want to find clusters that cannot be explained by these covariates. We employed two-stage sampling technique to select mother-child pairs. First, nine rural kebeles (smallest administrative unit) were randomly selected from 39 rural kebeles of Boricha district. Secondly, out of the nine rural kebeles, we recruited study subjects from gouts (villages) through cluster sampling technique. This cohort had included 935 children between 6 months to 47 months and 892 biological mothers between 15–49 years old from the total of 897 households at the beginning of the study. Children from 6 months to 47 months were recruited to accommodate age increment due to a one-year follow-up period of time. See earlier publication for detailed description of the study population [11]. We utilized eighteen data collectors and three supervisors for data collection, who were familiar with the local context and fluent speakers of the local language (Sidamu Afoo). They were trained and conducted pre-test outside of the selected kebeles that had similar socio-economic characteristics. We collected on socio-demographic data such as maternal age, educational status of mothers and occupational status of mothers. Each household had a unique code and geo-referenced using a handheld global positioning system (GPS) device (Garmin’s GPSMAP60CSx, Garmin International Inc., Olathe, Kansas, USA). Household food insecurity was assessed by using the Household Food Insecurity Access Scale (HFIAS) tool developed by the FANTA project and validated in different seasons of Ethiopia [13,14,15]. Food consumption was assessed by Household Dietary Diversity Score (HDDS) of 24 hours recall measurements. Twelve food groups were measured: Meat or Poultry, Eggs, Fish, Cereals, Root or tubers, Vegetables, Fruits, Pulses or legumes or nuts, milk and milk products, oil or fats, sugar or honey, and miscellaneous [13]. See earlier publication for detailed information [11]. We constructed wealth index using principal component analysis [6]. Household assets-related variables such as type and number of herds, ownership of improved sanitation, type of fuel used for cooking food, materials used for construction of house wall, floor and roof, number of sleeping rooms, ownership of chair and mobile telephone. The principal component analysis Kaiser-Meyer-Olkin measure of sampling adequacy was 67% and significance level of below 0.001. The households were then ranked into five categories such as poorest, poor, medium, rich and richest. The term malnutrition denotes both to undernutrition and over-nutrition. It is a condition that results from eating a diet in which various nutrients are either an insufficient or excessive. However, this paper focuses on the maternal and child undernutrition aspect such as child stunting and maternal undernutrition based on BMI measurements. Child anthropometric measurements were evaluated based on children’s height and recorded using Emergency Nutrition Assessment for Standardized Monitoring and Assessment of Relief and Transitions software (NutriSurvey for SMART, version 2011). Children up to 24 months of age were measured in a recumbent position using a length board to the nearest 0.1cm. Children who are able to stand unassisted measured in the standing position to the nearest 0.1cm. We used mother’s recall and memorable events to assess child age. Child stunting was defined as height-for-age Z-score of less than two standard deviation of the World Health Organization Child Growth Standards Median [16]. The presence of maternal malnutrition was defined as a body mass index [BMI] <18.5 kg/m2 [17]. The weight was recorded to the nearest 100 g using a digital SECA scale (SECA GmbH, Germany) and the height was measured with a locally prepared apparatus that had a 0.1 cm resolution. Data were double-entered and checked using EpiData v. 3.1 (Odense, Denmark), and transferred to STATA 15 (StataCorp, College Station, TX) for further cleaning and analysis. Descriptive statistics such as frequency counts, percentages, means and 95% CI were used to summarize the data. For the anthropometric measurements we deleted extreme values of child stunting (height-for-age Z-scores greater than six or less than minus six) which represented 4.9% of the measurements. Similarly, we deleted cases with improbable heights (about 1%). Data visualization was done using ESRI ArcMap 10.4.1 (ESRI, Redlands, CA, USA) software. The coordinates’ projection was defined using Universal Transverse Mercator Zone 37°N and World Geodetic system 1984. We used SaTScan version 9.7 software (Free software, Kulldorff’s spatial scan statistics) and specified three Microsoft Excel files (case, population, and coordinates) as an input data to identify locations of statistically significant clusters after controlling for risk factors. Kulldorff’s spatial scan statistics was used to identify statistically significant clusters (purely spatial) of high stunting and maternal malnutrition ([BMI] <18.5 kg/m2) rates using a discrete scan statistics of Poisson probability model [18]. Scan statistics computed data across space through a circle for space and covered the entire period [18]. Spatial analysis used a circular window shape that was performed with maximum spatial cluster size as a 50% of the population at risk; 50% was the upper limit for SaTScan clusters and did not account SaTScan clusters of the population greater than 50%. The circular window with the maximum log likelihood was considered as the most likely cluster area if P-value < 0.05 and the remaining clusters were reported as secondary cluster if they are geographically non-overlapping windows with the P-value < 0.05 [18]. The maximum number of Monte Carlo replications was 999 and the minimum number of two cases was required for high rates of clusters. SaTScan Version 9.7 calculated the P-values by using the combination of Monte Carlo, sequential Monte Carlo, and Gumbel approximation [18]. To evaluate whether there is clustering or not when the known risk factors for clustering in the study area are controlled, we compared crude analysis of SaTScan with the adjusted analysis after including covariates in the SaTScan Version 9.7. The dependent variable was a binary outcome (yes/no) based on the presence or absence of stunting and maternal malnutrition [BMI] <18.5 kg/m2. We considered the known predictor variables such as wealth index, food insecurity, altitude, maternal education, maternal occupation and maternal age. These covariates were selected based on their significant association with child stunting and maternal malnutrition ([BMI] <18.5 kg/m2) according to previous research conducted in Boricha district [11,12]. Although identifying the presence of clustering after controlling for known risk factors is the primary objective, we also identified the effects of the risk factors for the observed clustering [19]. Hence, we did a logistic regression model with declaring data to be time-series using Stata version 15 for further evaluation of the predictors of spatial clustering. The time setting of the model used season of the year as time variable, and child code was considered as panel ID variable. The model construction used an exchangeable correlation matrix, and a main effect term builder. The stunted children identified within the spatial cluster were compared with stunted children outside the cluster. The presence or absence of differences of risk factors between the groups (stunted children in the cluster versus stunted children outside the cluster) could give information about the underlying risk factors that may be responsible for the observed clustering. The potential risk factors considered were wealth status, altitude, household food insecurity, maternal age, maternal education, and maternal occupation. However, because of the very wide cluster radius size in the spatial cluster of maternal malnutrition, we limited our analysis only for stunting [20]. We had secured ethical approval from Institutional Review Board (IRB) at Hawassa University (Ref. No: IRB/001/09, Date: 13/09/2016) and the Regional Committee for Medical and Health Research Ethics, Western Norway (ref: 2016/1631/REK Vest) before we had started data collection. All study participants were informed about the benefit and harm of the study, and then informed written consent was obtained from all study participants. Those who could not sign on used their thumb print. The authors thank the study subjects for their willingness to participate, and the data collectors, supervisors, and Boricha district authorities for their positive input.

AI Digest

Study Justification:

– The study aimed to understand the spatial clustering of child stunting and maternal malnutrition in a drought-prone rural community in Southern Ethiopia.
– Previous studies in the area have shown seasonal patterns of malnutrition, but did not evaluate spatial clustering.
– Understanding spatial analysis of malnutrition is important for targeted interventions and resource allocation.

Study Highlights:

– The study used a community-based cohort design and SaTScan software to identify high rates of child stunting and maternal malnutrition clustering.
– The analysis showed areas with a higher risk of stunting and maternal malnutrition within identified spatial clusters.
– Factors associated with higher risk within clusters included poverty, younger and illiterate mothers, and maternal occupation as farmers and housewives.
– After adjusting for known risk factors, the study found that child stunting and maternal malnutrition were not spatially clustered.

Study Recommendations:

– Geographically targeted nutritional interventions are needed in drought-prone areas like Boricha.
– Interventions should focus on addressing poverty, improving maternal education, and providing support for vulnerable households.
– Further research is needed to explore other potential risk factors and their impact on malnutrition in the study area.

Key Role Players:

– Researchers and data collectors
– Community health workers
– Local government authorities
– Non-governmental organizations (NGOs) working in nutrition and health

Cost Items for Planning Recommendations:

– Training and capacity building for researchers and data collectors
– Community outreach and education programs
– Nutritional supplements and food assistance for vulnerable households
– Monitoring and evaluation of intervention programs
– Coordination and collaboration with local government and NGOs

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study design is a community-based cohort study, which is generally considered a robust design. The researchers used SaTScan software to identify high rates of child stunting and maternal malnutrition clustering, and they controlled for known risk factors such as wealth status, food insecurity, altitude, and maternal characteristics. The results showed that child stunting and maternal malnutrition were not spatially clustered after adjusting for these factors. The study provides valuable insights into the non-random distribution of risk factors and the need for geographically targeted nutritional interventions in a drought-prone area. To improve the evidence, the abstract could include more specific information about the sample size, data collection methods, and statistical analysis techniques used. Additionally, it would be helpful to provide information on the limitations of the study and any potential biases that may have influenced the results.

Abstract

This study was conducted in Boricha, which is geographically located at 6º 46’N and 38º 04’E to 7º 01’N and 38º 24’E in southern Ethiopia. Boricha district is a drought prone area, approximately located 34 km south of Hawassa, the capital city of Southern Nations Nationalities of Peoples Region (SNNPR). Sidama is the largest ethnic group in the district and more than 90% of the population lives in rural areas. Protestant Christianity is the main religion in the district and most of people make their living directly from subsistence farming and livestock rearing. Based on the 2007 national census, approximately 315,000 people lived in the district in 2017. The district has 39 rural kebeles (smallest administrative units), each with an average population of 1,000 to 6,000 people. In 2017, there was one government hospital, five public health centres, 39 health posts in the district. Each kebele has at least one health post staffed by two health extension workers who report to the health centre. As Boricha is the drought prone area, malnutrition is the major health problem. Highest peak of acute malnutrition cases occurs between March and June during Belg rainy season, and the lowest occurrence of acute malnutrition occurs between September and December, following rains in March and June [11]. Moreover, the absence of rivers and far to reach to underground water serve as potential malnutrition sites. Thus, study area is known for drought prone and included in Safety Net Programme. This study is a part of a community-based cohort study conducted to evaluate child and maternal malnutrition at the community level. During 2017, all households were visited four times. The aim of our study was to measure the patterns and determinants of child stunting and child wasting, determine the risk factors of maternal malnutrition, and evaluate spatial clustering of child and maternal malnutrition. The main outcomes of the study were the spatial clustering of child stunting and maternal malnutrition. The main exposure variables were wealth, altitude, household food insecurity access, maternal education, maternal occupation, and maternal age. The aforementioned covariates were related to child and maternal malnutrition according to their significance level in the previous studies conducted in Boricha district and they are assumed to be non-randomly distributed geographically [11,12]. Thus, we want to find clusters that cannot be explained by these covariates. We employed two-stage sampling technique to select mother-child pairs. First, nine rural kebeles (smallest administrative unit) were randomly selected from 39 rural kebeles of Boricha district. Secondly, out of the nine rural kebeles, we recruited study subjects from gouts (villages) through cluster sampling technique. This cohort had included 935 children between 6 months to 47 months and 892 biological mothers between 15–49 years old from the total of 897 households at the beginning of the study. Children from 6 months to 47 months were recruited to accommodate age increment due to a one-year follow-up period of time. See earlier publication for detailed description of the study population [11]. We utilized eighteen data collectors and three supervisors for data collection, who were familiar with the local context and fluent speakers of the local language (Sidamu Afoo). They were trained and conducted pre-test outside of the selected kebeles that had similar socio-economic characteristics. We collected on socio-demographic data such as maternal age, educational status of mothers and occupational status of mothers. Each household had a unique code and geo-referenced using a handheld global positioning system (GPS) device (Garmin’s GPSMAP60CSx, Garmin International Inc., Olathe, Kansas, USA). Household food insecurity was assessed by using the Household Food Insecurity Access Scale (HFIAS) tool developed by the FANTA project and validated in different seasons of Ethiopia [13,14,15]. Food consumption was assessed by Household Dietary Diversity Score (HDDS) of 24 hours recall measurements. Twelve food groups were measured: Meat or Poultry, Eggs, Fish, Cereals, Root or tubers, Vegetables, Fruits, Pulses or legumes or nuts, milk and milk products, oil or fats, sugar or honey, and miscellaneous [13]. See earlier publication for detailed information [11]. We constructed wealth index using principal component analysis [6]. Household assets-related variables such as type and number of herds, ownership of improved sanitation, type of fuel used for cooking food, materials used for construction of house wall, floor and roof, number of sleeping rooms, ownership of chair and mobile telephone. The principal component analysis Kaiser-Meyer-Olkin measure of sampling adequacy was 67% and significance level of below 0.001. The households were then ranked into five categories such as poorest, poor, medium, rich and richest. The term malnutrition denotes both to undernutrition and over-nutrition. It is a condition that results from eating a diet in which various nutrients are either an insufficient or excessive. However, this paper focuses on the maternal and child undernutrition aspect such as child stunting and maternal undernutrition based on BMI measurements. Child anthropometric measurements were evaluated based on children’s height and recorded using Emergency Nutrition Assessment for Standardized Monitoring and Assessment of Relief and Transitions software (NutriSurvey for SMART, version 2011). Children up to 24 months of age were measured in a recumbent position using a length board to the nearest 0.1cm. Children who are able to stand unassisted measured in the standing position to the nearest 0.1cm. We used mother’s recall and memorable events to assess child age. Child stunting was defined as height-for-age Z-score of less than two standard deviation of the World Health Organization Child Growth Standards Median [16]. The presence of maternal malnutrition was defined as a body mass index [BMI] <18.5 kg/m2 [17]. The weight was recorded to the nearest 100 g using a digital SECA scale (SECA GmbH, Germany) and the height was measured with a locally prepared apparatus that had a 0.1 cm resolution. Data were double-entered and checked using EpiData v. 3.1 (Odense, Denmark), and transferred to STATA 15 (StataCorp, College Station, TX) for further cleaning and analysis. Descriptive statistics such as frequency counts, percentages, means and 95% CI were used to summarize the data. For the anthropometric measurements we deleted extreme values of child stunting (height-for-age Z-scores greater than six or less than minus six) which represented 4.9% of the measurements. Similarly, we deleted cases with improbable heights (about 1%). Data visualization was done using ESRI ArcMap 10.4.1 (ESRI, Redlands, CA, USA) software. The coordinates’ projection was defined using Universal Transverse Mercator Zone 37°N and World Geodetic system 1984. We used SaTScan version 9.7 software (Free software, Kulldorff’s spatial scan statistics) and specified three Microsoft Excel files (case, population, and coordinates) as an input data to identify locations of statistically significant clusters after controlling for risk factors. Kulldorff’s spatial scan statistics was used to identify statistically significant clusters (purely spatial) of high stunting and maternal malnutrition ([BMI] <18.5 kg/m2) rates using a discrete scan statistics of Poisson probability model [18]. Scan statistics computed data across space through a circle for space and covered the entire period [18]. Spatial analysis used a circular window shape that was performed with maximum spatial cluster size as a 50% of the population at risk; 50% was the upper limit for SaTScan clusters and did not account SaTScan clusters of the population greater than 50%. The circular window with the maximum log likelihood was considered as the most likely cluster area if P-value < 0.05 and the remaining clusters were reported as secondary cluster if they are geographically non-overlapping windows with the P-value < 0.05 [18]. The maximum number of Monte Carlo replications was 999 and the minimum number of two cases was required for high rates of clusters. SaTScan Version 9.7 calculated the P-values by using the combination of Monte Carlo, sequential Monte Carlo, and Gumbel approximation [18]. To evaluate whether there is clustering or not when the known risk factors for clustering in the study area are controlled, we compared crude analysis of SaTScan with the adjusted analysis after including covariates in the SaTScan Version 9.7. The dependent variable was a binary outcome (yes/no) based on the presence or absence of stunting and maternal malnutrition [BMI] <18.5 kg/m2. We considered the known predictor variables such as wealth index, food insecurity, altitude, maternal education, maternal occupation and maternal age. These covariates were selected based on their significant association with child stunting and maternal malnutrition ([BMI] <18.5 kg/m2) according to previous research conducted in Boricha district [11,12]. Although identifying the presence of clustering after controlling for known risk factors is the primary objective, we also identified the effects of the risk factors for the observed clustering [19]. Hence, we did a logistic regression model with declaring data to be time-series using Stata version 15 for further evaluation of the predictors of spatial clustering. The time setting of the model used season of the year as time variable, and child code was considered as panel ID variable. The model construction used an exchangeable correlation matrix, and a main effect term builder. The stunted children identified within the spatial cluster were compared with stunted children outside the cluster. The presence or absence of differences of risk factors between the groups (stunted children in the cluster versus stunted children outside the cluster) could give information about the underlying risk factors that may be responsible for the observed clustering. The potential risk factors considered were wealth status, altitude, household food insecurity, maternal age, maternal education, and maternal occupation. However, because of the very wide cluster radius size in the spatial cluster of maternal malnutrition, we limited our analysis only for stunting [20]. We had secured ethical approval from Institutional Review Board (IRB) at Hawassa University (Ref. No: IRB/001/09, Date: 13/09/2016) and the Regional Committee for Medical and Health Research Ethics, Western Norway (ref: 2016/1631/REK Vest) before we had started data collection. All study participants were informed about the benefit and harm of the study, and then informed written consent was obtained from all study participants. Those who could not sign on used their thumb print. The authors thank the study subjects for their willingness to participate, and the data collectors, supervisors, and Boricha district authorities for their positive input.

Materials

N/A

Innovations

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

1. Mobile Health Clinics: Implementing mobile health clinics that can travel to remote areas to provide maternal health services, including prenatal care, vaccinations, and nutritional support.

2. Telemedicine: Introducing telemedicine services that allow pregnant women to consult with healthcare professionals remotely, reducing the need for travel and improving access to medical advice and support.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in rural areas where access to healthcare facilities is limited.

4. Nutritional Interventions: Implementing targeted nutritional interventions, such as providing fortified food supplements and educating women on proper nutrition during pregnancy, to address maternal malnutrition in drought-prone areas.

5. Water and Sanitation Infrastructure: Improving access to clean water and sanitation facilities in rural communities to reduce the risk of waterborne diseases and improve overall maternal health outcomes.

6. Health Education Programs: Developing and implementing health education programs that focus on maternal health, including family planning, prenatal care, and breastfeeding, to empower women with knowledge and promote healthy behaviors.

7. Strengthening Healthcare Facilities: Investing in the infrastructure and resources of healthcare facilities in drought-prone areas to ensure they have the capacity to provide quality maternal health services, including skilled birth attendance and emergency obstetric care.

8. Maternal Health Insurance: Introducing affordable and accessible maternal health insurance schemes to provide financial protection and ensure that women can access necessary maternal health services without facing financial barriers.

9. Partnerships and Collaboration: Encouraging partnerships and collaboration between government agencies, non-governmental organizations, and local communities to collectively address the challenges of maternal health in drought-prone areas and develop sustainable solutions.

10. Research and Data Collection: Conducting further research and data collection to better understand the specific challenges and needs of maternal health in drought-prone areas, and using this information to inform targeted interventions and policies.

AI Innovations Description

The study conducted in Boricha, a drought-prone area in southern Ethiopia, aimed to assess the spatial clustering of child stunting and maternal malnutrition after controlling for known risk factors. The researchers used a community-based cohort study design and SaTScan software to identify high rates of clustering. They found that there were areas with a higher risk of stunting and maternal malnutrition within identified spatial clusters. These clusters were associated with factors such as poverty, maternal characteristics (young age, illiteracy, occupation as farmers or housewives), and poor households. However, after adjusting for known risk factors, the study concluded that child stunting and maternal malnutrition were not spatially clustered. The findings suggest the need for geographically targeted nutritional interventions in drought-prone areas like Boricha.

AI Innovations Methodology

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

1. Strengthening healthcare infrastructure: Invest in improving the healthcare infrastructure in drought-prone areas like Boricha district in Southern Ethiopia. This could involve building more health centers, hospitals, and health posts, as well as ensuring they are adequately staffed and equipped to provide quality maternal healthcare services.

2. Mobile health clinics: Implement mobile health clinics that can reach remote and underserved areas. These clinics can provide essential maternal health services, including prenatal care, postnatal care, and family planning services.

3. Community health workers: Train and deploy community health workers (CHWs) to provide maternal health education, counseling, and basic healthcare services at the community level. CHWs can play a crucial role in reaching pregnant women and new mothers who may have limited access to healthcare facilities.

4. Maternal health awareness campaigns: Conduct targeted awareness campaigns to educate communities about the importance of maternal health and the available services. These campaigns can help dispel myths and misconceptions, promote early antenatal care, and encourage women to seek timely healthcare during pregnancy and childbirth.

5. Addressing socioeconomic factors: Implement interventions that address socioeconomic factors contributing to maternal malnutrition, such as poverty and food insecurity. This could involve providing economic support, improving agricultural practices, and promoting income-generating activities to improve household food security and overall well-being.

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

1. Data collection: Gather baseline data on maternal health indicators, such as maternal mortality rates, antenatal care coverage, and access to skilled birth attendants. This data can be obtained from existing health records, surveys, and interviews with key stakeholders.

2. Modeling the interventions: Use a modeling approach, such as a mathematical or statistical model, to simulate the impact of the recommended interventions on the selected maternal health indicators. This could involve estimating the potential increase in antenatal care coverage, reduction in maternal mortality rates, or improvement in access to skilled birth attendants based on the proposed interventions.

3. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the model and explore the potential variations in the outcomes based on different assumptions or scenarios. This can help identify the key factors that influence the impact of the interventions and guide decision-making.

4. Validation: Validate the model’s predictions by comparing them with real-world data or conducting pilot studies to assess the feasibility and effectiveness of the recommended interventions in improving access to maternal health.

5. Policy recommendations: Based on the simulation results, provide evidence-based policy recommendations to stakeholders, policymakers, and healthcare providers. These recommendations should highlight the potential benefits of the interventions and guide the allocation of resources and implementation strategies to improve access to maternal health.

It is important to note that the methodology described above is a general framework and may need to be adapted based on the specific context and available data in Boricha district or any other setting.

Author

Dima Health

Statistics:

Citations: 3

Identifiers:

DOI: 10.5334/aogh.3286

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative