Determinants of wasting among children aged 6-59 months in North-East Ethiopia: a community-based case-control study

BMJ Open, Volume 12, No. 8, Year 2022

Interpretation

Objective Childhood acute malnutrition, in the form of wasting defined by Weight-for-Height Z-Scores, is a major public health concern. It is one of the main reasons for the death of children in developing countries like Ethiopia. Accordingly, this study aimed to assess determinants of wasting among children aged 6-59 months in Meket district, North Wollo zone, North-East Ethiopia. Setting The study was conducted among communities in Meket district, North Wollo zone, North-East Ethiopia. Participants A total of 327 (109 cases and 218 controls) children aged 6-59 months participated in the study. Children from 6 months to 59 months of age who match the definition of case/wasted/ and control/not wasted were eligible for the study. However, children who had physical deformities which make anthropometric measurements inconvenient were excluded from the study. Primary and secondary outcome measures The main outcome measure was wasting. Result The mean ages of the cases and controls were 21.77±11.41 months and 20.13±11.39 months, respectively. Factors that were significantly associated with wasting were: maternal decision making on the use of household money (adjusted odd ratio (AOR)=3.04, 95% CI 1.08 to 7.83), complementary feeding started in a month (AOR=3.02, 95% CI 1.097 to 6.97), food diversity score (AOR=2.64, 95% CI 1.64 to 5.23), frequency of complementary feeding (AOR=6.68, 95% CI 3.6 to 11.25) and history of acute respiratory infections (ARIs) 2 weeks preceding the survey (AOR=3.21, 95% CI 1.07 to 7.86). Conclusion Our result implies that the right time to introduce complementary foods, the frequency of feeding and also the amount of food consumed were some of the crucial factors that needed to be changed in child nutrition to reduce wasting. Furthermore, within the framework of our study, the empowerment of women in the decision-making process and the prevention of ARI should be seen as a necessary benchmark for acute malnutrition. A community-based case-control study was undertaken to identify determinants of wasting among children aged 6–59 months in Meket district, North Wollo zone, North-East Ethiopia, from January to February 2020. Meket district is located in Amhara regional state and is situated about 670 km north of the capital city of Ethiopia, Addis Ababa. The district is also 245 km away from Bahir Dar and 145 km away from the city of Woldia in North Wollo Zone. It has 2 urban and 32 rural kebeles. Based on Meket district administration reports, the catchment population includes 208 687 people (106 430female and 102 257 male) in 48 532 households. The total number of children aged 6–59 months in this district was 26 879; out of those 13 708 of them were female and 13 171 male.42 There is 1 primary hospital, 13 health centres and 36 health posts in this district. All mothers or caregivers that had children aged 6–59 months and who were present in Meket district kebeles during the study period were the source population. Children aged 6–59 months who were admitted due to wasting (WHZ<−2Z score) with their caregivers or mothers were included in the study as cases. Children aged 6–59 months and attending without wasting who came for integrated community case management, screening, immunisation, growth monitoring promotion, and for other purposes were included as controls. Children who had physical deformities which make anthropometric measurements inconvenient were excluded from the study. For instance, children who were born without hands due to congenital deformities, were wounded or had burnt hands were excluded from the study since they had physical deformities. The sample size was calculated using Epi Info V.7 statistical software, and a case-control study was used. The size of the sample was determined from a previous study that was conducted in North-West Ethiopia, which was similar to our study setting. All candidate variables of wasting were considered and the largest was taken. Accordingly, we took into account children from households of large family size as it was the main associated factor of wasting in the previous study.43 The percentages of exposure among cases and controls in the abovementioned study were 64.4% and 46.6%, respectively. Detecting an OR of 2.7 with 95% CI (Zα/2=1.96), a power of 80% (Zβ=0.84) and a case to control ratio of 1:2 were taken from the previous study. Therefore, the total sample size after adding 5% possible contingency for the non-response rate was 327. Of those, 109 cases and 218 controls were approached. Among the 36 kebeles found in Meket district, 10 were selected using simple random sampling methods. The number of study participants, that is children aged 6–59 months, was assigned for each selected kebele proportionally to its size. The number of children in each kebele was found from the vital statistics report of kebele offices. After establishing the sampling frame, cases were identified and selected during a house-to-house visit in each selected kebele. A simple random sampling technique was used to select households until the sample size was achieved. For more than one wasted child per house, the lottery method was used. Whereas controls were selected after the matching criterion of age was fulfilled according to other inclusion and exclusion criterias. Individual matching was carried out as one case followed by two controls, based on three age categories from the same neighbourhood found through transect walks. Controls were matched to cases accordingly with an age interval similar to that of the cases (±3 months) and based on their place of residence (village or neighbourhood).27 43–47 Wasting is the nutritional deficient state of recent onset related to sudden food deprivation or malabsorption, utilisation of nutrients which results from weight loss, weight-for-height below −2 SD from the WHO median value.48 In this study, acute malnutrition or wasting was used interchangeably which was incorporated in both SAM and MAM. MAM is defined as WHZ between −2 and −3 or MUAC between 115 mm and <125 mm. On the other hand, SAM is defined as WHZ < −3 or MUAC <115 mm, or the presence of bilateral pitting oedema, or both.30 Children aged 6–59 months who were wasted according to the above definition including SAM or MAM. Children who weren’t wasted or did not fulfil the definition of cases. Data were collected from all eligible children by data collectors using an interviewer-administered questionnaire and anthropometric measurements. MUAC was also taken from all children with standardising procedures. In addition to child anthropometry measurement, the mothers or caregivers of the children were interviewed face to face. The mothers or caregivers of the children provided answers on variables such as the socioeconomic and demographic characteristics of the participants. Five nurses and 10 health extension workers for data collection and five supervisors were recruited. The data collectors and supervisors were provided with training for 3 days before the data collection period. The supervisors regularly monitored and supervised the overall activity to ensure the quality of data during the entire data collection period. The questionnaire was adopted from different literatures.27 29 32 34 36 49 50 It was originally prepared in English and then translated to the local language, Amharic. Finally, it was translated back to the English language by a skilled person, who had good proficiency in both English and Amharic, to check its consistency. The questionnaire was also pretested on 5% of actual respondents in Wadla district which is almost similar to the study population of this study. The questionnaire was modified based on the pretest. Moreover, the questionnaire was comprised of different variables including socioeconomic and demographic factors, child medical characteristics, child-caring practices (feeding practice, immunisation), maternal caring characteristics, and environmental health conditions. Household food insecurity was assessed by using the nine standards of the Household Food Insecurity Access Scale Questionnaire.51 We also used the WHO validated 7-item Food Frequency Questionnaire to quantify food diversity score.48 Additionally, the data collectors observed expanded program on immunisation (EPI) cards to check the date of birth of the child and immunisation status. To assess the physical growth and nutritional status of the children, measurements of height and weight were taken. Additionally, their age was determined by interviewing mothers or caregivers or by checking their birthday cards. These anthropometric data were collected using the procedure stipulated by WHO by trained data collectors, measured two times and then the average was taken.51 Anthropometric data were collected through the measurement of the height and weight of children. For those less than 2 years of age, measurement of the height was done without shoes. The height is read to the nearest 0.1 cm by using a horizontal wooden measuring board with the infant in a recumbent position on a hard and flat surface. However, the heights of children 24 months and above were measured using a vertical wooden board by placing the child on the measuring board. In this case, the child was standing upright in the middle of the board. The child’s head, shoulders, buttocks and heels touched the board. The heights (lengths) of the children were recorded to the nearest 0.1 cm. Length is usually greater than standing height by 0.5 cm if the child is 85 cm or more. But, if length cannot be measured standing, 0.5 cm were subtracted from the supine length.51 The weight of the child was measured by one health professional, with a 25 kg hanging sprint, the scale graduated to the nearest 100 gm with minimum clothing and no shoes. Also, the scale should be at eye level to read easily when the child is calm. Calibration was done before weighing each child. This was done by setting it to zero and checking the normality by weighing a material of preknown weight. If there was a difference of 0.01 kg or more between duplicate weighing, or if a measured weight differs by 0.01 kg or more from the known standard, check the scales. Then, adjust or replace them if necessary.51 See online supplemental file 1 for details of tools. bmjopen-2021-057887supp001.pdf Epi Info V.7 and SPSS V.24 were used for data entry and analysis, respectively. Besides, anthropometric data were analysed using the WHO Anthro V.2006 software.52 The outcome variables were dichotomised into cases (1) and controls (0). Then, frequencies and cross-tabulation were used to describe the study population with regard to the relevant variables. Conditional logistic regression was used to fit the data to identify the predictors for wasting. Bivariate logistic regression analysis was conducted to discover the effect of each study variable on the outcome variable. Variables having a value of p<0.2553 on the bivariate analysis entered into a multivariate logistic regression analysis to control the possible confounding. In the multivariate logistic regression analysis, variables with a value of p<0.05 were considered statistically significant. The Hosmer-Lemeshow goodness-of-fit test (χ2/df=4.92; Root Mean Square Error of Approximation (RMSEA)=0.05; Comparative Fit Index (CFI)=0.95; Tucker-Lewis Index (TLI)=0.91) was applied to test the appropriateness of the model. Multicollinearity between independent variables was checked and all of the variables scored variance inflation factors <10. No patient was involved.

AI Digest

Study Justification:

– Childhood acute malnutrition, specifically wasting, is a significant public health issue and a leading cause of death among children in developing countries like Ethiopia.
– Understanding the determinants of wasting is crucial for developing effective interventions and policies to reduce malnutrition and improve child health.
– This study aimed to assess the determinants of wasting among children aged 6-59 months in Meket district, North-East Ethiopia, to provide evidence-based recommendations for addressing this issue.

Study Highlights:

– The study was conducted in Meket district, North-East Ethiopia, among 327 children aged 6-59 months (109 cases and 218 controls).
– Factors significantly associated with wasting included maternal decision making on the use of household money, timing of complementary feeding, food diversity score, frequency of complementary feeding, and history of acute respiratory infections.
– The study highlights the importance of introducing complementary foods at the right time, ensuring an adequate frequency of feeding, and improving the amount of food consumed to reduce wasting.
– Empowering women in decision-making processes and preventing acute respiratory infections are also crucial for addressing acute malnutrition.

Recommendations for Lay Readers and Policy Makers:

– Interventions should focus on educating mothers and caregivers about the appropriate timing of introducing complementary foods and the importance of regular and diverse feeding practices.
– Efforts should be made to empower women in decision-making processes, particularly regarding the use of household money for child nutrition.
– Preventive measures, such as promoting good hygiene practices and providing access to healthcare services, should be implemented to reduce the incidence of acute respiratory infections.
– Collaboration between government agencies, healthcare providers, and community organizations is essential for implementing and monitoring these interventions.

Key Role Players:

– Government agencies responsible for health and nutrition policies and programs.
– Healthcare providers, including doctors, nurses, and health extension workers, who can deliver education and support to mothers and caregivers.
– Community organizations and local leaders who can facilitate community engagement and mobilization.
– Non-governmental organizations (NGOs) and international partners who can provide technical expertise and resources for implementing interventions.

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community workers.
– Development and dissemination of educational materials and resources.
– Monitoring and evaluation activities to assess the effectiveness of interventions.
– Health system strengthening to improve access to healthcare services.
– Community engagement and mobilization initiatives.
– Research and data collection to monitor progress and inform future interventions.
Please note that the cost items provided are general categories and not actual cost estimates. The specific costs will depend on the context and implementation strategies.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a clear description of the study objectives, setting, participants, and outcome measures. The abstract also presents the significant factors associated with wasting among children. However, it lacks information on the study design, data collection methods, and statistical analysis. To improve the evidence, the abstract should include details on the study design (e.g., case-control), data collection methods (e.g., questionnaire, anthropometric measurements), and statistical analysis (e.g., bivariate and multivariate logistic regression). This additional information would enhance the transparency and replicability of the study.

Abstract

A community-based case-control study was undertaken to identify determinants of wasting among children aged 6–59 months in Meket district, North Wollo zone, North-East Ethiopia, from January to February 2020. Meket district is located in Amhara regional state and is situated about 670 km north of the capital city of Ethiopia, Addis Ababa. The district is also 245 km away from Bahir Dar and 145 km away from the city of Woldia in North Wollo Zone. It has 2 urban and 32 rural kebeles. Based on Meket district administration reports, the catchment population includes 208 687 people (106 430female and 102 257 male) in 48 532 households. The total number of children aged 6–59 months in this district was 26 879; out of those 13 708 of them were female and 13 171 male.42 There is 1 primary hospital, 13 health centres and 36 health posts in this district. All mothers or caregivers that had children aged 6–59 months and who were present in Meket district kebeles during the study period were the source population. Children aged 6–59 months who were admitted due to wasting (WHZ<−2Z score) with their caregivers or mothers were included in the study as cases. Children aged 6–59 months and attending without wasting who came for integrated community case management, screening, immunisation, growth monitoring promotion, and for other purposes were included as controls. Children who had physical deformities which make anthropometric measurements inconvenient were excluded from the study. For instance, children who were born without hands due to congenital deformities, were wounded or had burnt hands were excluded from the study since they had physical deformities. The sample size was calculated using Epi Info V.7 statistical software, and a case-control study was used. The size of the sample was determined from a previous study that was conducted in North-West Ethiopia, which was similar to our study setting. All candidate variables of wasting were considered and the largest was taken. Accordingly, we took into account children from households of large family size as it was the main associated factor of wasting in the previous study.43 The percentages of exposure among cases and controls in the abovementioned study were 64.4% and 46.6%, respectively. Detecting an OR of 2.7 with 95% CI (Zα/2=1.96), a power of 80% (Zβ=0.84) and a case to control ratio of 1:2 were taken from the previous study. Therefore, the total sample size after adding 5% possible contingency for the non-response rate was 327. Of those, 109 cases and 218 controls were approached. Among the 36 kebeles found in Meket district, 10 were selected using simple random sampling methods. The number of study participants, that is children aged 6–59 months, was assigned for each selected kebele proportionally to its size. The number of children in each kebele was found from the vital statistics report of kebele offices. After establishing the sampling frame, cases were identified and selected during a house-to-house visit in each selected kebele. A simple random sampling technique was used to select households until the sample size was achieved. For more than one wasted child per house, the lottery method was used. Whereas controls were selected after the matching criterion of age was fulfilled according to other inclusion and exclusion criterias. Individual matching was carried out as one case followed by two controls, based on three age categories from the same neighbourhood found through transect walks. Controls were matched to cases accordingly with an age interval similar to that of the cases (±3 months) and based on their place of residence (village or neighbourhood).27 43–47 Wasting is the nutritional deficient state of recent onset related to sudden food deprivation or malabsorption, utilisation of nutrients which results from weight loss, weight-for-height below −2 SD from the WHO median value.48 In this study, acute malnutrition or wasting was used interchangeably which was incorporated in both SAM and MAM. MAM is defined as WHZ between −2 and −3 or MUAC between 115 mm and <125 mm. On the other hand, SAM is defined as WHZ < −3 or MUAC <115 mm, or the presence of bilateral pitting oedema, or both.30 Children aged 6–59 months who were wasted according to the above definition including SAM or MAM. Children who weren’t wasted or did not fulfil the definition of cases. Data were collected from all eligible children by data collectors using an interviewer-administered questionnaire and anthropometric measurements. MUAC was also taken from all children with standardising procedures. In addition to child anthropometry measurement, the mothers or caregivers of the children were interviewed face to face. The mothers or caregivers of the children provided answers on variables such as the socioeconomic and demographic characteristics of the participants. Five nurses and 10 health extension workers for data collection and five supervisors were recruited. The data collectors and supervisors were provided with training for 3 days before the data collection period. The supervisors regularly monitored and supervised the overall activity to ensure the quality of data during the entire data collection period. The questionnaire was adopted from different literatures.27 29 32 34 36 49 50 It was originally prepared in English and then translated to the local language, Amharic. Finally, it was translated back to the English language by a skilled person, who had good proficiency in both English and Amharic, to check its consistency. The questionnaire was also pretested on 5% of actual respondents in Wadla district which is almost similar to the study population of this study. The questionnaire was modified based on the pretest. Moreover, the questionnaire was comprised of different variables including socioeconomic and demographic factors, child medical characteristics, child-caring practices (feeding practice, immunisation), maternal caring characteristics, and environmental health conditions. Household food insecurity was assessed by using the nine standards of the Household Food Insecurity Access Scale Questionnaire.51 We also used the WHO validated 7-item Food Frequency Questionnaire to quantify food diversity score.48 Additionally, the data collectors observed expanded program on immunisation (EPI) cards to check the date of birth of the child and immunisation status. To assess the physical growth and nutritional status of the children, measurements of height and weight were taken. Additionally, their age was determined by interviewing mothers or caregivers or by checking their birthday cards. These anthropometric data were collected using the procedure stipulated by WHO by trained data collectors, measured two times and then the average was taken.51 Anthropometric data were collected through the measurement of the height and weight of children. For those less than 2 years of age, measurement of the height was done without shoes. The height is read to the nearest 0.1 cm by using a horizontal wooden measuring board with the infant in a recumbent position on a hard and flat surface. However, the heights of children 24 months and above were measured using a vertical wooden board by placing the child on the measuring board. In this case, the child was standing upright in the middle of the board. The child’s head, shoulders, buttocks and heels touched the board. The heights (lengths) of the children were recorded to the nearest 0.1 cm. Length is usually greater than standing height by 0.5 cm if the child is 85 cm or more. But, if length cannot be measured standing, 0.5 cm were subtracted from the supine length.51 The weight of the child was measured by one health professional, with a 25 kg hanging sprint, the scale graduated to the nearest 100 gm with minimum clothing and no shoes. Also, the scale should be at eye level to read easily when the child is calm. Calibration was done before weighing each child. This was done by setting it to zero and checking the normality by weighing a material of preknown weight. If there was a difference of 0.01 kg or more between duplicate weighing, or if a measured weight differs by 0.01 kg or more from the known standard, check the scales. Then, adjust or replace them if necessary.51 See online supplemental file 1 for details of tools. bmjopen-2021-057887supp001.pdf Epi Info V.7 and SPSS V.24 were used for data entry and analysis, respectively. Besides, anthropometric data were analysed using the WHO Anthro V.2006 software.52 The outcome variables were dichotomised into cases (1) and controls (0). Then, frequencies and cross-tabulation were used to describe the study population with regard to the relevant variables. Conditional logistic regression was used to fit the data to identify the predictors for wasting. Bivariate logistic regression analysis was conducted to discover the effect of each study variable on the outcome variable. Variables having a value of p<0.2553 on the bivariate analysis entered into a multivariate logistic regression analysis to control the possible confounding. In the multivariate logistic regression analysis, variables with a value of p<0.05 were considered statistically significant. The Hosmer-Lemeshow goodness-of-fit test (χ2/df=4.92; Root Mean Square Error of Approximation (RMSEA)=0.05; Comparative Fit Index (CFI)=0.95; Tucker-Lewis Index (TLI)=0.91) was applied to test the appropriateness of the model. Multicollinearity between independent variables was checked and all of the variables scored variance inflation factors <10. No patient was involved.

Materials

Innovations

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide pregnant women and new mothers with access to important health information, such as nutrition guidelines, prenatal care schedules, and postpartum care instructions. These apps can also include features like appointment reminders and emergency contact information.

2. Telemedicine: Establish telemedicine services that allow pregnant women in remote or underserved areas to consult with healthcare professionals through video calls or phone consultations. This can help address the shortage of healthcare providers in certain regions and provide timely advice and guidance to expectant mothers.

3. Community Health Workers: Train and deploy community health workers who can provide basic maternal health services, such as prenatal check-ups, health education, and referrals to healthcare facilities. These workers can bridge the gap between communities and healthcare systems, ensuring that pregnant women receive the care they need.

4. Maternal Health Vouchers: Implement voucher programs that provide pregnant women with financial assistance to access maternal health services, including prenatal care, delivery, and postpartum care. These vouchers can be distributed through community health centers or local organizations, ensuring that cost is not a barrier to receiving quality care.

5. Maternal Health Clinics: Establish dedicated maternal health clinics in underserved areas, staffed with skilled healthcare professionals who specialize in maternal and child health. These clinics can provide comprehensive care, including prenatal check-ups, vaccinations, family planning services, and postpartum support.

6. Health Education Campaigns: Launch targeted health education campaigns that focus on raising awareness about the importance of maternal health and the available services. These campaigns can use various mediums, such as radio, television, posters, and community meetings, to reach a wide audience and promote positive health-seeking behaviors.

7. Transportation Support: Address transportation barriers by providing transportation support to pregnant women in remote or rural areas, ensuring they can access healthcare facilities for prenatal care, delivery, and postpartum check-ups. This can be done through partnerships with local transportation providers or by establishing community-based transportation services.

8. Maternal Health Hotline: Set up a dedicated hotline that pregnant women can call to receive information, advice, and support related to maternal health. Trained healthcare professionals can staff the hotline and provide guidance on topics such as prenatal care, nutrition, breastfeeding, and postpartum recovery.

9. Maternal Health Monitoring Systems: Develop and implement digital systems that allow healthcare providers to remotely monitor the health status of pregnant women and identify any potential complications. These systems can use wearable devices or mobile apps to collect and transmit data, enabling early detection and intervention.

10. Public-Private Partnerships: Foster collaborations between government agencies, non-profit organizations, and private sector entities to improve access to maternal health services. These partnerships can leverage resources, expertise, and technology to expand healthcare infrastructure, train healthcare workers, and implement innovative solutions.

It’s important to note that the specific context and needs of the community should be considered when implementing these innovations. Local stakeholders, including healthcare providers, community leaders, and women themselves, should be involved in the planning and implementation process to ensure the solutions are culturally appropriate and sustainable.

AI Innovations Description

The study conducted in Meket district, North-East Ethiopia aimed to identify determinants of wasting among children aged 6-59 months. The study found several factors that were significantly associated with wasting, including maternal decision making on the use of household money, timing of complementary feeding, food diversity score, frequency of complementary feeding, and history of acute respiratory infections.

Based on the study’s findings, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Empowerment of women in decision-making: The study found that maternal decision making on the use of household money was significantly associated with wasting. To address this, it is recommended to implement programs that empower women and promote gender equality. This can include providing education and training on financial management, entrepreneurship, and decision-making skills. By empowering women, they can have greater control over household resources, including funds allocated for maternal health.

2. Timely introduction of complementary foods: The study found that the timing of complementary feeding was a significant factor associated with wasting. To improve access to maternal health, it is important to educate mothers and caregivers about the appropriate time to introduce complementary foods to infants. This can be done through community-based education programs, antenatal and postnatal care visits, and the involvement of community health workers. By ensuring that mothers have the knowledge and understanding of when to start complementary feeding, the nutritional needs of infants can be better met, reducing the risk of wasting.

3. Promotion of food diversity and frequency of feeding: The study found that food diversity score and frequency of complementary feeding were associated with wasting. To address this, it is recommended to promote food diversity and increase the frequency of feeding for children aged 6-59 months. This can be achieved through nutrition education programs that emphasize the importance of a balanced diet and provide practical guidance on meal planning and preparation. Additionally, community-based initiatives such as community gardens and nutrition supplementation programs can help improve access to diverse and nutritious foods for mothers and children.

4. Prevention of acute respiratory infections (ARIs): The study found that a history of ARIs was significantly associated with wasting. To improve access to maternal health, it is important to prioritize the prevention and management of ARIs in children. This can be done through immunization programs, promotion of good hygiene practices, and early detection and treatment of respiratory infections. By reducing the burden of ARIs, the overall health and well-being of children can be improved, reducing the risk of wasting.

In summary, the study’s findings suggest that addressing factors such as maternal decision making, timing of complementary feeding, food diversity, frequency of feeding, and prevention of ARIs can contribute to improving access to maternal health and reducing wasting among children. Implementing the recommended strategies can help drive innovation in maternal health programs and ultimately improve the health outcomes of mothers and children in Ethiopia.

AI Innovations Methodology

Based on the provided information, the study aimed to assess the determinants of wasting among children aged 6-59 months in Meket district, North Wollo zone, North-East Ethiopia. The study was conducted through a community-based case-control design, with a total of 327 children (109 cases and 218 controls) participating.

To improve access to maternal health, the following innovations could be considered:

1. Mobile Health (mHealth) Solutions: Develop mobile applications or SMS-based systems to provide pregnant women and new mothers with information on prenatal care, nutrition, immunizations, and postnatal care. These solutions can also be used to send reminders and alerts for appointments and medication adherence.

2. Telemedicine: Implement telemedicine services to enable remote consultations between pregnant women and healthcare providers. This can help overcome geographical barriers and provide access to specialized care for high-risk pregnancies.

3. Community Health Workers (CHWs): Train and deploy CHWs in rural areas to provide maternal health education, conduct antenatal and postnatal visits, and facilitate referrals to healthcare facilities. CHWs can play a crucial role in improving access to maternal health services, especially in underserved communities.

4. Maternal Waiting Homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes can provide a safe and supportive environment for women during the final weeks of pregnancy, ensuring timely access to skilled birth attendants.

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

1. Define the indicators: Identify key indicators to measure the impact of the recommendations, such as the number of pregnant women receiving prenatal care, the percentage of women delivering with skilled birth attendants, or the reduction in maternal mortality rates.

2. Collect baseline data: Gather data on the current status of maternal health access in the target area, including the number of healthcare facilities, the availability of skilled birth attendants, and the utilization of maternal health services.

3. Model the interventions: Use modeling techniques, such as mathematical modeling or simulation software, to estimate the potential impact of each recommendation on the identified indicators. This can involve creating different scenarios based on the implementation of one or more interventions.

4. Input data and assumptions: Input relevant data, such as population demographics, healthcare infrastructure, and resource availability, into the simulation model. Make assumptions about the coverage and effectiveness of the recommended interventions.

5. Run simulations: Run the simulation model using the input data and assumptions to generate projections of the impact of the recommendations on the selected indicators. This can help estimate the potential improvements in access to maternal health services.

6. Analyze results: Analyze the simulation results to assess the potential benefits and challenges of implementing the recommendations. Identify any trade-offs or unintended consequences that may arise from the interventions.

7. Refine and iterate: Based on the analysis, refine the recommendations and simulation model as needed. Iterate the process to further optimize the interventions and improve the accuracy of the impact projections.

By following this methodology, stakeholders can gain insights into the potential impact of innovative interventions on improving access to maternal health services and make informed decisions regarding their implementation.

Author

Dima Health

Identifiers:

DOI: 10.1136/bmjopen-2021-057887

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female