Trends of infant mortality and its determinants in Ethiopia: mixed-effect binary logistic regression and multivariate decomposition analysis

BMC Pregnancy and Childbirth, Volume 21, No. 1, Year 2021

Interpretation

Background: Infant mortality remains a serious global public health problem. The global infant mortality rate has decreased significantly over time, but the rate of decline in most African countries, including Ethiopia, is far below the rate expected to meet the SDG targets. Therefore, this study aimed to investigate the trends of infant mortality and its determinants in Ethiopia based on the four consecutive Ethiopian Demographic and Health Surveys (EDHSs). Methods: This analysis was based on the data from four EDHSs (EDHS 2000, 2005, 2011, and 2016). A total weighted sample of 46,317 live births was included for the final analysis. The logit-based multivariate decomposition analysis was used to identify significantly contributing factors for the decrease in infant mortality in Ethiopia over the last 16 years. To identify determinants, a mixed-effect logistic regression model was fitted. The Intra-class Correlation Coefficient (ICC) and Likelihood Ratio (LR) test were used to assess the presence of a significant clustering effect. Deviance, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) were used for model comparison. Variables with a p-value of less than 0.2 in the bi-variable analysis were considered for the multivariable analysis. In the multivariable analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were reported to identify the statistically significant determinants of infant mortality. Results: Infant mortality rate has decreased from 96.9 per 1000 births in 2000 to 48 per 1000 births in 2016, with an annual rate of reduction of 4.2%. According to the logit based multivariate decomposition analysis, about 18.1% of the overall decrease in infant mortality was due to the difference in composition of the respondents with respect to residence, maternal age, type of birth, and parity across the surveys, while the remaining 81.9% was due to the difference in the effect of residence, parity, type of birth and parity across the surveys. In the mixed-effect binary logistic regression analysis; preceding interval < 24 months (AOR = 1.79, 95% CI; 1.46, 2.19), small size at birth (AOR = 1.55, 95% CI; 1.25, 1.92), large size at birth (AOR = 1.26, 95% CI; 1.01, 1.57), BMI < 18.5 kg/m2 (AOR = 1.22, 95% CI; 1.05, 1.50), and twins (AOR = 4.25, 95% CI; 3.01, 6.01), parity> 6 (1.51, 95% CI; 1.01, 2.26), maternal age and male sex (AOR = 1.50, 95% CI: 1.25, 1.79) were significantly associated with increased odds of infant mortality. Conclusion: This study found that the infant mortality rate has declined over time in Ethiopia since 2000. Preceding birth interval, child-size at birth, BMI, type of birth, parity, maternal age, and sex of child were significant predictors of infant mortality. Public health programs aimed at rural communities, and multiparous mothers through enhancing health facility delivery would help maintain Ethiopia’s declining infant mortality rate. Furthermore, improving the use of ANC services and maternal nutrition is crucial to reducing infant mortality and achieving the SDG targets in Ethiopia. A community-based time-series cross-sectional study was used to answer the research objectives. All the Demographic and Health Surveys (DHSs) (EDHS 2000, 2005, 2011, and 2016) conducted in Ethiopia were used. The EDHS was employed in every five-year interval to generate updated health and health-related indicators. The majority of the country’s population lives in the regional states of Amhara, Oromia, and Southern Nations Nationalities and People’s Regions (SNNPR) [25]. Ethiopia is the 13th in the world and 2nd most populous country in Africa [26]. In 2016, there were an estimated 102 million people. A two-stage sampling technique was employed to select the sample and a total of 539 Enumeration Areas (EAs) in EDHS 2000, 540 EAs in EDHS 2005, 624 EAs in EDHS 2011, and 645 EAs in EDHS 2016 were randomly selected. Then, on average 27 to 32 households per EA were selected. The source population was all live births from reproductive-age women within 5 years before the survey in Ethiopia whereas all live births from reproductive-age women in the selected enumeration areas were the study population. A total weighted sample of 46,317 live births (12,260 in EDHS 2000, 11,163 in EDHS 2005, 11,872 in EDHS 2011, and 11,022 in EDHS 2016) from reproductive-age women were used for analysis. The detailed sampling procedure was presented in the full EDHSs report [18, 19, 31, 32]. The outcome variable for this study was infant mortality (the death of live birth within 1 year of birth). In EDHS there was a question about whether the child was alive or died at the time of the survey and for dead infants-age at death were recorded. Death of a child within 1 year of age was coded as 1, and 0 if the child was alive. The unit of analysis in this study was all live births in the 5 years preceding the survey. The infant mortality rate is defined as the number of infant deaths per 1000 live births [33]. The independent variables considered in this study were region (coded as Tigray, Afar, Amhara, Oromia, Somali, Benishangul, SNNPR, Gambella, Harari, Addis Ababa, and Dire Dawa), residence (coded as rural, and urban), sex of household head (coded as male and female), maternal age (recoded as < 20, 20–29, 30–39 and 40–49 years), women education (recoded as no, primary, and secondary and higher), paternal education (recoded as no, primary, and secondary and above), preceding birth interval (recoded as < 24 and ≥ 24 months), Body Mass Index (BMI) of the mother (recoded as < 18.5, 18.5–24.9, and ≥ 25 kg/m2), wealth index (coded as poor, middle and rich), parity (recoded as 1–3, 4–6 and > 6 births), type of birth (coded as single and multiple), place of delivery (coded as home and health facility), ANC visit during pregnancy (no visit, 1–4 and > 4 ANC visits), cigarettes smoking (coded as no and yes), mode of delivery (coded as vaginal and caesarean delivery), child nutritional status (stunting; coded as normal, moderately stunted, and severely stunted; wasting coded as normal, moderately wasted, and severely stunted; and underweight coded as normal, moderately underweight and severely underweight), media exposure (coded as no and yes), religion (coded as orthodox, muslim, protestant, catholic and others), sex of child (coded as male and female), covered by health insurance (coded as no and yes), and birth weight (large, average and small). Wealth Index (WI) was considered as a living standard measure for each respective year and generated using the Principal Component Analyses (PCA). The variables included in the PCA were ownership of durable assets, like radios, cars, refrigerators, TV sets, motorcycles, and bicycles; housing characteristics, such as the number of rooms for sleeping and building materials (walls, floors, and roofs); access to utilities and infrastructures, like electricity supply, source of drinking water, and sanitation facilities. The Ethiopian Demographic and Health survey consists of different datasets including men, women, kids (KR), birth, household, and household datasets. For this study, we used the Kids Record (KR) data set. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the representativeness of the survey to get reliable statistical estimates. Descriptive and summary statistics were done using STATA version 14 software. For the decomposition analysis, we appended the extracted data of 2000, 2005, 2011 and 2016 using the STATA command “append using” after we kept the similar variables across the surveys. The change in infant mortality rate in Ethiopia for the last 16 years was examined. To determine the factors that contributed to the decrease in the infant mortality rate over the last 16 years, the Multivariate Decomposition Analysis for the Non-linear Response variable (MVDCMP) was used. The multivariate decomposition analysis based on the logit link function uses the output from the binary logistic regression model to divide into components. The decrease in infant mortality can be explained by the difference in composition between the surveys (i.e., differences in characteristics or endowment) and/or the difference in effects of the explanatory variables across the surveys (i.e., differences in coefficients). The multivariate decomposition analysis of the logit or log-odd of infant mortality is taken as: The E component refers to the part of the overall decrease in infant mortality explained by the change in the composition of the study participants across the surveys. There is no error term in the logit-based multivariate decomposition analysis because we used the logit link function. The C component refers to the percentage of the overall decrease in infant mortality attributable to the differences in coefficients or effects of the explanatory variable across the surveys. For the decomposition analysis of infant mortality using the mvdcmp STATA command (28). Variables with a p-value < 0.2 in the bi-variable Logit-based multivariate decomposition analysis were considered for the multivariable Logit-based multivariate decomposition analysis. Finally, p-value < 0.05 and the corresponding coefficient (B) with a 95% confidence interval were used to declare significant factors that contributed to the decrease in infant mortality. As the data used for this study had nested structure, infants within the same cluster might share similar characteristics than infants out of that cluster. In hierarchical data, advanced statistical models such as mixed-effect regression analysis to get reliable estimate. Therefore, a two-level mixed-effect logistic regression model (both fixed and random effect) was fitted using EAs as a random variable to draw a valid conclusion. The assumptions of the mixed-effect binary logistic regression model were checked using the Intra-class Correlation Coefficient (ICC) and Likelihood Ratio (LR) test. The Median Odds Ratio (MOR) and Proportional Change in Variance (PCV) were computed to measure the variation across clusters. ICC quantifies the degree of heterogeneity of infant mortality between clusters (the proportion of the total observed individual-level variation in infant mortality that is attributable to between cluster variations). The MOR measures the between cluster variation in terms of odds ratio. The median value of the odds ratio between the cluster at high risk of infant mortality and cluster at lower risk of the infant when randomly picking out two clusters (EAs). ∂2 indicates that cluster variance PCV measures the total variation in infant mortality explained by the final model compared to the null model. Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and deviance were used for model comparison and a model with the lower deviance was chosen since the model was nested. We identified the independent variables based on previous literature conducted on determinants of infant mortality. As the data used for this study was secondary there was missing on the outcome variable (age at death), and we drop the observation that has missing value on the outcome variables. In the bi-variable mixed-effect binary logistic regression analysis; residence, sex of household head, maternal age, maternal education, wealth status, maternal BMI, preceding birth interval, parity, covered by health insurance, size at birth, ANC visit during pregnancy, sex of the child, place of delivery and type of birth had a p-value less than 0.2 and were considered for multivariable analysis. However, in the multivariable analysis; parity, type of birth, maternal age, maternal BMI, number of ANC visits, preceding birth interval, sex of a child, and size at birth were significantly associated with infant mortality. The Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) and p-value < 0.05 in the multivariable model were used to declare significant determinant factors of infant mortality. As the study was a secondary data analysis accessed from the MEASURE DHS program, this study did not require ethical approval and participant consent. We have granted permission from http:/www.dhsprogram.com to download and use the data for this study. In the data sets, there is no name of persons or household addresses.

AI Digest

Study Justification:

– Infant mortality remains a significant global public health problem.
– The rate of decline in infant mortality in Ethiopia is below the rate expected to meet the SDG targets.
– Understanding the trends and determinants of infant mortality in Ethiopia is crucial for developing effective interventions and policies.

Highlights:

– The study analyzed data from four Ethiopian Demographic and Health Surveys (EDHS) conducted in 2000, 2005, 2011, and 2016.
– The infant mortality rate in Ethiopia decreased from 96.9 per 1000 births in 2000 to 48 per 1000 births in 2016, with an annual rate of reduction of 4.2%.
– Factors such as preceding birth interval, child size at birth, maternal BMI, type of birth, parity, maternal age, and sex of the child were significantly associated with increased odds of infant mortality.
– The study found that 18.1% of the overall decrease in infant mortality was due to differences in composition (residence, maternal age, type of birth, and parity) across the surveys, while 81.9% was due to differences in the effects of these factors.

Recommendations:

– Public health programs should focus on rural communities and multiparous mothers to maintain the declining infant mortality rate in Ethiopia.
– Enhancing health facility delivery and improving the use of ANC services can contribute to reducing infant mortality.
– Maternal nutrition should be prioritized to achieve the SDG targets for infant mortality in Ethiopia.

Key Role Players:

– Ministry of Health: Responsible for implementing and coordinating public health programs targeting infant mortality reduction.
– Health professionals: Provide healthcare services and education to pregnant women and mothers.
– Community health workers: Play a crucial role in reaching rural communities and providing essential healthcare services.
– Non-governmental organizations: Support the implementation of public health programs and interventions.
– Researchers and academics: Conduct further studies to explore additional determinants and interventions for reducing infant mortality.

Cost Items for Planning Recommendations:

– Health facility infrastructure and equipment: Budget for the construction, renovation, and maintenance of health facilities.
– Training and capacity building: Allocate funds for training healthcare professionals and community health workers.
– Outreach and awareness campaigns: Budget for community engagement activities, health education materials, and media campaigns.
– Maternal and child health services: Allocate resources for ANC services, nutrition programs, and access to healthcare during pregnancy and childbirth.
– Monitoring and evaluation: Set aside funds for data collection, analysis, and monitoring the impact of interventions on infant mortality.

Strength of Evidence

N/A

Abstract

A community-based time-series cross-sectional study was used to answer the research objectives. All the Demographic and Health Surveys (DHSs) (EDHS 2000, 2005, 2011, and 2016) conducted in Ethiopia were used. The EDHS was employed in every five-year interval to generate updated health and health-related indicators. The majority of the country’s population lives in the regional states of Amhara, Oromia, and Southern Nations Nationalities and People’s Regions (SNNPR) [25]. Ethiopia is the 13th in the world and 2nd most populous country in Africa [26]. In 2016, there were an estimated 102 million people. A two-stage sampling technique was employed to select the sample and a total of 539 Enumeration Areas (EAs) in EDHS 2000, 540 EAs in EDHS 2005, 624 EAs in EDHS 2011, and 645 EAs in EDHS 2016 were randomly selected. Then, on average 27 to 32 households per EA were selected. The source population was all live births from reproductive-age women within 5 years before the survey in Ethiopia whereas all live births from reproductive-age women in the selected enumeration areas were the study population. A total weighted sample of 46,317 live births (12,260 in EDHS 2000, 11,163 in EDHS 2005, 11,872 in EDHS 2011, and 11,022 in EDHS 2016) from reproductive-age women were used for analysis. The detailed sampling procedure was presented in the full EDHSs report [18, 19, 31, 32]. The outcome variable for this study was infant mortality (the death of live birth within 1 year of birth). In EDHS there was a question about whether the child was alive or died at the time of the survey and for dead infants-age at death were recorded. Death of a child within 1 year of age was coded as 1, and 0 if the child was alive. The unit of analysis in this study was all live births in the 5 years preceding the survey. The infant mortality rate is defined as the number of infant deaths per 1000 live births [33]. The independent variables considered in this study were region (coded as Tigray, Afar, Amhara, Oromia, Somali, Benishangul, SNNPR, Gambella, Harari, Addis Ababa, and Dire Dawa), residence (coded as rural, and urban), sex of household head (coded as male and female), maternal age (recoded as < 20, 20–29, 30–39 and 40–49 years), women education (recoded as no, primary, and secondary and higher), paternal education (recoded as no, primary, and secondary and above), preceding birth interval (recoded as < 24 and ≥ 24 months), Body Mass Index (BMI) of the mother (recoded as 6 births), type of birth (coded as single and multiple), place of delivery (coded as home and health facility), ANC visit during pregnancy (no visit, 1–4 and > 4 ANC visits), cigarettes smoking (coded as no and yes), mode of delivery (coded as vaginal and caesarean delivery), child nutritional status (stunting; coded as normal, moderately stunted, and severely stunted; wasting coded as normal, moderately wasted, and severely stunted; and underweight coded as normal, moderately underweight and severely underweight), media exposure (coded as no and yes), religion (coded as orthodox, muslim, protestant, catholic and others), sex of child (coded as male and female), covered by health insurance (coded as no and yes), and birth weight (large, average and small). Wealth Index (WI) was considered as a living standard measure for each respective year and generated using the Principal Component Analyses (PCA). The variables included in the PCA were ownership of durable assets, like radios, cars, refrigerators, TV sets, motorcycles, and bicycles; housing characteristics, such as the number of rooms for sleeping and building materials (walls, floors, and roofs); access to utilities and infrastructures, like electricity supply, source of drinking water, and sanitation facilities. The Ethiopian Demographic and Health survey consists of different datasets including men, women, kids (KR), birth, household, and household datasets. For this study, we used the Kids Record (KR) data set. The data were weighted using sampling weight, primary sampling unit, and strata before any statistical analysis to restore the representativeness of the survey to get reliable statistical estimates. Descriptive and summary statistics were done using STATA version 14 software. For the decomposition analysis, we appended the extracted data of 2000, 2005, 2011 and 2016 using the STATA command “append using” after we kept the similar variables across the surveys. The change in infant mortality rate in Ethiopia for the last 16 years was examined. To determine the factors that contributed to the decrease in the infant mortality rate over the last 16 years, the Multivariate Decomposition Analysis for the Non-linear Response variable (MVDCMP) was used. The multivariate decomposition analysis based on the logit link function uses the output from the binary logistic regression model to divide into components. The decrease in infant mortality can be explained by the difference in composition between the surveys (i.e., differences in characteristics or endowment) and/or the difference in effects of the explanatory variables across the surveys (i.e., differences in coefficients). The multivariate decomposition analysis of the logit or log-odd of infant mortality is taken as: The E component refers to the part of the overall decrease in infant mortality explained by the change in the composition of the study participants across the surveys. There is no error term in the logit-based multivariate decomposition analysis because we used the logit link function. The C component refers to the percentage of the overall decrease in infant mortality attributable to the differences in coefficients or effects of the explanatory variable across the surveys. For the decomposition analysis of infant mortality using the mvdcmp STATA command (28). Variables with a p-value < 0.2 in the bi-variable Logit-based multivariate decomposition analysis were considered for the multivariable Logit-based multivariate decomposition analysis. Finally, p-value < 0.05 and the corresponding coefficient (B) with a 95% confidence interval were used to declare significant factors that contributed to the decrease in infant mortality. As the data used for this study had nested structure, infants within the same cluster might share similar characteristics than infants out of that cluster. In hierarchical data, advanced statistical models such as mixed-effect regression analysis to get reliable estimate. Therefore, a two-level mixed-effect logistic regression model (both fixed and random effect) was fitted using EAs as a random variable to draw a valid conclusion. The assumptions of the mixed-effect binary logistic regression model were checked using the Intra-class Correlation Coefficient (ICC) and Likelihood Ratio (LR) test. The Median Odds Ratio (MOR) and Proportional Change in Variance (PCV) were computed to measure the variation across clusters. ICC quantifies the degree of heterogeneity of infant mortality between clusters (the proportion of the total observed individual-level variation in infant mortality that is attributable to between cluster variations). The MOR measures the between cluster variation in terms of odds ratio. The median value of the odds ratio between the cluster at high risk of infant mortality and cluster at lower risk of the infant when randomly picking out two clusters (EAs). ∂2 indicates that cluster variance PCV measures the total variation in infant mortality explained by the final model compared to the null model. Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and deviance were used for model comparison and a model with the lower deviance was chosen since the model was nested. We identified the independent variables based on previous literature conducted on determinants of infant mortality. As the data used for this study was secondary there was missing on the outcome variable (age at death), and we drop the observation that has missing value on the outcome variables. In the bi-variable mixed-effect binary logistic regression analysis; residence, sex of household head, maternal age, maternal education, wealth status, maternal BMI, preceding birth interval, parity, covered by health insurance, size at birth, ANC visit during pregnancy, sex of the child, place of delivery and type of birth had a p-value less than 0.2 and were considered for multivariable analysis. However, in the multivariable analysis; parity, type of birth, maternal age, maternal BMI, number of ANC visits, preceding birth interval, sex of a child, and size at birth were significantly associated with infant mortality. The Adjusted Odds Ratio (AOR) with a 95% Confidence Interval (CI) and p-value < 0.05 in the multivariable model were used to declare significant determinant factors of infant mortality. As the study was a secondary data analysis accessed from the MEASURE DHS program, this study did not require ethical approval and participant consent. We have granted permission from http:/www.dhsprogram.com to download and use the data for this study. In the data sets, there is no name of persons or household addresses.

Materials

N/A

Innovations

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

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals for pregnant women in rural or underserved areas. This allows them to receive prenatal care, consultations, and medical advice without having to travel long distances.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take control of their own health and make informed decisions.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, health education, and referrals to pregnant women in their communities can improve access to maternal health services, especially in remote areas.

4. Maternal health clinics: Establishing dedicated maternal health clinics that offer comprehensive prenatal care, delivery services, and postnatal care can ensure that pregnant women have access to quality healthcare throughout their pregnancy and after childbirth.

5. Transportation services: Providing transportation services, such as ambulances or mobile clinics, can help overcome geographical barriers and ensure that pregnant women can reach healthcare facilities in a timely manner, especially during emergencies.

6. Maternal health education programs: Developing and implementing educational programs that focus on maternal health, including prenatal care, nutrition, and birth preparedness, can empower women with knowledge and help them make informed decisions about their health and the health of their babies.

7. Maternity waiting homes: Establishing maternity waiting homes near healthcare facilities can provide a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy, ensuring that they are close to medical care when they go into labor.

8. Financial incentives: Implementing financial incentives, such as conditional cash transfers or subsidies, can help alleviate the financial burden of seeking maternal healthcare services and encourage pregnant women to access and utilize these services.

9. Public-private partnerships: Collaborating with private healthcare providers and organizations can help expand access to maternal health services, improve infrastructure, and enhance the quality of care available to pregnant women.

10. Data-driven decision-making: Utilizing data from surveys, research studies, and health information systems can help identify gaps in maternal health services and inform evidence-based policies and interventions to improve access and outcomes.

It is important to note that the implementation of these innovations should be context-specific and tailored to the unique needs and challenges of the Ethiopian healthcare system and population.

AI Innovations Description

Based on the provided description, here is a recommendation that can be developed into an innovation to improve access to maternal health:

1. Strengthening Rural Health Facilities: Since public health programs aimed at rural communities were found to be effective in maintaining Ethiopia’s declining infant mortality rate, it is recommended to focus on strengthening rural health facilities. This can be done by improving infrastructure, ensuring availability of essential medical equipment and supplies, and increasing the number of skilled healthcare providers in rural areas.

2. Enhancing Health Facility Delivery: The study identified that delivering babies in health facilities was associated with lower infant mortality rates. To improve access to maternal health, it is important to promote and encourage health facility deliveries. This can be achieved through community awareness campaigns, providing incentives for facility deliveries, and ensuring that health facilities are equipped to handle childbirth complications.

3. Increasing the Use of Antenatal Care (ANC) Services: The study found that the number of ANC visits was significantly associated with infant mortality. To reduce infant mortality, it is crucial to improve the utilization of ANC services. This can be done by raising awareness about the importance of ANC, providing accessible and affordable ANC services, and addressing barriers such as transportation and financial constraints.

4. Improving Maternal Nutrition: Maternal nutrition was identified as a significant predictor of infant mortality. To address this, it is important to implement programs that focus on improving maternal nutrition during pregnancy. This can include providing nutritional supplements, promoting healthy eating habits, and educating women about the importance of a balanced diet during pregnancy.

5. Strengthening Health Insurance Coverage: The study found that being covered by health insurance was associated with lower infant mortality rates. Therefore, it is recommended to strengthen health insurance coverage, particularly among vulnerable populations. This can be achieved by expanding health insurance programs, reducing financial barriers to access, and ensuring that health insurance schemes cover maternal health services.

By implementing these recommendations, it is possible to improve access to maternal health and reduce infant mortality rates in Ethiopia.

AI Innovations Methodology

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

1. Strengthening rural healthcare infrastructure: Focus on improving healthcare facilities, equipment, and staffing in rural areas where access to maternal health services is limited.

2. Enhancing community-based healthcare services: Implement community-based programs that provide maternal health education, prenatal care, and postnatal support to pregnant women in remote areas.

3. Increasing awareness and education: Develop targeted campaigns to raise awareness about the importance of maternal health, including the benefits of antenatal care, skilled birth attendance, and postnatal care.

4. Improving transportation systems: Address transportation barriers by providing reliable and affordable transportation options for pregnant women to reach healthcare facilities in a timely manner.

5. Promoting maternal nutrition: Implement programs that educate women about the importance of proper nutrition during pregnancy and provide access to nutritious food and supplements.

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

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the percentage of pregnant women receiving antenatal care, the percentage of births attended by skilled health personnel, or the percentage of women receiving postnatal care.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or region.

3. Introduce the recommendations: Implement the recommended interventions or innovations to improve access to maternal health services.

4. Monitor and collect data: Continuously collect data on the selected indicators after the implementation of the recommendations. This can be done through surveys, interviews, or monitoring systems.

5. Analyze the data: Use statistical analysis techniques to compare the baseline data with the post-implementation data. This analysis can help determine the impact of the recommendations on the selected indicators.

6. Evaluate the results: Assess the effectiveness of the recommendations by comparing the post-implementation data with the desired targets or benchmarks. This evaluation can help identify areas of success and areas that may require further improvement.

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

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different interventions on improving access to maternal health and make informed decisions to prioritize and implement the most effective strategies.

Author

Dima Health

Statistics:

Citations: 6

Identifiers:

DOI: 10.1186/s12884-021-03835-0

Research Areas:

Community Interventions, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female