Quality of antenatal care predicts retention in skilled birth attendance: A multilevel analysis of 28 African countries

BMC Pregnancy and Childbirth, Volume 17, No. 1, Year 2017

Interprétation

Background: An effective continuum of maternal care ensures that mothers receive essential health packages from pre-pregnancy to delivery, and postnatally, reducing the risk of maternal death. However, across Africa, coverage of skilled birth attendance is lower than coverage for antenatal care, indicating mothers are not retained in the continuum between antenatal care and delivery. This paper explores predictors of retention of antenatal care clients in skilled birth attendance across Africa, including sociodemographic factors and quality of antenatal care received. Methods: We pooled nationally representative data from Demographic and Health Surveys conducted in 28 African countries between 2006 and 2015. For the 115,374 births in our sample, we estimated logistic multilevel models of retention in skilled birth attendance (SBA) among clients that received skilled antenatal care (ANC). Results: Among ANC clients in the study sample, 66% received SBA. Adjusting for all demographic covariates and country indicators, the odds of retention in SBA were higher among ANC clients that had their blood pressure checked, received information about pregnancy complications, had blood tests conducted, received at least one tetanus injection, and had urine tests conducted. Conclusions: Higher quality of ANC predicts retention in SBA in Africa. Improving quality of skilled care received prenatally may increase client retention during delivery, reducing maternal mortality. The study sample was drawn from the births recode data files of the latest Standard DHS conducted in each sub-Saharan African country between 2000 and 2016, where the full complement of variables for the study was collected. The DHS samples were based on a stratified two-stage cluster design. In the first stage, clusters are drawn from census files. In the second stage, a sample of households is drawn from each selected cluster. The birth recode data files of the nationally representative Demographic and Health Surveys include the full birth histories over the 3–5 preceding years of women in these households including information on pregnancy, postnatal care, immunization, and child health. The final sample covers surveys from 28 countries with unrestricted data access and that include the full complement of variables explored in the study. This sample represents a population of 740 million or 70% of the total population in sub-Saharan Africa in 2015. The following surveys were included: Benin, 2011–2012; Burkina Faso, 2010; Burundi, 2010; Cameroon, 2011; Chad, 2014–2015; Comoros, 2012; Congo, 2011–2012; Democratic Republic of Congo/DRC, 2013–2014; Ethiopia, 2011; Gabon, 2012; Gambia, 2013; Ghana, 2014; Ivory Coast, 2011–2012; Kenya, 2014; Lesotho, 2014; Liberia, 2013; Madagascar, 2008–2009; Malawi, 2010; Mali, 2012–2013; Mozambique, 2011; Namibia, 2013; Niger, 2012; Nigeria, 2013; Sierra Leone, 2013; Swaziland, 2006–2007; Tanzania, 2010; Togo, 2013–2014; Zambia, 2013–2014; and Zimbabwe, 2010–2011. The dependent variable in this study is retention in skilled birth attendance (SBA) among skilled antenatal care (ANC) clients. This variable is coded as ‘1’ if the respondent received any ANC (that is attended ANC at least once) and SBA in the index pregnancy, and ‘0’ if the respondent did not receive SBA, but had received any ANC in the index pregnancy. We defined skilled care as care provided by a doctor, nurse, or midwife, in line with the World Health Organization policy guidelines, as several countries did not have standardized definitions for skilled maternal care providers [6]. To fit a model of retention in SBA for ANC clients, we drew on the framework for health care access by Penchansky and Thomas [7]. The framework captures demand and supply-side determinants of care access along five dimensions (availability, accessibility, accommodation, affordability, and acceptability). We conducted a review of the literature on factors demonstrated to be associated with the use of maternal health care [8], [9]. We then included covariates, collected consistently across the 28 countries that represented at least one dimension of access within the framework. The availability dimension refers to the adequacy of the supply of skilled health workers, facilities, and services, and provides information on the quality of care received during ANC, where good quality of care corresponds to the recommended model by the World Health Organization of focused ANC based on at least four goal-oriented-visits [2]. We included indicators for the following variables: location of care in the facility, the conduct of any urine test, the conduct of any blood test, having had a blood pressure check, receiving at least one tetanus injection, attending up to 4 visits, and receiving any information on potential pregnancy complications. The accessibility dimension accounts for client transportation resources, distance and travel time to care. We thus included an indicator for living in an urban area, as poor physical access to social services correlates with rural dwelling across Africa [10]. Under the affordability dimension, that is the ability to pay and financial protection during care-seeking, we included indicators for having health insurance, possessing any primary education or higher, having a partner who has any primary education or higher and belonging to the richest two wealth quintiles. The acceptability dimension refers to the influences of personal characteristics of the provider and client on care-seeking. We thus included indicators for parity (primiparous for the first birth and grand multiparous for more than five previous births, so that women with 1 to 4 previous births were considered the reference category). We also included indicators for women’s age. Women below 18 years and those above 35 years were collapsed into one category and considered as the reference category (compared with women between 18 and 35 years old), as young and older maternal age has been shown to influence both maternal decisions to initiate care-seeking and the interaction with health care providers during pregnancy [11]. We also included an indicator variable for each country included in the study as a proxy for the national context. For each included country, we calculated the mean levels of ANC, SBA, and the gap in coverage between ANC and SBA (calculated as the difference between mean ANC and mean SBA levels). For the observations with the complete set of covariates (the analytic sample), we estimated the means and standard errors for the study dependent and independent variables, weighted based on client sampling weights. On the analytic sample, we then estimated a two-level logistic regression model of SBA retention, nesting each birth (individual-level) within a cluster. As several mothers reported only one birth over the survey period, we did not construct a three-level model that included random effects at the maternal level. The empirical model included random intercepts for the cluster, fixed effects for each country, and was weighted using respondent sample weights to ensure representativeness at the national level. We categorized the covariates into three blocks: country indicators (binary variables indicating the country in which the survey was conducted), ANC characteristics (corresponding to the availability dimension of the access to care framework) and demographic characteristics. We progressively added these blocks of covariates into the empirical model and computed the intraclass correlation (ICC), that is the DHS cluster-level correlation, to estimate the extent to which the individual probability of retention in SBA for ANC clients in the same DHS cluster was similar compared to individuals from other DHS clusters. The ICC expresses the proportion of the total variance that is at the DHS cluster level. We estimated the ICC using the latent variable method [12] as follows: Where Var DHS Cluster is the variance between DHS clusters and π2/3 is the variance between individuals. We then estimated the proportion of the cluster-level variance that is explained by different blocks of covariates as follows: Where Var 0 is the variance in the initial or empty model, and Var 1 is the second-level variance in the models with various blocks of covariates. For each covariate, we reported the odds ratio (OR) and 95% confidence interval (CI). As Benin had the highest percentage of ANC clients retained in SBA in the fully-adjusted models, we considered this the reference category in our multilevel models. All analyses were conducted using STATA 14.2.

Résumé de l’IA

Study Justification:

This study aims to investigate the factors that predict the retention of antenatal care (ANC) clients in skilled birth attendance (SBA) across 28 African countries. The study is important because it addresses the issue of low coverage of skilled birth attendance compared to antenatal care, which indicates a gap in the continuum of maternal care. By identifying predictors of retention in SBA, the study can inform interventions and policies to improve the quality of care and reduce maternal mortality.

Highlights:

– The study analyzed nationally representative data from 28 African countries between 2006 and 2015.
– Among ANC clients in the study sample, 66% received SBA.
– Factors associated with higher odds of retention in SBA included having blood pressure checked, receiving information about pregnancy complications, blood tests, tetanus injection, and urine tests.
– The study findings suggest that improving the quality of antenatal care can increase client retention during delivery, thereby reducing maternal mortality.

Recommendations:

Based on the study findings, the following recommendations can be made:

1. Improve the quality of antenatal care services by ensuring that blood pressure checks, information about pregnancy complications, blood tests, tetanus injections, and urine tests are provided to all ANC clients.
2. Strengthen the training and capacity of healthcare providers to deliver high-quality antenatal care.
3. Enhance accessibility and affordability of skilled birth attendance services, particularly in rural areas.
4. Promote health insurance coverage and financial protection for pregnant women to ensure affordability of care.
5. Implement targeted interventions to address specific barriers to retention in skilled birth attendance, such as transportation challenges and cultural beliefs.

Key Role Players:

1. Ministries of Health: Responsible for developing and implementing policies to improve maternal healthcare services.
2. Healthcare Providers: Involved in delivering antenatal care and skilled birth attendance services.
3. Community Health Workers: Play a crucial role in promoting maternal healthcare utilization and providing education and support to pregnant women.
4. Non-Governmental Organizations (NGOs): Can contribute by implementing programs to improve the quality and accessibility of maternal healthcare services.
5. International Donors: Provide funding and support for initiatives aimed at improving maternal healthcare in African countries.

Cost Items for Planning Recommendations:

1. Training and Capacity Building: Budget for training healthcare providers on delivering high-quality antenatal care.
2. Infrastructure Development: Allocate funds for improving healthcare facilities and equipment to ensure the provision of quality care.
3. Transportation Services: Consider the cost of providing transportation services for pregnant women in remote areas to access skilled birth attendance.
4. Health Insurance: Budget for expanding health insurance coverage for pregnant women to ensure financial protection during care-seeking.
5. Community Outreach Programs: Allocate funds for community health workers to conduct outreach programs and provide education and support to pregnant women.
Please note that the cost items provided are general categories and the actual cost will vary depending on the specific context and country.

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a large sample size of 115,374 births from 28 African countries. The study uses nationally representative data from Demographic and Health Surveys conducted between 2006 and 2015. The study also adjusts for demographic covariates and country indicators. However, to improve the evidence, the abstract could provide more information on the methodology used, such as the specific statistical models employed and any potential limitations of the study.

Abstrait

The study sample was drawn from the births recode data files of the latest Standard DHS conducted in each sub-Saharan African country between 2000 and 2016, where the full complement of variables for the study was collected. The DHS samples were based on a stratified two-stage cluster design. In the first stage, clusters are drawn from census files. In the second stage, a sample of households is drawn from each selected cluster. The birth recode data files of the nationally representative Demographic and Health Surveys include the full birth histories over the 3–5 preceding years of women in these households including information on pregnancy, postnatal care, immunization, and child health. The final sample covers surveys from 28 countries with unrestricted data access and that include the full complement of variables explored in the study. This sample represents a population of 740 million or 70% of the total population in sub-Saharan Africa in 2015. The following surveys were included: Benin, 2011–2012; Burkina Faso, 2010; Burundi, 2010; Cameroon, 2011; Chad, 2014–2015; Comoros, 2012; Congo, 2011–2012; Democratic Republic of Congo/DRC, 2013–2014; Ethiopia, 2011; Gabon, 2012; Gambia, 2013; Ghana, 2014; Ivory Coast, 2011–2012; Kenya, 2014; Lesotho, 2014; Liberia, 2013; Madagascar, 2008–2009; Malawi, 2010; Mali, 2012–2013; Mozambique, 2011; Namibia, 2013; Niger, 2012; Nigeria, 2013; Sierra Leone, 2013; Swaziland, 2006–2007; Tanzania, 2010; Togo, 2013–2014; Zambia, 2013–2014; and Zimbabwe, 2010–2011. The dependent variable in this study is retention in skilled birth attendance (SBA) among skilled antenatal care (ANC) clients. This variable is coded as ‘1’ if the respondent received any ANC (that is attended ANC at least once) and SBA in the index pregnancy, and ‘0’ if the respondent did not receive SBA, but had received any ANC in the index pregnancy. We defined skilled care as care provided by a doctor, nurse, or midwife, in line with the World Health Organization policy guidelines, as several countries did not have standardized definitions for skilled maternal care providers [6]. To fit a model of retention in SBA for ANC clients, we drew on the framework for health care access by Penchansky and Thomas [7]. The framework captures demand and supply-side determinants of care access along five dimensions (availability, accessibility, accommodation, affordability, and acceptability). We conducted a review of the literature on factors demonstrated to be associated with the use of maternal health care [8], [9]. We then included covariates, collected consistently across the 28 countries that represented at least one dimension of access within the framework. The availability dimension refers to the adequacy of the supply of skilled health workers, facilities, and services, and provides information on the quality of care received during ANC, where good quality of care corresponds to the recommended model by the World Health Organization of focused ANC based on at least four goal-oriented-visits [2]. We included indicators for the following variables: location of care in the facility, the conduct of any urine test, the conduct of any blood test, having had a blood pressure check, receiving at least one tetanus injection, attending up to 4 visits, and receiving any information on potential pregnancy complications. The accessibility dimension accounts for client transportation resources, distance and travel time to care. We thus included an indicator for living in an urban area, as poor physical access to social services correlates with rural dwelling across Africa [10]. Under the affordability dimension, that is the ability to pay and financial protection during care-seeking, we included indicators for having health insurance, possessing any primary education or higher, having a partner who has any primary education or higher and belonging to the richest two wealth quintiles. The acceptability dimension refers to the influences of personal characteristics of the provider and client on care-seeking. We thus included indicators for parity (primiparous for the first birth and grand multiparous for more than five previous births, so that women with 1 to 4 previous births were considered the reference category). We also included indicators for women’s age. Women below 18 years and those above 35 years were collapsed into one category and considered as the reference category (compared with women between 18 and 35 years old), as young and older maternal age has been shown to influence both maternal decisions to initiate care-seeking and the interaction with health care providers during pregnancy [11]. We also included an indicator variable for each country included in the study as a proxy for the national context. For each included country, we calculated the mean levels of ANC, SBA, and the gap in coverage between ANC and SBA (calculated as the difference between mean ANC and mean SBA levels). For the observations with the complete set of covariates (the analytic sample), we estimated the means and standard errors for the study dependent and independent variables, weighted based on client sampling weights. On the analytic sample, we then estimated a two-level logistic regression model of SBA retention, nesting each birth (individual-level) within a cluster. As several mothers reported only one birth over the survey period, we did not construct a three-level model that included random effects at the maternal level. The empirical model included random intercepts for the cluster, fixed effects for each country, and was weighted using respondent sample weights to ensure representativeness at the national level. We categorized the covariates into three blocks: country indicators (binary variables indicating the country in which the survey was conducted), ANC characteristics (corresponding to the availability dimension of the access to care framework) and demographic characteristics. We progressively added these blocks of covariates into the empirical model and computed the intraclass correlation (ICC), that is the DHS cluster-level correlation, to estimate the extent to which the individual probability of retention in SBA for ANC clients in the same DHS cluster was similar compared to individuals from other DHS clusters. The ICC expresses the proportion of the total variance that is at the DHS cluster level. We estimated the ICC using the latent variable method [12] as follows: Where Var DHS Cluster is the variance between DHS clusters and π2/3 is the variance between individuals. We then estimated the proportion of the cluster-level variance that is explained by different blocks of covariates as follows: Where Var 0 is the variance in the initial or empty model, and Var 1 is the second-level variance in the models with various blocks of covariates. For each covariate, we reported the odds ratio (OR) and 95% confidence interval (CI). As Benin had the highest percentage of ANC clients retained in SBA in the fully-adjusted models, we considered this the reference category in our multilevel models. All analyses were conducted using STATA 14.2.

Matériaux

N/A

Innovations

The study recommends focusing on improving the quality of antenatal care (ANC) received by pregnant women to improve access to maternal health. This includes ensuring that ANC providers are trained and equipped to provide comprehensive and high-quality care, promoting the recommended model of focused ANC based on at least four goal-oriented visits, improving physical access to ANC facilities, enhancing financial protection and affordability of ANC services, and addressing social and cultural barriers that may affect women’s decision to seek ANC and skilled birth attendance (SBA).

Some innovations for implementing these recommendations could include:

1. Training programs for ANC providers: Implementing training programs to improve the skills and knowledge of ANC providers, ensuring they are equipped to provide comprehensive care and conduct essential tests.

2. Mobile ANC clinics: Introducing mobile ANC clinics that can reach remote areas and provide ANC services to pregnant women who have limited access to healthcare facilities.

3. Telemedicine services: Using telemedicine services to provide ANC consultations and support to pregnant women in areas with limited access to healthcare facilities.

4. Community-based ANC programs: Establishing community-based ANC programs where trained community health workers can provide ANC services and support to pregnant women in their own communities.

5. Public-private partnerships: Collaborating with private healthcare providers to expand access to ANC services and improve the quality of care provided.

6. Health insurance schemes: Implementing health insurance schemes that cover ANC services, making them more affordable and accessible to pregnant women, particularly those from low-income backgrounds.

7. Public awareness campaigns: Conducting public awareness campaigns to educate communities about the importance of ANC and SBA, addressing any stigma or misconceptions surrounding maternal health.

These innovations can help improve access to maternal health services and ensure that pregnant women receive high-quality ANC, leading to better retention in skilled birth attendance and ultimately reducing maternal mortality rates in Africa.

AI Innovations Description

The recommendation from the study to improve access to maternal health is to focus on improving the quality of antenatal care (ANC) received by pregnant women. The study found that higher quality ANC predicts retention in skilled birth attendance (SBA) in Africa. This means that if pregnant women receive better quality care during their pregnancy, they are more likely to continue seeking skilled care during delivery, which can reduce maternal mortality.

To implement this recommendation, healthcare systems should prioritize the following interventions:

1. Ensure that ANC providers are trained and equipped to provide comprehensive and high-quality care. This includes conducting essential tests such as blood pressure checks, blood tests, and urine tests, as well as providing information about potential pregnancy complications.

2. Promote the recommended model of focused ANC based on at least four goal-oriented visits. This ensures that pregnant women receive the necessary care and support throughout their pregnancy.

3. Improve physical access to ANC facilities, especially for women living in rural areas. This can be achieved by increasing the number of healthcare facilities in remote areas or providing transportation services for pregnant women to reach the nearest facility.

4. Enhance financial protection and affordability of ANC services. This can be done by implementing health insurance schemes or providing subsidies for ANC services, particularly for women from low-income backgrounds.

5. Address social and cultural barriers that may affect women’s decision to seek ANC and SBA. This includes raising awareness about the importance of skilled care during pregnancy and delivery, as well as addressing any stigma or misconceptions surrounding maternal health.

By implementing these recommendations, healthcare systems can improve access to maternal health services and ultimately reduce maternal mortality rates in Africa.

AI Innovations Methodology

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

1. Define the target population: Determine the specific population group that will be the focus of the simulation, such as pregnant women in rural areas of Africa.

2. Collect baseline data: Gather data on the current status of access to maternal health services in the target population. This includes information on the quality of antenatal care received, retention in skilled birth attendance, and other relevant factors such as demographic characteristics and socio-economic status.

3. Implement the interventions: Simulate the implementation of the recommended interventions in the target population. This involves improving the quality of antenatal care, promoting the recommended model of focused ANC, improving physical access to ANC facilities, enhancing financial protection, and addressing social and cultural barriers.

4. Measure the impact: Assess the impact of the interventions on access to maternal health services. This can be done by comparing the indicators of access (e.g., retention in skilled birth attendance) before and after the implementation of the interventions.

5. Analyze the results: Analyze the data collected to determine the extent to which the interventions have improved access to maternal health services. This may involve statistical analysis to identify significant changes and trends.

6. Draw conclusions and make recommendations: Based on the findings of the simulation, draw conclusions about the effectiveness of the interventions in improving access to maternal health. Identify any limitations or challenges encountered during the simulation and make recommendations for further improvement.

It is important to note that this methodology is a general framework and the specific details of the simulation may vary depending on the available data, resources, and context.

Auteur

Dima Health

Statistics:

Citations: 34

Identifiers:

DOI: 10.1186/s12884-017-1337-1

Research Areas:

Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health

Study Countries:

Benin, Burkina Faso, Burundi, Cameroon, Chad, Comoros, Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Ivory Coast, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Multi-Countries, Namibia, Niger, Nigeria, Sierra Leone, Tanzania, Togo, Zambia, Zimbabwe

Study Design:

Case-Control Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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