Determinants of adverse birth outcome in Sub-Saharan Africa: analysis of recent demographic and health surveys

BMC Public Health, Volume 21, No. 1, Year 2021

Interpretation

Background: More than 75% of neonatal deaths occurred in the first weeks of life as a result of adverse birth outcomes. Low birth weight, preterm births are associated with a variety of acute and long-term complications. In Sub-Saharan Africa, there is insufficient evidence of adverse birth outcomes. Hence, this study aimed to determine the pooled prevalence and determinants of adverse birth outcomes in Sub-Saharan Africa. Method: Data of this study were obtained from a cross-sectional survey of the most recent Demographic and Health Surveys (DHS) of ten Sub-African (SSA) countries. A total of 76,853 children born five years preceding the survey were included in the final analysis. A Generalized Linear Mixed Models (GLMM) were fitted and an adjusted odds ratio (AOR) with a 95% Confidence Interval (CI) was computed to declare statistically significant determinants of adverse birth outcomes. Result: The pooled prevalence of adverse birth outcomes were 29.7% (95% CI: 29.4 to 30.03). Female child (AOR = 0.94, 95%CI: 0.91 0.97), women attended secondary level of education (AOR = 0.87, 95%CI: 0.82 0.92), middle (AOR = 0.94,95%CI: 0.90 0.98) and rich socioeconomic status (AOR = 0.94, 95%CI: 0.90 0.99), intimate-partner physical violence (beating) (AOR = 1.18, 95%CI: 1.14 1.22), big problems of long-distance travel (AOR = 1.08, 95%CI: 1.04 1.11), antenatal care follow-ups (AOR = 0.86, 95%CI: 0.83 0.86), multiparty (AOR = 0.88, 95%CI: 0.84 0.91), twin births (AOR = 2.89, 95%CI: 2.67 3.14), and lack of women involvement in healthcare decision-making process (AOR = 1.10, 95%CI: 1.06 1.13) were determinants of adverse birth outcomes. Conclusion: This study showed that the magnitude of adverse birth outcomes was high, abnormal baby size and preterm births were the most common adverse birth outcomes. This finding suggests that encouraging antenatal care follow-ups and socio-economic conditions of women are essential. Moreover, special attention should be given to multiple pregnancies, improving healthcare accessibilities to rural areas, and women’s involvement in healthcare decision-making. The most recent Demographic and Health Surveys (DHS) of ten Sub-African (SSA) countries (Angola, Congo, Cote d’Ivoire, Gambia, Lesotho, Liberia, Madagascar, Nigeria, Rwanda, Togo) data were used to make analysis of this study. The DHS is a part of the measure DHS programs that collect national information on basic health measures such as mortality, morbidity, and maternal and child health service utilization. Using the Kids Record (KR file) dataset, all births in the preceding five years before the survey were the study population. In the selected enumeration areas (EAs) births that had data about birth weight, gestational age at birth, and perinatal death records were included in the study. During the measure DHS survey, a multi-stage (two-stage) stratified sampling technique was used to select study participants; children were nested within the enumeration areas. After the dataset was appended, the weighted sample size became 76,853 children and women who had given birth five years preceding the survey. The methodology section of the DHS report goes into great detail about the study participant selection and data collection [23]. The main outcome variable of this study was adverse birth outcomes, which is defined as the presence of at least one or more of the following conditions in recent pregnancy (low birth weight, macrosomia, preterm birth, or stillbirth) [13, 19]. The outcome variable was generated by composite low birth weight, macrosomia, stillbirth, and gestational age less than 37 weeks of pregnancy. Finally, the variable takes 1 if at least one of adverse birth outcomes reported which was labeled as “adverse birth outcome”, and 0 otherwise. Socio-demographic characteristics (residence, maternal education, husband education, maternal age, mother marital status, sex of the child, media exposure, household wealth index, and maternal working status), health service utilization and accessibility (women healthcare decision-making autonomy, ANC follow up, and distance to health facility), and obstetrics related characteristics (preceding birth interval, parity, type of birth, and delivery by CS) were explanatory variables identified after thorough review of literatures [13, 24–31] . Short birth interval is defined as the time between two births which is less than 24 months [32]. Also, women’s healthcare decision-making autonomy is the ability of the women to make decisions to use health care services and treatment options [25]. Finally, media exposure was defined as when a woman reads a newspaper or listens to the radio, or watches television at least three times per week. Before any statistical analysis, the data were weighted using sampling weight based on primary sampling unit, and strata to restore the representativeness of the survey and take sampling design when calculating standard errors and reliable estimates. Cross-tabulations and summary statistics were done using STATA software version 14 (StataCorp.2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). The pooled prevalence of adverse birth outcomes with a 95% Confidence Interval (CI) was reported using a forest plot. The DHS dataset has a hierarchical structure that failed to meet the standard logistic regression model assumptions of independent observation and equal variance. Meanwhile, the children were nested within a cluster household, and children from the same cluster were more similar than from other clusters. Therefore, a mixed effect logistic regression model (both fixed and random effect) was fitted to account for cluster variability by using the advanced models. The outcome variable of the study was binary, a standard logistic regression and Generalized Linear Mixed Models (GLMM) were fitted step by step. Because the models were nested, model fitness was compared using the Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR), Median Odds Ratio (MOR), and deviance (−2LLR) values. As a result, the mixed-effect logistic regression model with the lowest deviance value was selected as the most parsimonious model. (Shown on Supplementary file Table 1). In the bivariable analysis, variables with less than 0.2 p-values were selected and entered into the multivariable mixed-effect logistic regression model. Adjusted Odds Ratios (AOR) with a 95% CI were calculated in the multivariable model to see the strength of association between independent variables and adverse birth outcomes. Variables with a 0.05 p-value in the final model being used as a statistically significant determinant of adverse birth outcomes. Permission for data access was obtained from measure demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifier.

AI Digest

Study Justification:

– Neonatal deaths resulting from adverse birth outcomes are a significant concern, with over 75% occurring in the first weeks of life.
– Sub-Saharan Africa lacks sufficient evidence on adverse birth outcomes.
– This study aimed to determine the prevalence and determinants of adverse birth outcomes in Sub-Saharan Africa.

Highlights:

– Pooled prevalence of adverse birth outcomes in Sub-Saharan Africa was 29.7%.
– Determinants of adverse birth outcomes included: female child, women with secondary education, middle and rich socioeconomic status, intimate-partner physical violence, long-distance travel, lack of antenatal care follow-ups, multiparty, twin births, and lack of women involvement in healthcare decision-making.
– Abnormal baby size and preterm births were the most common adverse birth outcomes.

Recommendations:

– Encourage antenatal care follow-ups and improve the socio-economic conditions of women.
– Special attention should be given to multiple pregnancies, improving healthcare accessibilities to rural areas, and women’s involvement in healthcare decision-making.

Key Role Players:

– Ministry of Health officials
– Healthcare providers
– Community health workers
– Non-governmental organizations (NGOs)
– Policy makers

Cost Items for Planning Recommendations:

– Training and capacity building for healthcare providers and community health workers
– Infrastructure development for healthcare facilities in rural areas
– Awareness campaigns and education materials for women and communities
– Support for women’s empowerment programs
– Monitoring and evaluation of interventions
Please note that the cost items provided are general categories and not actual cost estimates. The actual cost will depend on the specific 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 moderately strong. The study utilized a large sample size and a cross-sectional survey design, which increases the generalizability of the findings. The study also used a mixed-effect logistic regression model to account for cluster variability. However, the abstract does not provide information on the specific methods used for data collection and analysis, which limits the ability to assess the rigor of the study. To improve the strength of the evidence, the abstract should include more details on the study design, data collection procedures, and statistical analysis methods.

Abstract

The most recent Demographic and Health Surveys (DHS) of ten Sub-African (SSA) countries (Angola, Congo, Cote d’Ivoire, Gambia, Lesotho, Liberia, Madagascar, Nigeria, Rwanda, Togo) data were used to make analysis of this study. The DHS is a part of the measure DHS programs that collect national information on basic health measures such as mortality, morbidity, and maternal and child health service utilization. Using the Kids Record (KR file) dataset, all births in the preceding five years before the survey were the study population. In the selected enumeration areas (EAs) births that had data about birth weight, gestational age at birth, and perinatal death records were included in the study. During the measure DHS survey, a multi-stage (two-stage) stratified sampling technique was used to select study participants; children were nested within the enumeration areas. After the dataset was appended, the weighted sample size became 76,853 children and women who had given birth five years preceding the survey. The methodology section of the DHS report goes into great detail about the study participant selection and data collection [23]. The main outcome variable of this study was adverse birth outcomes, which is defined as the presence of at least one or more of the following conditions in recent pregnancy (low birth weight, macrosomia, preterm birth, or stillbirth) [13, 19]. The outcome variable was generated by composite low birth weight, macrosomia, stillbirth, and gestational age less than 37 weeks of pregnancy. Finally, the variable takes 1 if at least one of adverse birth outcomes reported which was labeled as “adverse birth outcome”, and 0 otherwise. Socio-demographic characteristics (residence, maternal education, husband education, maternal age, mother marital status, sex of the child, media exposure, household wealth index, and maternal working status), health service utilization and accessibility (women healthcare decision-making autonomy, ANC follow up, and distance to health facility), and obstetrics related characteristics (preceding birth interval, parity, type of birth, and delivery by CS) were explanatory variables identified after thorough review of literatures [13, 24–31] . Short birth interval is defined as the time between two births which is less than 24 months [32]. Also, women’s healthcare decision-making autonomy is the ability of the women to make decisions to use health care services and treatment options [25]. Finally, media exposure was defined as when a woman reads a newspaper or listens to the radio, or watches television at least three times per week. Before any statistical analysis, the data were weighted using sampling weight based on primary sampling unit, and strata to restore the representativeness of the survey and take sampling design when calculating standard errors and reliable estimates. Cross-tabulations and summary statistics were done using STATA software version 14 (StataCorp.2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). The pooled prevalence of adverse birth outcomes with a 95% Confidence Interval (CI) was reported using a forest plot. The DHS dataset has a hierarchical structure that failed to meet the standard logistic regression model assumptions of independent observation and equal variance. Meanwhile, the children were nested within a cluster household, and children from the same cluster were more similar than from other clusters. Therefore, a mixed effect logistic regression model (both fixed and random effect) was fitted to account for cluster variability by using the advanced models. The outcome variable of the study was binary, a standard logistic regression and Generalized Linear Mixed Models (GLMM) were fitted step by step. Because the models were nested, model fitness was compared using the Intra-class Correlation Coefficient (ICC), Likelihood Ratio (LR), Median Odds Ratio (MOR), and deviance (−2LLR) values. As a result, the mixed-effect logistic regression model with the lowest deviance value was selected as the most parsimonious model. (Shown on Supplementary file Table 1). In the bivariable analysis, variables with less than 0.2 p-values were selected and entered into the multivariable mixed-effect logistic regression model. Adjusted Odds Ratios (AOR) with a 95% CI were calculated in the multivariable model to see the strength of association between independent variables and adverse birth outcomes. Variables with a 0.05 p-value in the final model being used as a statistically significant determinant of adverse birth outcomes. Permission for data access was obtained from measure demographic and health survey through an online request from http://www.dhsprogram.com. The data used for this study were publicly available with no personal identifier.

Materials

https://efashare.b-cdn.net/materials/83a1751d638f636a197c9e121a14c78a1578b6c73a37db6c73a253d41b5bd9b1/12889_2021_11113_MOESM1_ESM.docx

Innovations

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

1. Mobile health (mHealth) interventions: Develop and implement mobile phone applications or text messaging services to provide pregnant women with information and reminders about antenatal care visits, nutrition, and healthy behaviors during pregnancy.

2. Telemedicine: Establish telemedicine programs to enable remote consultations between pregnant women and healthcare providers, especially in rural areas where access to healthcare facilities is limited.

3. Community health workers: Train and deploy community health workers to provide education, support, and basic healthcare services to pregnant women in underserved areas. These workers can help identify high-risk pregnancies and refer women to appropriate healthcare facilities.

4. Transportation solutions: Improve transportation infrastructure and services to ensure that pregnant women can easily access healthcare facilities for antenatal care visits, delivery, and emergency obstetric care.

5. Financial incentives: Implement financial incentive programs to encourage pregnant women to seek antenatal care and deliver in healthcare facilities. This can include cash transfers, vouchers, or insurance schemes.

6. Public-private partnerships: Foster collaborations between the public and private sectors to improve access to maternal health services. This can involve leveraging private healthcare providers and facilities to expand service coverage and reduce waiting times.

7. Maternal waiting homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel long distances for delivery. These homes can provide a safe and comfortable environment for women to stay before and after childbirth.

8. Task-shifting and training: Train and empower non-specialist healthcare providers, such as nurses and midwives, to perform certain tasks traditionally done by doctors. This can help alleviate the shortage of skilled healthcare professionals and improve access to maternal health services.

9. Quality improvement initiatives: Implement quality improvement programs in healthcare facilities to ensure that pregnant women receive timely and appropriate care. This can involve training healthcare providers, improving infrastructure and equipment, and strengthening referral systems.

10. Health education and awareness campaigns: Conduct targeted health education and awareness campaigns to increase knowledge and understanding of maternal health issues among pregnant women and their families. This can help promote early detection of complications and encourage timely healthcare-seeking behavior.

It is important to note that the specific innovations to be implemented should be tailored to the local context and needs of the target population.

AI Innovations Description

Based on the description provided, the recommendation to improve access to maternal health and address the determinants of adverse birth outcomes in Sub-Saharan Africa is as follows:

1. Increase access to antenatal care (ANC) follow-ups: Encouraging pregnant women to attend regular ANC visits can help identify and address potential complications early on. This can include providing education on the importance of ANC, improving transportation options for women in rural areas, and ensuring that ANC services are available and accessible.

2. Improve women’s socio-economic conditions: Women who have higher levels of education and come from wealthier backgrounds have been found to have lower rates of adverse birth outcomes. Therefore, efforts should be made to improve access to education for women and address socio-economic disparities that may hinder access to quality maternal healthcare.

3. Address intimate partner violence: Intimate partner violence, specifically physical violence, has been identified as a determinant of adverse birth outcomes. Implementing interventions to prevent and address intimate partner violence can contribute to improving maternal health outcomes.

4. Enhance healthcare decision-making for women: Lack of women’s involvement in healthcare decision-making has been associated with adverse birth outcomes. Empowering women to actively participate in decisions regarding their healthcare can lead to better outcomes. This can be achieved through education, awareness campaigns, and promoting gender equality.

5. Focus on multiple pregnancies: Twin births were found to be a significant determinant of adverse birth outcomes. Special attention should be given to monitoring and providing appropriate care for women with multiple pregnancies to reduce the risk of complications.

6. Improve healthcare access in rural areas: Long-distance travel to access healthcare services was identified as a determinant of adverse birth outcomes. Expanding healthcare facilities and services in rural areas can help reduce barriers to access and ensure that women in these areas receive timely and appropriate care.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in adverse birth outcomes in Sub-Saharan Africa.

AI Innovations Methodology

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

1. Strengthen Antenatal Care (ANC) Services: Enhance the availability and quality of ANC services, including regular check-ups, screenings, and health education for pregnant women. This can help identify and manage potential risks and complications early on.

2. Improve Healthcare Access in Rural Areas: Increase the accessibility of healthcare facilities, particularly in rural areas where access to maternal health services may be limited. This can be achieved through the establishment of mobile clinics, community health centers, or telemedicine services.

3. Enhance Women’s Empowerment and Involvement: Promote women’s involvement in healthcare decision-making processes, ensuring their autonomy and empowerment. This can be done through education, awareness campaigns, and community engagement programs.

4. Address Socioeconomic Factors: Address socioeconomic factors that contribute to adverse birth outcomes, such as poverty, low education levels, and limited resources. Implement programs that provide financial support, education, and skill-building opportunities for women and families.

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 number of ANC visits, percentage of women receiving skilled birth attendance, or maternal mortality rates.

2. Collect baseline data: Gather data on the current status of these indicators in the target population or region. This can be obtained from existing surveys, health records, or other relevant sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, healthcare infrastructure, and resource allocation.

4. Input data and parameters: Input the baseline data and parameters into the simulation model. This includes information on the target population, healthcare facilities, and the expected effects of the recommendations.

5. Run simulations: Run multiple simulations using different scenarios, varying the implementation strategies and intensity of the recommendations. This will help assess the potential impact on access to maternal health under different conditions.

6. Analyze results: Analyze the simulation results to determine the potential impact of the recommendations on the selected indicators. Compare the outcomes of different scenarios to identify the most effective strategies.

7. Refine and validate the model: Continuously refine and validate the simulation model based on feedback, additional data, and real-world observations. This will improve the accuracy and reliability of the simulations.

8. Communicate findings: Present the findings of the simulation study to relevant stakeholders, policymakers, and healthcare providers. Use the results to advocate for the implementation of effective strategies to improve access to maternal health.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

Authors & Co-Authors

Statistics:

Citations: 7

Authors: 4

Identifiers:

DOI: 10.1186/s12889-021-11113-z

Research Areas:

Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Angola, Congo, Gambia, Lesotho, Liberia, Madagascar, Multi-Countries, Nigeria, Rwanda, Togo

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female