Observed trends in the magnitude of socioeconomic and area-based inequalities in use of caesarean section in Ethiopia: A cross-sectional study

BMC Public Health, Volume 20, No. 1, Year 2020

Interpretation

Background: In Ethiopia, there is a paucity of studies on inequality in caesarean section using methodologically rigorous and well-established approaches. In this study, we showed extent and the overtime dynamics of inequality in caesarean section in Ethiopia following rigorous methodologies. Methods: The data for analysis came from Ethiopia Demographic and Health Surveys (EDHS) conducted between 2000 and 2016. We used the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) to analyze the data. Caesarean delivery was disaggregated by four equity stratifiers, namely education, wealth, residence and regions. Relative and absolute summary measures were calculated for each equity stratifier to capture inequality from different perspectives. 95% Uncertainty Interval was calculated around a point estimate to measure statistical significance. Results: We found large socioeconomic and area-based inequalities in use of caesarean section in all study surveys. The inequalities have occurred in favour of socioeconomically advantaged women and those living in urban areas and certain regions such as Addis Ababa. While area-related inequality had generally increased with time, socioeconomic inequality showed fluctuation. Adoption of different measures in the study for the inequality analysis has caused the emergence of mix of patterns in caesarean section inequality over time. Conclusions: In all the surveys, wealthy and more educated women, and those residing in urban areas had higher chance of obtaining caesarean delivery. Policy makers should work to ensure caesarean section that is in the accepted normal range. More emphasis should be drawn to subpopulation with under use of caesarean section while at the same time, discouraging unjustified use of it. Maternal death in Ethiopia is one of the highest in the world [15] and signals substantial inequality in access to maternal health care services [16, 17]. However, the country has remarkably reduced the burden of maternal and child mortalities over the last several years [15, 18]. Ethiopia had a Maternal Mortality Ratio (MMR) of 401 in 2017; MMR decreased from 298 deaths per 100,000 live births in 2000, to 401 deaths per 100,000 live births in 2017 [15]. The averted deaths may be linked to a concomitant rise in coverage of maternal and child health services interventions during the same time period [19]. However, the gains are not enough; the current maternal health care service coverage is far from the level required to achieve the SDG of 70 deaths per 100, 000 live births [20]. In 2015, Ethiopia adopted an equity-laden five-year rolling Health Sector Transformational Plan (HSTP) [21] to help facilitate implementation of effective interventions and ultimately meet the 2030 SDG. This study was performed using cross sectional data obtained from the Ethiopia Demographic and Health Survey (EDHS) conducted in 2000, 2005, 2011 and 2016. All were nationally representative household surveys conducted by the Central Statistical Agency (CSA) of Ethiopia. The EDHS is the only source of nationally representative data on several health indicators including caesarean sections in Ethiopia. Two interim EDHSs were conducted in 2014 and 2019, and both were not included in the analysis because they are not available in the HEAT software. The DHS collects data on a wide range of health topics such as maternal health, child health, domestic violence, female genital mutilation, HIV/AIDS, maternal and child nutritional status, to mention just a few. Samples in the DHS are deemed to be representative nationally as well as regionally and for urban and rural residence. The number of women age 15 to 49 years included in the surveys were 15,683, 16,515, 14,070 and 15,367 in 2016, 2011, 2005 and 2000, respectively. The sample share of each of the waves was presented in the results section (Table 1) [14]. Cesarean section rate among non-pregnant women aged 15 to 49 years disaggregated by education, economic status, place of residence and region, the EDHS between 2000 and 2016 a STATcompiler. The DHS Program. [Internet]. [cited 2020 Jul 13]. Available from: https://www.statcompiler.com/en/ The detailed sampling methodology of the DHS has been described elsewhere [22]. Briefly, DHS follows a stratified two stage clustered sample design. Stratification is done based on the sub-national regions and place of residence. Following stratification, samples are drawn from each stratum through a two-step process. In the first stage, census enumeration areas (EA) are selected through a sampling technique known as Probability Proportion to Size (PPS), where the probability of selection of an EAs is contingent upon its size (measured through number of households), and the larger the EA is, the higher its chance of being in the sample. Information on the number of EA is basically obtained from most recent census data. In the second stage, 25–30 households are selected systematically from each stratum. The surveys covered all women aged 15 to 49 years who gave birth in the 5 years preceding the survey. For the CS variable, all women who gave birth two or 3 years preceding the surveys were included. The inequality variable measured in this study is birth delivered through caesarean section. It is measured as the proportion of all births by caesarean section in the two or 3 years prior to the surveys. Restricting our analysis to two or 3 years prior to the surveys period allowed us to present a more recent status of the CS rate and its disparity. We disaggregated the caesarean birth by the four equity stratifiers: economic status, education, place of residence, and the subnational regions. The economic status (or wealth) has five categories: poorest, poor, middle, rich and richest. Educational status of the woman was classified as no-education, primary, secondary of higher; place of residence as urban vs. rural, and the sub-national regions included the nine regions and two city administrations. The disaggregated caesarean delivery (reported as a percent) was presented for each of the four EDHS time periods. Point estimates were calculated and presented with corresponding 95% Uncertainty Intervals. The educational status and wealth have a natural ordering and are known as ordered equity stratifiers whereas place of residence and regions are non-ordered equity stratifiers. Whether an equity stratifier is ordered or not affects the choice of summary measures to be calculated [14]. We used the 2019 updated version of the WHO’s HEAT software [23] for analyzing the socioeconomic and area-based inequalities in the CS rate in Ethiopia between 2000 and 2016. The software description and access have been described in detail elsewhere [24]. In summary, the WHO released the software in 2016 using free and publicly available R programming language and R packages. The software allows assessment of within country health inequalities of more than 30 indicators for the reproductive, maternal, newborn, and child health (RMNCH). It also permits bench-marking inequality in one country with that of another country, allowing direct comparison of inequality in two or more countries at the same time [23, 24]. Since the software is publicly available, there is no ethical concern regarding access. Type of health indicator of interest (favorable vs. adverse) and the inherent properties of dimensions of inequality determine choices and interpretations of summary measures for this inequality study. Caesarean section was disaggregated by the commonly used dimensions of inequality discussed above. In addition, we adopted summary measures of different use and statistical properties. We employed a combination of absolute and relative inequality summary measures. These were Difference (D), Population Attributable Risk (PAR), Slope Index of Inequality (SII) and Relative Concentration Index (RCI); the first three are absolute inequality measures and the last is a relative measure of inequality. These measures were calculated for each of the four equity stratifiers; for the wealth and education dimensions of inequality, we calculated all of the four inequality measures, and for the region and place of residence, we calculated the D and PAR. The detailed methods of calculation, interpretation and all other detailed properties of the measures employed in the study have been described elsewhere in detail [23]. We offer a brief description of properties and interpretations of the measures. Whilst positive values of the measures are indicative of a disproportionately higher coverage of the service among the advantaged sub-groups (women who have completed secondary education or higher, are wealthy, are urban dwellers and live in big cities such as Addis Ababa are deemed advantaged); negative values indicate the disadvantaged nature of the service. When the absolute inequality measures are zero or the relative inequality measures are one, there is no inequality. The higher the absolute value, the greater the inequality is. D is a simple measure suitable for showing the absolute difference between two categories within a dimension of inequality (i.e. urban vs rural for residence) or between 2 or more categories (i.e. regions) based on an identified reference subgroup in the category. The other three (PAR, SII, RCI) are weighted complex measures of inequality that take into account sizes of subpopulations used in the calculation, thereby producing estimates reflective of the subpopulation size [14, 23]. Complex measures are the one that take account of sizes of subpopulation used in the calculation of a measure in question, thereby producing estimates reflective of size of the subpopulation [14, 23]. The computation of SII and RCI was restricted to education and wealth dimensions of inequality since they require an ordered equity stratifier. To declare that CS shows statistically significant disparities across the sub-groups of each equity stratifer, and to determine whether or not the inequality changed with time, we computed 95% Uncertainty Intervals (UI) around point estimates of each measure for each survey. For all inequality measures, the lower and upper bounds of the UI must not include zero to interpret that inequality exists. We assessed the trend of inequality for each summary measure by referring to the UIs for the different survey years; if the UIs did not overlap, inequality existed. The findings were presented per the recommendation of the Strengthening Reporting of Observational studies in Epidemiology (STROBE) reporting guidelines [25]. Ethics approval is not needed as the data is retrospective, anonymous and publicly available. Ethical procedures were the responsibility of the institutions that commissioned, funded, and managed the surveys. All DHS surveys are approved by Inner City Fund (ICF) International as well as an Institutional Review Board (IRB) in the respective country to ensure that the protocols are in compliance with the U.S. Department of Health and Human Services regulations for the protection of human subjects.

AI Digest

Study Justification:

– In Ethiopia, there is a lack of studies on inequality in caesarean section using rigorous methodologies.
– This study aims to fill that gap by examining the extent and dynamics of inequality in caesarean section in Ethiopia.
– The findings of this study will provide valuable insights for policymakers and healthcare providers to address and reduce inequalities in access to caesarean section.

Study Highlights:

– The study analyzed data from the Ethiopia Demographic and Health Surveys conducted between 2000 and 2016.
– Large socioeconomic and area-based inequalities in the use of caesarean section were found in all study surveys.
– Socioeconomically advantaged women, those living in urban areas, and certain regions had a higher chance of obtaining caesarean delivery.
– Area-related inequality generally increased over time, while socioeconomic inequality showed fluctuation.
– The adoption of different measures for inequality analysis resulted in a mix of patterns in caesarean section inequality over time.

Study Recommendations:

– Policy makers should work towards ensuring that the use of caesarean section falls within the accepted normal range.
– More emphasis should be placed on subpopulations with underuse of caesarean section, while discouraging unjustified use of it.

Key Role Players:

– Policy makers and government officials responsible for healthcare planning and implementation.
– Healthcare providers, including doctors, nurses, and midwives.
– Public health researchers and experts.
– Non-governmental organizations (NGOs) working in the field of maternal and child health.

Cost Items for Planning Recommendations:

– Training programs for healthcare providers on appropriate use of caesarean section.
– Infrastructure development to improve access to caesarean section services in underserved areas.
– Awareness campaigns to educate the public about the risks and benefits of caesarean section.
– Monitoring and evaluation systems to track the use of caesarean section and identify areas of improvement.
– Research and data collection to further understand the factors contributing to inequality in caesarean section use.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study used rigorous methodologies and analyzed nationally representative data from Ethiopia Demographic and Health Surveys. The study identified large socioeconomic and area-based inequalities in the use of caesarean section in Ethiopia. The findings were supported by the calculation of relative and absolute summary measures and the use of 95% Uncertainty Intervals to measure statistical significance. The study also provided actionable steps by recommending policy makers to ensure caesarean section within the accepted normal range and to focus on subpopulations with underuse while discouraging unjustified use. However, the abstract could be improved by providing more specific details about the methodologies used, such as the specific measures employed and their interpretations. Additionally, it would be helpful to include information about the sample size and any limitations of the study.

Abstract

Maternal death in Ethiopia is one of the highest in the world [15] and signals substantial inequality in access to maternal health care services [16, 17]. However, the country has remarkably reduced the burden of maternal and child mortalities over the last several years [15, 18]. Ethiopia had a Maternal Mortality Ratio (MMR) of 401 in 2017; MMR decreased from 298 deaths per 100,000 live births in 2000, to 401 deaths per 100,000 live births in 2017 [15]. The averted deaths may be linked to a concomitant rise in coverage of maternal and child health services interventions during the same time period [19]. However, the gains are not enough; the current maternal health care service coverage is far from the level required to achieve the SDG of 70 deaths per 100, 000 live births [20]. In 2015, Ethiopia adopted an equity-laden five-year rolling Health Sector Transformational Plan (HSTP) [21] to help facilitate implementation of effective interventions and ultimately meet the 2030 SDG. This study was performed using cross sectional data obtained from the Ethiopia Demographic and Health Survey (EDHS) conducted in 2000, 2005, 2011 and 2016. All were nationally representative household surveys conducted by the Central Statistical Agency (CSA) of Ethiopia. The EDHS is the only source of nationally representative data on several health indicators including caesarean sections in Ethiopia. Two interim EDHSs were conducted in 2014 and 2019, and both were not included in the analysis because they are not available in the HEAT software. The DHS collects data on a wide range of health topics such as maternal health, child health, domestic violence, female genital mutilation, HIV/AIDS, maternal and child nutritional status, to mention just a few. Samples in the DHS are deemed to be representative nationally as well as regionally and for urban and rural residence. The number of women age 15 to 49 years included in the surveys were 15,683, 16,515, 14,070 and 15,367 in 2016, 2011, 2005 and 2000, respectively. The sample share of each of the waves was presented in the results section (Table 1) [14]. Cesarean section rate among non-pregnant women aged 15 to 49 years disaggregated by education, economic status, place of residence and region, the EDHS between 2000 and 2016 a STATcompiler. The DHS Program. [Internet]. [cited 2020 Jul 13]. Available from: https://www.statcompiler.com/en/ The detailed sampling methodology of the DHS has been described elsewhere [22]. Briefly, DHS follows a stratified two stage clustered sample design. Stratification is done based on the sub-national regions and place of residence. Following stratification, samples are drawn from each stratum through a two-step process. In the first stage, census enumeration areas (EA) are selected through a sampling technique known as Probability Proportion to Size (PPS), where the probability of selection of an EAs is contingent upon its size (measured through number of households), and the larger the EA is, the higher its chance of being in the sample. Information on the number of EA is basically obtained from most recent census data. In the second stage, 25–30 households are selected systematically from each stratum. The surveys covered all women aged 15 to 49 years who gave birth in the 5 years preceding the survey. For the CS variable, all women who gave birth two or 3 years preceding the surveys were included. The inequality variable measured in this study is birth delivered through caesarean section. It is measured as the proportion of all births by caesarean section in the two or 3 years prior to the surveys. Restricting our analysis to two or 3 years prior to the surveys period allowed us to present a more recent status of the CS rate and its disparity. We disaggregated the caesarean birth by the four equity stratifiers: economic status, education, place of residence, and the subnational regions. The economic status (or wealth) has five categories: poorest, poor, middle, rich and richest. Educational status of the woman was classified as no-education, primary, secondary of higher; place of residence as urban vs. rural, and the sub-national regions included the nine regions and two city administrations. The disaggregated caesarean delivery (reported as a percent) was presented for each of the four EDHS time periods. Point estimates were calculated and presented with corresponding 95% Uncertainty Intervals. The educational status and wealth have a natural ordering and are known as ordered equity stratifiers whereas place of residence and regions are non-ordered equity stratifiers. Whether an equity stratifier is ordered or not affects the choice of summary measures to be calculated [14]. We used the 2019 updated version of the WHO’s HEAT software [23] for analyzing the socioeconomic and area-based inequalities in the CS rate in Ethiopia between 2000 and 2016. The software description and access have been described in detail elsewhere [24]. In summary, the WHO released the software in 2016 using free and publicly available R programming language and R packages. The software allows assessment of within country health inequalities of more than 30 indicators for the reproductive, maternal, newborn, and child health (RMNCH). It also permits bench-marking inequality in one country with that of another country, allowing direct comparison of inequality in two or more countries at the same time [23, 24]. Since the software is publicly available, there is no ethical concern regarding access. Type of health indicator of interest (favorable vs. adverse) and the inherent properties of dimensions of inequality determine choices and interpretations of summary measures for this inequality study. Caesarean section was disaggregated by the commonly used dimensions of inequality discussed above. In addition, we adopted summary measures of different use and statistical properties. We employed a combination of absolute and relative inequality summary measures. These were Difference (D), Population Attributable Risk (PAR), Slope Index of Inequality (SII) and Relative Concentration Index (RCI); the first three are absolute inequality measures and the last is a relative measure of inequality. These measures were calculated for each of the four equity stratifiers; for the wealth and education dimensions of inequality, we calculated all of the four inequality measures, and for the region and place of residence, we calculated the D and PAR. The detailed methods of calculation, interpretation and all other detailed properties of the measures employed in the study have been described elsewhere in detail [23]. We offer a brief description of properties and interpretations of the measures. Whilst positive values of the measures are indicative of a disproportionately higher coverage of the service among the advantaged sub-groups (women who have completed secondary education or higher, are wealthy, are urban dwellers and live in big cities such as Addis Ababa are deemed advantaged); negative values indicate the disadvantaged nature of the service. When the absolute inequality measures are zero or the relative inequality measures are one, there is no inequality. The higher the absolute value, the greater the inequality is. D is a simple measure suitable for showing the absolute difference between two categories within a dimension of inequality (i.e. urban vs rural for residence) or between 2 or more categories (i.e. regions) based on an identified reference subgroup in the category. The other three (PAR, SII, RCI) are weighted complex measures of inequality that take into account sizes of subpopulations used in the calculation, thereby producing estimates reflective of the subpopulation size [14, 23]. Complex measures are the one that take account of sizes of subpopulation used in the calculation of a measure in question, thereby producing estimates reflective of size of the subpopulation [14, 23]. The computation of SII and RCI was restricted to education and wealth dimensions of inequality since they require an ordered equity stratifier. To declare that CS shows statistically significant disparities across the sub-groups of each equity stratifer, and to determine whether or not the inequality changed with time, we computed 95% Uncertainty Intervals (UI) around point estimates of each measure for each survey. For all inequality measures, the lower and upper bounds of the UI must not include zero to interpret that inequality exists. We assessed the trend of inequality for each summary measure by referring to the UIs for the different survey years; if the UIs did not overlap, inequality existed. The findings were presented per the recommendation of the Strengthening Reporting of Observational studies in Epidemiology (STROBE) reporting guidelines [25]. Ethics approval is not needed as the data is retrospective, anonymous and publicly available. Ethical procedures were the responsibility of the institutions that commissioned, funded, and managed the surveys. All DHS surveys are approved by Inner City Fund (ICF) International as well as an Institutional Review Board (IRB) in the respective country to ensure that the protocols are in compliance with the U.S. Department of Health and Human Services regulations for the protection of human subjects.

Materials

N/A

Innovations

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

1. Mobile Health (mHealth) Solutions: Develop and implement mobile applications or text messaging services to provide pregnant women with information and reminders about prenatal care, nutrition, and potential warning signs during pregnancy. This can help improve access to important health information, particularly for women in remote areas.

2. Telemedicine: Establish telemedicine services to connect pregnant women in rural areas with healthcare providers in urban areas. This can enable remote consultations, monitoring, and advice, reducing the need for women to travel long distances for routine check-ups.

3. Community Health Worker Programs: Expand and strengthen community health worker programs to provide maternal health education, support, and referrals in underserved areas. Community health workers can play a crucial role in bridging the gap between communities and healthcare facilities.

4. Transportation Support: Develop transportation initiatives to address the challenges of accessing healthcare facilities, particularly in remote areas. This could include providing transportation vouchers or organizing community-based transportation services to ensure pregnant women can reach healthcare facilities in a timely manner.

5. Maternal Waiting Homes: Establish maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away. These homes can provide a safe and comfortable place for women to stay during the final weeks of pregnancy, ensuring they are close to the facility when labor begins.

6. Financial Incentives: Implement financial incentives, such as conditional cash transfers or subsidies, to encourage pregnant women to seek antenatal care and deliver at healthcare facilities. This can help address financial barriers that prevent women from accessing maternal health services.

7. Quality Improvement Initiatives: Implement quality improvement initiatives in healthcare facilities to ensure that maternal health services are provided in a respectful, timely, and effective manner. This can help increase trust in the healthcare system and encourage more women to seek care.

8. Public Awareness Campaigns: Launch public awareness campaigns to educate communities about the importance of maternal health and the available services. This can help reduce stigma, increase demand for services, and promote community support for pregnant women.

It is important to note that the specific implementation and effectiveness of these innovations would require further research and evaluation.

AI Innovations Description

Based on the information provided, the recommendation to improve access to maternal health in Ethiopia is as follows:

1. Address socioeconomic and area-based inequalities: The study highlights the large socioeconomic and area-based inequalities in the use of caesarean section in Ethiopia. To improve access to maternal health, it is crucial to address these inequalities. Policy makers should focus on implementing strategies that target disadvantaged populations, including women with lower education levels, lower economic status, and those residing in rural areas and regions with limited access to healthcare services.

2. Increase coverage of maternal health services: Although Ethiopia has made progress in reducing maternal and child mortalities, the current coverage of maternal health services is still insufficient to meet the Sustainable Development Goal (SDG) target. Efforts should be made to increase the coverage of essential maternal health interventions, including antenatal care, skilled birth attendance, and emergency obstetric care. This can be achieved through the implementation of the Health Sector Transformational Plan (HSTP) and the expansion of healthcare facilities in underserved areas.

3. Ensure appropriate use of caesarean section: The study found that wealthier and more educated women, as well as those residing in urban areas, had a higher chance of obtaining caesarean delivery. It is important to ensure that caesarean sections are performed when medically necessary and within the accepted normal range. Unjustified use of caesarean sections should be discouraged, while simultaneously addressing the underuse of this intervention among certain subpopulations.

4. Continuously monitor and evaluate progress: To track the impact of interventions and identify areas for improvement, it is essential to establish a robust monitoring and evaluation system. Regular data collection, analysis, and reporting on maternal health indicators, including caesarean section rates and inequalities, will provide valuable insights for policy makers and stakeholders. This will enable evidence-based decision-making and the adjustment of strategies as needed.

By implementing these recommendations, Ethiopia can work towards reducing maternal mortality rates and improving access to maternal health services for all women, regardless of their socioeconomic status or geographic location.

AI Innovations Methodology

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

1. Strengthening Health Infrastructure: Invest in improving healthcare facilities, including hospitals, clinics, and maternity centers, especially in rural areas where access to maternal health services is limited.

2. Skilled Birth Attendants: Increase the number of skilled birth attendants, such as midwives and obstetricians, to ensure safe deliveries and reduce maternal mortality rates.

3. Community Health Workers: Train and deploy community health workers to provide basic maternal health services, education, and referrals in remote areas where access to healthcare is limited.

4. Mobile Health Technologies: Utilize mobile health technologies, such as telemedicine and mobile apps, to provide remote consultations, health information, and reminders for prenatal and postnatal care.

5. Health Education and Awareness: Implement comprehensive health education programs to raise awareness about the importance of maternal health, family planning, and the availability of healthcare services.

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

1. Data Collection: Gather data on the current state of maternal health access, including maternal mortality rates, availability of healthcare facilities, number of skilled birth attendants, and geographical distribution of healthcare services.

2. Define Indicators: Identify key indicators to measure the impact of the recommendations, such as the number of healthcare facilities per population, the percentage of births attended by skilled birth attendants, and the reduction in maternal mortality rates.

3. Baseline Assessment: Establish a baseline assessment of the current situation by analyzing the collected data and calculating the selected indicators.

4. Modeling and Simulation: Use modeling techniques, such as mathematical models or simulation software, to simulate the impact of the recommendations on the selected indicators. This could involve adjusting variables, such as the number of healthcare facilities or skilled birth attendants, and observing the resulting changes in the indicators.

5. Sensitivity Analysis: Conduct sensitivity analysis to assess the robustness of the simulation results by varying input parameters and evaluating the impact on the indicators.

6. Evaluation and Recommendations: Analyze the simulation results and evaluate the potential impact of the recommendations on improving access to maternal health. Based on the findings, provide recommendations for policy-makers and stakeholders on the most effective strategies to implement.

It is important to note that the methodology described above is a general framework and may require further customization based on the specific context and available data in Ethiopia.

Author

Dima Health

Statistics:

Citations: 4

Identifiers:

DOI: 10.1186/s12889-020-09297-x

Research Areas:

Community Interventions, Disparities, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Violence and Injury

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female