Analysis of cesarean section rates using Robson ten group classification system in a tertiary teaching hospital, Addis Ababa, Ethiopia: a cross-sectional study

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Study Justification:
– Cesarean section (CS) rates are an important indicator of access to and quality of maternal health services.
– The World Health Organization recommends the Robson ten group classification system as a global standard for assessing, monitoring, and comparing CS rates.
– This study aimed to assess the rate of CS and analyze it based on the Robson classification system.
Highlights:
– A total of 4,200 deliveries were analyzed, and the overall CS rate was found to be 34.7%.
– The largest contributors to the overall CS rate were Group 10 (19.1%), Group 2 (18.3%), Group 5 (17.1%), and Group 4 (15.8%).
– There was a high rate of pre-labor CS in Group 2, Group 4, and Group 10.
– Group 10 was identified as the leading contributor to the overall CS rate.
Recommendations for Lay Reader:
– The study highlights the importance of monitoring and assessing CS rates in maternal health services.
– Targeted interventions should be developed to reduce the CS rate among low-risk groups.
– Further studies are needed to identify modifiable factors and improve outcomes.
Recommendations for Policy Maker:
– Implement the Robson ten group classification system to assess and monitor CS rates.
– Develop tailored strategies to reduce the CS rate among low-risk groups.
– Evaluate existing management protocols and conduct further studies on indications of CS and outcomes.
Key Role Players:
– Researchers and data collectors
– Medical staff and obstetricians
– Hospital administrators and policymakers
– Maternal health experts and consultants
Cost Items for Planning Recommendations:
– Training and capacity building for researchers and data collectors
– Data collection tools and technology (e.g., Open Data Kit for android platform)
– Data analysis software (e.g., IBM SPSS Statistics)
– Research and publication expenses
– Implementation of tailored strategies and interventions
– Evaluation of existing management protocols
– Further studies and research on indications of CS and outcomes

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study conducted a facility-based cross-sectional study at a tertiary hospital, which provides valuable data. The Robson ten group classification system was used to assess the rate of cesarean section (CS) and analyze the contribution of each group to the overall CS rate. The study included a large sample size of 4,200 deliveries, which enhances the reliability of the findings. However, the study design is cross-sectional, which limits the ability to establish causality. To improve the strength of the evidence, future studies could consider using a longitudinal design to assess the impact of interventions on CS rates over time.

Background: Cesarean section (CS) is an important indicator of access to, and quality of maternal health services. The World Health Organization recommends the Robson ten group classification system as a global standard for assessing, monitoring and comparing CS rates at all levels. This study aimed to assess the rate of CS and perform an analysis based on Robson classification system. Methods: A facility-based cross-sectional study was conducted at a tertiary hospital in Addis Ababa, Ethiopia. Data were collected from medical charts of all women who delivered from January-June 2018. The overall CS rate was calculated then women were categorized into one of the ten Robson groups. Relative size of each group, contribution of each group to the overall CS rate, and CS rate within each group were calculated. Results: A total of 4,200 deliveries were analyzed. Of these 1,459 (34.7%) were CS. The largest contributors to the overall CS rate were Group 10 (19.1%), Group 2 (18.3%), Group 5 (17.1%), and Group 4 (15.8%). There was also a high rate of pre-labor CS in Group 2, Group 4, and Group 10. Conclusion: Through implementation of the Robson ten group classification system, we identified the contribution of each group to the overall CS rate as well as the CS rate within each group. Group 10 was the leading contributor to the overall CS rate. This study also revealed a high rate of CS among low-risk groups. These target groups require more in-depth analysis to identify possible modifiable factors and to apply specific interventions to reduce the CS rate. Evaluation of existing management protocols and further studies into indications of CS and outcomes are needed to design tailored strategies and improve outcomes.

This was a cross-sectional study conducted at Saint Paul’s Hospital Millennium Medical College (SPHMMC) in Addis Ababa, Ethiopia. SPHMMC is a tertiary care facility conducting close to 10,000 deliveries per annum. It is also a public teaching hospital and mainly serves as a referral center for high-risk cases. The study population included all women who gave birth from January to June 2018. We excluded laparotomy done for uterine rupture and deliveries before fetal viability. In the Ethiopian context, viability is considered after gestational age of 28 weeks or birth weight ≥ 1,000 g, if gestational age is unknown [17]. Data were collected by trained data collectors using a structured data extraction template on Open Data Kit for an android platform and saved on a central server. Medical charts were reviewed to collect relevant obstetric information. This includes past obstetric history (parity and previous CS), onset of labor (spontaneous, induced, or CS before labor), fetal presentation or lie (cephalic, breech, transverse or oblique), number of fetuses (single or multiple), mode of delivery (vaginal or CS), and gestational age (term or preterm). Gestational age was calculated either from menstrual date or obstetric ultrasound done before 24 weeks of pregnancy. For cases with no gestational age milestone, we used birth weight as a proxy indicator of gestational age. Birth weight < 2,500 g was considered preterm and birth weight ≥ 2,500 g was considered term [18]. This strategy has been employed in other studies conducted in similar settings [14, 19, 20]. Data was exported to and analyzed using IBM SPSS Statistics for Windows, version 20 (IBM Corp., Armonk, N.Y., USA). The overall CS rate at the institution was calculated first. We coded all abstracted data and women were categorized into one of the ten Robson groups. For each group, size relative to the entire obstetric population, contribution to the overall CS rate, and CS rate within the group were calculated.

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

1. Implement the Robson ten group classification system: The study highlights the importance of using the Robson classification system to assess and monitor cesarean section rates. Implementing this system in other healthcare facilities can provide valuable data for identifying areas of improvement and developing targeted interventions.

2. Conduct in-depth analysis of high-risk groups: The study found a high rate of cesarean sections among low-risk groups. Further analysis of these groups can help identify modifiable factors and develop specific interventions to reduce the cesarean section rate.

3. Evaluate existing management protocols: Assessing the effectiveness of current management protocols for maternal health can help identify areas for improvement. This evaluation can lead to the development of tailored strategies that address specific challenges and improve outcomes.

4. Study indications of cesarean sections and outcomes: Conducting further research on the indications for cesarean sections and their outcomes can provide valuable insights into the factors influencing decision-making and potential areas for improvement. This information can guide the development of evidence-based guidelines and interventions.

5. Utilize technology for data collection and analysis: The study used a structured data extraction template on an android platform for data collection. Expanding the use of technology, such as mobile applications or electronic health records, for data collection and analysis can streamline processes, improve accuracy, and facilitate data sharing for research and decision-making purposes.

6. Strengthen referral systems: As a tertiary care facility, the hospital in the study serves as a referral center for high-risk cases. Strengthening referral systems between primary, secondary, and tertiary healthcare facilities can ensure timely access to appropriate care for pregnant women, especially those with high-risk pregnancies.

7. Enhance training and capacity-building: Providing comprehensive training and capacity-building programs for healthcare providers can improve their skills and knowledge in managing maternal health. This can include training on evidence-based practices, emergency obstetric care, and communication skills to ensure quality care and better outcomes.

8. Promote community awareness and engagement: Increasing community awareness about maternal health and the importance of accessing healthcare services can help reduce barriers to access. Engaging communities through education programs, outreach initiatives, and community health workers can empower women and their families to seek timely and appropriate care during pregnancy and childbirth.

It is important to note that these recommendations are based on the information provided and may need to be adapted to specific contexts and resources available.
AI Innovations Description
Based on the analysis of cesarean section rates using the Robson ten group classification system in a tertiary teaching hospital in Addis Ababa, Ethiopia, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Implement the Robson ten group classification system: The study highlights the importance of using the Robson classification system as a global standard for assessing, monitoring, and comparing cesarean section rates. By implementing this system in other healthcare facilities, it will enable a standardized approach to analyzing cesarean section rates and identifying areas for improvement.

2. Targeted interventions for high-risk groups: The study identified specific groups (Group 10, Group 2, Group 5, and Group 4) that contributed significantly to the overall cesarean section rate. These groups require more in-depth analysis to identify possible modifiable factors and develop targeted interventions to reduce the cesarean section rate. By focusing on these high-risk groups, healthcare providers can provide tailored strategies and interventions to improve outcomes.

3. Evaluation of existing management protocols: The study suggests the need for evaluating existing management protocols to identify areas where improvements can be made. By assessing the current protocols and guidelines for cesarean section, healthcare facilities can identify any gaps or areas for improvement and make necessary adjustments to ensure optimal maternal health outcomes.

4. Further studies into indications of cesarean section and outcomes: The study recommends conducting further studies to explore the indications of cesarean section and their impact on outcomes. By gaining a deeper understanding of the reasons behind cesarean section deliveries and their associated outcomes, healthcare providers can develop evidence-based strategies to reduce unnecessary cesarean sections and improve maternal health outcomes.

Overall, by implementing the Robson ten group classification system, targeting interventions for high-risk groups, evaluating existing management protocols, and conducting further studies, healthcare facilities can innovate and improve access to maternal health services, leading to better outcomes for mothers and babies.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Increase availability and accessibility of antenatal care: Ensure that pregnant women have access to regular check-ups, screenings, and education about pregnancy and childbirth. This can be achieved by establishing more antenatal care clinics, especially in rural areas, and providing transportation services for women who live far from healthcare facilities.

2. Strengthen referral systems: Develop and implement effective referral systems to ensure that pregnant women with high-risk pregnancies are promptly referred to appropriate healthcare facilities. This can involve training healthcare providers on identifying high-risk pregnancies, establishing clear communication channels between healthcare facilities, and providing transportation support for referrals.

3. Enhance emergency obstetric care services: Improve the availability and quality of emergency obstetric care services, including skilled birth attendants, emergency obstetric surgery, blood transfusion services, and neonatal resuscitation. This can be achieved by training healthcare providers, equipping healthcare facilities with necessary supplies and equipment, and ensuring 24/7 availability of emergency obstetric care.

4. Promote community-based interventions: Implement community-based interventions to raise awareness about maternal health, promote healthy behaviors during pregnancy, and encourage early recognition of danger signs. This can involve community health workers conducting home visits, organizing community education sessions, and engaging local leaders and influencers to advocate for maternal health.

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 reflect access to maternal health, such as the number of antenatal care visits, the percentage of pregnant women referred to higher-level facilities, the availability of emergency obstetric care services, and the knowledge level of the community regarding maternal health.

2. Collect baseline data: Gather data on the current status of the indicators before implementing the recommendations. This can involve surveys, interviews, and data collection from healthcare facilities, community members, and relevant stakeholders.

3. Implement the recommendations: Put the recommended interventions into practice, ensuring proper implementation and monitoring of each intervention. This may involve training healthcare providers, establishing new healthcare facilities or services, conducting community awareness campaigns, and improving referral systems.

4. Collect post-intervention data: After a sufficient period of time, collect data on the indicators again to assess the impact of the recommendations. This can involve repeating the same data collection methods used in the baseline assessment.

5. Analyze and compare data: Compare the baseline and post-intervention data to determine the changes in the indicators. This can be done through statistical analysis, such as calculating percentages, rates, and trends. Assess the significance of the changes and evaluate the effectiveness of the recommendations in improving access to maternal health.

6. Adjust and refine interventions: Based on the findings of the impact assessment, make necessary adjustments and refinements to the interventions. This may involve scaling up successful interventions, addressing any challenges or barriers identified, and continuously monitoring and evaluating the impact of the interventions.

By following this methodology, it is possible to simulate the impact of the recommendations on improving access to maternal health and make evidence-based decisions for further interventions and improvements.

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