Pregnancy outcomes in facility deliveries in Kenya and Uganda: A large cross-sectional analysis of maternity registers illuminating opportunities for mortality prevention

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Study Justification:
– The study aims to contribute to global quality improvement efforts by characterizing facility-based pregnancy outcomes in Kenya and Uganda.
– It focuses on understanding opportunities for perinatal mortality prevention and improving care for preterm and low birthweight infants.
– The study utilizes strengthened maternity registers as a valuable data source for understanding pregnancy outcomes.
Study Highlights:
– Among 50,981 deliveries, 91.3% were live births, 1.6% died before discharge, 0.5% were early stillbirths, 3.6% were late stillbirths, and 4.7% were spontaneous abortions.
– Maternal mortality rate was 0.1%.
– Preterm and low birthweight infants represented a disproportionate number of stillbirths and pre-discharge deaths.
– More pre-discharge deaths and stillbirths occurred after maternal referral and with cesarean section.
– Half of maternal deaths occurred in women who had undergone cesarean section.
Recommendations:
– Renewed focus on improving care of preterm and low birthweight infants.
– Expanding access to emergency obstetric care.
– Emphasizing accurate gestational age assessments.
– Improving care after maternal referral and cesarean section.
Key Role Players:
– Health workers and health authorities from research areas.
– University of California San Francisco.
– Kenya Medical Research Institute.
– University of Rwanda.
– Rwanda Biomedical Center.
– Makerere University.
Cost Items for Planning Recommendations:
– Supplies (pregnancy wheels, tape measures, digital scales).
– Training sessions for labor and delivery staff.
– Mentoring of labor and delivery staff.
– Monthly feedback on the completeness of registers.
– Access to ultrasound for accurate gestational age assessments.
– Staffing of newborn special care units.
– Staffing of pediatricians or general doctors.
– Budget for quality improvement efforts.
Please note that the provided information is a summary of the study and its findings. For more detailed information, please refer to the publication in PLoS ONE, Volume 15, No. 6, Year 2020.

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 cross-sectional analysis of maternity registers in Kenya and Uganda. The study collected data from 23 facilities over 18 months and made efforts to strengthen the data through provision of supplies, training, and feedback on completeness. The study provides detailed information on pregnancy outcomes, including maternal, neonatal, and fetal outcomes, as well as discharge outcomes. The study also examines maternal mortality. To improve the evidence, the study could have included a comparison group or control group to evaluate the impact of the intervention being studied. Additionally, the study could have provided more information on the representativeness of the included facilities and the generalizability of the findings to the overall population.

Introduction As facility-based deliveries increase globally, maternity registers offer a promising way of documenting pregnancy outcomes and understanding opportunities for perinatal mortality prevention. This study aims to contribute to global quality improvement efforts by characterizing facility-based pregnancy outcomes in Kenya and Uganda including maternal, neonatal, and fetal outcomes at the time of delivery and neonatal discharge outcomes using strengthened maternity registers. Methods Cross sectional data were collected from strengthened maternity registers at 23 facilities over 18 months. Data strengthening efforts included provision of supplies, training on standard indicator definitions, and monthly feedback on completeness. Pregnancy outcomes were classified as live births, early stillbirths, late stillbirths, or spontaneous abortions according to birth weight or gestational age. Discharge outcomes were assessed for all live births. Outcomes were assessed by country and by infant, maternal, and facility characteristics. Maternal mortality was also examined. Results Among 50,981 deliveries, 91.3% were live born and, of those, 1.6% died before discharge. An additional 0.5% of deliveries were early stillbirths, 3.6% late stillbirths, and 4.7% spontaneous abortions. There were 64 documented maternal deaths (0.1%). Preterm and low birthweight infants represented a disproportionate number of stillbirths and pre-discharge deaths, yet very few were born at ≤1500g or <28w. More pre-discharge deaths and stillbirths occurred after maternal referral and with cesarean section. Half of maternal deaths occurred in women who had undergone cesarean section. Conclusion Maternity registers are a valuable data source for understanding pregnancy outcomes including those mothers and infants at highest risk of perinatal mortality. Strengthened register data in Kenya and Uganda highlight the need for renewed focus on improving care of preterm and low birthweight infants and expanding access to emergency obstetric care. Registers also permit enumeration of pregnancy loss <28 weeks. Documenting these earlier losses is an important step towards further mortality reduction for the most vulnerable infants.

This study is a descriptive, cross-sectional analysis of labor ward maternity registers in Kenya and Uganda between October 1st, 2016 and March 31st, 2018. Data were collected as part of the East Africa Preterm Birth Initiative (PTBi) [13]. This initiative is a partnership between the University of California San Francisco, Kenya Medical Research Institute, University of Rwanda, Rwanda Biomedical Center, and Makerere University in Uganda. In Kenya and Uganda specifically, PTBi is conducting a randomized cluster trial to evaluate the impact of an intrapartum quality improvement package on neonatal survival in preterm and low birthweight infants (clinicaltrials.gov, {"type":"clinical-trial","attrs":{"text":"NCT03112018","term_id":"NCT03112018"}}NCT03112018). The full study protocol is available elsewhere [14]. This cross-sectional analysis includes both control and intervention sites and is not an evaluation of the impact of the trial. Maternity register data were gathered from 23 health facilities including 17 in Migori county in western Kenya and six in Busoga region in eastern Uganda. In Migori county, facility births represent 53% of all births [15] and in Busoga approximately 77% of deliveries occur in facilities [16]. The facilities included in this analysis were the largest facilities in each location and based on population and reported births, it is estimated that included facilities covered approximately 20–30% of all births in the two regions [17–19]. Within each country the level of care of included facilities varied. However, across both countries facilities ranging from level III through VI were represented [for facility level definitions see references 20, 21]. In Kenya, the 17 facilities included nine level III health centers and eight level IV district referral hospitals. Cesarean sections were performed in level IV facilities only. Two of the 17 Kenyan facilities had newborn special care units. However, only one had a pediatrician on staff. Six facilities had a general doctor, six had a clinical officer, and the remaining five employed nurse midwives only [22]. In Uganda by contrast, all facilities were hospitals including five level V and one level VI facility. All Ugandan facilities were capable of performing cesarean sections and all had newborn special care units. Two hospitals had staff pediatricians and the remainder employed a general doctor [22]. Anonymized patient level delivery data were extracted monthly from maternity registers. Pre-existing national maternity registers were used for this study. However, prior to the study period, data strengthening efforts were completed as part of the PTBi trial to improve the accuracy and completeness of these maternity registers. These efforts included provision of supplies (pregnancy wheels, tape measures, digital scales) with skill building sessions, monthly training and mentoring of labor and delivery staff on standard indicator definitions, and monthly feedback on the completeness of registers. Particular emphasis was placed on the accuracy of gestational age assessments, which were estimated by labor and delivery providers based on reported last menstrual period, fundal height, or antenatal records carried by the mother. Ultrasound was not universally available during antenatal care or at the time of delivery. The impact of data strengthening on register completeness has been evaluated and full results are available elsewhere [23]. In brief, in Kenya average completion rates increased from 93 to 97% for gestational age, 87 to 98% for birthweight, 97 to 99% for 1-minute APGAR, and 74 to 88% for infant status at discharge from the preliminary assessment to 6 months post data strengthening [23]. In Uganda, average completion rates increased from 52 to 87% for gestational age, 89 to 94% for birthweight, 93 to 96% for 1-minute APGAR, and 86 to 88% for infant status at discharge [23]. Infant, maternal and facility characteristics abstracted from registers and their completeness in this study are as follows: infant sex 91%, multiple gestation 97%, gestational age 86%, birth weight 92%, maternal age 99%, incoming maternal referral status 59% (only available in Uganda), delivery mode 93%, and facility level 100%. Register entries were identified as deliveries if at least one of the following indices was documented: 1-minute Apgar score, birth weight, infant sex, birth outcome, or discharge status. Pregnancy outcomes were then classified as 1) live birth, 2) early stillbirth, 3) late stillbirth, or 4) spontaneous abortion. Live births were defined in this study as infants born with signs of life (as noted by the health care provider at the time of birth and validated by non-zero 1-minute Apgar score) weighing ≥500 grams or, if no birth weight was recorded, ≥24 weeks completed gestation. This differs from the WHO definition of live birth, which includes any infant born with signs of life regardless of gestational age or birth weight [12]. The definition was chosen in part to permit classification of spontaneous abortions, which were defined as any fetus born weighing <500 grams or, if no birthweight was recorded, <24 weeks gestational age. Stillbirths were classified as early or late. The WHO definition of stillbirth was used to define late stillbirths in this analysis—infants born without signs of life weighing ≥1000 grams or, if no birth weight was recorded, ≥28 weeks completed gestation [2]. Early stillbirths were defined as infants born without signs of life weighing between 500 and 999 grams or, if no birth weight was recorded, between 24 and 27 weeks completed gestation. Some stillbirths were further identified as fresh (i.e. intrapartum) or macerated based on infant appearance to the provider at the time of delivery, although not a required field in registers. Training was provided on visual differentiation of fresh versus macerated stillbirths but fetal heart tone monitoring was not routinely available in study facilities. Discharge outcomes were examined for all live born infants. In Kenya, registers included a field for discharge outcome distinct from birth outcome. In Uganda, there was only one field for infant status. In both countries, when delivery and discharge status could not be distinguished or there was conflicting information (i.e. non-zero 1-minute Apgar categorized as a stillbirth), Apgar scores were used to differentiate stillbirths from live births experiencing an immediate neonatal or pre-discharge death. Pre-discharge maternal mortality was also examined and was a unique field in registers in both countries. Entries excluded from this analysis included 1) births before arrival (n = 606), as the aim was to characterize facility-based outcomes and 2) births with no documented birth weight or gestational age (n = 36), as this prohibited outcome classification. Mothers were excluded if 1) they delivered before arrival (n = 562) or 2) were discharged pregnant (n = 9202). A unique maternal identification code was used to link maternal and neonatal data. Data are summarized using descriptive frequencies. Pearson chi square test was used to compare pregnancy outcomes by country as well as by maternal, infant, and facility-based co-variates. The Fisher’s exact test was substituted for cases of small sample size (n<5) in instances where models converged. Early stillbirths and spontaneous abortions were excluded from the analyses by birth weight and gestational age as these outcomes were pre-defined by a narrow range of birth weights and gestational ages. A sub analysis was performed to compare fresh versus macerated late stillbirths. Other analyses available upon request include: country specific analyses and a sub analysis of multiple gestation vs singletons. All analyses except Fisher’s exact tests were performed using SPSS 23 [24]. Fisher’s exact tests were performed in STATA 14 [25]. This study was approved by Institutional Review Board at the University of California San Francisco (Study no: 16–19162), the Kenyan Medical Institute Scientific and Ethics Review Unit (SERU protocol no: KEMRI/SERU/CCR/0034/3251), the Makerere University Higher Degrees, Research, and Ethics Committee (Protocol ID: IRB00011353), and the Uganda National Council of Science and Technology. There was a waiver of consent to obtain line-item level data from maternity registers. De-identified data were collected from maternity registers, so there was no direct patient contact or time spent for this analysis. Results will be disseminated to health workers and health authorities from research areas.

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

1. Digital Maternity Registers: Implementing digital systems for maternity registers can improve accuracy, completeness, and accessibility of data. This can streamline data collection, analysis, and reporting, leading to more effective decision-making and quality improvement efforts.

2. Telemedicine and Teleconsultations: Introducing telemedicine services can enhance access to healthcare for pregnant women, especially in remote or underserved areas. Teleconsultations can provide timely and convenient access to healthcare professionals, allowing for early detection and management of pregnancy complications.

3. Mobile Health (mHealth) Applications: Developing mobile health applications specifically designed for maternal health can empower women with information and resources. These apps can provide personalized health education, appointment reminders, medication tracking, and emergency contact information, improving overall maternal health outcomes.

4. Community Health Worker Programs: Expanding community health worker programs can increase access to maternal healthcare services, particularly in rural or marginalized communities. Trained community health workers can provide antenatal care, education, and support, bridging the gap between communities and formal healthcare systems.

5. Transport and Referral Systems: Establishing efficient transport and referral systems can ensure timely access to emergency obstetric care. This can involve partnerships with local transportation providers, ambulance services, and coordination between healthcare facilities to facilitate quick and safe transfers of pregnant women in need of specialized care.

6. Maternal Health Financing Models: Exploring innovative financing models, such as community-based health insurance or conditional cash transfer programs, can help overcome financial barriers to accessing maternal healthcare. These models can provide financial protection and incentivize women to seek timely and appropriate care during pregnancy and childbirth.

7. Maternity Waiting Homes: Establishing maternity waiting homes near healthcare facilities can provide a safe and supportive environment for pregnant women who live far away. These homes can offer accommodation, nutrition, and antenatal care services, ensuring that women have access to skilled birth attendants when the time comes for delivery.

8. Task-Shifting and Training Programs: Implementing task-shifting programs, where certain healthcare tasks are delegated to trained non-physician healthcare providers, can help alleviate the shortage of skilled birth attendants. This can involve training nurses, midwives, or community health workers to provide comprehensive maternal healthcare services.

9. Quality Improvement Initiatives: Implementing quality improvement initiatives, such as regular audits, feedback mechanisms, and continuous training programs, can enhance the quality of maternal healthcare services. These initiatives can identify gaps in care, promote evidence-based practices, and improve overall maternal health outcomes.

10. Public-Private Partnerships: Collaborating with private healthcare providers and organizations can help expand access to maternal healthcare services. Public-private partnerships can leverage the resources, expertise, and innovation of both sectors to improve the availability and quality of maternal health services.

It’s important to note that the specific context, resources, and needs of the target population should be considered when implementing these innovations.
AI Innovations Description
The study mentioned in the description is a cross-sectional analysis of maternity registers in Kenya and Uganda, conducted as part of the East Africa Preterm Birth Initiative (PTBi). The goal of the study was to document pregnancy outcomes and identify opportunities for perinatal mortality prevention.

The study collected data from 23 health facilities over a period of 18 months. Efforts were made to strengthen the accuracy and completeness of the maternity registers, including providing supplies, training on standard indicator definitions, and monthly feedback on completeness.

The findings of the study showed that among 50,981 deliveries, 91.3% were live births, 1.6% died before discharge, 0.5% were early stillbirths, 3.6% were late stillbirths, and 4.7% were spontaneous abortions. Maternal mortality was documented in 0.1% of cases.

The study highlighted the need for improved care for preterm and low birthweight infants, as they represented a disproportionate number of stillbirths and pre-discharge deaths. It also emphasized the importance of expanding access to emergency obstetric care.

The study concluded that maternity registers are a valuable source of data for understanding pregnancy outcomes and identifying high-risk mothers and infants. The findings can be used to inform quality improvement efforts and reduce perinatal mortality.

Based on these findings, a recommendation to develop an innovation to improve access to maternal health could be to implement a system that uses strengthened maternity registers to track and monitor pregnancy outcomes in real-time. This system could include automated data entry, regular feedback on completeness and accuracy, and alerts for high-risk cases. By using technology to improve the efficiency and effectiveness of maternity registers, healthcare providers can better identify and address the needs of pregnant women, especially those at high risk of complications or adverse outcomes. This innovation would help improve access to timely and appropriate care, ultimately leading to better maternal and neonatal health outcomes.
AI Innovations Methodology
Based on the provided study, here are some potential recommendations for improving access to maternal health:

1. Strengthening maternity registers: Continue efforts to improve the accuracy and completeness of maternity registers by providing necessary supplies, training on standard indicator definitions, and regular feedback on completeness. This will ensure that reliable data is available for monitoring and improving maternal health outcomes.

2. Focus on preterm and low birthweight infants: Develop targeted interventions and strategies to improve the care of preterm and low birthweight infants, as they represent a disproportionate number of stillbirths and pre-discharge deaths. This may include specialized training for healthcare providers, access to neonatal intensive care units, and improved monitoring and support for these vulnerable infants.

3. Expand access to emergency obstetric care: Increase access to emergency obstetric care, particularly in cases where maternal referral is needed. This can help reduce maternal mortality and improve outcomes for both mothers and infants. Strategies may include improving transportation systems, strengthening referral networks, and ensuring that healthcare facilities have the necessary resources and expertise to provide emergency obstetric care.

4. Enhance access to ultrasound services: Improve access to ultrasound services during antenatal care and at the time of delivery. Ultrasound can provide valuable information for accurate gestational age assessment, which is important for determining appropriate care and interventions. This may involve training healthcare providers in ultrasound use, increasing the availability of ultrasound machines in healthcare facilities, and addressing any logistical or financial barriers to accessing ultrasound services.

Methodology to simulate the impact of these recommendations on improving access to maternal health:

1. Define the target population: Identify the specific population or region for which the impact of the recommendations will be simulated. This could be a specific country, region, or healthcare facility.

2. Collect baseline data: Gather relevant data on the current state of maternal health in the target population. This may include information on maternal mortality rates, access to emergency obstetric care, availability of ultrasound services, and other relevant indicators.

3. Develop a simulation model: Create a simulation model that incorporates the recommendations and their potential impact on improving access to maternal health. This model should consider factors such as the number of healthcare facilities, the availability of resources and trained healthcare providers, and the potential reach and effectiveness of the interventions.

4. Input data and run simulations: Input the baseline data into the simulation model and run simulations to estimate the potential impact of the recommendations. This may involve adjusting various parameters and assumptions to explore different scenarios and outcomes.

5. Analyze results: Analyze the results of the simulations to assess the potential impact of the recommendations on improving access to maternal health. This may include evaluating changes in maternal mortality rates, improvements in access to emergency obstetric care, and other relevant outcomes.

6. Refine and validate the model: Continuously refine and validate the simulation model based on feedback, additional data, and real-world observations. This will help ensure the accuracy and reliability of the simulations and their applicability to the target population.

7. Communicate findings and recommendations: Share the findings of the simulations with relevant stakeholders, including policymakers, healthcare providers, and community members. Use the results to advocate for the implementation of the recommendations and to guide decision-making and resource allocation for improving access to maternal health.

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