Perinatal death in Northern Uganda: incidence and risk factors in a community-based prospective cohort study

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Study Justification:
– Perinatal mortality in Uganda is high, exceeding the target set by the Every Newborn Action Plan.
– Understanding the risk factors for perinatal death is crucial for improving perinatal survival.
– This study aimed to determine the incidence, risk factors, and causes of perinatal death in Northern Uganda.
Study Highlights:
– The study enrolled 1,877 pregnant women in Lira district, Northern Uganda.
– The majority of participants were young, married or cohabiting, and had primary education.
– The perinatal mortality rate was 43/1,000 births, with stillbirths and early neonatal deaths contributing to the majority of deaths.
– Birth asphyxia, respiratory failure, infections, and intra-partum events were the major probable contributors to perinatal death.
– Nulliparity and maternal age >30 years were identified as risk factors for perinatal death.
– Improved access to care during pregnancy and childbirth is needed for pregnant women in the region.
Recommendations:
– Enhance antenatal care services to address the identified risk factors, particularly for nulliparous women and those over 30 years old.
– Strengthen efforts to prevent and manage birth asphyxia, respiratory failure, infections, and intra-partum events.
– Improve overall access to healthcare services during pregnancy and childbirth in the study region.
Key Role Players:
– Community volunteers: Identify and refer pregnant women for enrollment in the study.
– Research assistants: Collect data using standardized questionnaires and conduct follow-up visits.
– Paediatricians: Assign causes of death based on verbal autopsy data.
– Coordinators and supervisors: Check data for completeness and consistency.
– Doctors, nurses, and data analysts: Provide training and support to research assistants.
Cost Items for Planning Recommendations:
– Training: Budget for the one-week intensive training of research assistants.
– Communication: Provide mobile cell phones for community volunteers and research assistants.
– Transportation: Allocate funds for motorcycles to facilitate timely follow-up visits.
– Data collection tools: Cover the costs of translating, pretesting, and adjusting questionnaires.
– Verbal autopsy: Allocate resources for conducting verbal autopsies and assigning causes of death.
– Data management: Include costs for using an android-based mobile application and data analysis software.
– Personnel: Consider the salaries and allowances for the key role players involved in the study.
Please note that the provided cost items are general categories and not actual cost estimates.

Background: Perinatal mortality in Uganda remains high at 38 deaths/1,000 births, an estimate greater than the every newborn action plan (ENAP) target of ≤24/1,000 births by 2030. To improve perinatal survival, there is a need to understand the persisting risk factors for death. Objective: We determined the incidence, risk factors, and causes of perinatal death in Lira district, Northern Uganda. Methods: This was a community-based prospective cohort study among pregnant women in Lira district, Northern Uganda. Female community volunteers identified pregnant women in each household who were recruited at ≥28 weeks of gestation and followed until 50 days postpartum. Information on perinatal survival was gathered from participants within 24 hours after childbirth and at 7 days postpartum. The cause of death was ascertained using verbal autopsies. We used generalized estimating equations of the Poisson family to determine the risk factors for perinatal death. Results: Of the 1,877 women enrolled, the majority were ≤30 years old (79.8%), married or cohabiting (91.3%), and had attained only a primary education (77.7%). There were 81 perinatal deaths among them, giving a perinatal mortality rate of 43/1,000 births [95% confidence interval (95% CI: 35, 53)], of these 37 were stillbirths (20 deaths/1,000 total births) and 44 were early neonatal deaths (23 deaths/1,000 live births). Birth asphyxia, respiratory failure, infections and intra-partum events were the major probable contributors to perinatal death. The risk factors for perinatal death were nulliparity at enrolment (adjusted IRR 2.7, [95% CI: 1.3, 5.6]) and maternal age >30 years (adjusted IRR 2.5, [95% CI: 1.1, 5.8]). Conclusion: The incidence of perinatal death in this region was higher than had previously been reported in Uganda. Risk factors for perinatal mortality were nulliparity and maternal age >30 years. Pregnant women in this region need improved access to care during pregnancy and childbirth.

This was a prospective cohort study among pregnant women recruited at ≥28 weeks and followed up for the first 50 days of life. It was nested in the Survival Pluss trial (NCT0260505369), a cluster-randomized community-based trial. The more detailed description of this study can be found in a previous publication [27]. All pregnant women in the study were identified by community volunteers and followed up until 1 week after birth. The study was conducted in Lira, a district in Northern Uganda, between January 2018 and March 2019. Lira district has 3 administrative counties, 13 sub-counties, 89 parishes and 751 villages. The district had a population of 410,000 in 2014 [28], served by 31 healthcare facilities, including one referral hospital, 3 healthcare centres with operation rooms, 17 healthcare centres with maternity ward but no surgical facilities, and 10 healthcare centres with only out-patient services (dispensary). The main economic activity in the region is subsistence farming. The study was carried out in 3 sub-counties, Aromo, Agweng and Ogur. These sub-counties were chosen based on the poor maternal and perinatal indicators and the location in a rural and hard-to-reach area of the district. Participants were identified by 250 community female volunteers who had received training on ethical conduct and record keeping. They contacted the research team via mobile communication whenever they identified a pregnant woman in their communities. A research assistant, accompanied by the community volunteer, then visited the pregnant woman at home; she was included if she was found to be at least 28 weeks pregnant, resident in the study area and willing to participate in the study. The gestational age was determined based on the last normal menstrual period. Participants were recruited irrespective of antenatal care utilization. Follow-up phone calls and visits were made to ensure that the mothers were visited within 24 hours after childbirth and 7 days postpartum. During recruitment, pregnant women who were likely to leave the study area before the end of 6 months and those with psychiatric illness were excluded. For this study, the required sample size of 1,812 was estimated using Fleiss statistical methods for rates and proportions [29]. It was assumed that 35% of the pregnant women who experienced a perinatal death had no formal education and 28% with perinatal death had a secondary education [30]. This estimation factored in 0.05 alpha, 80% power and 10% non-response. A team of 42 trained research assistants, fluent in the local language Lango collected the data using a standardized questionnaire in face-to-face interviews conducted at the participant’s home. The standardized questionnaire, with structured questions on demographic, socio-economic and current pregnancy was administered at recruitment. The sections on birth and perinatal survival status were administered within 24 hours after childbirth and at 7 days postpartum. Research assistants (university graduates) underwent a one-week intensive training on data collection procedures and tools before deployment in the field. This training was facilitated by doctors, nurses and a data analyst. The research assistants that were in two groups lived within the community in Aromo and Agweng sub-counties. They were given mobile cell phones and motorcycles to make timely follow-up visits. Data were checked on a daily basis by the coordinators and supervisors for completeness and consistency before submission. A verbal autopsy was carried out in cases of perinatal death, using standard verbal autopsy questionnaires developed by the WHO [31]. A verbal autopsy was taken within 2 weeks if relatives felt able and willing to provide information. Deaths were grouped according to timing; if it occurred during the antepartum period, intrapartum period or in the early neonatal period. The cause of death was assigned by two paediatricians after independently reviewing the collected data. A consensus was reached on the verbal autopsy data collected to determine the probable cause of death, which were later assigned by one of the authors to the new International Classification of Death – Perinatal Mortality (ICD-PM) grouping [32]. All data collection tools were translated in Lango, pretested, and adjusted as necessary. The primary study outcome for this study was perinatal death. According to the WHO definition, a stillbirth was a baby born with a gestation of 28 weeks or more and an early neonatal death was a live-born baby that died within the first 7 days of life. Perinatal death was used as an umbrella term for both stillbirth and early neonatal death [33]. The birth weight of stillborn infants was not used to determine gestational age, as this was culturally unacceptable in the study area. Therefore, gestational age was determined based on the last normal menstrual period. The perinatal mortality rate was defined on the WHO definition as the number of perinatal deaths per 1,000 births included in the study (≥28 weeks) [18]. The stillbirth rate was defined as the number of stillbirths per 1,000 total births [18]. The early neonatal death rate was the probability that a child born alive within the study period died during the first 7 days after birth, expressed per 1,000 live births [34]. Twin pregnancies were counted as one (last twin), irrespective of the number who died. An asset-based wealth index was used to estimate the economic status of the participant’s household, using the principal component analysis. The ‘wealth index’ was computed using the first principal component and based on the availability of nine different household assets. The wealth index was later reduced into three groups; lowest 40%, middle 40% and top 20%. Maternal age was categorized as 30 years. Marital status was regrouped as married or unmarried. Maternal education was categorized as no education, only primary education, and secondary education or higher education. Parity (at enrolment) was defined as the number of deliveries a woman had had and grouped as Para 0 (nullipara), Para 1–4 (multipara) and Para 5+ (Grand multipara). Antenatal care utilization was collected as ‘yes/no’ at enrolment. Obesity was defined as a body mass index (BMI) >30 kg/m2. Participants’ place of residence was included to assess for the difference in perinatal mortality between the two administrative divisions (sub-counties). A variable representing the intervention or the control arm of the parent trial was included in the analysis as a potential confounder. Data were collected using an android-based mobile application (Open Data Kit: https://opendatakit.org) and analysed using STATA version 14.0 (StataCorp; College Station, TX, USA). Categorical variables were summarized as proportions and continuous variables as means with their standard deviations as appropriate. We used generalized estimating equations of the Poisson family, with a log link, taking into account clustering, and assuming an exchangeable correlation to calculate the risk ratios estimating the magnitude of any associations between exposure variables and perinatal death. Based on scientific literature and biological plausibility, we included the following factors into our model: maternal age, marital status [10,11], maternal education, wealth index, antenatal care attendance [9], parity [11,13,14], intervention of the community trial [12] and maternal obesity [15,35]. We also included the place of residence to assess whether perinatal death varied in the two sub-counties represented in our study. All the variables in the model were assessed for collinearity, which was considered present if the variables had a variance inflation factor (VIF) of >10. In situations of collinearity, we retained the variable with the greater biological plausibility. Multivariable regression analysis was used to take into account potential confounding.

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

1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that can be used by pregnant women and healthcare providers to improve communication, provide educational resources, and track important health information during pregnancy and postpartum.

2. Telemedicine: Establish telemedicine services that allow pregnant women in remote or hard-to-reach areas to consult with healthcare providers and receive prenatal care through video calls or phone consultations.

3. Community-Based Education and Awareness Programs: Conduct community-based education programs to raise awareness about the importance of prenatal care, safe childbirth practices, and early recognition of danger signs during pregnancy and childbirth.

4. Transportation Support: Provide transportation support for pregnant women in rural areas to access healthcare facilities for prenatal care, delivery, and postpartum care.

5. Task-Shifting and Training: Train community health workers or midwives to provide basic prenatal care and conduct home visits to monitor the health of pregnant women and newborns, especially in areas with limited healthcare resources.

6. Strengthening Healthcare Facilities: Improve the infrastructure and resources of healthcare facilities in underserved areas to ensure they are equipped to provide quality maternal healthcare services.

7. Maternal Health Insurance: Implement affordable and accessible health insurance schemes specifically for maternal health to reduce financial barriers and increase access to quality care.

8. Partnerships and Collaboration: Foster partnerships between government agencies, non-governmental organizations, and private sector entities to combine resources and expertise in addressing maternal health challenges.

These innovations can help improve access to maternal health services, enhance the quality of care, and ultimately reduce perinatal mortality rates in regions like Northern Uganda.
AI Innovations Description
Based on the information provided, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Improve access to antenatal care: Enhance the availability and accessibility of antenatal care services in the study area. This can be achieved by establishing more healthcare facilities with maternity wards and surgical facilities, especially in rural and hard-to-reach areas. Additionally, efforts should be made to ensure that pregnant women are aware of the importance of antenatal care and are encouraged to utilize these services.

2. Strengthen community-based interventions: Build upon the existing community volunteer program by providing further training and support. These volunteers play a crucial role in identifying pregnant women and linking them to healthcare services. By enhancing their knowledge and skills, they can better educate and support pregnant women throughout their pregnancy and postpartum period.

3. Address risk factors: Focus on addressing the identified risk factors for perinatal death, such as nulliparity and maternal age over 30 years. Implement targeted interventions to support and educate women in these specific groups, providing them with the necessary resources and information to improve their pregnancy outcomes.

4. Enhance transportation services: Improve transportation options for pregnant women, particularly in rural areas. This can be achieved by providing reliable and affordable transportation services, such as ambulances or community transport systems, to ensure that women can access healthcare facilities in a timely manner during emergencies or for routine check-ups.

5. Increase awareness and education: Conduct community-wide awareness campaigns to educate both women and their families about the importance of maternal health and the available services. This can help reduce cultural barriers and misconceptions surrounding pregnancy and childbirth, encouraging more women to seek appropriate care.

6. Strengthen data collection and analysis: Continue conducting research and collecting data on maternal health indicators to monitor progress and identify areas for improvement. Regular analysis of this data can help inform evidence-based interventions and policies to further enhance access to maternal health services.

By implementing these recommendations, it is expected that access to maternal health will be improved, leading to a reduction in perinatal mortality rates in the study area.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening Antenatal Care (ANC) Services: Enhance the quality and accessibility of ANC services by ensuring that pregnant women receive comprehensive care, including regular check-ups, health education, and screenings for potential complications.

2. Improving Transportation Infrastructure: Enhance transportation infrastructure in rural and hard-to-reach areas to facilitate timely access to healthcare facilities during pregnancy and childbirth. This can include improving road networks, providing ambulances, or implementing telemedicine services.

3. Community-Based Interventions: Implement community-based interventions to increase awareness about maternal health, promote early antenatal care seeking, and encourage women to give birth in healthcare facilities with skilled birth attendants.

4. Training and Capacity Building: Provide training and capacity building programs for healthcare providers, including midwives and community health workers, to improve their skills in managing maternal complications and providing quality care during pregnancy and childbirth.

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

1. Data Collection: Gather baseline data on key indicators related to maternal health, such as the number of pregnant women receiving ANC, the number of births attended by skilled birth attendants, and the incidence of perinatal mortality.

2. Define Simulation Parameters: Determine the specific parameters to be simulated, such as the increase in ANC coverage, the improvement in transportation infrastructure, or the implementation of community-based interventions. Assign values to these parameters based on available evidence or expert opinion.

3. Model Development: Develop a simulation model that incorporates the defined parameters and their potential impact on maternal health outcomes. This could be a mathematical model, such as a system dynamics model or an agent-based model, which simulates the interactions between different factors influencing access to maternal health.

4. Simulation Runs: Run the simulation model multiple times, varying the values of the parameters to explore different scenarios. This can help assess the potential impact of each recommendation individually and in combination.

5. Analysis of Results: Analyze the simulation results to determine the potential impact of the recommendations on improving access to maternal health. This can include quantifying changes in key indicators, such as the increase in ANC coverage or the reduction in perinatal mortality rates.

6. Sensitivity Analysis: Conduct sensitivity analysis to assess the robustness of the simulation results to changes in the input parameters. This can help identify the most influential factors and potential uncertainties in the simulation model.

7. Policy Recommendations: Based on the simulation results, provide evidence-based policy recommendations for implementing the identified recommendations to improve access to maternal health. Consider the feasibility, cost-effectiveness, and sustainability of each recommendation in the local context.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data. Therefore, it is recommended to consult with experts in the field of maternal health and simulation modeling to tailor the methodology to the specific needs of the study.

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