Perinatal mortality in eastern uganda: A community based prospective cohort study

PLoS ONE, Volume 6, No. 5, Year 2011

Ukuhunyushwa

Background: To achieve a child mortality reduction according to millennium development goal 4, it is necessary to considerably reduce neonatal mortality. We report stillbirth and early neonatal mortality risks as well as determinants of perinatal mortality in Eastern Uganda. Methods: A community-based prospective cohort study was conducted between 2006 and 2008. A total of 835 pregnant women were followed up for pregnancy outcome and survival of their children until 7 days after delivery. Mother’s residence, age, parity, bed net use and whether delivery took place at home were included in multivariable regression analyses to identify risk factors for perinatal death. Results: The stillbirth risk was 19 per 1,000 pregnancies and the early neonatal death risk 22 per 1,000 live births. Overall, the perinatal mortality risk was 41 [95%CI: 27, 54] per 1,000 pregnancies. Of the deaths, 47% followed complicated deliveries and 24% preterm births. Perinatal mortality was 63/1,000 pregnancies among teenage mothers, 76/1,000 pregnancies among nulliparous women and 61/1,000 pregnancies among women delivering at home who, after controlling for potential confounders, had a 3.7 (95%CI: 1.8, 7.4) times higher perinatal mortality than women who gave birth in a health facility. This association was considerably stronger among nulliparous women [RR 8.0 (95%CI: 2.9, 21.6)] than among women with a previous live birth [RR 1.8 (95%CI: 0.7, 4.5)]. All perinatal deaths occurred among women who did not sleep under a mosquito net. Women living in urban slums had a higher risk of losing their babies than those in rural areas [RR: 2.7 (95%CI: 1.4, 5.3)]. Conclusion: Our findings strengthen arguments for ensuring that pregnant women have access to and use adequate delivery facilities and bed nets. © 2011 Nankabirwa et al. We obtained written informed consent from each study participant. Ethical approval was obtained from the Makerere University Research and Ethics Committee, the Uganda National Council for Science and Technology, and from the Regional Committee for Medical and Research Ethics for Western Norway (REK VEST, approval number 05/8197). The study was undertaken during the cluster-randomized PROMISE EBF intervention trial, where we promoted exclusive breastfeeding by individual peer counselling in the intervention areas (Clinical trials gov: {“type”:”clinical-trial”,”attrs”:{“text”:”NCT00397150″,”term_id”:”NCT00397150″}}NCT00397150) [6]. Data collection for this study started in Uganda in January 2006 and ended in May 2008. The study was conducted in Mbale district which had an estimated population of 720,000 [7] and is located 300 km North-East of Kampala. The study area is served by Mbale Hospital, which doubles as the district and regional referral hospital. The HIV prevalence among pregnant women in antenatal clinics in Mbale was approximately 5–6% during the study period. Most of the people living in the area were subsistence farmers. Mbale district comprised of 7 counties; the study was conducted in the two biggest counties, namely Bungokho County (rural) and Mbale Municipality (urban). Twenty four clusters were included in the study, 18 rural and 6 urban. Six clusters in Mbale municipality were selected from all its three municipal divisions. Most of the urban areas were large informal settlements. Eighteen clusters in Bugonkho County were chosen from eight out of its eleven sub-counties. Clusters were included if they neighboured the main road out from Mbale Municipality or were on the 1st or 2nd branch off the main road, had a population of at least 1,000 inhabitants and represented a social and administrative unit. Between January 2006 and September 2007, all pregnant women in the selected clusters were approached by the study team. They were eligible if they resided in the study area, were seven or more months pregnant, opted to breastfeed their infants and consented to participate in the study. Women were excluded if they had an intention to leave the area during the study period. Both singletons and twin deliveries were included in the study. In the PROMISE-EBF trial, 886 pregnant women were identified and approached. Of these, 875 (99%) accepted to participate in the study. Of the 875, 12 (1%) women did not meet the eligibility criteria and 28 (3%) relocated out of the study area after recruitment but before the endpoint of 7 days after delivery and were lost to follow-up. We analyzed data from the remaining 835 women. At recruitment, trained data collectors fluent in the local language, Lumasaaba, administered a pre-tested structured Lumasaaba questionnaire. Information was collected on socio-demographic characteristics, antenatal care attendance, marital status and main source of income. Information was also collected on the current pregnancy and use of bed nets. The women were followed up through the pregnancy until 6 months postpartum. All births, deaths and details of the delivery were recorded within four weeks of delivery. For the perinatal deaths, a standard World Health Organization (WHO) verbal autopsy questionnaire was used to collect information for a standard algorithm determining the likely cause of death [8]. The questionnaire had both an open-ended section for reporting verbatim and a closed-ended section with filter questions. The verbal autopsies were done as soon as socially acceptable (2 weeks to 2 months after the loss of the baby). Using EpiHandy software (www.openXdata.org, version 165.528-142 RC) on handheld computers, the data was entered in the field. We undertook data analysis using Stata version 9 (StataCorp LP, TX, U.S.). Continuous variables were categorized to avoid doubtful assumptions about linearity. The primary outcome was perinatal death, defined as pregnancy loss occurring after seven completed months of gestation (still birth) or deaths within the first seven days of delivery of a live born child (early neonatal death), its confidence limits calculated using the exact method. Secondary outcomes included stillbirths and early neonatal deaths. The stillbirth risk was defined as the number of babies born dead after 28 weeks of gestation per 1,000 pregnancies and the early neonatal mortality risk was defined as the number of deaths in the first 7 days of life per 1,000 live births. The exposure variables were maternal age, parity, mother’s education, place of delivery, antenatal care attendance, marital status, residence, household wealth index, and use of bed nets. Crude risk ratios (RR) and 95% confidence intervals were estimated for the exposure variables. We used multivariable generalized linear model (GLM) regression analysis with a log link to estimate the adjusted RR of the independent variables on perinatal mortality. Initially, all these variables were included in the crude analyses. However, only variables that were associated with perinatal deaths yielding a P-value <0.2 were retained in the model. We calculated the difference in perinatal mortality between women delivering at home and in health facilities or with a traditional birth attendant. We also calculated the perinatal mortality difference for sleeping under a mosquito net; the corresponding RR could not be calculated because no perinatal deaths were recorded in this category. We categorized marital status into two categories: ‘married’ and ‘un-married’. The unmarried category comprised cohabiting, single, divorced, widowed and separated. We categorised place of delivery into two groups: ‘hospital/clinic/local maternity/traditional birth attendant's place or on the way to hospital’, and ‘at home’, i.e. without a skilled birth attendant. We defined parity according to the number of previous live births and grouped education into two categories: ‘less than or equal to 7 years of education’ and ‘more than 7 years of education’. To measure bed net use, mothers were asked whether or not they slept under a bed net. We created a composite index of wealth (socio-economic status) using multiple correspondence analysis (MCA). Because the MCA technique allows combination and ranking of a large number of variables into smaller and fewer variables without prejudgment, it is considered a more accurate indicator of socioeconomic status (SES) than single items such as occupation or possession of particular items. Also, in comparison to principal component analysis, the MCA technique is more appropriate for discrete variables. This was important in this study because several relevant variables could only be categorical. Furthermore, unlike principal component analysis, which clusters variables together, MCA clusters the categories within these variables together. We used MCA on possession of a TV, radio, mobile phone, chair, cupboard, refrigerator, type of toilet, type of house walls as well as electricity and water source in the home. We used dates of the last menstrual period to estimate gestational age. Preterm births were infants born less than 37 weeks of gestation. Complicated deliveries were those associated with haemorrhage, cord complications, obstructed, prolonged labour, pre-eclampsia, eclampsia, and breech or other non-cephalic presentations.

I-AI Digest

Study Justification:

– The study aims to address the need for reducing neonatal mortality in order to achieve the Millennium Development Goal 4, which focuses on child mortality reduction.
– By identifying the stillbirth and early neonatal mortality risks, as well as the determinants of perinatal mortality in Eastern Uganda, the study provides valuable insights into the factors contributing to these deaths.

Study Highlights:

– The stillbirth risk was found to be 19 per 1,000 pregnancies, while the early neonatal death risk was 22 per 1,000 live births.
– The overall perinatal mortality risk was 41 per 1,000 pregnancies.
– Complicated deliveries and preterm births accounted for 47% and 24% of the deaths, respectively.
– Teenage mothers, nulliparous women, and women delivering at home had higher perinatal mortality rates.
– Women who did not sleep under a mosquito net had a higher risk of perinatal death.
– Women living in urban slums had a higher risk of losing their babies compared to those in rural areas.

Study Recommendations:

– Ensure that pregnant women have access to and use adequate delivery facilities.
– Promote the use of bed nets to protect against mosquito-borne diseases.
– Provide targeted interventions for teenage mothers, nulliparous women, and women delivering at home.
– Improve healthcare services and infrastructure in urban slums to reduce perinatal mortality rates.

Key Role Players:

– Healthcare providers: Doctors, nurses, midwives, and other medical professionals involved in antenatal care, delivery, and postnatal care.
– Community health workers: Individuals who can provide education and support to pregnant women and new mothers in the community.
– Policy makers: Government officials and organizations responsible for developing and implementing healthcare policies and programs.
– Non-governmental organizations (NGOs): Organizations that can provide resources, funding, and support for interventions aimed at reducing perinatal mortality.

Cost Items for Planning Recommendations:

– Infrastructure development: Funding for the construction or improvement of healthcare facilities, including maternity wards and delivery rooms.
– Training and capacity building: Budget for training healthcare providers and community health workers to improve their skills and knowledge in perinatal care.
– Education and awareness campaigns: Funds for creating and implementing campaigns to educate pregnant women and their families about the importance of accessing healthcare services and using bed nets.
– Supply and equipment procurement: Budget for purchasing necessary medical supplies, equipment, and bed nets.
– Monitoring and evaluation: Resources for monitoring the implementation and impact of interventions, as well as evaluating their effectiveness in reducing perinatal mortality rates.

Amandla Obufakazi

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 community-based prospective cohort study with a large sample size (835 pregnant women). The study provides specific data on stillbirth and early neonatal mortality risks, as well as determinants of perinatal mortality in Eastern Uganda. The study also includes multivariable regression analyses to identify risk factors for perinatal death. However, to improve the evidence, it would be helpful to provide more information on the methodology, such as the sampling technique and data collection procedures. Additionally, including information on the statistical significance of the findings would further strengthen the evidence.

Abstract

We obtained written informed consent from each study participant. Ethical approval was obtained from the Makerere University Research and Ethics Committee, the Uganda National Council for Science and Technology, and from the Regional Committee for Medical and Research Ethics for Western Norway (REK VEST, approval number 05/8197). The study was undertaken during the cluster-randomized PROMISE EBF intervention trial, where we promoted exclusive breastfeeding by individual peer counselling in the intervention areas (Clinical trials gov: {“type”:”clinical-trial”,”attrs”:{“text”:”NCT00397150″,”term_id”:”NCT00397150″}}NCT00397150) [6]. Data collection for this study started in Uganda in January 2006 and ended in May 2008. The study was conducted in Mbale district which had an estimated population of 720,000 [7] and is located 300 km North-East of Kampala. The study area is served by Mbale Hospital, which doubles as the district and regional referral hospital. The HIV prevalence among pregnant women in antenatal clinics in Mbale was approximately 5–6% during the study period. Most of the people living in the area were subsistence farmers. Mbale district comprised of 7 counties; the study was conducted in the two biggest counties, namely Bungokho County (rural) and Mbale Municipality (urban). Twenty four clusters were included in the study, 18 rural and 6 urban. Six clusters in Mbale municipality were selected from all its three municipal divisions. Most of the urban areas were large informal settlements. Eighteen clusters in Bugonkho County were chosen from eight out of its eleven sub-counties. Clusters were included if they neighboured the main road out from Mbale Municipality or were on the 1st or 2nd branch off the main road, had a population of at least 1,000 inhabitants and represented a social and administrative unit. Between January 2006 and September 2007, all pregnant women in the selected clusters were approached by the study team. They were eligible if they resided in the study area, were seven or more months pregnant, opted to breastfeed their infants and consented to participate in the study. Women were excluded if they had an intention to leave the area during the study period. Both singletons and twin deliveries were included in the study. In the PROMISE-EBF trial, 886 pregnant women were identified and approached. Of these, 875 (99%) accepted to participate in the study. Of the 875, 12 (1%) women did not meet the eligibility criteria and 28 (3%) relocated out of the study area after recruitment but before the endpoint of 7 days after delivery and were lost to follow-up. We analyzed data from the remaining 835 women. At recruitment, trained data collectors fluent in the local language, Lumasaaba, administered a pre-tested structured Lumasaaba questionnaire. Information was collected on socio-demographic characteristics, antenatal care attendance, marital status and main source of income. Information was also collected on the current pregnancy and use of bed nets. The women were followed up through the pregnancy until 6 months postpartum. All births, deaths and details of the delivery were recorded within four weeks of delivery. For the perinatal deaths, a standard World Health Organization (WHO) verbal autopsy questionnaire was used to collect information for a standard algorithm determining the likely cause of death [8]. The questionnaire had both an open-ended section for reporting verbatim and a closed-ended section with filter questions. The verbal autopsies were done as soon as socially acceptable (2 weeks to 2 months after the loss of the baby). Using EpiHandy software (www.openXdata.org, version 165.528-142 RC) on handheld computers, the data was entered in the field. We undertook data analysis using Stata version 9 (StataCorp LP, TX, U.S.). Continuous variables were categorized to avoid doubtful assumptions about linearity. The primary outcome was perinatal death, defined as pregnancy loss occurring after seven completed months of gestation (still birth) or deaths within the first seven days of delivery of a live born child (early neonatal death), its confidence limits calculated using the exact method. Secondary outcomes included stillbirths and early neonatal deaths. The stillbirth risk was defined as the number of babies born dead after 28 weeks of gestation per 1,000 pregnancies and the early neonatal mortality risk was defined as the number of deaths in the first 7 days of life per 1,000 live births. The exposure variables were maternal age, parity, mother’s education, place of delivery, antenatal care attendance, marital status, residence, household wealth index, and use of bed nets. Crude risk ratios (RR) and 95% confidence intervals were estimated for the exposure variables. We used multivariable generalized linear model (GLM) regression analysis with a log link to estimate the adjusted RR of the independent variables on perinatal mortality. Initially, all these variables were included in the crude analyses. However, only variables that were associated with perinatal deaths yielding a P-value <0.2 were retained in the model. We calculated the difference in perinatal mortality between women delivering at home and in health facilities or with a traditional birth attendant. We also calculated the perinatal mortality difference for sleeping under a mosquito net; the corresponding RR could not be calculated because no perinatal deaths were recorded in this category. We categorized marital status into two categories: ‘married’ and ‘un-married’. The unmarried category comprised cohabiting, single, divorced, widowed and separated. We categorised place of delivery into two groups: ‘hospital/clinic/local maternity/traditional birth attendant's place or on the way to hospital’, and ‘at home’, i.e. without a skilled birth attendant. We defined parity according to the number of previous live births and grouped education into two categories: ‘less than or equal to 7 years of education’ and ‘more than 7 years of education’. To measure bed net use, mothers were asked whether or not they slept under a bed net. We created a composite index of wealth (socio-economic status) using multiple correspondence analysis (MCA). Because the MCA technique allows combination and ranking of a large number of variables into smaller and fewer variables without prejudgment, it is considered a more accurate indicator of socioeconomic status (SES) than single items such as occupation or possession of particular items. Also, in comparison to principal component analysis, the MCA technique is more appropriate for discrete variables. This was important in this study because several relevant variables could only be categorical. Furthermore, unlike principal component analysis, which clusters variables together, MCA clusters the categories within these variables together. We used MCA on possession of a TV, radio, mobile phone, chair, cupboard, refrigerator, type of toilet, type of house walls as well as electricity and water source in the home. We used dates of the last menstrual period to estimate gestational age. Preterm births were infants born less than 37 weeks of gestation. Complicated deliveries were those associated with haemorrhage, cord complications, obstructed, prolonged labour, pre-eclampsia, eclampsia, and breech or other non-cephalic presentations.

Izinto zokwakha

N/A

Emisha

Innovation 1: Develop a mobile health (mHealth) application that provides pregnant women in Eastern Uganda with information and reminders about the importance of delivering in a health facility and using bed nets. The app can also provide a directory of nearby health facilities and their services, making it easier for women to access appropriate care. Additionally, the app can include educational resources on prenatal and postnatal care, breastfeeding, and nutrition.

Innovation 2: Establish community-based outreach programs that provide education and support to pregnant women in Eastern Uganda. These programs can include home visits by trained healthcare workers who can provide information on the benefits of delivering in a health facility and using bed nets. They can also offer guidance on prenatal and postnatal care, breastfeeding, and nutrition. These programs can help increase awareness and encourage positive health behaviors among pregnant women.

Innovation 3: Collaborate with local community leaders and organizations to organize community events and workshops focused on maternal health. These events can include educational sessions on the importance of delivering in a health facility and using bed nets, as well as interactive activities to promote positive health behaviors. By engaging the community, these events can help raise awareness and encourage pregnant women to seek appropriate care and use bed nets.

Innovation 4: Improve the availability and accessibility of delivery facilities in Eastern Uganda by establishing new health centers or upgrading existing ones. This can include providing necessary equipment and training healthcare workers to ensure safe and quality care for pregnant women. By increasing the number of delivery facilities, more women will have access to skilled birth attendants and reduce the risk of perinatal mortality.

Innovation 5: Implement a comprehensive maternal health program that includes regular antenatal care visits for pregnant women in Eastern Uganda. These visits can provide opportunities for healthcare providers to educate women about the importance of delivering in a health facility and using bed nets. They can also monitor the progress of the pregnancy and address any potential complications early on. Regular antenatal care can help improve maternal and neonatal outcomes and reduce the risk of perinatal mortality.

AI Innovations Description

Based on the information provided in the study, here is a recommendation that can be developed into an innovation to improve access to maternal health:

Recommendation: Implement a comprehensive maternal health program that focuses on increasing access to delivery facilities and promoting the use of bed nets among pregnant women in Eastern Uganda.

Explanation: The study found that women delivering at home without a skilled birth attendant had a significantly higher risk of perinatal mortality compared to those who gave birth in a health facility. Additionally, all perinatal deaths occurred among women who did not sleep under a mosquito net. Therefore, it is crucial to ensure that pregnant women have access to and use adequate delivery facilities and bed nets to reduce perinatal mortality.

Innovation: Develop a mobile health (mHealth) application that provides pregnant women in Eastern Uganda with information and reminders about the importance of delivering in a health facility and using bed nets. The app can also provide a directory of nearby health facilities and their services, making it easier for women to access appropriate care. Additionally, the app can include educational resources on prenatal and postnatal care, breastfeeding, and nutrition.

By leveraging technology and providing targeted information and reminders, this innovation can help improve access to maternal health services and promote positive health behaviors among pregnant women in Eastern Uganda.

AI Innovations Methodology

To simulate the impact of the main recommendations on improving access to maternal health, you can follow these steps:

1. Define the target population: Identify the specific group of pregnant women in Eastern Uganda who would benefit from the implementation of the recommendations. This could include factors such as age, parity, residence, and socioeconomic status.

2. Collect baseline data: Gather information on the current access to delivery facilities and bed net usage among the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a mathematical or statistical model that simulates the impact of the recommendations on improving access to maternal health. This model should take into account factors such as the number of pregnant women, the availability and proximity of delivery facilities, the distribution and usage of bed nets, and the potential impact on perinatal mortality.

4. Input data and parameters: Input the baseline data and relevant parameters into the simulation model. This includes information on the number of pregnant women, the current perinatal mortality rate, the percentage of women delivering at home, and the percentage of women using bed nets.

5. Run simulations: Run multiple simulations using different scenarios that reflect the implementation of the recommendations. This could include increasing the number of delivery facilities, promoting the use of bed nets through educational campaigns, and providing access to a mobile health application.

6. Analyze results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. This can include measuring changes in perinatal mortality rates, the percentage of women delivering at home, and the percentage of women using bed nets.

7. Validate the model: Validate the simulation model by comparing the simulated results with real-world data, if available. This helps ensure the accuracy and reliability of the model.

8. Refine and iterate: Based on the results and feedback from stakeholders, refine and iterate the simulation model to improve its accuracy and effectiveness in predicting the impact of the recommendations.

By following these steps, you can simulate the potential impact of the recommendations on improving access to maternal health in Eastern Uganda. This can help inform decision-making and resource allocation for implementing the recommendations effectively.

Umbhali

Dima Health

Statistics:

Citations: 49

Identifiers:

DOI: 10.1371/journal.pone.0019674

Research Areas:

Community Interventions, Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Uganda

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female

Yabelana ngalokhu: