Causes of perinatal death at a tertiary care hospital in Northern Tanzania 2000-2010: A registry based study

BMC Pregnancy and Childbirth, Volume 12, Year 2012

Interprétation

Background: Perinatal mortality reflects maternal health as well as antenatal, intrapartum and newborn care, and is an important health indicator. This study aimed at classifying causes of perinatal death in order to identify categories of potentially preventable deaths.Methods: We studied a total of 1958 stillbirths and early neonatal deaths above 500 g between July 2000 and October 2010 registered in the Medical Birth Registry and neonatal registry at Kilimanjaro Christian Medical Centre (KCMC) in Northern Tanzania. The deaths were classified according to the Neonatal and Intrauterine deaths Classification according to Etiology (NICE).Results: Overall perinatal mortality was 57.7/1000 (1958 out of 33 929), of which 1219 (35.9/1000) were stillbirths and 739 (21.8/1000) were early neonatal deaths. Major causes of perinatal mortality were unexplained asphyxia (n=425, 12.5/1000), obstetric complications (n=303, 8.9/1000), maternal disease (n=287, 8.5/1000), unexplained antepartum stillbirths after 37 weeks of gestation (n= 219, 6.5/1000), and unexplained antepartum stillbirths before 37 weeks of gestation (n=184, 5.4/1000). Obstructed/prolonged labour was the leading condition (251/303, 82.8%) among the obstetric complications. Preeclampsia/eclampsia was the leading cause (253/287, 88.2%) among the maternal conditions. When we excluded women who were referred for delivery at KCMC due to medical reasons (19.1% of all births and 36.0% of all deaths), perinatal mortality was reduced to 45.6/1000. This reduction was mainly due to fewer deaths from obstetric complications (from 8.9 to 2.1/1000) and maternal conditions (from 8.5 to 5.5/1000).Conclusion: The distribution of causes of death in this population suggests a great potential for prevention. Early identification of mothers at risk of pregnancy complications through antenatal care screening, teaching pregnant women to recognize signs of pregnancy complications, timely access to obstetric care, monitoring of labour for fetal distress, and proper newborn resuscitation may reduce some of the categories of deaths. © 2012 Mmbaga et al.; licensee BioMed Central Ltd. This study is based on data collected at KCMC hospital in Northern Tanzania. The hospital is a tertiary care and zonal referral hospital which serves about 10 million people from mainly four regions in Northern Tanzania, namely Kilimanjaro, Arusha, Tanga and Manyara. Being a tertiary referral hospital the KCMC labour ward receives normal deliveries as well as high risk mothers with maternal or obstetric complications referred at various stages of pregnancy or labour from Moshi urban area or from other health facilities in the Northern zone. The KCMC obstetrics and gynaecology department has a team on call which includes one specialist or consultant obstetrician, one obstetric resident and one intern doctor, two anaesthesiologists and 3 midwives who take care of the department outside regular working hours for comprehensive emergency obstetrics and gynaecological care. The department has two operative theatres in labour ward for emergency caesarean sections. In the Kilimanjaro region 70% of all births take place at health facilities [18]. Around 50% of the deliveries at KCMC are from Moshi urban area. The caesarean section rate at the institution is about 33% [19]. Based on records from the birth registry linked to the neonatal registry from July 2000 to October 2010 [20], we established a cohort of births with birth weight 500 g or more. A total of 34087 births were recorded of which 158 (0.4%) the birth weights were either missing or below 500 g (Figure (Figure1).1). Therefore, our study population was 33929 births with birth weight 500 g or more, of which 1958 died perinatally. Description of the study population. Numbers in brackets are proportions of all births 2000–2010 (N=34087). Information on all mothers who delivered at KCMC was obtained through a structured questionnaire and the mothers being interviewed within the first 24 hours after delivery. Informed consent was obtained from mothers prior to the interview. Information was also extracted from the antenatal care record cards. Detailed description of the data collection procedure and data collected for the birth registry and neonatal registry have been previously published [20,21]. For stillbirths, time of death was recorded as before labour, during labour, or unknown. The status of the fetus was also recorded, whether it was a macerated stillbirth or fresh one. The information was also sought whether the fetus died before or after admission to labour ward. Reporting of early neonatal deaths included date of death, time of death (died within first 24 hours, died within first week), and up to three diagnoses of cause of death [20]. Early neonatal deaths include newborns that die during first week of life. We define perinatal mortality as stillbirth or early neonatal death with birth weight 500 grams or more [22]. Perinatal mortality rate (PNMR), stillbirth rate (SBR) and early neonatal mortality rate (ENMR), were calculated as follows: PNMR = (stillbirths + early neonatal deaths/total births) × 1000, SBR = (stillbirths/total births) × 1000 and ENMR = (early neonatal deaths/live births) × 1000. Outcome was perinatal death, overall and according to cause of death. Causes of death were classified on the basis of maternal, obstetric, fetal and neonatal characteristics identified in the linked registry data, according to the NICE classification [23], with a mild modification of the unexplained asphyxia category (Table (Table1)1) based on our previous modification [20]. In a strictly hierarchical order, each stillborn or early neonatal death was classified into one of the 13 specific, mutually exclusive causes of death. For the two causes of death categories maternal disease and obstetric complications, we also investigated co-morbidity. Definitions of the characteristics included in the 13 categories of causes of perinatal deaths by NICE classification *Characteristics included in the 13 categories adapted and modified from Winbo et al. [23]. Modifications are bolded. Main results were stratified according to referral status (mother referred for delivery due to medical condition yes/no). The following conditions recorded in the birth registry were considered; obstructed labour, malpresentation, prolonged labour, retained twin, fetal distress, cord prolapse, premature/prolonged rupture of membrane, abruption placenta, placenta previa, antepartum haemorrhage, ruptured uterus, preeclampsia, eclampsia, gestational or diabetic mellitus, hypertension, and malaria. Referral due to previous caesarean section without any of the complications above was not regarded a medical referral. Data were analyzed using Statistical Package for Social Science (SPSS) program for Windows Version 19.0 (SPSS 19.0 Chicago Inc. III, USA). Descriptive measures such as mean, standard deviation, rate per 1000 and relative risk were calculated. The protocol for this study was approved by Kilimanjaro Christian Medical college (KCM-College) research ethics committee, with certificate no. 333 of 15th July 2010.

Résumé de l’IA

Study Justification:

– Perinatal mortality is an important health indicator that reflects maternal health and the quality of antenatal, intrapartum, and newborn care.
– Understanding the causes of perinatal death can help identify categories of deaths that may be preventable.
– This study aimed to classify the causes of perinatal death in order to identify areas for potential prevention.

Study Highlights:

– The study analyzed data from 1958 stillbirths and early neonatal deaths at a tertiary care hospital in Northern Tanzania between 2000 and 2010.
– The overall perinatal mortality rate was 57.7 per 1000 births, with 35.9 per 1000 stillbirths and 21.8 per 1000 early neonatal deaths.
– The major causes of perinatal mortality were unexplained asphyxia, obstetric complications, maternal disease, and unexplained antepartum stillbirths.
– Obstructed/prolonged labor was the leading condition among obstetric complications, while preeclampsia/eclampsia was the leading cause among maternal conditions.
– Excluding women referred for delivery due to medical reasons reduced perinatal mortality to 45.6 per 1000, mainly due to fewer deaths from obstetric complications and maternal conditions.
– The study suggests that early identification of mothers at risk, timely access to obstetric care, and proper newborn resuscitation may help reduce perinatal deaths.

Recommendations:

– Implement antenatal care screening to identify mothers at risk of pregnancy complications.
– Educate pregnant women on recognizing signs of pregnancy complications.
– Ensure timely access to obstetric care for high-risk mothers.
– Monitor labor for fetal distress.
– Provide proper newborn resuscitation.

Key Role Players:

– Obstetricians and gynecologists
– Midwives
– Anesthesiologists
– Policy makers
– Health educators

Cost Items for Planning Recommendations:

– Training programs for healthcare professionals on antenatal care screening, obstetric care, and newborn resuscitation.
– Equipment and supplies for monitoring labor and newborn resuscitation.
– Public health campaigns to educate pregnant women on recognizing signs of pregnancy complications.
– Infrastructure improvements to ensure timely access to obstetric care.
– Research and monitoring to evaluate the effectiveness of interventions.
Please note that the cost items provided are general suggestions and may vary depending on the specific context and resources available.

Force de la preuve

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 registry-based study with a large sample size (1958 stillbirths and early neonatal deaths) over a 10-year period. The study provides detailed information on the causes of perinatal death in a specific population (Northern Tanzania) and highlights the potential for prevention. However, to improve the evidence, the study could have included more information on the methodology, such as the data collection process and the statistical analysis used. Additionally, the abstract could have provided more context on the limitations of the study and the generalizability of the findings.

Abstrait

This study is based on data collected at KCMC hospital in Northern Tanzania. The hospital is a tertiary care and zonal referral hospital which serves about 10 million people from mainly four regions in Northern Tanzania, namely Kilimanjaro, Arusha, Tanga and Manyara. Being a tertiary referral hospital the KCMC labour ward receives normal deliveries as well as high risk mothers with maternal or obstetric complications referred at various stages of pregnancy or labour from Moshi urban area or from other health facilities in the Northern zone. The KCMC obstetrics and gynaecology department has a team on call which includes one specialist or consultant obstetrician, one obstetric resident and one intern doctor, two anaesthesiologists and 3 midwives who take care of the department outside regular working hours for comprehensive emergency obstetrics and gynaecological care. The department has two operative theatres in labour ward for emergency caesarean sections. In the Kilimanjaro region 70% of all births take place at health facilities [18]. Around 50% of the deliveries at KCMC are from Moshi urban area. The caesarean section rate at the institution is about 33% [19]. Based on records from the birth registry linked to the neonatal registry from July 2000 to October 2010 [20], we established a cohort of births with birth weight 500 g or more. A total of 34087 births were recorded of which 158 (0.4%) the birth weights were either missing or below 500 g (Figure (Figure1).1). Therefore, our study population was 33929 births with birth weight 500 g or more, of which 1958 died perinatally. Description of the study population. Numbers in brackets are proportions of all births 2000–2010 (N=34087). Information on all mothers who delivered at KCMC was obtained through a structured questionnaire and the mothers being interviewed within the first 24 hours after delivery. Informed consent was obtained from mothers prior to the interview. Information was also extracted from the antenatal care record cards. Detailed description of the data collection procedure and data collected for the birth registry and neonatal registry have been previously published [20,21]. For stillbirths, time of death was recorded as before labour, during labour, or unknown. The status of the fetus was also recorded, whether it was a macerated stillbirth or fresh one. The information was also sought whether the fetus died before or after admission to labour ward. Reporting of early neonatal deaths included date of death, time of death (died within first 24 hours, died within first week), and up to three diagnoses of cause of death [20]. Early neonatal deaths include newborns that die during first week of life. We define perinatal mortality as stillbirth or early neonatal death with birth weight 500 grams or more [22]. Perinatal mortality rate (PNMR), stillbirth rate (SBR) and early neonatal mortality rate (ENMR), were calculated as follows: PNMR = (stillbirths + early neonatal deaths/total births) × 1000, SBR = (stillbirths/total births) × 1000 and ENMR = (early neonatal deaths/live births) × 1000. Outcome was perinatal death, overall and according to cause of death. Causes of death were classified on the basis of maternal, obstetric, fetal and neonatal characteristics identified in the linked registry data, according to the NICE classification [23], with a mild modification of the unexplained asphyxia category (Table (Table1)1) based on our previous modification [20]. In a strictly hierarchical order, each stillborn or early neonatal death was classified into one of the 13 specific, mutually exclusive causes of death. For the two causes of death categories maternal disease and obstetric complications, we also investigated co-morbidity. Definitions of the characteristics included in the 13 categories of causes of perinatal deaths by NICE classification *Characteristics included in the 13 categories adapted and modified from Winbo et al. [23]. Modifications are bolded. Main results were stratified according to referral status (mother referred for delivery due to medical condition yes/no). The following conditions recorded in the birth registry were considered; obstructed labour, malpresentation, prolonged labour, retained twin, fetal distress, cord prolapse, premature/prolonged rupture of membrane, abruption placenta, placenta previa, antepartum haemorrhage, ruptured uterus, preeclampsia, eclampsia, gestational or diabetic mellitus, hypertension, and malaria. Referral due to previous caesarean section without any of the complications above was not regarded a medical referral. Data were analyzed using Statistical Package for Social Science (SPSS) program for Windows Version 19.0 (SPSS 19.0 Chicago Inc. III, USA). Descriptive measures such as mean, standard deviation, rate per 1000 and relative risk were calculated. The protocol for this study was approved by Kilimanjaro Christian Medical college (KCM-College) research ethics committee, with certificate no. 333 of 15th July 2010.

Matériaux

N/A

Innovations

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

1. Early identification of mothers at risk: Implementing antenatal care screening programs to identify mothers who may be at risk of pregnancy complications. This can help healthcare providers intervene early and provide appropriate care.

2. Education for pregnant women: Providing education to pregnant women on recognizing signs of pregnancy complications. This can empower women to seek timely medical attention when needed.

3. Timely access to obstetric care: Ensuring that pregnant women have timely access to obstetric care, especially in cases of high-risk pregnancies or complications. This may involve improving transportation systems or establishing referral networks between healthcare facilities.

4. Monitoring of labor for fetal distress: Implementing protocols for monitoring labor to detect signs of fetal distress. This can help healthcare providers intervene promptly and prevent adverse outcomes.

5. Proper newborn resuscitation: Ensuring that healthcare providers are trained in proper newborn resuscitation techniques. This can help reduce neonatal deaths and improve outcomes for newborns.

These innovations have the potential to address some of the categories of preventable deaths identified in the study and improve access to maternal health in the region.

AI Innovations Description

Based on the study conducted at KCMC hospital in Northern Tanzania, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Early identification of mothers at risk: Implement a comprehensive antenatal care screening program to identify mothers at risk of pregnancy complications. This can include regular check-ups, screenings, and assessments to detect any potential issues early on.

2. Education for pregnant women: Provide education and awareness programs for pregnant women to recognize signs of pregnancy complications. This can include information on warning signs, symptoms, and when to seek medical help.

3. Timely access to obstetric care: Ensure that pregnant women have timely access to obstetric care, especially in cases of high-risk pregnancies or complications. This can involve improving transportation systems, establishing referral networks, and reducing barriers to accessing healthcare facilities.

4. Monitoring of labor for fetal distress: Implement protocols and guidelines for monitoring labor to detect fetal distress. This can include regular monitoring of fetal heart rate, timely interventions, and appropriate management of complications during labor.

5. Proper newborn resuscitation: Train healthcare providers in proper newborn resuscitation techniques to reduce neonatal mortality. This can involve providing training programs, ensuring availability of necessary equipment, and promoting adherence to best practices.

By implementing these recommendations, it is possible to reduce the categories of preventable deaths and improve access to maternal health in the region.

AI Innovations Methodology

Based on the study “Causes of perinatal death at a tertiary care hospital in Northern Tanzania 2000-2010: A registry based study,” here are some potential recommendations to improve access to maternal health:

1. Strengthen Antenatal Care: Implement comprehensive antenatal care programs that include regular screenings and assessments to identify mothers at risk of pregnancy complications. This can help in early identification and management of potential issues, reducing the risk of perinatal death.

2. Education and Awareness: Develop educational programs to teach pregnant women about the signs and symptoms of pregnancy complications. This will empower them to recognize warning signs and seek timely medical care, improving maternal and neonatal outcomes.

3. Timely Access to Obstetric Care: Improve access to obstetric care by ensuring that pregnant women have timely access to quality healthcare facilities. This can be achieved by increasing the number of healthcare facilities, improving transportation infrastructure, and reducing financial barriers to healthcare services.

4. Monitoring of Labor: Implement protocols for monitoring labor to detect fetal distress early. This can help in timely interventions, such as emergency cesarean sections, to prevent adverse outcomes.

5. Newborn Resuscitation: Provide training and resources for healthcare providers to ensure proper newborn resuscitation techniques are followed. This can help reduce neonatal deaths due to birth asphyxia.

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

1. Collect Baseline Data: Gather data on the current access to maternal health services, perinatal mortality rates, and causes of perinatal deaths in the target population.

2. Define Indicators: Identify key indicators to measure the impact of the recommendations, such as the reduction in perinatal mortality rate, improvement in access to antenatal care, and increase in the utilization of obstetric care services.

3. Develop a Simulation Model: Create a simulation model that incorporates the baseline data and simulates the impact of the recommendations over a specific time period. The model should consider factors such as population demographics, healthcare infrastructure, and resource availability.

4. Input Data and Parameters: Input the data on the recommended interventions, such as the number of additional healthcare facilities, the percentage increase in antenatal care coverage, and the training programs for healthcare providers. Set the parameters for the simulation model, such as the time period and population size.

5. Run Simulations: Run multiple simulations using different scenarios and assumptions to assess the potential impact of the recommendations on improving access to maternal health. Analyze the results to identify the most effective interventions and their expected outcomes.

6. Evaluate Results: Evaluate the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. Compare the outcomes with the baseline data to assess the effectiveness of the proposed interventions.

7. Refine and Implement: Based on the simulation results, refine the recommendations and develop an implementation plan. Monitor and evaluate the implementation to measure the actual impact on improving access to maternal health.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific context and available data.

Auteur

Dima Health

Statistics:

Citations: 29

Identifiers:

DOI: 10.1186/1471-2393-12-139

Research Areas:

Community Interventions, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Workforce

Study Countries:

Tanzania

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female

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